The Prediction of Botulinum Toxin Structure Based on in Silico and in Vitro Analysis

NASA Astrophysics Data System (ADS)

Suzuki, Tomonori; Miyazaki, Satoru

2011-01-01

Many of biological system mediated through protein-protein interactions. Knowledge of protein-protein complex structure is required for understanding the function. The determination of huge size and flexible protein-protein complex structure by experimental studies remains difficult, costly and five-consuming, therefore computational prediction of protein structures by homolog modeling and docking studies is valuable method. In addition, MD simulation is also one of the most powerful methods allowing to see the real dynamics of proteins. Here, we predict protein-protein complex structure of botulinum toxin to analyze its property. These bioinformatics methods are useful to report the relation between the flexibility of backbone structure and the activity.

Sixty-five years of the long march in protein secondary structure prediction: the final stretch?

PubMed Central

Yang, Yuedong; Gao, Jianzhao; Wang, Jihua; Heffernan, Rhys; Hanson, Jack; Paliwal, Kuldip; Zhou, Yaoqi

2018-01-01

Abstract Protein secondary structure prediction began in 1951 when Pauling and Corey predicted helical and sheet conformations for protein polypeptide backbone even before the first protein structure was determined. Sixty-five years later, powerful new methods breathe new life into this field. The highest three-state accuracy without relying on structure templates is now at 82–84%, a number unthinkable just a few years ago. These improvements came from increasingly larger databases of protein sequences and structures for training, the use of template secondary structure information and more powerful deep learning techniques. As we are approaching to the theoretical limit of three-state prediction (88–90%), alternative to secondary structure prediction (prediction of backbone torsion angles and Cα-atom-based angles and torsion angles) not only has more room for further improvement but also allows direct prediction of three-dimensional fragment structures with constantly improved accuracy. About 20% of all 40-residue fragments in a database of 1199 non-redundant proteins have <6 Å root-mean-squared distance from the native conformations by SPIDER2. More powerful deep learning methods with improved capability of capturing long-range interactions begin to emerge as the next generation of techniques for secondary structure prediction. The time has come to finish off the final stretch of the long march towards protein secondary structure prediction. PMID:28040746

Assessment of Protein Side-Chain Conformation Prediction Methods in Different Residue Environments

PubMed Central

Peterson, Lenna X.; Kang, Xuejiao; Kihara, Daisuke

2016-01-01

Computational prediction of side-chain conformation is an important component of protein structure prediction. Accurate side-chain prediction is crucial for practical applications of protein structure models that need atomic detailed resolution such as protein and ligand design. We evaluated the accuracy of eight side-chain prediction methods in reproducing the side-chain conformations of experimentally solved structures deposited to the Protein Data Bank. Prediction accuracy was evaluated for a total of four different structural environments (buried, surface, interface, and membrane-spanning) in three different protein types (monomeric, multimeric, and membrane). Overall, the highest accuracy was observed for buried residues in monomeric and multimeric proteins. Notably, side-chains at protein interfaces and membrane-spanning regions were better predicted than surface residues even though the methods did not all use multimeric and membrane proteins for training. Thus, we conclude that the current methods are as practically useful for modeling protein docking interfaces and membrane-spanning regions as for modeling monomers. PMID:24619909

CONFOLD2: improved contact-driven ab initio protein structure modeling.

PubMed

Adhikari, Badri; Cheng, Jianlin

2018-01-25

Contact-guided protein structure prediction methods are becoming more and more successful because of the latest advances in residue-residue contact prediction. To support contact-driven structure prediction, effective tools that can quickly build tertiary structural models of good quality from predicted contacts need to be developed. We develop an improved contact-driven protein modelling method, CONFOLD2, and study how it may be effectively used for ab initio protein structure prediction with predicted contacts as input. It builds models using various subsets of input contacts to explore the fold space under the guidance of a soft square energy function, and then clusters the models to obtain the top five models. CONFOLD2 obtains an average reconstruction accuracy of 0.57 TM-score for the 150 proteins in the PSICOV contact prediction dataset. When benchmarked on the CASP11 contacts predicted using CONSIP2 and CASP12 contacts predicted using Raptor-X, CONFOLD2 achieves a mean TM-score of 0.41 on both datasets. CONFOLD2 allows to quickly generate top five structural models for a protein sequence when its secondary structures and contacts predictions at hand. The source code of CONFOLD2 is publicly available at https://github.com/multicom-toolbox/CONFOLD2/ .

In Silico Analysis for the Study of Botulinum Toxin Structure

NASA Astrophysics Data System (ADS)

Suzuki, Tomonori; Miyazaki, Satoru

2010-01-01

Protein-protein interactions play many important roles in biological function. Knowledge of protein-protein complex structure is required for understanding the function. The determination of protein-protein complex structure by experimental studies remains difficult, therefore computational prediction of protein structures by structure modeling and docking studies is valuable method. In addition, MD simulation is also one of the most popular methods for protein structure modeling and characteristics. Here, we attempt to predict protein-protein complex structure and property using some of bioinformatic methods, and we focus botulinum toxin complex as target structure.

Predicting the helix packing of globular proteins by self-correcting distance geometry.

PubMed

Mumenthaler, C; Braun, W

1995-05-01

A new self-correcting distance geometry method for predicting the three-dimensional structure of small globular proteins was assessed with a test set of 8 helical proteins. With the knowledge of the amino acid sequence and the helical segments, our completely automated method calculated the correct backbone topology of six proteins. The accuracy of the predicted structures ranged from 2.3 A to 3.1 A for the helical segments compared to the experimentally determined structures. For two proteins, the predicted constraints were not restrictive enough to yield a conclusive prediction. The method can be applied to all small globular proteins, provided the secondary structure is known from NMR analysis or can be predicted with high reliability.

Protein Structure Prediction by Protein Threading

NASA Astrophysics Data System (ADS)

Xu, Ying; Liu, Zhijie; Cai, Liming; Xu, Dong

The seminal work of Bowie, Lüthy, and Eisenberg (Bowie et al., 1991) on "the inverse protein folding problem" laid the foundation of protein structure prediction by protein threading. By using simple measures for fitness of different amino acid types to local structural environments defined in terms of solvent accessibility and protein secondary structure, the authors derived a simple and yet profoundly novel approach to assessing if a protein sequence fits well with a given protein structural fold. Their follow-up work (Elofsson et al., 1996; Fischer and Eisenberg, 1996; Fischer et al., 1996a,b) and the work by Jones, Taylor, and Thornton (Jones et al., 1992) on protein fold recognition led to the development of a new brand of powerful tools for protein structure prediction, which we now term "protein threading." These computational tools have played a key role in extending the utility of all the experimentally solved structures by X-ray crystallography and nuclear magnetic resonance (NMR), providing structural models and functional predictions for many of the proteins encoded in the hundreds of genomes that have been sequenced up to now.

BindML/BindML+: Detecting Protein-Protein Interaction Interface Propensity from Amino Acid Substitution Patterns.

PubMed

Wei, Qing; La, David; Kihara, Daisuke

2017-01-01

Prediction of protein-protein interaction sites in a protein structure provides important information for elucidating the mechanism of protein function and can also be useful in guiding a modeling or design procedures of protein complex structures. Since prediction methods essentially assess the propensity of amino acids that are likely to be part of a protein docking interface, they can help in designing protein-protein interactions. Here, we introduce BindML and BindML+ protein-protein interaction sites prediction methods. BindML predicts protein-protein interaction sites by identifying mutation patterns found in known protein-protein complexes using phylogenetic substitution models. BindML+ is an extension of BindML for distinguishing permanent and transient types of protein-protein interaction sites. We developed an interactive web-server that provides a convenient interface to assist in structural visualization of protein-protein interactions site predictions. The input data for the web-server are a tertiary structure of interest. BindML and BindML+ are available at http://kiharalab.org/bindml/ and http://kiharalab.org/bindml/plus/ .

A scoring function based on solvation thermodynamics for protein structure prediction

PubMed Central

Du, Shiqiao; Harano, Yuichi; Kinoshita, Masahiro; Sakurai, Minoru

2012-01-01

We predict protein structure using our recently developed free energy function for describing protein stability, which is focused on solvation thermodynamics. The function is combined with the current most reliable sampling methods, i.e., fragment assembly (FA) and comparative modeling (CM). The prediction is tested using 11 small proteins for which high-resolution crystal structures are available. For 8 of these proteins, sequence similarities are found in the database, and the prediction is performed with CM. Fairly accurate models with average Cα root mean square deviation (RMSD) ∼ 2.0 Å are successfully obtained for all cases. For the rest of the target proteins, we perform the prediction following FA protocols. For 2 cases, we obtain predicted models with an RMSD ∼ 3.0 Å as the best-scored structures. For the other case, the RMSD remains larger than 7 Å. For all the 11 target proteins, our scoring function identifies the experimentally determined native structure as the best structure. Starting from the predicted structure, replica exchange molecular dynamics is performed to further refine the structures. However, we are unable to improve its RMSD toward the experimental structure. The exhaustive sampling by coarse-grained normal mode analysis around the native structures reveals that our function has a linear correlation with RMSDs < 3.0 Å. These results suggest that the function is quite reliable for the protein structure prediction while the sampling method remains one of the major limiting factors in it. The aspects through which the methodology could further be improved are discussed. PMID:27493529

Protein Structure and Function Prediction Using I-TASSER

PubMed Central

Yang, Jianyi; Zhang, Yang

2016-01-01

I-TASSER is a hierarchical protocol for automated protein structure prediction and structure-based function annotation. Starting from the amino acid sequence of target proteins, I-TASSER first generates full-length atomic structural models from multiple threading alignments and iterative structural assembly simulations followed by atomic-level structure refinement. The biological functions of the protein, including ligand-binding sites, enzyme commission number, and gene ontology terms, are then inferred from known protein function databases based on sequence and structure profile comparisons. I-TASSER is freely available as both an on-line server and a stand-alone package. This unit describes how to use the I-TASSER protocol to generate structure and function prediction and how to interpret the prediction results, as well as alternative approaches for further improving the I-TASSER modeling quality for distant-homologous and multi-domain protein targets. PMID:26678386

PredictProtein—an open resource for online prediction of protein structural and functional features

PubMed Central

Yachdav, Guy; Kloppmann, Edda; Kajan, Laszlo; Hecht, Maximilian; Goldberg, Tatyana; Hamp, Tobias; Hönigschmid, Peter; Schafferhans, Andrea; Roos, Manfred; Bernhofer, Michael; Richter, Lothar; Ashkenazy, Haim; Punta, Marco; Schlessinger, Avner; Bromberg, Yana; Schneider, Reinhard; Vriend, Gerrit; Sander, Chris; Ben-Tal, Nir; Rost, Burkhard

2014-01-01

PredictProtein is a meta-service for sequence analysis that has been predicting structural and functional features of proteins since 1992. Queried with a protein sequence it returns: multiple sequence alignments, predicted aspects of structure (secondary structure, solvent accessibility, transmembrane helices (TMSEG) and strands, coiled-coil regions, disulfide bonds and disordered regions) and function. The service incorporates analysis methods for the identification of functional regions (ConSurf), homology-based inference of Gene Ontology terms (metastudent), comprehensive subcellular localization prediction (LocTree3), protein–protein binding sites (ISIS2), protein–polynucleotide binding sites (SomeNA) and predictions of the effect of point mutations (non-synonymous SNPs) on protein function (SNAP2). Our goal has always been to develop a system optimized to meet the demands of experimentalists not highly experienced in bioinformatics. To this end, the PredictProtein results are presented as both text and a series of intuitive, interactive and visually appealing figures. The web server and sources are available at http://ppopen.rostlab.org. PMID:24799431

Physics-based protein-structure prediction using a hierarchical protocol based on the UNRES force field: assessment in two blind tests.

PubMed

Ołdziej, S; Czaplewski, C; Liwo, A; Chinchio, M; Nanias, M; Vila, J A; Khalili, M; Arnautova, Y A; Jagielska, A; Makowski, M; Schafroth, H D; Kaźmierkiewicz, R; Ripoll, D R; Pillardy, J; Saunders, J A; Kang, Y K; Gibson, K D; Scheraga, H A

2005-05-24

Recent improvements in the protein-structure prediction method developed in our laboratory, based on the thermodynamic hypothesis, are described. The conformational space is searched extensively at the united-residue level by using our physics-based UNRES energy function and the conformational space annealing method of global optimization. The lowest-energy coarse-grained structures are then converted to an all-atom representation and energy-minimized with the ECEPP/3 force field. The procedure was assessed in two recent blind tests of protein-structure prediction. During the first blind test, we predicted large fragments of alpha and alpha+beta proteins [60-70 residues with C(alpha) rms deviation (rmsd) <6 A]. However, for alpha+beta proteins, significant topological errors occurred despite low rmsd values. In the second exercise, we predicted whole structures of five proteins (two alpha and three alpha+beta, with sizes of 53-235 residues) with remarkably good accuracy. In particular, for the genomic target TM0487 (a 102-residue alpha+beta protein from Thermotoga maritima), we predicted the complete, topologically correct structure with 7.3-A C(alpha) rmsd. So far this protein is the largest alpha+beta protein predicted based solely on the amino acid sequence and a physics-based potential-energy function and search procedure. For target T0198, a phosphate transport system regulator PhoU from T. maritima (a 235-residue mainly alpha-helical protein), we predicted the topology of the whole six-helix bundle correctly within 8 A rmsd, except the 32 C-terminal residues, most of which form a beta-hairpin. These and other examples described in this work demonstrate significant progress in physics-based protein-structure prediction.

A Particle Swarm Optimization-Based Approach with Local Search for Predicting Protein Folding.

PubMed

Yang, Cheng-Hong; Lin, Yu-Shiun; Chuang, Li-Yeh; Chang, Hsueh-Wei

2017-10-01

The hydrophobic-polar (HP) model is commonly used for predicting protein folding structures and hydrophobic interactions. This study developed a particle swarm optimization (PSO)-based algorithm combined with local search algorithms; specifically, the high exploration PSO (HEPSO) algorithm (which can execute global search processes) was combined with three local search algorithms (hill-climbing algorithm, greedy algorithm, and Tabu table), yielding the proposed HE-L-PSO algorithm. By using 20 known protein structures, we evaluated the performance of the HE-L-PSO algorithm in predicting protein folding in the HP model. The proposed HE-L-PSO algorithm exhibited favorable performance in predicting both short and long amino acid sequences with high reproducibility and stability, compared with seven reported algorithms. The HE-L-PSO algorithm yielded optimal solutions for all predicted protein folding structures. All HE-L-PSO-predicted protein folding structures possessed a hydrophobic core that is similar to normal protein folding.

Accurate Prediction of Contact Numbers for Multi-Spanning Helical Membrane Proteins

PubMed Central

Li, Bian; Mendenhall, Jeffrey; Nguyen, Elizabeth Dong; Weiner, Brian E.; Fischer, Axel W.; Meiler, Jens

2017-01-01

Prediction of the three-dimensional (3D) structures of proteins by computational methods is acknowledged as an unsolved problem. Accurate prediction of important structural characteristics such as contact number is expected to accelerate the otherwise slow progress being made in the prediction of 3D structure of proteins. Here, we present a dropout neural network-based method, TMH-Expo, for predicting the contact number of transmembrane helix (TMH) residues from sequence. Neuronal dropout is a strategy where certain neurons of the network are excluded from back-propagation to prevent co-adaptation of hidden-layer neurons. By using neuronal dropout, overfitting was significantly reduced and performance was noticeably improved. For multi-spanning helical membrane proteins, TMH-Expo achieved a remarkable Pearson correlation coefficient of 0.69 between predicted and experimental values and a mean absolute error of only 1.68. In addition, among those membrane protein–membrane protein interface residues, 76.8% were correctly predicted. Mapping of predicted contact numbers onto structures indicates that contact numbers predicted by TMH-Expo reflect the exposure patterns of TMHs and reveal membrane protein–membrane protein interfaces, reinforcing the potential of predicted contact numbers to be used as restraints for 3D structure prediction and protein–protein docking. TMH-Expo can be accessed via a Web server at www.meilerlab.org. PMID:26804342

Predicting residue-wise contact orders in proteins by support vector regression.

PubMed

Song, Jiangning; Burrage, Kevin

2006-10-03

The residue-wise contact order (RWCO) describes the sequence separations between the residues of interest and its contacting residues in a protein sequence. It is a new kind of one-dimensional protein structure that represents the extent of long-range contacts and is considered as a generalization of contact order. Together with secondary structure, accessible surface area, the B factor, and contact number, RWCO provides comprehensive and indispensable important information to reconstructing the protein three-dimensional structure from a set of one-dimensional structural properties. Accurately predicting RWCO values could have many important applications in protein three-dimensional structure prediction and protein folding rate prediction, and give deep insights into protein sequence-structure relationships. We developed a novel approach to predict residue-wise contact order values in proteins based on support vector regression (SVR), starting from primary amino acid sequences. We explored seven different sequence encoding schemes to examine their effects on the prediction performance, including local sequence in the form of PSI-BLAST profiles, local sequence plus amino acid composition, local sequence plus molecular weight, local sequence plus secondary structure predicted by PSIPRED, local sequence plus molecular weight and amino acid composition, local sequence plus molecular weight and predicted secondary structure, and local sequence plus molecular weight, amino acid composition and predicted secondary structure. When using local sequences with multiple sequence alignments in the form of PSI-BLAST profiles, we could predict the RWCO distribution with a Pearson correlation coefficient (CC) between the predicted and observed RWCO values of 0.55, and root mean square error (RMSE) of 0.82, based on a well-defined dataset with 680 protein sequences. Moreover, by incorporating global features such as molecular weight and amino acid composition we could further improve the prediction performance with the CC to 0.57 and an RMSE of 0.79. In addition, combining the predicted secondary structure by PSIPRED was found to significantly improve the prediction performance and could yield the best prediction accuracy with a CC of 0.60 and RMSE of 0.78, which provided at least comparable performance compared with the other existing methods. The SVR method shows a prediction performance competitive with or at least comparable to the previously developed linear regression-based methods for predicting RWCO values. In contrast to support vector classification (SVC), SVR is very good at estimating the raw value profiles of the samples. The successful application of the SVR approach in this study reinforces the fact that support vector regression is a powerful tool in extracting the protein sequence-structure relationship and in estimating the protein structural profiles from amino acid sequences.

Blind test of physics-based prediction of protein structures.

PubMed

Shell, M Scott; Ozkan, S Banu; Voelz, Vincent; Wu, Guohong Albert; Dill, Ken A

2009-02-01

We report here a multiprotein blind test of a computer method to predict native protein structures based solely on an all-atom physics-based force field. We use the AMBER 96 potential function with an implicit (GB/SA) model of solvation, combined with replica-exchange molecular-dynamics simulations. Coarse conformational sampling is performed using the zipping and assembly method (ZAM), an approach that is designed to mimic the putative physical routes of protein folding. ZAM was applied to the folding of six proteins, from 76 to 112 monomers in length, in CASP7, a community-wide blind test of protein structure prediction. Because these predictions have about the same level of accuracy as typical bioinformatics methods, and do not utilize information from databases of known native structures, this work opens up the possibility of predicting the structures of membrane proteins, synthetic peptides, or other foldable polymers, for which there is little prior knowledge of native structures. This approach may also be useful for predicting physical protein folding routes, non-native conformations, and other physical properties from amino acid sequences.

Blind Test of Physics-Based Prediction of Protein Structures

PubMed Central

Shell, M. Scott; Ozkan, S. Banu; Voelz, Vincent; Wu, Guohong Albert; Dill, Ken A.

2009-01-01

We report here a multiprotein blind test of a computer method to predict native protein structures based solely on an all-atom physics-based force field. We use the AMBER 96 potential function with an implicit (GB/SA) model of solvation, combined with replica-exchange molecular-dynamics simulations. Coarse conformational sampling is performed using the zipping and assembly method (ZAM), an approach that is designed to mimic the putative physical routes of protein folding. ZAM was applied to the folding of six proteins, from 76 to 112 monomers in length, in CASP7, a community-wide blind test of protein structure prediction. Because these predictions have about the same level of accuracy as typical bioinformatics methods, and do not utilize information from databases of known native structures, this work opens up the possibility of predicting the structures of membrane proteins, synthetic peptides, or other foldable polymers, for which there is little prior knowledge of native structures. This approach may also be useful for predicting physical protein folding routes, non-native conformations, and other physical properties from amino acid sequences. PMID:19186130

Shaping up the protein folding funnel by local interaction: lesson from a structure prediction study.

PubMed

Chikenji, George; Fujitsuka, Yoshimi; Takada, Shoji

2006-02-28

Predicting protein tertiary structure by folding-like simulations is one of the most stringent tests of how much we understand the principle of protein folding. Currently, the most successful method for folding-based structure prediction is the fragment assembly (FA) method. Here, we address why the FA method is so successful and its lesson for the folding problem. To do so, using the FA method, we designed a structure prediction test of "chimera proteins." In the chimera proteins, local structural preference is specific to the target sequences, whereas nonlocal interactions are only sequence-independent compaction forces. We find that these chimera proteins can find the native folds of the intact sequences with high probability indicating dominant roles of the local interactions. We further explore roles of local structural preference by exact calculation of the HP lattice model of proteins. From these results, we suggest principles of protein folding: For small proteins, compact structures that are fully compatible with local structural preference are few, one of which is the native fold. These local biases shape up the funnel-like energy landscape.

Shaping up the protein folding funnel by local interaction: Lesson from a structure prediction study

PubMed Central

Chikenji, George; Fujitsuka, Yoshimi; Takada, Shoji

2006-01-01

Predicting protein tertiary structure by folding-like simulations is one of the most stringent tests of how much we understand the principle of protein folding. Currently, the most successful method for folding-based structure prediction is the fragment assembly (FA) method. Here, we address why the FA method is so successful and its lesson for the folding problem. To do so, using the FA method, we designed a structure prediction test of “chimera proteins.” In the chimera proteins, local structural preference is specific to the target sequences, whereas nonlocal interactions are only sequence-independent compaction forces. We find that these chimera proteins can find the native folds of the intact sequences with high probability indicating dominant roles of the local interactions. We further explore roles of local structural preference by exact calculation of the HP lattice model of proteins. From these results, we suggest principles of protein folding: For small proteins, compact structures that are fully compatible with local structural preference are few, one of which is the native fold. These local biases shape up the funnel-like energy landscape. PMID:16488978

Fast computational methods for predicting protein structure from primary amino acid sequence

DOEpatents

Agarwal, Pratul Kumar [Knoxville, TN

2011-07-19

The present invention provides a method utilizing primary amino acid sequence of a protein, energy minimization, molecular dynamics and protein vibrational modes to predict three-dimensional structure of a protein. The present invention also determines possible intermediates in the protein folding pathway. The present invention has important applications to the design of novel drugs as well as protein engineering. The present invention predicts the three-dimensional structure of a protein independent of size of the protein, overcoming a significant limitation in the prior art.

Structural features that predict real-value fluctuations of globular proteins.

PubMed

Jamroz, Michal; Kolinski, Andrzej; Kihara, Daisuke

2012-05-01

It is crucial to consider dynamics for understanding the biological function of proteins. We used a large number of molecular dynamics (MD) trajectories of nonhomologous proteins as references and examined static structural features of proteins that are most relevant to fluctuations. We examined correlation of individual structural features with fluctuations and further investigated effective combinations of features for predicting the real value of residue fluctuations using the support vector regression (SVR). It was found that some structural features have higher correlation than crystallographic B-factors with fluctuations observed in MD trajectories. Moreover, SVR that uses combinations of static structural features showed accurate prediction of fluctuations with an average Pearson's correlation coefficient of 0.669 and a root mean square error of 1.04 Å. This correlation coefficient is higher than the one observed in predictions by the Gaussian network model (GNM). An advantage of the developed method over the GNMs is that the former predicts the real value of fluctuation. The results help improve our understanding of relationships between protein structure and fluctuation. Furthermore, the developed method provides a convienient practial way to predict fluctuations of proteins using easily computed static structural features of proteins. Copyright © 2012 Wiley Periodicals, Inc.

Structural features that predict real-value fluctuations of globular proteins

PubMed Central

Jamroz, Michal; Kolinski, Andrzej; Kihara, Daisuke

2012-01-01

It is crucial to consider dynamics for understanding the biological function of proteins. We used a large number of molecular dynamics trajectories of non-homologous proteins as references and examined static structural features of proteins that are most relevant to fluctuations. We examined correlation of individual structural features with fluctuations and further investigated effective combinations of features for predicting the real-value of residue fluctuations using the support vector regression. It was found that some structural features have higher correlation than crystallographic B-factors with fluctuations observed in molecular dynamics trajectories. Moreover, support vector regression that uses combinations of static structural features showed accurate prediction of fluctuations with an average Pearson’s correlation coefficient of 0.669 and a root mean square error of 1.04 Å. This correlation coefficient is higher than the one observed for the prediction by the Gaussian network model. An advantage of the developed method over the Gaussian network models is that the former predicts the real-value of fluctuation. The results help improve our understanding of relationships between protein structure and fluctuation. Furthermore, the developed method provides a convienient practial way to predict fluctuations of proteins using easily computed static structural features of proteins. PMID:22328193

Contact Prediction for Beta and Alpha-Beta Proteins Using Integer Linear Optimization and its Impact on the First Principles 3D Structure Prediction Method ASTRO-FOLD

PubMed Central

Rajgaria, R.; Wei, Y.; Floudas, C. A.

2010-01-01

An integer linear optimization model is presented to predict residue contacts in β, α + β, and α/β proteins. The total energy of a protein is expressed as sum of a Cα – Cα distance dependent contact energy contribution and a hydrophobic contribution. The model selects contacts that assign lowest energy to the protein structure while satisfying a set of constraints that are included to enforce certain physically observed topological information. A new method based on hydrophobicity is proposed to find the β-sheet alignments. These β-sheet alignments are used as constraints for contacts between residues of β-sheets. This model was tested on three independent protein test sets and CASP8 test proteins consisting of β, α + β, α/β proteins and was found to perform very well. The average accuracy of the predictions (separated by at least six residues) was approximately 61%. The average true positive and false positive distances were also calculated for each of the test sets and they are 7.58 Å and 15.88 Å, respectively. Residue contact prediction can be directly used to facilitate the protein tertiary structure prediction. This proposed residue contact prediction model is incorporated into the first principles protein tertiary structure prediction approach, ASTRO-FOLD. The effectiveness of the contact prediction model was further demonstrated by the improvement in the quality of the protein structure ensemble generated using the predicted residue contacts for a test set of 10 proteins. PMID:20225257

Defining and predicting structurally conserved regions in protein superfamilies

PubMed Central

Huang, Ivan K.; Grishin, Nick V.

2013-01-01

Motivation: The structures of homologous proteins are generally better conserved than their sequences. This phenomenon is demonstrated by the prevalence of structurally conserved regions (SCRs) even in highly divergent protein families. Defining SCRs requires the comparison of two or more homologous structures and is affected by their availability and divergence, and our ability to deduce structurally equivalent positions among them. In the absence of multiple homologous structures, it is necessary to predict SCRs of a protein using information from only a set of homologous sequences and (if available) a single structure. Accurate SCR predictions can benefit homology modelling and sequence alignment. Results: Using pairwise DaliLite alignments among a set of homologous structures, we devised a simple measure of structural conservation, termed structural conservation index (SCI). SCI was used to distinguish SCRs from non-SCRs. A database of SCRs was compiled from 386 SCOP superfamilies containing 6489 protein domains. Artificial neural networks were then trained to predict SCRs with various features deduced from a single structure and homologous sequences. Assessment of the predictions via a 5-fold cross-validation method revealed that predictions based on features derived from a single structure perform similarly to ones based on homologous sequences, while combining sequence and structural features was optimal in terms of accuracy (0.755) and Matthews correlation coefficient (0.476). These results suggest that even without information from multiple structures, it is still possible to effectively predict SCRs for a protein. Finally, inspection of the structures with the worst predictions pinpoints difficulties in SCR definitions. Availability: The SCR database and the prediction server can be found at http://prodata.swmed.edu/SCR. Contact: 91huangi@gmail.com or grishin@chop.swmed.edu Supplementary information: Supplementary data are available at Bioinformatics Online PMID:23193223

Prediction of Spontaneous Protein Deamidation from Sequence-Derived Secondary Structure and Intrinsic Disorder.

PubMed

Lorenzo, J Ramiro; Alonso, Leonardo G; Sánchez, Ignacio E

2015-01-01

Asparagine residues in proteins undergo spontaneous deamidation, a post-translational modification that may act as a molecular clock for the regulation of protein function and turnover. Asparagine deamidation is modulated by protein local sequence, secondary structure and hydrogen bonding. We present NGOME, an algorithm able to predict non-enzymatic deamidation of internal asparagine residues in proteins in the absence of structural data, using sequence-based predictions of secondary structure and intrinsic disorder. Compared to previous algorithms, NGOME does not require three-dimensional structures yet yields better predictions than available sequence-only methods. Four case studies of specific proteins show how NGOME may help the user identify deamidation-prone asparagine residues, often related to protein gain of function, protein degradation or protein misfolding in pathological processes. A fifth case study applies NGOME at a proteomic scale and unveils a correlation between asparagine deamidation and protein degradation in yeast. NGOME is freely available as a webserver at the National EMBnet node Argentina, URL: http://www.embnet.qb.fcen.uba.ar/ in the subpage "Protein and nucleic acid structure and sequence analysis".

Predicting nucleic acid binding interfaces from structural models of proteins

PubMed Central

Dror, Iris; Shazman, Shula; Mukherjee, Srayanta; Zhang, Yang; Glaser, Fabian; Mandel-Gutfreund, Yael

2011-01-01

The function of DNA- and RNA-binding proteins can be inferred from the characterization and accurate prediction of their binding interfaces. However the main pitfall of various structure-based methods for predicting nucleic acid binding function is that they are all limited to a relatively small number of proteins for which high-resolution three dimensional structures are available. In this study, we developed a pipeline for extracting functional electrostatic patches from surfaces of protein structural models, obtained using the I-TASSER protein structure predictor. The largest positive patches are extracted from the protein surface using the patchfinder algorithm. We show that functional electrostatic patches extracted from an ensemble of structural models highly overlap the patches extracted from high-resolution structures. Furthermore, by testing our pipeline on a set of 55 known nucleic acid binding proteins for which I-TASSER produces high-quality models, we show that the method accurately identifies the nucleic acids binding interface on structural models of proteins. Employing a combined patch approach we show that patches extracted from an ensemble of models better predicts the real nucleic acid binding interfaces compared to patches extracted from independent models. Overall, these results suggest that combining information from a collection of low-resolution structural models could be a valuable approach for functional annotation. We suggest that our method will be further applicable for predicting other functional surfaces of proteins with unknown structure. PMID:22086767

A Template-Based Protein Structure Reconstruction Method Using Deep Autoencoder Learning.

PubMed

Li, Haiou; Lyu, Qiang; Cheng, Jianlin

2016-12-01

Protein structure prediction is an important problem in computational biology, and is widely applied to various biomedical problems such as protein function study, protein design, and drug design. In this work, we developed a novel deep learning approach based on a deeply stacked denoising autoencoder for protein structure reconstruction. We applied our approach to a template-based protein structure prediction using only the 3D structural coordinates of homologous template proteins as input. The templates were identified for a target protein by a PSI-BLAST search. 3DRobot (a program that automatically generates diverse and well-packed protein structure decoys) was used to generate initial decoy models for the target from the templates. A stacked denoising autoencoder was trained on the decoys to obtain a deep learning model for the target protein. The trained deep model was then used to reconstruct the final structural model for the target sequence. With target proteins that have highly similar template proteins as benchmarks, the GDT-TS score of the predicted structures is greater than 0.7, suggesting that the deep autoencoder is a promising method for protein structure reconstruction.

Building a Better Fragment Library for De Novo Protein Structure Prediction

PubMed Central

de Oliveira, Saulo H. P.; Shi, Jiye; Deane, Charlotte M.

2015-01-01

Fragment-based approaches are the current standard for de novo protein structure prediction. These approaches rely on accurate and reliable fragment libraries to generate good structural models. In this work, we describe a novel method for structure fragment library generation and its application in fragment-based de novo protein structure prediction. The importance of correct testing procedures in assessing the quality of fragment libraries is demonstrated. In particular, the exclusion of homologs to the target from the libraries to correctly simulate a de novo protein structure prediction scenario, something which surprisingly is not always done. We demonstrate that fragments presenting different predominant predicted secondary structures should be treated differently during the fragment library generation step and that exhaustive and random search strategies should both be used. This information was used to develop a novel method, Flib. On a validation set of 41 structurally diverse proteins, Flib libraries presents both a higher precision and coverage than two of the state-of-the-art methods, NNMake and HHFrag. Flib also achieves better precision and coverage on the set of 275 protein domains used in the two previous experiments of the the Critical Assessment of Structure Prediction (CASP9 and CASP10). We compared Flib libraries against NNMake libraries in a structure prediction context. Of the 13 cases in which a correct answer was generated, Flib models were more accurate than NNMake models for 10. “Flib is available for download at: http://www.stats.ox.ac.uk/research/proteins/resources”. PMID:25901595

Building protein-protein interaction networks for Leishmania species through protein structural information.

PubMed

Dos Santos Vasconcelos, Crhisllane Rafaele; de Lima Campos, Túlio; Rezende, Antonio Mauro

2018-03-06

Systematic analysis of a parasite interactome is a key approach to understand different biological processes. It makes possible to elucidate disease mechanisms, to predict protein functions and to select promising targets for drug development. Currently, several approaches for protein interaction prediction for non-model species incorporate only small fractions of the entire proteomes and their interactions. Based on this perspective, this study presents an integration of computational methodologies, protein network predictions and comparative analysis of the protozoan species Leishmania braziliensis and Leishmania infantum. These parasites cause Leishmaniasis, a worldwide distributed and neglected disease, with limited treatment options using currently available drugs. The predicted interactions were obtained from a meta-approach, applying rigid body docking tests and template-based docking on protein structures predicted by different comparative modeling techniques. In addition, we trained a machine-learning algorithm (Gradient Boosting) using docking information performed on a curated set of positive and negative protein interaction data. Our final model obtained an AUC = 0.88, with recall = 0.69, specificity = 0.88 and precision = 0.83. Using this approach, it was possible to confidently predict 681 protein structures and 6198 protein interactions for L. braziliensis, and 708 protein structures and 7391 protein interactions for L. infantum. The predicted networks were integrated to protein interaction data already available, analyzed using several topological features and used to classify proteins as essential for network stability. The present study allowed to demonstrate the importance of integrating different methodologies of interaction prediction to increase the coverage of the protein interaction of the studied protocols, besides it made available protein structures and interactions not previously reported.

PSSP-RFE: accurate prediction of protein structural class by recursive feature extraction from PSI-BLAST profile, physical-chemical property and functional annotations.

PubMed

Li, Liqi; Cui, Xiang; Yu, Sanjiu; Zhang, Yuan; Luo, Zhong; Yang, Hua; Zhou, Yue; Zheng, Xiaoqi

2014-01-01

Protein structure prediction is critical to functional annotation of the massively accumulated biological sequences, which prompts an imperative need for the development of high-throughput technologies. As a first and key step in protein structure prediction, protein structural class prediction becomes an increasingly challenging task. Amongst most homological-based approaches, the accuracies of protein structural class prediction are sufficiently high for high similarity datasets, but still far from being satisfactory for low similarity datasets, i.e., below 40% in pairwise sequence similarity. Therefore, we present a novel method for accurate and reliable protein structural class prediction for both high and low similarity datasets. This method is based on Support Vector Machine (SVM) in conjunction with integrated features from position-specific score matrix (PSSM), PROFEAT and Gene Ontology (GO). A feature selection approach, SVM-RFE, is also used to rank the integrated feature vectors through recursively removing the feature with the lowest ranking score. The definitive top features selected by SVM-RFE are input into the SVM engines to predict the structural class of a query protein. To validate our method, jackknife tests were applied to seven widely used benchmark datasets, reaching overall accuracies between 84.61% and 99.79%, which are significantly higher than those achieved by state-of-the-art tools. These results suggest that our method could serve as an accurate and cost-effective alternative to existing methods in protein structural classification, especially for low similarity datasets.

SCPRED: accurate prediction of protein structural class for sequences of twilight-zone similarity with predicting sequences.

PubMed

Kurgan, Lukasz; Cios, Krzysztof; Chen, Ke

2008-05-01

Protein structure prediction methods provide accurate results when a homologous protein is predicted, while poorer predictions are obtained in the absence of homologous templates. However, some protein chains that share twilight-zone pairwise identity can form similar folds and thus determining structural similarity without the sequence similarity would be desirable for the structure prediction. The folding type of a protein or its domain is defined as the structural class. Current structural class prediction methods that predict the four structural classes defined in SCOP provide up to 63% accuracy for the datasets in which sequence identity of any pair of sequences belongs to the twilight-zone. We propose SCPRED method that improves prediction accuracy for sequences that share twilight-zone pairwise similarity with sequences used for the prediction. SCPRED uses a support vector machine classifier that takes several custom-designed features as its input to predict the structural classes. Based on extensive design that considers over 2300 index-, composition- and physicochemical properties-based features along with features based on the predicted secondary structure and content, the classifier's input includes 8 features based on information extracted from the secondary structure predicted with PSI-PRED and one feature computed from the sequence. Tests performed with datasets of 1673 protein chains, in which any pair of sequences shares twilight-zone similarity, show that SCPRED obtains 80.3% accuracy when predicting the four SCOP-defined structural classes, which is superior when compared with over a dozen recent competing methods that are based on support vector machine, logistic regression, and ensemble of classifiers predictors. The SCPRED can accurately find similar structures for sequences that share low identity with sequence used for the prediction. The high predictive accuracy achieved by SCPRED is attributed to the design of the features, which are capable of separating the structural classes in spite of their low dimensionality. We also demonstrate that the SCPRED's predictions can be successfully used as a post-processing filter to improve performance of modern fold classification methods.

SCPRED: Accurate prediction of protein structural class for sequences of twilight-zone similarity with predicting sequences

PubMed Central

Kurgan, Lukasz; Cios, Krzysztof; Chen, Ke

2008-01-01

Background Protein structure prediction methods provide accurate results when a homologous protein is predicted, while poorer predictions are obtained in the absence of homologous templates. However, some protein chains that share twilight-zone pairwise identity can form similar folds and thus determining structural similarity without the sequence similarity would be desirable for the structure prediction. The folding type of a protein or its domain is defined as the structural class. Current structural class prediction methods that predict the four structural classes defined in SCOP provide up to 63% accuracy for the datasets in which sequence identity of any pair of sequences belongs to the twilight-zone. We propose SCPRED method that improves prediction accuracy for sequences that share twilight-zone pairwise similarity with sequences used for the prediction. Results SCPRED uses a support vector machine classifier that takes several custom-designed features as its input to predict the structural classes. Based on extensive design that considers over 2300 index-, composition- and physicochemical properties-based features along with features based on the predicted secondary structure and content, the classifier's input includes 8 features based on information extracted from the secondary structure predicted with PSI-PRED and one feature computed from the sequence. Tests performed with datasets of 1673 protein chains, in which any pair of sequences shares twilight-zone similarity, show that SCPRED obtains 80.3% accuracy when predicting the four SCOP-defined structural classes, which is superior when compared with over a dozen recent competing methods that are based on support vector machine, logistic regression, and ensemble of classifiers predictors. Conclusion The SCPRED can accurately find similar structures for sequences that share low identity with sequence used for the prediction. The high predictive accuracy achieved by SCPRED is attributed to the design of the features, which are capable of separating the structural classes in spite of their low dimensionality. We also demonstrate that the SCPRED's predictions can be successfully used as a post-processing filter to improve performance of modern fold classification methods. PMID:18452616

Functional classification of protein structures by local structure matching in graph representation.

PubMed

Mills, Caitlyn L; Garg, Rohan; Lee, Joslynn S; Tian, Liang; Suciu, Alexandru; Cooperman, Gene; Beuning, Penny J; Ondrechen, Mary Jo

2018-03-31

As a result of high-throughput protein structure initiatives, over 14,400 protein structures have been solved by structural genomics (SG) centers and participating research groups. While the totality of SG data represents a tremendous contribution to genomics and structural biology, reliable functional information for these proteins is generally lacking. Better functional predictions for SG proteins will add substantial value to the structural information already obtained. Our method described herein, Graph Representation of Active Sites for Prediction of Function (GRASP-Func), predicts quickly and accurately the biochemical function of proteins by representing residues at the predicted local active site as graphs rather than in Cartesian coordinates. We compare the GRASP-Func method to our previously reported method, structurally aligned local sites of activity (SALSA), using the ribulose phosphate binding barrel (RPBB), 6-hairpin glycosidase (6-HG), and Concanavalin A-like Lectins/Glucanase (CAL/G) superfamilies as test cases. In each of the superfamilies, SALSA and the much faster method GRASP-Func yield similar correct classification of previously characterized proteins, providing a validated benchmark for the new method. In addition, we analyzed SG proteins using our SALSA and GRASP-Func methods to predict function. Forty-one SG proteins in the RPBB superfamily, nine SG proteins in the 6-HG superfamily, and one SG protein in the CAL/G superfamily were successfully classified into one of the functional families in their respective superfamily by both methods. This improved, faster, validated computational method can yield more reliable predictions of function that can be used for a wide variety of applications by the community. © 2018 The Authors Protein Science published by Wiley Periodicals, Inc. on behalf of The Protein Society.

Local backbone structure prediction of proteins

PubMed Central

De Brevern, Alexandre G.; Benros, Cristina; Gautier, Romain; Valadié, Hélène; Hazout, Serge; Etchebest, Catherine

2004-01-01

Summary A statistical analysis of the PDB structures has led us to define a new set of small 3D structural prototypes called Protein Blocks (PBs). This structural alphabet includes 16 PBs, each one is defined by the (φ, Ψ) dihedral angles of 5 consecutive residues. The amino acid distributions observed in sequence windows encompassing these PBs are used to predict by a Bayesian approach the local 3D structure of proteins from the sole knowledge of their sequences. LocPred is a software which allows the users to submit a protein sequence and performs a prediction in terms of PBs. The prediction results are given both textually and graphically. PMID:15724288

Protein asparagine deamidation prediction based on structures with machine learning methods.

PubMed

Jia, Lei; Sun, Yaxiong

2017-01-01

Chemical stability is a major concern in the development of protein therapeutics due to its impact on both efficacy and safety. Protein "hotspots" are amino acid residues that are subject to various chemical modifications, including deamidation, isomerization, glycosylation, oxidation etc. A more accurate prediction method for potential hotspot residues would allow their elimination or reduction as early as possible in the drug discovery process. In this work, we focus on prediction models for asparagine (Asn) deamidation. Sequence-based prediction method simply identifies the NG motif (amino acid asparagine followed by a glycine) to be liable to deamidation. It still dominates deamidation evaluation process in most pharmaceutical setup due to its convenience. However, the simple sequence-based method is less accurate and often causes over-engineering a protein. We introduce structure-based prediction models by mining available experimental and structural data of deamidated proteins. Our training set contains 194 Asn residues from 25 proteins that all have available high-resolution crystal structures. Experimentally measured deamidation half-life of Asn in penta-peptides as well as 3D structure-based properties, such as solvent exposure, crystallographic B-factors, local secondary structure and dihedral angles etc., were used to train prediction models with several machine learning algorithms. The prediction tools were cross-validated as well as tested with an external test data set. The random forest model had high enrichment in ranking deamidated residues higher than non-deamidated residues while effectively eliminated false positive predictions. It is possible that such quantitative protein structure-function relationship tools can also be applied to other protein hotspot predictions. In addition, we extensively discussed metrics being used to evaluate the performance of predicting unbalanced data sets such as the deamidation case.

Prediction of Carbohydrate Binding Sites on Protein Surfaces with 3-Dimensional Probability Density Distributions of Interacting Atoms

PubMed Central

Tsai, Keng-Chang; Jian, Jhih-Wei; Yang, Ei-Wen; Hsu, Po-Chiang; Peng, Hung-Pin; Chen, Ching-Tai; Chen, Jun-Bo; Chang, Jeng-Yih; Hsu, Wen-Lian; Yang, An-Suei

2012-01-01

Non-covalent protein-carbohydrate interactions mediate molecular targeting in many biological processes. Prediction of non-covalent carbohydrate binding sites on protein surfaces not only provides insights into the functions of the query proteins; information on key carbohydrate-binding residues could suggest site-directed mutagenesis experiments, design therapeutics targeting carbohydrate-binding proteins, and provide guidance in engineering protein-carbohydrate interactions. In this work, we show that non-covalent carbohydrate binding sites on protein surfaces can be predicted with relatively high accuracy when the query protein structures are known. The prediction capabilities were based on a novel encoding scheme of the three-dimensional probability density maps describing the distributions of 36 non-covalent interacting atom types around protein surfaces. One machine learning model was trained for each of the 30 protein atom types. The machine learning algorithms predicted tentative carbohydrate binding sites on query proteins by recognizing the characteristic interacting atom distribution patterns specific for carbohydrate binding sites from known protein structures. The prediction results for all protein atom types were integrated into surface patches as tentative carbohydrate binding sites based on normalized prediction confidence level. The prediction capabilities of the predictors were benchmarked by a 10-fold cross validation on 497 non-redundant proteins with known carbohydrate binding sites. The predictors were further tested on an independent test set with 108 proteins. The residue-based Matthews correlation coefficient (MCC) for the independent test was 0.45, with prediction precision and sensitivity (or recall) of 0.45 and 0.49 respectively. In addition, 111 unbound carbohydrate-binding protein structures for which the structures were determined in the absence of the carbohydrate ligands were predicted with the trained predictors. The overall prediction MCC was 0.49. Independent tests on anti-carbohydrate antibodies showed that the carbohydrate antigen binding sites were predicted with comparable accuracy. These results demonstrate that the predictors are among the best in carbohydrate binding site predictions to date. PMID:22848404

Rigid-Docking Approaches to Explore Protein-Protein Interaction Space.

PubMed

Matsuzaki, Yuri; Uchikoga, Nobuyuki; Ohue, Masahito; Akiyama, Yutaka

Protein-protein interactions play core roles in living cells, especially in the regulatory systems. As information on proteins has rapidly accumulated on publicly available databases, much effort has been made to obtain a better picture of protein-protein interaction networks using protein tertiary structure data. Predicting relevant interacting partners from their tertiary structure is a challenging task and computer science methods have the potential to assist with this. Protein-protein rigid docking has been utilized by several projects, docking-based approaches having the advantages that they can suggest binding poses of predicted binding partners which would help in understanding the interaction mechanisms and that comparing docking results of both non-binders and binders can lead to understanding the specificity of protein-protein interactions from structural viewpoints. In this review we focus on explaining current computational prediction methods to predict pairwise direct protein-protein interactions that form protein complexes.

A protein block based fold recognition method for the annotation of twilight zone sequences.

PubMed

Suresh, V; Ganesan, K; Parthasarathy, S

2013-03-01

The description of protein backbone was recently improved with a group of structural fragments called Structural Alphabets instead of the regular three states (Helix, Sheet and Coil) secondary structure description. Protein Blocks is one of the Structural Alphabets used to describe each and every region of protein backbone including the coil. According to de Brevern (2000) the Protein Blocks has 16 structural fragments and each one has 5 residues in length. Protein Blocks fragments are highly informative among the available Structural Alphabets and it has been used for many applications. Here, we present a protein fold recognition method based on Protein Blocks for the annotation of twilight zone sequences. In our method, we align the predicted Protein Blocks of a query amino acid sequence with a library of assigned Protein Blocks of 953 known folds using the local pair-wise alignment. The alignment results with z-value ≥ 2.5 and P-value ≤ 0.08 are predicted as possible folds. Our method is able to recognize the possible folds for nearly 35.5% of the twilight zone sequences with their predicted Protein Block sequence obtained by pb_prediction, which is available at Protein Block Export server.

RaptorX-Angle: real-value prediction of protein backbone dihedral angles through a hybrid method of clustering and deep learning.

PubMed

Gao, Yujuan; Wang, Sheng; Deng, Minghua; Xu, Jinbo

2018-05-08

Protein dihedral angles provide a detailed description of protein local conformation. Predicted dihedral angles can be used to narrow down the conformational space of the whole polypeptide chain significantly, thus aiding protein tertiary structure prediction. However, direct angle prediction from sequence alone is challenging. In this article, we present a novel method (named RaptorX-Angle) to predict real-valued angles by combining clustering and deep learning. Tested on a subset of PDB25 and the targets in the latest two Critical Assessment of protein Structure Prediction (CASP), our method outperforms the existing state-of-art method SPIDER2 in terms of Pearson Correlation Coefficient (PCC) and Mean Absolute Error (MAE). Our result also shows approximately linear relationship between the real prediction errors and our estimated bounds. That is, the real prediction error can be well approximated by our estimated bounds. Our study provides an alternative and more accurate prediction of dihedral angles, which may facilitate protein structure prediction and functional study.

A computational tool to predict the evolutionarily conserved protein-protein interaction hot-spot residues from the structure of the unbound protein.

PubMed

Agrawal, Neeraj J; Helk, Bernhard; Trout, Bernhardt L

2014-01-21

Identifying hot-spot residues - residues that are critical to protein-protein binding - can help to elucidate a protein's function and assist in designing therapeutic molecules to target those residues. We present a novel computational tool, termed spatial-interaction-map (SIM), to predict the hot-spot residues of an evolutionarily conserved protein-protein interaction from the structure of an unbound protein alone. SIM can predict the protein hot-spot residues with an accuracy of 36-57%. Thus, the SIM tool can be used to predict the yet unknown hot-spot residues for many proteins for which the structure of the protein-protein complexes are not available, thereby providing a clue to their functions and an opportunity to design therapeutic molecules to target these proteins. Copyright © 2013 Federation of European Biochemical Societies. Published by Elsevier B.V. All rights reserved.

Protein Secondary Structure Prediction Using AutoEncoder Network and Bayes Classifier

NASA Astrophysics Data System (ADS)

Wang, Leilei; Cheng, Jinyong

2018-03-01

Protein secondary structure prediction is belong to bioinformatics,and it's important in research area. In this paper, we propose a new prediction way of protein using bayes classifier and autoEncoder network. Our experiments show some algorithms including the construction of the model, the classification of parameters and so on. The data set is a typical CB513 data set for protein. In terms of accuracy, the method is the cross validation based on the 3-fold. Then we can get the Q3 accuracy. Paper results illustrate that the autoencoder network improved the prediction accuracy of protein secondary structure.

Performance of protein-structure predictions with the physics-based UNRES force field in CASP11.

PubMed

Krupa, Paweł; Mozolewska, Magdalena A; Wiśniewska, Marta; Yin, Yanping; He, Yi; Sieradzan, Adam K; Ganzynkowicz, Robert; Lipska, Agnieszka G; Karczyńska, Agnieszka; Ślusarz, Magdalena; Ślusarz, Rafał; Giełdoń, Artur; Czaplewski, Cezary; Jagieła, Dawid; Zaborowski, Bartłomiej; Scheraga, Harold A; Liwo, Adam

2016-11-01

Participating as the Cornell-Gdansk group, we have used our physics-based coarse-grained UNited RESidue (UNRES) force field to predict protein structure in the 11th Community Wide Experiment on the Critical Assessment of Techniques for Protein Structure Prediction (CASP11). Our methodology involved extensive multiplexed replica exchange simulations of the target proteins with a recently improved UNRES force field to provide better reproductions of the local structures of polypeptide chains. All simulations were started from fully extended polypeptide chains, and no external information was included in the simulation process except for weak restraints on secondary structure to enable us to finish each prediction within the allowed 3-week time window. Because of simplified UNRES representation of polypeptide chains, use of enhanced sampling methods, code optimization and parallelization and sufficient computational resources, we were able to treat, for the first time, all 55 human prediction targets with sizes from 44 to 595 amino acid residues, the average size being 251 residues. Complete structures of six single-domain proteins were predicted accurately, with the highest accuracy being attained for the T0769, for which the CαRMSD was 3.8 Å for 97 residues of the experimental structure. Correct structures were also predicted for 13 domains of multi-domain proteins with accuracy comparable to that of the best template-based modeling methods. With further improvements of the UNRES force field that are now underway, our physics-based coarse-grained approach to protein-structure prediction will eventually reach global prediction capacity and, consequently, reliability in simulating protein structure and dynamics that are important in biochemical processes. Freely available on the web at http://www.unres.pl/ CONTACT: has5@cornell.edu. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Predicting nucleic acid binding interfaces from structural models of proteins.

PubMed

Dror, Iris; Shazman, Shula; Mukherjee, Srayanta; Zhang, Yang; Glaser, Fabian; Mandel-Gutfreund, Yael

2012-02-01

The function of DNA- and RNA-binding proteins can be inferred from the characterization and accurate prediction of their binding interfaces. However, the main pitfall of various structure-based methods for predicting nucleic acid binding function is that they are all limited to a relatively small number of proteins for which high-resolution three-dimensional structures are available. In this study, we developed a pipeline for extracting functional electrostatic patches from surfaces of protein structural models, obtained using the I-TASSER protein structure predictor. The largest positive patches are extracted from the protein surface using the patchfinder algorithm. We show that functional electrostatic patches extracted from an ensemble of structural models highly overlap the patches extracted from high-resolution structures. Furthermore, by testing our pipeline on a set of 55 known nucleic acid binding proteins for which I-TASSER produces high-quality models, we show that the method accurately identifies the nucleic acids binding interface on structural models of proteins. Employing a combined patch approach we show that patches extracted from an ensemble of models better predicts the real nucleic acid binding interfaces compared with patches extracted from independent models. Overall, these results suggest that combining information from a collection of low-resolution structural models could be a valuable approach for functional annotation. We suggest that our method will be further applicable for predicting other functional surfaces of proteins with unknown structure. Copyright © 2011 Wiley Periodicals, Inc.

Dynameomics: data-driven methods and models for utilizing large-scale protein structure repositories for improving fragment-based loop prediction.

PubMed

Rysavy, Steven J; Beck, David A C; Daggett, Valerie

2014-11-01

Protein function is intimately linked to protein structure and dynamics yet experimentally determined structures frequently omit regions within a protein due to indeterminate data, which is often due protein dynamics. We propose that atomistic molecular dynamics simulations provide a diverse sampling of biologically relevant structures for these missing segments (and beyond) to improve structural modeling and structure prediction. Here we make use of the Dynameomics data warehouse, which contains simulations of representatives of essentially all known protein folds. We developed novel computational methods to efficiently identify, rank and retrieve small peptide structures, or fragments, from this database. We also created a novel data model to analyze and compare large repositories of structural data, such as contained within the Protein Data Bank and the Dynameomics data warehouse. Our evaluation compares these structural repositories for improving loop predictions and analyzes the utility of our methods and models. Using a standard set of loop structures, containing 510 loops, 30 for each loop length from 4 to 20 residues, we find that the inclusion of Dynameomics structures in fragment-based methods improves the quality of the loop predictions without being dependent on sequence homology. Depending on loop length, ∼ 25-75% of the best predictions came from the Dynameomics set, resulting in lower main chain root-mean-square deviations for all fragment lengths using the combined fragment library. We also provide specific cases where Dynameomics fragments provide better predictions for NMR loop structures than fragments from crystal structures. Online access to these fragment libraries is available at http://www.dynameomics.org/fragments. © 2014 The Protein Society.

Some of the most interesting CASP11 targets through the eyes of their authors.

PubMed

Kryshtafovych, Andriy; Moult, John; Baslé, Arnaud; Burgin, Alex; Craig, Timothy K; Edwards, Robert A; Fass, Deborah; Hartmann, Marcus D; Korycinski, Mateusz; Lewis, Richard J; Lorimer, Donald; Lupas, Andrei N; Newman, Janet; Peat, Thomas S; Piepenbrink, Kurt H; Prahlad, Janani; van Raaij, Mark J; Rohwer, Forest; Segall, Anca M; Seguritan, Victor; Sundberg, Eric J; Singh, Abhimanyu K; Wilson, Mark A; Schwede, Torsten

2016-09-01

The Critical Assessment of protein Structure Prediction (CASP) experiment would not have been possible without the prediction targets provided by the experimental structural biology community. In this article, selected crystallographers providing targets for the CASP11 experiment discuss the functional and biological significance of the target proteins, highlight their most interesting structural features, and assess whether these features were correctly reproduced in the predictions submitted to CASP11. Proteins 2016; 84(Suppl 1):34-50. © 2015 The Authors. Proteins: Structure, Function, and Bioinformatics Published by Wiley Periodicals, Inc. © 2015 The Authors. Proteins: Structure, Function, and Bioinformatics Published by Wiley Periodicals, Inc.

Construction of ontology augmented networks for protein complex prediction.

PubMed

Zhang, Yijia; Lin, Hongfei; Yang, Zhihao; Wang, Jian

2013-01-01

Protein complexes are of great importance in understanding the principles of cellular organization and function. The increase in available protein-protein interaction data, gene ontology and other resources make it possible to develop computational methods for protein complex prediction. Most existing methods focus mainly on the topological structure of protein-protein interaction networks, and largely ignore the gene ontology annotation information. In this article, we constructed ontology augmented networks with protein-protein interaction data and gene ontology, which effectively unified the topological structure of protein-protein interaction networks and the similarity of gene ontology annotations into unified distance measures. After constructing ontology augmented networks, a novel method (clustering based on ontology augmented networks) was proposed to predict protein complexes, which was capable of taking into account the topological structure of the protein-protein interaction network, as well as the similarity of gene ontology annotations. Our method was applied to two different yeast protein-protein interaction datasets and predicted many well-known complexes. The experimental results showed that (i) ontology augmented networks and the unified distance measure can effectively combine the structure closeness and gene ontology annotation similarity; (ii) our method is valuable in predicting protein complexes and has higher F1 and accuracy compared to other competing methods.

Utilization of protein intrinsic disorder knowledge in structural proteomics

PubMed Central

Oldfield, Christopher J.; Xue, Bin; Van, Ya-Yue; Ulrich, Eldon L.; Markley, John L.; Dunker, A. Keith; Uversky, Vladimir N.

2014-01-01

Intrinsically disordered proteins (IDPs) and proteins with long disordered regions are highly abundant in various proteomes. Despite their lack of well-defined ordered structure, these proteins and regions are frequently involved in crucial biological processes. Although in recent years these proteins have attracted the attention of many researchers, IDPs represent a significant challenge for structural characterization since these proteins can impact many of the processes in the structure determination pipeline. Here we investigate the effects of IDPs on the structure determination process and the utility of disorder prediction in selecting and improving proteins for structural characterization. Examination of the extent of intrinsic disorder in existing crystal structures found that relatively few protein crystal structures contain extensive regions of intrinsic disorder. Although intrinsic disorder is not the only cause of crystallization failures and many structured proteins cannot be crystallized, filtering out highly disordered proteins from structure-determination target lists is still likely to be cost effective. Therefore it is desirable to avoid highly disordered proteins from structure-determination target lists and we show that disorder prediction can be applied effectively to enrich structure determination pipelines with proteins more likely to yield crystal structures. For structural investigation of specific proteins, disorder prediction can be used to improve targets for structure determination. Finally, a framework for considering intrinsic disorder in the structure determination pipeline is proposed. PMID:23232152

Structure and non-structure of centrosomal proteins.

PubMed

Dos Santos, Helena G; Abia, David; Janowski, Robert; Mortuza, Gulnahar; Bertero, Michela G; Boutin, Maïlys; Guarín, Nayibe; Méndez-Giraldez, Raúl; Nuñez, Alfonso; Pedrero, Juan G; Redondo, Pilar; Sanz, María; Speroni, Silvia; Teichert, Florian; Bruix, Marta; Carazo, José M; Gonzalez, Cayetano; Reina, José; Valpuesta, José M; Vernos, Isabelle; Zabala, Juan C; Montoya, Guillermo; Coll, Miquel; Bastolla, Ugo; Serrano, Luis

2013-01-01

Here we perform a large-scale study of the structural properties and the expression of proteins that constitute the human Centrosome. Centrosomal proteins tend to be larger than generic human proteins (control set), since their genes contain in average more exons (20.3 versus 14.6). They are rich in predicted disordered regions, which cover 57% of their length, compared to 39% in the general human proteome. They also contain several regions that are dually predicted to be disordered and coiled-coil at the same time: 55 proteins (15%) contain disordered and coiled-coil fragments that cover more than 20% of their length. Helices prevail over strands in regions homologous to known structures (47% predicted helical residues against 17% predicted as strands), and even more in the whole centrosomal proteome (52% against 7%), while for control human proteins 34.5% of the residues are predicted as helical and 12.8% are predicted as strands. This difference is mainly due to residues predicted as disordered and helical (30% in centrosomal and 9.4% in control proteins), which may correspond to alpha-helix forming molecular recognition features (α-MoRFs). We performed expression assays for 120 full-length centrosomal proteins and 72 domain constructs that we have predicted to be globular. These full-length proteins are often insoluble: Only 39 out of 120 expressed proteins (32%) and 19 out of 72 domains (26%) were soluble. We built or retrieved structural models for 277 out of 361 human proteins whose centrosomal localization has been experimentally verified. We could not find any suitable structural template with more than 20% sequence identity for 84 centrosomal proteins (23%), for which around 74% of the residues are predicted to be disordered or coiled-coils. The three-dimensional models that we built are available at http://ub.cbm.uam.es/centrosome/models/index.php.

Target Highlights in CASP9: Experimental Target Structures for the Critical Assessment of Techniques for Protein Structure Prediction

PubMed Central

Kryshtafovych, Andriy; Moult, John; Bartual, Sergio G.; Bazan, J. Fernando; Berman, Helen; Casteel, Darren E.; Christodoulou, Evangelos; Everett, John K.; Hausmann, Jens; Heidebrecht, Tatjana; Hills, Tanya; Hui, Raymond; Hunt, John F.; Jayaraman, Seetharaman; Joachimiak, Andrzej; Kennedy, Michael A.; Kim, Choel; Lingel, Andreas; Michalska, Karolina; Montelione, Gaetano T.; Otero, José M.; Perrakis, Anastassis; Pizarro, Juan C.; van Raaij, Mark J.; Ramelot, Theresa A.; Rousseau, Francois; Tong, Liang; Wernimont, Amy K.; Young, Jasmine; Schwede, Torsten

2011-01-01

One goal of the CASP Community Wide Experiment on the Critical Assessment of Techniques for Protein Structure Prediction is to identify the current state of the art in protein structure prediction and modeling. A fundamental principle of CASP is blind prediction on a set of relevant protein targets, i.e. the participating computational methods are tested on a common set of experimental target proteins, for which the experimental structures are not known at the time of modeling. Therefore, the CASP experiment would not have been possible without broad support of the experimental protein structural biology community. In this manuscript, several experimental groups discuss the structures of the proteins which they provided as prediction targets for CASP9, highlighting structural and functional peculiarities of these structures: the long tail fibre protein gp37 from bacteriophage T4, the cyclic GMP-dependent protein kinase Iβ (PKGIβ) dimerization/docking domain, the ectodomain of the JTB (Jumping Translocation Breakpoint) transmembrane receptor, Autotaxin (ATX) in complex with an inhibitor, the DNA-Binding J-Binding Protein 1 (JBP1) domain essential for biosynthesis and maintenance of DNA base-J (β-D-glucosyl-hydroxymethyluracil) in Trypanosoma and Leishmania, an so far uncharacterized 73 residue domain from Ruminococcus gnavus with a fold typical for PDZ-like domains, a domain from the Phycobilisome (PBS) core-membrane linker (LCM) phycobiliprotein ApcE from Synechocystis, the Heat shock protein 90 (Hsp90) activators PFC0360w and PFC0270w from Plasmodium falciparum, and 2-oxo-3-deoxygalactonate kinase from Klebsiella pneumoniae. PMID:22020785

Cloud prediction of protein structure and function with PredictProtein for Debian.

PubMed

Kaján, László; Yachdav, Guy; Vicedo, Esmeralda; Steinegger, Martin; Mirdita, Milot; Angermüller, Christof; Böhm, Ariane; Domke, Simon; Ertl, Julia; Mertes, Christian; Reisinger, Eva; Staniewski, Cedric; Rost, Burkhard

2013-01-01

We report the release of PredictProtein for the Debian operating system and derivatives, such as Ubuntu, Bio-Linux, and Cloud BioLinux. The PredictProtein suite is available as a standard set of open source Debian packages. The release covers the most popular prediction methods from the Rost Lab, including methods for the prediction of secondary structure and solvent accessibility (profphd), nuclear localization signals (predictnls), and intrinsically disordered regions (norsnet). We also present two case studies that successfully utilize PredictProtein packages for high performance computing in the cloud: the first analyzes protein disorder for whole organisms, and the second analyzes the effect of all possible single sequence variants in protein coding regions of the human genome.

Cloud Prediction of Protein Structure and Function with PredictProtein for Debian

PubMed Central

Kaján, László; Yachdav, Guy; Vicedo, Esmeralda; Steinegger, Martin; Mirdita, Milot; Angermüller, Christof; Böhm, Ariane; Domke, Simon; Ertl, Julia; Mertes, Christian; Reisinger, Eva; Rost, Burkhard

2013-01-01

We report the release of PredictProtein for the Debian operating system and derivatives, such as Ubuntu, Bio-Linux, and Cloud BioLinux. The PredictProtein suite is available as a standard set of open source Debian packages. The release covers the most popular prediction methods from the Rost Lab, including methods for the prediction of secondary structure and solvent accessibility (profphd), nuclear localization signals (predictnls), and intrinsically disordered regions (norsnet). We also present two case studies that successfully utilize PredictProtein packages for high performance computing in the cloud: the first analyzes protein disorder for whole organisms, and the second analyzes the effect of all possible single sequence variants in protein coding regions of the human genome. PMID:23971032

Structure Prediction and Analysis of Neuraminidase Sequence Variants

ERIC Educational Resources Information Center

Thayer, Kelly M.

2016-01-01

Analyzing protein structure has become an integral aspect of understanding systems of biochemical import. The laboratory experiment endeavors to introduce protein folding to ascertain structures of proteins for which the structure is unavailable, as well as to critically evaluate the quality of the prediction obtained. The model system used is the…

CMsearch: simultaneous exploration of protein sequence space and structure space improves not only protein homology detection but also protein structure prediction.

PubMed

Cui, Xuefeng; Lu, Zhiwu; Wang, Sheng; Jing-Yan Wang, Jim; Gao, Xin

2016-06-15

Protein homology detection, a fundamental problem in computational biology, is an indispensable step toward predicting protein structures and understanding protein functions. Despite the advances in recent decades on sequence alignment, threading and alignment-free methods, protein homology detection remains a challenging open problem. Recently, network methods that try to find transitive paths in the protein structure space demonstrate the importance of incorporating network information of the structure space. Yet, current methods merge the sequence space and the structure space into a single space, and thus introduce inconsistency in combining different sources of information. We present a novel network-based protein homology detection method, CMsearch, based on cross-modal learning. Instead of exploring a single network built from the mixture of sequence and structure space information, CMsearch builds two separate networks to represent the sequence space and the structure space. It then learns sequence-structure correlation by simultaneously taking sequence information, structure information, sequence space information and structure space information into consideration. We tested CMsearch on two challenging tasks, protein homology detection and protein structure prediction, by querying all 8332 PDB40 proteins. Our results demonstrate that CMsearch is insensitive to the similarity metrics used to define the sequence and the structure spaces. By using HMM-HMM alignment as the sequence similarity metric, CMsearch clearly outperforms state-of-the-art homology detection methods and the CASP-winning template-based protein structure prediction methods. Our program is freely available for download from http://sfb.kaust.edu.sa/Pages/Software.aspx : xin.gao@kaust.edu.sa Supplementary data are available at Bioinformatics online. © The Author 2016. Published by Oxford University Press.

Structure prediction of polyglutamine disease proteins: comparison of methods

PubMed Central

2014-01-01

Background The expansion of polyglutamine (poly-Q) repeats in several unrelated proteins is associated with at least ten neurodegenerative diseases. The length of the poly-Q regions plays an important role in the progression of the diseases. The number of glutamines (Q) is inversely related to the onset age of these polyglutamine diseases, and the expansion of poly-Q repeats has been associated with protein misfolding. However, very little is known about the structural changes induced by the expansion of the repeats. Computational methods can provide an alternative to determine the structure of these poly-Q proteins, but it is important to evaluate their performance before large scale prediction work is done. Results In this paper, two popular protein structure prediction programs, I-TASSER and Rosetta, have been used to predict the structure of the N-terminal fragment of a protein associated with Huntington's disease with 17 glutamines. Results show that both programs have the ability to find the native structures, but I-TASSER performs better for the overall task. Conclusions Both I-TASSER and Rosetta can be used for structure prediction of proteins with poly-Q repeats. Knowledge of poly-Q structure may significantly contribute to development of therapeutic strategies for poly-Q diseases. PMID:25080018

Thermodynamic prediction of protein neutrality.

PubMed

Bloom, Jesse D; Silberg, Jonathan J; Wilke, Claus O; Drummond, D Allan; Adami, Christoph; Arnold, Frances H

2005-01-18

We present a simple theory that uses thermodynamic parameters to predict the probability that a protein retains the wild-type structure after one or more random amino acid substitutions. Our theory predicts that for large numbers of substitutions the probability that a protein retains its structure will decline exponentially with the number of substitutions, with the severity of this decline determined by properties of the structure. Our theory also predicts that a protein can gain extra robustness to the first few substitutions by increasing its thermodynamic stability. We validate our theory with simulations on lattice protein models and by showing that it quantitatively predicts previously published experimental measurements on subtilisin and our own measurements on variants of TEM1 beta-lactamase. Our work unifies observations about the clustering of functional proteins in sequence space, and provides a basis for interpreting the response of proteins to substitutions in protein engineering applications.

Thermodynamic prediction of protein neutrality

PubMed Central

Bloom, Jesse D.; Silberg, Jonathan J.; Wilke, Claus O.; Drummond, D. Allan; Adami, Christoph; Arnold, Frances H.

2005-01-01

We present a simple theory that uses thermodynamic parameters to predict the probability that a protein retains the wild-type structure after one or more random amino acid substitutions. Our theory predicts that for large numbers of substitutions the probability that a protein retains its structure will decline exponentially with the number of substitutions, with the severity of this decline determined by properties of the structure. Our theory also predicts that a protein can gain extra robustness to the first few substitutions by increasing its thermodynamic stability. We validate our theory with simulations on lattice protein models and by showing that it quantitatively predicts previously published experimental measurements on subtilisin and our own measurements on variants of TEM1 β-lactamase. Our work unifies observations about the clustering of functional proteins in sequence space, and provides a basis for interpreting the response of proteins to substitutions in protein engineering applications. PMID:15644440

GeneSilico protein structure prediction meta-server.

PubMed

Kurowski, Michal A; Bujnicki, Janusz M

2003-07-01

Rigorous assessments of protein structure prediction have demonstrated that fold recognition methods can identify remote similarities between proteins when standard sequence search methods fail. It has been shown that the accuracy of predictions is improved when refined multiple sequence alignments are used instead of single sequences and if different methods are combined to generate a consensus model. There are several meta-servers available that integrate protein structure predictions performed by various methods, but they do not allow for submission of user-defined multiple sequence alignments and they seldom offer confidentiality of the results. We developed a novel WWW gateway for protein structure prediction, which combines the useful features of other meta-servers available, but with much greater flexibility of the input. The user may submit an amino acid sequence or a multiple sequence alignment to a set of methods for primary, secondary and tertiary structure prediction. Fold-recognition results (target-template alignments) are converted into full-atom 3D models and the quality of these models is uniformly assessed. A consensus between different FR methods is also inferred. The results are conveniently presented on-line on a single web page over a secure, password-protected connection. The GeneSilico protein structure prediction meta-server is freely available for academic users at http://genesilico.pl/meta.

GeneSilico protein structure prediction meta-server

PubMed Central

Kurowski, Michal A.; Bujnicki, Janusz M.

2003-01-01

Rigorous assessments of protein structure prediction have demonstrated that fold recognition methods can identify remote similarities between proteins when standard sequence search methods fail. It has been shown that the accuracy of predictions is improved when refined multiple sequence alignments are used instead of single sequences and if different methods are combined to generate a consensus model. There are several meta-servers available that integrate protein structure predictions performed by various methods, but they do not allow for submission of user-defined multiple sequence alignments and they seldom offer confidentiality of the results. We developed a novel WWW gateway for protein structure prediction, which combines the useful features of other meta-servers available, but with much greater flexibility of the input. The user may submit an amino acid sequence or a multiple sequence alignment to a set of methods for primary, secondary and tertiary structure prediction. Fold-recognition results (target-template alignments) are converted into full-atom 3D models and the quality of these models is uniformly assessed. A consensus between different FR methods is also inferred. The results are conveniently presented on-line on a single web page over a secure, password-protected connection. The GeneSilico protein structure prediction meta-server is freely available for academic users at http://genesilico.pl/meta. PMID:12824313

Large-scale model quality assessment for improving protein tertiary structure prediction.

PubMed

Cao, Renzhi; Bhattacharya, Debswapna; Adhikari, Badri; Li, Jilong; Cheng, Jianlin

2015-06-15

Sampling structural models and ranking them are the two major challenges of protein structure prediction. Traditional protein structure prediction methods generally use one or a few quality assessment (QA) methods to select the best-predicted models, which cannot consistently select relatively better models and rank a large number of models well. Here, we develop a novel large-scale model QA method in conjunction with model clustering to rank and select protein structural models. It unprecedentedly applied 14 model QA methods to generate consensus model rankings, followed by model refinement based on model combination (i.e. averaging). Our experiment demonstrates that the large-scale model QA approach is more consistent and robust in selecting models of better quality than any individual QA method. Our method was blindly tested during the 11th Critical Assessment of Techniques for Protein Structure Prediction (CASP11) as MULTICOM group. It was officially ranked third out of all 143 human and server predictors according to the total scores of the first models predicted for 78 CASP11 protein domains and second according to the total scores of the best of the five models predicted for these domains. MULTICOM's outstanding performance in the extremely competitive 2014 CASP11 experiment proves that our large-scale QA approach together with model clustering is a promising solution to one of the two major problems in protein structure modeling. The web server is available at: http://sysbio.rnet.missouri.edu/multicom_cluster/human/. © The Author 2015. Published by Oxford University Press.

Aromatic claw: A new fold with high aromatic content that evades structural prediction: Aromatic Claw

DOE Office of Scientific and Technical Information (OSTI.GOV)

Sachleben, Joseph R.; Adhikari, Aashish N.; Gawlak, Grzegorz

2016-11-10

We determined the NMR structure of a highly aromatic (13%) protein of unknown function, Aq1974 from Aquifex aeolicus (PDB ID: 5SYQ). The unusual sequence of this protein has a tryptophan content five times the normal (six tryptophan residues of 114 or 5.2% while the average tryptophan content is 1.0%) with the tryptophans occurring in a WXW motif. It has no detectable sequence homology with known protein structures. Although its NMR spectrum suggested that the protein was rich in β-sheet, upon resonance assignment and solution structure determination, the protein was found to be primarily α-helical with a small two-stranded β-sheet withmore » a novel fold that we have termed an Aromatic Claw. As this fold was previously unknown and the sequence unique, we submitted the sequence to CASP10 as a target for blind structural prediction. At the end of the competition, the sequence was classified a hard template based model; the structural relationship between the template and the experimental structure was small and the predictions all failed to predict the structure. CSRosetta was found to predict the secondary structure and its packing; however, it was found that there was little correlation between CSRosetta score and the RMSD between the CSRosetta structure and the NMR determined one. This work demonstrates that even in relatively small proteins, we do not yet have the capacity to accurately predict the fold for all primary sequences. The experimental discovery of new folds helps guide the improvement of structural prediction methods.« less

Protein complex prediction in large ontology attributed protein-protein interaction networks.

PubMed

Zhang, Yijia; Lin, Hongfei; Yang, Zhihao; Wang, Jian; Li, Yanpeng; Xu, Bo

2013-01-01

Protein complexes are important for unraveling the secrets of cellular organization and function. Many computational approaches have been developed to predict protein complexes in protein-protein interaction (PPI) networks. However, most existing approaches focus mainly on the topological structure of PPI networks, and largely ignore the gene ontology (GO) annotation information. In this paper, we constructed ontology attributed PPI networks with PPI data and GO resource. After constructing ontology attributed networks, we proposed a novel approach called CSO (clustering based on network structure and ontology attribute similarity). Structural information and GO attribute information are complementary in ontology attributed networks. CSO can effectively take advantage of the correlation between frequent GO annotation sets and the dense subgraph for protein complex prediction. Our proposed CSO approach was applied to four different yeast PPI data sets and predicted many well-known protein complexes. The experimental results showed that CSO was valuable in predicting protein complexes and achieved state-of-the-art performance.

Biological and functional relevance of CASP predictions.

PubMed

Liu, Tianyun; Ish-Shalom, Shirbi; Torng, Wen; Lafita, Aleix; Bock, Christian; Mort, Matthew; Cooper, David N; Bliven, Spencer; Capitani, Guido; Mooney, Sean D; Altman, Russ B

2018-03-01

Our goal is to answer the question: compared with experimental structures, how useful are predicted models for functional annotation? We assessed the functional utility of predicted models by comparing the performances of a suite of methods for functional characterization on the predictions and the experimental structures. We identified 28 sites in 25 protein targets to perform functional assessment. These 28 sites included nine sites with known ligand binding (holo-sites), nine sites that are expected or suggested by experimental authors for small molecule binding (apo-sites), and Ten sites containing important motifs, loops, or key residues with important disease-associated mutations. We evaluated the utility of the predictions by comparing their microenvironments to the experimental structures. Overall structural quality correlates with functional utility. However, the best-ranked predictions (global) may not have the best functional quality (local). Our assessment provides an ability to discriminate between predictions with high structural quality. When assessing ligand-binding sites, most prediction methods have higher performance on apo-sites than holo-sites. Some servers show consistently high performance for certain types of functional sites. Finally, many functional sites are associated with protein-protein interaction. We also analyzed biologically relevant features from the protein assemblies of two targets where the active site spanned the protein-protein interface. For the assembly targets, we find that the features in the models are mainly determined by the choice of template. © 2017 The Authors Proteins: Structure, Function and Bioinformatics Published by Wiley Periodicals, Inc.

Assessing the performance of MM/PBSA and MM/GBSA methods. 8. Predicting binding free energies and poses of protein-RNA complexes.

PubMed

Chen, Fu; Sun, Huiyong; Wang, Junmei; Zhu, Feng; Liu, Hui; Wang, Zhe; Lei, Tailong; Li, Youyong; Hou, Tingjun

2018-06-21

Molecular docking provides a computationally efficient way to predict the atomic structural details of protein-RNA interactions (PRI), but accurate prediction of the three-dimensional structures and binding affinities for PRI is still notoriously difficult, partly due to the unreliability of the existing scoring functions for PRI. MM/PBSA and MM/GBSA are more theoretically rigorous than most scoring functions for protein-RNA docking, but their prediction performance for protein-RNA systems remains unclear. Here, we systemically evaluated the capability of MM/PBSA and MM/GBSA to predict the binding affinities and recognize the near-native binding structures for protein-RNA systems with different solvent models and interior dielectric constants (ϵ in ). For predicting the binding affinities, the predictions given by MM/GBSA based on the minimized structures in explicit solvent and the GBGBn1 model with ϵ in = 2 yielded the highest correlation with the experimental data. Moreover, the MM/GBSA calculations based on the minimized structures in implicit solvent and the GBGBn1 model distinguished the near-native binding structures within the top 10 decoys for 118 out of the 149 protein-RNA systems (79.2%). This performance is better than all docking scoring functions studied here. Therefore, the MM/GBSA rescoring is an efficient way to improve the prediction capability of scoring functions for protein-RNA systems. Published by Cold Spring Harbor Laboratory Press for the RNA Society.

Predicting disulfide connectivity from protein sequence using multiple sequence feature vectors and secondary structure.

PubMed

Song, Jiangning; Yuan, Zheng; Tan, Hao; Huber, Thomas; Burrage, Kevin

2007-12-01

Disulfide bonds are primary covalent crosslinks between two cysteine residues in proteins that play critical roles in stabilizing the protein structures and are commonly found in extracy-toplasmatic or secreted proteins. In protein folding prediction, the localization of disulfide bonds can greatly reduce the search in conformational space. Therefore, there is a great need to develop computational methods capable of accurately predicting disulfide connectivity patterns in proteins that could have potentially important applications. We have developed a novel method to predict disulfide connectivity patterns from protein primary sequence, using a support vector regression (SVR) approach based on multiple sequence feature vectors and predicted secondary structure by the PSIPRED program. The results indicate that our method could achieve a prediction accuracy of 74.4% and 77.9%, respectively, when averaged on proteins with two to five disulfide bridges using 4-fold cross-validation, measured on the protein and cysteine pair on a well-defined non-homologous dataset. We assessed the effects of different sequence encoding schemes on the prediction performance of disulfide connectivity. It has been shown that the sequence encoding scheme based on multiple sequence feature vectors coupled with predicted secondary structure can significantly improve the prediction accuracy, thus enabling our method to outperform most of other currently available predictors. Our work provides a complementary approach to the current algorithms that should be useful in computationally assigning disulfide connectivity patterns and helps in the annotation of protein sequences generated by large-scale whole-genome projects. The prediction web server and Supplementary Material are accessible at http://foo.maths.uq.edu.au/~huber/disulfide

Visualisation of variable binding pockets on protein surfaces by probabilistic analysis of related structure sets.

PubMed

Ashford, Paul; Moss, David S; Alex, Alexander; Yeap, Siew K; Povia, Alice; Nobeli, Irene; Williams, Mark A

2012-03-14

Protein structures provide a valuable resource for rational drug design. For a protein with no known ligand, computational tools can predict surface pockets that are of suitable size and shape to accommodate a complementary small-molecule drug. However, pocket prediction against single static structures may miss features of pockets that arise from proteins' dynamic behaviour. In particular, ligand-binding conformations can be observed as transiently populated states of the apo protein, so it is possible to gain insight into ligand-bound forms by considering conformational variation in apo proteins. This variation can be explored by considering sets of related structures: computationally generated conformers, solution NMR ensembles, multiple crystal structures, homologues or homology models. It is non-trivial to compare pockets, either from different programs or across sets of structures. For a single structure, difficulties arise in defining particular pocket's boundaries. For a set of conformationally distinct structures the challenge is how to make reasonable comparisons between them given that a perfect structural alignment is not possible. We have developed a computational method, Provar, that provides a consistent representation of predicted binding pockets across sets of related protein structures. The outputs are probabilities that each atom or residue of the protein borders a predicted pocket. These probabilities can be readily visualised on a protein using existing molecular graphics software. We show how Provar simplifies comparison of the outputs of different pocket prediction algorithms, of pockets across multiple simulated conformations and between homologous structures. We demonstrate the benefits of use of multiple structures for protein-ligand and protein-protein interface analysis on a set of complexes and consider three case studies in detail: i) analysis of a kinase superfamily highlights the conserved occurrence of surface pockets at the active and regulatory sites; ii) a simulated ensemble of unliganded Bcl2 structures reveals extensions of a known ligand-binding pocket not apparent in the apo crystal structure; iii) visualisations of interleukin-2 and its homologues highlight conserved pockets at the known receptor interfaces and regions whose conformation is known to change on inhibitor binding. Through post-processing of the output of a variety of pocket prediction software, Provar provides a flexible approach to the analysis and visualization of the persistence or variability of pockets in sets of related protein structures.

Dynameomics: Data-driven methods and models for utilizing large-scale protein structure repositories for improving fragment-based loop prediction

PubMed Central

Rysavy, Steven J; Beck, David AC; Daggett, Valerie

2014-01-01

Protein function is intimately linked to protein structure and dynamics yet experimentally determined structures frequently omit regions within a protein due to indeterminate data, which is often due protein dynamics. We propose that atomistic molecular dynamics simulations provide a diverse sampling of biologically relevant structures for these missing segments (and beyond) to improve structural modeling and structure prediction. Here we make use of the Dynameomics data warehouse, which contains simulations of representatives of essentially all known protein folds. We developed novel computational methods to efficiently identify, rank and retrieve small peptide structures, or fragments, from this database. We also created a novel data model to analyze and compare large repositories of structural data, such as contained within the Protein Data Bank and the Dynameomics data warehouse. Our evaluation compares these structural repositories for improving loop predictions and analyzes the utility of our methods and models. Using a standard set of loop structures, containing 510 loops, 30 for each loop length from 4 to 20 residues, we find that the inclusion of Dynameomics structures in fragment-based methods improves the quality of the loop predictions without being dependent on sequence homology. Depending on loop length, ∼25–75% of the best predictions came from the Dynameomics set, resulting in lower main chain root-mean-square deviations for all fragment lengths using the combined fragment library. We also provide specific cases where Dynameomics fragments provide better predictions for NMR loop structures than fragments from crystal structures. Online access to these fragment libraries is available at http://www.dynameomics.org/fragments. PMID:25142412

An object programming based environment for protein secondary structure prediction.

PubMed

Giacomini, M; Ruggiero, C; Sacile, R

1996-01-01

The most frequently used methods for protein secondary structure prediction are empirical statistical methods and rule based methods. A consensus system based on object-oriented programming is presented, which integrates the two approaches with the aim of improving the prediction quality. This system uses an object-oriented knowledge representation based on the concepts of conformation, residue and protein, where the conformation class is the basis, the residue class derives from it and the protein class derives from the residue class. The system has been tested with satisfactory results on several proteins of the Brookhaven Protein Data Bank. Its results have been compared with the results of the most widely used prediction methods, and they show a higher prediction capability and greater stability. Moreover, the system itself provides an index of the reliability of its current prediction. This system can also be regarded as a basis structure for programs of this kind.

De novo protein structure prediction by dynamic fragment assembly and conformational space annealing.

PubMed

Lee, Juyong; Lee, Jinhyuk; Sasaki, Takeshi N; Sasai, Masaki; Seok, Chaok; Lee, Jooyoung

2011-08-01

Ab initio protein structure prediction is a challenging problem that requires both an accurate energetic representation of a protein structure and an efficient conformational sampling method for successful protein modeling. In this article, we present an ab initio structure prediction method which combines a recently suggested novel way of fragment assembly, dynamic fragment assembly (DFA) and conformational space annealing (CSA) algorithm. In DFA, model structures are scored by continuous functions constructed based on short- and long-range structural restraint information from a fragment library. Here, DFA is represented by the full-atom model by CHARMM with the addition of the empirical potential of DFIRE. The relative contributions between various energy terms are optimized using linear programming. The conformational sampling was carried out with CSA algorithm, which can find low energy conformations more efficiently than simulated annealing used in the existing DFA study. The newly introduced DFA energy function and CSA sampling algorithm are implemented into CHARMM. Test results on 30 small single-domain proteins and 13 template-free modeling targets of the 8th Critical Assessment of protein Structure Prediction show that the current method provides comparable and complementary prediction results to existing top methods. Copyright © 2011 Wiley-Liss, Inc.

3D RNA and functional interactions from evolutionary couplings

PubMed Central

Weinreb, Caleb; Riesselman, Adam; Ingraham, John B.; Gross, Torsten; Sander, Chris; Marks, Debora S.

2016-01-01

Summary Non-coding RNAs are ubiquitous, but the discovery of new RNA gene sequences far outpaces research on their structure and functional interactions. We mine the evolutionary sequence record to derive precise information about function and structure of RNAs and RNA-protein complexes. As in protein structure prediction, we use maximum entropy global probability models of sequence co-variation to infer evolutionarily constrained nucleotide-nucleotide interactions within RNA molecules, and nucleotide-amino acid interactions in RNA-protein complexes. The predicted contacts allow all-atom blinded 3D structure prediction at good accuracy for several known RNA structures and RNA-protein complexes. For unknown structures, we predict contacts in 160 non-coding RNA families. Beyond 3D structure prediction, evolutionary couplings help identify important functional interactions, e.g., at switch points in riboswitches and at a complex nucleation site in HIV. Aided by accelerating sequence accumulation, evolutionary coupling analysis can accelerate the discovery of functional interactions and 3D structures involving RNA. PMID:27087444

Critical Features of Fragment Libraries for Protein Structure Prediction

PubMed Central

dos Santos, Karina Baptista

2017-01-01

The use of fragment libraries is a popular approach among protein structure prediction methods and has proven to substantially improve the quality of predicted structures. However, some vital aspects of a fragment library that influence the accuracy of modeling a native structure remain to be determined. This study investigates some of these features. Particularly, we analyze the effect of using secondary structure prediction guiding fragments selection, different fragments sizes and the effect of structural clustering of fragments within libraries. To have a clearer view of how these factors affect protein structure prediction, we isolated the process of model building by fragment assembly from some common limitations associated with prediction methods, e.g., imprecise energy functions and optimization algorithms, by employing an exact structure-based objective function under a greedy algorithm. Our results indicate that shorter fragments reproduce the native structure more accurately than the longer. Libraries composed of multiple fragment lengths generate even better structures, where longer fragments show to be more useful at the beginning of the simulations. The use of many different fragment sizes shows little improvement when compared to predictions carried out with libraries that comprise only three different fragment sizes. Models obtained from libraries built using only sequence similarity are, on average, better than those built with a secondary structure prediction bias. However, we found that the use of secondary structure prediction allows greater reduction of the search space, which is invaluable for prediction methods. The results of this study can be critical guidelines for the use of fragment libraries in protein structure prediction. PMID:28085928

Critical Features of Fragment Libraries for Protein Structure Prediction.

PubMed

Trevizani, Raphael; Custódio, Fábio Lima; Dos Santos, Karina Baptista; Dardenne, Laurent Emmanuel

2017-01-01

The use of fragment libraries is a popular approach among protein structure prediction methods and has proven to substantially improve the quality of predicted structures. However, some vital aspects of a fragment library that influence the accuracy of modeling a native structure remain to be determined. This study investigates some of these features. Particularly, we analyze the effect of using secondary structure prediction guiding fragments selection, different fragments sizes and the effect of structural clustering of fragments within libraries. To have a clearer view of how these factors affect protein structure prediction, we isolated the process of model building by fragment assembly from some common limitations associated with prediction methods, e.g., imprecise energy functions and optimization algorithms, by employing an exact structure-based objective function under a greedy algorithm. Our results indicate that shorter fragments reproduce the native structure more accurately than the longer. Libraries composed of multiple fragment lengths generate even better structures, where longer fragments show to be more useful at the beginning of the simulations. The use of many different fragment sizes shows little improvement when compared to predictions carried out with libraries that comprise only three different fragment sizes. Models obtained from libraries built using only sequence similarity are, on average, better than those built with a secondary structure prediction bias. However, we found that the use of secondary structure prediction allows greater reduction of the search space, which is invaluable for prediction methods. The results of this study can be critical guidelines for the use of fragment libraries in protein structure prediction.

Mathematical methods for protein science

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hart, W.; Istrail, S.; Atkins, J.

1997-12-31

Understanding the structure and function of proteins is a fundamental endeavor in molecular biology. Currently, over 100,000 protein sequences have been determined by experimental methods. The three dimensional structure of the protein determines its function, but there are currently less than 4,000 structures known to atomic resolution. Accordingly, techniques to predict protein structure from sequence have an important role in aiding the understanding of the Genome and the effects of mutations in genetic disease. The authors describe current efforts at Sandia to better understand the structure of proteins through rigorous mathematical analyses of simple lattice models. The efforts have focusedmore » on two aspects of protein science: mathematical structure prediction, and inverse protein folding.« less

Antibody-protein interactions: benchmark datasets and prediction tools evaluation

PubMed Central

Ponomarenko, Julia V; Bourne, Philip E

2007-01-01

Background The ability to predict antibody binding sites (aka antigenic determinants or B-cell epitopes) for a given protein is a precursor to new vaccine design and diagnostics. Among the various methods of B-cell epitope identification X-ray crystallography is one of the most reliable methods. Using these experimental data computational methods exist for B-cell epitope prediction. As the number of structures of antibody-protein complexes grows, further interest in prediction methods using 3D structure is anticipated. This work aims to establish a benchmark for 3D structure-based epitope prediction methods. Results Two B-cell epitope benchmark datasets inferred from the 3D structures of antibody-protein complexes were defined. The first is a dataset of 62 representative 3D structures of protein antigens with inferred structural epitopes. The second is a dataset of 82 structures of antibody-protein complexes containing different structural epitopes. Using these datasets, eight web-servers developed for antibody and protein binding sites prediction have been evaluated. In no method did performance exceed a 40% precision and 46% recall. The values of the area under the receiver operating characteristic curve for the evaluated methods were about 0.6 for ConSurf, DiscoTope, and PPI-PRED methods and above 0.65 but not exceeding 0.70 for protein-protein docking methods when the best of the top ten models for the bound docking were considered; the remaining methods performed close to random. The benchmark datasets are included as a supplement to this paper. Conclusion It may be possible to improve epitope prediction methods through training on datasets which include only immune epitopes and through utilizing more features characterizing epitopes, for example, the evolutionary conservation score. Notwithstanding, overall poor performance may reflect the generality of antigenicity and hence the inability to decipher B-cell epitopes as an intrinsic feature of the protein. It is an open question as to whether ultimately discriminatory features can be found. PMID:17910770

TMDIM: an improved algorithm for the structure prediction of transmembrane domains of bitopic dimers.

PubMed

Cao, Han; Ng, Marcus C K; Jusoh, Siti Azma; Tai, Hio Kuan; Siu, Shirley W I

2017-09-01

[Formula: see text]-Helical transmembrane proteins are the most important drug targets in rational drug development. However, solving the experimental structures of these proteins remains difficult, therefore computational methods to accurately and efficiently predict the structures are in great demand. We present an improved structure prediction method TMDIM based on Park et al. (Proteins 57:577-585, 2004) for predicting bitopic transmembrane protein dimers. Three major algorithmic improvements are introduction of the packing type classification, the multiple-condition decoy filtering, and the cluster-based candidate selection. In a test of predicting nine known bitopic dimers, approximately 78% of our predictions achieved a successful fit (RMSD <2.0 Å) and 78% of the cases are better predicted than the two other methods compared. Our method provides an alternative for modeling TM bitopic dimers of unknown structures for further computational studies. TMDIM is freely available on the web at https://cbbio.cis.umac.mo/TMDIM . Website is implemented in PHP, MySQL and Apache, with all major browsers supported.

TMDIM: an improved algorithm for the structure prediction of transmembrane domains of bitopic dimers

NASA Astrophysics Data System (ADS)

Cao, Han; Ng, Marcus C. K.; Jusoh, Siti Azma; Tai, Hio Kuan; Siu, Shirley W. I.

2017-09-01

α-Helical transmembrane proteins are the most important drug targets in rational drug development. However, solving the experimental structures of these proteins remains difficult, therefore computational methods to accurately and efficiently predict the structures are in great demand. We present an improved structure prediction method TMDIM based on Park et al. (Proteins 57:577-585, 2004) for predicting bitopic transmembrane protein dimers. Three major algorithmic improvements are introduction of the packing type classification, the multiple-condition decoy filtering, and the cluster-based candidate selection. In a test of predicting nine known bitopic dimers, approximately 78% of our predictions achieved a successful fit (RMSD <2.0 Å) and 78% of the cases are better predicted than the two other methods compared. Our method provides an alternative for modeling TM bitopic dimers of unknown structures for further computational studies. TMDIM is freely available on the web at https://cbbio.cis.umac.mo/TMDIM. Website is implemented in PHP, MySQL and Apache, with all major browsers supported.

Post processing of protein-compound docking for fragment-based drug discovery (FBDD): in-silico structure-based drug screening and ligand-binding pose prediction.

PubMed

Fukunishi, Yoshifumi

2010-01-01

For fragment-based drug development, both hit (active) compound prediction and docking-pose (protein-ligand complex structure) prediction of the hit compound are important, since chemical modification (fragment linking, fragment evolution) subsequent to the hit discovery must be performed based on the protein-ligand complex structure. However, the naïve protein-compound docking calculation shows poor accuracy in terms of docking-pose prediction. Thus, post-processing of the protein-compound docking is necessary. Recently, several methods for the post-processing of protein-compound docking have been proposed. In FBDD, the compounds are smaller than those for conventional drug screening. This makes it difficult to perform the protein-compound docking calculation. A method to avoid this problem has been reported. Protein-ligand binding free energy estimation is useful to reduce the procedures involved in the chemical modification of the hit fragment. Several prediction methods have been proposed for high-accuracy estimation of protein-ligand binding free energy. This paper summarizes the various computational methods proposed for docking-pose prediction and their usefulness in FBDD.

RECURSIVE PROTEIN MODELING: A DIVIDE AND CONQUER STRATEGY FOR PROTEIN STRUCTURE PREDICTION AND ITS CASE STUDY IN CASP9

PubMed Central

CHENG, JIANLIN; EICKHOLT, JESSE; WANG, ZHENG; DENG, XIN

2013-01-01

After decades of research, protein structure prediction remains a very challenging problem. In order to address the different levels of complexity of structural modeling, two types of modeling techniques — template-based modeling and template-free modeling — have been developed. Template-based modeling can often generate a moderate- to high-resolution model when a similar, homologous template structure is found for a query protein but fails if no template or only incorrect templates are found. Template-free modeling, such as fragment-based assembly, may generate models of moderate resolution for small proteins of low topological complexity. Seldom have the two techniques been integrated together to improve protein modeling. Here we develop a recursive protein modeling approach to selectively and collaboratively apply template-based and template-free modeling methods to model template-covered (i.e. certain) and template-free (i.e. uncertain) regions of a protein. A preliminary implementation of the approach was tested on a number of hard modeling cases during the 9th Critical Assessment of Techniques for Protein Structure Prediction (CASP9) and successfully improved the quality of modeling in most of these cases. Recursive modeling can signicantly reduce the complexity of protein structure modeling and integrate template-based and template-free modeling to improve the quality and efficiency of protein structure prediction. PMID:22809379

Conformational Transitions upon Ligand Binding: Holo-Structure Prediction from Apo Conformations

PubMed Central

Seeliger, Daniel; de Groot, Bert L.

2010-01-01

Biological function of proteins is frequently associated with the formation of complexes with small-molecule ligands. Experimental structure determination of such complexes at atomic resolution, however, can be time-consuming and costly. Computational methods for structure prediction of protein/ligand complexes, particularly docking, are as yet restricted by their limited consideration of receptor flexibility, rendering them not applicable for predicting protein/ligand complexes if large conformational changes of the receptor upon ligand binding are involved. Accurate receptor models in the ligand-bound state (holo structures), however, are a prerequisite for successful structure-based drug design. Hence, if only an unbound (apo) structure is available distinct from the ligand-bound conformation, structure-based drug design is severely limited. We present a method to predict the structure of protein/ligand complexes based solely on the apo structure, the ligand and the radius of gyration of the holo structure. The method is applied to ten cases in which proteins undergo structural rearrangements of up to 7.1 Å backbone RMSD upon ligand binding. In all cases, receptor models within 1.6 Å backbone RMSD to the target were predicted and close-to-native ligand binding poses were obtained for 8 of 10 cases in the top-ranked complex models. A protocol is presented that is expected to enable structure modeling of protein/ligand complexes and structure-based drug design for cases where crystal structures of ligand-bound conformations are not available. PMID:20066034

MUFOLD-SS: New deep inception-inside-inception networks for protein secondary structure prediction.

PubMed

Fang, Chao; Shang, Yi; Xu, Dong

2018-05-01

Protein secondary structure prediction can provide important information for protein 3D structure prediction and protein functions. Deep learning offers a new opportunity to significantly improve prediction accuracy. In this article, a new deep neural network architecture, named the Deep inception-inside-inception (Deep3I) network, is proposed for protein secondary structure prediction and implemented as a software tool MUFOLD-SS. The input to MUFOLD-SS is a carefully designed feature matrix corresponding to the primary amino acid sequence of a protein, which consists of a rich set of information derived from individual amino acid, as well as the context of the protein sequence. Specifically, the feature matrix is a composition of physio-chemical properties of amino acids, PSI-BLAST profile, and HHBlits profile. MUFOLD-SS is composed of a sequence of nested inception modules and maps the input matrix to either eight states or three states of secondary structures. The architecture of MUFOLD-SS enables effective processing of local and global interactions between amino acids in making accurate prediction. In extensive experiments on multiple datasets, MUFOLD-SS outperformed the best existing methods and other deep neural networks significantly. MUFold-SS can be downloaded from http://dslsrv8.cs.missouri.edu/~cf797/MUFoldSS/download.html. © 2018 Wiley Periodicals, Inc.

Some of the most interesting CASP11 targets through the eyes of their authors

PubMed Central

Kryshtafovych, Andriy; Moult, John; Baslé, Arnaud; Burgin, Alex; Craig, Timothy K.; Edwards, Robert A.; Fass, Deborah; Hartmann, Marcus D.; Korycinski, Mateusz; Lewis, Richard J.; Lorimer, Donald; Lupas, Andrei N.; Newman, Janet; Peat, Thomas S.; Piepenbrink, Kurt H.; Prahlad, Janani; van Raaij, Mark J.; Rohwer, Forest; Segall, Anca M.; Seguritan, Victor; Sundberg, Eric J.; Singh, Abhimanyu K.; Wilson, Mark A.

2015-01-01

ABSTRACT The Critical Assessment of protein Structure Prediction (CASP) experiment would not have been possible without the prediction targets provided by the experimental structural biology community. In this article, selected crystallographers providing targets for the CASP11 experiment discuss the functional and biological significance of the target proteins, highlight their most interesting structural features, and assess whether these features were correctly reproduced in the predictions submitted to CASP11. Proteins 2016; 84(Suppl 1):34–50. © 2015 The Authors. Proteins: Structure, Function, and Bioinformatics Published by Wiley Periodicals, Inc. PMID:26473983

Prediction of Protein Structural Classes for Low-Similarity Sequences Based on Consensus Sequence and Segmented PSSM.

PubMed

Liang, Yunyun; Liu, Sanyang; Zhang, Shengli

2015-01-01

Prediction of protein structural classes for low-similarity sequences is useful for understanding fold patterns, regulation, functions, and interactions of proteins. It is well known that feature extraction is significant to prediction of protein structural class and it mainly uses protein primary sequence, predicted secondary structure sequence, and position-specific scoring matrix (PSSM). Currently, prediction solely based on the PSSM has played a key role in improving the prediction accuracy. In this paper, we propose a novel method called CSP-SegPseP-SegACP by fusing consensus sequence (CS), segmented PsePSSM, and segmented autocovariance transformation (ACT) based on PSSM. Three widely used low-similarity datasets (1189, 25PDB, and 640) are adopted in this paper. Then a 700-dimensional (700D) feature vector is constructed and the dimension is decreased to 224D by using principal component analysis (PCA). To verify the performance of our method, rigorous jackknife cross-validation tests are performed on 1189, 25PDB, and 640 datasets. Comparison of our results with the existing PSSM-based methods demonstrates that our method achieves the favorable and competitive performance. This will offer an important complementary to other PSSM-based methods for prediction of protein structural classes for low-similarity sequences.

UNRES server for physics-based coarse-grained simulations and prediction of protein structure, dynamics and thermodynamics.

PubMed

Czaplewski, Cezary; Karczynska, Agnieszka; Sieradzan, Adam K; Liwo, Adam

2018-04-30

A server implementation of the UNRES package (http://www.unres.pl) for coarse-grained simulations of protein structures with the physics-based UNRES model, coined a name UNRES server, is presented. In contrast to most of the protein coarse-grained models, owing to its physics-based origin, the UNRES force field can be used in simulations, including those aimed at protein-structure prediction, without ancillary information from structural databases; however, the implementation includes the possibility of using restraints. Local energy minimization, canonical molecular dynamics simulations, replica exchange and multiplexed replica exchange molecular dynamics simulations can be run with the current UNRES server; the latter are suitable for protein-structure prediction. The user-supplied input includes protein sequence and, optionally, restraints from secondary-structure prediction or small x-ray scattering data, and simulation type and parameters which are selected or typed in. Oligomeric proteins, as well as those containing D-amino-acid residues and disulfide links can be treated. The output is displayed graphically (minimized structures, trajectories, final models, analysis of trajectory/ensembles); however, all output files can be downloaded by the user. The UNRES server can be freely accessed at http://unres-server.chem.ug.edu.pl.

Prelude and Fugue, predicting local protein structure, early folding regions and structural weaknesses.

PubMed

Kwasigroch, Jean Marc; Rooman, Marianne

2006-07-15

Prelude&Fugue are bioinformatics tools aiming at predicting the local 3D structure of a protein from its amino acid sequence in terms of seven backbone torsion angle domains, using database-derived potentials. Prelude(&Fugue) computes all lowest free energy conformations of a protein or protein region, ranked by increasing energy, and possibly satisfying some interresidue distance constraints specified by the user. (Prelude&)Fugue detects sequence regions whose predicted structure is significantly preferred relative to other conformations in the absence of tertiary interactions. These programs can be used for predicting secondary structure, tertiary structure of short peptides, flickering early folding sequences and peptides that adopt a preferred conformation in solution. They can also be used for detecting structural weaknesses, i.e. sequence regions that are not optimal with respect to the tertiary fold. http://babylone.ulb.ac.be/Prelude_and_Fugue.

ProBiS-CHARMMing: Web Interface for Prediction and Optimization of Ligands in Protein Binding Sites.

PubMed

Konc, Janez; Miller, Benjamin T; Štular, Tanja; Lešnik, Samo; Woodcock, H Lee; Brooks, Bernard R; Janežič, Dušanka

2015-11-23

Proteins often exist only as apo structures (unligated) in the Protein Data Bank, with their corresponding holo structures (with ligands) unavailable. However, apoproteins may not represent the amino-acid residue arrangement upon ligand binding well, which is especially problematic for molecular docking. We developed the ProBiS-CHARMMing web interface by connecting the ProBiS ( http://probis.cmm.ki.si ) and CHARMMing ( http://www.charmming.org ) web servers into one functional unit that enables prediction of protein-ligand complexes and allows for their geometry optimization and interaction energy calculation. The ProBiS web server predicts ligands (small compounds, proteins, nucleic acids, and single-atom ligands) that may bind to a query protein. This is achieved by comparing its surface structure against a nonredundant database of protein structures and finding those that have binding sites similar to that of the query protein. Existing ligands found in the similar binding sites are then transposed to the query according to predictions from ProBiS. The CHARMMing web server enables, among other things, minimization and potential energy calculation for a wide variety of biomolecular systems, and it is used here to optimize the geometry of the predicted protein-ligand complex structures using the CHARMM force field and to calculate their interaction energies with the corresponding query proteins. We show how ProBiS-CHARMMing can be used to predict ligands and their poses for a particular binding site, and minimize the predicted protein-ligand complexes to obtain representations of holoproteins. The ProBiS-CHARMMing web interface is freely available for academic users at http://probis.nih.gov.

WeFold: A Coopetition for Protein Structure Prediction

PubMed Central

Khoury, George A.; Liwo, Adam; Khatib, Firas; Zhou, Hongyi; Chopra, Gaurav; Bacardit, Jaume; Bortot, Leandro O.; Faccioli, Rodrigo A.; Deng, Xin; He, Yi; Krupa, Pawel; Li, Jilong; Mozolewska, Magdalena A.; Sieradzan, Adam K.; Smadbeck, James; Wirecki, Tomasz; Cooper, Seth; Flatten, Jeff; Xu, Kefan; Baker, David; Cheng, Jianlin; Delbem, Alexandre C. B.; Floudas, Christodoulos A.; Keasar, Chen; Levitt, Michael; Popović, Zoran; Scheraga, Harold A.; Skolnick, Jeffrey; Crivelli, Silvia N.; Players, Foldit

2014-01-01

The protein structure prediction problem continues to elude scientists. Despite the introduction of many methods, only modest gains were made over the last decade for certain classes of prediction targets. To address this challenge, a social-media based worldwide collaborative effort, named WeFold, was undertaken by thirteen labs. During the collaboration, the labs were simultaneously competing with each other. Here, we present the first attempt at “coopetition” in scientific research applied to the protein structure prediction and refinement problems. The coopetition was possible by allowing the participating labs to contribute different components of their protein structure prediction pipelines and create new hybrid pipelines that they tested during CASP10. This manuscript describes both successes and areas needing improvement as identified throughout the first WeFold experiment and discusses the efforts that are underway to advance this initiative. A footprint of all contributions and structures are publicly accessible at http://www.wefold.org. PMID:24677212

Text Mining Improves Prediction of Protein Functional Sites

PubMed Central

Cohn, Judith D.; Ravikumar, Komandur E.

2012-01-01

We present an approach that integrates protein structure analysis and text mining for protein functional site prediction, called LEAP-FS (Literature Enhanced Automated Prediction of Functional Sites). The structure analysis was carried out using Dynamics Perturbation Analysis (DPA), which predicts functional sites at control points where interactions greatly perturb protein vibrations. The text mining extracts mentions of residues in the literature, and predicts that residues mentioned are functionally important. We assessed the significance of each of these methods by analyzing their performance in finding known functional sites (specifically, small-molecule binding sites and catalytic sites) in about 100,000 publicly available protein structures. The DPA predictions recapitulated many of the functional site annotations and preferentially recovered binding sites annotated as biologically relevant vs. those annotated as potentially spurious. The text-based predictions were also substantially supported by the functional site annotations: compared to other residues, residues mentioned in text were roughly six times more likely to be found in a functional site. The overlap of predictions with annotations improved when the text-based and structure-based methods agreed. Our analysis also yielded new high-quality predictions of many functional site residues that were not catalogued in the curated data sources we inspected. We conclude that both DPA and text mining independently provide valuable high-throughput protein functional site predictions, and that integrating the two methods using LEAP-FS further improves the quality of these predictions. PMID:22393388

Protein Secondary Structure Prediction Using Deep Convolutional Neural Fields.

PubMed

Wang, Sheng; Peng, Jian; Ma, Jianzhu; Xu, Jinbo

2016-01-11

Protein secondary structure (SS) prediction is important for studying protein structure and function. When only the sequence (profile) information is used as input feature, currently the best predictors can obtain ~80% Q3 accuracy, which has not been improved in the past decade. Here we present DeepCNF (Deep Convolutional Neural Fields) for protein SS prediction. DeepCNF is a Deep Learning extension of Conditional Neural Fields (CNF), which is an integration of Conditional Random Fields (CRF) and shallow neural networks. DeepCNF can model not only complex sequence-structure relationship by a deep hierarchical architecture, but also interdependency between adjacent SS labels, so it is much more powerful than CNF. Experimental results show that DeepCNF can obtain ~84% Q3 accuracy, ~85% SOV score, and ~72% Q8 accuracy, respectively, on the CASP and CAMEO test proteins, greatly outperforming currently popular predictors. As a general framework, DeepCNF can be used to predict other protein structure properties such as contact number, disorder regions, and solvent accessibility.

Protein Secondary Structure Prediction Using Deep Convolutional Neural Fields

NASA Astrophysics Data System (ADS)

Wang, Sheng; Peng, Jian; Ma, Jianzhu; Xu, Jinbo

2016-01-01

Protein secondary structure (SS) prediction is important for studying protein structure and function. When only the sequence (profile) information is used as input feature, currently the best predictors can obtain ~80% Q3 accuracy, which has not been improved in the past decade. Here we present DeepCNF (Deep Convolutional Neural Fields) for protein SS prediction. DeepCNF is a Deep Learning extension of Conditional Neural Fields (CNF), which is an integration of Conditional Random Fields (CRF) and shallow neural networks. DeepCNF can model not only complex sequence-structure relationship by a deep hierarchical architecture, but also interdependency between adjacent SS labels, so it is much more powerful than CNF. Experimental results show that DeepCNF can obtain ~84% Q3 accuracy, ~85% SOV score, and ~72% Q8 accuracy, respectively, on the CASP and CAMEO test proteins, greatly outperforming currently popular predictors. As a general framework, DeepCNF can be used to predict other protein structure properties such as contact number, disorder regions, and solvent accessibility.

Modularity of Protein Folds as a Tool for Template-Free Modeling of Structures.

PubMed

Vallat, Brinda; Madrid-Aliste, Carlos; Fiser, Andras

2015-08-01

Predicting the three-dimensional structure of proteins from their amino acid sequences remains a challenging problem in molecular biology. While the current structural coverage of proteins is almost exclusively provided by template-based techniques, the modeling of the rest of the protein sequences increasingly require template-free methods. However, template-free modeling methods are much less reliable and are usually applicable for smaller proteins, leaving much space for improvement. We present here a novel computational method that uses a library of supersecondary structure fragments, known as Smotifs, to model protein structures. The library of Smotifs has saturated over time, providing a theoretical foundation for efficient modeling. The method relies on weak sequence signals from remotely related protein structures to create a library of Smotif fragments specific to the target protein sequence. This Smotif library is exploited in a fragment assembly protocol to sample decoys, which are assessed by a composite scoring function. Since the Smotif fragments are larger in size compared to the ones used in other fragment-based methods, the proposed modeling algorithm, SmotifTF, can employ an exhaustive sampling during decoy assembly. SmotifTF successfully predicts the overall fold of the target proteins in about 50% of the test cases and performs competitively when compared to other state of the art prediction methods, especially when sequence signal to remote homologs is diminishing. Smotif-based modeling is complementary to current prediction methods and provides a promising direction in addressing the structure prediction problem, especially when targeting larger proteins for modeling.

Fourier-based classification of protein secondary structures.

PubMed

Shu, Jian-Jun; Yong, Kian Yan

2017-04-15

The correct prediction of protein secondary structures is one of the key issues in predicting the correct protein folded shape, which is used for determining gene function. Existing methods make use of amino acids properties as indices to classify protein secondary structures, but are faced with a significant number of misclassifications. The paper presents a technique for the classification of protein secondary structures based on protein "signal-plotting" and the use of the Fourier technique for digital signal processing. New indices are proposed to classify protein secondary structures by analyzing hydrophobicity profiles. The approach is simple and straightforward. Results show that the more types of protein secondary structures can be classified by means of these newly-proposed indices. Copyright © 2017 Elsevier Inc. All rights reserved.

SeqRate: sequence-based protein folding type classification and rates prediction

PubMed Central

2010-01-01

Background Protein folding rate is an important property of a protein. Predicting protein folding rate is useful for understanding protein folding process and guiding protein design. Most previous methods of predicting protein folding rate require the tertiary structure of a protein as an input. And most methods do not distinguish the different kinetic nature (two-state folding or multi-state folding) of the proteins. Here we developed a method, SeqRate, to predict both protein folding kinetic type (two-state versus multi-state) and real-value folding rate using sequence length, amino acid composition, contact order, contact number, and secondary structure information predicted from only protein sequence with support vector machines. Results We systematically studied the contributions of individual features to folding rate prediction. On a standard benchmark dataset, the accuracy of folding kinetic type classification is 80%. The Pearson correlation coefficient and the mean absolute difference between predicted and experimental folding rates (sec-1) in the base-10 logarithmic scale are 0.81 and 0.79 for two-state protein folders, and 0.80 and 0.68 for three-state protein folders. SeqRate is the first sequence-based method for protein folding type classification and its accuracy of fold rate prediction is improved over previous sequence-based methods. Its performance can be further enhanced with additional information, such as structure-based geometric contacts, as inputs. Conclusions Both the web server and software of predicting folding rate are publicly available at http://casp.rnet.missouri.edu/fold_rate/index.html. PMID:20438647

Sequence co-evolution gives 3D contacts and structures of protein complexes

PubMed Central

Hopf, Thomas A; Schärfe, Charlotta P I; Rodrigues, João P G L M; Green, Anna G; Kohlbacher, Oliver; Sander, Chris; Bonvin, Alexandre M J J; Marks, Debora S

2014-01-01

Protein–protein interactions are fundamental to many biological processes. Experimental screens have identified tens of thousands of interactions, and structural biology has provided detailed functional insight for select 3D protein complexes. An alternative rich source of information about protein interactions is the evolutionary sequence record. Building on earlier work, we show that analysis of correlated evolutionary sequence changes across proteins identifies residues that are close in space with sufficient accuracy to determine the three-dimensional structure of the protein complexes. We evaluate prediction performance in blinded tests on 76 complexes of known 3D structure, predict protein–protein contacts in 32 complexes of unknown structure, and demonstrate how evolutionary couplings can be used to distinguish between interacting and non-interacting protein pairs in a large complex. With the current growth of sequences, we expect that the method can be generalized to genome-wide elucidation of protein–protein interaction networks and used for interaction predictions at residue resolution. DOI: http://dx.doi.org/10.7554/eLife.03430.001 PMID:25255213

Accurate prediction of RNA-binding protein residues with two discriminative structural descriptors.

PubMed

Sun, Meijian; Wang, Xia; Zou, Chuanxin; He, Zenghui; Liu, Wei; Li, Honglin

2016-06-07

RNA-binding proteins participate in many important biological processes concerning RNA-mediated gene regulation, and several computational methods have been recently developed to predict the protein-RNA interactions of RNA-binding proteins. Newly developed discriminative descriptors will help to improve the prediction accuracy of these prediction methods and provide further meaningful information for researchers. In this work, we designed two structural features (residue electrostatic surface potential and triplet interface propensity) and according to the statistical and structural analysis of protein-RNA complexes, the two features were powerful for identifying RNA-binding protein residues. Using these two features and other excellent structure- and sequence-based features, a random forest classifier was constructed to predict RNA-binding residues. The area under the receiver operating characteristic curve (AUC) of five-fold cross-validation for our method on training set RBP195 was 0.900, and when applied to the test set RBP68, the prediction accuracy (ACC) was 0.868, and the F-score was 0.631. The good prediction performance of our method revealed that the two newly designed descriptors could be discriminative for inferring protein residues interacting with RNAs. To facilitate the use of our method, a web-server called RNAProSite, which implements the proposed method, was constructed and is freely available at http://lilab.ecust.edu.cn/NABind .

Accurate disulfide-bonding network predictions improve ab initio structure prediction of cysteine-rich proteins

PubMed Central

Yang, Jing; He, Bao-Ji; Jang, Richard; Zhang, Yang; Shen, Hong-Bin

2015-01-01

Abstract Motivation: Cysteine-rich proteins cover many important families in nature but there are currently no methods specifically designed for modeling the structure of these proteins. The accuracy of disulfide connectivity pattern prediction, particularly for the proteins of higher-order connections, e.g. >3 bonds, is too low to effectively assist structure assembly simulations. Results: We propose a new hierarchical order reduction protocol called Cyscon for disulfide-bonding prediction. The most confident disulfide bonds are first identified and bonding prediction is then focused on the remaining cysteine residues based on SVR training. Compared with purely machine learning-based approaches, Cyscon improved the average accuracy of connectivity pattern prediction by 21.9%. For proteins with more than 5 disulfide bonds, Cyscon improved the accuracy by 585% on the benchmark set of PDBCYS. When applied to 158 non-redundant cysteine-rich proteins, Cyscon predictions helped increase (or decrease) the TM-score (or RMSD) of the ab initio QUARK modeling by 12.1% (or 14.4%). This result demonstrates a new avenue to improve the ab initio structure modeling for cysteine-rich proteins. Availability and implementation: http://www.csbio.sjtu.edu.cn/bioinf/Cyscon/ Contact: zhng@umich.edu or hbshen@sjtu.edu.cn Supplementary information: Supplementary data are available at Bioinformatics online. PMID:26254435

Molecular dynamics simulations of the Bcl-2 protein to predict the structure of its unordered flexible loop domain.

PubMed

Raghav, Pawan Kumar; Verma, Yogesh Kumar; Gangenahalli, Gurudutta U

2012-05-01

B-cell lymphoma (Bcl-2) protein is an anti-apoptotic member of the Bcl-2 family. It is functionally demarcated into four Bcl-2 homology (BH) domains: BH1, BH2, BH3, BH4, one flexible loop domain (FLD), a transmembrane domain (TM), and an X domain. Bcl-2's BH domains have clearly been elucidated from a structural perspective, whereas the conformation of FLD has not yet been predicted, despite its important role in regulating apoptosis through its interactions with JNK-1, PKC, PP2A phosphatase, caspase 3, MAP kinase, ubiquitin, PS1, and FKBP38. Many important residues that regulate Bcl-2 anti-apoptotic activity are present in this domain, for example Asp34, Thr56, Thr69, Ser70, Thr74, and Ser87. The structural elucidation of the FLD would likely help in attempts to accurately predict the effect of mutating these residues on the overall structure of the protein and the interactions of other proteins in this domain. Therefore, we have generated an increased quality model of the Bcl-2 protein including the FLD through modeling. Further, molecular dynamics (MD) simulations were used for FLD optimization, to predict the flexibility, and to determine the stability of the folded FLD. In addition, essential dynamics (ED) was used to predict the collective motions and the essential subspace relevant to Bcl-2 protein function. The predicted average structure and ensemble of MD-simulated structures were submitted to the Protein Model Database (PMDB), and the Bcl-2 structures obtained exhibited enhanced quality. This study should help to elucidate the structural basis for Bcl-2 anti-apoptotic activity regulation through its binding to other proteins via the FLD.

All-atom 3D structure prediction of transmembrane β-barrel proteins from sequences.

PubMed

Hayat, Sikander; Sander, Chris; Marks, Debora S; Elofsson, Arne

2015-04-28

Transmembrane β-barrels (TMBs) carry out major functions in substrate transport and protein biogenesis but experimental determination of their 3D structure is challenging. Encouraged by successful de novo 3D structure prediction of globular and α-helical membrane proteins from sequence alignments alone, we developed an approach to predict the 3D structure of TMBs. The approach combines the maximum-entropy evolutionary coupling method for predicting residue contacts (EVfold) with a machine-learning approach (boctopus2) for predicting β-strands in the barrel. In a blinded test for 19 TMB proteins of known structure that have a sufficient number of diverse homologous sequences available, this combined method (EVfold_bb) predicts hydrogen-bonded residue pairs between adjacent β-strands at an accuracy of ∼70%. This accuracy is sufficient for the generation of all-atom 3D models. In the transmembrane barrel region, the average 3D structure accuracy [template-modeling (TM) score] of top-ranked models is 0.54 (ranging from 0.36 to 0.85), with a higher (44%) number of residue pairs in correct strand-strand registration than in earlier methods (18%). Although the nonbarrel regions are predicted less accurately overall, the evolutionary couplings identify some highly constrained loop residues and, for FecA protein, the barrel including the structure of a plug domain can be accurately modeled (TM score = 0.68). Lower prediction accuracy tends to be associated with insufficient sequence information and we therefore expect increasing numbers of β-barrel families to become accessible to accurate 3D structure prediction as the number of available sequences increases.

Functional insights from proteome-wide structural modeling of Treponema pallidum subspecies pallidum, the causative agent of syphilis.

PubMed

Houston, Simon; Lithgow, Karen Vivien; Osbak, Kara Krista; Kenyon, Chris Richard; Cameron, Caroline E

2018-05-16

Syphilis continues to be a major global health threat with 11 million new infections each year, and a global burden of 36 million cases. The causative agent of syphilis, Treponema pallidum subspecies pallidum, is a highly virulent bacterium, however the molecular mechanisms underlying T. pallidum pathogenesis remain to be definitively identified. This is due to the fact that T. pallidum is currently uncultivatable, inherently fragile and thus difficult to work with, and phylogenetically distinct with no conventional virulence factor homologs found in other pathogens. In fact, approximately 30% of its predicted protein-coding genes have no known orthologs or assigned functions. Here we employed a structural bioinformatics approach using Phyre2-based tertiary structure modeling to improve our understanding of T. pallidum protein function on a proteome-wide scale. Phyre2-based tertiary structure modeling generated high-confidence predictions for 80% of the T. pallidum proteome (780/978 predicted proteins). Tertiary structure modeling also inferred the same function as primary structure-based annotations from genome sequencing pipelines for 525/605 proteins (87%), which represents 54% (525/978) of all T. pallidum proteins. Of the 175 T. pallidum proteins modeled with high confidence that were not assigned functions in the previously annotated published proteome, 167 (95%) were able to be assigned predicted functions. Twenty-one of the 175 hypothetical proteins modeled with high confidence were also predicted to exhibit significant structural similarity with proteins experimentally confirmed to be required for virulence in other pathogens. Phyre2-based structural modeling is a powerful bioinformatics tool that has provided insight into the potential structure and function of the majority of T. pallidum proteins and helped validate the primary structure-based annotation of more than 50% of all T. pallidum proteins with high confidence. This work represents the first T. pallidum proteome-wide structural modeling study and is one of few studies to apply this approach for the functional annotation of a whole proteome.

LECTINPred: web Server that Uses Complex Networks of Protein Structure for Prediction of Lectins with Potential Use as Cancer Biomarkers or in Parasite Vaccine Design.

PubMed

Munteanu, Cristian R; Pedreira, Nieves; Dorado, Julián; Pazos, Alejandro; Pérez-Montoto, Lázaro G; Ubeira, Florencio M; González-Díaz, Humberto

2014-04-01

Lectins (Ls) play an important role in many diseases such as different types of cancer, parasitic infections and other diseases. Interestingly, the Protein Data Bank (PDB) contains +3000 protein 3D structures with unknown function. Thus, we can in principle, discover new Ls mining non-annotated structures from PDB or other sources. However, there are no general models to predict new biologically relevant Ls based on 3D chemical structures. We used the MARCH-INSIDE software to calculate the Markov-Shannon 3D electrostatic entropy parameters for the complex networks of protein structure of 2200 different protein 3D structures, including 1200 Ls. We have performed a Linear Discriminant Analysis (LDA) using these parameters as inputs in order to seek a new Quantitative Structure-Activity Relationship (QSAR) model, which is able to discriminate 3D structure of Ls from other proteins. We implemented this predictor in the web server named LECTINPred, freely available at http://bio-aims.udc.es/LECTINPred.php. This web server showed the following goodness-of-fit statistics: Sensitivity=96.7 % (for Ls), Specificity=87.6 % (non-active proteins), and Accuracy=92.5 % (for all proteins), considering altogether both the training and external prediction series. In mode 2, users can carry out an automatic retrieval of protein structures from PDB. We illustrated the use of this server, in operation mode 1, performing a data mining of PDB. We predicted Ls scores for +2000 proteins with unknown function and selected the top-scored ones as possible lectins. In operation mode 2, LECTINPred can also upload 3D structural models generated with structure-prediction tools like LOMETS or PHYRE2. The new Ls are expected to be of relevance as cancer biomarkers or useful in parasite vaccine design. © 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Recent developments in structural proteomics for protein structure determination.

PubMed

Liu, Hsuan-Liang; Hsu, Jyh-Ping

2005-05-01

The major challenges in structural proteomics include identifying all the proteins on the genome-wide scale, determining their structure-function relationships, and outlining the precise three-dimensional structures of the proteins. Protein structures are typically determined by experimental approaches such as X-ray crystallography or nuclear magnetic resonance (NMR) spectroscopy. However, the knowledge of three-dimensional space by these techniques is still limited. Thus, computational methods such as comparative and de novo approaches and molecular dynamic simulations are intensively used as alternative tools to predict the three-dimensional structures and dynamic behavior of proteins. This review summarizes recent developments in structural proteomics for protein structure determination; including instrumental methods such as X-ray crystallography and NMR spectroscopy, and computational methods such as comparative and de novo structure prediction and molecular dynamics simulations.

Prediction of protein tertiary structure from sequences using a very large back-propagation neural network

DOE Office of Scientific and Technical Information (OSTI.GOV)

Liu, X.; Wilcox, G.L.

1993-12-31

We have implemented large scale back-propagation neural networks on a 544 node Connection Machine, CM-5, using the C language in MIMD mode. The program running on 512 processors performs backpropagation learning at 0.53 Gflops, which provides 76 million connection updates per second. We have applied the network to the prediction of protein tertiary structure from sequence information alone. A neural network with one hidden layer and 40 million connections is trained to learn the relationship between sequence and tertiary structure. The trained network yields predicted structures of some proteins on which it has not been trained given only their sequences.more » Presentation of the Fourier transform of the sequences accentuates periodicity in the sequence and yields good generalization with greatly increased training efficiency. Training simulations with a large, heterologous set of protein structures (111 proteins from CM-5 time) to solutions with under 2% RMS residual error within the training set (random responses give an RMS error of about 20%). Presentation of 15 sequences of related proteins in a testing set of 24 proteins yields predicted structures with less than 8% RMS residual error, indicating good apparent generalization.« less

Predicting turns in proteins with a unified model.

PubMed

Song, Qi; Li, Tonghua; Cong, Peisheng; Sun, Jiangming; Li, Dapeng; Tang, Shengnan

2012-01-01

Turns are a critical element of the structure of a protein; turns play a crucial role in loops, folds, and interactions. Current prediction methods are well developed for the prediction of individual turn types, including α-turn, β-turn, and γ-turn, etc. However, for further protein structure and function prediction it is necessary to develop a uniform model that can accurately predict all types of turns simultaneously. In this study, we present a novel approach, TurnP, which offers the ability to investigate all the turns in a protein based on a unified model. The main characteristics of TurnP are: (i) using newly exploited features of structural evolution information (secondary structure and shape string of protein) based on structure homologies, (ii) considering all types of turns in a unified model, and (iii) practical capability of accurate prediction of all turns simultaneously for a query. TurnP utilizes predicted secondary structures and predicted shape strings, both of which have greater accuracy, based on innovative technologies which were both developed by our group. Then, sequence and structural evolution features, which are profile of sequence, profile of secondary structures and profile of shape strings are generated by sequence and structure alignment. When TurnP was validated on a non-redundant dataset (4,107 entries) by five-fold cross-validation, we achieved an accuracy of 88.8% and a sensitivity of 71.8%, which exceeded the most state-of-the-art predictors of certain type of turn. Newly determined sequences, the EVA and CASP9 datasets were used as independent tests and the results we achieved were outstanding for turn predictions and confirmed the good performance of TurnP for practical applications.

Predicting Turns in Proteins with a Unified Model

PubMed Central

Song, Qi; Li, Tonghua; Cong, Peisheng; Sun, Jiangming; Li, Dapeng; Tang, Shengnan

2012-01-01

Motivation Turns are a critical element of the structure of a protein; turns play a crucial role in loops, folds, and interactions. Current prediction methods are well developed for the prediction of individual turn types, including α-turn, β-turn, and γ-turn, etc. However, for further protein structure and function prediction it is necessary to develop a uniform model that can accurately predict all types of turns simultaneously. Results In this study, we present a novel approach, TurnP, which offers the ability to investigate all the turns in a protein based on a unified model. The main characteristics of TurnP are: (i) using newly exploited features of structural evolution information (secondary structure and shape string of protein) based on structure homologies, (ii) considering all types of turns in a unified model, and (iii) practical capability of accurate prediction of all turns simultaneously for a query. TurnP utilizes predicted secondary structures and predicted shape strings, both of which have greater accuracy, based on innovative technologies which were both developed by our group. Then, sequence and structural evolution features, which are profile of sequence, profile of secondary structures and profile of shape strings are generated by sequence and structure alignment. When TurnP was validated on a non-redundant dataset (4,107 entries) by five-fold cross-validation, we achieved an accuracy of 88.8% and a sensitivity of 71.8%, which exceeded the most state-of-the-art predictors of certain type of turn. Newly determined sequences, the EVA and CASP9 datasets were used as independent tests and the results we achieved were outstanding for turn predictions and confirmed the good performance of TurnP for practical applications. PMID:23144872

Reduced Fragment Diversity for Alpha and Alpha-Beta Protein Structure Prediction using Rosetta.

PubMed

Abbass, Jad; Nebel, Jean-Christophe

2017-01-01

Protein structure prediction is considered a main challenge in computational biology. The biannual international competition, Critical Assessment of protein Structure Prediction (CASP), has shown in its eleventh experiment that free modelling target predictions are still beyond reliable accuracy, therefore, much effort should be made to improve ab initio methods. Arguably, Rosetta is considered as the most competitive method when it comes to targets with no homologues. Relying on fragments of length 9 and 3 from known structures, Rosetta creates putative structures by assembling candidate fragments. Generally, the structure with the lowest energy score, also known as first model, is chosen to be the "predicted one". A thorough study has been conducted on the role and diversity of 3-mers involved in Rosetta's model "refinement" phase. Usage of the standard number of 3-mers - i.e. 200 - has been shown to degrade alpha and alpha-beta protein conformations initially achieved by assembling 9-mers. Therefore, a new prediction pipeline is proposed for Rosetta where the "refinement" phase is customised according to a target's structural class prediction. Over 8% improvement in terms of first model structure accuracy is reported for alpha and alpha-beta classes when decreasing the number of 3- mers. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

Structure-Templated Predictions of Novel Protein Interactions from Sequence Information

PubMed Central

Betel, Doron; Breitkreuz, Kevin E; Isserlin, Ruth; Dewar-Darch, Danielle; Tyers, Mike; Hogue, Christopher W. V

2007-01-01

The multitude of functions performed in the cell are largely controlled by a set of carefully orchestrated protein interactions often facilitated by specific binding of conserved domains in the interacting proteins. Interacting domains commonly exhibit distinct binding specificity to short and conserved recognition peptides called binding profiles. Although many conserved domains are known in nature, only a few have well-characterized binding profiles. Here, we describe a novel predictive method known as domain–motif interactions from structural topology (D-MIST) for elucidating the binding profiles of interacting domains. A set of domains and their corresponding binding profiles were derived from extant protein structures and protein interaction data and then used to predict novel protein interactions in yeast. A number of the predicted interactions were verified experimentally, including new interactions of the mitotic exit network, RNA polymerases, nucleotide metabolism enzymes, and the chaperone complex. These results demonstrate that new protein interactions can be predicted exclusively from sequence information. PMID:17892321

NMRDSP: an accurate prediction of protein shape strings from NMR chemical shifts and sequence data.

PubMed

Mao, Wusong; Cong, Peisheng; Wang, Zhiheng; Lu, Longjian; Zhu, Zhongliang; Li, Tonghua

2013-01-01

Shape string is structural sequence and is an extremely important structure representation of protein backbone conformations. Nuclear magnetic resonance chemical shifts give a strong correlation with the local protein structure, and are exploited to predict protein structures in conjunction with computational approaches. Here we demonstrate a novel approach, NMRDSP, which can accurately predict the protein shape string based on nuclear magnetic resonance chemical shifts and structural profiles obtained from sequence data. The NMRDSP uses six chemical shifts (HA, H, N, CA, CB and C) and eight elements of structure profiles as features, a non-redundant set (1,003 entries) as the training set, and a conditional random field as a classification algorithm. For an independent testing set (203 entries), we achieved an accuracy of 75.8% for S8 (the eight states accuracy) and 87.8% for S3 (the three states accuracy). This is higher than only using chemical shifts or sequence data, and confirms that the chemical shift and the structure profile are significant features for shape string prediction and their combination prominently improves the accuracy of the predictor. We have constructed the NMRDSP web server and believe it could be employed to provide a solid platform to predict other protein structures and functions. The NMRDSP web server is freely available at http://cal.tongji.edu.cn/NMRDSP/index.jsp.

Electrostatics, structure prediction, and the energy landscapes for protein folding and binding.

PubMed

Tsai, Min-Yeh; Zheng, Weihua; Balamurugan, D; Schafer, Nicholas P; Kim, Bobby L; Cheung, Margaret S; Wolynes, Peter G

2016-01-01

While being long in range and therefore weakly specific, electrostatic interactions are able to modulate the stability and folding landscapes of some proteins. The relevance of electrostatic forces for steering the docking of proteins to each other is widely acknowledged, however, the role of electrostatics in establishing specifically funneled landscapes and their relevance for protein structure prediction are still not clear. By introducing Debye-Hückel potentials that mimic long-range electrostatic forces into the Associative memory, Water mediated, Structure, and Energy Model (AWSEM), a transferable protein model capable of predicting tertiary structures, we assess the effects of electrostatics on the landscapes of thirteen monomeric proteins and four dimers. For the monomers, we find that adding electrostatic interactions does not improve structure prediction. Simulations of ribosomal protein S6 show, however, that folding stability depends monotonically on electrostatic strength. The trend in predicted melting temperatures of the S6 variants agrees with experimental observations. Electrostatic effects can play a range of roles in binding. The binding of the protein complex KIX-pKID is largely assisted by electrostatic interactions, which provide direct charge-charge stabilization of the native state and contribute to the funneling of the binding landscape. In contrast, for several other proteins, including the DNA-binding protein FIS, electrostatics causes frustration in the DNA-binding region, which favors its binding with DNA but not with its protein partner. This study highlights the importance of long-range electrostatics in functional responses to problems where proteins interact with their charged partners, such as DNA, RNA, as well as membranes. © 2015 The Protein Society.

Predicting protein interactions by Brownian dynamics simulations.

PubMed

Meng, Xuan-Yu; Xu, Yu; Zhang, Hong-Xing; Mezei, Mihaly; Cui, Meng

2012-01-01

We present a newly adapted Brownian-Dynamics (BD)-based protein docking method for predicting native protein complexes. The approach includes global BD conformational sampling, compact complex selection, and local energy minimization. In order to reduce the computational costs for energy evaluations, a shell-based grid force field was developed to represent the receptor protein and solvation effects. The performance of this BD protein docking approach has been evaluated on a test set of 24 crystal protein complexes. Reproduction of experimental structures in the test set indicates the adequate conformational sampling and accurate scoring of this BD protein docking approach. Furthermore, we have developed an approach to account for the flexibility of proteins, which has been successfully applied to reproduce the experimental complex structure from the structure of two unbounded proteins. These results indicate that this adapted BD protein docking approach can be useful for the prediction of protein-protein interactions.

Why Is There a Glass Ceiling for Threading Based Protein Structure Prediction Methods?

PubMed

Skolnick, Jeffrey; Zhou, Hongyi

2017-04-20

Despite their different implementations, comparison of the best threading approaches to the prediction of evolutionary distant protein structures reveals that they tend to succeed or fail on the same protein targets. This is true despite the fact that the structural template library has good templates for all cases. Thus, a key question is why are certain protein structures threadable while others are not. Comparison with threading results on a set of artificial sequences selected for stability further argues that the failure of threading is due to the nature of the protein structures themselves. Using a new contact map based alignment algorithm, we demonstrate that certain folds are highly degenerate in that they can have very similar coarse grained fractions of native contacts aligned and yet differ significantly from the native structure. For threadable proteins, this is not the case. Thus, contemporary threading approaches appear to have reached a plateau, and new approaches to structure prediction are required.

Automated protein structure modeling in CASP9 by I-TASSER pipeline combined with QUARK-based ab initio folding and FG-MD-based structure refinement

PubMed Central

Xu, Dong; Zhang, Jian; Roy, Ambrish; Zhang, Yang

2011-01-01

I-TASSER is an automated pipeline for protein tertiary structure prediction using multiple threading alignments and iterative structure assembly simulations. In CASP9 experiments, two new algorithms, QUARK and FG-MD, were added to the I-TASSER pipeline for improving the structural modeling accuracy. QUARK is a de novo structure prediction algorithm used for structure modeling of proteins that lack detectable template structures. For distantly homologous targets, QUARK models are found useful as a reference structure for selecting good threading alignments and guiding the I-TASSER structure assembly simulations. FG-MD is an atomic-level structural refinement program that uses structural fragments collected from the PDB structures to guide molecular dynamics simulation and improve the local structure of predicted model, including hydrogen-bonding networks, torsion angles and steric clashes. Despite considerable progress in both the template-based and template-free structure modeling, significant improvements on protein target classification, domain parsing, model selection, and ab initio folding of beta-proteins are still needed to further improve the I-TASSER pipeline. PMID:22069036

Computational modeling of membrane proteins

PubMed Central

Leman, Julia Koehler; Ulmschneider, Martin B.; Gray, Jeffrey J.

2014-01-01

The determination of membrane protein (MP) structures has always trailed that of soluble proteins due to difficulties in their overexpression, reconstitution into membrane mimetics, and subsequent structure determination. The percentage of MP structures in the protein databank (PDB) has been at a constant 1-2% for the last decade. In contrast, over half of all drugs target MPs, only highlighting how little we understand about drug-specific effects in the human body. To reduce this gap, researchers have attempted to predict structural features of MPs even before the first structure was experimentally elucidated. In this review, we present current computational methods to predict MP structure, starting with secondary structure prediction, prediction of trans-membrane spans, and topology. Even though these methods generate reliable predictions, challenges such as predicting kinks or precise beginnings and ends of secondary structure elements are still waiting to be addressed. We describe recent developments in the prediction of 3D structures of both α-helical MPs as well as β-barrels using comparative modeling techniques, de novo methods, and molecular dynamics (MD) simulations. The increase of MP structures has (1) facilitated comparative modeling due to availability of more and better templates, and (2) improved the statistics for knowledge-based scoring functions. Moreover, de novo methods have benefitted from the use of correlated mutations as restraints. Finally, we outline current advances that will likely shape the field in the forthcoming decade. PMID:25355688

Protein loop modeling using a new hybrid energy function and its application to modeling in inaccurate structural environments.

PubMed

Park, Hahnbeom; Lee, Gyu Rie; Heo, Lim; Seok, Chaok

2014-01-01

Protein loop modeling is a tool for predicting protein local structures of particular interest, providing opportunities for applications involving protein structure prediction and de novo protein design. Until recently, the majority of loop modeling methods have been developed and tested by reconstructing loops in frameworks of experimentally resolved structures. In many practical applications, however, the protein loops to be modeled are located in inaccurate structural environments. These include loops in model structures, low-resolution experimental structures, or experimental structures of different functional forms. Accordingly, discrepancies in the accuracy of the structural environment assumed in development of the method and that in practical applications present additional challenges to modern loop modeling methods. This study demonstrates a new strategy for employing a hybrid energy function combining physics-based and knowledge-based components to help tackle this challenge. The hybrid energy function is designed to combine the strengths of each energy component, simultaneously maintaining accurate loop structure prediction in a high-resolution framework structure and tolerating minor environmental errors in low-resolution structures. A loop modeling method based on global optimization of this new energy function is tested on loop targets situated in different levels of environmental errors, ranging from experimental structures to structures perturbed in backbone as well as side chains and template-based model structures. The new method performs comparably to force field-based approaches in loop reconstruction in crystal structures and better in loop prediction in inaccurate framework structures. This result suggests that higher-accuracy predictions would be possible for a broader range of applications. The web server for this method is available at http://galaxy.seoklab.org/loop with the PS2 option for the scoring function.

PDB-UF: database of predicted enzymatic functions for unannotated protein structures from structural genomics.

PubMed

von Grotthuss, Marcin; Plewczynski, Dariusz; Ginalski, Krzysztof; Rychlewski, Leszek; Shakhnovich, Eugene I

2006-02-06

The number of protein structures from structural genomics centers dramatically increases in the Protein Data Bank (PDB). Many of these structures are functionally unannotated because they have no sequence similarity to proteins of known function. However, it is possible to successfully infer function using only structural similarity. Here we present the PDB-UF database, a web-accessible collection of predictions of enzymatic properties using structure-function relationship. The assignments were conducted for three-dimensional protein structures of unknown function that come from structural genomics initiatives. We show that 4 hypothetical proteins (with PDB accession codes: 1VH0, 1NS5, 1O6D, and 1TO0), for which standard BLAST tools such as PSI-BLAST or RPS-BLAST failed to assign any function, are probably methyltransferase enzymes. We suggest that the structure-based prediction of an EC number should be conducted having the different similarity score cutoff for different protein folds. Moreover, performing the annotation using two different algorithms can reduce the rate of false positive assignments. We believe, that the presented web-based repository will help to decrease the number of protein structures that have functions marked as "unknown" in the PDB file. http://paradox.harvard.edu/PDB-UF and http://bioinfo.pl/PDB-UF.

Topology of membrane proteins-predictions, limitations and variations.

PubMed

Tsirigos, Konstantinos D; Govindarajan, Sudha; Bassot, Claudio; Västermark, Åke; Lamb, John; Shu, Nanjiang; Elofsson, Arne

2017-10-26

Transmembrane proteins perform a variety of important biological functions necessary for the survival and growth of the cells. Membrane proteins are built up by transmembrane segments that span the lipid bilayer. The segments can either be in the form of hydrophobic alpha-helices or beta-sheets which create a barrel. A fundamental aspect of the structure of transmembrane proteins is the membrane topology, that is, the number of transmembrane segments, their position in the protein sequence and their orientation in the membrane. Along these lines, many predictive algorithms for the prediction of the topology of alpha-helical and beta-barrel transmembrane proteins exist. The newest algorithms obtain an accuracy close to 80% both for alpha-helical and beta-barrel transmembrane proteins. However, lately it has been shown that the simplified picture presented when describing a protein family by its topology is limited. To demonstrate this, we highlight examples where the topology is either not conserved in a protein superfamily or where the structure cannot be described solely by the topology of a protein. The prediction of these non-standard features from sequence alone was not successful until the recent revolutionary progress in 3D-structure prediction of proteins. Copyright © 2017 Elsevier Ltd. All rights reserved.

RepeatsDB-lite: a web server for unit annotation of tandem repeat proteins.

PubMed

Hirsh, Layla; Paladin, Lisanna; Piovesan, Damiano; Tosatto, Silvio C E

2018-05-09

RepeatsDB-lite (http://protein.bio.unipd.it/repeatsdb-lite) is a web server for the prediction of repetitive structural elements and units in tandem repeat (TR) proteins. TRs are a widespread but poorly annotated class of non-globular proteins carrying heterogeneous functions. RepeatsDB-lite extends the prediction to all TR types and strongly improves the performance both in terms of computational time and accuracy over previous methods, with precision above 95% for solenoid structures. The algorithm exploits an improved TR unit library derived from the RepeatsDB database to perform an iterative structural search and assignment. The web interface provides tools for analyzing the evolutionary relationships between units and manually refine the prediction by changing unit positions and protein classification. An all-against-all structure-based sequence similarity matrix is calculated and visualized in real-time for every user edit. Reviewed predictions can be submitted to RepeatsDB for review and inclusion.

Toward a structure determination method for biomineral-associated protein using combined solid- state NMR and computational structure prediction.

PubMed

Masica, David L; Ash, Jason T; Ndao, Moise; Drobny, Gary P; Gray, Jeffrey J

2010-12-08

Protein-biomineral interactions are paramount to materials production in biology, including the mineral phase of hard tissue. Unfortunately, the structure of biomineral-associated proteins cannot be determined by X-ray crystallography or solution nuclear magnetic resonance (NMR). Here we report a method for determining the structure of biomineral-associated proteins. The method combines solid-state NMR (ssNMR) and ssNMR-biased computational structure prediction. In addition, the algorithm is able to identify lattice geometries most compatible with ssNMR constraints, representing a quantitative, novel method for investigating crystal-face binding specificity. We use this method to determine most of the structure of human salivary statherin interacting with the mineral phase of tooth enamel. Computation and experiment converge on an ensemble of related structures and identify preferential binding at three crystal surfaces. The work represents a significant advance toward determining structure of biomineral-adsorbed protein using experimentally biased structure prediction. This method is generally applicable to proteins that can be chemically synthesized. Copyright © 2010 Elsevier Ltd. All rights reserved.

Performance of MDockPP in CAPRI rounds 28-29 and 31-35 including the prediction of water-mediated interactions.

PubMed

Xu, Xianjin; Qiu, Liming; Yan, Chengfei; Ma, Zhiwei; Grinter, Sam Z; Zou, Xiaoqin

2017-03-01

Protein-protein interactions are either through direct contacts between two binding partners or mediated by structural waters. Both direct contacts and water-mediated interactions are crucial to the formation of a protein-protein complex. During the recent CAPRI rounds, a novel parallel searching strategy for predicting water-mediated interactions is introduced into our protein-protein docking method, MDockPP. Briefly, a FFT-based docking algorithm is employed in generating putative binding modes, and an iteratively derived statistical potential-based scoring function, ITScorePP, in conjunction with biological information is used to assess and rank the binding modes. Up to 10 binding modes are selected as the initial protein-protein complex structures for MD simulations in explicit solvent. Water molecules near the interface are clustered based on the snapshots extracted from independent equilibrated trajectories. Then, protein-ligand docking is employed for a parallel search for water molecules near the protein-protein interface. The water molecules generated by ligand docking and the clustered water molecules generated by MD simulations are merged, referred to as the predicted structural water molecules. Here, we report the performance of this protocol for CAPRI rounds 28-29 and 31-35 containing 20 valid docking targets and 11 scoring targets. In the docking experiments, we predicted correct binding modes for nine targets, including one high-accuracy, two medium-accuracy, and six acceptable predictions. Regarding the two targets for the prediction of water-mediated interactions, we achieved models ranked as "excellent" in accordance with the CAPRI evaluation criteria; one of these two targets is considered as a difficult target for structural water prediction. Proteins 2017; 85:424-434. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

Applications of Protein Thermodynamic Database for Understanding Protein Mutant Stability and Designing Stable Mutants.

PubMed

Gromiha, M Michael; Anoosha, P; Huang, Liang-Tsung

2016-01-01

Protein stability is the free energy difference between unfolded and folded states of a protein, which lies in the range of 5-25 kcal/mol. Experimentally, protein stability is measured with circular dichroism, differential scanning calorimetry, and fluorescence spectroscopy using thermal and denaturant denaturation methods. These experimental data have been accumulated in the form of a database, ProTherm, thermodynamic database for proteins and mutants. It also contains sequence and structure information of a protein, experimental methods and conditions, and literature information. Different features such as search, display, and sorting options and visualization tools have been incorporated in the database. ProTherm is a valuable resource for understanding/predicting the stability of proteins and it can be accessed at http://www.abren.net/protherm/ . ProTherm has been effectively used to examine the relationship among thermodynamics, structure, and function of proteins. We describe the recent progress on the development of methods for understanding/predicting protein stability, such as (1) general trends on mutational effects on stability, (2) relationship between the stability of protein mutants and amino acid properties, (3) applications of protein three-dimensional structures for predicting their stability upon point mutations, (4) prediction of protein stability upon single mutations from amino acid sequence, and (5) prediction methods for addressing double mutants. A list of online resources for predicting has also been provided.

A novel Multi-Agent Ada-Boost algorithm for predicting protein structural class with the information of protein secondary structure.

PubMed

Fan, Ming; Zheng, Bin; Li, Lihua

2015-10-01

Knowledge of the structural class of a given protein is important for understanding its folding patterns. Although a lot of efforts have been made, it still remains a challenging problem for prediction of protein structural class solely from protein sequences. The feature extraction and classification of proteins are the main problems in prediction. In this research, we extended our earlier work regarding these two aspects. In protein feature extraction, we proposed a scheme by calculating the word frequency and word position from sequences of amino acid, reduced amino acid, and secondary structure. For an accurate classification of the structural class of protein, we developed a novel Multi-Agent Ada-Boost (MA-Ada) method by integrating the features of Multi-Agent system into Ada-Boost algorithm. Extensive experiments were taken to test and compare the proposed method using four benchmark datasets in low homology. The results showed classification accuracies of 88.5%, 96.0%, 88.4%, and 85.5%, respectively, which are much better compared with the existing methods. The source code and dataset are available on request.

StruLocPred: structure-based protein subcellular localisation prediction using multi-class support vector machine.

PubMed

Zhou, Wengang; Dickerson, Julie A

2012-01-01

Knowledge of protein subcellular locations can help decipher a protein's biological function. This work proposes new features: sequence-based: Hybrid Amino Acid Pair (HAAP) and two structure-based: Secondary Structural Element Composition (SSEC) and solvent accessibility state frequency. A multi-class Support Vector Machine is developed to predict the locations. Testing on two established data sets yields better prediction accuracies than the best available systems. Comparisons with existing methods show comparable results to ESLPred2. When StruLocPred is applied to the entire Arabidopsis proteome, over 77% of proteins with known locations match the prediction results. An implementation of this system is at http://wgzhou.ece. iastate.edu/StruLocPred/.

Structure Prediction of the Second Extracellular Loop in G-Protein-Coupled Receptors

PubMed Central

Kmiecik, Sebastian; Jamroz, Michal; Kolinski, Michal

2014-01-01

G-protein-coupled receptors (GPCRs) play key roles in living organisms. Therefore, it is important to determine their functional structures. The second extracellular loop (ECL2) is a functionally important region of GPCRs, which poses significant challenge for computational structure prediction methods. In this work, we evaluated CABS, a well-established protein modeling tool for predicting ECL2 structure in 13 GPCRs. The ECL2s (with between 13 and 34 residues) are predicted in an environment of other extracellular loops being fully flexible and the transmembrane domain fixed in its x-ray conformation. The modeling procedure used theoretical predictions of ECL2 secondary structure and experimental constraints on disulfide bridges. Our approach yielded ensembles of low-energy conformers and the most populated conformers that contained models close to the available x-ray structures. The level of similarity between the predicted models and x-ray structures is comparable to that of other state-of-the-art computational methods. Our results extend other studies by including newly crystallized GPCRs. PMID:24896119

A sparse autoencoder-based deep neural network for protein solvent accessibility and contact number prediction.

PubMed

Deng, Lei; Fan, Chao; Zeng, Zhiwen

2017-12-28

Direct prediction of the three-dimensional (3D) structures of proteins from one-dimensional (1D) sequences is a challenging problem. Significant structural characteristics such as solvent accessibility and contact number are essential for deriving restrains in modeling protein folding and protein 3D structure. Thus, accurately predicting these features is a critical step for 3D protein structure building. In this study, we present DeepSacon, a computational method that can effectively predict protein solvent accessibility and contact number by using a deep neural network, which is built based on stacked autoencoder and a dropout method. The results demonstrate that our proposed DeepSacon achieves a significant improvement in the prediction quality compared with the state-of-the-art methods. We obtain 0.70 three-state accuracy for solvent accessibility, 0.33 15-state accuracy and 0.74 Pearson Correlation Coefficient (PCC) for the contact number on the 5729 monomeric soluble globular protein dataset. We also evaluate the performance on the CASP11 benchmark dataset, DeepSacon achieves 0.68 three-state accuracy and 0.69 PCC for solvent accessibility and contact number, respectively. We have shown that DeepSacon can reliably predict solvent accessibility and contact number with stacked sparse autoencoder and a dropout approach.

Simultaneous optimization of biomolecular energy function on features from small molecules and macromolecules

PubMed Central

Park, Hahnbeom; Bradley, Philip; Greisen, Per; Liu, Yuan; Mulligan, Vikram Khipple; Kim, David E.; Baker, David; DiMaio, Frank

2017-01-01

Most biomolecular modeling energy functions for structure prediction, sequence design, and molecular docking, have been parameterized using existing macromolecular structural data; this contrasts molecular mechanics force fields which are largely optimized using small-molecule data. In this study, we describe an integrated method that enables optimization of a biomolecular modeling energy function simultaneously against small-molecule thermodynamic data and high-resolution macromolecular structural data. We use this approach to develop a next-generation Rosetta energy function that utilizes a new anisotropic implicit solvation model, and an improved electrostatics and Lennard-Jones model, illustrating how energy functions can be considerably improved in their ability to describe large-scale energy landscapes by incorporating both small-molecule and macromolecule data. The energy function improves performance in a wide range of protein structure prediction challenges, including monomeric structure prediction, protein-protein and protein-ligand docking, protein sequence design, and prediction of the free energy changes by mutation, while reasonably recapitulating small-molecule thermodynamic properties. PMID:27766851

Building a knowledge-based statistical potential by capturing high-order inter-residue interactions and its applications in protein secondary structure assessment.

PubMed

Li, Yaohang; Liu, Hui; Rata, Ionel; Jakobsson, Eric

2013-02-25

The rapidly increasing number of protein crystal structures available in the Protein Data Bank (PDB) has naturally made statistical analyses feasible in studying complex high-order inter-residue correlations. In this paper, we report a context-based secondary structure potential (CSSP) for assessing the quality of predicted protein secondary structures generated by various prediction servers. CSSP is a sequence-position-specific knowledge-based potential generated based on the potentials of mean force approach, where high-order inter-residue interactions are taken into consideration. The CSSP potential is effective in identifying secondary structure predictions with good quality. In 56% of the targets in the CB513 benchmark, the optimal CSSP potential is able to recognize the native secondary structure or a prediction with Q3 accuracy higher than 90% as best scored in the predicted secondary structures generated by 10 popularly used secondary structure prediction servers. In more than 80% of the CB513 targets, the predicted secondary structures with the lowest CSSP potential values yield higher than 80% Q3 accuracy. Similar performance of CSSP is found on the CASP9 targets as well. Moreover, our computational results also show that the CSSP potential using triplets outperforms the CSSP potential using doublets and is currently better than the CSSP potential using quartets.

Exploring the Sequence-based Prediction of Folding Initiation Sites in Proteins.

PubMed

Raimondi, Daniele; Orlando, Gabriele; Pancsa, Rita; Khan, Taushif; Vranken, Wim F

2017-08-18

Protein folding is a complex process that can lead to disease when it fails. Especially poorly understood are the very early stages of protein folding, which are likely defined by intrinsic local interactions between amino acids close to each other in the protein sequence. We here present EFoldMine, a method that predicts, from the primary amino acid sequence of a protein, which amino acids are likely involved in early folding events. The method is based on early folding data from hydrogen deuterium exchange (HDX) data from NMR pulsed labelling experiments, and uses backbone and sidechain dynamics as well as secondary structure propensities as features. The EFoldMine predictions give insights into the folding process, as illustrated by a qualitative comparison with independent experimental observations. Furthermore, on a quantitative proteome scale, the predicted early folding residues tend to become the residues that interact the most in the folded structure, and they are often residues that display evolutionary covariation. The connection of the EFoldMine predictions with both folding pathway data and the folded protein structure suggests that the initial statistical behavior of the protein chain with respect to local structure formation has a lasting effect on its subsequent states.

Identification of family-specific residue packing motifs and their use for structure-based protein function prediction: I. Method development.

PubMed

Bandyopadhyay, Deepak; Huan, Jun; Prins, Jan; Snoeyink, Jack; Wang, Wei; Tropsha, Alexander

2009-11-01

Protein function prediction is one of the central problems in computational biology. We present a novel automated protein structure-based function prediction method using libraries of local residue packing patterns that are common to most proteins in a known functional family. Critical to this approach is the representation of a protein structure as a graph where residue vertices (residue name used as a vertex label) are connected by geometrical proximity edges. The approach employs two steps. First, it uses a fast subgraph mining algorithm to find all occurrences of family-specific labeled subgraphs for all well characterized protein structural and functional families. Second, it queries a new structure for occurrences of a set of motifs characteristic of a known family, using a graph index to speed up Ullman's subgraph isomorphism algorithm. The confidence of function inference from structure depends on the number of family-specific motifs found in the query structure compared with their distribution in a large non-redundant database of proteins. This method can assign a new structure to a specific functional family in cases where sequence alignments, sequence patterns, structural superposition and active site templates fail to provide accurate annotation.

FINDSITE-metal: Integrating evolutionary information and machine learning for structure-based metal binding site prediction at the proteome level

PubMed Central

Brylinski, Michal; Skolnick, Jeffrey

2010-01-01

The rapid accumulation of gene sequences, many of which are hypothetical proteins with unknown function, has stimulated the development of accurate computational tools for protein function prediction with evolution/structure-based approaches showing considerable promise. In this paper, we present FINDSITE-metal, a new threading-based method designed specifically to detect metal binding sites in modeled protein structures. Comprehensive benchmarks using different quality protein structures show that weakly homologous protein models provide sufficient structural information for quite accurate annotation by FINDSITE-metal. Combining structure/evolutionary information with machine learning results in highly accurate metal binding annotations; for protein models constructed by TASSER, whose average Cα RMSD from the native structure is 8.9 Å, 59.5% (71.9%) of the best of top five predicted metal locations are within 4 Å (8 Å) from a bound metal in the crystal structure. For most of the targets, multiple metal binding sites are detected with the best predicted binding site at rank 1 and within the top 2 ranks in 65.6% and 83.1% of the cases, respectively. Furthermore, for iron, copper, zinc, calcium and magnesium ions, the binding metal can be predicted with high, typically 70-90%, accuracy. FINDSITE-metal also provides a set of confidence indexes that help assess the reliability of predictions. Finally, we describe the proteome-wide application of FINDSITE-metal that quantifies the metal binding complement of the human proteome. FINDSITE-metal is freely available to the academic community at http://cssb.biology.gatech.edu/findsite-metal/. PMID:21287609

Quantitative Protein Topography Analysis and High-Resolution Structure Prediction Using Hydroxyl Radical Labeling and Tandem-Ion Mass Spectrometry (MS)*

PubMed Central

Kaur, Parminder; Kiselar, Janna; Yang, Sichun; Chance, Mark R.

2015-01-01

Hydroxyl radical footprinting based MS for protein structure assessment has the goal of understanding ligand induced conformational changes and macromolecular interactions, for example, protein tertiary and quaternary structure, but the structural resolution provided by typical peptide-level quantification is limiting. In this work, we present experimental strategies using tandem-MS fragmentation to increase the spatial resolution of the technique to the single residue level to provide a high precision tool for molecular biophysics research. Overall, in this study we demonstrated an eightfold increase in structural resolution compared with peptide level assessments. In addition, to provide a quantitative analysis of residue based solvent accessibility and protein topography as a basis for high-resolution structure prediction; we illustrate strategies of data transformation using the relative reactivity of side chains as a normalization strategy and predict side-chain surface area from the footprinting data. We tested the methods by examination of Ca+2-calmodulin showing highly significant correlations between surface area and side-chain contact predictions for individual side chains and the crystal structure. Tandem ion based hydroxyl radical footprinting-MS provides quantitative high-resolution protein topology information in solution that can fill existing gaps in structure determination for large proteins and macromolecular complexes. PMID:25687570

Struct2Net: a web service to predict protein–protein interactions using a structure-based approach

PubMed Central

Singh, Rohit; Park, Daniel; Xu, Jinbo; Hosur, Raghavendra; Berger, Bonnie

2010-01-01

Struct2Net is a web server for predicting interactions between arbitrary protein pairs using a structure-based approach. Prediction of protein–protein interactions (PPIs) is a central area of interest and successful prediction would provide leads for experiments and drug design; however, the experimental coverage of the PPI interactome remains inadequate. We believe that Struct2Net is the first community-wide resource to provide structure-based PPI predictions that go beyond homology modeling. Also, most web-resources for predicting PPIs currently rely on functional genomic data (e.g. GO annotation, gene expression, cellular localization, etc.). Our structure-based approach is independent of such methods and only requires the sequence information of the proteins being queried. The web service allows multiple querying options, aimed at maximizing flexibility. For the most commonly studied organisms (fly, human and yeast), predictions have been pre-computed and can be retrieved almost instantaneously. For proteins from other species, users have the option of getting a quick-but-approximate result (using orthology over pre-computed results) or having a full-blown computation performed. The web service is freely available at http://struct2net.csail.mit.edu. PMID:20513650

NIAS-Server: Neighbors Influence of Amino acids and Secondary Structures in Proteins.

PubMed

Borguesan, Bruno; Inostroza-Ponta, Mario; Dorn, Márcio

2017-03-01

The exponential growth in the number of experimentally determined three-dimensional protein structures provide a new and relevant knowledge about the conformation of amino acids in proteins. Only a few of probability densities of amino acids are publicly available for use in structure validation and prediction methods. NIAS (Neighbors Influence of Amino acids and Secondary structures) is a web-based tool used to extract information about conformational preferences of amino acid residues and secondary structures in experimental-determined protein templates. This information is useful, for example, to characterize folds and local motifs in proteins, molecular folding, and can help the solution of complex problems such as protein structure prediction, protein design, among others. The NIAS-Server and supplementary data are available at http://sbcb.inf.ufrgs.br/nias .

Improve the prediction of RNA-binding residues using structural neighbours.

PubMed

Li, Quan; Cao, Zanxia; Liu, Haiyan

2010-03-01

The interactions between RNA-binding proteins (RBPs) with RNA play key roles in managing some of the cell's basic functions. The identification and prediction of RNA binding sites is important for understanding the RNA-binding mechanism. Computational approaches are being developed to predict RNA-binding residues based on the sequence- or structure-derived features. To achieve higher prediction accuracy, improvements on current prediction methods are necessary. We identified that the structural neighbors of RNA-binding and non-RNA-binding residues have different amino acid compositions. Combining this structure-derived feature with evolutionary (PSSM) and other structural information (secondary structure and solvent accessibility) significantly improves the predictions over existing methods. Using a multiple linear regression approach and 6-fold cross validation, our best model can achieve an overall correct rate of 87.8% and MCC of 0.47, with a specificity of 93.4%, correctly predict 52.4% of the RNA-binding residues for a dataset containing 107 non-homologous RNA-binding proteins. Compared with existing methods, including the amino acid compositions of structure neighbors lead to clearly improvement. A web server was developed for predicting RNA binding residues in a protein sequence (or structure),which is available at http://mcgill.3322.org/RNA/.

ProBiS-ligands: a web server for prediction of ligands by examination of protein binding sites.

PubMed

Konc, Janez; Janežič, Dušanka

2014-07-01

The ProBiS-ligands web server predicts binding of ligands to a protein structure. Starting with a protein structure or binding site, ProBiS-ligands first identifies template proteins in the Protein Data Bank that share similar binding sites. Based on the superimpositions of the query protein and the similar binding sites found, the server then transposes the ligand structures from those sites to the query protein. Such ligand prediction supports many activities, e.g. drug repurposing. The ProBiS-ligands web server, an extension of the ProBiS web server, is open and free to all users at http://probis.cmm.ki.si/ligands. © The Author(s) 2014. Published by Oxford University Press on behalf of Nucleic Acids Research.

Structural motif screening reveals a novel, conserved carbohydrate-binding surface in the pathogenesis-related protein PR-5d.

PubMed

Doxey, Andrew C; Cheng, Zhenyu; Moffatt, Barbara A; McConkey, Brendan J

2010-08-03

Aromatic amino acids play a critical role in protein-glycan interactions. Clusters of surface aromatic residues and their features may therefore be useful in distinguishing glycan-binding sites as well as predicting novel glycan-binding proteins. In this work, a structural bioinformatics approach was used to screen the Protein Data Bank (PDB) for coplanar aromatic motifs similar to those found in known glycan-binding proteins. The proteins identified in the screen were significantly associated with carbohydrate-related functions according to gene ontology (GO) enrichment analysis, and predicted motifs were found frequently within novel folds and glycan-binding sites not included in the training set. In addition to numerous binding sites predicted in structural genomics proteins of unknown function, one novel prediction was a surface motif (W34/W36/W192) in the tobacco pathogenesis-related protein, PR-5d. Phylogenetic analysis revealed that the surface motif is exclusive to a subfamily of PR-5 proteins from the Solanaceae family of plants, and is absent completely in more distant homologs. To confirm PR-5d's insoluble-polysaccharide binding activity, a cellulose-pulldown assay of tobacco proteins was performed and PR-5d was identified in the cellulose-binding fraction by mass spectrometry. Based on the combined results, we propose that the putative binding site in PR-5d may be an evolutionary adaptation of Solanaceae plants including potato, tomato, and tobacco, towards defense against cellulose-containing pathogens such as species of the deadly oomycete genus, Phytophthora. More generally, the results demonstrate that coplanar aromatic clusters on protein surfaces are a structural signature of glycan-binding proteins, and can be used to computationally predict novel glycan-binding proteins from 3 D structure.

Learning To Fold Proteins Using Energy Landscape Theory

PubMed Central

Schafer, N.P.; Kim, B.L.; Zheng, W.; Wolynes, P.G.

2014-01-01

This review is a tutorial for scientists interested in the problem of protein structure prediction, particularly those interested in using coarse-grained molecular dynamics models that are optimized using lessons learned from the energy landscape theory of protein folding. We also present a review of the results of the AMH/AMC/AMW/AWSEM family of coarse-grained molecular dynamics protein folding models to illustrate the points covered in the first part of the article. Accurate coarse-grained structure prediction models can be used to investigate a wide range of conceptual and mechanistic issues outside of protein structure prediction; specifically, the paper concludes by reviewing how AWSEM has in recent years been able to elucidate questions related to the unusual kinetic behavior of artificially designed proteins, multidomain protein misfolding, and the initial stages of protein aggregation. PMID:25308991

Building proteins from C alpha coordinates using the dihedral probability grid Monte Carlo method.

PubMed Central

Mathiowetz, A. M.; Goddard, W. A.

1995-01-01

Dihedral probability grid Monte Carlo (DPG-MC) is a general-purpose method of conformational sampling that can be applied to many problems in peptide and protein modeling. Here we present the DPG-MC method and apply it to predicting complete protein structures from C alpha coordinates. This is useful in such endeavors as homology modeling, protein structure prediction from lattice simulations, or fitting protein structures to X-ray crystallographic data. It also serves as an example of how DPG-MC can be applied to systems with geometric constraints. The conformational propensities for individual residues are used to guide conformational searches as the protein is built from the amino-terminus to the carboxyl-terminus. Results for a number of proteins show that both the backbone and side chain can be accurately modeled using DPG-MC. Backbone atoms are generally predicted with RMS errors of about 0.5 A (compared to X-ray crystal structure coordinates) and all atoms are predicted to an RMS error of 1.7 A or better. PMID:7549885

First Principles Predictions of the Structure and Function of G-Protein-Coupled Receptors: Validation for Bovine Rhodopsin

PubMed Central

Trabanino, Rene J.; Hall, Spencer E.; Vaidehi, Nagarajan; Floriano, Wely B.; Kam, Victor W. T.; Goddard, William A.

2004-01-01

G-protein-coupled receptors (GPCRs) are involved in cell communication processes and with mediating such senses as vision, smell, taste, and pain. They constitute a prominent superfamily of drug targets, but an atomic-level structure is available for only one GPCR, bovine rhodopsin, making it difficult to use structure-based methods to design receptor-specific drugs. We have developed the MembStruk first principles computational method for predicting the three-dimensional structure of GPCRs. In this article we validate the MembStruk procedure by comparing its predictions with the high-resolution crystal structure of bovine rhodopsin. The crystal structure of bovine rhodopsin has the second extracellular (EC-II) loop closed over the transmembrane regions by making a disulfide linkage between Cys-110 and Cys-187, but we speculate that opening this loop may play a role in the activation process of the receptor through the cysteine linkage with helix 3. Consequently we predicted two structures for bovine rhodopsin from the primary sequence (with no input from the crystal structure)—one with the EC-II loop closed as in the crystal structure, and the other with the EC-II loop open. The MembStruk-predicted structure of bovine rhodopsin with the closed EC-II loop deviates from the crystal by 2.84 Å coordinate root mean-square (CRMS) in the transmembrane region main-chain atoms. The predicted three-dimensional structures for other GPCRs can be validated only by predicting binding sites and energies for various ligands. For such predictions we developed the HierDock first principles computational method. We validate HierDock by predicting the binding site of 11-cis-retinal in the crystal structure of bovine rhodopsin. Scanning the whole protein without using any prior knowledge of the binding site, we find that the best scoring conformation in rhodopsin is 1.1 Å CRMS from the crystal structure for the ligand atoms. This predicted conformation has the carbonyl O only 2.82 Å from the N of Lys-296. Making this Schiff base bond and minimizing leads to a final conformation only 0.62 Å CRMS from the crystal structure. We also used HierDock to predict the binding site of 11-cis-retinal in the MembStruk-predicted structure of bovine rhodopsin (closed loop). Scanning the whole protein structure leads to a structure in which the carbonyl O is only 2.85 Å from the N of Lys-296. Making this Schiff base bond and minimizing leads to a final conformation only 2.92 Å CRMS from the crystal structure. The good agreement of the ab initio-predicted protein structures and ligand binding site with experiment validates the use of the MembStruk and HierDock first principles' methods. Since these methods are generic and applicable to any GPCR, they should be useful in predicting the structures of other GPCRs and the binding site of ligands to these proteins. PMID:15041637

ORION: a web server for protein fold recognition and structure prediction using evolutionary hybrid profiles

PubMed Central

Ghouzam, Yassine; Postic, Guillaume; Guerin, Pierre-Edouard; de Brevern, Alexandre G.; Gelly, Jean-Christophe

2016-01-01

Protein structure prediction based on comparative modeling is the most efficient way to produce structural models when it can be performed. ORION is a dedicated webserver based on a new strategy that performs this task. The identification by ORION of suitable templates is performed using an original profile-profile approach that combines sequence and structure evolution information. Structure evolution information is encoded into profiles using structural features, such as solvent accessibility and local conformation —with Protein Blocks—, which give an accurate description of the local protein structure. ORION has recently been improved, increasing by 5% the quality of its results. The ORION web server accepts a single protein sequence as input and searches homologous protein structures within minutes. Various databases such as PDB, SCOP and HOMSTRAD can be mined to find an appropriate structural template. For the modeling step, a protein 3D structure can be directly obtained from the selected template by MODELLER and displayed with global and local quality model estimation measures. The sequence and the predicted structure of 4 examples from the CAMEO server and a recent CASP11 target from the ‘Hard’ category (T0818-D1) are shown as pertinent examples. Our web server is accessible at http://www.dsimb.inserm.fr/ORION/. PMID:27319297

ORION: a web server for protein fold recognition and structure prediction using evolutionary hybrid profiles.

PubMed

Ghouzam, Yassine; Postic, Guillaume; Guerin, Pierre-Edouard; de Brevern, Alexandre G; Gelly, Jean-Christophe

2016-06-20

Protein structure prediction based on comparative modeling is the most efficient way to produce structural models when it can be performed. ORION is a dedicated webserver based on a new strategy that performs this task. The identification by ORION of suitable templates is performed using an original profile-profile approach that combines sequence and structure evolution information. Structure evolution information is encoded into profiles using structural features, such as solvent accessibility and local conformation -with Protein Blocks-, which give an accurate description of the local protein structure. ORION has recently been improved, increasing by 5% the quality of its results. The ORION web server accepts a single protein sequence as input and searches homologous protein structures within minutes. Various databases such as PDB, SCOP and HOMSTRAD can be mined to find an appropriate structural template. For the modeling step, a protein 3D structure can be directly obtained from the selected template by MODELLER and displayed with global and local quality model estimation measures. The sequence and the predicted structure of 4 examples from the CAMEO server and a recent CASP11 target from the 'Hard' category (T0818-D1) are shown as pertinent examples. Our web server is accessible at http://www.dsimb.inserm.fr/ORION/.

Protein Tertiary Structure Prediction Based on Main Chain Angle Using a Hybrid Bees Colony Optimization Algorithm

NASA Astrophysics Data System (ADS)

Mahmood, Zakaria N.; Mahmuddin, Massudi; Mahmood, Mohammed Nooraldeen

Encoding proteins of amino acid sequence to predict classified into their respective families and subfamilies is important research area. However for a given protein, knowing the exact action whether hormonal, enzymatic, transmembranal or nuclear receptors does not depend solely on amino acid sequence but on the way the amino acid thread folds as well. This study provides a prototype system that able to predict a protein tertiary structure. Several methods are used to develop and evaluate the system to produce better accuracy in protein 3D structure prediction. The Bees Optimization algorithm which inspired from the honey bees food foraging method, is used in the searching phase. In this study, the experiment is conducted on short sequence proteins that have been used by the previous researches using well-known tools. The proposed approach shows a promising result.

KFC Server: interactive forecasting of protein interaction hot spots.

PubMed

Darnell, Steven J; LeGault, Laura; Mitchell, Julie C

2008-07-01

The KFC Server is a web-based implementation of the KFC (Knowledge-based FADE and Contacts) model-a machine learning approach for the prediction of binding hot spots, or the subset of residues that account for most of a protein interface's; binding free energy. The server facilitates the automated analysis of a user submitted protein-protein or protein-DNA interface and the visualization of its hot spot predictions. For each residue in the interface, the KFC Server characterizes its local structural environment, compares that environment to the environments of experimentally determined hot spots and predicts if the interface residue is a hot spot. After the computational analysis, the user can visualize the results using an interactive job viewer able to quickly highlight predicted hot spots and surrounding structural features within the protein structure. The KFC Server is accessible at http://kfc.mitchell-lab.org.

Automated prediction of protein function and detection of functional sites from structure.

PubMed

Pazos, Florencio; Sternberg, Michael J E

2004-10-12

Current structural genomics projects are yielding structures for proteins whose functions are unknown. Accordingly, there is a pressing requirement for computational methods for function prediction. Here we present PHUNCTIONER, an automatic method for structure-based function prediction using automatically extracted functional sites (residues associated to functions). The method relates proteins with the same function through structural alignments and extracts 3D profiles of conserved residues. Functional features to train the method are extracted from the Gene Ontology (GO) database. The method extracts these features from the entire GO hierarchy and hence is applicable across the whole range of function specificity. 3D profiles associated with 121 GO annotations were extracted. We tested the power of the method both for the prediction of function and for the extraction of functional sites. The success of function prediction by our method was compared with the standard homology-based method. In the zone of low sequence similarity (approximately 15%), our method assigns the correct GO annotation in 90% of the protein structures considered, approximately 20% higher than inheritance of function from the closest homologue.

Computational prediction of hinge axes in proteins

PubMed Central

2014-01-01

Background A protein's function is determined by the wide range of motions exhibited by its 3D structure. However, current experimental techniques are not able to reliably provide the level of detail required for elucidating the exact mechanisms of protein motion essential for effective drug screening and design. Computational tools are instrumental in the study of the underlying structure-function relationship. We focus on a special type of proteins called "hinge proteins" which exhibit a motion that can be interpreted as a rotation of one domain relative to another. Results This work proposes a computational approach that uses the geometric structure of a single conformation to predict the feasible motions of the protein and is founded in recent work from rigidity theory, an area of mathematics that studies flexibility properties of general structures. Given a single conformational state, our analysis predicts a relative axis of motion between two specified domains. We analyze a dataset of 19 structures known to exhibit this hinge-like behavior. For 15, the predicted axis is consistent with a motion to a second, known conformation. We present a detailed case study for three proteins whose dynamics have been well-studied in the literature: calmodulin, the LAO binding protein and the Bence-Jones protein. Conclusions Our results show that incorporating rigidity-theoretic analyses can lead to effective computational methods for understanding hinge motions in macromolecules. This initial investigation is the first step towards a new tool for probing the structure-dynamics relationship in proteins. PMID:25080829

Multiobjective evolutionary algorithm with many tables for purely ab initio protein structure prediction.

PubMed

Brasil, Christiane Regina Soares; Delbem, Alexandre Claudio Botazzo; da Silva, Fernando Luís Barroso

2013-07-30

This article focuses on the development of an approach for ab initio protein structure prediction (PSP) without using any earlier knowledge from similar protein structures, as fragment-based statistics or inference of secondary structures. Such an approach is called purely ab initio prediction. The article shows that well-designed multiobjective evolutionary algorithms can predict relevant protein structures in a purely ab initio way. One challenge for purely ab initio PSP is the prediction of structures with β-sheets. To work with such proteins, this research has also developed procedures to efficiently estimate hydrogen bond and solvation contribution energies. Considering van der Waals, electrostatic, hydrogen bond, and solvation contribution energies, the PSP is a problem with four energetic terms to be minimized. Each interaction energy term can be considered an objective of an optimization method. Combinatorial problems with four objectives have been considered too complex for the available multiobjective optimization (MOO) methods. The proposed approach, called "Multiobjective evolutionary algorithms with many tables" (MEAMT), can efficiently deal with four objectives through the combination thereof, performing a more adequate sampling of the objective space. Therefore, this method can better map the promising regions in this space, predicting structures in a purely ab initio way. In other words, MEAMT is an efficient optimization method for MOO, which explores simultaneously the search space as well as the objective space. MEAMT can predict structures with one or two domains with RMSDs comparable to values obtained by recently developed ab initio methods (GAPFCG , I-PAES, and Quark) that use different levels of earlier knowledge. Copyright © 2013 Wiley Periodicals, Inc.

HomPPI: a class of sequence homology based protein-protein interface prediction methods

PubMed Central

2011-01-01

Background Although homology-based methods are among the most widely used methods for predicting the structure and function of proteins, the question as to whether interface sequence conservation can be effectively exploited in predicting protein-protein interfaces has been a subject of debate. Results We studied more than 300,000 pair-wise alignments of protein sequences from structurally characterized protein complexes, including both obligate and transient complexes. We identified sequence similarity criteria required for accurate homology-based inference of interface residues in a query protein sequence. Based on these analyses, we developed HomPPI, a class of sequence homology-based methods for predicting protein-protein interface residues. We present two variants of HomPPI: (i) NPS-HomPPI (Non partner-specific HomPPI), which can be used to predict interface residues of a query protein in the absence of knowledge of the interaction partner; and (ii) PS-HomPPI (Partner-specific HomPPI), which can be used to predict the interface residues of a query protein with a specific target protein. Our experiments on a benchmark dataset of obligate homodimeric complexes show that NPS-HomPPI can reliably predict protein-protein interface residues in a given protein, with an average correlation coefficient (CC) of 0.76, sensitivity of 0.83, and specificity of 0.78, when sequence homologs of the query protein can be reliably identified. NPS-HomPPI also reliably predicts the interface residues of intrinsically disordered proteins. Our experiments suggest that NPS-HomPPI is competitive with several state-of-the-art interface prediction servers including those that exploit the structure of the query proteins. The partner-specific classifier, PS-HomPPI can, on a large dataset of transient complexes, predict the interface residues of a query protein with a specific target, with a CC of 0.65, sensitivity of 0.69, and specificity of 0.70, when homologs of both the query and the target can be reliably identified. The HomPPI web server is available at http://homppi.cs.iastate.edu/. Conclusions Sequence homology-based methods offer a class of computationally efficient and reliable approaches for predicting the protein-protein interface residues that participate in either obligate or transient interactions. For query proteins involved in transient interactions, the reliability of interface residue prediction can be improved by exploiting knowledge of putative interaction partners. PMID:21682895

TANGLE: Two-Level Support Vector Regression Approach for Protein Backbone Torsion Angle Prediction from Primary Sequences

PubMed Central

Song, Jiangning; Tan, Hao; Wang, Mingjun; Webb, Geoffrey I.; Akutsu, Tatsuya

2012-01-01

Protein backbone torsion angles (Phi) and (Psi) involve two rotation angles rotating around the Cα-N bond (Phi) and the Cα-C bond (Psi). Due to the planarity of the linked rigid peptide bonds, these two angles can essentially determine the backbone geometry of proteins. Accordingly, the accurate prediction of protein backbone torsion angle from sequence information can assist the prediction of protein structures. In this study, we develop a new approach called TANGLE (Torsion ANGLE predictor) to predict the protein backbone torsion angles from amino acid sequences. TANGLE uses a two-level support vector regression approach to perform real-value torsion angle prediction using a variety of features derived from amino acid sequences, including the evolutionary profiles in the form of position-specific scoring matrices, predicted secondary structure, solvent accessibility and natively disordered region as well as other global sequence features. When evaluated based on a large benchmark dataset of 1,526 non-homologous proteins, the mean absolute errors (MAEs) of the Phi and Psi angle prediction are 27.8° and 44.6°, respectively, which are 1% and 3% respectively lower than that using one of the state-of-the-art prediction tools ANGLOR. Moreover, the prediction of TANGLE is significantly better than a random predictor that was built on the amino acid-specific basis, with the p-value<1.46e-147 and 7.97e-150, respectively by the Wilcoxon signed rank test. As a complementary approach to the current torsion angle prediction algorithms, TANGLE should prove useful in predicting protein structural properties and assisting protein fold recognition by applying the predicted torsion angles as useful restraints. TANGLE is freely accessible at http://sunflower.kuicr.kyoto-u.ac.jp/~sjn/TANGLE/. PMID:22319565

Complete fold annotation of the human proteome using a novel structural feature space.

PubMed

Middleton, Sarah A; Illuminati, Joseph; Kim, Junhyong

2017-04-13

Recognition of protein structural fold is the starting point for many structure prediction tools and protein function inference. Fold prediction is computationally demanding and recognizing novel folds is difficult such that the majority of proteins have not been annotated for fold classification. Here we describe a new machine learning approach using a novel feature space that can be used for accurate recognition of all 1,221 currently known folds and inference of unknown novel folds. We show that our method achieves better than 94% accuracy even when many folds have only one training example. We demonstrate the utility of this method by predicting the folds of 34,330 human protein domains and showing that these predictions can yield useful insights into potential biological function, such as prediction of RNA-binding ability. Our method can be applied to de novo fold prediction of entire proteomes and identify candidate novel fold families.

Complete fold annotation of the human proteome using a novel structural feature space

PubMed Central

Middleton, Sarah A.; Illuminati, Joseph; Kim, Junhyong

2017-01-01

Recognition of protein structural fold is the starting point for many structure prediction tools and protein function inference. Fold prediction is computationally demanding and recognizing novel folds is difficult such that the majority of proteins have not been annotated for fold classification. Here we describe a new machine learning approach using a novel feature space that can be used for accurate recognition of all 1,221 currently known folds and inference of unknown novel folds. We show that our method achieves better than 94% accuracy even when many folds have only one training example. We demonstrate the utility of this method by predicting the folds of 34,330 human protein domains and showing that these predictions can yield useful insights into potential biological function, such as prediction of RNA-binding ability. Our method can be applied to de novo fold prediction of entire proteomes and identify candidate novel fold families. PMID:28406174

Addressing recent docking challenges: A hybrid strategy to integrate template-based and free protein-protein docking.

PubMed

Yan, Yumeng; Wen, Zeyu; Wang, Xinxiang; Huang, Sheng-You

2017-03-01

Protein-protein docking is an important computational tool for predicting protein-protein interactions. With the rapid development of proteomics projects, more and more experimental binding information ranging from mutagenesis data to three-dimensional structures of protein complexes are becoming available. Therefore, how to appropriately incorporate the biological information into traditional ab initio docking has been an important issue and challenge in the field of protein-protein docking. To address these challenges, we have developed a Hybrid DOCKing protocol of template-based and template-free approaches, referred to as HDOCK. The basic procedure of HDOCK is to model the structures of individual components based on the template complex by a template-based method if a template is available; otherwise, the component structures will be modeled based on monomer proteins by regular homology modeling. Then, the complex structure of the component models is predicted by traditional protein-protein docking. With the HDOCK protocol, we have participated in the CPARI experiment for rounds 28-35. Out of the 25 CASP-CAPRI targets for oligomer modeling, our HDOCK protocol predicted correct models for 16 targets, ranking one of the top algorithms in this challenge. Our docking method also made correct predictions on other CAPRI challenges such as protein-peptide binding for 6 out of 8 targets and water predictions for 2 out of 2 targets. The advantage of our hybrid docking approach over pure template-based docking was further confirmed by a comparative evaluation on 20 CASP-CAPRI targets. Proteins 2017; 85:497-512. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

TOUCHSTONE II: a new approach to ab initio protein structure prediction.

PubMed

Zhang, Yang; Kolinski, Andrzej; Skolnick, Jeffrey

2003-08-01

We have developed a new combined approach for ab initio protein structure prediction. The protein conformation is described as a lattice chain connecting C(alpha) atoms, with attached C(beta) atoms and side-chain centers of mass. The model force field includes various short-range and long-range knowledge-based potentials derived from a statistical analysis of the regularities of protein structures. The combination of these energy terms is optimized through the maximization of correlation for 30 x 60,000 decoys between the root mean square deviation (RMSD) to native and energies, as well as the energy gap between native and the decoy ensemble. To accelerate the conformational search, a newly developed parallel hyperbolic sampling algorithm with a composite movement set is used in the Monte Carlo simulation processes. We exploit this strategy to successfully fold 41/100 small proteins (36 approximately 120 residues) with predicted structures having a RMSD from native below 6.5 A in the top five cluster centroids. To fold larger-size proteins as well as to improve the folding yield of small proteins, we incorporate into the basic force field side-chain contact predictions from our threading program PROSPECTOR where homologous proteins were excluded from the data base. With these threading-based restraints, the program can fold 83/125 test proteins (36 approximately 174 residues) with structures having a RMSD to native below 6.5 A in the top five cluster centroids. This shows the significant improvement of folding by using predicted tertiary restraints, especially when the accuracy of side-chain contact prediction is >20%. For native fold selection, we introduce quantities dependent on the cluster density and the combination of energy and free energy, which show a higher discriminative power to select the native structure than the previously used cluster energy or cluster size, and which can be used in native structure identification in blind simulations. These procedures are readily automated and are being implemented on a genomic scale.

Text Mining for Protein Docking

PubMed Central

Badal, Varsha D.; Kundrotas, Petras J.; Vakser, Ilya A.

2015-01-01

The rapidly growing amount of publicly available information from biomedical research is readily accessible on the Internet, providing a powerful resource for predictive biomolecular modeling. The accumulated data on experimentally determined structures transformed structure prediction of proteins and protein complexes. Instead of exploring the enormous search space, predictive tools can simply proceed to the solution based on similarity to the existing, previously determined structures. A similar major paradigm shift is emerging due to the rapidly expanding amount of information, other than experimentally determined structures, which still can be used as constraints in biomolecular structure prediction. Automated text mining has been widely used in recreating protein interaction networks, as well as in detecting small ligand binding sites on protein structures. Combining and expanding these two well-developed areas of research, we applied the text mining to structural modeling of protein-protein complexes (protein docking). Protein docking can be significantly improved when constraints on the docking mode are available. We developed a procedure that retrieves published abstracts on a specific protein-protein interaction and extracts information relevant to docking. The procedure was assessed on protein complexes from Dockground (http://dockground.compbio.ku.edu). The results show that correct information on binding residues can be extracted for about half of the complexes. The amount of irrelevant information was reduced by conceptual analysis of a subset of the retrieved abstracts, based on the bag-of-words (features) approach. Support Vector Machine models were trained and validated on the subset. The remaining abstracts were filtered by the best-performing models, which decreased the irrelevant information for ~ 25% complexes in the dataset. The extracted constraints were incorporated in the docking protocol and tested on the Dockground unbound benchmark set, significantly increasing the docking success rate. PMID:26650466

Utilizing knowledge base of amino acids structural neighborhoods to predict protein-protein interaction sites.

PubMed

Jelínek, Jan; Škoda, Petr; Hoksza, David

2017-12-06

Protein-protein interactions (PPI) play a key role in an investigation of various biochemical processes, and their identification is thus of great importance. Although computational prediction of which amino acids take part in a PPI has been an active field of research for some time, the quality of in-silico methods is still far from perfect. We have developed a novel prediction method called INSPiRE which benefits from a knowledge base built from data available in Protein Data Bank. All proteins involved in PPIs were converted into labeled graphs with nodes corresponding to amino acids and edges to pairs of neighboring amino acids. A structural neighborhood of each node was then encoded into a bit string and stored in the knowledge base. When predicting PPIs, INSPiRE labels amino acids of unknown proteins as interface or non-interface based on how often their structural neighborhood appears as interface or non-interface in the knowledge base. We evaluated INSPiRE's behavior with respect to different types and sizes of the structural neighborhood. Furthermore, we examined the suitability of several different features for labeling the nodes. Our evaluations showed that INSPiRE clearly outperforms existing methods with respect to Matthews correlation coefficient. In this paper we introduce a new knowledge-based method for identification of protein-protein interaction sites called INSPiRE. Its knowledge base utilizes structural patterns of known interaction sites in the Protein Data Bank which are then used for PPI prediction. Extensive experiments on several well-established datasets show that INSPiRE significantly surpasses existing PPI approaches.

Proteins and Their Interacting Partners: An Introduction to Protein-Ligand Binding Site Prediction Methods.

PubMed

Roche, Daniel Barry; Brackenridge, Danielle Allison; McGuffin, Liam James

2015-12-15

Elucidating the biological and biochemical roles of proteins, and subsequently determining their interacting partners, can be difficult and time consuming using in vitro and/or in vivo methods, and consequently the majority of newly sequenced proteins will have unknown structures and functions. However, in silico methods for predicting protein-ligand binding sites and protein biochemical functions offer an alternative practical solution. The characterisation of protein-ligand binding sites is essential for investigating new functional roles, which can impact the major biological research spheres of health, food, and energy security. In this review we discuss the role in silico methods play in 3D modelling of protein-ligand binding sites, along with their role in predicting biochemical functionality. In addition, we describe in detail some of the key alternative in silico prediction approaches that are available, as well as discussing the Critical Assessment of Techniques for Protein Structure Prediction (CASP) and the Continuous Automated Model EvaluatiOn (CAMEO) projects, and their impact on developments in the field. Furthermore, we discuss the importance of protein function prediction methods for tackling 21st century problems.

Analysis of periplasmic sensor domains from Anaeromyxobacter dehalogenans 2CP-C: Structure of one sensor domain from a histidine kinase and another from a chemotaxis protein

PubMed Central

Pokkuluri, P Raj; Dwulit-Smith, Jeff; Duke, Norma E; Wilton, Rosemarie; Mack, Jamey C; Bearden, Jessica; Rakowski, Ella; Babnigg, Gyorgy; Szurmant, Hendrik; Joachimiak, Andrzej; Schiffer, Marianne

2013-01-01

Anaeromyxobacter dehalogenans is a δ-proteobacterium found in diverse soils and sediments. It is of interest in bioremediation efforts due to its dechlorination and metal-reducing capabilities. To gain an understanding on A. dehalogenans' abilities to adapt to diverse environments we analyzed its signal transduction proteins. The A. dehalogenans genome codes for a large number of sensor histidine kinases (HK) and methyl-accepting chemotaxis proteins (MCP); among these 23 HK and 11 MCP proteins have a sensor domain in the periplasm. These proteins most likely contribute to adaptation to the organism's surroundings. We predicted their three-dimensional folds and determined the structures of two of the periplasmic sensor domains by X-ray diffraction. Most of the domains are predicted to have either PAS-like or helical bundle structures, with two predicted to have solute-binding protein fold, and another predicted to have a 6-phosphogluconolactonase like fold. Atomic structures of two sensor domains confirmed the respective fold predictions. The Adeh_2942 sensor (HK) was found to have a helical bundle structure, and the Adeh_3718 sensor (MCP) has a PAS-like structure. Interestingly, the Adeh_3718 sensor has an acetate moiety bound in a binding site typical for PAS-like domains. Future work is needed to determine whether Adeh_3718 is involved in acetate sensing by A. dehalogenans. PMID:23897711

(PS)2: protein structure prediction server version 3.0.

PubMed

Huang, Tsun-Tsao; Hwang, Jenn-Kang; Chen, Chu-Huang; Chu, Chih-Sheng; Lee, Chi-Wen; Chen, Chih-Chieh

2015-07-01

Protein complexes are involved in many biological processes. Examining coupling between subunits of a complex would be useful to understand the molecular basis of protein function. Here, our updated (PS)(2) web server predicts the three-dimensional structures of protein complexes based on comparative modeling; furthermore, this server examines the coupling between subunits of the predicted complex by combining structural and evolutionary considerations. The predicted complex structure could be indicated and visualized by Java-based 3D graphics viewers and the structural and evolutionary profiles are shown and compared chain-by-chain. For each subunit, considerations with or without the packing contribution of other subunits cause the differences in similarities between structural and evolutionary profiles, and these differences imply which form, complex or monomeric, is preferred in the biological condition for the subunit. We believe that the (PS)(2) server would be a useful tool for biologists who are interested not only in the structures of protein complexes but also in the coupling between subunits of the complexes. The (PS)(2) is freely available at http://ps2v3.life.nctu.edu.tw/. © The Author(s) 2015. Published by Oxford University Press on behalf of Nucleic Acids Research.

Ensemble Generation and the Influence of Protein Flexibility on Geometric Tunnel Prediction in Cytochrome P450 Enzymes

PubMed Central

Kingsley, Laura J.; Lill, Markus A.

2014-01-01

Computational prediction of ligand entry and egress paths in proteins has become an emerging topic in computational biology and has proven useful in fields such as protein engineering and drug design. Geometric tunnel prediction programs, such as Caver3.0 and MolAxis, are computationally efficient methods to identify potential ligand entry and egress routes in proteins. Although many geometric tunnel programs are designed to accommodate a single input structure, the increasingly recognized importance of protein flexibility in tunnel formation and behavior has led to the more widespread use of protein ensembles in tunnel prediction. However, there has not yet been an attempt to directly investigate the influence of ensemble size and composition on geometric tunnel prediction. In this study, we compared tunnels found in a single crystal structure to ensembles of various sizes generated using different methods on both the apo and holo forms of cytochrome P450 enzymes CYP119, CYP2C9, and CYP3A4. Several protein structure clustering methods were tested in an attempt to generate smaller ensembles that were capable of reproducing the data from larger ensembles. Ultimately, we found that by including members from both the apo and holo data sets, we could produce ensembles containing less than 15 members that were comparable to apo or holo ensembles containing over 100 members. Furthermore, we found that, in the absence of either apo or holo crystal structure data, pseudo-apo or –holo ensembles (e.g. adding ligand to apo protein throughout MD simulations) could be used to resemble the structural ensembles of the corresponding apo and holo ensembles, respectively. Our findings not only further highlight the importance of including protein flexibility in geometric tunnel prediction, but also suggest that smaller ensembles can be as capable as larger ensembles at capturing many of the protein motions important for tunnel prediction at a lower computational cost. PMID:24956479

A new test set for validating predictions of protein-ligand interaction.

PubMed

Nissink, J Willem M; Murray, Chris; Hartshorn, Mike; Verdonk, Marcel L; Cole, Jason C; Taylor, Robin

2002-12-01

We present a large test set of protein-ligand complexes for the purpose of validating algorithms that rely on the prediction of protein-ligand interactions. The set consists of 305 complexes with protonation states assigned by manual inspection. The following checks have been carried out to identify unsuitable entries in this set: (1) assessing the involvement of crystallographically related protein units in ligand binding; (2) identification of bad clashes between protein side chains and ligand; and (3) assessment of structural errors, and/or inconsistency of ligand placement with crystal structure electron density. In addition, the set has been pruned to assure diversity in terms of protein-ligand structures, and subsets are supplied for different protein-structure resolution ranges. A classification of the set by protein type is available. As an illustration, validation results are shown for GOLD and SuperStar. GOLD is a program that performs flexible protein-ligand docking, and SuperStar is used for the prediction of favorable interaction sites in proteins. The new CCDC/Astex test set is freely available to the scientific community (http://www.ccdc.cam.ac.uk). Copyright 2002 Wiley-Liss, Inc.

A protein-dependent side-chain rotamer library.

PubMed

Bhuyan, Md Shariful Islam; Gao, Xin

2011-12-14

Protein side-chain packing problem has remained one of the key open problems in bioinformatics. The three main components of protein side-chain prediction methods are a rotamer library, an energy function and a search algorithm. Rotamer libraries summarize the existing knowledge of the experimentally determined structures quantitatively. Depending on how much contextual information is encoded, there are backbone-independent rotamer libraries and backbone-dependent rotamer libraries. Backbone-independent libraries only encode sequential information, whereas backbone-dependent libraries encode both sequential and locally structural information. However, side-chain conformations are determined by spatially local information, rather than sequentially local information. Since in the side-chain prediction problem, the backbone structure is given, spatially local information should ideally be encoded into the rotamer libraries. In this paper, we propose a new type of backbone-dependent rotamer library, which encodes structural information of all the spatially neighboring residues. We call it protein-dependent rotamer libraries. Given any rotamer library and a protein backbone structure, we first model the protein structure as a Markov random field. Then the marginal distributions are estimated by the inference algorithms, without doing global optimization or search. The rotamers from the given library are then re-ranked and associated with the updated probabilities. Experimental results demonstrate that the proposed protein-dependent libraries significantly outperform the widely used backbone-dependent libraries in terms of the side-chain prediction accuracy and the rotamer ranking ability. Furthermore, without global optimization/search, the side-chain prediction power of the protein-dependent library is still comparable to the global-search-based side-chain prediction methods.

STRUM: structure-based prediction of protein stability changes upon single-point mutation.

PubMed

Quan, Lijun; Lv, Qiang; Zhang, Yang

2016-10-01

Mutations in human genome are mainly through single nucleotide polymorphism, some of which can affect stability and function of proteins, causing human diseases. Several methods have been proposed to predict the effect of mutations on protein stability; but most require features from experimental structure. Given the fast progress in protein structure prediction, this work explores the possibility to improve the mutation-induced stability change prediction using low-resolution structure modeling. We developed a new method (STRUM) for predicting stability change caused by single-point mutations. Starting from wild-type sequences, 3D models are constructed by the iterative threading assembly refinement (I-TASSER) simulations, where physics- and knowledge-based energy functions are derived on the I-TASSER models and used to train STRUM models through gradient boosting regression. STRUM was assessed by 5-fold cross validation on 3421 experimentally determined mutations from 150 proteins. The Pearson correlation coefficient (PCC) between predicted and measured changes of Gibbs free-energy gap, ΔΔG, upon mutation reaches 0.79 with a root-mean-square error 1.2 kcal/mol in the mutation-based cross-validations. The PCC reduces if separating training and test mutations from non-homologous proteins, which reflects inherent correlations in the current mutation sample. Nevertheless, the results significantly outperform other state-of-the-art methods, including those built on experimental protein structures. Detailed analyses show that the most sensitive features in STRUM are the physics-based energy terms on I-TASSER models and the conservation scores from multiple-threading template alignments. However, the ΔΔG prediction accuracy has only a marginal dependence on the accuracy of protein structure models as long as the global fold is correct. These data demonstrate the feasibility to use low-resolution structure modeling for high-accuracy stability change prediction upon point mutations. http://zhanglab.ccmb.med.umich.edu/STRUM/ CONTACT: qiang@suda.edu.cn and zhng@umich.edu Supplementary data are available at Bioinformatics online. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

STRUM: structure-based prediction of protein stability changes upon single-point mutation

PubMed Central

Quan, Lijun; Lv, Qiang; Zhang, Yang

2016-01-01

Motivation: Mutations in human genome are mainly through single nucleotide polymorphism, some of which can affect stability and function of proteins, causing human diseases. Several methods have been proposed to predict the effect of mutations on protein stability; but most require features from experimental structure. Given the fast progress in protein structure prediction, this work explores the possibility to improve the mutation-induced stability change prediction using low-resolution structure modeling. Results: We developed a new method (STRUM) for predicting stability change caused by single-point mutations. Starting from wild-type sequences, 3D models are constructed by the iterative threading assembly refinement (I-TASSER) simulations, where physics- and knowledge-based energy functions are derived on the I-TASSER models and used to train STRUM models through gradient boosting regression. STRUM was assessed by 5-fold cross validation on 3421 experimentally determined mutations from 150 proteins. The Pearson correlation coefficient (PCC) between predicted and measured changes of Gibbs free-energy gap, ΔΔG, upon mutation reaches 0.79 with a root-mean-square error 1.2 kcal/mol in the mutation-based cross-validations. The PCC reduces if separating training and test mutations from non-homologous proteins, which reflects inherent correlations in the current mutation sample. Nevertheless, the results significantly outperform other state-of-the-art methods, including those built on experimental protein structures. Detailed analyses show that the most sensitive features in STRUM are the physics-based energy terms on I-TASSER models and the conservation scores from multiple-threading template alignments. However, the ΔΔG prediction accuracy has only a marginal dependence on the accuracy of protein structure models as long as the global fold is correct. These data demonstrate the feasibility to use low-resolution structure modeling for high-accuracy stability change prediction upon point mutations. Availability and Implementation: http://zhanglab.ccmb.med.umich.edu/STRUM/ Contact: qiang@suda.edu.cn and zhng@umich.edu Supplementary information: Supplementary data are available at Bioinformatics online. PMID:27318206

Coverage of whole proteome by structural genomics observed through protein homology modeling database

PubMed Central

Yamaguchi, Akihiro; Go, Mitiko

2006-01-01

We have been developing FAMSBASE, a protein homology-modeling database of whole ORFs predicted from genome sequences. The latest update of FAMSBASE (http://daisy.nagahama-i-bio.ac.jp/Famsbase/), which is based on the protein three-dimensional (3D) structures released by November 2003, contains modeled 3D structures for 368,724 open reading frames (ORFs) derived from genomes of 276 species, namely 17 archaebacterial, 130 eubacterial, 18 eukaryotic and 111 phage genomes. Those 276 genomes are predicted to have 734,193 ORFs in total and the current FAMSBASE contains protein 3D structure of approximately 50% of the ORF products. However, cases that a modeled 3D structure covers the whole part of an ORF product are rare. When portion of an ORF with 3D structure is compared in three kingdoms of life, in archaebacteria and eubacteria, approximately 60% of the ORFs have modeled 3D structures covering almost the entire amino acid sequences, however, the percentage falls to about 30% in eukaryotes. When annual differences in the number of ORFs with modeled 3D structure are calculated, the fraction of modeled 3D structures of soluble protein for archaebacteria is increased by 5%, and that for eubacteria by 7% in the last 3 years. Assuming that this rate would be maintained and that determination of 3D structures for predicted disordered regions is unattainable, whole soluble protein model structures of prokaryotes without the putative disordered regions will be in hand within 15 years. For eukaryotic proteins, they will be in hand within 25 years. The 3D structures we will have at those times are not the 3D structure of the entire proteins encoded in single ORFs, but the 3D structures of separate structural domains. Measuring or predicting spatial arrangements of structural domains in an ORF will then be a coming issue of structural genomics. PMID:17146617

Artificial Intelligence in Prediction of Secondary Protein Structure Using CB513 Database

PubMed Central

Avdagic, Zikrija; Purisevic, Elvir; Omanovic, Samir; Coralic, Zlatan

2009-01-01

In this paper we describe CB513 a non-redundant dataset, suitable for development of algorithms for prediction of secondary protein structure. A program was made in Borland Delphi for transforming data from our dataset to make it suitable for learning of neural network for prediction of secondary protein structure implemented in MATLAB Neural-Network Toolbox. Learning (training and testing) of neural network is researched with different sizes of windows, different number of neurons in the hidden layer and different number of training epochs, while using dataset CB513. PMID:21347158

A sampling-based method for ranking protein structural models by integrating multiple scores and features.

PubMed

Shi, Xiaohu; Zhang, Jingfen; He, Zhiquan; Shang, Yi; Xu, Dong

2011-09-01

One of the major challenges in protein tertiary structure prediction is structure quality assessment. In many cases, protein structure prediction tools generate good structural models, but fail to select the best models from a huge number of candidates as the final output. In this study, we developed a sampling-based machine-learning method to rank protein structural models by integrating multiple scores and features. First, features such as predicted secondary structure, solvent accessibility and residue-residue contact information are integrated by two Radial Basis Function (RBF) models trained from different datasets. Then, the two RBF scores and five selected scoring functions developed by others, i.e., Opus-CA, Opus-PSP, DFIRE, RAPDF, and Cheng Score are synthesized by a sampling method. At last, another integrated RBF model ranks the structural models according to the features of sampling distribution. We tested the proposed method by using two different datasets, including the CASP server prediction models of all CASP8 targets and a set of models generated by our in-house software MUFOLD. The test result shows that our method outperforms any individual scoring function on both best model selection, and overall correlation between the predicted ranking and the actual ranking of structural quality.

Structure prediction of the second extracellular loop in G-protein-coupled receptors.

PubMed

Kmiecik, Sebastian; Jamroz, Michal; Kolinski, Michal

2014-06-03

G-protein-coupled receptors (GPCRs) play key roles in living organisms. Therefore, it is important to determine their functional structures. The second extracellular loop (ECL2) is a functionally important region of GPCRs, which poses significant challenge for computational structure prediction methods. In this work, we evaluated CABS, a well-established protein modeling tool for predicting ECL2 structure in 13 GPCRs. The ECL2s (with between 13 and 34 residues) are predicted in an environment of other extracellular loops being fully flexible and the transmembrane domain fixed in its x-ray conformation. The modeling procedure used theoretical predictions of ECL2 secondary structure and experimental constraints on disulfide bridges. Our approach yielded ensembles of low-energy conformers and the most populated conformers that contained models close to the available x-ray structures. The level of similarity between the predicted models and x-ray structures is comparable to that of other state-of-the-art computational methods. Our results extend other studies by including newly crystallized GPCRs. Copyright © 2014 The Authors. Published by Elsevier Inc. All rights reserved.

Prediction of Protein-Protein Interaction Sites by Random Forest Algorithm with mRMR and IFS

PubMed Central

Li, Bi-Qing; Feng, Kai-Yan; Chen, Lei; Huang, Tao; Cai, Yu-Dong

2012-01-01

Prediction of protein-protein interaction (PPI) sites is one of the most challenging problems in computational biology. Although great progress has been made by employing various machine learning approaches with numerous characteristic features, the problem is still far from being solved. In this study, we developed a novel predictor based on Random Forest (RF) algorithm with the Minimum Redundancy Maximal Relevance (mRMR) method followed by incremental feature selection (IFS). We incorporated features of physicochemical/biochemical properties, sequence conservation, residual disorder, secondary structure and solvent accessibility. We also included five 3D structural features to predict protein-protein interaction sites and achieved an overall accuracy of 0.672997 and MCC of 0.347977. Feature analysis showed that 3D structural features such as Depth Index (DPX) and surface curvature (SC) contributed most to the prediction of protein-protein interaction sites. It was also shown via site-specific feature analysis that the features of individual residues from PPI sites contribute most to the determination of protein-protein interaction sites. It is anticipated that our prediction method will become a useful tool for identifying PPI sites, and that the feature analysis described in this paper will provide useful insights into the mechanisms of interaction. PMID:22937126

Optimization of protein-protein docking for predicting Fc-protein interactions.

PubMed

Agostino, Mark; Mancera, Ricardo L; Ramsland, Paul A; Fernández-Recio, Juan

2016-11-01

The antibody crystallizable fragment (Fc) is recognized by effector proteins as part of the immune system. Pathogens produce proteins that bind Fc in order to subvert or evade the immune response. The structural characterization of the determinants of Fc-protein association is essential to improve our understanding of the immune system at the molecular level and to develop new therapeutic agents. Furthermore, Fc-binding peptides and proteins are frequently used to purify therapeutic antibodies. Although several structures of Fc-protein complexes are available, numerous others have not yet been determined. Protein-protein docking could be used to investigate Fc-protein complexes; however, improved approaches are necessary to efficiently model such cases. In this study, a docking-based structural bioinformatics approach is developed for predicting the structures of Fc-protein complexes. Based on the available set of X-ray structures of Fc-protein complexes, three regions of the Fc, loosely corresponding to three turns within the structure, were defined as containing the essential features for protein recognition and used as restraints to filter the initial docking search. Rescoring the filtered poses with an optimal scoring strategy provided a success rate of approximately 80% of the test cases examined within the top ranked 20 poses, compared to approximately 20% by the initial unrestrained docking. The developed docking protocol provides a significant improvement over the initial unrestrained docking and will be valuable for predicting the structures of currently undetermined Fc-protein complexes, as well as in the design of peptides and proteins that target Fc. Copyright © 2016 John Wiley & Sons, Ltd.

The Proteome Folding Project: Proteome-scale prediction of structure and function

PubMed Central

Drew, Kevin; Winters, Patrick; Butterfoss, Glenn L.; Berstis, Viktors; Uplinger, Keith; Armstrong, Jonathan; Riffle, Michael; Schweighofer, Erik; Bovermann, Bill; Goodlett, David R.; Davis, Trisha N.; Shasha, Dennis; Malmström, Lars; Bonneau, Richard

2011-01-01

The incompleteness of proteome structure and function annotation is a critical problem for biologists and, in particular, severely limits interpretation of high-throughput and next-generation experiments. We have developed a proteome annotation pipeline based on structure prediction, where function and structure annotations are generated using an integration of sequence comparison, fold recognition, and grid-computing-enabled de novo structure prediction. We predict protein domain boundaries and three-dimensional (3D) structures for protein domains from 94 genomes (including human, Arabidopsis, rice, mouse, fly, yeast, Escherichia coli, and worm). De novo structure predictions were distributed on a grid of more than 1.5 million CPUs worldwide (World Community Grid). We generated significant numbers of new confident fold annotations (9% of domains that are otherwise unannotated in these genomes). We demonstrate that predicted structures can be combined with annotations from the Gene Ontology database to predict new and more specific molecular functions. PMID:21824995

Protein Folding and Structure Prediction from the Ground Up: The Atomistic Associative Memory, Water Mediated, Structure and Energy Model.

PubMed

Chen, Mingchen; Lin, Xingcheng; Zheng, Weihua; Onuchic, José N; Wolynes, Peter G

2016-08-25

The associative memory, water mediated, structure and energy model (AWSEM) is a coarse-grained force field with transferable tertiary interactions that incorporates local in sequence energetic biases using bioinformatically derived structural information about peptide fragments with locally similar sequences that we call memories. The memory information from the protein data bank (PDB) database guides proper protein folding. The structural information about available sequences in the database varies in quality and can sometimes lead to frustrated free energy landscapes locally. One way out of this difficulty is to construct the input fragment memory information from all-atom simulations of portions of the complete polypeptide chain. In this paper, we investigate this approach first put forward by Kwac and Wolynes in a more complete way by studying the structure prediction capabilities of this approach for six α-helical proteins. This scheme which we call the atomistic associative memory, water mediated, structure and energy model (AAWSEM) amounts to an ab initio protein structure prediction method that starts from the ground up without using bioinformatic input. The free energy profiles from AAWSEM show that atomistic fragment memories are sufficient to guide the correct folding when tertiary forces are included. AAWSEM combines the efficiency of coarse-grained simulations on the full protein level with the local structural accuracy achievable from all-atom simulations of only parts of a large protein. The results suggest that a hybrid use of atomistic fragment memory and database memory in structural predictions may well be optimal for many practical applications.

SMOQ: a tool for predicting the absolute residue-specific quality of a single protein model with support vector machines

PubMed Central

2014-01-01

Background It is important to predict the quality of a protein structural model before its native structure is known. The method that can predict the absolute local quality of individual residues in a single protein model is rare, yet particularly needed for using, ranking and refining protein models. Results We developed a machine learning tool (SMOQ) that can predict the distance deviation of each residue in a single protein model. SMOQ uses support vector machines (SVM) with protein sequence and structural features (i.e. basic feature set), including amino acid sequence, secondary structures, solvent accessibilities, and residue-residue contacts to make predictions. We also trained a SVM model with two new additional features (profiles and SOV scores) on 20 CASP8 targets and found that including them can only improve the performance when real deviations between native and model are higher than 5Å. The SMOQ tool finally released uses the basic feature set trained on 85 CASP8 targets. Moreover, SMOQ implemented a way to convert predicted local quality scores into a global quality score. SMOQ was tested on the 84 CASP9 single-domain targets. The average difference between the residue-specific distance deviation predicted by our method and the actual distance deviation on the test data is 2.637Å. The global quality prediction accuracy of the tool is comparable to other good tools on the same benchmark. Conclusion SMOQ is a useful tool for protein single model quality assessment. Its source code and executable are available at: http://sysbio.rnet.missouri.edu/multicom_toolbox/. PMID:24776231

SMOQ: a tool for predicting the absolute residue-specific quality of a single protein model with support vector machines.

PubMed

Cao, Renzhi; Wang, Zheng; Wang, Yiheng; Cheng, Jianlin

2014-04-28

It is important to predict the quality of a protein structural model before its native structure is known. The method that can predict the absolute local quality of individual residues in a single protein model is rare, yet particularly needed for using, ranking and refining protein models. We developed a machine learning tool (SMOQ) that can predict the distance deviation of each residue in a single protein model. SMOQ uses support vector machines (SVM) with protein sequence and structural features (i.e. basic feature set), including amino acid sequence, secondary structures, solvent accessibilities, and residue-residue contacts to make predictions. We also trained a SVM model with two new additional features (profiles and SOV scores) on 20 CASP8 targets and found that including them can only improve the performance when real deviations between native and model are higher than 5Å. The SMOQ tool finally released uses the basic feature set trained on 85 CASP8 targets. Moreover, SMOQ implemented a way to convert predicted local quality scores into a global quality score. SMOQ was tested on the 84 CASP9 single-domain targets. The average difference between the residue-specific distance deviation predicted by our method and the actual distance deviation on the test data is 2.637Å. The global quality prediction accuracy of the tool is comparable to other good tools on the same benchmark. SMOQ is a useful tool for protein single model quality assessment. Its source code and executable are available at: http://sysbio.rnet.missouri.edu/multicom_toolbox/.

An Evolution-Based Approach to De Novo Protein Design and Case Study on Mycobacterium tuberculosis

PubMed Central

Brender, Jeffrey R.; Czajka, Jeff; Marsh, David; Gray, Felicia; Cierpicki, Tomasz; Zhang, Yang

2013-01-01

Computational protein design is a reverse procedure of protein folding and structure prediction, where constructing structures from evolutionarily related proteins has been demonstrated to be the most reliable method for protein 3-dimensional structure prediction. Following this spirit, we developed a novel method to design new protein sequences based on evolutionarily related protein families. For a given target structure, a set of proteins having similar fold are identified from the PDB library by structural alignments. A structural profile is then constructed from the protein templates and used to guide the conformational search of amino acid sequence space, where physicochemical packing is accommodated by single-sequence based solvation, torsion angle, and secondary structure predictions. The method was tested on a computational folding experiment based on a large set of 87 protein structures covering different fold classes, which showed that the evolution-based design significantly enhances the foldability and biological functionality of the designed sequences compared to the traditional physics-based force field methods. Without using homologous proteins, the designed sequences can be folded with an average root-mean-square-deviation of 2.1 Å to the target. As a case study, the method is extended to redesign all 243 structurally resolved proteins in the pathogenic bacteria Mycobacterium tuberculosis, which is the second leading cause of death from infectious disease. On a smaller scale, five sequences were randomly selected from the design pool and subjected to experimental validation. The results showed that all the designed proteins are soluble with distinct secondary structure and three have well ordered tertiary structure, as demonstrated by circular dichroism and NMR spectroscopy. Together, these results demonstrate a new avenue in computational protein design that uses knowledge of evolutionary conservation from protein structural families to engineer new protein molecules of improved fold stability and biological functionality. PMID:24204234

Protein single-model quality assessment by feature-based probability density functions.

PubMed

Cao, Renzhi; Cheng, Jianlin

2016-04-04

Protein quality assessment (QA) has played an important role in protein structure prediction. We developed a novel single-model quality assessment method-Qprob. Qprob calculates the absolute error for each protein feature value against the true quality scores (i.e. GDT-TS scores) of protein structural models, and uses them to estimate its probability density distribution for quality assessment. Qprob has been blindly tested on the 11th Critical Assessment of Techniques for Protein Structure Prediction (CASP11) as MULTICOM-NOVEL server. The official CASP result shows that Qprob ranks as one of the top single-model QA methods. In addition, Qprob makes contributions to our protein tertiary structure predictor MULTICOM, which is officially ranked 3rd out of 143 predictors. The good performance shows that Qprob is good at assessing the quality of models of hard targets. These results demonstrate that this new probability density distribution based method is effective for protein single-model quality assessment and is useful for protein structure prediction. The webserver of Qprob is available at: http://calla.rnet.missouri.edu/qprob/. The software is now freely available in the web server of Qprob.

BetaTPred: prediction of beta-TURNS in a protein using statistical algorithms.

PubMed

Kaur, Harpreet; Raghava, G P S

2002-03-01

beta-turns play an important role from a structural and functional point of view. beta-turns are the most common type of non-repetitive structures in proteins and comprise on average, 25% of the residues. In the past numerous methods have been developed to predict beta-turns in a protein. Most of these prediction methods are based on statistical approaches. In order to utilize the full potential of these methods, there is a need to develop a web server. This paper describes a web server called BetaTPred, developed for predicting beta-TURNS in a protein from its amino acid sequence. BetaTPred allows the user to predict turns in a protein using existing statistical algorithms. It also allows to predict different types of beta-TURNS e.g. type I, I', II, II', VI, VIII and non-specific. This server assists the users in predicting the consensus beta-TURNS in a protein. The server is accessible from http://imtech.res.in/raghava/betatpred/

Prediction of protein tertiary structure to low resolution: performance for a large and structurally diverse test set.

PubMed

Eyrich, V A; Standley, D M; Friesner, R A

1999-05-14

We report the tertiary structure predictions for 95 proteins ranging in size from 17 to 160 residues starting from known secondary structure. Predictions are obtained from global minimization of an empirical potential function followed by the application of a refined atomic overlap potential. The minimization strategy employed represents a variant of the Monte Carlo plus minimization scheme of Li and Scheraga applied to a reduced model of the protein chain. For all of the cases except beta-proteins larger than 75 residues, a native-like structure, usually 4-6 A root-mean-square deviation from the native, is located. For beta-proteins larger than 75 residues, the energy gap between native-like structures and the lowest energy structures produced in the simulation is large, so that low RMSD structures are not generated starting from an unfolded state. This is attributed to the lack of an explicit hydrogen bond term in the potential function, which we hypothesize is necessary to stabilize large assemblies of beta-strands. Copyright 1999 Academic Press.

AIDA: ab initio domain assembly for automated multi-domain protein structure prediction and domain–domain interaction prediction

PubMed Central

Xu, Dong; Jaroszewski, Lukasz; Li, Zhanwen; Godzik, Adam

2015-01-01

Motivation: Most proteins consist of multiple domains, independent structural and evolutionary units that are often reshuffled in genomic rearrangements to form new protein architectures. Template-based modeling methods can often detect homologous templates for individual domains, but templates that could be used to model the entire query protein are often not available. Results: We have developed a fast docking algorithm ab initio domain assembly (AIDA) for assembling multi-domain protein structures, guided by the ab initio folding potential. This approach can be extended to discontinuous domains (i.e. domains with ‘inserted’ domains). When tested on experimentally solved structures of multi-domain proteins, the relative domain positions were accurately found among top 5000 models in 86% of cases. AIDA server can use domain assignments provided by the user or predict them from the provided sequence. The latter approach is particularly useful for automated protein structure prediction servers. The blind test consisting of 95 CASP10 targets shows that domain boundaries could be successfully determined for 97% of targets. Availability and implementation: The AIDA package as well as the benchmark sets used here are available for download at http://ffas.burnham.org/AIDA/. Contact: adam@sanfordburnham.org Supplementary information: Supplementary data are available at Bioinformatics online. PMID:25701568

CABS-flex predictions of protein flexibility compared with NMR ensembles

PubMed Central

Jamroz, Michal; Kolinski, Andrzej; Kmiecik, Sebastian

2014-01-01

Motivation: Identification of flexible regions of protein structures is important for understanding of their biological functions. Recently, we have developed a fast approach for predicting protein structure fluctuations from a single protein model: the CABS-flex. CABS-flex was shown to be an efficient alternative to conventional all-atom molecular dynamics (MD). In this work, we evaluate CABS-flex and MD predictions by comparison with protein structural variations within NMR ensembles. Results: Based on a benchmark set of 140 proteins, we show that the relative fluctuations of protein residues obtained from CABS-flex are well correlated to those of NMR ensembles. On average, this correlation is stronger than that between MD and NMR ensembles. In conclusion, CABS-flex is useful and complementary to MD in predicting protein regions that undergo conformational changes as well as the extent of such changes. Availability and implementation: The CABS-flex is freely available to all users at http://biocomp.chem.uw.edu.pl/CABSflex. Contact: sekmi@chem.uw.edu.pl Supplementary information: Supplementary data are available at Bioinformatics online. PMID:24735558

CABS-flex predictions of protein flexibility compared with NMR ensembles.

PubMed

Jamroz, Michal; Kolinski, Andrzej; Kmiecik, Sebastian

2014-08-01

Identification of flexible regions of protein structures is important for understanding of their biological functions. Recently, we have developed a fast approach for predicting protein structure fluctuations from a single protein model: the CABS-flex. CABS-flex was shown to be an efficient alternative to conventional all-atom molecular dynamics (MD). In this work, we evaluate CABS-flex and MD predictions by comparison with protein structural variations within NMR ensembles. Based on a benchmark set of 140 proteins, we show that the relative fluctuations of protein residues obtained from CABS-flex are well correlated to those of NMR ensembles. On average, this correlation is stronger than that between MD and NMR ensembles. In conclusion, CABS-flex is useful and complementary to MD in predicting protein regions that undergo conformational changes as well as the extent of such changes. The CABS-flex is freely available to all users at http://biocomp.chem.uw.edu.pl/CABSflex. sekmi@chem.uw.edu.pl Supplementary data are available at Bioinformatics online. © The Author 2014. Published by Oxford University Press.

Accurate secondary structure prediction and fold recognition for circular dichroism spectroscopy

PubMed Central

Micsonai, András; Wien, Frank; Kernya, Linda; Lee, Young-Ho; Goto, Yuji; Réfrégiers, Matthieu; Kardos, József

2015-01-01

Circular dichroism (CD) spectroscopy is a widely used technique for the study of protein structure. Numerous algorithms have been developed for the estimation of the secondary structure composition from the CD spectra. These methods often fail to provide acceptable results on α/β-mixed or β-structure–rich proteins. The problem arises from the spectral diversity of β-structures, which has hitherto been considered as an intrinsic limitation of the technique. The predictions are less reliable for proteins of unusual β-structures such as membrane proteins, protein aggregates, and amyloid fibrils. Here, we show that the parallel/antiparallel orientation and the twisting of the β-sheets account for the observed spectral diversity. We have developed a method called β-structure selection (BeStSel) for the secondary structure estimation that takes into account the twist of β-structures. This method can reliably distinguish parallel and antiparallel β-sheets and accurately estimates the secondary structure for a broad range of proteins. Moreover, the secondary structure components applied by the method are characteristic to the protein fold, and thus the fold can be predicted to the level of topology in the CATH classification from a single CD spectrum. By constructing a web server, we offer a general tool for a quick and reliable structure analysis using conventional CD or synchrotron radiation CD (SRCD) spectroscopy for the protein science research community. The method is especially useful when X-ray or NMR techniques fail. Using BeStSel on data collected by SRCD spectroscopy, we investigated the structure of amyloid fibrils of various disease-related proteins and peptides. PMID:26038575

Improving binding mode and binding affinity predictions of docking by ligand-based search of protein conformations: evaluation in D3R grand challenge 2015

NASA Astrophysics Data System (ADS)

Xu, Xianjin; Yan, Chengfei; Zou, Xiaoqin

2017-08-01

The growing number of protein-ligand complex structures, particularly the structures of proteins co-bound with different ligands, in the Protein Data Bank helps us tackle two major challenges in molecular docking studies: the protein flexibility and the scoring function. Here, we introduced a systematic strategy by using the information embedded in the known protein-ligand complex structures to improve both binding mode and binding affinity predictions. Specifically, a ligand similarity calculation method was employed to search a receptor structure with a bound ligand sharing high similarity with the query ligand for the docking use. The strategy was applied to the two datasets (HSP90 and MAP4K4) in recent D3R Grand Challenge 2015. In addition, for the HSP90 dataset, a system-specific scoring function (ITScore2_hsp90) was generated by recalibrating our statistical potential-based scoring function (ITScore2) using the known protein-ligand complex structures and the statistical mechanics-based iterative method. For the HSP90 dataset, better performances were achieved for both binding mode and binding affinity predictions comparing with the original ITScore2 and with ensemble docking. For the MAP4K4 dataset, although there were only eight known protein-ligand complex structures, our docking strategy achieved a comparable performance with ensemble docking. Our method for receptor conformational selection and iterative method for the development of system-specific statistical potential-based scoring functions can be easily applied to other protein targets that have a number of protein-ligand complex structures available to improve predictions on binding.

Advances in Homology Protein Structure Modeling

PubMed Central

Xiang, Zhexin

2007-01-01

Homology modeling plays a central role in determining protein structure in the structural genomics project. The importance of homology modeling has been steadily increasing because of the large gap that exists between the overwhelming number of available protein sequences and experimentally solved protein structures, and also, more importantly, because of the increasing reliability and accuracy of the method. In fact, a protein sequence with over 30% identity to a known structure can often be predicted with an accuracy equivalent to a low-resolution X-ray structure. The recent advances in homology modeling, especially in detecting distant homologues, aligning sequences with template structures, modeling of loops and side chains, as well as detecting errors in a model, have contributed to reliable prediction of protein structure, which was not possible even several years ago. The ongoing efforts in solving protein structures, which can be time-consuming and often difficult, will continue to spur the development of a host of new computational methods that can fill in the gap and further contribute to understanding the relationship between protein structure and function. PMID:16787261

Prediction of physical protein protein interactions

NASA Astrophysics Data System (ADS)

Szilágyi, András; Grimm, Vera; Arakaki, Adrián K.; Skolnick, Jeffrey

2005-06-01

Many essential cellular processes such as signal transduction, transport, cellular motion and most regulatory mechanisms are mediated by protein-protein interactions. In recent years, new experimental techniques have been developed to discover the protein-protein interaction networks of several organisms. However, the accuracy and coverage of these techniques have proven to be limited, and computational approaches remain essential both to assist in the design and validation of experimental studies and for the prediction of interaction partners and detailed structures of protein complexes. Here, we provide a critical overview of existing structure-independent and structure-based computational methods. Although these techniques have significantly advanced in the past few years, we find that most of them are still in their infancy. We also provide an overview of experimental techniques for the detection of protein-protein interactions. Although the developments are promising, false positive and false negative results are common, and reliable detection is possible only by taking a consensus of different experimental approaches. The shortcomings of experimental techniques affect both the further development and the fair evaluation of computational prediction methods. For an adequate comparative evaluation of prediction and high-throughput experimental methods, an appropriately large benchmark set of biophysically characterized protein complexes would be needed, but is sorely lacking.

Ligand Binding Site Detection by Local Structure Alignment and Its Performance Complementarity

PubMed Central

Lee, Hui Sun; Im, Wonpil

2013-01-01

Accurate determination of potential ligand binding sites (BS) is a key step for protein function characterization and structure-based drug design. Despite promising results of template-based BS prediction methods using global structure alignment (GSA), there is a room to improve the performance by properly incorporating local structure alignment (LSA) because BS are local structures and often similar for proteins with dissimilar global folds. We present a template-based ligand BS prediction method using G-LoSA, our LSA tool. A large benchmark set validation shows that G-LoSA predicts drug-like ligands’ positions in single-chain protein targets more precisely than TM-align, a GSA-based method, while the overall success rate of TM-align is better. G-LoSA is particularly efficient for accurate detection of local structures conserved across proteins with diverse global topologies. Recognizing the performance complementarity of G-LoSA to TM-align and a non-template geometry-based method, fpocket, a robust consensus scoring method, CMCS-BSP (Complementary Methods and Consensus Scoring for ligand Binding Site Prediction), is developed and shows improvement on prediction accuracy. The G-LoSA source code is freely available at http://im.bioinformatics.ku.edu/GLoSA. PMID:23957286

Crysalis: an integrated server for computational analysis and design of protein crystallization.

PubMed

Wang, Huilin; Feng, Liubin; Zhang, Ziding; Webb, Geoffrey I; Lin, Donghai; Song, Jiangning

2016-02-24

The failure of multi-step experimental procedures to yield diffraction-quality crystals is a major bottleneck in protein structure determination. Accordingly, several bioinformatics methods have been successfully developed and employed to select crystallizable proteins. Unfortunately, the majority of existing in silico methods only allow the prediction of crystallization propensity, seldom enabling computational design of protein mutants that can be targeted for enhancing protein crystallizability. Here, we present Crysalis, an integrated crystallization analysis tool that builds on support-vector regression (SVR) models to facilitate computational protein crystallization prediction, analysis, and design. More specifically, the functionality of this new tool includes: (1) rapid selection of target crystallizable proteins at the proteome level, (2) identification of site non-optimality for protein crystallization and systematic analysis of all potential single-point mutations that might enhance protein crystallization propensity, and (3) annotation of target protein based on predicted structural properties. We applied the design mode of Crysalis to identify site non-optimality for protein crystallization on a proteome-scale, focusing on proteins currently classified as non-crystallizable. Our results revealed that site non-optimality is based on biases related to residues, predicted structures, physicochemical properties, and sequence loci, which provides in-depth understanding of the features influencing protein crystallization. Crysalis is freely available at http://nmrcen.xmu.edu.cn/crysalis/.

Crysalis: an integrated server for computational analysis and design of protein crystallization

PubMed Central

Wang, Huilin; Feng, Liubin; Zhang, Ziding; Webb, Geoffrey I.; Lin, Donghai; Song, Jiangning

2016-01-01

The failure of multi-step experimental procedures to yield diffraction-quality crystals is a major bottleneck in protein structure determination. Accordingly, several bioinformatics methods have been successfully developed and employed to select crystallizable proteins. Unfortunately, the majority of existing in silico methods only allow the prediction of crystallization propensity, seldom enabling computational design of protein mutants that can be targeted for enhancing protein crystallizability. Here, we present Crysalis, an integrated crystallization analysis tool that builds on support-vector regression (SVR) models to facilitate computational protein crystallization prediction, analysis, and design. More specifically, the functionality of this new tool includes: (1) rapid selection of target crystallizable proteins at the proteome level, (2) identification of site non-optimality for protein crystallization and systematic analysis of all potential single-point mutations that might enhance protein crystallization propensity, and (3) annotation of target protein based on predicted structural properties. We applied the design mode of Crysalis to identify site non-optimality for protein crystallization on a proteome-scale, focusing on proteins currently classified as non-crystallizable. Our results revealed that site non-optimality is based on biases related to residues, predicted structures, physicochemical properties, and sequence loci, which provides in-depth understanding of the features influencing protein crystallization. Crysalis is freely available at http://nmrcen.xmu.edu.cn/crysalis/. PMID:26906024

Computational Approaches for Revealing the Structure of Membrane Transporters: Case Study on Bilitranslocase.

PubMed

Venko, Katja; Roy Choudhury, A; Novič, Marjana

2017-01-01

The structural and functional details of transmembrane proteins are vastly underexplored, mostly due to experimental difficulties regarding their solubility and stability. Currently, the majority of transmembrane protein structures are still unknown and this present a huge experimental and computational challenge. Nowadays, thanks to X-ray crystallography or NMR spectroscopy over 3000 structures of membrane proteins have been solved, among them only a few hundred unique ones. Due to the vast biological and pharmaceutical interest in the elucidation of the structure and the functional mechanisms of transmembrane proteins, several computational methods have been developed to overcome the experimental gap. If combined with experimental data the computational information enables rapid, low cost and successful predictions of the molecular structure of unsolved proteins. The reliability of the predictions depends on the availability and accuracy of experimental data associated with structural information. In this review, the following methods are proposed for in silico structure elucidation: sequence-dependent predictions of transmembrane regions, predictions of transmembrane helix-helix interactions, helix arrangements in membrane models, and testing their stability with molecular dynamics simulations. We also demonstrate the usage of the computational methods listed above by proposing a model for the molecular structure of the transmembrane protein bilitranslocase. Bilitranslocase is bilirubin membrane transporter, which shares similar tissue distribution and functional properties with some of the members of the Organic Anion Transporter family and is the only member classified in the Bilirubin Transporter Family. Regarding its unique properties, bilitranslocase is a potentially interesting drug target.

Identification of Extracellular Segments by Mass Spectrometry Improves Topology Prediction of Transmembrane Proteins.

PubMed

Langó, Tamás; Róna, Gergely; Hunyadi-Gulyás, Éva; Turiák, Lilla; Varga, Julia; Dobson, László; Várady, György; Drahos, László; Vértessy, Beáta G; Medzihradszky, Katalin F; Szakács, Gergely; Tusnády, Gábor E

2017-02-13

Transmembrane proteins play crucial role in signaling, ion transport, nutrient uptake, as well as in maintaining the dynamic equilibrium between the internal and external environment of cells. Despite their important biological functions and abundance, less than 2% of all determined structures are transmembrane proteins. Given the persisting technical difficulties associated with high resolution structure determination of transmembrane proteins, additional methods, including computational and experimental techniques remain vital in promoting our understanding of their topologies, 3D structures, functions and interactions. Here we report a method for the high-throughput determination of extracellular segments of transmembrane proteins based on the identification of surface labeled and biotin captured peptide fragments by LC/MS/MS. We show that reliable identification of extracellular protein segments increases the accuracy and reliability of existing topology prediction algorithms. Using the experimental topology data as constraints, our improved prediction tool provides accurate and reliable topology models for hundreds of human transmembrane proteins.

D2N: Distance to the native.

PubMed

Mishra, Avinash; Rana, Prashant Singh; Mittal, Aditya; Jayaram, B

2014-10-01

Root-mean-square-deviation (RMSD), of computationally-derived protein structures from experimentally determined structures, is a critical index to assessing protein-structure-prediction-algorithms (PSPAs). The development of PSPAs to obtain 0Å RMSD from native structures is considered central to computational biology. However, till date it has been quite challenging to measure how far a predicted protein structure is from its native - in the absence of a known experimental/native structure. In this work, we report the development of a metric "D2N" (distance to the native) - that predicts the "RMSD" of any structure without actually knowing the native structure. By combining physico-chemical properties and known universalities in spatial organization of soluble proteins to develop D2N, we demonstrate the ability to predict the distance of a proposed structure to within ±1.5Ǻ error with a remarkable average accuracy of 93.6% for structures below 5Ǻ from the native. We believe that this work opens up a completely new avenue towards assigning reliable structures to whole proteomes even in the absence of experimentally determined native structures. The D2N tool is freely available at http://www.scfbio-iitd.res.in/software/d2n.jsp. Copyright © 2014 Elsevier B.V. All rights reserved.

Principles of assembly reveal a periodic table of protein complexes.

PubMed

Ahnert, Sebastian E; Marsh, Joseph A; Hernández, Helena; Robinson, Carol V; Teichmann, Sarah A

2015-12-11

Structural insights into protein complexes have had a broad impact on our understanding of biological function and evolution. In this work, we sought a comprehensive understanding of the general principles underlying quaternary structure organization in protein complexes. We first examined the fundamental steps by which protein complexes can assemble, using experimental and structure-based characterization of assembly pathways. Most assembly transitions can be classified into three basic types, which can then be used to exhaustively enumerate a large set of possible quaternary structure topologies. These topologies, which include the vast majority of observed protein complex structures, enable a natural organization of protein complexes into a periodic table. On the basis of this table, we can accurately predict the expected frequencies of quaternary structure topologies, including those not yet observed. These results have important implications for quaternary structure prediction, modeling, and engineering. Copyright © 2015, American Association for the Advancement of Science.

Binding ligand prediction for proteins using partial matching of local surface patches.

PubMed

Sael, Lee; Kihara, Daisuke

2010-01-01

Functional elucidation of uncharacterized protein structures is an important task in bioinformatics. We report our new approach for structure-based function prediction which captures local surface features of ligand binding pockets. Function of proteins, specifically, binding ligands of proteins, can be predicted by finding similar local surface regions of known proteins. To enable partial comparison of binding sites in proteins, a weighted bipartite matching algorithm is used to match pairs of surface patches. The surface patches are encoded with the 3D Zernike descriptors. Unlike the existing methods which compare global characteristics of the protein fold or the global pocket shape, the local surface patch method can find functional similarity between non-homologous proteins and binding pockets for flexible ligand molecules. The proposed method improves prediction results over global pocket shape-based method which was previously developed by our group.

Binding Ligand Prediction for Proteins Using Partial Matching of Local Surface Patches

PubMed Central

Sael, Lee; Kihara, Daisuke

2010-01-01

Functional elucidation of uncharacterized protein structures is an important task in bioinformatics. We report our new approach for structure-based function prediction which captures local surface features of ligand binding pockets. Function of proteins, specifically, binding ligands of proteins, can be predicted by finding similar local surface regions of known proteins. To enable partial comparison of binding sites in proteins, a weighted bipartite matching algorithm is used to match pairs of surface patches. The surface patches are encoded with the 3D Zernike descriptors. Unlike the existing methods which compare global characteristics of the protein fold or the global pocket shape, the local surface patch method can find functional similarity between non-homologous proteins and binding pockets for flexible ligand molecules. The proposed method improves prediction results over global pocket shape-based method which was previously developed by our group. PMID:21614188

Designing and evaluating the MULTICOM protein local and global model quality prediction methods in the CASP10 experiment

PubMed Central

2014-01-01

Background Protein model quality assessment is an essential component of generating and using protein structural models. During the Tenth Critical Assessment of Techniques for Protein Structure Prediction (CASP10), we developed and tested four automated methods (MULTICOM-REFINE, MULTICOM-CLUSTER, MULTICOM-NOVEL, and MULTICOM-CONSTRUCT) that predicted both local and global quality of protein structural models. Results MULTICOM-REFINE was a clustering approach that used the average pairwise structural similarity between models to measure the global quality and the average Euclidean distance between a model and several top ranked models to measure the local quality. MULTICOM-CLUSTER and MULTICOM-NOVEL were two new support vector machine-based methods of predicting both the local and global quality of a single protein model. MULTICOM-CONSTRUCT was a new weighted pairwise model comparison (clustering) method that used the weighted average similarity between models in a pool to measure the global model quality. Our experiments showed that the pairwise model assessment methods worked better when a large portion of models in the pool were of good quality, whereas single-model quality assessment methods performed better on some hard targets when only a small portion of models in the pool were of reasonable quality. Conclusions Since digging out a few good models from a large pool of low-quality models is a major challenge in protein structure prediction, single model quality assessment methods appear to be poised to make important contributions to protein structure modeling. The other interesting finding was that single-model quality assessment scores could be used to weight the models by the consensus pairwise model comparison method to improve its accuracy. PMID:24731387

Designing and evaluating the MULTICOM protein local and global model quality prediction methods in the CASP10 experiment.

PubMed

Cao, Renzhi; Wang, Zheng; Cheng, Jianlin

2014-04-15

Protein model quality assessment is an essential component of generating and using protein structural models. During the Tenth Critical Assessment of Techniques for Protein Structure Prediction (CASP10), we developed and tested four automated methods (MULTICOM-REFINE, MULTICOM-CLUSTER, MULTICOM-NOVEL, and MULTICOM-CONSTRUCT) that predicted both local and global quality of protein structural models. MULTICOM-REFINE was a clustering approach that used the average pairwise structural similarity between models to measure the global quality and the average Euclidean distance between a model and several top ranked models to measure the local quality. MULTICOM-CLUSTER and MULTICOM-NOVEL were two new support vector machine-based methods of predicting both the local and global quality of a single protein model. MULTICOM-CONSTRUCT was a new weighted pairwise model comparison (clustering) method that used the weighted average similarity between models in a pool to measure the global model quality. Our experiments showed that the pairwise model assessment methods worked better when a large portion of models in the pool were of good quality, whereas single-model quality assessment methods performed better on some hard targets when only a small portion of models in the pool were of reasonable quality. Since digging out a few good models from a large pool of low-quality models is a major challenge in protein structure prediction, single model quality assessment methods appear to be poised to make important contributions to protein structure modeling. The other interesting finding was that single-model quality assessment scores could be used to weight the models by the consensus pairwise model comparison method to improve its accuracy.

Protein–DNA Interactions: The Story so Far and a New Method for Prediction

DOE PAGES

Jones, Susan; Thornton, Janet M.

2003-01-01

This review describes methods for the prediction of DNA binding function, and specifically summarizes a new method using 3D structural templates. The new method features the HTH motif that is found in approximately one-third of DNAbinding protein families. A library of 3D structural templates of HTH motifs was derived from proteins in the PDB. Templates were scanned against complete protein structures and the optimal superposition of a template on a structure calculated. Significance thresholds in terms of a minimum root mean squared deviation (rmsd) of an optimal superposition, and a minimum motif accessible surface area (ASA), have been calculated. Inmore » this way, it is possible to scan the template library against proteins of unknown function to make predictions about DNA-binding functionality.« less

Tertiary alphabet for the observable protein structural universe.

PubMed

Mackenzie, Craig O; Zhou, Jianfu; Grigoryan, Gevorg

2016-11-22

Here, we systematically decompose the known protein structural universe into its basic elements, which we dub tertiary structural motifs (TERMs). A TERM is a compact backbone fragment that captures the secondary, tertiary, and quaternary environments around a given residue, comprising one or more disjoint segments (three on average). We seek the set of universal TERMs that capture all structure in the Protein Data Bank (PDB), finding remarkable degeneracy. Only ∼600 TERMs are sufficient to describe 50% of the PDB at sub-Angstrom resolution. However, more rare geometries also exist, and the overall structural coverage grows logarithmically with the number of TERMs. We go on to show that universal TERMs provide an effective mapping between sequence and structure. We demonstrate that TERM-based statistics alone are sufficient to recapitulate close-to-native sequences given either NMR or X-ray backbones. Furthermore, sequence variability predicted from TERM data agrees closely with evolutionary variation. Finally, locations of TERMs in protein chains can be predicted from sequence alone based on sequence signatures emergent from TERM instances in the PDB. For multisegment motifs, this method identifies spatially adjacent fragments that are not contiguous in sequence-a major bottleneck in structure prediction. Although all TERMs recur in diverse proteins, some appear specialized for certain functions, such as interface formation, metal coordination, or even water binding. Structural biology has benefited greatly from previously observed degeneracies in structure. The decomposition of the known structural universe into a finite set of compact TERMs offers exciting opportunities toward better understanding, design, and prediction of protein structure.

Tertiary alphabet for the observable protein structural universe

PubMed Central

Mackenzie, Craig O.; Zhou, Jianfu; Grigoryan, Gevorg

2016-01-01

Here, we systematically decompose the known protein structural universe into its basic elements, which we dub tertiary structural motifs (TERMs). A TERM is a compact backbone fragment that captures the secondary, tertiary, and quaternary environments around a given residue, comprising one or more disjoint segments (three on average). We seek the set of universal TERMs that capture all structure in the Protein Data Bank (PDB), finding remarkable degeneracy. Only ∼600 TERMs are sufficient to describe 50% of the PDB at sub-Angstrom resolution. However, more rare geometries also exist, and the overall structural coverage grows logarithmically with the number of TERMs. We go on to show that universal TERMs provide an effective mapping between sequence and structure. We demonstrate that TERM-based statistics alone are sufficient to recapitulate close-to-native sequences given either NMR or X-ray backbones. Furthermore, sequence variability predicted from TERM data agrees closely with evolutionary variation. Finally, locations of TERMs in protein chains can be predicted from sequence alone based on sequence signatures emergent from TERM instances in the PDB. For multisegment motifs, this method identifies spatially adjacent fragments that are not contiguous in sequence—a major bottleneck in structure prediction. Although all TERMs recur in diverse proteins, some appear specialized for certain functions, such as interface formation, metal coordination, or even water binding. Structural biology has benefited greatly from previously observed degeneracies in structure. The decomposition of the known structural universe into a finite set of compact TERMs offers exciting opportunities toward better understanding, design, and prediction of protein structure. PMID:27810958

Optimizing physical energy functions for protein folding.

PubMed

Fujitsuka, Yoshimi; Takada, Shoji; Luthey-Schulten, Zaida A; Wolynes, Peter G

2004-01-01

We optimize a physical energy function for proteins with the use of the available structural database and perform three benchmark tests of the performance: (1) recognition of native structures in the background of predefined decoy sets of Levitt, (2) de novo structure prediction using fragment assembly sampling, and (3) molecular dynamics simulations. The energy parameter optimization is based on the energy landscape theory and uses a Monte Carlo search to find a set of parameters that seeks the largest ratio deltaE(s)/DeltaE for all proteins in a training set simultaneously. Here, deltaE(s) is the stability gap between the native and the average in the denatured states and DeltaE is the energy fluctuation among these states. Some of the energy parameters optimized are found to show significant correlation with experimentally observed quantities: (1) In the recognition test, the optimized function assigns the lowest energy to either the native or a near-native structure among many decoy structures for all the proteins studied. (2) Structure prediction with the fragment assembly sampling gives structure models with root mean square deviation less than 6 A in one of the top five cluster centers for five of six proteins studied. (3) Structure prediction using molecular dynamics simulation gives poorer performance, implying the importance of having a more precise description of local structures. The physical energy function solely inferred from a structural database neither utilizes sequence information from the family of the target nor the outcome of the secondary structure prediction but can produce the correct native fold for many small proteins. Copyright 2003 Wiley-Liss, Inc.

Constraint Logic Programming approach to protein structure prediction.

PubMed

Dal Palù, Alessandro; Dovier, Agostino; Fogolari, Federico

2004-11-30

The protein structure prediction problem is one of the most challenging problems in biological sciences. Many approaches have been proposed using database information and/or simplified protein models. The protein structure prediction problem can be cast in the form of an optimization problem. Notwithstanding its importance, the problem has very seldom been tackled by Constraint Logic Programming, a declarative programming paradigm suitable for solving combinatorial optimization problems. Constraint Logic Programming techniques have been applied to the protein structure prediction problem on the face-centered cube lattice model. Molecular dynamics techniques, endowed with the notion of constraint, have been also exploited. Even using a very simplified model, Constraint Logic Programming on the face-centered cube lattice model allowed us to obtain acceptable results for a few small proteins. As a test implementation their (known) secondary structure and the presence of disulfide bridges are used as constraints. Simplified structures obtained in this way have been converted to all atom models with plausible structure. Results have been compared with a similar approach using a well-established technique as molecular dynamics. The results obtained on small proteins show that Constraint Logic Programming techniques can be employed for studying protein simplified models, which can be converted into realistic all atom models. The advantage of Constraint Logic Programming over other, much more explored, methodologies, resides in the rapid software prototyping, in the easy way of encoding heuristics, and in exploiting all the advances made in this research area, e.g. in constraint propagation and its use for pruning the huge search space.

MemBrain: An Easy-to-Use Online Webserver for Transmembrane Protein Structure Prediction

NASA Astrophysics Data System (ADS)

Yin, Xi; Yang, Jing; Xiao, Feng; Yang, Yang; Shen, Hong-Bin

2018-03-01

Membrane proteins are an important kind of proteins embedded in the membranes of cells and play crucial roles in living organisms, such as ion channels, transporters, receptors. Because it is difficult to determinate the membrane protein's structure by wet-lab experiments, accurate and fast amino acid sequence-based computational methods are highly desired. In this paper, we report an online prediction tool called MemBrain, whose input is the amino acid sequence. MemBrain consists of specialized modules for predicting transmembrane helices, residue-residue contacts and relative accessible surface area of α-helical membrane proteins. MemBrain achieves a prediction accuracy of 97.9% of A TMH, 87.1% of A P, 3.2 ± 3.0 of N-score, 3.1 ± 2.8 of C-score. MemBrain-Contact obtains 62%/64.1% prediction accuracy on training and independent dataset on top L/5 contact prediction, respectively. And MemBrain-Rasa achieves Pearson correlation coefficient of 0.733 and its mean absolute error of 13.593. These prediction results provide valuable hints for revealing the structure and function of membrane proteins. MemBrain web server is free for academic use and available at www.csbio.sjtu.edu.cn/bioinf/MemBrain/. [Figure not available: see fulltext.

MQAPRank: improved global protein model quality assessment by learning-to-rank.

PubMed

Jing, Xiaoyang; Dong, Qiwen

2017-05-25

Protein structure prediction has achieved a lot of progress during the last few decades and a greater number of models for a certain sequence can be predicted. Consequently, assessing the qualities of predicted protein models in perspective is one of the key components of successful protein structure prediction. Over the past years, a number of methods have been developed to address this issue, which could be roughly divided into three categories: single methods, quasi-single methods and clustering (or consensus) methods. Although these methods achieve much success at different levels, accurate protein model quality assessment is still an open problem. Here, we present the MQAPRank, a global protein model quality assessment program based on learning-to-rank. The MQAPRank first sorts the decoy models by using single method based on learning-to-rank algorithm to indicate their relative qualities for the target protein. And then it takes the first five models as references to predict the qualities of other models by using average GDT_TS scores between reference models and other models. Benchmarked on CASP11 and 3DRobot datasets, the MQAPRank achieved better performances than other leading protein model quality assessment methods. Recently, the MQAPRank participated in the CASP12 under the group name FDUBio and achieved the state-of-the-art performances. The MQAPRank provides a convenient and powerful tool for protein model quality assessment with the state-of-the-art performances, it is useful for protein structure prediction and model quality assessment usages.

Sequential search leads to faster, more efficient fragment-based de novo protein structure prediction.

PubMed

de Oliveira, Saulo H P; Law, Eleanor C; Shi, Jiye; Deane, Charlotte M

2018-04-01

Most current de novo structure prediction methods randomly sample protein conformations and thus require large amounts of computational resource. Here, we consider a sequential sampling strategy, building on ideas from recent experimental work which shows that many proteins fold cotranslationally. We have investigated whether a pseudo-greedy search approach, which begins sequentially from one of the termini, can improve the performance and accuracy of de novo protein structure prediction. We observed that our sequential approach converges when fewer than 20 000 decoys have been produced, fewer than commonly expected. Using our software, SAINT2, we also compared the run time and quality of models produced in a sequential fashion against a standard, non-sequential approach. Sequential prediction produces an individual decoy 1.5-2.5 times faster than non-sequential prediction. When considering the quality of the best model, sequential prediction led to a better model being produced for 31 out of 41 soluble protein validation cases and for 18 out of 24 transmembrane protein cases. Correct models (TM-Score > 0.5) were produced for 29 of these cases by the sequential mode and for only 22 by the non-sequential mode. Our comparison reveals that a sequential search strategy can be used to drastically reduce computational time of de novo protein structure prediction and improve accuracy. Data are available for download from: http://opig.stats.ox.ac.uk/resources. SAINT2 is available for download from: https://github.com/sauloho/SAINT2. saulo.deoliveira@dtc.ox.ac.uk. Supplementary data are available at Bioinformatics online.

3dRPC: a web server for 3D RNA-protein structure prediction.

PubMed

Huang, Yangyu; Li, Haotian; Xiao, Yi

2018-04-01

RNA-protein interactions occur in many biological processes. To understand the mechanism of these interactions one needs to know three-dimensional (3D) structures of RNA-protein complexes. 3dRPC is an algorithm for prediction of 3D RNA-protein complex structures and consists of a docking algorithm RPDOCK and a scoring function 3dRPC-Score. RPDOCK is used to sample possible complex conformations of an RNA and a protein by calculating the geometric and electrostatic complementarities and stacking interactions at the RNA-protein interface according to the features of atom packing of the interface. 3dRPC-Score is a knowledge-based potential that uses the conformations of nucleotide-amino-acid pairs as statistical variables and that is used to choose the near-native complex-conformations obtained from the docking method above. Recently, we built a web server for 3dRPC. The users can easily use 3dRPC without installing it locally. RNA and protein structures in PDB (Protein Data Bank) format are the only needed input files. It can also incorporate the information of interface residues or residue-pairs obtained from experiments or theoretical predictions to improve the prediction. The address of 3dRPC web server is http://biophy.hust.edu.cn/3dRPC. yxiao@hust.edu.cn.

Protein 8-class secondary structure prediction using conditional neural fields.

PubMed

Wang, Zhiyong; Zhao, Feng; Peng, Jian; Xu, Jinbo

2011-10-01

Compared with the protein 3-class secondary structure (SS) prediction, the 8-class prediction gains less attention and is also much more challenging, especially for proteins with few sequence homologs. This paper presents a new probabilistic method for 8-class SS prediction using conditional neural fields (CNFs), a recently invented probabilistic graphical model. This CNF method not only models the complex relationship between sequence features and SS, but also exploits the interdependency among SS types of adjacent residues. In addition to sequence profiles, our method also makes use of non-evolutionary information for SS prediction. Tested on the CB513 and RS126 data sets, our method achieves Q8 accuracy of 64.9 and 64.7%, respectively, which are much better than the SSpro8 web server (51.0 and 48.0%, respectively). Our method can also be used to predict other structure properties (e.g. solvent accessibility) of a protein or the SS of RNA. Copyright © 2011 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Complete fold annotation of the human proteome using a novel structural feature space

DOE PAGES

Middleton, Sarah A.; Illuminati, Joseph; Kim, Junhyong

2017-04-13

Recognition of protein structural fold is the starting point for many structure prediction tools and protein function inference. Fold prediction is computationally demanding and recognizing novel folds is difficult such that the majority of proteins have not been annotated for fold classification. Here we describe a new machine learning approach using a novel feature space that can be used for accurate recognition of all 1,221 currently known folds and inference of unknown novel folds. We show that our method achieves better than 94% accuracy even when many folds have only one training example. We demonstrate the utility of this methodmore » by predicting the folds of 34,330 human protein domains and showing that these predictions can yield useful insights into potential biological function, such as prediction of RNA-binding ability. Finally, our method can be applied to de novo fold prediction of entire proteomes and identify candidate novel fold families.« less

Survey of predictors of propensity for protein production and crystallization with application to predict resolution of crystal structures

DOE Office of Scientific and Technical Information (OSTI.GOV)

Gao, Jianzhao; Wu, Zhonghua; Hu, Gang

Selection of proper targets for the X-ray crystallography will benefit biological research community immensely. Several computational models were proposed to predict propensity of successful protein production and diffraction quality crystallization from protein sequences. We reviewed a comprehensive collection of 22 such predictors that were developed in the last decade. We found that almost all of these models are easily accessible as webservers and/or standalone software and we demonstrated that some of them are widely used by the research community. We empirically evaluated and compared the predictive performance of seven representative methods. The analysis suggests that these methods produce quite accuratemore » propensities for the diffraction-quality crystallization. We also summarized results of the first study of the relation between these predictive propensities and the resolution of the crystallizable proteins. We found that the propensities predicted by several methods are significantly higher for proteins that have high resolution structures compared to those with the low resolution structures. Moreover, we tested a new meta-predictor, MetaXXC, which averages the propensities generated by the three most accurate predictors of the diffraction-quality crystallization. MetaXXC generates putative values of resolution that have modest levels of correlation with the experimental resolutions and it offers the lowest mean absolute error when compared to the seven considered methods. We conclude that protein sequences can be used to fairly accurately predict whether their corresponding protein structures can be solved using X-ray crystallography. Moreover, we also ascertain that sequences can be used to reasonably well predict the resolution of the resulting protein crystals.« less

Large-scale structure prediction by improved contact predictions and model quality assessment.

PubMed

Michel, Mirco; Menéndez Hurtado, David; Uziela, Karolis; Elofsson, Arne

2017-07-15

Accurate contact predictions can be used for predicting the structure of proteins. Until recently these methods were limited to very big protein families, decreasing their utility. However, recent progress by combining direct coupling analysis with machine learning methods has made it possible to predict accurate contact maps for smaller families. To what extent these predictions can be used to produce accurate models of the families is not known. We present the PconsFold2 pipeline that uses contact predictions from PconsC3, the CONFOLD folding algorithm and model quality estimations to predict the structure of a protein. We show that the model quality estimation significantly increases the number of models that reliably can be identified. Finally, we apply PconsFold2 to 6379 Pfam families of unknown structure and find that PconsFold2 can, with an estimated 90% specificity, predict the structure of up to 558 Pfam families of unknown structure. Out of these, 415 have not been reported before. Datasets as well as models of all the 558 Pfam families are available at http://c3.pcons.net/ . All programs used here are freely available. arne@bioinfo.se. © The Author 2017. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com

Biological and functional relevance of CASP predictions

PubMed Central

Liu, Tianyun; Ish‐Shalom, Shirbi; Torng, Wen; Lafita, Aleix; Bock, Christian; Mort, Matthew; Cooper, David N; Bliven, Spencer; Capitani, Guido; Mooney, Sean D.

2017-01-01

Abstract Our goal is to answer the question: compared with experimental structures, how useful are predicted models for functional annotation? We assessed the functional utility of predicted models by comparing the performances of a suite of methods for functional characterization on the predictions and the experimental structures. We identified 28 sites in 25 protein targets to perform functional assessment. These 28 sites included nine sites with known ligand binding (holo‐sites), nine sites that are expected or suggested by experimental authors for small molecule binding (apo‐sites), and Ten sites containing important motifs, loops, or key residues with important disease‐associated mutations. We evaluated the utility of the predictions by comparing their microenvironments to the experimental structures. Overall structural quality correlates with functional utility. However, the best‐ranked predictions (global) may not have the best functional quality (local). Our assessment provides an ability to discriminate between predictions with high structural quality. When assessing ligand‐binding sites, most prediction methods have higher performance on apo‐sites than holo‐sites. Some servers show consistently high performance for certain types of functional sites. Finally, many functional sites are associated with protein‐protein interaction. We also analyzed biologically relevant features from the protein assemblies of two targets where the active site spanned the protein‐protein interface. For the assembly targets, we find that the features in the models are mainly determined by the choice of template. PMID:28975675

Isolation and in silico analysis of a novel H+-pyrophosphatase gene orthologue from the halophytic grass Leptochloa fusca

NASA Astrophysics Data System (ADS)

Rauf, Muhammad; Saeed, Nasir A.; Habib, Imran; Ahmed, Moddassir; Shahzad, Khurram; Mansoor, Shahid; Ali, Rashid

2017-02-01

Structure prediction can provide information about function and active sites of protein which helps to design new functional proteins. H+-pyrophosphatase is transmembrane protein involved in establishing proton motive force for active transport of Na+ across membrane by Na+/H+ antiporters. A full length novel H+-pyrophosphatase gene was isolated from halophytic grass Leptochloa fusca using RT-PCR and RACE method. Full length LfVP1 gene sequence of 2292 nucleotides encodes protein of 764 amino acids. DNA and protein sequences were used for characterization using bioinformatics tools. Various important potential sites were predicted by PROSITE webserver. Primary structural analysis showed LfVP1 as stable protein and Grand average hydropathy (GRAVY) indicated that LfVP1 protein has good hydrosolubility. Secondary structure analysis showed that LfVP1 protein sequence contains significant proportion of alpha helix and random coil. Protein membrane topology suggested the presence of 14 transmembrane domains and presence of catalytic domain in TM3. Three dimensional structure from LfVP1 protein sequence also indicated the presence of 14 transmembrane domains and hydrophobicity surface model showed amino acid hydrophobicity. Ramachandran plot showed that 98% amino acid residues were predicted in the favored region.

Application of Machine Learning Approaches for Protein-protein Interactions Prediction.

PubMed

Zhang, Mengying; Su, Qiang; Lu, Yi; Zhao, Manman; Niu, Bing

2017-01-01

Proteomics endeavors to study the structures, functions and interactions of proteins. Information of the protein-protein interactions (PPIs) helps to improve our knowledge of the functions and the 3D structures of proteins. Thus determining the PPIs is essential for the study of the proteomics. In this review, in order to study the application of machine learning in predicting PPI, some machine learning approaches such as support vector machine (SVM), artificial neural networks (ANNs) and random forest (RF) were selected, and the examples of its applications in PPIs were listed. SVM and RF are two commonly used methods. Nowadays, more researchers predict PPIs by combining more than two methods. This review presents the application of machine learning approaches in predicting PPI. Many examples of success in identification and prediction in the area of PPI prediction have been discussed, and the PPIs research is still in progress. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

Prediction of protein secondary structure content for the twilight zone sequences.

PubMed

Homaeian, Leila; Kurgan, Lukasz A; Ruan, Jishou; Cios, Krzysztof J; Chen, Ke

2007-11-15

Secondary protein structure carries information about local structural arrangements, which include three major conformations: alpha-helices, beta-strands, and coils. Significant majority of successful methods for prediction of the secondary structure is based on multiple sequence alignment. However, multiple alignment fails to provide accurate results when a sequence comes from the twilight zone, that is, it is characterized by low (<30%) homology. To this end, we propose a novel method for prediction of secondary structure content through comprehensive sequence representation, called PSSC-core. The method uses a multiple linear regression model and introduces a comprehensive feature-based sequence representation to predict amount of helices and strands for sequences from the twilight zone. The PSSC-core method was tested and compared with two other state-of-the-art prediction methods on a set of 2187 twilight zone sequences. The results indicate that our method provides better predictions for both helix and strand content. The PSSC-core is shown to provide statistically significantly better results when compared with the competing methods, reducing the prediction error by 5-7% for helix and 7-9% for strand content predictions. The proposed feature-based sequence representation uses a comprehensive set of physicochemical properties that are custom-designed for each of the helix and strand content predictions. It includes composition and composition moment vectors, frequency of tetra-peptides associated with helical and strand conformations, various property-based groups like exchange groups, chemical groups of the side chains and hydrophobic group, auto-correlations based on hydrophobicity, side-chain masses, hydropathy, and conformational patterns for beta-sheets. The PSSC-core method provides an alternative for predicting the secondary structure content that can be used to validate and constrain results of other structure prediction methods. At the same time, it also provides useful insight into design of successful protein sequence representations that can be used in developing new methods related to prediction of different aspects of the secondary protein structure. (c) 2007 Wiley-Liss, Inc.

LIBP-Pred: web server for lipid binding proteins using structural network parameters; PDB mining of human cancer biomarkers and drug targets in parasites and bacteria.

PubMed

González-Díaz, Humberto; Munteanu, Cristian R; Postelnicu, Lucian; Prado-Prado, Francisco; Gestal, Marcos; Pazos, Alejandro

2012-03-01

Lipid-Binding Proteins (LIBPs) or Fatty Acid-Binding Proteins (FABPs) play an important role in many diseases such as different types of cancer, kidney injury, atherosclerosis, diabetes, intestinal ischemia and parasitic infections. Thus, the computational methods that can predict LIBPs based on 3D structure parameters became a goal of major importance for drug-target discovery, vaccine design and biomarker selection. In addition, the Protein Data Bank (PDB) contains 3000+ protein 3D structures with unknown function. This list, as well as new experimental outcomes in proteomics research, is a very interesting source to discover relevant proteins, including LIBPs. However, to the best of our knowledge, there are no general models to predict new LIBPs based on 3D structures. We developed new Quantitative Structure-Activity Relationship (QSAR) models based on 3D electrostatic parameters of 1801 different proteins, including 801 LIBPs. We calculated these electrostatic parameters with the MARCH-INSIDE software and they correspond to the entire protein or to specific protein regions named core, inner, middle, and surface. We used these parameters as inputs to develop a simple Linear Discriminant Analysis (LDA) classifier to discriminate 3D structure of LIBPs from other proteins. We implemented this predictor in the web server named LIBP-Pred, freely available at , along with other important web servers of the Bio-AIMS portal. The users can carry out an automatic retrieval of protein structures from PDB or upload their custom protein structural models from their disk created with LOMETS server. We demonstrated the PDB mining option performing a predictive study of 2000+ proteins with unknown function. Interesting results regarding the discovery of new Cancer Biomarkers in humans or drug targets in parasites have been discussed here in this sense.

POOL server: machine learning application for functional site prediction in proteins.

PubMed

Somarowthu, Srinivas; Ondrechen, Mary Jo

2012-08-01

We present an automated web server for partial order optimum likelihood (POOL), a machine learning application that combines computed electrostatic and geometric information for high-performance prediction of catalytic residues from 3D structures. Input features consist of THEMATICS electrostatics data and pocket information from ConCavity. THEMATICS measures deviation from typical, sigmoidal titration behavior to identify functionally important residues and ConCavity identifies binding pockets by analyzing the surface geometry of protein structures. Both THEMATICS and ConCavity (structure only) do not require the query protein to have any sequence or structure similarity to other proteins. Hence, POOL is applicable to proteins with novel folds and engineered proteins. As an additional option for cases where sequence homologues are available, users can include evolutionary information from INTREPID for enhanced accuracy in site prediction. The web site is free and open to all users with no login requirements at http://www.pool.neu.edu. m.ondrechen@neu.edu Supplementary data are available at Bioinformatics online.

SVM-Fold: a tool for discriminative multi-class protein fold and superfamily recognition

PubMed Central

Melvin, Iain; Ie, Eugene; Kuang, Rui; Weston, Jason; Stafford, William Noble; Leslie, Christina

2007-01-01

Background Predicting a protein's structural class from its amino acid sequence is a fundamental problem in computational biology. Much recent work has focused on developing new representations for protein sequences, called string kernels, for use with support vector machine (SVM) classifiers. However, while some of these approaches exhibit state-of-the-art performance at the binary protein classification problem, i.e. discriminating between a particular protein class and all other classes, few of these studies have addressed the real problem of multi-class superfamily or fold recognition. Moreover, there are only limited software tools and systems for SVM-based protein classification available to the bioinformatics community. Results We present a new multi-class SVM-based protein fold and superfamily recognition system and web server called SVM-Fold, which can be found at . Our system uses an efficient implementation of a state-of-the-art string kernel for sequence profiles, called the profile kernel, where the underlying feature representation is a histogram of inexact matching k-mer frequencies. We also employ a novel machine learning approach to solve the difficult multi-class problem of classifying a sequence of amino acids into one of many known protein structural classes. Binary one-vs-the-rest SVM classifiers that are trained to recognize individual structural classes yield prediction scores that are not comparable, so that standard "one-vs-all" classification fails to perform well. Moreover, SVMs for classes at different levels of the protein structural hierarchy may make useful predictions, but one-vs-all does not try to combine these multiple predictions. To deal with these problems, our method learns relative weights between one-vs-the-rest classifiers and encodes information about the protein structural hierarchy for multi-class prediction. In large-scale benchmark results based on the SCOP database, our code weighting approach significantly improves on the standard one-vs-all method for both the superfamily and fold prediction in the remote homology setting and on the fold recognition problem. Moreover, our code weight learning algorithm strongly outperforms nearest-neighbor methods based on PSI-BLAST in terms of prediction accuracy on every structure classification problem we consider. Conclusion By combining state-of-the-art SVM kernel methods with a novel multi-class algorithm, the SVM-Fold system delivers efficient and accurate protein fold and superfamily recognition. PMID:17570145

Structural Analysis of PTM Hotspots (SAPH-ire) – A Quantitative Informatics Method Enabling the Discovery of Novel Regulatory Elements in Protein Families*

PubMed Central

Dewhurst, Henry M.; Choudhury, Shilpa; Torres, Matthew P.

2015-01-01

Predicting the biological function potential of post-translational modifications (PTMs) is becoming increasingly important in light of the exponential increase in available PTM data from high-throughput proteomics. We developed structural analysis of PTM hotspots (SAPH-ire)—a quantitative PTM ranking method that integrates experimental PTM observations, sequence conservation, protein structure, and interaction data to allow rank order comparisons within or between protein families. Here, we applied SAPH-ire to the study of PTMs in diverse G protein families, a conserved and ubiquitous class of proteins essential for maintenance of intracellular structure (tubulins) and signal transduction (large and small Ras-like G proteins). A total of 1728 experimentally verified PTMs from eight unique G protein families were clustered into 451 unique hotspots, 51 of which have a known and cited biological function or response. Using customized software, the hotspots were analyzed in the context of 598 unique protein structures. By comparing distributions of hotspots with known versus unknown function, we show that SAPH-ire analysis is predictive for PTM biological function. Notably, SAPH-ire revealed high-ranking hotspots for which a functional impact has not yet been determined, including phosphorylation hotspots in the N-terminal tails of G protein gamma subunits—conserved protein structures never before reported as regulators of G protein coupled receptor signaling. To validate this prediction we used the yeast model system for G protein coupled receptor signaling, revealing that gamma subunit–N-terminal tail phosphorylation is activated in response to G protein coupled receptor stimulation and regulates protein stability in vivo. These results demonstrate the utility of integrating protein structural and sequence features into PTM prioritization schemes that can improve the analysis and functional power of modification-specific proteomics data. PMID:26070665

Accurate prediction of interfacial residues in two-domain proteins using evolutionary information: implications for three-dimensional modeling.

PubMed

Bhaskara, Ramachandra M; Padhi, Amrita; Srinivasan, Narayanaswamy

2014-07-01

With the preponderance of multidomain proteins in eukaryotic genomes, it is essential to recognize the constituent domains and their functions. Often function involves communications across the domain interfaces, and the knowledge of the interacting sites is essential to our understanding of the structure-function relationship. Using evolutionary information extracted from homologous domains in at least two diverse domain architectures (single and multidomain), we predict the interface residues corresponding to domains from the two-domain proteins. We also use information from the three-dimensional structures of individual domains of two-domain proteins to train naïve Bayes classifier model to predict the interfacial residues. Our predictions are highly accurate (∼85%) and specific (∼95%) to the domain-domain interfaces. This method is specific to multidomain proteins which contain domains in at least more than one protein architectural context. Using predicted residues to constrain domain-domain interaction, rigid-body docking was able to provide us with accurate full-length protein structures with correct orientation of domains. We believe that these results can be of considerable interest toward rational protein and interaction design, apart from providing us with valuable information on the nature of interactions. © 2013 Wiley Periodicals, Inc.

Prediction of cis/trans isomerization in proteins using PSI-BLAST profiles and secondary structure information.

PubMed

Song, Jiangning; Burrage, Kevin; Yuan, Zheng; Huber, Thomas

2006-03-09

The majority of peptide bonds in proteins are found to occur in the trans conformation. However, for proline residues, a considerable fraction of Prolyl peptide bonds adopt the cis form. Proline cis/trans isomerization is known to play a critical role in protein folding, splicing, cell signaling and transmembrane active transport. Accurate prediction of proline cis/trans isomerization in proteins would have many important applications towards the understanding of protein structure and function. In this paper, we propose a new approach to predict the proline cis/trans isomerization in proteins using support vector machine (SVM). The preliminary results indicated that using Radial Basis Function (RBF) kernels could lead to better prediction performance than that of polynomial and linear kernel functions. We used single sequence information of different local window sizes, amino acid compositions of different local sequences, multiple sequence alignment obtained from PSI-BLAST and the secondary structure information predicted by PSIPRED. We explored these different sequence encoding schemes in order to investigate their effects on the prediction performance. The training and testing of this approach was performed on a newly enlarged dataset of 2424 non-homologous proteins determined by X-Ray diffraction method using 5-fold cross-validation. Selecting the window size 11 provided the best performance for determining the proline cis/trans isomerization based on the single amino acid sequence. It was found that using multiple sequence alignments in the form of PSI-BLAST profiles could significantly improve the prediction performance, the prediction accuracy increased from 62.8% with single sequence to 69.8% and Matthews Correlation Coefficient (MCC) improved from 0.26 with single local sequence to 0.40. Furthermore, if coupled with the predicted secondary structure information by PSIPRED, our method yielded a prediction accuracy of 71.5% and MCC of 0.43, 9% and 0.17 higher than the accuracy achieved based on the singe sequence information, respectively. A new method has been developed to predict the proline cis/trans isomerization in proteins based on support vector machine, which used the single amino acid sequence with different local window sizes, the amino acid compositions of local sequence flanking centered proline residues, the position-specific scoring matrices (PSSMs) extracted by PSI-BLAST and the predicted secondary structures generated by PSIPRED. The successful application of SVM approach in this study reinforced that SVM is a powerful tool in predicting proline cis/trans isomerization in proteins and biological sequence analysis.

What are the structural features that drive partitioning of proteins in aqueous two-phase systems?

PubMed

Wu, Zhonghua; Hu, Gang; Wang, Kui; Zaslavsky, Boris Yu; Kurgan, Lukasz; Uversky, Vladimir N

2017-01-01

Protein partitioning in aqueous two-phase systems (ATPSs) represents a convenient, inexpensive, and easy to scale-up protein separation technique. Since partition behavior of a protein dramatically depends on an ATPS composition, it would be highly beneficial to have reliable means for (even qualitative) prediction of partitioning of a target protein under different conditions. Our aim was to understand which structural features of proteins contribute to partitioning of a query protein in a given ATPS. We undertook a systematic empirical analysis of relations between 57 numerical structural descriptors derived from the corresponding amino acid sequences and crystal structures of 10 well-characterized proteins and the partition behavior of these proteins in 29 different ATPSs. This analysis revealed that just a few structural characteristics of proteins can accurately determine behavior of these proteins in a given ATPS. However, partition behavior of proteins in different ATPSs relies on different structural features. In other words, we could not find a unique set of protein structural features derived from their crystal structures that could be used for the description of the protein partition behavior of all proteins in all ATPSs analyzed in this study. We likely need to gain better insight into relationships between protein-solvent interactions and protein structure peculiarities, in particular given limitations of the used here crystal structures, to be able to construct a model that accurately predicts protein partition behavior across all ATPSs. Copyright © 2016 Elsevier B.V. All rights reserved.

Genome scale enzyme–metabolite and drug–target interaction predictions using the signature molecular descriptor

DOE PAGES

Faulon, Jean-Loup; Misra, Milind; Martin, Shawn; ...

2007-11-23

Motivation: Identifying protein enzymatic or pharmacological activities are important areas of research in biology and chemistry. Biological and chemical databases are increasingly being populated with linkages between protein sequences and chemical structures. Additionally, there is now sufficient information to apply machine-learning techniques to predict interactions between chemicals and proteins at a genome scale. Current machine-learning techniques use as input either protein sequences and structures or chemical information. We propose here a method to infer protein–chemical interactions using heterogeneous input consisting of both protein sequence and chemical information. Results: Our method relies on expressing proteins and chemicals with a common cheminformaticsmore » representation. We demonstrate our approach by predicting whether proteins can catalyze reactions not present in training sets. We also predict whether a given drug can bind a target, in the absence of prior binding information for that drug and target. Lastly, such predictions cannot be made with current machine-learning techniques requiring binding information for individual reactions or individual targets.« less

FragBag, an accurate representation of protein structure, retrieves structural neighbors from the entire PDB quickly and accurately.

PubMed

Budowski-Tal, Inbal; Nov, Yuval; Kolodny, Rachel

2010-02-23

Fast identification of protein structures that are similar to a specified query structure in the entire Protein Data Bank (PDB) is fundamental in structure and function prediction. We present FragBag: An ultrafast and accurate method for comparing protein structures. We describe a protein structure by the collection of its overlapping short contiguous backbone segments, and discretize this set using a library of fragments. Then, we succinctly represent the protein as a "bags-of-fragments"-a vector that counts the number of occurrences of each fragment-and measure the similarity between two structures by the similarity between their vectors. Our representation has two additional benefits: (i) it can be used to construct an inverted index, for implementing a fast structural search engine of the entire PDB, and (ii) one can specify a structure as a collection of substructures, without combining them into a single structure; this is valuable for structure prediction, when there are reliable predictions only of parts of the protein. We use receiver operating characteristic curve analysis to quantify the success of FragBag in identifying neighbor candidate sets in a dataset of over 2,900 structures. The gold standard is the set of neighbors found by six state of the art structural aligners. Our best FragBag library finds more accurate candidate sets than the three other filter methods: The SGM, PRIDE, and a method by Zotenko et al. More interestingly, FragBag performs on a par with the computationally expensive, yet highly trusted structural aligners STRUCTAL and CE.

Detection of functionally important regions in "hypothetical proteins" of known structure.

PubMed

Nimrod, Guy; Schushan, Maya; Steinberg, David M; Ben-Tal, Nir

2008-12-10

Structural genomics initiatives provide ample structures of "hypothetical proteins" (i.e., proteins of unknown function) at an ever increasing rate. However, without function annotation, this structural goldmine is of little use to biologists who are interested in particular molecular systems. To this end, we used (an improved version of) the PatchFinder algorithm for the detection of functional regions on the protein surface, which could mediate its interactions with, e.g., substrates, ligands, and other proteins. Examination, using a data set of annotated proteins, showed that PatchFinder outperforms similar methods. We collected 757 structures of hypothetical proteins and their predicted functional regions in the N-Func database. Inspection of several of these regions demonstrated that they are useful for function prediction. For example, we suggested an interprotein interface and a putative nucleotide-binding site. A web-server implementation of PatchFinder and the N-Func database are available at http://patchfinder.tau.ac.il/.

Prediction of β-turns in proteins from multiple alignment using neural network

PubMed Central

Kaur, Harpreet; Raghava, Gajendra Pal Singh

2003-01-01

A neural network-based method has been developed for the prediction of β-turns in proteins by using multiple sequence alignment. Two feed-forward back-propagation networks with a single hidden layer are used where the first-sequence structure network is trained with the multiple sequence alignment in the form of PSI-BLAST–generated position-specific scoring matrices. The initial predictions from the first network and PSIPRED-predicted secondary structure are used as input to the second structure-structure network to refine the predictions obtained from the first net. A significant improvement in prediction accuracy has been achieved by using evolutionary information contained in the multiple sequence alignment. The final network yields an overall prediction accuracy of 75.5% when tested by sevenfold cross-validation on a set of 426 nonhomologous protein chains. The corresponding Qpred, Qobs, and Matthews correlation coefficient values are 49.8%, 72.3%, and 0.43, respectively, and are the best among all the previously published β-turn prediction methods. The Web server BetaTPred2 (http://www.imtech.res.in/raghava/betatpred2/) has been developed based on this approach. PMID:12592033

Molecular Dynamics Simulations of Hydrophobic Residues

NASA Astrophysics Data System (ADS)

Caballero, Diego; Zhou, Alice; Regan, Lynne; O'Hern, Corey

2013-03-01

Molecular recognition and protein-protein interactions are involved in important biological processes. However, despite recent improvements in computational methods for protein design, we still lack a predictive understanding of protein structure and interactions. To begin to address these shortcomings, we performed molecular dynamics simulations of hydrophobic residues modeled as hard spheres with stereo-chemical constraints initially at high temperature, and then quenched to low temperature to obtain local energy minima. We find that there is a range of quench rates over which the probabilities of side-chain dihedral angles for hydrophobic residues match the probabilities obtained for known protein structures. In addition, we predict the side-chain dihedral angle propensities in the core region of the proteins T4, ROP, and several mutants. These studies serve as a first step in developing the ability to quantitatively rank the energies of designed protein constructs. The success of these studies suggests that only hard-sphere dynamics with geometrical constraints are needed for accurate protein structure prediction in hydrophobic cavities and binding interfaces. NSF Grant PHY-1019147

Predicting Real-Valued Protein Residue Fluctuation Using FlexPred.

PubMed

Peterson, Lenna; Jamroz, Michal; Kolinski, Andrzej; Kihara, Daisuke

2017-01-01

The conventional view of a protein structure as static provides only a limited picture. There is increasing evidence that protein dynamics are often vital to protein function including interaction with partners such as other proteins, nucleic acids, and small molecules. Considering flexibility is also important in applications such as computational protein docking and protein design. While residue flexibility is partially indicated by experimental measures such as the B-factor from X-ray crystallography and ensemble fluctuation from nuclear magnetic resonance (NMR) spectroscopy as well as computational molecular dynamics (MD) simulation, these techniques are resource-intensive. In this chapter, we describe the web server and stand-alone version of FlexPred, which rapidly predicts absolute per-residue fluctuation from a three-dimensional protein structure. On a set of 592 nonredundant structures, comparing the fluctuations predicted by FlexPred to the observed fluctuations in MD simulations showed an average correlation coefficient of 0.669 and an average root mean square error of 1.07 Å. FlexPred is available at http://kiharalab.org/flexPred/ .

From laptop to benchtop to bedside: Structure-based Drug Design on Protein Targets

PubMed Central

Chen, Lu; Morrow, John K.; Tran, Hoang T.; Phatak, Sharangdhar S.; Du-Cuny, Lei; Zhang, Shuxing

2013-01-01

As an important aspect of computer-aided drug design, structure-based drug design brought a new horizon to pharmaceutical development. This in silico method permeates all aspects of drug discovery today, including lead identification, lead optimization, ADMET prediction and drug repurposing. Structure-based drug design has resulted in fruitful successes drug discovery targeting protein-ligand and protein-protein interactions. Meanwhile, challenges, noted by low accuracy and combinatoric issues, may also cause failures. In this review, state-of-the-art techniques for protein modeling (e.g. structure prediction, modeling protein flexibility, etc.), hit identification/optimization (e.g. molecular docking, focused library design, fragment-based design, molecular dynamic, etc.), and polypharmacology design will be discussed. We will explore how structure-based techniques can facilitate the drug discovery process and interplay with other experimental approaches. PMID:22316152

Prediction of pi-turns in proteins using PSI-BLAST profiles and secondary structure information.

PubMed

Wang, Yan; Xue, Zhi-Dong; Shi, Xiao-Hong; Xu, Jin

2006-09-01

Due to the structural and functional importance of tight turns, some methods have been proposed to predict gamma-turns, beta-turns, and alpha-turns in proteins. In the past, studies of pi-turns were made, but not a single prediction approach has been developed so far. It will be useful to develop a method for identifying pi-turns in a protein sequence. In this paper, the support vector machine (SVM) method has been introduced to predict pi-turns from the amino acid sequence. The training and testing of this approach is performed with a newly collected data set of 640 non-homologous protein chains containing 1931 pi-turns. Different sequence encoding schemes have been explored in order to investigate their effects on the prediction performance. With multiple sequence alignment and predicted secondary structure, the final SVM model yields a Matthews correlation coefficient (MCC) of 0.556 by a 7-fold cross-validation. A web server implementing the prediction method is available at the following URL: http://210.42.106.80/piturn/.

Rebelling for a Reason: Protein Structural “Outliers”

PubMed Central

Arumugam, Gandhimathi; Nair, Anu G.; Hariharaputran, Sridhar; Ramanathan, Sowdhamini

2013-01-01

Analysis of structural variation in domain superfamilies can reveal constraints in protein evolution which aids protein structure prediction and classification. Structure-based sequence alignment of distantly related proteins, organized in PASS2 database, provides clues about structurally conserved regions among different functional families. Some superfamily members show large structural differences which are functionally relevant. This paper analyses the impact of structural divergence on function for multi-member superfamilies, selected from the PASS2 superfamily alignment database. Functional annotations within superfamilies, with structural outliers or ‘rebels’, are discussed in the context of structural variations. Overall, these data reinforce the idea that functional similarities cannot be extrapolated from mere structural conservation. The implication for fold-function prediction is that the functional annotations can only be inherited with very careful consideration, especially at low sequence identities. PMID:24073209

Quantifying side-chain conformational variations in protein structure

PubMed Central

Miao, Zhichao; Cao, Yang

2016-01-01

Protein side-chain conformation is closely related to their biological functions. The side-chain prediction is a key step in protein design, protein docking and structure optimization. However, side-chain polymorphism comprehensively exists in protein as various types and has been long overlooked by side-chain prediction. But such conformational variations have not been quantitatively studied and the correlations between these variations and residue features are vague. Here, we performed statistical analyses on large scale data sets and found that the side-chain conformational flexibility is closely related to the exposure to solvent, degree of freedom and hydrophilicity. These analyses allowed us to quantify different types of side-chain variabilities in PDB. The results underscore that protein side-chain conformation prediction is not a single-answer problem, leading us to reconsider the assessment approaches of side-chain prediction programs. PMID:27845406

Quantifying side-chain conformational variations in protein structure

NASA Astrophysics Data System (ADS)

Miao, Zhichao; Cao, Yang

2016-11-01

Protein side-chain conformation is closely related to their biological functions. The side-chain prediction is a key step in protein design, protein docking and structure optimization. However, side-chain polymorphism comprehensively exists in protein as various types and has been long overlooked by side-chain prediction. But such conformational variations have not been quantitatively studied and the correlations between these variations and residue features are vague. Here, we performed statistical analyses on large scale data sets and found that the side-chain conformational flexibility is closely related to the exposure to solvent, degree of freedom and hydrophilicity. These analyses allowed us to quantify different types of side-chain variabilities in PDB. The results underscore that protein side-chain conformation prediction is not a single-answer problem, leading us to reconsider the assessment approaches of side-chain prediction programs.

Quantifying side-chain conformational variations in protein structure.

PubMed

Miao, Zhichao; Cao, Yang

2016-11-15

Protein side-chain conformation is closely related to their biological functions. The side-chain prediction is a key step in protein design, protein docking and structure optimization. However, side-chain polymorphism comprehensively exists in protein as various types and has been long overlooked by side-chain prediction. But such conformational variations have not been quantitatively studied and the correlations between these variations and residue features are vague. Here, we performed statistical analyses on large scale data sets and found that the side-chain conformational flexibility is closely related to the exposure to solvent, degree of freedom and hydrophilicity. These analyses allowed us to quantify different types of side-chain variabilities in PDB. The results underscore that protein side-chain conformation prediction is not a single-answer problem, leading us to reconsider the assessment approaches of side-chain prediction programs.

Predicting beta-turns in proteins using support vector machines with fractional polynomials

PubMed Central

2013-01-01

Background β-turns are secondary structure type that have essential role in molecular recognition, protein folding, and stability. They are found to be the most common type of non-repetitive structures since 25% of amino acids in protein structures are situated on them. Their prediction is considered to be one of the crucial problems in bioinformatics and molecular biology, which can provide valuable insights and inputs for the fold recognition and drug design. Results We propose an approach that combines support vector machines (SVMs) and logistic regression (LR) in a hybrid prediction method, which we call (H-SVM-LR) to predict β-turns in proteins. Fractional polynomials are used for LR modeling. We utilize position specific scoring matrices (PSSMs) and predicted secondary structure (PSS) as features. Our simulation studies show that H-SVM-LR achieves Qtotal of 82.87%, 82.84%, and 82.32% on the BT426, BT547, and BT823 datasets respectively. These values are the highest among other β-turns prediction methods that are based on PSSMs and secondary structure information. H-SVM-LR also achieves favorable performance in predicting β-turns as measured by the Matthew's correlation coefficient (MCC) on these datasets. Furthermore, H-SVM-LR shows good performance when considering shape strings as additional features. Conclusions In this paper, we present a comprehensive approach for β-turns prediction. Experiments show that our proposed approach achieves better performance compared to other competing prediction methods. PMID:24565438

Predicting beta-turns in proteins using support vector machines with fractional polynomials.

PubMed

Elbashir, Murtada; Wang, Jianxin; Wu, Fang-Xiang; Wang, Lusheng

2013-11-07

β-turns are secondary structure type that have essential role in molecular recognition, protein folding, and stability. They are found to be the most common type of non-repetitive structures since 25% of amino acids in protein structures are situated on them. Their prediction is considered to be one of the crucial problems in bioinformatics and molecular biology, which can provide valuable insights and inputs for the fold recognition and drug design. We propose an approach that combines support vector machines (SVMs) and logistic regression (LR) in a hybrid prediction method, which we call (H-SVM-LR) to predict β-turns in proteins. Fractional polynomials are used for LR modeling. We utilize position specific scoring matrices (PSSMs) and predicted secondary structure (PSS) as features. Our simulation studies show that H-SVM-LR achieves Qtotal of 82.87%, 82.84%, and 82.32% on the BT426, BT547, and BT823 datasets respectively. These values are the highest among other β-turns prediction methods that are based on PSSMs and secondary structure information. H-SVM-LR also achieves favorable performance in predicting β-turns as measured by the Matthew's correlation coefficient (MCC) on these datasets. Furthermore, H-SVM-LR shows good performance when considering shape strings as additional features. In this paper, we present a comprehensive approach for β-turns prediction. Experiments show that our proposed approach achieves better performance compared to other competing prediction methods.

The calcium binding properties and structure prediction of the Hax-1 protein.

PubMed

Balcerak, Anna; Rowinski, Sebastian; Szafron, Lukasz M; Grzybowska, Ewa A

2017-01-01

Hax-1 is a protein involved in regulation of different cellular processes, but its properties and exact mechanisms of action remain unknown. In this work, using purified, recombinant Hax-1 and by applying an in vitro autoradiography assay we have shown that this protein binds Ca 2+ . Additionally, we performed structure prediction analysis which shows that Hax-1 displays definitive structural features, such as two α-helices, short β-strands and four disordered segments.

Conformational switching upon phosphorylation: a predictive framework based on energy landscape principles.

PubMed

Lätzer, Joachim; Shen, Tongye; Wolynes, Peter G

2008-02-19

We investigate how post-translational phosphorylation modifies the global conformation of a protein by changing its free energy landscape using two test proteins, cystatin and NtrC. We first examine the changes in a free energy landscape caused by phosphorylation using a model containing information about both structural forms. For cystatin the free energy cost is fairly large indicating a low probability of sampling the phosphorylated conformation in a perfectly funneled landscape. The predicted barrier for NtrC conformational transition is several times larger than the barrier for cystatin, indicating that the switch protein NtrC most probably follows a partial unfolding mechanism to move from one basin to the other. Principal component analysis and linear response theory show how the naturally occurring conformational changes in unmodified proteins are captured and stabilized by the change of interaction potential. We also develop a partially guided structure prediction Hamiltonian which is capable of predicting the global structure of a phosphorylated protein using only knowledge of the structure of the unphosphorylated protein or vice versa. This algorithm makes use of a generic transferable long-range residue contact potential along with details of structure short range in sequence. By comparing the results obtained with this guided transferable potential to those from the native-only, perfectly funneled Hamiltonians, we show that the transferable Hamiltonian correctly captures the nature of the global conformational changes induced by phosphorylation and can sample substantially correct structures for the modified protein with high probability.

Using RNA Sequence and Structure for the Prediction of Riboswitch Aptamer: A Comprehensive Review of Available Software and Tools

PubMed Central

Antunes, Deborah; Jorge, Natasha A. N.; Caffarena, Ernesto R.; Passetti, Fabio

2018-01-01

RNA molecules are essential players in many fundamental biological processes. Prokaryotes and eukaryotes have distinct RNA classes with specific structural features and functional roles. Computational prediction of protein structures is a research field in which high confidence three-dimensional protein models can be proposed based on the sequence alignment between target and templates. However, to date, only a few approaches have been developed for the computational prediction of RNA structures. Similar to proteins, RNA structures may be altered due to the interaction with various ligands, including proteins, other RNAs, and metabolites. A riboswitch is a molecular mechanism, found in the three kingdoms of life, in which the RNA structure is modified by the binding of a metabolite. It can regulate multiple gene expression mechanisms, such as transcription, translation initiation, and mRNA splicing and processing. Due to their nature, these entities also act on the regulation of gene expression and detection of small metabolites and have the potential to helping in the discovery of new classes of antimicrobial agents. In this review, we describe software and web servers currently available for riboswitch aptamer identification and secondary and tertiary structure prediction, including applications. PMID:29403526

CNNH_PSS: protein 8-class secondary structure prediction by convolutional neural network with highway.

PubMed

Zhou, Jiyun; Wang, Hongpeng; Zhao, Zhishan; Xu, Ruifeng; Lu, Qin

2018-05-08

Protein secondary structure is the three dimensional form of local segments of proteins and its prediction is an important problem in protein tertiary structure prediction. Developing computational approaches for protein secondary structure prediction is becoming increasingly urgent. We present a novel deep learning based model, referred to as CNNH_PSS, by using multi-scale CNN with highway. In CNNH_PSS, any two neighbor convolutional layers have a highway to deliver information from current layer to the output of the next one to keep local contexts. As lower layers extract local context while higher layers extract long-range interdependencies, the highways between neighbor layers allow CNNH_PSS to have ability to extract both local contexts and long-range interdependencies. We evaluate CNNH_PSS on two commonly used datasets: CB6133 and CB513. CNNH_PSS outperforms the multi-scale CNN without highway by at least 0.010 Q8 accuracy and also performs better than CNF, DeepCNF and SSpro8, which cannot extract long-range interdependencies, by at least 0.020 Q8 accuracy, demonstrating that both local contexts and long-range interdependencies are indeed useful for prediction. Furthermore, CNNH_PSS also performs better than GSM and DCRNN which need extra complex model to extract long-range interdependencies. It demonstrates that CNNH_PSS not only cost less computer resource, but also achieves better predicting performance. CNNH_PSS have ability to extracts both local contexts and long-range interdependencies by combing multi-scale CNN and highway network. The evaluations on common datasets and comparisons with state-of-the-art methods indicate that CNNH_PSS is an useful and efficient tool for protein secondary structure prediction.

Correlation of chemical shifts predicted by molecular dynamics simulations for partially disordered proteins.

PubMed

Karp, Jerome M; Eryilmaz, Ertan; Erylimaz, Ertan; Cowburn, David

2015-01-01

There has been a longstanding interest in being able to accurately predict NMR chemical shifts from structural data. Recent studies have focused on using molecular dynamics (MD) simulation data as input for improved prediction. Here we examine the accuracy of chemical shift prediction for intein systems, which have regions of intrinsic disorder. We find that using MD simulation data as input for chemical shift prediction does not consistently improve prediction accuracy over use of a static X-ray crystal structure. This appears to result from the complex conformational ensemble of the disordered protein segments. We show that using accelerated molecular dynamics (aMD) simulations improves chemical shift prediction, suggesting that methods which better sample the conformational ensemble like aMD are more appropriate tools for use in chemical shift prediction for proteins with disordered regions. Moreover, our study suggests that data accurately reflecting protein dynamics must be used as input for chemical shift prediction in order to correctly predict chemical shifts in systems with disorder.

Comprehensive comparative analysis and identification of RNA-binding protein domains: multi-class classification and feature selection.

PubMed

Jahandideh, Samad; Srinivasasainagendra, Vinodh; Zhi, Degui

2012-11-07

RNA-protein interaction plays an important role in various cellular processes, such as protein synthesis, gene regulation, post-transcriptional gene regulation, alternative splicing, and infections by RNA viruses. In this study, using Gene Ontology Annotated (GOA) and Structural Classification of Proteins (SCOP) databases an automatic procedure was designed to capture structurally solved RNA-binding protein domains in different subclasses. Subsequently, we applied tuned multi-class SVM (TMCSVM), Random Forest (RF), and multi-class ℓ1/ℓq-regularized logistic regression (MCRLR) for analysis and classifying RNA-binding protein domains based on a comprehensive set of sequence and structural features. In this study, we compared prediction accuracy of three different state-of-the-art predictor methods. From our results, TMCSVM outperforms the other methods and suggests the potential of TMCSVM as a useful tool for facilitating the multi-class prediction of RNA-binding protein domains. On the other hand, MCRLR by elucidating importance of features for their contribution in predictive accuracy of RNA-binding protein domains subclasses, helps us to provide some biological insights into the roles of sequences and structures in protein-RNA interactions.

Improving predictions of protein-protein interfaces by combining amino acid-specific classifiers based on structural and physicochemical descriptors with their weighted neighbor averages.

PubMed

de Moraes, Fábio R; Neshich, Izabella A P; Mazoni, Ivan; Yano, Inácio H; Pereira, José G C; Salim, José A; Jardine, José G; Neshich, Goran

2014-01-01

Protein-protein interactions are involved in nearly all regulatory processes in the cell and are considered one of the most important issues in molecular biology and pharmaceutical sciences but are still not fully understood. Structural and computational biology contributed greatly to the elucidation of the mechanism of protein interactions. In this paper, we present a collection of the physicochemical and structural characteristics that distinguish interface-forming residues (IFR) from free surface residues (FSR). We formulated a linear discriminative analysis (LDA) classifier to assess whether chosen descriptors from the BlueStar STING database (http://www.cbi.cnptia.embrapa.br/SMS/) are suitable for such a task. Receiver operating characteristic (ROC) analysis indicates that the particular physicochemical and structural descriptors used for building the linear classifier perform much better than a random classifier and in fact, successfully outperform some of the previously published procedures, whose performance indicators were recently compared by other research groups. The results presented here show that the selected set of descriptors can be utilized to predict IFRs, even when homologue proteins are missing (particularly important for orphan proteins where no homologue is available for comparative analysis/indication) or, when certain conformational changes accompany interface formation. The development of amino acid type specific classifiers is shown to increase IFR classification performance. Also, we found that the addition of an amino acid conservation attribute did not improve the classification prediction. This result indicates that the increase in predictive power associated with amino acid conservation is exhausted by adequate use of an extensive list of independent physicochemical and structural parameters that, by themselves, fully describe the nano-environment at protein-protein interfaces. The IFR classifier developed in this study is now integrated into the BlueStar STING suite of programs. Consequently, the prediction of protein-protein interfaces for all proteins available in the PDB is possible through STING_interfaces module, accessible at the following website: (http://www.cbi.cnptia.embrapa.br/SMS/predictions/index.html).

Improving Predictions of Protein-Protein Interfaces by Combining Amino Acid-Specific Classifiers Based on Structural and Physicochemical Descriptors with Their Weighted Neighbor Averages

PubMed Central

de Moraes, Fábio R.; Neshich, Izabella A. P.; Mazoni, Ivan; Yano, Inácio H.; Pereira, José G. C.; Salim, José A.; Jardine, José G.; Neshich, Goran

2014-01-01

Protein-protein interactions are involved in nearly all regulatory processes in the cell and are considered one of the most important issues in molecular biology and pharmaceutical sciences but are still not fully understood. Structural and computational biology contributed greatly to the elucidation of the mechanism of protein interactions. In this paper, we present a collection of the physicochemical and structural characteristics that distinguish interface-forming residues (IFR) from free surface residues (FSR). We formulated a linear discriminative analysis (LDA) classifier to assess whether chosen descriptors from the BlueStar STING database (http://www.cbi.cnptia.embrapa.br/SMS/) are suitable for such a task. Receiver operating characteristic (ROC) analysis indicates that the particular physicochemical and structural descriptors used for building the linear classifier perform much better than a random classifier and in fact, successfully outperform some of the previously published procedures, whose performance indicators were recently compared by other research groups. The results presented here show that the selected set of descriptors can be utilized to predict IFRs, even when homologue proteins are missing (particularly important for orphan proteins where no homologue is available for comparative analysis/indication) or, when certain conformational changes accompany interface formation. The development of amino acid type specific classifiers is shown to increase IFR classification performance. Also, we found that the addition of an amino acid conservation attribute did not improve the classification prediction. This result indicates that the increase in predictive power associated with amino acid conservation is exhausted by adequate use of an extensive list of independent physicochemical and structural parameters that, by themselves, fully describe the nano-environment at protein-protein interfaces. The IFR classifier developed in this study is now integrated into the BlueStar STING suite of programs. Consequently, the prediction of protein-protein interfaces for all proteins available in the PDB is possible through STING_interfaces module, accessible at the following website: (http://www.cbi.cnptia.embrapa.br/SMS/predictions/index.html). PMID:24489849

Modular prediction of protein structural classes from sequences of twilight-zone identity with predicting sequences.

PubMed

Mizianty, Marcin J; Kurgan, Lukasz

2009-12-13

Knowledge of structural class is used by numerous methods for identification of structural/functional characteristics of proteins and could be used for the detection of remote homologues, particularly for chains that share twilight-zone similarity. In contrast to existing sequence-based structural class predictors, which target four major classes and which are designed for high identity sequences, we predict seven classes from sequences that share twilight-zone identity with the training sequences. The proposed MODular Approach to Structural class prediction (MODAS) method is unique as it allows for selection of any subset of the classes. MODAS is also the first to utilize a novel, custom-built feature-based sequence representation that combines evolutionary profiles and predicted secondary structure. The features quantify information relevant to the definition of the classes including conservation of residues and arrangement and number of helix/strand segments. Our comprehensive design considers 8 feature selection methods and 4 classifiers to develop Support Vector Machine-based classifiers that are tailored for each of the seven classes. Tests on 5 twilight-zone and 1 high-similarity benchmark datasets and comparison with over two dozens of modern competing predictors show that MODAS provides the best overall accuracy that ranges between 80% and 96.7% (83.5% for the twilight-zone datasets), depending on the dataset. This translates into 19% and 8% error rate reduction when compared against the best performing competing method on two largest datasets. The proposed predictor provides accurate predictions at 58% accuracy for membrane proteins class, which is not considered by majority of existing methods, in spite that this class accounts for only 2% of the data. Our predictive model is analyzed to demonstrate how and why the input features are associated with the corresponding classes. The improved predictions stem from the novel features that express collocation of the secondary structure segments in the protein sequence and that combine evolutionary and secondary structure information. Our work demonstrates that conservation and arrangement of the secondary structure segments predicted along the protein chain can successfully predict structural classes which are defined based on the spatial arrangement of the secondary structures. A web server is available at http://biomine.ece.ualberta.ca/MODAS/.

Modular prediction of protein structural classes from sequences of twilight-zone identity with predicting sequences

PubMed Central

2009-01-01

Background Knowledge of structural class is used by numerous methods for identification of structural/functional characteristics of proteins and could be used for the detection of remote homologues, particularly for chains that share twilight-zone similarity. In contrast to existing sequence-based structural class predictors, which target four major classes and which are designed for high identity sequences, we predict seven classes from sequences that share twilight-zone identity with the training sequences. Results The proposed MODular Approach to Structural class prediction (MODAS) method is unique as it allows for selection of any subset of the classes. MODAS is also the first to utilize a novel, custom-built feature-based sequence representation that combines evolutionary profiles and predicted secondary structure. The features quantify information relevant to the definition of the classes including conservation of residues and arrangement and number of helix/strand segments. Our comprehensive design considers 8 feature selection methods and 4 classifiers to develop Support Vector Machine-based classifiers that are tailored for each of the seven classes. Tests on 5 twilight-zone and 1 high-similarity benchmark datasets and comparison with over two dozens of modern competing predictors show that MODAS provides the best overall accuracy that ranges between 80% and 96.7% (83.5% for the twilight-zone datasets), depending on the dataset. This translates into 19% and 8% error rate reduction when compared against the best performing competing method on two largest datasets. The proposed predictor provides accurate predictions at 58% accuracy for membrane proteins class, which is not considered by majority of existing methods, in spite that this class accounts for only 2% of the data. Our predictive model is analyzed to demonstrate how and why the input features are associated with the corresponding classes. Conclusions The improved predictions stem from the novel features that express collocation of the secondary structure segments in the protein sequence and that combine evolutionary and secondary structure information. Our work demonstrates that conservation and arrangement of the secondary structure segments predicted along the protein chain can successfully predict structural classes which are defined based on the spatial arrangement of the secondary structures. A web server is available at http://biomine.ece.ualberta.ca/MODAS/. PMID:20003388

Comparative Protein Structure Modeling Using MODELLER

PubMed Central

Webb, Benjamin; Sali, Andrej

2016-01-01

Comparative protein structure modeling predicts the three-dimensional structure of a given protein sequence (target) based primarily on its alignment to one or more proteins of known structure (templates). The prediction process consists of fold assignment, target-template alignment, model building, and model evaluation. This unit describes how to calculate comparative models using the program MODELLER and how to use the ModBase database of such models, and discusses all four steps of comparative modeling, frequently observed errors, and some applications. Modeling lactate dehydrogenase from Trichomonas vaginalis (TvLDH) is described as an example. The download and installation of the MODELLER software is also described. PMID:27322406

FPGA accelerator for protein secondary structure prediction based on the GOR algorithm

PubMed Central

2011-01-01

Background Protein is an important molecule that performs a wide range of functions in biological systems. Recently, the protein folding attracts much more attention since the function of protein can be generally derived from its molecular structure. The GOR algorithm is one of the most successful computational methods and has been widely used as an efficient analysis tool to predict secondary structure from protein sequence. However, the execution time is still intolerable with the steep growth in protein database. Recently, FPGA chips have emerged as one promising application accelerator to accelerate bioinformatics algorithms by exploiting fine-grained custom design. Results In this paper, we propose a complete fine-grained parallel hardware implementation on FPGA to accelerate the GOR-IV package for 2D protein structure prediction. To improve computing efficiency, we partition the parameter table into small segments and access them in parallel. We aggressively exploit data reuse schemes to minimize the need for loading data from external memory. The whole computation structure is carefully pipelined to overlap the sequence loading, computing and back-writing operations as much as possible. We implemented a complete GOR desktop system based on an FPGA chip XC5VLX330. Conclusions The experimental results show a speedup factor of more than 430x over the original GOR-IV version and 110x speedup over the optimized version with multi-thread SIMD implementation running on a PC platform with AMD Phenom 9650 Quad CPU for 2D protein structure prediction. However, the power consumption is only about 30% of that of current general-propose CPUs. PMID:21342582

JET2 Viewer: a database of predicted multiple, possibly overlapping, protein-protein interaction sites for PDB structures.

PubMed

Ripoche, Hugues; Laine, Elodie; Ceres, Nicoletta; Carbone, Alessandra

2017-01-04

The database JET2 Viewer, openly accessible at http://www.jet2viewer.upmc.fr/, reports putative protein binding sites for all three-dimensional (3D) structures available in the Protein Data Bank (PDB). This knowledge base was generated by applying the computational method JET 2 at large-scale on more than 20 000 chains. JET 2 strategy yields very precise predictions of interacting surfaces and unravels their evolutionary process and complexity. JET2 Viewer provides an online intelligent display, including interactive 3D visualization of the binding sites mapped onto PDB structures and suitable files recording JET 2 analyses. Predictions were evaluated on more than 15 000 experimentally characterized protein interfaces. This is, to our knowledge, the largest evaluation of a protein binding site prediction method. The overall performance of JET 2 on all interfaces are: Sen = 52.52, PPV = 51.24, Spe = 80.05, Acc = 75.89. The data can be used to foster new strategies for protein-protein interactions modulation and interaction surface redesign. © The Author(s) 2016. Published by Oxford University Press on behalf of Nucleic Acids Research.

Protein secondary structure and stability determined by combining exoproteolysis and matrix-assisted laser desorption/ionization time-of-flight mass spectrometry.

PubMed

Villanueva, Josep; Villegas, Virtudes; Querol, Enrique; Avilés, Francesc X; Serrano, Luis

2002-09-01

In the post-genomic era, several projects focused on the massive experimental resolution of the three-dimensional structures of all the proteins of different organisms have been initiated. Simultaneously, significant progress has been made in the ab initio prediction of protein three-dimensional structure. One of the keys to the success of such a prediction is the use of local information (i.e. secondary structure). Here we describe a new limited proteolysis methodology, based on the use of unspecific exoproteases coupled with matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS), to map quickly secondary structure elements of a protein from both ends, the N- and C-termini. We show that the proteolytic patterns (mass spectra series) obtained can be interpreted in the light of the conformation and local stability of the analyzed proteins, a direct correlation being observed between the predicted and the experimentally derived protein secondary structure. Further, this methodology can be easily applied to check rapidly the folding state of a protein and characterize mutational effects on protein conformation and stability. Moreover, given global stability information, this methodology allows one to locate the protein regions of increased or decreased conformational stability. All of this can be done with a small fraction of the amount of protein required by most of the other methods for conformational analysis. Thus limited exoproteolysis, together with MALDI-TOF MS, can be a useful tool to achieve quickly the elucidation of protein structure and stability. Copyright 2002 John Wiley & Sons, Ltd.

Prediction of protein structural classes by Chou's pseudo amino acid composition: approached using continuous wavelet transform and principal component analysis.

PubMed

Li, Zhan-Chao; Zhou, Xi-Bin; Dai, Zong; Zou, Xiao-Yong

2009-07-01

A prior knowledge of protein structural classes can provide useful information about its overall structure, so it is very important for quick and accurate determination of protein structural class with computation method in protein science. One of the key for computation method is accurate protein sample representation. Here, based on the concept of Chou's pseudo-amino acid composition (AAC, Chou, Proteins: structure, function, and genetics, 43:246-255, 2001), a novel method of feature extraction that combined continuous wavelet transform (CWT) with principal component analysis (PCA) was introduced for the prediction of protein structural classes. Firstly, the digital signal was obtained by mapping each amino acid according to various physicochemical properties. Secondly, CWT was utilized to extract new feature vector based on wavelet power spectrum (WPS), which contains more abundant information of sequence order in frequency domain and time domain, and PCA was then used to reorganize the feature vector to decrease information redundancy and computational complexity. Finally, a pseudo-amino acid composition feature vector was further formed to represent primary sequence by coupling AAC vector with a set of new feature vector of WPS in an orthogonal space by PCA. As a showcase, the rigorous jackknife cross-validation test was performed on the working datasets. The results indicated that prediction quality has been improved, and the current approach of protein representation may serve as a useful complementary vehicle in classifying other attributes of proteins, such as enzyme family class, subcellular localization, membrane protein types and protein secondary structure, etc.

The Phyre2 web portal for protein modelling, prediction and analysis

PubMed Central

Kelley, Lawrence A; Mezulis, Stefans; Yates, Christopher M; Wass, Mark N; Sternberg, Michael JE

2017-01-01

Summary Phyre2 is a suite of tools available on the web to predict and analyse protein structure, function and mutations. The focus of Phyre2 is to provide biologists with a simple and intuitive interface to state-of-the-art protein bioinformatics tools. Phyre2 replaces Phyre, the original version of the server for which we previously published a protocol. In this updated protocol, we describe Phyre2, which uses advanced remote homology detection methods to build 3D models, predict ligand binding sites, and analyse the effect of amino-acid variants (e.g. nsSNPs) for a user’s protein sequence. Users are guided through results by a simple interface at a level of detail determined by them. This protocol will guide a user from submitting a protein sequence to interpreting the secondary and tertiary structure of their models, their domain composition and model quality. A range of additional available tools is described to find a protein structure in a genome, to submit large number of sequences at once and to automatically run weekly searches for proteins difficult to model. The server is available at http://www.sbg.bio.ic.ac.uk/phyre2. A typical structure prediction will be returned between 30mins and 2 hours after submission. PMID:25950237

Year 2 Report: Protein Function Prediction Platform

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zhou, C E

2012-04-27

Upon completion of our second year of development in a 3-year development cycle, we have completed a prototype protein structure-function annotation and function prediction system: Protein Function Prediction (PFP) platform (v.0.5). We have met our milestones for Years 1 and 2 and are positioned to continue development in completion of our original statement of work, or a reasonable modification thereof, in service to DTRA Programs involved in diagnostics and medical countermeasures research and development. The PFP platform is a multi-scale computational modeling system for protein structure-function annotation and function prediction. As of this writing, PFP is the only existing fullymore » automated, high-throughput, multi-scale modeling, whole-proteome annotation platform, and represents a significant advance in the field of genome annotation (Fig. 1). PFP modules perform protein functional annotations at the sequence, systems biology, protein structure, and atomistic levels of biological complexity (Fig. 2). Because these approaches provide orthogonal means of characterizing proteins and suggesting protein function, PFP processing maximizes the protein functional information that can currently be gained by computational means. Comprehensive annotation of pathogen genomes is essential for bio-defense applications in pathogen characterization, threat assessment, and medical countermeasure design and development in that it can short-cut the time and effort required to select and characterize protein biomarkers.« less

CABS-fold: Server for the de novo and consensus-based prediction of protein structure.

PubMed

Blaszczyk, Maciej; Jamroz, Michal; Kmiecik, Sebastian; Kolinski, Andrzej

2013-07-01

The CABS-fold web server provides tools for protein structure prediction from sequence only (de novo modeling) and also using alternative templates (consensus modeling). The web server is based on the CABS modeling procedures ranked in previous Critical Assessment of techniques for protein Structure Prediction competitions as one of the leading approaches for de novo and template-based modeling. Except for template data, fragmentary distance restraints can also be incorporated into the modeling process. The web server output is a coarse-grained trajectory of generated conformations, its Jmol representation and predicted models in all-atom resolution (together with accompanying analysis). CABS-fold can be freely accessed at http://biocomp.chem.uw.edu.pl/CABSfold.

CABS-fold: server for the de novo and consensus-based prediction of protein structure

PubMed Central

Blaszczyk, Maciej; Jamroz, Michal; Kmiecik, Sebastian; Kolinski, Andrzej

2013-01-01

The CABS-fold web server provides tools for protein structure prediction from sequence only (de novo modeling) and also using alternative templates (consensus modeling). The web server is based on the CABS modeling procedures ranked in previous Critical Assessment of techniques for protein Structure Prediction competitions as one of the leading approaches for de novo and template-based modeling. Except for template data, fragmentary distance restraints can also be incorporated into the modeling process. The web server output is a coarse-grained trajectory of generated conformations, its Jmol representation and predicted models in all-atom resolution (together with accompanying analysis). CABS-fold can be freely accessed at http://biocomp.chem.uw.edu.pl/CABSfold. PMID:23748950

Real-Time Ligand Binding Pocket Database Search Using Local Surface Descriptors

PubMed Central

Chikhi, Rayan; Sael, Lee; Kihara, Daisuke

2010-01-01

Due to the increasing number of structures of unknown function accumulated by ongoing structural genomics projects, there is an urgent need for computational methods for characterizing protein tertiary structures. As functions of many of these proteins are not easily predicted by conventional sequence database searches, a legitimate strategy is to utilize structure information in function characterization. Of a particular interest is prediction of ligand binding to a protein, as ligand molecule recognition is a major part of molecular function of proteins. Predicting whether a ligand molecule binds a protein is a complex problem due to the physical nature of protein-ligand interactions and the flexibility of both binding sites and ligand molecules. However, geometric and physicochemical complementarity is observed between the ligand and its binding site in many cases. Therefore, ligand molecules which bind to a local surface site in a protein can be predicted by finding similar local pockets of known binding ligands in the structure database. Here, we present two representations of ligand binding pockets and utilize them for ligand binding prediction by pocket shape comparison. These representations are based on mapping of surface properties of binding pockets, which are compactly described either by the two dimensional pseudo-Zernike moments or the 3D Zernike descriptors. These compact representations allow a fast real-time pocket searching against a database. Thorough benchmark study employing two different datasets show that our representations are competitive with the other existing methods. Limitations and potentials of the shape-based methods as well as possible improvements are discussed. PMID:20455259

Real-time ligand binding pocket database search using local surface descriptors.

PubMed

Chikhi, Rayan; Sael, Lee; Kihara, Daisuke

2010-07-01

Because of the increasing number of structures of unknown function accumulated by ongoing structural genomics projects, there is an urgent need for computational methods for characterizing protein tertiary structures. As functions of many of these proteins are not easily predicted by conventional sequence database searches, a legitimate strategy is to utilize structure information in function characterization. Of particular interest is prediction of ligand binding to a protein, as ligand molecule recognition is a major part of molecular function of proteins. Predicting whether a ligand molecule binds a protein is a complex problem due to the physical nature of protein-ligand interactions and the flexibility of both binding sites and ligand molecules. However, geometric and physicochemical complementarity is observed between the ligand and its binding site in many cases. Therefore, ligand molecules which bind to a local surface site in a protein can be predicted by finding similar local pockets of known binding ligands in the structure database. Here, we present two representations of ligand binding pockets and utilize them for ligand binding prediction by pocket shape comparison. These representations are based on mapping of surface properties of binding pockets, which are compactly described either by the two-dimensional pseudo-Zernike moments or the three-dimensional Zernike descriptors. These compact representations allow a fast real-time pocket searching against a database. Thorough benchmark studies employing two different datasets show that our representations are competitive with the other existing methods. Limitations and potentials of the shape-based methods as well as possible improvements are discussed.

Prediction of Protein Structure by Template-Based Modeling Combined with the UNRES Force Field.

PubMed

Krupa, Paweł; Mozolewska, Magdalena A; Joo, Keehyoung; Lee, Jooyoung; Czaplewski, Cezary; Liwo, Adam

2015-06-22

A new approach to the prediction of protein structures that uses distance and backbone virtual-bond dihedral angle restraints derived from template-based models and simulations with the united residue (UNRES) force field is proposed. The approach combines the accuracy and reliability of template-based methods for the segments of the target sequence with high similarity to those having known structures with the ability of UNRES to pack the domains correctly. Multiplexed replica-exchange molecular dynamics with restraints derived from template-based models of a given target, in which each restraint is weighted according to the accuracy of the prediction of the corresponding section of the molecule, is used to search the conformational space, and the weighted histogram analysis method and cluster analysis are applied to determine the families of the most probable conformations, from which candidate predictions are selected. To test the capability of the method to recover template-based models from restraints, five single-domain proteins with structures that have been well-predicted by template-based methods were used; it was found that the resulting structures were of the same quality as the best of the original models. To assess whether the new approach can improve template-based predictions with incorrectly predicted domain packing, four such targets were selected from the CASP10 targets; for three of them the new approach resulted in significantly better predictions compared with the original template-based models. The new approach can be used to predict the structures of proteins for which good templates can be found for sections of the sequence or an overall good template can be found for the entire sequence but the prediction quality is remarkably weaker in putative domain-linker regions.

Massive integration of diverse protein quality assessment methods to improve template based modeling in CASP11.

PubMed

Cao, Renzhi; Bhattacharya, Debswapna; Adhikari, Badri; Li, Jilong; Cheng, Jianlin

2016-09-01

Model evaluation and selection is an important step and a big challenge in template-based protein structure prediction. Individual model quality assessment methods designed for recognizing some specific properties of protein structures often fail to consistently select good models from a model pool because of their limitations. Therefore, combining multiple complimentary quality assessment methods is useful for improving model ranking and consequently tertiary structure prediction. Here, we report the performance and analysis of our human tertiary structure predictor (MULTICOM) based on the massive integration of 14 diverse complementary quality assessment methods that was successfully benchmarked in the 11th Critical Assessment of Techniques of Protein Structure prediction (CASP11). The predictions of MULTICOM for 39 template-based domains were rigorously assessed by six scoring metrics covering global topology of Cα trace, local all-atom fitness, side chain quality, and physical reasonableness of the model. The results show that the massive integration of complementary, diverse single-model and multi-model quality assessment methods can effectively leverage the strength of single-model methods in distinguishing quality variation among similar good models and the advantage of multi-model quality assessment methods of identifying reasonable average-quality models. The overall excellent performance of the MULTICOM predictor demonstrates that integrating a large number of model quality assessment methods in conjunction with model clustering is a useful approach to improve the accuracy, diversity, and consequently robustness of template-based protein structure prediction. Proteins 2016; 84(Suppl 1):247-259. © 2015 Wiley Periodicals, Inc. © 2015 Wiley Periodicals, Inc.

Comparative Protein Structure Modeling Using MODELLER.

PubMed

Webb, Benjamin; Sali, Andrej

2014-09-08

Functional characterization of a protein sequence is one of the most frequent problems in biology. This task is usually facilitated by accurate three-dimensional (3-D) structure of the studied protein. In the absence of an experimentally determined structure, comparative or homology modeling can sometimes provide a useful 3-D model for a protein that is related to at least one known protein structure. Comparative modeling predicts the 3-D structure of a given protein sequence (target) based primarily on its alignment to one or more proteins of known structure (templates). The prediction process consists of fold assignment, target-template alignment, model building, and model evaluation. This unit describes how to calculate comparative models using the program MODELLER and discusses all four steps of comparative modeling, frequently observed errors, and some applications. Modeling lactate dehydrogenase from Trichomonas vaginalis (TvLDH) is described as an example. The download and installation of the MODELLER software is also described. Copyright © 2014 John Wiley & Sons, Inc.

Folding and Stabilization of Native-Sequence-Reversed Proteins

PubMed Central

Zhang, Yuanzhao; Weber, Jeffrey K; Zhou, Ruhong

2016-01-01

Though the problem of sequence-reversed protein folding is largely unexplored, one might speculate that reversed native protein sequences should be significantly more foldable than purely random heteropolymer sequences. In this article, we investigate how the reverse-sequences of native proteins might fold by examining a series of small proteins of increasing structural complexity (α-helix, β-hairpin, α-helix bundle, and α/β-protein). Employing a tandem protein structure prediction algorithmic and molecular dynamics simulation approach, we find that the ability of reverse sequences to adopt native-like folds is strongly influenced by protein size and the flexibility of the native hydrophobic core. For β-hairpins with reverse-sequences that fail to fold, we employ a simple mutational strategy for guiding stable hairpin formation that involves the insertion of amino acids into the β-turn region. This systematic look at reverse sequence duality sheds new light on the problem of protein sequence-structure mapping and may serve to inspire new protein design and protein structure prediction protocols. PMID:27113844

Folding and Stabilization of Native-Sequence-Reversed Proteins

NASA Astrophysics Data System (ADS)

Zhang, Yuanzhao; Weber, Jeffrey K.; Zhou, Ruhong

2016-04-01

Though the problem of sequence-reversed protein folding is largely unexplored, one might speculate that reversed native protein sequences should be significantly more foldable than purely random heteropolymer sequences. In this article, we investigate how the reverse-sequences of native proteins might fold by examining a series of small proteins of increasing structural complexity (α-helix, β-hairpin, α-helix bundle, and α/β-protein). Employing a tandem protein structure prediction algorithmic and molecular dynamics simulation approach, we find that the ability of reverse sequences to adopt native-like folds is strongly influenced by protein size and the flexibility of the native hydrophobic core. For β-hairpins with reverse-sequences that fail to fold, we employ a simple mutational strategy for guiding stable hairpin formation that involves the insertion of amino acids into the β-turn region. This systematic look at reverse sequence duality sheds new light on the problem of protein sequence-structure mapping and may serve to inspire new protein design and protein structure prediction protocols.

Inferences from structural comparison: flexibility, secondary structure wobble and sequence alignment optimization.

PubMed

Zhang, Gaihua; Su, Zhen

2012-01-01

Work on protein structure prediction is very useful in biological research. To evaluate their accuracy, experimental protein structures or their derived data are used as the 'gold standard'. However, as proteins are dynamic molecular machines with structural flexibility such a standard may be unreliable. To investigate the influence of the structure flexibility, we analysed 3,652 protein structures of 137 unique sequences from 24 protein families. The results showed that (1) the three-dimensional (3D) protein structures were not rigid: the root-mean-square deviation (RMSD) of the backbone Cα of structures with identical sequences was relatively large, with the average of the maximum RMSD from each of the 137 sequences being 1.06 Å; (2) the derived data of the 3D structure was not constant, e.g. the highest ratio of the secondary structure wobble site was 60.69%, with the sequence alignments from structural comparisons of two proteins in the same family sometimes being completely different. Proteins may have several stable conformations and the data derived from resolved structures as a 'gold standard' should be optimized before being utilized as criteria to evaluate the prediction methods, e.g. sequence alignment from structural comparison. Helix/β-sheet transition exists in normal free proteins. The coil ratio of the 3D structure could affect its resolution as determined by X-ray crystallography.

In silico prediction of a disease-associated STIL mutant and its affect on the recruitment of centromere protein J (CENPJ).

PubMed

Kumar, Ambuj; Rajendran, Vidya; Sethumadhavan, Rao; Purohit, Rituraj

2012-01-01

Human STIL (SCL/TAL1 interrupting locus) protein maintains centriole stability and spindle pole localisation. It helps in recruitment of CENPJ (Centromere protein J)/CPAP (centrosomal P4.1-associated protein) and other centrosomal proteins. Mutations in STIL protein are reported in several disorders, especially in deregulation of cell cycle cascades. In this work, we examined the non-synonymous single nucleotide polymorphisms (nsSNPs) reported in STIL protein for their disease association. Different SNP prediction tools were used to predict disease-associated nsSNPs. Our evaluation technique predicted rs147744459 (R242C) as a highly deleterious disease-associated nsSNP and its interaction behaviour with CENPJ protein. Molecular modelling, docking and molecular dynamics simulation were conducted to examine the structural consequences of the predicted disease-associated mutation. By molecular dynamic simulation we observed structural consequences of R242C mutation which affects interaction of STIL and CENPJ functional domains. The result obtained in this study will provide a biophysical insight into future investigations of pathological nsSNPs using a computational platform.

Contingency Table Browser - prediction of early stage protein structure.

PubMed

Kalinowska, Barbara; Krzykalski, Artur; Roterman, Irena

2015-01-01

The Early Stage (ES) intermediate represents the starting structure in protein folding simulations based on the Fuzzy Oil Drop (FOD) model. The accuracy of FOD predictions is greatly dependent on the accuracy of the chosen intermediate. A suitable intermediate can be constructed using the sequence-structure relationship information contained in the so-called contingency table - this table expresses the likelihood of encountering various structural motifs for each tetrapeptide fragment in the amino acid sequence. The limited accuracy with which such structures could previously be predicted provided the motivation for a more indepth study of the contingency table itself. The Contingency Table Browser is a tool which can visualize, search and analyze the table. Our work presents possible applications of Contingency Table Browser, among them - analysis of specific protein sequences from the point of view of their structural ambiguity.

KFC Server: interactive forecasting of protein interaction hot spots

PubMed Central

Darnell, Steven J.; LeGault, Laura; Mitchell, Julie C.

2008-01-01

The KFC Server is a web-based implementation of the KFC (Knowledge-based FADE and Contacts) model—a machine learning approach for the prediction of binding hot spots, or the subset of residues that account for most of a protein interface's; binding free energy. The server facilitates the automated analysis of a user submitted protein–protein or protein–DNA interface and the visualization of its hot spot predictions. For each residue in the interface, the KFC Server characterizes its local structural environment, compares that environment to the environments of experimentally determined hot spots and predicts if the interface residue is a hot spot. After the computational analysis, the user can visualize the results using an interactive job viewer able to quickly highlight predicted hot spots and surrounding structural features within the protein structure. The KFC Server is accessible at http://kfc.mitchell-lab.org. PMID:18539611

Prediction protein structural classes with pseudo-amino acid composition: approximate entropy and hydrophobicity pattern.

PubMed

Zhang, Tong-Liang; Ding, Yong-Sheng; Chou, Kuo-Chen

2008-01-07

Compared with the conventional amino acid (AA) composition, the pseudo-amino acid (PseAA) composition as originally introduced for protein subcellular location prediction can incorporate much more information of a protein sequence, so as to remarkably enhance the power of using a discrete model to predict various attributes of a protein. In this study, based on the concept of PseAA composition, the approximate entropy and hydrophobicity pattern of a protein sequence are used to characterize the PseAA components. Also, the immune genetic algorithm (IGA) is applied to search the optimal weight factors in generating the PseAA composition. Thus, for a given protein sequence sample, a 27-D (dimensional) PseAA composition is generated as its descriptor. The fuzzy K nearest neighbors (FKNN) classifier is adopted as the prediction engine. The results thus obtained in predicting protein structural classification are quite encouraging, indicating that the current approach may also be used to improve the prediction quality of other protein attributes, or at least can play a complimentary role to the existing methods in the relevant areas. Our algorithm is written in Matlab that is available by contacting the corresponding author.

Prediction and Dissection of Protein-RNA Interactions by Molecular Descriptors.

PubMed

Liu, Zhi-Ping; Chen, Luonan

2016-01-01

Protein-RNA interactions play crucial roles in numerous biological processes. However, detecting the interactions and binding sites between protein and RNA by traditional experiments is still time consuming and labor costing. Thus, it is of importance to develop bioinformatics methods for predicting protein-RNA interactions and binding sites. Accurate prediction of protein-RNA interactions and recognitions will highly benefit to decipher the interaction mechanisms between protein and RNA, as well as to improve the RNA-related protein engineering and drug design. In this work, we summarize the current bioinformatics strategies of predicting protein-RNA interactions and dissecting protein-RNA interaction mechanisms from local structure binding motifs. In particular, we focus on the feature-based machine learning methods, in which the molecular descriptors of protein and RNA are extracted and integrated as feature vectors of representing the interaction events and recognition residues. In addition, the available methods are classified and compared comprehensively. The molecular descriptors are expected to elucidate the binding mechanisms of protein-RNA interaction and reveal the functional implications from structural complementary perspective.

GalaxyHomomer: a web server for protein homo-oligomer structure prediction from a monomer sequence or structure.

PubMed

Baek, Minkyung; Park, Taeyong; Heo, Lim; Park, Chiwook; Seok, Chaok

2017-07-03

Homo-oligomerization of proteins is abundant in nature, and is often intimately related with the physiological functions of proteins, such as in metabolism, signal transduction or immunity. Information on the homo-oligomer structure is therefore important to obtain a molecular-level understanding of protein functions and their regulation. Currently available web servers predict protein homo-oligomer structures either by template-based modeling using homo-oligomer templates selected from the protein structure database or by ab initio docking of monomer structures resolved by experiment or predicted by computation. The GalaxyHomomer server, freely accessible at http://galaxy.seoklab.org/homomer, carries out template-based modeling, ab initio docking or both depending on the availability of proper oligomer templates. It also incorporates recently developed model refinement methods that can consistently improve model quality. Moreover, the server provides additional options that can be chosen by the user depending on the availability of information on the monomer structure, oligomeric state and locations of unreliable/flexible loops or termini. The performance of the server was better than or comparable to that of other available methods when tested on benchmark sets and in a recent CASP performed in a blind fashion. © The Author(s) 2017. Published by Oxford University Press on behalf of Nucleic Acids Research.

EVAcon: a protein contact prediction evaluation service

PubMed Central

Graña, Osvaldo; Eyrich, Volker A.; Pazos, Florencio; Rost, Burkhard; Valencia, Alfonso

2005-01-01

Here we introduce EVAcon, an automated web service that evaluates the performance of contact prediction servers. Currently, EVAcon is monitoring nine servers, four of which are specialized in contact prediction and five are general structure prediction servers. Results are compared for all newly determined experimental structures deposited into PDB (∼5–50 per week). EVAcon allows for a precise comparison of the results based on a system of common protein subsets and the commonly accepted evaluation criteria that are also used in the corresponding category of the CASP assessment. EVAcon is a new service added to the functionality of the EVA system for the continuous evaluation of protein structure prediction servers. The new service is accesible from any of the three EVA mirrors: PDG (CNB-CSIC, Madrid) (); CUBIC (Columbia University, NYC) (); and Sali Lab (UCSF, San Francisco) (). PMID:15980486

Ab initio protein structure assembly using continuous structure fragments and optimized knowledge-based force field.

PubMed

Xu, Dong; Zhang, Yang

2012-07-01

Ab initio protein folding is one of the major unsolved problems in computational biology owing to the difficulties in force field design and conformational search. We developed a novel program, QUARK, for template-free protein structure prediction. Query sequences are first broken into fragments of 1-20 residues where multiple fragment structures are retrieved at each position from unrelated experimental structures. Full-length structure models are then assembled from fragments using replica-exchange Monte Carlo simulations, which are guided by a composite knowledge-based force field. A number of novel energy terms and Monte Carlo movements are introduced and the particular contributions to enhancing the efficiency of both force field and search engine are analyzed in detail. QUARK prediction procedure is depicted and tested on the structure modeling of 145 nonhomologous proteins. Although no global templates are used and all fragments from experimental structures with template modeling score >0.5 are excluded, QUARK can successfully construct 3D models of correct folds in one-third cases of short proteins up to 100 residues. In the ninth community-wide Critical Assessment of protein Structure Prediction experiment, QUARK server outperformed the second and third best servers by 18 and 47% based on the cumulative Z-score of global distance test-total scores in the FM category. Although ab initio protein folding remains a significant challenge, these data demonstrate new progress toward the solution of the most important problem in the field. Copyright © 2012 Wiley Periodicals, Inc.

VITAL NMR: Using Chemical Shift Derived Secondary Structure Information for a Limited Set of Amino Acids to Assess Homology Model Accuracy

DOE Office of Scientific and Technical Information (OSTI.GOV)

Brothers, Michael C; Nesbitt, Anna E; Hallock, Michael J

2011-01-01

Homology modeling is a powerful tool for predicting protein structures, whose success depends on obtaining a reasonable alignment between a given structural template and the protein sequence being analyzed. In order to leverage greater predictive power for proteins with few structural templates, we have developed a method to rank homology models based upon their compliance to secondary structure derived from experimental solid-state NMR (SSNMR) data. Such data is obtainable in a rapid manner by simple SSNMR experiments (e.g., (13)C-(13)C 2D correlation spectra). To test our homology model scoring procedure for various amino acid labeling schemes, we generated a library ofmore » 7,474 homology models for 22 protein targets culled from the TALOS+/SPARTA+ training set of protein structures. Using subsets of amino acids that are plausibly assigned by SSNMR, we discovered that pairs of the residues Val, Ile, Thr, Ala and Leu (VITAL) emulate an ideal dataset where all residues are site specifically assigned. Scoring the models with a predicted VITAL site-specific dataset and calculating secondary structure with the Chemical Shift Index resulted in a Pearson correlation coefficient (-0.75) commensurate to the control (-0.77), where secondary structure was scored site specifically for all amino acids (ALL 20) using STRIDE. This method promises to accelerate structure procurement by SSNMR for proteins with unknown folds through guiding the selection of remotely homologous protein templates and assessing model quality.« less

Knowledge-based fragment binding prediction.

PubMed

Tang, Grace W; Altman, Russ B

2014-04-01

Target-based drug discovery must assess many drug-like compounds for potential activity. Focusing on low-molecular-weight compounds (fragments) can dramatically reduce the chemical search space. However, approaches for determining protein-fragment interactions have limitations. Experimental assays are time-consuming, expensive, and not always applicable. At the same time, computational approaches using physics-based methods have limited accuracy. With increasing high-resolution structural data for protein-ligand complexes, there is now an opportunity for data-driven approaches to fragment binding prediction. We present FragFEATURE, a machine learning approach to predict small molecule fragments preferred by a target protein structure. We first create a knowledge base of protein structural environments annotated with the small molecule substructures they bind. These substructures have low-molecular weight and serve as a proxy for fragments. FragFEATURE then compares the structural environments within a target protein to those in the knowledge base to retrieve statistically preferred fragments. It merges information across diverse ligands with shared substructures to generate predictions. Our results demonstrate FragFEATURE's ability to rediscover fragments corresponding to the ligand bound with 74% precision and 82% recall on average. For many protein targets, it identifies high scoring fragments that are substructures of known inhibitors. FragFEATURE thus predicts fragments that can serve as inputs to fragment-based drug design or serve as refinement criteria for creating target-specific compound libraries for experimental or computational screening.

Knowledge-based Fragment Binding Prediction

PubMed Central

Tang, Grace W.; Altman, Russ B.

2014-01-01

Target-based drug discovery must assess many drug-like compounds for potential activity. Focusing on low-molecular-weight compounds (fragments) can dramatically reduce the chemical search space. However, approaches for determining protein-fragment interactions have limitations. Experimental assays are time-consuming, expensive, and not always applicable. At the same time, computational approaches using physics-based methods have limited accuracy. With increasing high-resolution structural data for protein-ligand complexes, there is now an opportunity for data-driven approaches to fragment binding prediction. We present FragFEATURE, a machine learning approach to predict small molecule fragments preferred by a target protein structure. We first create a knowledge base of protein structural environments annotated with the small molecule substructures they bind. These substructures have low-molecular weight and serve as a proxy for fragments. FragFEATURE then compares the structural environments within a target protein to those in the knowledge base to retrieve statistically preferred fragments. It merges information across diverse ligands with shared substructures to generate predictions. Our results demonstrate FragFEATURE's ability to rediscover fragments corresponding to the ligand bound with 74% precision and 82% recall on average. For many protein targets, it identifies high scoring fragments that are substructures of known inhibitors. FragFEATURE thus predicts fragments that can serve as inputs to fragment-based drug design or serve as refinement criteria for creating target-specific compound libraries for experimental or computational screening. PMID:24762971

Hidden Markov model-derived structural alphabet for proteins: the learning of protein local shapes captures sequence specificity.

PubMed

Camproux, A C; Tufféry, P

2005-08-05

Understanding and predicting protein structures depend on the complexity and the accuracy of the models used to represent them. We have recently set up a Hidden Markov Model to optimally compress protein three-dimensional conformations into a one-dimensional series of letters of a structural alphabet. Such a model learns simultaneously the shape of representative structural letters describing the local conformation and the logic of their connections, i.e. the transition matrix between the letters. Here, we move one step further and report some evidence that such a model of protein local architecture also captures some accurate amino acid features. All the letters have specific and distinct amino acid distributions. Moreover, we show that words of amino acids can have significant propensities for some letters. Perspectives point towards the prediction of the series of letters describing the structure of a protein from its amino acid sequence.

Massive integration of diverse protein quality assessment methods to improve template based modeling in CASP11

PubMed Central

Cao, Renzhi; Bhattacharya, Debswapna; Adhikari, Badri; Li, Jilong; Cheng, Jianlin

2015-01-01

Model evaluation and selection is an important step and a big challenge in template-based protein structure prediction. Individual model quality assessment methods designed for recognizing some specific properties of protein structures often fail to consistently select good models from a model pool because of their limitations. Therefore, combining multiple complimentary quality assessment methods is useful for improving model ranking and consequently tertiary structure prediction. Here, we report the performance and analysis of our human tertiary structure predictor (MULTICOM) based on the massive integration of 14 diverse complementary quality assessment methods that was successfully benchmarked in the 11th Critical Assessment of Techniques of Protein Structure prediction (CASP11). The predictions of MULTICOM for 39 template-based domains were rigorously assessed by six scoring metrics covering global topology of Cα trace, local all-atom fitness, side chain quality, and physical reasonableness of the model. The results show that the massive integration of complementary, diverse single-model and multi-model quality assessment methods can effectively leverage the strength of single-model methods in distinguishing quality variation among similar good models and the advantage of multi-model quality assessment methods of identifying reasonable average-quality models. The overall excellent performance of the MULTICOM predictor demonstrates that integrating a large number of model quality assessment methods in conjunction with model clustering is a useful approach to improve the accuracy, diversity, and consequently robustness of template-based protein structure prediction. PMID:26369671

Ab Initio structure prediction for Escherichia coli: towards genome-wide protein structure modeling and fold assignment

PubMed Central

Xu, Dong; Zhang, Yang

2013-01-01

Genome-wide protein structure prediction and structure-based function annotation have been a long-term goal in molecular biology but not yet become possible due to difficulties in modeling distant-homology targets. We developed a hybrid pipeline combining ab initio folding and template-based modeling for genome-wide structure prediction applied to the Escherichia coli genome. The pipeline was tested on 43 known sequences, where QUARK-based ab initio folding simulation generated models with TM-score 17% higher than that by traditional comparative modeling methods. For 495 unknown hard sequences, 72 are predicted to have a correct fold (TM-score > 0.5) and 321 have a substantial portion of structure correctly modeled (TM-score > 0.35). 317 sequences can be reliably assigned to a SCOP fold family based on structural analogy to existing proteins in PDB. The presented results, as a case study of E. coli, represent promising progress towards genome-wide structure modeling and fold family assignment using state-of-the-art ab initio folding algorithms. PMID:23719418

Protein structure refinement using a quantum mechanics-based chemical shielding predictor.

PubMed

Bratholm, Lars A; Jensen, Jan H

2017-03-01

The accurate prediction of protein chemical shifts using a quantum mechanics (QM)-based method has been the subject of intense research for more than 20 years but so far empirical methods for chemical shift prediction have proven more accurate. In this paper we show that a QM-based predictor of a protein backbone and CB chemical shifts (ProCS15, PeerJ , 2016, 3, e1344) is of comparable accuracy to empirical chemical shift predictors after chemical shift-based structural refinement that removes small structural errors. We present a method by which quantum chemistry based predictions of isotropic chemical shielding values (ProCS15) can be used to refine protein structures using Markov Chain Monte Carlo (MCMC) simulations, relating the chemical shielding values to the experimental chemical shifts probabilistically. Two kinds of MCMC structural refinement simulations were performed using force field geometry optimized X-ray structures as starting points: simulated annealing of the starting structure and constant temperature MCMC simulation followed by simulated annealing of a representative ensemble structure. Annealing of the CHARMM structure changes the CA-RMSD by an average of 0.4 Å but lowers the chemical shift RMSD by 1.0 and 0.7 ppm for CA and N. Conformational averaging has a relatively small effect (0.1-0.2 ppm) on the overall agreement with carbon chemical shifts but lowers the error for nitrogen chemical shifts by 0.4 ppm. If an amino acid specific offset is included the ProCS15 predicted chemical shifts have RMSD values relative to experiments that are comparable to popular empirical chemical shift predictors. The annealed representative ensemble structures differ in CA-RMSD relative to the initial structures by an average of 2.0 Å, with >2.0 Å difference for six proteins. In four of the cases, the largest structural differences arise in structurally flexible regions of the protein as determined by NMR, and in the remaining two cases, the large structural change may be due to force field deficiencies. The overall accuracy of the empirical methods are slightly improved by annealing the CHARMM structure with ProCS15, which may suggest that the minor structural changes introduced by ProCS15-based annealing improves the accuracy of the protein structures. Having established that QM-based chemical shift prediction can deliver the same accuracy as empirical shift predictors we hope this can help increase the accuracy of related approaches such as QM/MM or linear scaling approaches or interpreting protein structural dynamics from QM-derived chemical shift.

Order within disorder: Aggrecan chondroitin sulphate-attachment region provides new structural insights into protein sequences classified as disordered

PubMed Central

Jowitt, Thomas A; Murdoch, Alan D; Baldock, Clair; Berry, Richard; Day, Joanna M; Hardingham, Timothy E

2010-01-01

Structural investigation of proteins containing large stretches of sequences without predicted secondary structure is the focus of much increased attention. Here, we have produced an unglycosylated 30 kDa peptide from the chondroitin sulphate (CS)-attachment region of human aggrecan (CS-peptide), which was predicted to be intrinsically disordered and compared its structure with the adjacent aggrecan G3 domain. Biophysical analyses, including analytical ultracentrifugation, light scattering, and circular dichroism showed that the CS-peptide had an elongated and stiffened conformation in contrast to the globular G3 domain. The results suggested that it contained significant secondary structure, which was sensitive to urea, and we propose that the CS-peptide forms an elongated wormlike molecule based on a dynamic range of energetically equivalent secondary structures stabilized by hydrogen bonds. The dimensions of the structure predicted from small-angle X-ray scattering analysis were compatible with EM images of fully glycosylated aggrecan and a partly glycosylated aggrecan CS2-G3 construct. The semiordered structure identified in CS-peptide was not predicted by common structural algorithms and identified a potentially distinct class of semiordered structure within sequences currently identified as disordered. Sequence comparisons suggested some evidence for comparable structures in proteins encoded by other genes (PRG4, MUC5B, and CBP). The function of these semiordered sequences may serve to spatially position attached folded modules and/or to present polypeptides for modification, such as glycosylation, and to provide templates for the multiple pleiotropic interactions proposed for disordered proteins. Proteins 2010. © 2010 Wiley-Liss, Inc. PMID:20806220

Transmembrane helix prediction: a comparative evaluation and analysis.

PubMed

Cuthbertson, Jonathan M; Doyle, Declan A; Sansom, Mark S P

2005-06-01

The prediction of transmembrane (TM) helices plays an important role in the study of membrane proteins, given the relatively small number (approximately 0.5% of the PDB) of high-resolution structures for such proteins. We used two datasets (one redundant and one non-redundant) of high-resolution structures of membrane proteins to evaluate and analyse TM helix prediction. The redundant (non-redundant) dataset contains structure of 434 (268) TM helices, from 112 (73) polypeptide chains. Of the 434 helices in the dataset, 20 may be classified as 'half-TM' as they are too short to span a lipid bilayer. We compared 13 TM helix prediction methods, evaluating each method using per segment, per residue and termini scores. Four methods consistently performed well: SPLIT4, TMHMM2, HMMTOP2 and TMAP. However, even the best methods were in error by, on average, about two turns of helix at the TM helix termini. The best and worst case predictions for individual proteins were analysed. In particular, the performance of the various methods and of a consensus prediction method, were compared for a number of proteins (e.g. SecY, ClC, KvAP) containing half-TM helices. The difficulties of predicting half-TM helices suggests that current prediction methods successfully embody the two-state model of membrane protein folding, but do not accommodate a third stage in which, e.g., short helices and re-entrant loops fold within a bundle of stable TM helices.

Toxicity challenges in environmental chemicals: Prediction of human plasma protein binding through quantitative structure-activity relationship (QSAR) models

EPA Science Inventory

The present study explores the merit of utilizing available pharmaceutical data to construct a quantitative structure-activity relationship (QSAR) for prediction of the fraction of a chemical unbound to plasma protein (Fub) in environmentally relevant compounds. Independent model...

Prediction of homoprotein and heteroprotein complexes by protein docking and template‐based modeling: A CASP‐CAPRI experiment

PubMed Central

Velankar, Sameer; Kryshtafovych, Andriy; Huang, Shen‐You; Schneidman‐Duhovny, Dina; Sali, Andrej; Segura, Joan; Fernandez‐Fuentes, Narcis; Viswanath, Shruthi; Elber, Ron; Grudinin, Sergei; Popov, Petr; Neveu, Emilie; Lee, Hasup; Baek, Minkyung; Park, Sangwoo; Heo, Lim; Rie Lee, Gyu; Seok, Chaok; Qin, Sanbo; Zhou, Huan‐Xiang; Ritchie, David W.; Maigret, Bernard; Devignes, Marie‐Dominique; Ghoorah, Anisah; Torchala, Mieczyslaw; Chaleil, Raphaël A.G.; Bates, Paul A.; Ben‐Zeev, Efrat; Eisenstein, Miriam; Negi, Surendra S.; Weng, Zhiping; Vreven, Thom; Pierce, Brian G.; Borrman, Tyler M.; Yu, Jinchao; Ochsenbein, Françoise; Guerois, Raphaël; Vangone, Anna; Rodrigues, João P.G.L.M.; van Zundert, Gydo; Nellen, Mehdi; Xue, Li; Karaca, Ezgi; Melquiond, Adrien S.J.; Visscher, Koen; Kastritis, Panagiotis L.; Bonvin, Alexandre M.J.J.; Xu, Xianjin; Qiu, Liming; Yan, Chengfei; Li, Jilong; Ma, Zhiwei; Cheng, Jianlin; Zou, Xiaoqin; Shen, Yang; Peterson, Lenna X.; Kim, Hyung‐Rae; Roy, Amit; Han, Xusi; Esquivel‐Rodriguez, Juan; Kihara, Daisuke; Yu, Xiaofeng; Bruce, Neil J.; Fuller, Jonathan C.; Wade, Rebecca C.; Anishchenko, Ivan; Kundrotas, Petras J.; Vakser, Ilya A.; Imai, Kenichiro; Yamada, Kazunori; Oda, Toshiyuki; Nakamura, Tsukasa; Tomii, Kentaro; Pallara, Chiara; Romero‐Durana, Miguel; Jiménez‐García, Brian; Moal, Iain H.; Férnandez‐Recio, Juan; Joung, Jong Young; Kim, Jong Yun; Joo, Keehyoung; Lee, Jooyoung; Kozakov, Dima; Vajda, Sandor; Mottarella, Scott; Hall, David R.; Beglov, Dmitri; Mamonov, Artem; Xia, Bing; Bohnuud, Tanggis; Del Carpio, Carlos A.; Ichiishi, Eichiro; Marze, Nicholas; Kuroda, Daisuke; Roy Burman, Shourya S.; Gray, Jeffrey J.; Chermak, Edrisse; Cavallo, Luigi; Oliva, Romina; Tovchigrechko, Andrey

2016-01-01

ABSTRACT We present the results for CAPRI Round 30, the first joint CASP‐CAPRI experiment, which brought together experts from the protein structure prediction and protein–protein docking communities. The Round comprised 25 targets from amongst those submitted for the CASP11 prediction experiment of 2014. The targets included mostly homodimers, a few homotetramers, and two heterodimers, and comprised protein chains that could readily be modeled using templates from the Protein Data Bank. On average 24 CAPRI groups and 7 CASP groups submitted docking predictions for each target, and 12 CAPRI groups per target participated in the CAPRI scoring experiment. In total more than 9500 models were assessed against the 3D structures of the corresponding target complexes. Results show that the prediction of homodimer assemblies by homology modeling techniques and docking calculations is quite successful for targets featuring large enough subunit interfaces to represent stable associations. Targets with ambiguous or inaccurate oligomeric state assignments, often featuring crystal contact‐sized interfaces, represented a confounding factor. For those, a much poorer prediction performance was achieved, while nonetheless often providing helpful clues on the correct oligomeric state of the protein. The prediction performance was very poor for genuine tetrameric targets, where the inaccuracy of the homology‐built subunit models and the smaller pair‐wise interfaces severely limited the ability to derive the correct assembly mode. Our analysis also shows that docking procedures tend to perform better than standard homology modeling techniques and that highly accurate models of the protein components are not always required to identify their association modes with acceptable accuracy. Proteins 2016; 84(Suppl 1):323–348. © 2016 The Authors Proteins: Structure, Function, and Bioinformatics Published by Wiley Periodicals, Inc. PMID:27122118

DOE Office of Scientific and Technical Information (OSTI.GOV)

Middleton, Sarah A.; Illuminati, Joseph; Kim, Junhyong

Recognition of protein structural fold is the starting point for many structure prediction tools and protein function inference. Fold prediction is computationally demanding and recognizing novel folds is difficult such that the majority of proteins have not been annotated for fold classification. Here we describe a new machine learning approach using a novel feature space that can be used for accurate recognition of all 1,221 currently known folds and inference of unknown novel folds. We show that our method achieves better than 94% accuracy even when many folds have only one training example. We demonstrate the utility of this methodmore » by predicting the folds of 34,330 human protein domains and showing that these predictions can yield useful insights into potential biological function, such as prediction of RNA-binding ability. Finally, our method can be applied to de novo fold prediction of entire proteomes and identify candidate novel fold families.« less

Bio-knowledge based filters improve residue-residue contact prediction accuracy.

PubMed

Wozniak, P P; Pelc, J; Skrzypecki, M; Vriend, G; Kotulska, M

2018-05-29

Residue-residue contact prediction through direct coupling analysis has reached impressive accuracy, but yet higher accuracy will be needed to allow for routine modelling of protein structures. One way to improve the prediction accuracy is to filter predicted contacts using knowledge about the particular protein of interest or knowledge about protein structures in general. We focus on the latter and discuss a set of filters that can be used to remove false positive contact predictions. Each filter depends on one or a few cut-off parameters for which the filter performance was investigated. Combining all filters while using default parameters resulted for a test-set of 851 protein domains in the removal of 29% of the predictions of which 92% were indeed false positives. All data and scripts are available from http://comprec-lin.iiar.pwr.edu.pl/FPfilter/. malgorzata.kotulska@pwr.edu.pl. Supplementary data are available at Bioinformatics online.

ProSelection: A Novel Algorithm to Select Proper Protein Structure Subsets for in Silico Target Identification and Drug Discovery Research.

PubMed

Wang, Nanyi; Wang, Lirong; Xie, Xiang-Qun

2017-11-27

Molecular docking is widely applied to computer-aided drug design and has become relatively mature in the recent decades. Application of docking in modeling varies from single lead compound optimization to large-scale virtual screening. The performance of molecular docking is highly dependent on the protein structures selected. It is especially challenging for large-scale target prediction research when multiple structures are available for a single target. Therefore, we have established ProSelection, a docking preferred-protein selection algorithm, in order to generate the proper structure subset(s). By the ProSelection algorithm, protein structures of "weak selectors" are filtered out whereas structures of "strong selectors" are kept. Specifically, the structure which has a good statistical performance of distinguishing active ligands from inactive ligands is defined as a strong selector. In this study, 249 protein structures of 14 autophagy-related targets are investigated. Surflex-dock was used as the docking engine to distinguish active and inactive compounds against these protein structures. Both t test and Mann-Whitney U test were used to distinguish the strong from the weak selectors based on the normality of the docking score distribution. The suggested docking score threshold for active ligands (SDA) was generated for each strong selector structure according to the receiver operating characteristic (ROC) curve. The performance of ProSelection was further validated by predicting the potential off-targets of 43 U.S. Federal Drug Administration approved small molecule antineoplastic drugs. Overall, ProSelection will accelerate the computational work in protein structure selection and could be a useful tool for molecular docking, target prediction, and protein-chemical database establishment research.

Discrete Molecular Dynamics Can Predict Helical Prestructured Motifs in Disordered Proteins

PubMed Central

Han, Kyou-Hoon; Dokholyan, Nikolay V.; Tompa, Péter; Kalmár, Lajos; Hegedűs, Tamás

2014-01-01

Intrinsically disordered proteins (IDPs) lack a stable tertiary structure, but their short binding regions termed Pre-Structured Motifs (PreSMo) can form transient secondary structure elements in solution. Although disordered proteins are crucial in many biological processes and designing strategies to modulate their function is highly important, both experimental and computational tools to describe their conformational ensembles and the initial steps of folding are sparse. Here we report that discrete molecular dynamics (DMD) simulations combined with replica exchange (RX) method efficiently samples the conformational space and detects regions populating α-helical conformational states in disordered protein regions. While the available computational methods predict secondary structural propensities in IDPs based on the observation of protein-protein interactions, our ab initio method rests on physical principles of protein folding and dynamics. We show that RX-DMD predicts α-PreSMos with high confidence confirmed by comparison to experimental NMR data. Moreover, the method also can dissect α-PreSMos in close vicinity to each other and indicate helix stability. Importantly, simulations with disordered regions forming helices in X-ray structures of complexes indicate that a preformed helix is frequently the binding element itself, while in other cases it may have a role in initiating the binding process. Our results indicate that RX-DMD provides a breakthrough in the structural and dynamical characterization of disordered proteins by generating the structural ensembles of IDPs even when experimental data are not available. PMID:24763499

Unraveling the meaning of chemical shifts in protein NMR.

PubMed

Berjanskii, Mark V; Wishart, David S

2017-11-01

Chemical shifts are among the most informative parameters in protein NMR. They provide wealth of information about protein secondary and tertiary structure, protein flexibility, and protein-ligand binding. In this report, we review the progress in interpreting and utilizing protein chemical shifts that has occurred over the past 25years, with a particular focus on the large body of work arising from our group and other Canadian NMR laboratories. More specifically, this review focuses on describing, assessing, and providing some historical context for various chemical shift-based methods to: (1) determine protein secondary and super-secondary structure; (2) derive protein torsion angles; (3) assess protein flexibility; (4) predict residue accessible surface area; (5) refine 3D protein structures; (6) determine 3D protein structures and (7) characterize intrinsically disordered proteins. This review also briefly covers some of the methods that we previously developed to predict chemical shifts from 3D protein structures and/or protein sequence data. It is hoped that this review will help to increase awareness of the considerable utility of NMR chemical shifts in structural biology and facilitate more widespread adoption of chemical-shift based methods by the NMR spectroscopists, structural biologists, protein biophysicists, and biochemists worldwide. This article is part of a Special Issue entitled: Biophysics in Canada, edited by Lewis Kay, John Baenziger, Albert Berghuis and Peter Tieleman. Copyright © 2017 Elsevier B.V. All rights reserved.

Prediction of protein structural classes by recurrence quantification analysis based on chaos game representation.

PubMed

Yang, Jian-Yi; Peng, Zhen-Ling; Yu, Zu-Guo; Zhang, Rui-Jie; Anh, Vo; Wang, Desheng

2009-04-21

In this paper, we intend to predict protein structural classes (alpha, beta, alpha+beta, or alpha/beta) for low-homology data sets. Two data sets were used widely, 1189 (containing 1092 proteins) and 25PDB (containing 1673 proteins) with sequence homology being 40% and 25%, respectively. We propose to decompose the chaos game representation of proteins into two kinds of time series. Then, a novel and powerful nonlinear analysis technique, recurrence quantification analysis (RQA), is applied to analyze these time series. For a given protein sequence, a total of 16 characteristic parameters can be calculated with RQA, which are treated as feature representation of protein sequences. Based on such feature representation, the structural class for each protein is predicted with Fisher's linear discriminant algorithm. The jackknife test is used to test and compare our method with other existing methods. The overall accuracies with step-by-step procedure are 65.8% and 64.2% for 1189 and 25PDB data sets, respectively. With one-against-others procedure used widely, we compare our method with five other existing methods. Especially, the overall accuracies of our method are 6.3% and 4.1% higher for the two data sets, respectively. Furthermore, only 16 parameters are used in our method, which is less than that used by other methods. This suggests that the current method may play a complementary role to the existing methods and is promising to perform the prediction of protein structural classes.

Transmembrane protein topology prediction using support vector machines.

PubMed

Nugent, Timothy; Jones, David T

2009-05-26

Alpha-helical transmembrane (TM) proteins are involved in a wide range of important biological processes such as cell signaling, transport of membrane-impermeable molecules, cell-cell communication, cell recognition and cell adhesion. Many are also prime drug targets, and it has been estimated that more than half of all drugs currently on the market target membrane proteins. However, due to the experimental difficulties involved in obtaining high quality crystals, this class of protein is severely under-represented in structural databases. In the absence of structural data, sequence-based prediction methods allow TM protein topology to be investigated. We present a support vector machine-based (SVM) TM protein topology predictor that integrates both signal peptide and re-entrant helix prediction, benchmarked with full cross-validation on a novel data set of 131 sequences with known crystal structures. The method achieves topology prediction accuracy of 89%, while signal peptides and re-entrant helices are predicted with 93% and 44% accuracy respectively. An additional SVM trained to discriminate between globular and TM proteins detected zero false positives, with a low false negative rate of 0.4%. We present the results of applying these tools to a number of complete genomes. Source code, data sets and a web server are freely available from http://bioinf.cs.ucl.ac.uk/psipred/. The high accuracy of TM topology prediction which includes detection of both signal peptides and re-entrant helices, combined with the ability to effectively discriminate between TM and globular proteins, make this method ideally suited to whole genome annotation of alpha-helical transmembrane proteins.

Distance matrix-based approach to protein structure prediction.

PubMed

Kloczkowski, Andrzej; Jernigan, Robert L; Wu, Zhijun; Song, Guang; Yang, Lei; Kolinski, Andrzej; Pokarowski, Piotr

2009-03-01

Much structural information is encoded in the internal distances; a distance matrix-based approach can be used to predict protein structure and dynamics, and for structural refinement. Our approach is based on the square distance matrix D = [r(ij)(2)] containing all square distances between residues in proteins. This distance matrix contains more information than the contact matrix C, that has elements of either 0 or 1 depending on whether the distance r (ij) is greater or less than a cutoff value r (cutoff). We have performed spectral decomposition of the distance matrices D = sigma lambda(k)V(k)V(kT), in terms of eigenvalues lambda kappa and the corresponding eigenvectors v kappa and found that it contains at most five nonzero terms. A dominant eigenvector is proportional to r (2)--the square distance of points from the center of mass, with the next three being the principal components of the system of points. By predicting r (2) from the sequence we can approximate a distance matrix of a protein with an expected RMSD value of about 7.3 A, and by combining it with the prediction of the first principal component we can improve this approximation to 4.0 A. We can also explain the role of hydrophobic interactions for the protein structure, because r is highly correlated with the hydrophobic profile of the sequence. Moreover, r is highly correlated with several sequence profiles which are useful in protein structure prediction, such as contact number, the residue-wise contact order (RWCO) or mean square fluctuations (i.e. crystallographic temperature factors). We have also shown that the next three components are related to spatial directionality of the secondary structure elements, and they may be also predicted from the sequence, improving overall structure prediction. We have also shown that the large number of available HIV-1 protease structures provides a remarkable sampling of conformations, which can be viewed as direct structural information about the dynamics. After structure matching, we apply principal component analysis (PCA) to obtain the important apparent motions for both bound and unbound structures. There are significant similarities between the first few key motions and the first few low-frequency normal modes calculated from a static representative structure with an elastic network model (ENM) that is based on the contact matrix C (related to D), strongly suggesting that the variations among the observed structures and the corresponding conformational changes are facilitated by the low-frequency, global motions intrinsic to the structure. Similarities are also found when the approach is applied to an NMR ensemble, as well as to atomic molecular dynamics (MD) trajectories. Thus, a sufficiently large number of experimental structures can directly provide important information about protein dynamics, but ENM can also provide a similar sampling of conformations. Finally, we use distance constraints from databases of known protein structures for structure refinement. We use the distributions of distances of various types in known protein structures to obtain the most probable ranges or the mean-force potentials for the distances. We then impose these constraints on structures to be refined or include the mean-force potentials directly in the energy minimization so that more plausible structural models can be built. This approach has been successfully used by us in 2006 in the CASPR structure refinement (http://predictioncenter.org/caspR).

Motif discovery with data mining in 3D protein structure databases: discovery, validation and prediction of the U-shape zinc binding ("Huf-Zinc") motif.

PubMed

Maurer-Stroh, Sebastian; Gao, He; Han, Hao; Baeten, Lies; Schymkowitz, Joost; Rousseau, Frederic; Zhang, Louxin; Eisenhaber, Frank

2013-02-01

Data mining in protein databases, derivatives from more fundamental protein 3D structure and sequence databases, has considerable unearthed potential for the discovery of sequence motif--structural motif--function relationships as the finding of the U-shape (Huf-Zinc) motif, originally a small student's project, exemplifies. The metal ion zinc is critically involved in universal biological processes, ranging from protein-DNA complexes and transcription regulation to enzymatic catalysis and metabolic pathways. Proteins have evolved a series of motifs to specifically recognize and bind zinc ions. Many of these, so called zinc fingers, are structurally independent globular domains with discontinuous binding motifs made up of residues mostly far apart in sequence. Through a systematic approach starting from the BRIX structure fragment database, we discovered that there exists another predictable subset of zinc-binding motifs that not only have a conserved continuous sequence pattern but also share a characteristic local conformation, despite being included in totally different overall folds. While this does not allow general prediction of all Zn binding motifs, a HMM-based web server, Huf-Zinc, is available for prediction of these novel, as well as conventional, zinc finger motifs in protein sequences. The Huf-Zinc webserver can be freely accessed through this URL (http://mendel.bii.a-star.edu.sg/METHODS/hufzinc/).

Fast large-scale clustering of protein structures using Gauss integrals.

PubMed

Harder, Tim; Borg, Mikael; Boomsma, Wouter; Røgen, Peter; Hamelryck, Thomas

2012-02-15

Clustering protein structures is an important task in structural bioinformatics. De novo structure prediction, for example, often involves a clustering step for finding the best prediction. Other applications include assigning proteins to fold families and analyzing molecular dynamics trajectories. We present Pleiades, a novel approach to clustering protein structures with a rigorous mathematical underpinning. The method approximates clustering based on the root mean square deviation by first mapping structures to Gauss integral vectors--which were introduced by Røgen and co-workers--and subsequently performing K-means clustering. Compared to current methods, Pleiades dramatically improves on the time needed to perform clustering, and can cluster a significantly larger number of structures, while providing state-of-the-art results. The number of low energy structures generated in a typical folding study, which is in the order of 50,000 structures, can be clustered within seconds to minutes.

Polarization-dependent two-photon absorption for the determination of protein secondary structure: A theoretical study

NASA Astrophysics Data System (ADS)

Wanapun, Duangporn; Wampler, Ronald D.; Begue, Nathan J.; Simpson, Garth J.

2008-03-01

A new method for sensitive determination of protein secondary structure via multi-photon absorption is considered theoretically. Perturbation theory is developed to describe the polarization-dependent two-photon absorption (TPA) of α-helix and β-sheet protein secondary structures. The exciton coupling interactions responsible for relatively weak electronic circular dichroism in one-photon absorption are predicted to give rise to large changes in the TPA cross-section (>200%) for circular versus linear incident polarizations, defined as CLD. The CLD effect in TPA is electric dipole-allowed, which explains the much greater sensitivity. These predictions suggest TPA should be a viable means of sensitively probing protein secondary structure.

Rapid search for tertiary fragments reveals protein sequence–structure relationships

PubMed Central

Zhou, Jianfu; Grigoryan, Gevorg

2015-01-01

Finding backbone substructures from the Protein Data Bank that match an arbitrary query structural motif, composed of multiple disjoint segments, is a problem of growing relevance in structure prediction and protein design. Although numerous protein structure search approaches have been proposed, methods that address this specific task without additional restrictions and on practical time scales are generally lacking. Here, we propose a solution, dubbed MASTER, that is both rapid, enabling searches over the Protein Data Bank in a matter of seconds, and provably correct, finding all matches below a user-specified root-mean-square deviation cutoff. We show that despite the potentially exponential time complexity of the problem, running times in practice are modest even for queries with many segments. The ability to explore naturally plausible structural and sequence variations around a given motif has the potential to synthesize its design principles in an automated manner; so we go on to illustrate the utility of MASTER to protein structural biology. We demonstrate its capacity to rapidly establish structure–sequence relationships, uncover the native designability landscapes of tertiary structural motifs, identify structural signatures of binding, and automatically rewire protein topologies. Given the broad utility of protein tertiary fragment searches, we hope that providing MASTER in an open-source format will enable novel advances in understanding, predicting, and designing protein structure. PMID:25420575

A discriminatory function for prediction of protein-DNA interactions based on alpha shape modeling.

PubMed

Zhou, Weiqiang; Yan, Hong

2010-10-15

Protein-DNA interaction has significant importance in many biological processes. However, the underlying principle of the molecular recognition process is still largely unknown. As more high-resolution 3D structures of protein-DNA complex are becoming available, the surface characteristics of the complex become an important research topic. In our work, we apply an alpha shape model to represent the surface structure of the protein-DNA complex and developed an interface-atom curvature-dependent conditional probability discriminatory function for the prediction of protein-DNA interaction. The interface-atom curvature-dependent formalism captures atomic interaction details better than the atomic distance-based method. The proposed method provides good performance in discriminating the native structures from the docking decoy sets, and outperforms the distance-dependent formalism in terms of the z-score. Computer experiment results show that the curvature-dependent formalism with the optimal parameters can achieve a native z-score of -8.17 in discriminating the native structure from the highest surface-complementarity scored decoy set and a native z-score of -7.38 in discriminating the native structure from the lowest RMSD decoy set. The interface-atom curvature-dependent formalism can also be used to predict apo version of DNA-binding proteins. These results suggest that the interface-atom curvature-dependent formalism has a good prediction capability for protein-DNA interactions. The code and data sets are available for download on http://www.hy8.com/bioinformatics.htm kenandzhou@hotmail.com.

MetaGO: Predicting Gene Ontology of Non-homologous Proteins Through Low-Resolution Protein Structure Prediction and Protein-Protein Network Mapping.

PubMed

Zhang, Chengxin; Zheng, Wei; Freddolino, Peter L; Zhang, Yang

2018-03-10

Homology-based transferal remains the major approach to computational protein function annotations, but it becomes increasingly unreliable when the sequence identity between query and template decreases below 30%. We propose a novel pipeline, MetaGO, to deduce Gene Ontology attributes of proteins by combining sequence homology-based annotation with low-resolution structure prediction and comparison, and partner's homology-based protein-protein network mapping. The pipeline was tested on a large-scale set of 1000 non-redundant proteins from the CAFA3 experiment. Under the stringent benchmark conditions where templates with >30% sequence identity to the query are excluded, MetaGO achieves average F-measures of 0.487, 0.408, and 0.598, for Molecular Function, Biological Process, and Cellular Component, respectively, which are significantly higher than those achieved by other state-of-the-art function annotations methods. Detailed data analysis shows that the major advantage of the MetaGO lies in the new functional homolog detections from partner's homology-based network mapping and structure-based local and global structure alignments, the confidence scores of which can be optimally combined through logistic regression. These data demonstrate the power of using a hybrid model incorporating protein structure and interaction networks to deduce new functional insights beyond traditional sequence homology-based referrals, especially for proteins that lack homologous function templates. The MetaGO pipeline is available at http://zhanglab.ccmb.med.umich.edu/MetaGO/. Copyright © 2018. Published by Elsevier Ltd.

An improved stochastic fractal search algorithm for 3D protein structure prediction.

PubMed

Zhou, Changjun; Sun, Chuan; Wang, Bin; Wang, Xiaojun

2018-05-03

Protein structure prediction (PSP) is a significant area for biological information research, disease treatment, and drug development and so on. In this paper, three-dimensional structures of proteins are predicted based on the known amino acid sequences, and the structure prediction problem is transformed into a typical NP problem by an AB off-lattice model. This work applies a novel improved Stochastic Fractal Search algorithm (ISFS) to solve the problem. The Stochastic Fractal Search algorithm (SFS) is an effective evolutionary algorithm that performs well in exploring the search space but falls into local minimums sometimes. In order to avoid the weakness, Lvy flight and internal feedback information are introduced in ISFS. In the experimental process, simulations are conducted by ISFS algorithm on Fibonacci sequences and real peptide sequences. Experimental results prove that the ISFS performs more efficiently and robust in terms of finding the global minimum and avoiding getting stuck in local minimums.

Identification of Conserved Water Sites in Protein Structures for Drug Design.

PubMed

Jukič, Marko; Konc, Janez; Gobec, Stanislav; Janežič, Dušanka

2017-12-26

Identification of conserved waters in protein structures is a challenging task with applications in molecular docking and protein stability prediction. As an alternative to computationally demanding simulations of proteins in water, experimental cocrystallized waters in the Protein Data Bank (PDB) in combination with a local structure alignment algorithm can be used for reliable prediction of conserved water sites. We developed the ProBiS H2O approach based on the previously developed ProBiS algorithm, which enables identification of conserved water sites in proteins using experimental protein structures from the PDB or a set of custom protein structures available to the user. With a protein structure, a binding site, or an individual water molecule as a query, ProBiS H2O collects similar proteins from the PDB and performs local or binding site-specific superimpositions of the query structure with similar proteins using the ProBiS algorithm. It collects the experimental water molecules from the similar proteins and transposes them to the query protein. Transposed waters are clustered by their mutual proximity, which enables identification of discrete sites in the query protein with high water conservation. ProBiS H2O is a robust and fast new approach that uses existing experimental structural data to identify conserved water sites on the interfaces of protein complexes, for example protein-small molecule interfaces, and elsewhere on the protein structures. It has been successfully validated in several reported proteins in which conserved water molecules were found to play an important role in ligand binding with applications in drug design.

Restricted mobility of side chains on concave surfaces of solenoid proteins may impart heightened potential for intermolecular interactions.

PubMed

Ramya, L; Gautham, N; Chaloin, Laurent; Kajava, Andrey V

2015-09-01

Significant progress has been made in the determination of the protein structures with their number today passing over a hundred thousand structures. The next challenge is the understanding and prediction of protein-protein and protein-ligand interactions. In this work we address this problem by analyzing curved solenoid proteins. Many of these proteins are considered as "hub molecules" for their high potential to interact with many different molecules and to be a scaffold for multisubunit protein machineries. Our analysis of these structures through molecular dynamics simulations reveals that the mobility of the side-chains on the concave surfaces of the solenoids is lower than on the convex ones. This result provides an explanation to the observed preferential binding of the ligands, including small and flexible ligands, to the concave surface of the curved solenoid proteins. The relationship between the landscapes and dynamic properties of the protein surfaces can be further generalized to the other types of protein structures and eventually used in the computer algorithms, allowing prediction of protein-ligand interactions by analysis of protein surfaces. © 2015 Wiley Periodicals, Inc.

Combining Physicochemical and Evolutionary Information for Protein Contact Prediction

PubMed Central

Schneider, Michael; Brock, Oliver

2014-01-01

We introduce a novel contact prediction method that achieves high prediction accuracy by combining evolutionary and physicochemical information about native contacts. We obtain evolutionary information from multiple-sequence alignments and physicochemical information from predicted ab initio protein structures. These structures represent low-energy states in an energy landscape and thus capture the physicochemical information encoded in the energy function. Such low-energy structures are likely to contain native contacts, even if their overall fold is not native. To differentiate native from non-native contacts in those structures, we develop a graph-based representation of the structural context of contacts. We then use this representation to train an support vector machine classifier to identify most likely native contacts in otherwise non-native structures. The resulting contact predictions are highly accurate. As a result of combining two sources of information—evolutionary and physicochemical—we maintain prediction accuracy even when only few sequence homologs are present. We show that the predicted contacts help to improve ab initio structure prediction. A web service is available at http://compbio.robotics.tu-berlin.de/epc-map/. PMID:25338092

Metamorphic Proteins: Emergence of Dual Protein Folds from One Primary Sequence.

PubMed

Lella, Muralikrishna; Mahalakshmi, Radhakrishnan

2017-06-20

Every amino acid exhibits a different propensity for distinct structural conformations. Hence, decoding how the primary amino acid sequence undergoes the transition to a defined secondary structure and its final three-dimensional fold is presently considered predictable with reasonable certainty. However, protein sequences that defy the first principles of secondary structure prediction (they attain two different folds) have recently been discovered. Such proteins, aptly named metamorphic proteins, decrease the conformational constraint by increasing flexibility in the secondary structure and thereby result in efficient functionality. In this review, we discuss the major factors driving the conformational switch related both to protein sequence and to structure using illustrative examples. We discuss the concept of an evolutionary transition in sequence and structure, the functional impact of the tertiary fold, and the pressure of intrinsic and external factors that give rise to metamorphic proteins. We mainly focus on the major components of protein architecture, namely, the α-helix and β-sheet segments, which are involved in conformational switching within the same or highly similar sequences. These chameleonic sequences are widespread in both cytosolic and membrane proteins, and these folds are equally important for protein structure and function. We discuss the implications of metamorphic proteins and chameleonic peptide sequences in de novo peptide design.

Protein secondary structure prediction using modular reciprocal bidirectional recurrent neural networks.

PubMed

Babaei, Sepideh; Geranmayeh, Amir; Seyyedsalehi, Seyyed Ali

2010-12-01

The supervised learning of recurrent neural networks well-suited for prediction of protein secondary structures from the underlying amino acids sequence is studied. Modular reciprocal recurrent neural networks (MRR-NN) are proposed to model the strong correlations between adjacent secondary structure elements. Besides, a multilayer bidirectional recurrent neural network (MBR-NN) is introduced to capture the long-range intramolecular interactions between amino acids in formation of the secondary structure. The final modular prediction system is devised based on the interactive integration of the MRR-NN and the MBR-NN structures to arbitrarily engage the neighboring effects of the secondary structure types concurrent with memorizing the sequential dependencies of amino acids along the protein chain. The advanced combined network augments the percentage accuracy (Q₃) to 79.36% and boosts the segment overlap (SOV) up to 70.09% when tested on the PSIPRED dataset in three-fold cross-validation. Copyright © 2010 Elsevier Ireland Ltd. All rights reserved.

RaptorX server: a resource for template-based protein structure modeling.

PubMed

Källberg, Morten; Margaryan, Gohar; Wang, Sheng; Ma, Jianzhu; Xu, Jinbo

2014-01-01

Assigning functional properties to a newly discovered protein is a key challenge in modern biology. To this end, computational modeling of the three-dimensional atomic arrangement of the amino acid chain is often crucial in determining the role of the protein in biological processes. We present a community-wide web-based protocol, RaptorX server ( http://raptorx.uchicago.edu ), for automated protein secondary structure prediction, template-based tertiary structure modeling, and probabilistic alignment sampling.Given a target sequence, RaptorX server is able to detect even remotely related template sequences by means of a novel nonlinear context-specific alignment potential and probabilistic consistency algorithm. Using the protocol presented here it is thus possible to obtain high-quality structural models for many target protein sequences when only distantly related protein domains have experimentally solved structures. At present, RaptorX server can perform secondary and tertiary structure prediction of a 200 amino acid target sequence in approximately 30 min.

RaptorX-Property: a web server for protein structure property prediction.

PubMed

Wang, Sheng; Li, Wei; Liu, Shiwang; Xu, Jinbo

2016-07-08

RaptorX Property (http://raptorx2.uchicago.edu/StructurePropertyPred/predict/) is a web server predicting structure property of a protein sequence without using any templates. It outperforms other servers, especially for proteins without close homologs in PDB or with very sparse sequence profile (i.e. carries little evolutionary information). This server employs a powerful in-house deep learning model DeepCNF (Deep Convolutional Neural Fields) to predict secondary structure (SS), solvent accessibility (ACC) and disorder regions (DISO). DeepCNF not only models complex sequence-structure relationship by a deep hierarchical architecture, but also interdependency between adjacent property labels. Our experimental results show that, tested on CASP10, CASP11 and the other benchmarks, this server can obtain ∼84% Q3 accuracy for 3-state SS, ∼72% Q8 accuracy for 8-state SS, ∼66% Q3 accuracy for 3-state solvent accessibility, and ∼0.89 area under the ROC curve (AUC) for disorder prediction. © The Author(s) 2016. Published by Oxford University Press on behalf of Nucleic Acids Research.

Novel Computational Approaches to Drug Discovery

NASA Astrophysics Data System (ADS)

Skolnick, Jeffrey; Brylinski, Michal

2010-01-01

New approaches to protein functional inference based on protein structure and evolution are described. First, FINDSITE, a threading based approach to protein function prediction, is summarized. Then, the results of large scale benchmarking of ligand binding site prediction, ligand screening, including applications to HIV protease, and GO molecular functional inference are presented. A key advantage of FINDSITE is its ability to use low resolution, predicted structures as well as high resolution experimental structures. Then, an extension of FINDSITE to ligand screening in GPCRs using predicted GPCR structures, FINDSITE/QDOCKX, is presented. This is a particularly difficult case as there are few experimentally solved GPCR structures. Thus, we first train on a subset of known binding ligands for a set of GPCRs; this is then followed by benchmarking against a large ligand library. For the virtual ligand screening of a number of Dopamine receptors, encouraging results are seen, with significant enrichment in identified ligands over those found in the training set. Thus, FINDSITE and its extensions represent a powerful approach to the successful prediction of a variety of molecular functions.

Protein contact prediction using patterns of correlation.

PubMed

Hamilton, Nicholas; Burrage, Kevin; Ragan, Mark A; Huber, Thomas

2004-09-01

We describe a new method for using neural networks to predict residue contact pairs in a protein. The main inputs to the neural network are a set of 25 measures of correlated mutation between all pairs of residues in two "windows" of size 5 centered on the residues of interest. While the individual pair-wise correlations are a relatively weak predictor of contact, by training the network on windows of correlation the accuracy of prediction is significantly improved. The neural network is trained on a set of 100 proteins and then tested on a disjoint set of 1033 proteins of known structure. An average predictive accuracy of 21.7% is obtained taking the best L/2 predictions for each protein, where L is the sequence length. Taking the best L/10 predictions gives an average accuracy of 30.7%. The predictor is also tested on a set of 59 proteins from the CASP5 experiment. The accuracy is found to be relatively consistent across different sequence lengths, but to vary widely according to the secondary structure. Predictive accuracy is also found to improve by using multiple sequence alignments containing many sequences to calculate the correlations. Copyright 2004 Wiley-Liss, Inc.

Accurate De Novo Prediction of Protein Contact Map by Ultra-Deep Learning Model.

PubMed

Wang, Sheng; Sun, Siqi; Li, Zhen; Zhang, Renyu; Xu, Jinbo

2017-01-01

Protein contacts contain key information for the understanding of protein structure and function and thus, contact prediction from sequence is an important problem. Recently exciting progress has been made on this problem, but the predicted contacts for proteins without many sequence homologs is still of low quality and not very useful for de novo structure prediction. This paper presents a new deep learning method that predicts contacts by integrating both evolutionary coupling (EC) and sequence conservation information through an ultra-deep neural network formed by two deep residual neural networks. The first residual network conducts a series of 1-dimensional convolutional transformation of sequential features; the second residual network conducts a series of 2-dimensional convolutional transformation of pairwise information including output of the first residual network, EC information and pairwise potential. By using very deep residual networks, we can accurately model contact occurrence patterns and complex sequence-structure relationship and thus, obtain higher-quality contact prediction regardless of how many sequence homologs are available for proteins in question. Our method greatly outperforms existing methods and leads to much more accurate contact-assisted folding. Tested on 105 CASP11 targets, 76 past CAMEO hard targets, and 398 membrane proteins, the average top L long-range prediction accuracy obtained by our method, one representative EC method CCMpred and the CASP11 winner MetaPSICOV is 0.47, 0.21 and 0.30, respectively; the average top L/10 long-range accuracy of our method, CCMpred and MetaPSICOV is 0.77, 0.47 and 0.59, respectively. Ab initio folding using our predicted contacts as restraints but without any force fields can yield correct folds (i.e., TMscore>0.6) for 203 of the 579 test proteins, while that using MetaPSICOV- and CCMpred-predicted contacts can do so for only 79 and 62 of them, respectively. Our contact-assisted models also have much better quality than template-based models especially for membrane proteins. The 3D models built from our contact prediction have TMscore>0.5 for 208 of the 398 membrane proteins, while those from homology modeling have TMscore>0.5 for only 10 of them. Further, even if trained mostly by soluble proteins, our deep learning method works very well on membrane proteins. In the recent blind CAMEO benchmark, our fully-automated web server implementing this method successfully folded 6 targets with a new fold and only 0.3L-2.3L effective sequence homologs, including one β protein of 182 residues, one α+β protein of 125 residues, one α protein of 140 residues, one α protein of 217 residues, one α/β of 260 residues and one α protein of 462 residues. Our method also achieved the highest F1 score on free-modeling targets in the latest CASP (Critical Assessment of Structure Prediction), although it was not fully implemented back then. http://raptorx.uchicago.edu/ContactMap/.

Accurate De Novo Prediction of Protein Contact Map by Ultra-Deep Learning Model

PubMed Central

Li, Zhen; Zhang, Renyu

2017-01-01

Motivation Protein contacts contain key information for the understanding of protein structure and function and thus, contact prediction from sequence is an important problem. Recently exciting progress has been made on this problem, but the predicted contacts for proteins without many sequence homologs is still of low quality and not very useful for de novo structure prediction. Method This paper presents a new deep learning method that predicts contacts by integrating both evolutionary coupling (EC) and sequence conservation information through an ultra-deep neural network formed by two deep residual neural networks. The first residual network conducts a series of 1-dimensional convolutional transformation of sequential features; the second residual network conducts a series of 2-dimensional convolutional transformation of pairwise information including output of the first residual network, EC information and pairwise potential. By using very deep residual networks, we can accurately model contact occurrence patterns and complex sequence-structure relationship and thus, obtain higher-quality contact prediction regardless of how many sequence homologs are available for proteins in question. Results Our method greatly outperforms existing methods and leads to much more accurate contact-assisted folding. Tested on 105 CASP11 targets, 76 past CAMEO hard targets, and 398 membrane proteins, the average top L long-range prediction accuracy obtained by our method, one representative EC method CCMpred and the CASP11 winner MetaPSICOV is 0.47, 0.21 and 0.30, respectively; the average top L/10 long-range accuracy of our method, CCMpred and MetaPSICOV is 0.77, 0.47 and 0.59, respectively. Ab initio folding using our predicted contacts as restraints but without any force fields can yield correct folds (i.e., TMscore>0.6) for 203 of the 579 test proteins, while that using MetaPSICOV- and CCMpred-predicted contacts can do so for only 79 and 62 of them, respectively. Our contact-assisted models also have much better quality than template-based models especially for membrane proteins. The 3D models built from our contact prediction have TMscore>0.5 for 208 of the 398 membrane proteins, while those from homology modeling have TMscore>0.5 for only 10 of them. Further, even if trained mostly by soluble proteins, our deep learning method works very well on membrane proteins. In the recent blind CAMEO benchmark, our fully-automated web server implementing this method successfully folded 6 targets with a new fold and only 0.3L-2.3L effective sequence homologs, including one β protein of 182 residues, one α+β protein of 125 residues, one α protein of 140 residues, one α protein of 217 residues, one α/β of 260 residues and one α protein of 462 residues. Our method also achieved the highest F1 score on free-modeling targets in the latest CASP (Critical Assessment of Structure Prediction), although it was not fully implemented back then. Availability http://raptorx.uchicago.edu/ContactMap/ PMID:28056090

The PYRIN domain: A member of the death domain-fold superfamily

PubMed Central

Fairbrother, Wayne J.; Gordon, Nathaniel C.; Humke, Eric W.; O'Rourke, Karen M.; Starovasnik, Melissa A.; Yin, Jian-Ping; Dixit, Vishva M.

2001-01-01

PYRIN domains were identified recently as putative protein–protein interaction domains at the N-termini of several proteins thought to function in apoptotic and inflammatory signaling pathways. The ∼95 residue PYRIN domains have no statistically significant sequence homology to proteins with known three-dimensional structure. Using secondary structure prediction and potential-based fold recognition methods, however, the PYRIN domain is predicted to be a member of the six-helix bundle death domain-fold superfamily that includes death domains (DDs), death effector domains (DEDs), and caspase recruitment domains (CARDs). Members of the death domain-fold superfamily are well established mediators of protein–protein interactions found in many proteins involved in apoptosis and inflammation, indicating further that the PYRIN domains serve a similar function. An homology model of the PYRIN domain of CARD7/DEFCAP/NAC/NALP1, a member of the Apaf-1/Ced-4 family of proteins, was constructed using the three-dimensional structures of the FADD and p75 neurotrophin receptor DDs, and of the Apaf-1 and caspase-9 CARDs, as templates. Validation of the model using a variety of computational techniques indicates that the fold prediction is consistent with the sequence. Comparison of a circular dichroism spectrum of the PYRIN domain of CARD7/DEFCAP/NAC/NALP1 with spectra of several proteins known to adopt the death domain-fold provides experimental support for the structure prediction. PMID:11514682

Cascaded bidirectional recurrent neural networks for protein secondary structure prediction.

PubMed

Chen, Jinmiao; Chaudhari, Narendra

2007-01-01

Protein secondary structure (PSS) prediction is an important topic in bioinformatics. Our study on a large set of non-homologous proteins shows that long-range interactions commonly exist and negatively affect PSS prediction. Besides, we also reveal strong correlations between secondary structure (SS) elements. In order to take into account the long-range interactions and SS-SS correlations, we propose a novel prediction system based on cascaded bidirectional recurrent neural network (BRNN). We compare the cascaded BRNN against another two BRNN architectures, namely the original BRNN architecture used for speech recognition as well as Pollastri's BRNN that was proposed for PSS prediction. Our cascaded BRNN achieves an overall three state accuracy Q3 of 74.38\\%, and reaches a high Segment OVerlap (SOV) of 66.0455. It outperforms the original BRNN and Pollastri's BRNN in both Q3 and SOV. Specifically, it improves the SOV score by 4-6%.

Bio-AIMS Collection of Chemoinformatics Web Tools based on Molecular Graph Information and Artificial Intelligence Models.

PubMed

Munteanu, Cristian R; Gonzalez-Diaz, Humberto; Garcia, Rafael; Loza, Mabel; Pazos, Alejandro

2015-01-01

The molecular information encoding into molecular descriptors is the first step into in silico Chemoinformatics methods in Drug Design. The Machine Learning methods are a complex solution to find prediction models for specific biological properties of molecules. These models connect the molecular structure information such as atom connectivity (molecular graphs) or physical-chemical properties of an atom/group of atoms to the molecular activity (Quantitative Structure - Activity Relationship, QSAR). Due to the complexity of the proteins, the prediction of their activity is a complicated task and the interpretation of the models is more difficult. The current review presents a series of 11 prediction models for proteins, implemented as free Web tools on an Artificial Intelligence Model Server in Biosciences, Bio-AIMS (http://bio-aims.udc.es/TargetPred.php). Six tools predict protein activity, two models evaluate drug - protein target interactions and the other three calculate protein - protein interactions. The input information is based on the protein 3D structure for nine models, 1D peptide amino acid sequence for three tools and drug SMILES formulas for two servers. The molecular graph descriptor-based Machine Learning models could be useful tools for in silico screening of new peptides/proteins as future drug targets for specific treatments.

Modeling the assembly order of multimeric heteroprotein complexes

PubMed Central

Esquivel-Rodriguez, Juan; Terashi, Genki; Christoffer, Charles; Shin, Woong-Hee

2018-01-01

Protein-protein interactions are the cornerstone of numerous biological processes. Although an increasing number of protein complex structures have been determined using experimental methods, relatively fewer studies have been performed to determine the assembly order of complexes. In addition to the insights into the molecular mechanisms of biological function provided by the structure of a complex, knowing the assembly order is important for understanding the process of complex formation. Assembly order is also practically useful for constructing subcomplexes as a step toward solving the entire complex experimentally, designing artificial protein complexes, and developing drugs that interrupt a critical step in the complex assembly. There are several experimental methods for determining the assembly order of complexes; however, these techniques are resource-intensive. Here, we present a computational method that predicts the assembly order of protein complexes by building the complex structure. The method, named Path-LzerD, uses a multimeric protein docking algorithm that assembles a protein complex structure from individual subunit structures and predicts assembly order by observing the simulated assembly process of the complex. Benchmarked on a dataset of complexes with experimental evidence of assembly order, Path-LZerD was successful in predicting the assembly pathway for the majority of the cases. Moreover, when compared with a simple approach that infers the assembly path from the buried surface area of subunits in the native complex, Path-LZerD has the strong advantage that it can be used for cases where the complex structure is not known. The path prediction accuracy decreased when starting from unbound monomers, particularly for larger complexes of five or more subunits, for which only a part of the assembly path was correctly identified. As the first method of its kind, Path-LZerD opens a new area of computational protein structure modeling and will be an indispensable approach for studying protein complexes. PMID:29329283

Modeling the assembly order of multimeric heteroprotein complexes.

PubMed

Peterson, Lenna X; Togawa, Yoichiro; Esquivel-Rodriguez, Juan; Terashi, Genki; Christoffer, Charles; Roy, Amitava; Shin, Woong-Hee; Kihara, Daisuke

2018-01-01

Protein-protein interactions are the cornerstone of numerous biological processes. Although an increasing number of protein complex structures have been determined using experimental methods, relatively fewer studies have been performed to determine the assembly order of complexes. In addition to the insights into the molecular mechanisms of biological function provided by the structure of a complex, knowing the assembly order is important for understanding the process of complex formation. Assembly order is also practically useful for constructing subcomplexes as a step toward solving the entire complex experimentally, designing artificial protein complexes, and developing drugs that interrupt a critical step in the complex assembly. There are several experimental methods for determining the assembly order of complexes; however, these techniques are resource-intensive. Here, we present a computational method that predicts the assembly order of protein complexes by building the complex structure. The method, named Path-LzerD, uses a multimeric protein docking algorithm that assembles a protein complex structure from individual subunit structures and predicts assembly order by observing the simulated assembly process of the complex. Benchmarked on a dataset of complexes with experimental evidence of assembly order, Path-LZerD was successful in predicting the assembly pathway for the majority of the cases. Moreover, when compared with a simple approach that infers the assembly path from the buried surface area of subunits in the native complex, Path-LZerD has the strong advantage that it can be used for cases where the complex structure is not known. The path prediction accuracy decreased when starting from unbound monomers, particularly for larger complexes of five or more subunits, for which only a part of the assembly path was correctly identified. As the first method of its kind, Path-LZerD opens a new area of computational protein structure modeling and will be an indispensable approach for studying protein complexes.

Assessment of CAPRI predictions in rounds 3-5 shows progress in docking procedures.

PubMed

Méndez, Raúl; Leplae, Raphaël; Lensink, Marc F; Wodak, Shoshana J

2005-08-01

The current status of docking procedures for predicting protein-protein interactions starting from their three-dimensional (3D) structure is reassessed by evaluating blind predictions, performed during 2003-2004 as part of Rounds 3-5 of the community-wide experiment on Critical Assessment of PRedicted Interactions (CAPRI). Ten newly determined structures of protein-protein complexes were used as targets for these rounds. They comprised 2 enzyme-inhibitor complexes, 2 antigen-antibody complexes, 2 complexes involved in cellular signaling, 2 homo-oligomers, and a complex between 2 components of the bacterial cellulosome. For most targets, the predictors were given the experimental structures of 1 unbound and 1 bound component, with the latter in a random orientation. For some, the structure of the free component was derived from that of a related protein, requiring the use of homology modeling. In some of the targets, significant differences in conformation were displayed between the bound and unbound components, representing a major challenge for the docking procedures. For 1 target, predictions could not go to completion. In total, 1866 predictions submitted by 30 groups were evaluated. Over one-third of these groups applied completely novel docking algorithms and scoring functions, with several of them specifically addressing the challenge of dealing with side-chain and backbone flexibility. The quality of the predicted interactions was evaluated by comparison to the experimental structures of the targets, made available for the evaluation, using the well-agreed-upon criteria used previously. Twenty-four groups, which for the first time included an automatic Web server, produced predictions ranking from acceptable to highly accurate for all targets, including those where the structures of the bound and unbound forms differed substantially. These results and a brief survey of the methods used by participants of CAPRI Rounds 3-5 suggest that genuine progress in the performance of docking methods is being achieved, with CAPRI acting as the catalyst.

Prediction of redox-sensitive cysteines using sequential distance and other sequence-based features.

PubMed

Sun, Ming-An; Zhang, Qing; Wang, Yejun; Ge, Wei; Guo, Dianjing

2016-08-24

Reactive oxygen species can modify the structure and function of proteins and may also act as important signaling molecules in various cellular processes. Cysteine thiol groups of proteins are particularly susceptible to oxidation. Meanwhile, their reversible oxidation is of critical roles for redox regulation and signaling. Recently, several computational tools have been developed for predicting redox-sensitive cysteines; however, those methods either only focus on catalytic redox-sensitive cysteines in thiol oxidoreductases, or heavily depend on protein structural data, thus cannot be widely used. In this study, we analyzed various sequence-based features potentially related to cysteine redox-sensitivity, and identified three types of features for efficient computational prediction of redox-sensitive cysteines. These features are: sequential distance to the nearby cysteines, PSSM profile and predicted secondary structure of flanking residues. After further feature selection using SVM-RFE, we developed Redox-Sensitive Cysteine Predictor (RSCP), a SVM based classifier for redox-sensitive cysteine prediction using primary sequence only. Using 10-fold cross-validation on RSC758 dataset, the accuracy, sensitivity, specificity, MCC and AUC were estimated as 0.679, 0.602, 0.756, 0.362 and 0.727, respectively. When evaluated using 10-fold cross-validation with BALOSCTdb dataset which has structure information, the model achieved performance comparable to current structure-based method. Further validation using an independent dataset indicates it is robust and of relatively better accuracy for predicting redox-sensitive cysteines from non-enzyme proteins. In this study, we developed a sequence-based classifier for predicting redox-sensitive cysteines. The major advantage of this method is that it does not rely on protein structure data, which ensures more extensive application compared to other current implementations. Accurate prediction of redox-sensitive cysteines not only enhances our understanding about the redox sensitivity of cysteine, it may also complement the proteomics approach and facilitate further experimental investigation of important redox-sensitive cysteines.

The unfoldomics decade: an update on intrinsically disordered proteins.

PubMed

Dunker, A Keith; Oldfield, Christopher J; Meng, Jingwei; Romero, Pedro; Yang, Jack Y; Chen, Jessica Walton; Vacic, Vladimir; Obradovic, Zoran; Uversky, Vladimir N

2008-09-16

Our first predictor of protein disorder was published just over a decade ago in the Proceedings of the IEEE International Conference on Neural Networks (Romero P, Obradovic Z, Kissinger C, Villafranca JE, Dunker AK (1997) Identifying disordered regions in proteins from amino acid sequence. Proceedings of the IEEE International Conference on Neural Networks, 1: 90-95). By now more than twenty other laboratory groups have joined the efforts to improve the prediction of protein disorder. While the various prediction methodologies used for protein intrinsic disorder resemble those methodologies used for secondary structure prediction, the two types of structures are entirely different. For example, the two structural classes have very different dynamic properties, with the irregular secondary structure class being much less mobile than the disorder class. The prediction of secondary structure has been useful. On the other hand, the prediction of intrinsic disorder has been revolutionary, leading to major modifications of the more than 100 year-old views relating protein structure and function. Experimentalists have been providing evidence over many decades that some proteins lack fixed structure or are disordered (or unfolded) under physiological conditions. In addition, experimentalists are also showing that, for many proteins, their functions depend on the unstructured rather than structured state; such results are in marked contrast to the greater than hundred year old views such as the lock and key hypothesis. Despite extensive data on many important examples, including disease-associated proteins, the importance of disorder for protein function has been largely ignored. Indeed, to our knowledge, current biochemistry books don't present even one acknowledged example of a disorder-dependent function, even though some reports of disorder-dependent functions are more than 50 years old. The results from genome-wide predictions of intrinsic disorder and the results from other bioinformatics studies of intrinsic disorder are demanding attention for these proteins. Disorder prediction has been important for showing that the relatively few experimentally characterized examples are members of a very large collection of related disordered proteins that are wide-spread over all three domains of life. Many significant biological functions are now known to depend directly on, or are importantly associated with, the unfolded or partially folded state. Here our goal is to review the key discoveries and to weave these discoveries together to support novel approaches for understanding sequence-function relationships. Intrinsically disordered protein is common across the three domains of life, but especially common among the eukaryotic proteomes. Signaling sequences and sites of posttranslational modifications are frequently, or very likely most often, located within regions of intrinsic disorder. Disorder-to-order transitions are coupled with the adoption of different structures with different partners. Also, the flexibility of intrinsic disorder helps different disordered regions to bind to a common binding site on a common partner. Such capacity for binding diversity plays important roles in both protein-protein interaction networks and likely also in gene regulation networks. Such disorder-based signaling is further modulated in multicellular eukaryotes by alternative splicing, for which such splicing events map to regions of disorder much more often than to regions of structure. Associating alternative splicing with disorder rather than structure alleviates theoretical and experimentally observed problems associated with the folding of different length, isomeric amino acid sequences. The combination of disorder and alternative splicing is proposed to provide a mechanism for easily "trying out" different signaling pathways, thereby providing the mechanism for generating signaling diversity and enabling the evolution of cell differentiation and multicellularity. Finally, several recent small molecules of interest as potential drugs have been shown to act by blocking protein-protein interactions based on intrinsic disorder of one of the partners. Study of these examples has led to a new approach for drug discovery, and bioinformatics analysis of the human proteome suggests that various disease-associated proteins are very rich in such disorder-based drug discovery targets.

Analysis of free modeling predictions by RBO aleph in CASP11.

PubMed

Mabrouk, Mahmoud; Werner, Tim; Schneider, Michael; Putz, Ines; Brock, Oliver

2016-09-01

The CASP experiment is a biannual benchmark for assessing protein structure prediction methods. In CASP11, RBO Aleph ranked as one of the top-performing automated servers in the free modeling category. This category consists of targets for which structural templates are not easily retrievable. We analyze the performance of RBO Aleph and show that its success in CASP was a result of its ab initio structure prediction protocol. A detailed analysis of this protocol demonstrates that two components unique to our method greatly contributed to prediction quality: residue-residue contact prediction by EPC-map and contact-guided conformational space search by model-based search (MBS). Interestingly, our analysis also points to a possible fundamental problem in evaluating the performance of protein structure prediction methods: Improvements in components of the method do not necessarily lead to improvements of the entire method. This points to the fact that these components interact in ways that are poorly understood. This problem, if indeed true, represents a significant obstacle to community-wide progress. Proteins 2016; 84(Suppl 1):87-104. © 2015 Wiley Periodicals, Inc. © 2015 Wiley Periodicals, Inc.

Parallel protein secondary structure prediction based on neural networks.

PubMed

Zhong, Wei; Altun, Gulsah; Tian, Xinmin; Harrison, Robert; Tai, Phang C; Pan, Yi

2004-01-01

Protein secondary structure prediction has a fundamental influence on today's bioinformatics research. In this work, binary and tertiary classifiers of protein secondary structure prediction are implemented on Denoeux belief neural network (DBNN) architecture. Hydrophobicity matrix, orthogonal matrix, BLOSUM62 and PSSM (position specific scoring matrix) are experimented separately as the encoding schemes for DBNN. The experimental results contribute to the design of new encoding schemes. New binary classifier for Helix versus not Helix ( approximately H) for DBNN produces prediction accuracy of 87% when PSSM is used for the input profile. The performance of DBNN binary classifier is comparable to other best prediction methods. The good test results for binary classifiers open a new approach for protein structure prediction with neural networks. Due to the time consuming task of training the neural networks, Pthread and OpenMP are employed to parallelize DBNN in the hyperthreading enabled Intel architecture. Speedup for 16 Pthreads is 4.9 and speedup for 16 OpenMP threads is 4 in the 4 processors shared memory architecture. Both speedup performance of OpenMP and Pthread is superior to that of other research. With the new parallel training algorithm, thousands of amino acids can be processed in reasonable amount of time. Our research also shows that hyperthreading technology for Intel architecture is efficient for parallel biological algorithms.

Mining the protein data bank with CReF to predict approximate 3-D structures of polypeptides.

PubMed

Dorn, Márcio; de Souza, Osmar Norberto

2010-01-01

n this paper we describe CReF, a Central Residue Fragment-based method to predict approximate 3-D structures of polypeptides by mining the Protein Data Bank (PDB). The approximate predicted structures are good enough to be used as starting conformations in refinement procedures employing state-of-the-art molecular mechanics methods such as molecular dynamics simulations. CReF is very fast and we illustrate its efficacy in three case studies of polypeptides whose sizes vary from 34 to 70 amino acids. As indicated by the RMSD values, our initial results show that the predicted structures adopt the expected fold, similar to the experimental ones.

Structural Analysis of PTM Hotspots (SAPH-ire)--A Quantitative Informatics Method Enabling the Discovery of Novel Regulatory Elements in Protein Families.

PubMed

Dewhurst, Henry M; Choudhury, Shilpa; Torres, Matthew P

2015-08-01

Predicting the biological function potential of post-translational modifications (PTMs) is becoming increasingly important in light of the exponential increase in available PTM data from high-throughput proteomics. We developed structural analysis of PTM hotspots (SAPH-ire)--a quantitative PTM ranking method that integrates experimental PTM observations, sequence conservation, protein structure, and interaction data to allow rank order comparisons within or between protein families. Here, we applied SAPH-ire to the study of PTMs in diverse G protein families, a conserved and ubiquitous class of proteins essential for maintenance of intracellular structure (tubulins) and signal transduction (large and small Ras-like G proteins). A total of 1728 experimentally verified PTMs from eight unique G protein families were clustered into 451 unique hotspots, 51 of which have a known and cited biological function or response. Using customized software, the hotspots were analyzed in the context of 598 unique protein structures. By comparing distributions of hotspots with known versus unknown function, we show that SAPH-ire analysis is predictive for PTM biological function. Notably, SAPH-ire revealed high-ranking hotspots for which a functional impact has not yet been determined, including phosphorylation hotspots in the N-terminal tails of G protein gamma subunits--conserved protein structures never before reported as regulators of G protein coupled receptor signaling. To validate this prediction we used the yeast model system for G protein coupled receptor signaling, revealing that gamma subunit-N-terminal tail phosphorylation is activated in response to G protein coupled receptor stimulation and regulates protein stability in vivo. These results demonstrate the utility of integrating protein structural and sequence features into PTM prioritization schemes that can improve the analysis and functional power of modification-specific proteomics data. © 2015 by The American Society for Biochemistry and Molecular Biology, Inc.

Mutations that Cause Human Disease: A Computational/Experimental Approach

DOE Office of Scientific and Technical Information (OSTI.GOV)

Beernink, P; Barsky, D; Pesavento, B

International genome sequencing projects have produced billions of nucleotides (letters) of DNA sequence data, including the complete genome sequences of 74 organisms. These genome sequences have created many new scientific opportunities, including the ability to identify sequence variations among individuals within a species. These genetic differences, which are known as single nucleotide polymorphisms (SNPs), are particularly important in understanding the genetic basis for disease susceptibility. Since the report of the complete human genome sequence, over two million human SNPs have been identified, including a large-scale comparison of an entire chromosome from twenty individuals. Of the protein coding SNPs (cSNPs), approximatelymore » half leads to a single amino acid change in the encoded protein (non-synonymous coding SNPs). Most of these changes are functionally silent, while the remainder negatively impact the protein and sometimes cause human disease. To date, over 550 SNPs have been found to cause single locus (monogenic) diseases and many others have been associated with polygenic diseases. SNPs have been linked to specific human diseases, including late-onset Parkinson disease, autism, rheumatoid arthritis and cancer. The ability to predict accurately the effects of these SNPs on protein function would represent a major advance toward understanding these diseases. To date several attempts have been made toward predicting the effects of such mutations. The most successful of these is a computational approach called ''Sorting Intolerant From Tolerant'' (SIFT). This method uses sequence conservation among many similar proteins to predict which residues in a protein are functionally important. However, this method suffers from several limitations. First, a query sequence must have a sufficient number of relatives to infer sequence conservation. Second, this method does not make use of or provide any information on protein structure, which can be used to understand how an amino acid change affects the protein. The experimental methods that provide the most detailed structural information on proteins are X-ray crystallography and NMR spectroscopy. However, these methods are labor intensive and currently cannot be carried out on a genomic scale. Nonetheless, Structural Genomics projects are being pursued by more than a dozen groups and consortia worldwide and as a result the number of experimentally determined structures is rising exponentially. Based on the expectation that protein structures will continue to be determined at an ever-increasing rate, reliable structure prediction schemes will become increasingly valuable, leading to information on protein function and disease for many different proteins. Given known genetic variability and experimentally determined protein structures, can we accurately predict the effects of single amino acid substitutions? An objective assessment of this question would involve comparing predicted and experimentally determined structures, which thus far has not been rigorously performed. The completed research leveraged existing expertise at LLNL in computational and structural biology, as well as significant computing resources, to address this question.« less

Resource for structure related information on transmembrane proteins

NASA Astrophysics Data System (ADS)

Tusnády, Gábor E.; Simon, István

Transmembrane proteins are involved in a wide variety of vital biological processes including transport of water-soluble molecules, flow of information and energy production. Despite significant efforts to determine the structures of these proteins, only a few thousand solved structures are known so far. Here, we review the various resources for structure-related information on these types of proteins ranging from the 3D structure to the topology and from the up-to-date databases to the various Internet sites and servers dealing with structure prediction and structure analysis. Abbreviations: 3D, three dimensional; PDB, Protein Data Bank; TMP, transmembrane protein.

Structure Prediction and Analysis of DNA Transposon and LINE Retrotransposon Proteins*

PubMed Central

Abrusán, György; Zhang, Yang; Szilágyi, András

2013-01-01

Despite the considerable amount of research on transposable elements, no large-scale structural analyses of the TE proteome have been performed so far. We predicted the structures of hundreds of proteins from a representative set of DNA and LINE transposable elements and used the obtained structural data to provide the first general structural characterization of TE proteins and to estimate the frequency of TE domestication and horizontal transfer events. We show that 1) ORF1 and Gag proteins of retrotransposons contain high amounts of structural disorder; thus, despite their very low conservation, the presence of disordered regions and probably their chaperone function is conserved. 2) The distribution of SCOP classes in DNA transposons and LINEs indicates that the proteins of DNA transposons are more ancient, containing folds that already existed when the first cellular organisms appeared. 3) DNA transposon proteins have lower contact order than randomly selected reference proteins, indicating rapid folding, most likely to avoid protein aggregation. 4) Structure-based searches for TE homologs indicate that the overall frequency of TE domestication events is low, whereas we found a relatively high number of cases where horizontal transfer, frequently involving parasites, is the most likely explanation for the observed homology. PMID:23530042

3D Protein structure prediction with genetic tabu search algorithm

PubMed Central

2010-01-01

Background Protein structure prediction (PSP) has important applications in different fields, such as drug design, disease prediction, and so on. In protein structure prediction, there are two important issues. The first one is the design of the structure model and the second one is the design of the optimization technology. Because of the complexity of the realistic protein structure, the structure model adopted in this paper is a simplified model, which is called off-lattice AB model. After the structure model is assumed, optimization technology is needed for searching the best conformation of a protein sequence based on the assumed structure model. However, PSP is an NP-hard problem even if the simplest model is assumed. Thus, many algorithms have been developed to solve the global optimization problem. In this paper, a hybrid algorithm, which combines genetic algorithm (GA) and tabu search (TS) algorithm, is developed to complete this task. Results In order to develop an efficient optimization algorithm, several improved strategies are developed for the proposed genetic tabu search algorithm. The combined use of these strategies can improve the efficiency of the algorithm. In these strategies, tabu search introduced into the crossover and mutation operators can improve the local search capability, the adoption of variable population size strategy can maintain the diversity of the population, and the ranking selection strategy can improve the possibility of an individual with low energy value entering into next generation. Experiments are performed with Fibonacci sequences and real protein sequences. Experimental results show that the lowest energy obtained by the proposed GATS algorithm is lower than that obtained by previous methods. Conclusions The hybrid algorithm has the advantages from both genetic algorithm and tabu search algorithm. It makes use of the advantage of multiple search points in genetic algorithm, and can overcome poor hill-climbing capability in the conventional genetic algorithm by using the flexible memory functions of TS. Compared with some previous algorithms, GATS algorithm has better performance in global optimization and can predict 3D protein structure more effectively. PMID:20522256

Optimization of rotamers prior to template minimization improves stability predictions made by computational protein design.

PubMed

Davey, James A; Chica, Roberto A

2015-04-01

Computational protein design (CPD) predictions are highly dependent on the structure of the input template used. However, it is unclear how small differences in template geometry translate to large differences in stability prediction accuracy. Herein, we explored how structural changes to the input template affect the outcome of stability predictions by CPD. To do this, we prepared alternate templates by Rotamer Optimization followed by energy Minimization (ROM) and used them to recapitulate the stability of 84 protein G domain β1 mutant sequences. In the ROM process, side-chain rotamers for wild-type (WT) or mutant sequences are optimized on crystal or nuclear magnetic resonance (NMR) structures prior to template minimization, resulting in alternate structures termed ROM templates. We show that use of ROM templates prepared from sequences known to be stable results predominantly in improved prediction accuracy compared to using the minimized crystal or NMR structures. Conversely, ROM templates prepared from sequences that are less stable than the WT reduce prediction accuracy by increasing the number of false positives. These observed changes in prediction outcomes are attributed to differences in side-chain contacts made by rotamers in ROM templates. Finally, we show that ROM templates prepared from sequences that are unfolded or that adopt a nonnative fold result in the selective enrichment of sequences that are also unfolded or that adopt a nonnative fold, respectively. Our results demonstrate the existence of a rotamer bias caused by the input template that can be harnessed to skew predictions toward sequences displaying desired characteristics. © 2014 The Protein Society.

Lessons from making the Structural Classification of Proteins (SCOP) and their implications for protein structure modelling.

PubMed

Andreeva, Antonina

2016-06-15

The Structural Classification of Proteins (SCOP) database has facilitated the development of many tools and algorithms and it has been successfully used in protein structure prediction and large-scale genome annotations. During the development of SCOP, numerous exceptions were found to topological rules, along with complex evolutionary scenarios and peculiarities in proteins including the ability to fold into alternative structures. This article reviews cases of structural variations observed for individual proteins and among groups of homologues, knowledge of which is essential for protein structure modelling. © 2016 The Author(s). published by Portland Press Limited on behalf of the Biochemical Society.

Ab Initio Protein Structure Assembly Using Continuous Structure Fragments and Optimized Knowledge-based Force Field

PubMed Central

Xu, Dong; Zhang, Yang

2012-01-01

Ab initio protein folding is one of the major unsolved problems in computational biology due to the difficulties in force field design and conformational search. We developed a novel program, QUARK, for template-free protein structure prediction. Query sequences are first broken into fragments of 1–20 residues where multiple fragment structures are retrieved at each position from unrelated experimental structures. Full-length structure models are then assembled from fragments using replica-exchange Monte Carlo simulations, which are guided by a composite knowledge-based force field. A number of novel energy terms and Monte Carlo movements are introduced and the particular contributions to enhancing the efficiency of both force field and search engine are analyzed in detail. QUARK prediction procedure is depicted and tested on the structure modeling of 145 non-homologous proteins. Although no global templates are used and all fragments from experimental structures with template modeling score (TM-score) >0.5 are excluded, QUARK can successfully construct 3D models of correct folds in 1/3 cases of short proteins up to 100 residues. In the ninth community-wide Critical Assessment of protein Structure Prediction (CASP9) experiment, QUARK server outperformed the second and third best servers by 18% and 47% based on the cumulative Z-score of global distance test-total (GDT-TS) scores in the free modeling (FM) category. Although ab initio protein folding remains a significant challenge, these data demonstrate new progress towards the solution of the most important problem in the field. PMID:22411565

Assessing the Chemical Accuracy of Protein Structures via Peptide Acidity

PubMed Central

Anderson, Janet S.; Hernández, Griselda; LeMaster, David M.

2012-01-01

Although the protein native state is a Boltzmann conformational ensemble, practical applications often require a representative model from the most populated region of that distribution. The acidity of the backbone amides, as reflected in hydrogen exchange rates, is exquisitely sensitive to the surrounding charge and dielectric volume distribution. For each of four proteins, three independently determined X-ray structures of differing crystallographic resolution were used to predict exchange for the static solvent-exposed amide hydrogens. The average correlation coefficients range from 0.74 for ubiquitin to 0.93 for Pyrococcus furiosus rubredoxin, reflecting the larger range of experimental exchange rates exhibited by the latter protein. The exchange prediction errors modestly correlate with the crystallographic resolution. MODELLER 9v6-derived homology models at ~60% sequence identity (36% identity for chymotrypsin inhibitor CI2) yielded correlation coefficients that are ~0.1 smaller than for the cognate X-ray structures. The most recently deposited NOE-based ubiquitin structure and the original NMR structure of CI2 fail to provide statistically significant predictions of hydrogen exchange. However, the more recent RECOORD refinement study of CI2 yielded predictions comparable to the X-ray and homology model-based analyses. PMID:23182463

Screening and structure-based modeling of T-cell epitopes of Nipah virus proteome: an immunoinformatic approach for designing peptide-based vaccine.

PubMed

Kamthania, Mohit; Sharma, D K

2015-12-01

Identification of Nipah virus (NiV) T-cell-specific antigen is urgently needed for appropriate diagnostic and vaccination. In the present study, prediction and modeling of T-cell epitopes of Nipah virus antigenic proteins nucleocapsid, phosphoprotein, matrix, fusion, glycoprotein, L protein, W protein, V protein and C protein followed by the binding simulation studies of predicted highest binding scorers with their corresponding MHC class I alleles were done. Immunoinformatic tool ProPred1 was used to predict the promiscuous MHC class I epitopes of viral antigenic proteins. The molecular modelings of the epitopes were done by PEPstr server. And alleles structure were predicted by MODELLER 9.10. Molecular dynamics (MD) simulation studies were performed through the NAMD graphical user interface embedded in visual molecular dynamics. Epitopes VPATNSPEL, NPTAVPFTL and LLFVFGPNL of Nucleocapsid, V protein and Fusion protein have considerable binding energy and score with HLA-B7, HLA-B*2705 and HLA-A2MHC class I allele, respectively. These three predicted peptides are highly potential to induce T-cell-mediated immune response and are expected to be useful in designing epitope-based vaccines against Nipah virus after further testing by wet laboratory studies.

RosettaHoles: rapid assessment of protein core packing for structure prediction, refinement, design, and validation.

PubMed

Sheffler, Will; Baker, David

2009-01-01

We present a novel method called RosettaHoles for visual and quantitative assessment of underpacking in the protein core. RosettaHoles generates a set of spherical cavity balls that fill the empty volume between atoms in the protein interior. For visualization, the cavity balls are aggregated into contiguous overlapping clusters and small cavities are discarded, leaving an uncluttered representation of the unfilled regions of space in a structure. For quantitative analysis, the cavity ball data are used to estimate the probability of observing a given cavity in a high-resolution crystal structure. RosettaHoles provides excellent discrimination between real and computationally generated structures, is predictive of incorrect regions in models, identifies problematic structures in the Protein Data Bank, and promises to be a useful validation tool for newly solved experimental structures.

RosettaHoles: Rapid assessment of protein core packing for structure prediction, refinement, design, and validation

PubMed Central

Sheffler, Will; Baker, David

2009-01-01

We present a novel method called RosettaHoles for visual and quantitative assessment of underpacking in the protein core. RosettaHoles generates a set of spherical cavity balls that fill the empty volume between atoms in the protein interior. For visualization, the cavity balls are aggregated into contiguous overlapping clusters and small cavities are discarded, leaving an uncluttered representation of the unfilled regions of space in a structure. For quantitative analysis, the cavity ball data are used to estimate the probability of observing a given cavity in a high-resolution crystal structure. RosettaHoles provides excellent discrimination between real and computationally generated structures, is predictive of incorrect regions in models, identifies problematic structures in the Protein Data Bank, and promises to be a useful validation tool for newly solved experimental structures. PMID:19177366

Homology modeling a fast tool for drug discovery: current perspectives.

PubMed

Vyas, V K; Ukawala, R D; Ghate, M; Chintha, C

2012-01-01

Major goal of structural biology involve formation of protein-ligand complexes; in which the protein molecules act energetically in the course of binding. Therefore, perceptive of protein-ligand interaction will be very important for structure based drug design. Lack of knowledge of 3D structures has hindered efforts to understand the binding specificities of ligands with protein. With increasing in modeling software and the growing number of known protein structures, homology modeling is rapidly becoming the method of choice for obtaining 3D coordinates of proteins. Homology modeling is a representation of the similarity of environmental residues at topologically corresponding positions in the reference proteins. In the absence of experimental data, model building on the basis of a known 3D structure of a homologous protein is at present the only reliable method to obtain the structural information. Knowledge of the 3D structures of proteins provides invaluable insights into the molecular basis of their functions. The recent advances in homology modeling, particularly in detecting and aligning sequences with template structures, distant homologues, modeling of loops and side chains as well as detecting errors in a model contributed to consistent prediction of protein structure, which was not possible even several years ago. This review focused on the features and a role of homology modeling in predicting protein structure and described current developments in this field with victorious applications at the different stages of the drug design and discovery.

Homology Modeling a Fast Tool for Drug Discovery: Current Perspectives

PubMed Central

Vyas, V. K.; Ukawala, R. D.; Ghate, M.; Chintha, C.

2012-01-01

Major goal of structural biology involve formation of protein-ligand complexes; in which the protein molecules act energetically in the course of binding. Therefore, perceptive of protein-ligand interaction will be very important for structure based drug design. Lack of knowledge of 3D structures has hindered efforts to understand the binding specificities of ligands with protein. With increasing in modeling software and the growing number of known protein structures, homology modeling is rapidly becoming the method of choice for obtaining 3D coordinates of proteins. Homology modeling is a representation of the similarity of environmental residues at topologically corresponding positions in the reference proteins. In the absence of experimental data, model building on the basis of a known 3D structure of a homologous protein is at present the only reliable method to obtain the structural information. Knowledge of the 3D structures of proteins provides invaluable insights into the molecular basis of their functions. The recent advances in homology modeling, particularly in detecting and aligning sequences with template structures, distant homologues, modeling of loops and side chains as well as detecting errors in a model contributed to consistent prediction of protein structure, which was not possible even several years ago. This review focused on the features and a role of homology modeling in predicting protein structure and described current developments in this field with victorious applications at the different stages of the drug design and discovery. PMID:23204616

Protein modeling and molecular dynamics simulation of SlWRKY4 protein cloned from drought tolerant tomato (Solanum habrochaites) line EC520061.

PubMed

Karkute, Suhas G; Easwaran, Murugesh; Gujjar, Ranjit Singh; Piramanayagam, Shanmughavel; Singh, Major

2015-10-01

WRKY genes are members of one of the largest families of plant transcription factors and play an important role in response to biotic and abiotic stresses, and overall growth and development. Understanding the interaction of WRKY proteins with other proteins/ligands in plant cells is of utmost importance to develop plants having tolerance to biotic and abiotic stresses. The SlWRKY4 gene was cloned from a drought tolerant wild species of tomato (Solanum habrochaites) and the secondary structure and 3D modeling of this protein were predicted using Schrödinger Suite-Prime. Predicted structures were also subjected to plot against Ramachandran's conformation, and the modeled structure was minimized using Macromodel. Finally, the minimized structure was simulated in the water environment to check the protein stability. The behavior of the modeled structure was well-simulated and analyzed through RMSD and RMSF of the protein. The present work provides the modeled 3D structure of SlWRKY4 that will help in understanding the mechanism of gene regulation by further in silico interaction studies.

Improved cryoEM-Guided Iterative Molecular Dynamics–Rosetta Protein Structure Refinement Protocol for High Precision Protein Structure Prediction

PubMed Central

2016-01-01

Many excellent methods exist that incorporate cryo-electron microscopy (cryoEM) data to constrain computational protein structure prediction and refinement. Previously, it was shown that iteration of two such orthogonal sampling and scoring methods – Rosetta and molecular dynamics (MD) simulations – facilitated exploration of conformational space in principle. Here, we go beyond a proof-of-concept study and address significant remaining limitations of the iterative MD–Rosetta protein structure refinement protocol. Specifically, all parts of the iterative refinement protocol are now guided by medium-resolution cryoEM density maps, and previous knowledge about the native structure of the protein is no longer necessary. Models are identified solely based on score or simulation time. All four benchmark proteins showed substantial improvement through three rounds of the iterative refinement protocol. The best-scoring final models of two proteins had sub-Ångstrom RMSD to the native structure over residues in secondary structure elements. Molecular dynamics was most efficient in refining secondary structure elements and was thus highly complementary to the Rosetta refinement which is most powerful in refining side chains and loop regions. PMID:25883538

A Deep Learning Network Approach to ab initio Protein Secondary Structure Prediction

PubMed Central

Spencer, Matt; Eickholt, Jesse; Cheng, Jianlin

2014-01-01

Ab initio protein secondary structure (SS) predictions are utilized to generate tertiary structure predictions, which are increasingly demanded due to the rapid discovery of proteins. Although recent developments have slightly exceeded previous methods of SS prediction, accuracy has stagnated around 80% and many wonder if prediction cannot be advanced beyond this ceiling. Disciplines that have traditionally employed neural networks are experimenting with novel deep learning techniques in attempts to stimulate progress. Since neural networks have historically played an important role in SS prediction, we wanted to determine whether deep learning could contribute to the advancement of this field as well. We developed an SS predictor that makes use of the position-specific scoring matrix generated by PSI-BLAST and deep learning network architectures, which we call DNSS. Graphical processing units and CUDA software optimize the deep network architecture and efficiently train the deep networks. Optimal parameters for the training process were determined, and a workflow comprising three separately trained deep networks was constructed in order to make refined predictions. This deep learning network approach was used to predict SS for a fully independent test data set of 198 proteins, achieving a Q3 accuracy of 80.7% and a Sov accuracy of 74.2%. PMID:25750595

A Deep Learning Network Approach to ab initio Protein Secondary Structure Prediction.

PubMed

Spencer, Matt; Eickholt, Jesse; Jianlin Cheng

2015-01-01

Ab initio protein secondary structure (SS) predictions are utilized to generate tertiary structure predictions, which are increasingly demanded due to the rapid discovery of proteins. Although recent developments have slightly exceeded previous methods of SS prediction, accuracy has stagnated around 80 percent and many wonder if prediction cannot be advanced beyond this ceiling. Disciplines that have traditionally employed neural networks are experimenting with novel deep learning techniques in attempts to stimulate progress. Since neural networks have historically played an important role in SS prediction, we wanted to determine whether deep learning could contribute to the advancement of this field as well. We developed an SS predictor that makes use of the position-specific scoring matrix generated by PSI-BLAST and deep learning network architectures, which we call DNSS. Graphical processing units and CUDA software optimize the deep network architecture and efficiently train the deep networks. Optimal parameters for the training process were determined, and a workflow comprising three separately trained deep networks was constructed in order to make refined predictions. This deep learning network approach was used to predict SS for a fully independent test dataset of 198 proteins, achieving a Q3 accuracy of 80.7 percent and a Sov accuracy of 74.2 percent.

Assessment of the utility of contact-based restraints in accelerating the prediction of protein structure using molecular dynamics simulations.

PubMed

Raval, Alpan; Piana, Stefano; Eastwood, Michael P; Shaw, David E

2016-01-01

Molecular dynamics (MD) simulation is a well-established tool for the computational study of protein structure and dynamics, but its application to the important problem of protein structure prediction remains challenging, in part because extremely long timescales can be required to reach the native structure. Here, we examine the extent to which the use of low-resolution information in the form of residue-residue contacts, which can often be inferred from bioinformatics or experimental studies, can accelerate the determination of protein structure in simulation. We incorporated sets of 62, 31, or 15 contact-based restraints in MD simulations of ubiquitin, a benchmark system known to fold to the native state on the millisecond timescale in unrestrained simulations. One-third of the restrained simulations folded to the native state within a few tens of microseconds-a speedup of over an order of magnitude compared with unrestrained simulations and a demonstration of the potential for limited amounts of structural information to accelerate structure determination. Almost all of the remaining ubiquitin simulations reached near-native conformations within a few tens of microseconds, but remained trapped there, apparently due to the restraints. We discuss potential methodological improvements that would facilitate escape from these near-native traps and allow more simulations to quickly reach the native state. Finally, using a target from the Critical Assessment of protein Structure Prediction (CASP) experiment, we show that distance restraints can improve simulation accuracy: In our simulations, restraints stabilized the native state of the protein, enabling a reasonable structural model to be inferred. © 2015 The Authors Protein Science published by Wiley Periodicals, Inc. on behalf of The Protein Society.

Predicting β-turns and their types using predicted backbone dihedral angles and secondary structures

PubMed Central

2010-01-01

Background β-turns are secondary structure elements usually classified as coil. Their prediction is important, because of their role in protein folding and their frequent occurrence in protein chains. Results We have developed a novel method that predicts β-turns and their types using information from multiple sequence alignments, predicted secondary structures and, for the first time, predicted dihedral angles. Our method uses support vector machines, a supervised classification technique, and is trained and tested on three established datasets of 426, 547 and 823 protein chains. We achieve a Matthews correlation coefficient of up to 0.49, when predicting the location of β-turns, the highest reported value to date. Moreover, the additional dihedral information improves the prediction of β-turn types I, II, IV, VIII and "non-specific", achieving correlation coefficients up to 0.39, 0.33, 0.27, 0.14 and 0.38, respectively. Our results are more accurate than other methods. Conclusions We have created an accurate predictor of β-turns and their types. Our method, called DEBT, is available online at http://comp.chem.nottingham.ac.uk/debt/. PMID:20673368

Predicting beta-turns and their types using predicted backbone dihedral angles and secondary structures.

PubMed

Kountouris, Petros; Hirst, Jonathan D

2010-07-31

Beta-turns are secondary structure elements usually classified as coil. Their prediction is important, because of their role in protein folding and their frequent occurrence in protein chains. We have developed a novel method that predicts beta-turns and their types using information from multiple sequence alignments, predicted secondary structures and, for the first time, predicted dihedral angles. Our method uses support vector machines, a supervised classification technique, and is trained and tested on three established datasets of 426, 547 and 823 protein chains. We achieve a Matthews correlation coefficient of up to 0.49, when predicting the location of beta-turns, the highest reported value to date. Moreover, the additional dihedral information improves the prediction of beta-turn types I, II, IV, VIII and "non-specific", achieving correlation coefficients up to 0.39, 0.33, 0.27, 0.14 and 0.38, respectively. Our results are more accurate than other methods. We have created an accurate predictor of beta-turns and their types. Our method, called DEBT, is available online at http://comp.chem.nottingham.ac.uk/debt/.

Accurate high-throughput structure mapping and prediction with transition metal ion FRET

PubMed Central

Yu, Xiaozhen; Wu, Xiongwu; Bermejo, Guillermo A.; Brooks, Bernard R.; Taraska, Justin W.

2013-01-01

Mapping the landscape of a protein’s conformational space is essential to understanding its functions and regulation. The limitations of many structural methods have made this process challenging for most proteins. Here, we report that transition metal ion FRET (tmFRET) can be used in a rapid, highly parallel screen, to determine distances from multiple locations within a protein at extremely low concentrations. The distances generated through this screen for the protein Maltose Binding Protein (MBP) match distances from the crystal structure to within a few angstroms. Furthermore, energy transfer accurately detects structural changes during ligand binding. Finally, fluorescence-derived distances can be used to guide molecular simulations to find low energy states. Our results open the door to rapid, accurate mapping and prediction of protein structures at low concentrations, in large complex systems, and in living cells. PMID:23273426

Pushing the size limit of de novo structure ensemble prediction guided by sparse SDSL-EPR restraints to 200 residues: The monomeric and homodimeric forms of BAX

PubMed Central

Fischer, Axel W.; Bordignon, Enrica; Bleicken, Stephanie; García-Sáez, Ana J.; Jeschke, Gunnar; Meiler, Jens

2016-01-01

Structure determination remains a challenge for many biologically important proteins. In particular, proteins that adopt multiple conformations often evade crystallization in all biologically relevant states. Although computational de novo protein folding approaches often sample biologically relevant conformations, the selection of the most accurate model for different functional states remains a formidable challenge, in particular, for proteins with more than about 150 residues. Electron paramagnetic resonance (EPR) spectroscopy can obtain limited structural information for proteins in well-defined biological states and thereby assist in selecting biologically relevant conformations. The present study demonstrates that de novo folding methods are able to accurately sample the folds of 192-residue long soluble monomeric Bcl-2-associated X protein (BAX). The tertiary structures of the monomeric and homodimeric forms of BAX were predicted using the primary structure as well as 25 and 11 EPR distance restraints, respectively. The predicted models were subsequently compared to respective NMR/X-ray structures of BAX. EPR restraints improve the protein-size normalized root-mean-square-deviation (RMSD100) of the most accurate models with respect to the NMR/crystal structure from 5.9 Å to 3.9 Å and from 5.7 Å to 3.3 Å, respectively. Additionally, the model discrimination is improved, which is demonstrated by an improvement of the enrichment from 5% to 15% and from 13% to 21%, respectively. PMID:27129417

United3D: a protein model quality assessment program that uses two consensus based methods.

PubMed

Terashi, Genki; Oosawa, Makoto; Nakamura, Yuuki; Kanou, Kazuhiko; Takeda-Shitaka, Mayuko

2012-01-01

In protein structure prediction, such as template-based modeling and free modeling (ab initio modeling), the step that assesses the quality of protein models is very important. We have developed a model quality assessment (QA) program United3D that uses an optimized clustering method and a simple Cα atom contact-based potential. United3D automatically estimates the quality scores (Qscore) of predicted protein models that are highly correlated with the actual quality (GDT_TS). The performance of United3D was tested in the ninth Critical Assessment of protein Structure Prediction (CASP9) experiment. In CASP9, United3D showed the lowest average loss of GDT_TS (5.3) among the QA methods participated in CASP9. This result indicates that the performance of United3D to identify the high quality models from the models predicted by CASP9 servers on 116 targets was best among the QA methods that were tested in CASP9. United3D also produced high average Pearson correlation coefficients (0.93) and acceptable Kendall rank correlation coefficients (0.68) between the Qscore and GDT_TS. This performance was competitive with the other top ranked QA methods that were tested in CASP9. These results indicate that United3D is a useful tool for selecting high quality models from many candidate model structures provided by various modeling methods. United3D will improve the accuracy of protein structure prediction.

NegGOA: negative GO annotations selection using ontology structure.

PubMed

Fu, Guangyuan; Wang, Jun; Yang, Bo; Yu, Guoxian

2016-10-01

Predicting the biological functions of proteins is one of the key challenges in the post-genomic era. Computational models have demonstrated the utility of applying machine learning methods to predict protein function. Most prediction methods explicitly require a set of negative examples-proteins that are known not carrying out a particular function. However, Gene Ontology (GO) almost always only provides the knowledge that proteins carry out a particular function, and functional annotations of proteins are incomplete. GO structurally organizes more than tens of thousands GO terms and a protein is annotated with several (or dozens) of these terms. For these reasons, the negative examples of a protein can greatly help distinguishing true positive examples of the protein from such a large candidate GO space. In this paper, we present a novel approach (called NegGOA) to select negative examples. Specifically, NegGOA takes advantage of the ontology structure, available annotations and potentiality of additional annotations of a protein to choose negative examples of the protein. We compare NegGOA with other negative examples selection algorithms and find that NegGOA produces much fewer false negatives than them. We incorporate the selected negative examples into an efficient function prediction model to predict the functions of proteins in Yeast, Human, Mouse and Fly. NegGOA also demonstrates improved accuracy than these comparing algorithms across various evaluation metrics. In addition, NegGOA is less suffered from incomplete annotations of proteins than these comparing methods. The Matlab and R codes are available at https://sites.google.com/site/guoxian85/neggoa gxyu@swu.edu.cn Supplementary data are available at Bioinformatics online. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Bhageerath-H: A homology/ab initio hybrid server for predicting tertiary structures of monomeric soluble proteins

PubMed Central

2014-01-01

Background The advent of human genome sequencing project has led to a spurt in the number of protein sequences in the databanks. Success of structure based drug discovery severely hinges on the availability of structures. Despite significant progresses in the area of experimental protein structure determination, the sequence-structure gap is continually widening. Data driven homology based computational methods have proved successful in predicting tertiary structures for sequences sharing medium to high sequence similarities. With dwindling similarities of query sequences, advanced homology/ ab initio hybrid approaches are being explored to solve structure prediction problem. Here we describe Bhageerath-H, a homology/ ab initio hybrid software/server for predicting protein tertiary structures with advancing drug design attempts as one of the goals. Results Bhageerath-H web-server was validated on 75 CASP10 targets which showed TM-scores ≥0.5 in 91% of the cases and Cα RMSDs ≤5Å from the native in 58% of the targets, which is well above the CASP10 water mark. Comparison with some leading servers demonstrated the uniqueness of the hybrid methodology in effectively sampling conformational space, scoring best decoys and refining low resolution models to high and medium resolution. Conclusion Bhageerath-H methodology is web enabled for the scientific community as a freely accessible web server. The methodology is fielded in the on-going CASP11 experiment. PMID:25521245

A Web-Accessible Protein Structure Prediction Pipeline

DTIC Science & Technology

2009-06-01

Abstract Proteins are the molecular basis of nearly all structural, catalytic, sensory, and regulatory functions in living organisms. The biological...sensory, and regulatory functions in living organisms. The structure of a protein is essential in understanding its function at the molecular level...Characterizing sequence-structure and structure-function relationships have been the goals of molecular biology for more than three decades

AGGRESCAN3D (A3D): server for prediction of aggregation properties of protein structures

PubMed Central

Zambrano, Rafael; Jamroz, Michal; Szczasiuk, Agata; Pujols, Jordi; Kmiecik, Sebastian; Ventura, Salvador

2015-01-01

Protein aggregation underlies an increasing number of disorders and constitutes a major bottleneck in the development of therapeutic proteins. Our present understanding on the molecular determinants of protein aggregation has crystalized in a series of predictive algorithms to identify aggregation-prone sites. A majority of these methods rely only on sequence. Therefore, they find difficulties to predict the aggregation properties of folded globular proteins, where aggregation-prone sites are often not contiguous in sequence or buried inside the native structure. The AGGRESCAN3D (A3D) server overcomes these limitations by taking into account the protein structure and the experimental aggregation propensity scale from the well-established AGGRESCAN method. Using the A3D server, the identified aggregation-prone residues can be virtually mutated to design variants with increased solubility, or to test the impact of pathogenic mutations. Additionally, A3D server enables to take into account the dynamic fluctuations of protein structure in solution, which may influence aggregation propensity. This is possible in A3D Dynamic Mode that exploits the CABS-flex approach for the fast simulations of flexibility of globular proteins. The A3D server can be accessed at http://biocomp.chem.uw.edu.pl/A3D/. PMID:25883144

Protein Structure Prediction Using Gas Phase Molecular Dynamics Simulation: EOTAXIN-3 Cytokine as a Case Study

NASA Astrophysics Data System (ADS)

Khairudin, Nurul Bahiyah Ahmad; Wahab, Habibah A.

In the current work, the structure of the enzyme CC chemokine eotaxin-3 (1G2S) was chosen as a case study to investigate the effects of gas phase on the predicted protein conformation using molecular dynamics simulation. Generally, simulating proteins in the gas phase tend to suffer from various drawbacks, among which excessive numbers of protein-protein hydrogen bonds. However, current results showed that the effects of gas phase simulation on 1G2S did not amplify the protein-protein hydrogen bonds. It was also found that some of the hydrogen bonds which were crucial in maintaining the secondary structural elements were disrupted. The predicted models showed high values of RMSD, 11.5 Å and 13.5 Å for both vacuum and explicit solvent simulations, respectively, indicating that the conformers were very much different from the native conformation. Even though the RMSD value for the in vacuo model was slightly lower, it somehow suffered from lower fraction of native contacts, poor hydrogen bonding networks and fewer occurrences of secondary structural elements compared to the solvated model. This finding supports the notion that water plays a dominant role in guiding the protein to fold along the correct path.

Purely Structural Protein Scoring Functions Using Support Vector Machine and Ensemble Learning.

PubMed

Mirzaei, Shokoufeh; Sidi, Tomer; Keasar, Chen; Crivelli, Silvia

2016-08-24

The function of a protein is determined by its structure, which creates a need for efficient methods of protein structure determination to advance scientific and medical research. Because current experimental structure determination methods carry a high price tag, computational predictions are highly desirable. Given a protein sequence, computational methods produce numerous 3D structures known as decoys. However, selection of the best quality decoys is challenging as the end users can handle only a few ones. Therefore, scoring functions are central to decoy selection. They combine measurable features into a single number indicator of decoy quality. Unfortunately, current scoring functions do not consistently select the best decoys. Machine learning techniques offer great potential to improve decoy scoring. This paper presents two machine-learning based scoring functions to predict the quality of proteins structures, i.e., the similarity between the predicted structure and the experimental one without knowing the latter. We use different metrics to compare these scoring functions against three state-of-the-art scores. This is a first attempt at comparing different scoring functions using the same non-redundant dataset for training and testing and the same features. The results show that adding informative features may be more significant than the method used.

A Method for Predicting Protein Complexes from Dynamic Weighted Protein-Protein Interaction Networks.

PubMed

Liu, Lizhen; Sun, Xiaowu; Song, Wei; Du, Chao

2018-06-01

Predicting protein complexes from protein-protein interaction (PPI) network is of great significance to recognize the structure and function of cells. A protein may interact with different proteins under different time or conditions. Existing approaches only utilize static PPI network data that may lose much temporal biological information. First, this article proposed a novel method that combines gene expression data at different time points with traditional static PPI network to construct different dynamic subnetworks. Second, to further filter out the data noise, the semantic similarity based on gene ontology is regarded as the network weight together with the principal component analysis, which is introduced to deal with the weight computing by three traditional methods. Third, after building a dynamic PPI network, a predicting protein complexes algorithm based on "core-attachment" structural feature is applied to detect complexes from each dynamic subnetworks. Finally, it is revealed from the experimental results that our method proposed in this article performs well on detecting protein complexes from dynamic weighted PPI networks.

De Novo Protein Structure Prediction

NASA Astrophysics Data System (ADS)

Hung, Ling-Hong; Ngan, Shing-Chung; Samudrala, Ram

An unparalleled amount of sequence data is being made available from large-scale genome sequencing efforts. The data provide a shortcut to the determination of the function of a gene of interest, as long as there is an existing sequenced gene with similar sequence and of known function. This has spurred structural genomic initiatives with the goal of determining as many protein folds as possible (Brenner and Levitt, 2000; Burley, 2000; Brenner, 2001; Heinemann et al., 2001). The purpose of this is twofold: First, the structure of a gene product can often lead to direct inference of its function. Second, since the function of a protein is dependent on its structure, direct comparison of the structures of gene products can be more sensitive than the comparison of sequences of genes for detecting homology. Presently, structural determination by crystallography and NMR techniques is still slow and expensive in terms of manpower and resources, despite attempts to automate the processes. Computer structure prediction algorithms, while not providing the accuracy of the traditional techniques, are extremely quick and inexpensive and can provide useful low-resolution data for structure comparisons (Bonneau and Baker, 2001). Given the immense number of structures which the structural genomic projects are attempting to solve, there would be a considerable gain even if the computer structure prediction approach were applicable to a subset of proteins.

Predicting β-Turns in Protein Using Kernel Logistic Regression

PubMed Central

Elbashir, Murtada Khalafallah; Sheng, Yu; Wang, Jianxin; Wu, FangXiang; Li, Min

2013-01-01

A β-turn is a secondary protein structure type that plays a significant role in protein configuration and function. On average 25% of amino acids in protein structures are located in β-turns. It is very important to develope an accurate and efficient method for β-turns prediction. Most of the current successful β-turns prediction methods use support vector machines (SVMs) or neural networks (NNs). The kernel logistic regression (KLR) is a powerful classification technique that has been applied successfully in many classification problems. However, it is often not found in β-turns classification, mainly because it is computationally expensive. In this paper, we used KLR to obtain sparse β-turns prediction in short evolution time. Secondary structure information and position-specific scoring matrices (PSSMs) are utilized as input features. We achieved Q total of 80.7% and MCC of 50% on BT426 dataset. These results show that KLR method with the right algorithm can yield performance equivalent to or even better than NNs and SVMs in β-turns prediction. In addition, KLR yields probabilistic outcome and has a well-defined extension to multiclass case. PMID:23509793

Predicting β-turns in protein using kernel logistic regression.

PubMed

Elbashir, Murtada Khalafallah; Sheng, Yu; Wang, Jianxin; Wu, Fangxiang; Li, Min

2013-01-01

A β-turn is a secondary protein structure type that plays a significant role in protein configuration and function. On average 25% of amino acids in protein structures are located in β-turns. It is very important to develope an accurate and efficient method for β-turns prediction. Most of the current successful β-turns prediction methods use support vector machines (SVMs) or neural networks (NNs). The kernel logistic regression (KLR) is a powerful classification technique that has been applied successfully in many classification problems. However, it is often not found in β-turns classification, mainly because it is computationally expensive. In this paper, we used KLR to obtain sparse β-turns prediction in short evolution time. Secondary structure information and position-specific scoring matrices (PSSMs) are utilized as input features. We achieved Q total of 80.7% and MCC of 50% on BT426 dataset. These results show that KLR method with the right algorithm can yield performance equivalent to or even better than NNs and SVMs in β-turns prediction. In addition, KLR yields probabilistic outcome and has a well-defined extension to multiclass case.

Integration of QUARK and I-TASSER for Ab Initio Protein Structure Prediction in CASP11.

PubMed

Zhang, Wenxuan; Yang, Jianyi; He, Baoji; Walker, Sara Elizabeth; Zhang, Hongjiu; Govindarajoo, Brandon; Virtanen, Jouko; Xue, Zhidong; Shen, Hong-Bin; Zhang, Yang

2016-09-01

We tested two pipelines developed for template-free protein structure prediction in the CASP11 experiment. First, the QUARK pipeline constructs structure models by reassembling fragments of continuously distributed lengths excised from unrelated proteins. Five free-modeling (FM) targets have the model successfully constructed by QUARK with a TM-score above 0.4, including the first model of T0837-D1, which has a TM-score = 0.736 and RMSD = 2.9 Å to the native. Detailed analysis showed that the success is partly attributed to the high-resolution contact map prediction derived from fragment-based distance-profiles, which are mainly located between regular secondary structure elements and loops/turns and help guide the orientation of secondary structure assembly. In the Zhang-Server pipeline, weakly scoring threading templates are re-ordered by the structural similarity to the ab initio folding models, which are then reassembled by I-TASSER based structure assembly simulations; 60% more domains with length up to 204 residues, compared to the QUARK pipeline, were successfully modeled by the I-TASSER pipeline with a TM-score above 0.4. The robustness of the I-TASSER pipeline can stem from the composite fragment-assembly simulations that combine structures from both ab initio folding and threading template refinements. Despite the promising cases, challenges still exist in long-range beta-strand folding, domain parsing, and the uncertainty of secondary structure prediction; the latter of which was found to affect nearly all aspects of FM structure predictions, from fragment identification, target classification, structure assembly, to final model selection. Significant efforts are needed to solve these problems before real progress on FM could be made. Proteins 2016; 84(Suppl 1):76-86. © 2015 Wiley Periodicals, Inc. © 2015 Wiley Periodicals, Inc.

GeneBuilder: interactive in silico prediction of gene structure.

PubMed

Milanesi, L; D'Angelo, D; Rogozin, I B

1999-01-01

Prediction of gene structure in newly sequenced DNA becomes very important in large genome sequencing projects. This problem is complicated due to the exon-intron structure of eukaryotic genes and because gene expression is regulated by many different short nucleotide domains. In order to be able to analyse the full gene structure in different organisms, it is necessary to combine information about potential functional signals (promoter region, splice sites, start and stop codons, 3' untranslated region) together with the statistical properties of coding sequences (coding potential), information about homologous proteins, ESTs and repeated elements. We have developed the GeneBuilder system which is based on prediction of functional signals and coding regions by different approaches in combination with similarity searches in proteins and EST databases. The potential gene structure models are obtained by using a dynamic programming method. The program permits the use of several parameters for gene structure prediction and refinement. During gene model construction, selecting different exon homology levels with a protein sequence selected from a list of homologous proteins can improve the accuracy of the gene structure prediction. In the case of low homology, GeneBuilder is still able to predict the gene structure. The GeneBuilder system has been tested by using the standard set (Burset and Guigo, Genomics, 34, 353-367, 1996) and the performances are: 0.89 sensitivity and 0.91 specificity at the nucleotide level. The total correlation coefficient is 0.88. The GeneBuilder system is implemented as a part of the WebGene a the URL: http://www.itba.mi. cnr.it/webgene and TRADAT (TRAncription Database and Analysis Tools) launcher URL: http://www.itba.mi.cnr.it/tradat.

Residue contacts predicted by evolutionary covariance extend the application of ab initio molecular replacement to larger and more challenging protein folds.

PubMed

Simkovic, Felix; Thomas, Jens M H; Keegan, Ronan M; Winn, Martyn D; Mayans, Olga; Rigden, Daniel J

2016-07-01

For many protein families, the deluge of new sequence information together with new statistical protocols now allow the accurate prediction of contacting residues from sequence information alone. This offers the possibility of more accurate ab initio (non-homology-based) structure prediction. Such models can be used in structure solution by molecular replacement (MR) where the target fold is novel or is only distantly related to known structures. Here, AMPLE, an MR pipeline that assembles search-model ensembles from ab initio structure predictions ('decoys'), is employed to assess the value of contact-assisted ab initio models to the crystallographer. It is demonstrated that evolutionary covariance-derived residue-residue contact predictions improve the quality of ab initio models and, consequently, the success rate of MR using search models derived from them. For targets containing β-structure, decoy quality and MR performance were further improved by the use of a β-strand contact-filtering protocol. Such contact-guided decoys achieved 14 structure solutions from 21 attempted protein targets, compared with nine for simple Rosetta decoys. Previously encountered limitations were superseded in two key respects. Firstly, much larger targets of up to 221 residues in length were solved, which is far larger than the previously benchmarked threshold of 120 residues. Secondly, contact-guided decoys significantly improved success with β-sheet-rich proteins. Overall, the improved performance of contact-guided decoys suggests that MR is now applicable to a significantly wider range of protein targets than were previously tractable, and points to a direct benefit to structural biology from the recent remarkable advances in sequencing.

Residue contacts predicted by evolutionary covariance extend the application of ab initio molecular replacement to larger and more challenging protein folds

PubMed Central

Simkovic, Felix; Thomas, Jens M. H.; Keegan, Ronan M.; Winn, Martyn D.; Mayans, Olga; Rigden, Daniel J.

2016-01-01

For many protein families, the deluge of new sequence information together with new statistical protocols now allow the accurate prediction of contacting residues from sequence information alone. This offers the possibility of more accurate ab initio (non-homology-based) structure prediction. Such models can be used in structure solution by molecular replacement (MR) where the target fold is novel or is only distantly related to known structures. Here, AMPLE, an MR pipeline that assembles search-model ensembles from ab initio structure predictions (‘decoys’), is employed to assess the value of contact-assisted ab initio models to the crystallographer. It is demonstrated that evolutionary covariance-derived residue–residue contact predictions improve the quality of ab initio models and, consequently, the success rate of MR using search models derived from them. For targets containing β-structure, decoy quality and MR performance were further improved by the use of a β-strand contact-filtering protocol. Such contact-guided decoys achieved 14 structure solutions from 21 attempted protein targets, compared with nine for simple Rosetta decoys. Previously encountered limitations were superseded in two key respects. Firstly, much larger targets of up to 221 residues in length were solved, which is far larger than the previously benchmarked threshold of 120 residues. Secondly, contact-guided decoys significantly improved success with β-sheet-rich proteins. Overall, the improved performance of contact-guided decoys suggests that MR is now applicable to a significantly wider range of protein targets than were previously tractable, and points to a direct benefit to structural biology from the recent remarkable advances in sequencing. PMID:27437113

eF-seek: prediction of the functional sites of proteins by searching for similar electrostatic potential and molecular surface shape.

PubMed

Kinoshita, Kengo; Murakami, Yoichi; Nakamura, Haruki

2007-07-01

We have developed a method to predict ligand-binding sites in a new protein structure by searching for similar binding sites in the Protein Data Bank (PDB). The similarities are measured according to the shapes of the molecular surfaces and their electrostatic potentials. A new web server, eF-seek, provides an interface to our search method. It simply requires a coordinate file in the PDB format, and generates a prediction result as a virtual complex structure, with the putative ligands in a PDB format file as the output. In addition, the predicted interacting interface is displayed to facilitate the examination of the virtual complex structure on our own applet viewer with the web browser (URL: http://eF-site.hgc.jp/eF-seek).

Extracting physicochemical features to predict protein secondary structure.

PubMed

Huang, Yin-Fu; Chen, Shu-Ying

2013-01-01

We propose a protein secondary structure prediction method based on position-specific scoring matrix (PSSM) profiles and four physicochemical features including conformation parameters, net charges, hydrophobic, and side chain mass. First, the SVM with the optimal window size and the optimal parameters of the kernel function is found. Then, we train the SVM using the PSSM profiles generated from PSI-BLAST and the physicochemical features extracted from the CB513 data set. Finally, we use the filter to refine the predicted results from the trained SVM. For all the performance measures of our method, Q 3 reaches 79.52, SOV94 reaches 86.10, and SOV99 reaches 74.60; all the measures are higher than those of the SVMpsi method and the SVMfreq method. This validates that considering these physicochemical features in predicting protein secondary structure would exhibit better performances.

Extracting Physicochemical Features to Predict Protein Secondary Structure

PubMed Central

Chen, Shu-Ying

2013-01-01

We propose a protein secondary structure prediction method based on position-specific scoring matrix (PSSM) profiles and four physicochemical features including conformation parameters, net charges, hydrophobic, and side chain mass. First, the SVM with the optimal window size and the optimal parameters of the kernel function is found. Then, we train the SVM using the PSSM profiles generated from PSI-BLAST and the physicochemical features extracted from the CB513 data set. Finally, we use the filter to refine the predicted results from the trained SVM. For all the performance measures of our method, Q 3 reaches 79.52, SOV94 reaches 86.10, and SOV99 reaches 74.60; all the measures are higher than those of the SVMpsi method and the SVMfreq method. This validates that considering these physicochemical features in predicting protein secondary structure would exhibit better performances. PMID:23766688

Protein-Protein Docking in Drug Design and Discovery.

PubMed

Kaczor, Agnieszka A; Bartuzi, Damian; Stępniewski, Tomasz Maciej; Matosiuk, Dariusz; Selent, Jana

2018-01-01

Protein-protein interactions (PPIs) are responsible for a number of key physiological processes in the living cells and underlie the pathomechanism of many diseases. Nowadays, along with the concept of so-called "hot spots" in protein-protein interactions, which are well-defined interface regions responsible for most of the binding energy, these interfaces can be targeted with modulators. In order to apply structure-based design techniques to design PPIs modulators, a three-dimensional structure of protein complex has to be available. In this context in silico approaches, in particular protein-protein docking, are a valuable complement to experimental methods for elucidating 3D structure of protein complexes. Protein-protein docking is easy to use and does not require significant computer resources and time (in contrast to molecular dynamics) and it results in 3D structure of a protein complex (in contrast to sequence-based methods of predicting binding interfaces). However, protein-protein docking cannot address all the aspects of protein dynamics, in particular the global conformational changes during protein complex formation. In spite of this fact, protein-protein docking is widely used to model complexes of water-soluble proteins and less commonly to predict structures of transmembrane protein assemblies, including dimers and oligomers of G protein-coupled receptors (GPCRs). In this chapter we review the principles of protein-protein docking, available algorithms and software and discuss the recent examples, benefits, and drawbacks of protein-protein docking application to water-soluble proteins, membrane anchoring and transmembrane proteins, including GPCRs.

Small angle X-ray scattering and cross-linking for data assisted protein structure prediction in CASP 12 with prospects for improved accuracy.

PubMed

Ogorzalek, Tadeusz L; Hura, Greg L; Belsom, Adam; Burnett, Kathryn H; Kryshtafovych, Andriy; Tainer, John A; Rappsilber, Juri; Tsutakawa, Susan E; Fidelis, Krzysztof

2018-03-01

Experimental data offers empowering constraints for structure prediction. These constraints can be used to filter equivalently scored models or more powerfully within optimization functions toward prediction. In CASP12, Small Angle X-ray Scattering (SAXS) and Cross-Linking Mass Spectrometry (CLMS) data, measured on an exemplary set of novel fold targets, were provided to the CASP community of protein structure predictors. As solution-based techniques, SAXS and CLMS can efficiently measure states of the full-length sequence in its native solution conformation and assembly. However, this experimental data did not substantially improve prediction accuracy judged by fits to crystallographic models. One issue, beyond intrinsic limitations of the algorithms, was a disconnect between crystal structures and solution-based measurements. Our analyses show that many targets had substantial percentages of disordered regions (up to 40%) or were multimeric or both. Thus, solution measurements of flexibility and assembly support variations that may confound prediction algorithms trained on crystallographic data and expecting globular fully-folded monomeric proteins. Here, we consider the CLMS and SAXS data collected, the information in these solution measurements, and the challenges in incorporating them into computational prediction. As improvement opportunities were only partly realized in CASP12, we provide guidance on how data from the full-length biological unit and the solution state can better aid prediction of the folded monomer or subunit. We furthermore describe strategic integrations of solution measurements with computational prediction programs with the aim of substantially improving foundational knowledge and the accuracy of computational algorithms for biologically-relevant structure predictions for proteins in solution. © 2018 Wiley Periodicals, Inc.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Cang, Zixuan; Mu, Lin; Wu, Kedi

Here, protein function and dynamics are closely related to its sequence and structure. However, prediction of protein function and dynamics from its sequence and structure is still a fundamental challenge in molecular biology. Protein classification, which is typically done through measuring the similarity between proteins based on protein sequence or physical information, serves as a crucial step toward the understanding of protein function and dynamics.

Predicting PDZ domain mediated protein interactions from structure

PubMed Central

2013-01-01

Background PDZ domains are structural protein domains that recognize simple linear amino acid motifs, often at protein C-termini, and mediate protein-protein interactions (PPIs) in important biological processes, such as ion channel regulation, cell polarity and neural development. PDZ domain-peptide interaction predictors have been developed based on domain and peptide sequence information. Since domain structure is known to influence binding specificity, we hypothesized that structural information could be used to predict new interactions compared to sequence-based predictors. Results We developed a novel computational predictor of PDZ domain and C-terminal peptide interactions using a support vector machine trained with PDZ domain structure and peptide sequence information. Performance was estimated using extensive cross validation testing. We used the structure-based predictor to scan the human proteome for ligands of 218 PDZ domains and show that the predictions correspond to known PDZ domain-peptide interactions and PPIs in curated databases. The structure-based predictor is complementary to the sequence-based predictor, finding unique known and novel PPIs, and is less dependent on training–testing domain sequence similarity. We used a functional enrichment analysis of our hits to create a predicted map of PDZ domain biology. This map highlights PDZ domain involvement in diverse biological processes, some only found by the structure-based predictor. Based on this analysis, we predict novel PDZ domain involvement in xenobiotic metabolism and suggest new interactions for other processes including wound healing and Wnt signalling. Conclusions We built a structure-based predictor of PDZ domain-peptide interactions, which can be used to scan C-terminal proteomes for PDZ interactions. We also show that the structure-based predictor finds many known PDZ mediated PPIs in human that were not found by our previous sequence-based predictor and is less dependent on training–testing domain sequence similarity. Using both predictors, we defined a functional map of human PDZ domain biology and predict novel PDZ domain function. Users may access our structure-based and previous sequence-based predictors at http://webservice.baderlab.org/domains/POW. PMID:23336252

A topological approach for protein classification

DOE PAGES

Cang, Zixuan; Mu, Lin; Wu, Kedi; ...

2015-11-04

Here, protein function and dynamics are closely related to its sequence and structure. However, prediction of protein function and dynamics from its sequence and structure is still a fundamental challenge in molecular biology. Protein classification, which is typically done through measuring the similarity between proteins based on protein sequence or physical information, serves as a crucial step toward the understanding of protein function and dynamics.

Protein structure refinement using a quantum mechanics-based chemical shielding predictor† †Electronic supplementary information (ESI) available. See DOI: 10.1039/c6sc04344e Click here for additional data file.

PubMed Central

2017-01-01

The accurate prediction of protein chemical shifts using a quantum mechanics (QM)-based method has been the subject of intense research for more than 20 years but so far empirical methods for chemical shift prediction have proven more accurate. In this paper we show that a QM-based predictor of a protein backbone and CB chemical shifts (ProCS15, PeerJ, 2016, 3, e1344) is of comparable accuracy to empirical chemical shift predictors after chemical shift-based structural refinement that removes small structural errors. We present a method by which quantum chemistry based predictions of isotropic chemical shielding values (ProCS15) can be used to refine protein structures using Markov Chain Monte Carlo (MCMC) simulations, relating the chemical shielding values to the experimental chemical shifts probabilistically. Two kinds of MCMC structural refinement simulations were performed using force field geometry optimized X-ray structures as starting points: simulated annealing of the starting structure and constant temperature MCMC simulation followed by simulated annealing of a representative ensemble structure. Annealing of the CHARMM structure changes the CA-RMSD by an average of 0.4 Å but lowers the chemical shift RMSD by 1.0 and 0.7 ppm for CA and N. Conformational averaging has a relatively small effect (0.1–0.2 ppm) on the overall agreement with carbon chemical shifts but lowers the error for nitrogen chemical shifts by 0.4 ppm. If an amino acid specific offset is included the ProCS15 predicted chemical shifts have RMSD values relative to experiments that are comparable to popular empirical chemical shift predictors. The annealed representative ensemble structures differ in CA-RMSD relative to the initial structures by an average of 2.0 Å, with >2.0 Å difference for six proteins. In four of the cases, the largest structural differences arise in structurally flexible regions of the protein as determined by NMR, and in the remaining two cases, the large structural change may be due to force field deficiencies. The overall accuracy of the empirical methods are slightly improved by annealing the CHARMM structure with ProCS15, which may suggest that the minor structural changes introduced by ProCS15-based annealing improves the accuracy of the protein structures. Having established that QM-based chemical shift prediction can deliver the same accuracy as empirical shift predictors we hope this can help increase the accuracy of related approaches such as QM/MM or linear scaling approaches or interpreting protein structural dynamics from QM-derived chemical shift. PMID:28451325

Coevolutionary modeling of protein sequences: Predicting structure, function, and mutational landscapes

NASA Astrophysics Data System (ADS)

Weigt, Martin

Over the last years, biological research has been revolutionized by experimental high-throughput techniques, in particular by next-generation sequencing technology. Unprecedented amounts of data are accumulating, and there is a growing request for computational methods unveiling the information hidden in raw data, thereby increasing our understanding of complex biological systems. Statistical-physics models based on the maximum-entropy principle have, in the last few years, played an important role in this context. To give a specific example, proteins and many non-coding RNA show a remarkable degree of structural and functional conservation in the course of evolution, despite a large variability in amino acid sequences. We have developed a statistical-mechanics inspired inference approach - called Direct-Coupling Analysis - to link this sequence variability (easy to observe in sequence alignments, which are available in public sequence databases) to bio-molecular structure and function. In my presentation I will show, how this methodology can be used (i) to infer contacts between residues and thus to guide tertiary and quaternary protein structure prediction and RNA structure prediction, (ii) to discriminate interacting from non-interacting protein families, and thus to infer conserved protein-protein interaction networks, and (iii) to reconstruct mutational landscapes and thus to predict the phenotypic effect of mutations. References [1] M. Figliuzzi, H. Jacquier, A. Schug, O. Tenaillon and M. Weigt ''Coevolutionary landscape inference and the context-dependence of mutations in beta-lactamase TEM-1'', Mol. Biol. Evol. (2015), doi: 10.1093/molbev/msv211 [2] E. De Leonardis, B. Lutz, S. Ratz, S. Cocco, R. Monasson, A. Schug, M. Weigt ''Direct-Coupling Analysis of nucleotide coevolution facilitates RNA secondary and tertiary structure prediction'', Nucleic Acids Research (2015), doi: 10.1093/nar/gkv932 [3] F. Morcos, A. Pagnani, B. Lunt, A. Bertolino, D. Marks, C. Sander, R. Zecchina, J.N. Onuchic, T. Hwa, M. Weigt, ''Direct-coupling analysis of residue co-evolution captures native contacts across many protein families'', Proc. Natl. Acad. Sci. 108, E1293-E1301 (2011).

A generalized analysis of hydrophobic and loop clusters within globular protein sequences

PubMed Central

Eudes, Richard; Le Tuan, Khanh; Delettré, Jean; Mornon, Jean-Paul; Callebaut, Isabelle

2007-01-01

Background Hydrophobic Cluster Analysis (HCA) is an efficient way to compare highly divergent sequences through the implicit secondary structure information directly derived from hydrophobic clusters. However, its efficiency and application are currently limited by the need of user expertise. In order to help the analysis of HCA plots, we report here the structural preferences of hydrophobic cluster species, which are frequently encountered in globular domains of proteins. These species are characterized only by their hydrophobic/non-hydrophobic dichotomy. This analysis has been extended to loop-forming clusters, using an appropriate loop alphabet. Results The structural behavior of hydrophobic cluster species, which are typical of protein globular domains, was investigated within banks of experimental structures, considered at different levels of sequence redundancy. The 294 more frequent hydrophobic cluster species were analyzed with regard to their association with the different secondary structures (frequencies of association with secondary structures and secondary structure propensities). Hydrophobic cluster species are predominantly associated with regular secondary structures, and a large part (60 %) reveals preferences for α-helices or β-strands. Moreover, the analysis of the hydrophobic cluster amino acid composition generally allows for finer prediction of the regular secondary structure associated with the considered cluster within a cluster species. We also investigated the behavior of loop forming clusters, using a "PGDNS" alphabet. These loop clusters do not overlap with hydrophobic clusters and are highly associated with coils. Finally, the structural information contained in the hydrophobic structural words, as deduced from experimental structures, was compared to the PSI-PRED predictions, revealing that β-strands and especially α-helices are generally over-predicted within the limits of typical β and α hydrophobic clusters. Conclusion The dictionary of hydrophobic clusters described here can help the HCA user to interpret and compare the HCA plots of globular protein sequences, as well as provides an original fundamental insight into the structural bricks of protein folds. Moreover, the novel loop cluster analysis brings additional information for secondary structure prediction on the whole sequence through a generalized cluster analysis (GCA), and not only on regular secondary structures. Such information lays the foundations for developing a new and original tool for secondary structure prediction. PMID:17210072

PRISM-EM: template interface-based modelling of multi-protein complexes guided by cryo-electron microscopy density maps.

PubMed

Kuzu, Guray; Keskin, Ozlem; Nussinov, Ruth; Gursoy, Attila

2016-10-01

The structures of protein assemblies are important for elucidating cellular processes at the molecular level. Three-dimensional electron microscopy (3DEM) is a powerful method to identify the structures of assemblies, especially those that are challenging to study by crystallography. Here, a new approach, PRISM-EM, is reported to computationally generate plausible structural models using a procedure that combines crystallographic structures and density maps obtained from 3DEM. The predictions are validated against seven available structurally different crystallographic complexes. The models display mean deviations in the backbone of <5 Å. PRISM-EM was further tested on different benchmark sets; the accuracy was evaluated with respect to the structure of the complex, and the correlation with EM density maps and interface predictions were evaluated and compared with those obtained using other methods. PRISM-EM was then used to predict the structure of the ternary complex of the HIV-1 envelope glycoprotein trimer, the ligand CD4 and the neutralizing protein m36.

Quantitative theory of hydrophobic effect as a driving force of protein structure

PubMed Central

Perunov, Nikolay; England, Jeremy L

2014-01-01

Various studies suggest that the hydrophobic effect plays a major role in driving the folding of proteins. In the past, however, it has been challenging to translate this understanding into a predictive, quantitative theory of how the full pattern of sequence hydrophobicity in a protein shapes functionally important features of its tertiary structure. Here, we extend and apply such a phenomenological theory of the sequence-structure relationship in globular protein domains, which had previously been applied to the study of allosteric motion. In an effort to optimize parameters for the model, we first analyze the patterns of backbone burial found in single-domain crystal structures, and discover that classic hydrophobicity scales derived from bulk physicochemical properties of amino acids are already nearly optimal for prediction of burial using the model. Subsequently, we apply the model to studying structural fluctuations in proteins and establish a means of identifying ligand-binding and protein–protein interaction sites using this approach. PMID:24408023

Tertiary structural propensities reveal fundamental sequence/structure relationships.

PubMed

Zheng, Fan; Zhang, Jian; Grigoryan, Gevorg

2015-05-05

Extracting useful generalizations from the continually growing Protein Data Bank (PDB) is of central importance. We hypothesize that the PDB contains valuable quantitative information on the level of local tertiary structural motifs (TERMs). We show that by breaking a protein structure into its constituent TERMs, and querying the PDB to characterize the natural ensemble matching each, we can estimate the compatibility of the structure with a given amino acid sequence through a metric we term "structure score." Considering submissions from recent Critical Assessment of Structure Prediction (CASP) experiments, we found a strong correlation (R = 0.69) between structure score and model accuracy, with poorly predicted regions readily identifiable. This performance exceeds that of leading atomistic statistical energy functions. Furthermore, TERM-based analysis of two prototypical multi-state proteins rapidly produced structural insights fully consistent with prior extensive experimental studies. We thus find that TERM-based analysis should have considerable utility for protein structural biology. Copyright © 2015 Elsevier Ltd. All rights reserved.

Characterizing informative sequence descriptors and predicting binding affinities of heterodimeric protein complexes.

PubMed

Srinivasulu, Yerukala Sathipati; Wang, Jyun-Rong; Hsu, Kai-Ti; Tsai, Ming-Ju; Charoenkwan, Phasit; Huang, Wen-Lin; Huang, Hui-Ling; Ho, Shinn-Ying

2015-01-01

Protein-protein interactions (PPIs) are involved in various biological processes, and underlying mechanism of the interactions plays a crucial role in therapeutics and protein engineering. Most machine learning approaches have been developed for predicting the binding affinity of protein-protein complexes based on structure and functional information. This work aims to predict the binding affinity of heterodimeric protein complexes from sequences only. This work proposes a support vector machine (SVM) based binding affinity classifier, called SVM-BAC, to classify heterodimeric protein complexes based on the prediction of their binding affinity. SVM-BAC identified 14 of 580 sequence descriptors (physicochemical, energetic and conformational properties of the 20 amino acids) to classify 216 heterodimeric protein complexes into low and high binding affinity. SVM-BAC yielded the training accuracy, sensitivity, specificity, AUC and test accuracy of 85.80%, 0.89, 0.83, 0.86 and 83.33%, respectively, better than existing machine learning algorithms. The 14 features and support vector regression were further used to estimate the binding affinities (Pkd) of 200 heterodimeric protein complexes. Prediction performance of a Jackknife test was the correlation coefficient of 0.34 and mean absolute error of 1.4. We further analyze three informative physicochemical properties according to their contribution to prediction performance. Results reveal that the following properties are effective in predicting the binding affinity of heterodimeric protein complexes: apparent partition energy based on buried molar fractions, relations between chemical structure and biological activity in principal component analysis IV, and normalized frequency of beta turn. The proposed sequence-based prediction method SVM-BAC uses an optimal feature selection method to identify 14 informative features to classify and predict binding affinity of heterodimeric protein complexes. The characterization analysis revealed that the average numbers of beta turns and hydrogen bonds at protein-protein interfaces in high binding affinity complexes are more than those in low binding affinity complexes.

Characterizing informative sequence descriptors and predicting binding affinities of heterodimeric protein complexes

PubMed Central

2015-01-01

Background Protein-protein interactions (PPIs) are involved in various biological processes, and underlying mechanism of the interactions plays a crucial role in therapeutics and protein engineering. Most machine learning approaches have been developed for predicting the binding affinity of protein-protein complexes based on structure and functional information. This work aims to predict the binding affinity of heterodimeric protein complexes from sequences only. Results This work proposes a support vector machine (SVM) based binding affinity classifier, called SVM-BAC, to classify heterodimeric protein complexes based on the prediction of their binding affinity. SVM-BAC identified 14 of 580 sequence descriptors (physicochemical, energetic and conformational properties of the 20 amino acids) to classify 216 heterodimeric protein complexes into low and high binding affinity. SVM-BAC yielded the training accuracy, sensitivity, specificity, AUC and test accuracy of 85.80%, 0.89, 0.83, 0.86 and 83.33%, respectively, better than existing machine learning algorithms. The 14 features and support vector regression were further used to estimate the binding affinities (Pkd) of 200 heterodimeric protein complexes. Prediction performance of a Jackknife test was the correlation coefficient of 0.34 and mean absolute error of 1.4. We further analyze three informative physicochemical properties according to their contribution to prediction performance. Results reveal that the following properties are effective in predicting the binding affinity of heterodimeric protein complexes: apparent partition energy based on buried molar fractions, relations between chemical structure and biological activity in principal component analysis IV, and normalized frequency of beta turn. Conclusions The proposed sequence-based prediction method SVM-BAC uses an optimal feature selection method to identify 14 informative features to classify and predict binding affinity of heterodimeric protein complexes. The characterization analysis revealed that the average numbers of beta turns and hydrogen bonds at protein-protein interfaces in high binding affinity complexes are more than those in low binding affinity complexes. PMID:26681483

GDAP: a web tool for genome-wide protein disulfide bond prediction.

PubMed

O'Connor, Brian D; Yeates, Todd O

2004-07-01

The Genomic Disulfide Analysis Program (GDAP) provides web access to computationally predicted protein disulfide bonds for over one hundred microbial genomes, including both bacterial and achaeal species. In the GDAP process, sequences of unknown structure are mapped, when possible, to known homologous Protein Data Bank (PDB) structures, after which specific distance criteria are applied to predict disulfide bonds. GDAP also accepts user-supplied protein sequences and subsequently queries the PDB sequence database for the best matches, scans for possible disulfide bonds and returns the results to the client. These predictions are useful for a variety of applications and have previously been used to show a dramatic preference in certain thermophilic archaea and bacteria for disulfide bonds within intracellular proteins. Given the central role these stabilizing, covalent bonds play in such organisms, the predictions available from GDAP provide a rich data source for designing site-directed mutants with more stable thermal profiles. The GDAP web application is a gateway to this information and can be used to understand the role disulfide bonds play in protein stability both in these unusual organisms and in sequences of interest to the individual researcher. The prediction server can be accessed at http://www.doe-mbi.ucla.edu/Services/GDAP.

Coevolution at protein complex interfaces can be detected by the complementarity trace with important impact for predictive docking

PubMed Central

Madaoui, Hocine; Guerois, Raphaël

2008-01-01

Protein surfaces are under significant selection pressure to maintain interactions with their partners throughout evolution. Capturing how selection pressure acts at the interfaces of protein–protein complexes is a fundamental issue with high interest for the structural prediction of macromolecular assemblies. We tackled this issue under the assumption that, throughout evolution, mutations should minimally disrupt the physicochemical compatibility between specific clusters of interacting residues. This constraint drove the development of the so-called Surface COmplementarity Trace in Complex History score (SCOTCH), which was found to discriminate with high efficiency the structure of biological complexes. SCOTCH performances were assessed not only with respect to other evolution-based approaches, such as conservation and coevolution analyses, but also with respect to statistically based scoring methods. Validated on a set of 129 complexes of known structure exhibiting both permanent and transient intermolecular interactions, SCOTCH appears as a robust strategy to guide the prediction of protein–protein complex structures. Of particular interest, it also provides a basic framework to efficiently track how protein surfaces could evolve while keeping their partners in contact. PMID:18511568

Protein Structure Validation and Refinement Using Amide Proton Chemical Shifts Derived from Quantum Mechanics

PubMed Central

Christensen, Anders S.; Linnet, Troels E.; Borg, Mikael; Boomsma, Wouter; Lindorff-Larsen, Kresten; Hamelryck, Thomas; Jensen, Jan H.

2013-01-01

We present the ProCS method for the rapid and accurate prediction of protein backbone amide proton chemical shifts - sensitive probes of the geometry of key hydrogen bonds that determine protein structure. ProCS is parameterized against quantum mechanical (QM) calculations and reproduces high level QM results obtained for a small protein with an RMSD of 0.25 ppm (r = 0.94). ProCS is interfaced with the PHAISTOS protein simulation program and is used to infer statistical protein ensembles that reflect experimentally measured amide proton chemical shift values. Such chemical shift-based structural refinements, starting from high-resolution X-ray structures of Protein G, ubiquitin, and SMN Tudor Domain, result in average chemical shifts, hydrogen bond geometries, and trans-hydrogen bond (h3 JNC') spin-spin coupling constants that are in excellent agreement with experiment. We show that the structural sensitivity of the QM-based amide proton chemical shift predictions is needed to obtain this agreement. The ProCS method thus offers a powerful new tool for refining the structures of hydrogen bonding networks to high accuracy with many potential applications such as protein flexibility in ligand binding. PMID:24391900

Protein docking prediction using predicted protein-protein interface.

PubMed

Li, Bin; Kihara, Daisuke

2012-01-10

Many important cellular processes are carried out by protein complexes. To provide physical pictures of interacting proteins, many computational protein-protein prediction methods have been developed in the past. However, it is still difficult to identify the correct docking complex structure within top ranks among alternative conformations. We present a novel protein docking algorithm that utilizes imperfect protein-protein binding interface prediction for guiding protein docking. Since the accuracy of protein binding site prediction varies depending on cases, the challenge is to develop a method which does not deteriorate but improves docking results by using a binding site prediction which may not be 100% accurate. The algorithm, named PI-LZerD (using Predicted Interface with Local 3D Zernike descriptor-based Docking algorithm), is based on a pair wise protein docking prediction algorithm, LZerD, which we have developed earlier. PI-LZerD starts from performing docking prediction using the provided protein-protein binding interface prediction as constraints, which is followed by the second round of docking with updated docking interface information to further improve docking conformation. Benchmark results on bound and unbound cases show that PI-LZerD consistently improves the docking prediction accuracy as compared with docking without using binding site prediction or using the binding site prediction as post-filtering. We have developed PI-LZerD, a pairwise docking algorithm, which uses imperfect protein-protein binding interface prediction to improve docking accuracy. PI-LZerD consistently showed better prediction accuracy over alternative methods in the series of benchmark experiments including docking using actual docking interface site predictions as well as unbound docking cases.

Highly sensitive detection of individual HEAT and ARM repeats with HHpred and COACH.

PubMed

Kippert, Fred; Gerloff, Dietlind L

2009-09-24

HEAT and ARM repeats occur in a large number of eukaryotic proteins. As these repeats are often highly diverged, the prediction of HEAT or ARM domains can be challenging. Except for the most clear-cut cases, identification at the individual repeat level is indispensable, in particular for determining domain boundaries. However, methods using single sequence queries do not have the sensitivity required to deal with more divergent repeats and, when applied to proteins with known structures, in some cases failed to detect a single repeat. Testing algorithms which use multiple sequence alignments as queries, we found two of them, HHpred and COACH, to detect HEAT and ARM repeats with greatly enhanced sensitivity. Calibration against experimentally determined structures suggests the use of three score classes with increasing confidence in the prediction, and prediction thresholds for each method. When we applied a new protocol using both HHpred and COACH to these structures, it detected 82% of HEAT repeats and 90% of ARM repeats, with the minimum for a given protein of 57% for HEAT repeats and 60% for ARM repeats. Application to bona fide HEAT and ARM proteins or domains indicated that similar numbers can be expected for the full complement of HEAT/ARM proteins. A systematic screen of the Protein Data Bank for false positive hits revealed their number to be low, in particular for ARM repeats. Double false positive hits for a given protein were rare for HEAT and not at all observed for ARM repeats. In combination with fold prediction and consistency checking (multiple sequence alignments, secondary structure prediction, and position analysis), repeat prediction with the new HHpred/COACH protocol dramatically improves prediction in the twilight zone of fold prediction methods, as well as the delineation of HEAT/ARM domain boundaries. A protocol is presented for the identification of individual HEAT or ARM repeats which is straightforward to implement. It provides high sensitivity at a low false positive rate and will therefore greatly enhance the accuracy of predictions of HEAT and ARM domains.

Highly Sensitive Detection of Individual HEAT and ARM Repeats with HHpred and COACH

PubMed Central

Kippert, Fred; Gerloff, Dietlind L.

2009-01-01

Background HEAT and ARM repeats occur in a large number of eukaryotic proteins. As these repeats are often highly diverged, the prediction of HEAT or ARM domains can be challenging. Except for the most clear-cut cases, identification at the individual repeat level is indispensable, in particular for determining domain boundaries. However, methods using single sequence queries do not have the sensitivity required to deal with more divergent repeats and, when applied to proteins with known structures, in some cases failed to detect a single repeat. Methodology and Principal Findings Testing algorithms which use multiple sequence alignments as queries, we found two of them, HHpred and COACH, to detect HEAT and ARM repeats with greatly enhanced sensitivity. Calibration against experimentally determined structures suggests the use of three score classes with increasing confidence in the prediction, and prediction thresholds for each method. When we applied a new protocol using both HHpred and COACH to these structures, it detected 82% of HEAT repeats and 90% of ARM repeats, with the minimum for a given protein of 57% for HEAT repeats and 60% for ARM repeats. Application to bona fide HEAT and ARM proteins or domains indicated that similar numbers can be expected for the full complement of HEAT/ARM proteins. A systematic screen of the Protein Data Bank for false positive hits revealed their number to be low, in particular for ARM repeats. Double false positive hits for a given protein were rare for HEAT and not at all observed for ARM repeats. In combination with fold prediction and consistency checking (multiple sequence alignments, secondary structure prediction, and position analysis), repeat prediction with the new HHpred/COACH protocol dramatically improves prediction in the twilight zone of fold prediction methods, as well as the delineation of HEAT/ARM domain boundaries. Significance A protocol is presented for the identification of individual HEAT or ARM repeats which is straightforward to implement. It provides high sensitivity at a low false positive rate and will therefore greatly enhance the accuracy of predictions of HEAT and ARM domains. PMID:19777061

Physics and chemistry-driven artificial neural network for predicting bioactivity of peptides and proteins and their design.

PubMed

Huang, Ri-Bo; Du, Qi-Shi; Wei, Yu-Tuo; Pang, Zong-Wen; Wei, Hang; Chou, Kuo-Chen

2009-02-07

Predicting the bioactivity of peptides and proteins is an important challenge in drug development and protein engineering. In this study we introduce a novel approach, the so-called "physics and chemistry-driven artificial neural network (Phys-Chem ANN)", to deal with such a problem. Unlike the existing ANN approaches, which were designed under the inspiration of biological neural system, the Phys-Chem ANN approach is based on the physical and chemical principles, as well as the structural features of proteins. In the Phys-Chem ANN model the "hidden layers" are no longer virtual "neurons", but real structural units of proteins and peptides. It is a hybridization approach, which combines the linear free energy concept of quantitative structure-activity relationship (QSAR) with the advanced mathematical technique of ANN. The Phys-Chem ANN approach has adopted an iterative and feedback procedure, incorporating both machine-learning and artificial intelligence capabilities. In addition to making more accurate predictions for the bioactivities of proteins and peptides than is possible with the traditional QSAR approach, the Phys-Chem ANN approach can also provide more insights about the relationship between bioactivities and the structures involved than the ANN approach does. As an example of the application of the Phys-Chem ANN approach, a predictive model for the conformational stability of human lysozyme is presented.

Surflex-Dock: Docking benchmarks and real-world application

NASA Astrophysics Data System (ADS)

Spitzer, Russell; Jain, Ajay N.

2012-06-01

Benchmarks for molecular docking have historically focused on re-docking the cognate ligand of a well-determined protein-ligand complex to measure geometric pose prediction accuracy, and measurement of virtual screening performance has been focused on increasingly large and diverse sets of target protein structures, cognate ligands, and various types of decoy sets. Here, pose prediction is reported on the Astex Diverse set of 85 protein ligand complexes, and virtual screening performance is reported on the DUD set of 40 protein targets. In both cases, prepared structures of targets and ligands were provided by symposium organizers. The re-prepared data sets yielded results not significantly different than previous reports of Surflex-Dock on the two benchmarks. Minor changes to protein coordinates resulting from complex pre-optimization had large effects on observed performance, highlighting the limitations of cognate ligand re-docking for pose prediction assessment. Docking protocols developed for cross-docking, which address protein flexibility and produce discrete families of predicted poses, produced substantially better performance for pose prediction. Performance on virtual screening performance was shown to benefit by employing and combining multiple screening methods: docking, 2D molecular similarity, and 3D molecular similarity. In addition, use of multiple protein conformations significantly improved screening enrichment.

iDBPs: a web server for the identification of DNA binding proteins.

PubMed

Nimrod, Guy; Schushan, Maya; Szilágyi, András; Leslie, Christina; Ben-Tal, Nir

2010-03-01

The iDBPs server uses the three-dimensional (3D) structure of a query protein to predict whether it binds DNA. First, the algorithm predicts the functional region of the protein based on its evolutionary profile; the assumption is that large clusters of conserved residues are good markers of functional regions. Next, various characteristics of the predicted functional region as well as global features of the protein are calculated, such as the average surface electrostatic potential, the dipole moment and cluster-based amino acid conservation patterns. Finally, a random forests classifier is used to predict whether the query protein is likely to bind DNA and to estimate the prediction confidence. We have trained and tested the classifier on various datasets and shown that it outperformed related methods. On a dataset that reflects the fraction of DNA binding proteins (DBPs) in a proteome, the area under the ROC curve was 0.90. The application of the server to an updated version of the N-Func database, which contains proteins of unknown function with solved 3D-structure, suggested new putative DBPs for experimental studies. http://idbps.tau.ac.il/

Protein side chain conformation predictions with an MMGBSA energy function.

PubMed

Gaillard, Thomas; Panel, Nicolas; Simonson, Thomas

2016-06-01

The prediction of protein side chain conformations from backbone coordinates is an important task in structural biology, with applications in structure prediction and protein design. It is a difficult problem due to its combinatorial nature. We study the performance of an "MMGBSA" energy function, implemented in our protein design program Proteus, which combines molecular mechanics terms, a Generalized Born and Surface Area (GBSA) solvent model, with approximations that make the model pairwise additive. Proteus is not a competitor to specialized side chain prediction programs due to its cost, but it allows protein design applications, where side chain prediction is an important step and MMGBSA an effective energy model. We predict the side chain conformations for 18 proteins. The side chains are first predicted individually, with the rest of the protein in its crystallographic conformation. Next, all side chains are predicted together. The contributions of individual energy terms are evaluated and various parameterizations are compared. We find that the GB and SA terms, with an appropriate choice of the dielectric constant and surface energy coefficients, are beneficial for single side chain predictions. For the prediction of all side chains, however, errors due to the pairwise additive approximation overcome the improvement brought by these terms. We also show the crucial contribution of side chain minimization to alleviate the rigid rotamer approximation. Even without GB and SA terms, we obtain accuracies comparable to SCWRL4, a specialized side chain prediction program. In particular, we obtain a better RMSD than SCWRL4 for core residues (at a higher cost), despite our simpler rotamer library. Proteins 2016; 84:803-819. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

Prediction of Ras-effector interactions using position energy matrices.

PubMed

Kiel, Christina; Serrano, Luis

2007-09-01

One of the more challenging problems in biology is to determine the cellular protein interaction network. Progress has been made to predict protein-protein interactions based on structural information, assuming that structural similar proteins interact in a similar way. In a previous publication, we have determined a genome-wide Ras-effector interaction network based on homology models, with a high accuracy of predicting binding and non-binding domains. However, for a prediction on a genome-wide scale, homology modelling is a time-consuming process. Therefore, we here successfully developed a faster method using position energy matrices, where based on different Ras-effector X-ray template structures, all amino acids in the effector binding domain are sequentially mutated to all other amino acid residues and the effect on binding energy is calculated. Those pre-calculated matrices can then be used to score for binding any Ras or effector sequences. Based on position energy matrices, the sequences of putative Ras-binding domains can be scanned quickly to calculate an energy sum value. By calibrating energy sum values using quantitative experimental binding data, thresholds can be defined and thus non-binding domains can be excluded quickly. Sequences which have energy sum values above this threshold are considered to be potential binding domains, and could be further analysed using homology modelling. This prediction method could be applied to other protein families sharing conserved interaction types, in order to determine in a fast way large scale cellular protein interaction networks. Thus, it could have an important impact on future in silico structural genomics approaches, in particular with regard to increasing structural proteomics efforts, aiming to determine all possible domain folds and interaction types. All matrices are deposited in the ADAN database (http://adan-embl.ibmc.umh.es/). Supplementary data are available at Bioinformatics online.

Protein-protein structure prediction by scoring molecular dynamics trajectories of putative poses.

PubMed

Sarti, Edoardo; Gladich, Ivan; Zamuner, Stefano; Correia, Bruno E; Laio, Alessandro

2016-09-01

The prediction of protein-protein interactions and their structural configuration remains a largely unsolved problem. Most of the algorithms aimed at finding the native conformation of a protein complex starting from the structure of its monomers are based on searching the structure corresponding to the global minimum of a suitable scoring function. However, protein complexes are often highly flexible, with mobile side chains and transient contacts due to thermal fluctuations. Flexibility can be neglected if one aims at finding quickly the approximate structure of the native complex, but may play a role in structure refinement, and in discriminating solutions characterized by similar scores. We here benchmark the capability of some state-of-the-art scoring functions (BACH-SixthSense, PIE/PISA and Rosetta) in discriminating finite-temperature ensembles of structures corresponding to the native state and to non-native configurations. We produce the ensembles by running thousands of molecular dynamics simulations in explicit solvent starting from poses generated by rigid docking and optimized in vacuum. We find that while Rosetta outperformed the other two scoring functions in scoring the structures in vacuum, BACH-SixthSense and PIE/PISA perform better in distinguishing near-native ensembles of structures generated by molecular dynamics in explicit solvent. Proteins 2016; 84:1312-1320. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

Annotation of Alternatively Spliced Proteins and Transcripts with Protein-Folding Algorithms and Isoform-Level Functional Networks.

PubMed

Li, Hongdong; Zhang, Yang; Guan, Yuanfang; Menon, Rajasree; Omenn, Gilbert S

2017-01-01

Tens of thousands of splice isoforms of proteins have been catalogued as predicted sequences from transcripts in humans and other species. Relatively few have been characterized biochemically or structurally. With the extensive development of protein bioinformatics, the characterization and modeling of isoform features, isoform functions, and isoform-level networks have advanced notably. Here we present applications of the I-TASSER family of algorithms for folding and functional predictions and the IsoFunc, MIsoMine, and Hisonet data resources for isoform-level analyses of network and pathway-based functional predictions and protein-protein interactions. Hopefully, predictions and insights from protein bioinformatics will stimulate many experimental validation studies.

Fast and Accurate Multivariate Gaussian Modeling of Protein Families: Predicting Residue Contacts and Protein-Interaction Partners

PubMed Central

Feinauer, Christoph; Procaccini, Andrea; Zecchina, Riccardo; Weigt, Martin; Pagnani, Andrea

2014-01-01

In the course of evolution, proteins show a remarkable conservation of their three-dimensional structure and their biological function, leading to strong evolutionary constraints on the sequence variability between homologous proteins. Our method aims at extracting such constraints from rapidly accumulating sequence data, and thereby at inferring protein structure and function from sequence information alone. Recently, global statistical inference methods (e.g. direct-coupling analysis, sparse inverse covariance estimation) have achieved a breakthrough towards this aim, and their predictions have been successfully implemented into tertiary and quaternary protein structure prediction methods. However, due to the discrete nature of the underlying variable (amino-acids), exact inference requires exponential time in the protein length, and efficient approximations are needed for practical applicability. Here we propose a very efficient multivariate Gaussian modeling approach as a variant of direct-coupling analysis: the discrete amino-acid variables are replaced by continuous Gaussian random variables. The resulting statistical inference problem is efficiently and exactly solvable. We show that the quality of inference is comparable or superior to the one achieved by mean-field approximations to inference with discrete variables, as done by direct-coupling analysis. This is true for (i) the prediction of residue-residue contacts in proteins, and (ii) the identification of protein-protein interaction partner in bacterial signal transduction. An implementation of our multivariate Gaussian approach is available at the website http://areeweb.polito.it/ricerca/cmp/code. PMID:24663061

Improved performance in CAPRI round 37 using LZerD docking and template-based modeling with combined scoring functions.

PubMed

Peterson, Lenna X; Shin, Woong-Hee; Kim, Hyungrae; Kihara, Daisuke

2018-03-01

We report our group's performance for protein-protein complex structure prediction and scoring in Round 37 of the Critical Assessment of PRediction of Interactions (CAPRI), an objective assessment of protein-protein complex modeling. We demonstrated noticeable improvement in both prediction and scoring compared to previous rounds of CAPRI, with our human predictor group near the top of the rankings and our server scorer group at the top. This is the first time in CAPRI that a server has been the top scorer group. To predict protein-protein complex structures, we used both multi-chain template-based modeling (TBM) and our protein-protein docking program, LZerD. LZerD represents protein surfaces using 3D Zernike descriptors (3DZD), which are based on a mathematical series expansion of a 3D function. Because 3DZD are a soft representation of the protein surface, LZerD is tolerant to small conformational changes, making it well suited to docking unbound and TBM structures. The key to our improved performance in CAPRI Round 37 was to combine multi-chain TBM and docking. As opposed to our previous strategy of performing docking for all target complexes, we used TBM when multi-chain templates were available and docking otherwise. We also describe the combination of multiple scoring functions used by our server scorer group, which achieved the top rank for the scorer phase. © 2017 Wiley Periodicals, Inc.

Impact of the canine double-deletion β1 adrenoreceptor polymorphisms on protein structure and heart rate response to atenolol, a β1-selective β-blocker.

PubMed

Meurs, Kathryn M; Stern, Josh A; Reina-Doreste, Yamir; Maran, Brian A; Chdid, Lhoucine; Lahmers, Sunshine; Keene, Bruce W; Mealey, Katrina L

2015-09-01

β-Adrenergic receptor antagonists are widely utilized for the management of cardiac diseases in dogs. We have recently identified two deletion polymorphisms in the canine adrenoreceptor 1 (ADRB1) gene.We hypothesized that canine ADRB1 deletions would alter the structure of the protein, as well as the heart rate response to the β-adrenergic receptor antagonist, atenolol. The objectives of this study were to predict the impact of these deletions on the predicted structure of the protein and on the heart rate response to atenolol in a population of healthy adult dogs. Eighteen apparently healthy, mature dogs with (11) and without (seven) ADRB1 deletions were evaluated. The heart rate of the dogs was evaluated with a baseline ambulatory ECG before and 14-21 days after atenolol therapy (1 mg/kg orally q12 h). Minimum, average, and maximum heart rates were compared between groups of dogs (deletions, controls) using an unpaired t-test and within each group of dogs using a paired t-test. The protein structure of ADRB1 was predicted by computer modeling. Deletions were predicted to alter the structure of the ADRB1 protein. The heart rates of the dogs with deletions were lower than those of the control dogs (the average heart rates were significantly lower). ADRB1 deletions appear to have structural and functional consequences. Individual genome-based treatment recommendations could impact the management of dogs with heart disease.

Proteus: a random forest classifier to predict disorder-to-order transitioning binding regions in intrinsically disordered proteins

NASA Astrophysics Data System (ADS)

Basu, Sankar; Söderquist, Fredrik; Wallner, Björn

2017-05-01

The focus of the computational structural biology community has taken a dramatic shift over the past one-and-a-half decades from the classical protein structure prediction problem to the possible understanding of intrinsically disordered proteins (IDP) or proteins containing regions of disorder (IDPR). The current interest lies in the unraveling of a disorder-to-order transitioning code embedded in the amino acid sequences of IDPs/IDPRs. Disordered proteins are characterized by an enormous amount of structural plasticity which makes them promiscuous in binding to different partners, multi-functional in cellular activity and atypical in folding energy landscapes resembling partially folded molten globules. Also, their involvement in several deadly human diseases (e.g. cancer, cardiovascular and neurodegenerative diseases) makes them attractive drug targets, and important for a biochemical understanding of the disease(s). The study of the structural ensemble of IDPs is rather difficult, in particular for transient interactions. When bound to a structured partner, an IDPR adapts an ordered conformation in the complex. The residues that undergo this disorder-to-order transition are called protean residues, generally found in short contiguous stretches and the first step in understanding the modus operandi of an IDP/IDPR would be to predict these residues. There are a few available methods which predict these protean segments from their amino acid sequences; however, their performance reported in the literature leaves clear room for improvement. With this background, the current study presents `Proteus', a random forest classifier that predicts the likelihood of a residue undergoing a disorder-to-order transition upon binding to a potential partner protein. The prediction is based on features that can be calculated using the amino acid sequence alone. Proteus compares favorably with existing methods predicting twice as many true positives as the second best method (55 vs. 27%) with a much higher precision on an independent data set. The current study also sheds some light on a possible `disorder-to-order' transitioning consensus, untangled, yet embedded in the amino acid sequence of IDPs. Some guidelines have also been suggested for proceeding with a real-life structural modeling involving an IDPR using Proteus.

Small-angle X-Ray analysis of macromolecular structure: the structure of protein NS2 (NEP) in solution

NASA Astrophysics Data System (ADS)

Shtykova, E. V.; Bogacheva, E. N.; Dadinova, L. A.; Jeffries, C. M.; Fedorova, N. V.; Golovko, A. O.; Baratova, L. A.; Batishchev, O. V.

2017-11-01

A complex structural analysis of nuclear export protein NS2 (NEP) of influenza virus A has been performed using bioinformatics predictive methods and small-angle X-ray scattering data. The behavior of NEP molecules in a solution (their aggregation, oligomerization, and dissociation, depending on the buffer composition) has been investigated. It was shown that stable associates are formed even in a conventional aqueous salt solution at physiological pH value. For the first time we have managed to get NEP dimers in solution, to analyze their structure, and to compare the models obtained using the method of the molecular tectonics with the spatial protein structure predicted by us using the bioinformatics methods. The results of the study provide a new insight into the structural features of nuclear export protein NS2 (NEP) of the influenza virus A, which is very important for viral infection development.

Comprehensive inventory of protein complexes in the Protein Data Bank from consistent classification of interfaces

DOE PAGES

Bordner, Andrew J.; Gorin, Andrey A.

2008-05-12

Here, protein-protein interactions are ubiquitous and essential for cellular processes. High-resolution X-ray crystallographic structures of protein complexes can elucidate the details of their function and provide a basis for many computational and experimental approaches. Here we demonstrate that existing annotations of protein complexes, including those provided by the Protein Data Bank (PDB) itself, contain a significant fraction of incorrect annotations. Results: We have developed a method for identifying protein complexes in the PDB X-ray structures by a four step procedure: (1) comprehensively collecting all protein-protein interfaces; (2) clustering similar protein-protein interfaces together; (3) estimating the probability that each cluster ismore » relevant based on a diverse set of properties; and (4) finally combining these scores for each entry in order to predict the complex structure. Unlike previous annotation methods, consistent prediction of complexes with identical or almost identical protein content is insured. The resulting clusters of biologically relevant interfaces provide a reliable catalog of evolutionary conserved protein-protein interactions.« less

Great interactions: How binding incorrect partners can teach us about protein recognition and function.

PubMed

Vamparys, Lydie; Laurent, Benoist; Carbone, Alessandra; Sacquin-Mora, Sophie

2016-10-01

Protein-protein interactions play a key part in most biological processes and understanding their mechanism is a fundamental problem leading to numerous practical applications. The prediction of protein binding sites in particular is of paramount importance since proteins now represent a major class of therapeutic targets. Amongst others methods, docking simulations between two proteins known to interact can be a useful tool for the prediction of likely binding patches on a protein surface. From the analysis of the protein interfaces generated by a massive cross-docking experiment using the 168 proteins of the Docking Benchmark 2.0, where all possible protein pairs, and not only experimental ones, have been docked together, we show that it is also possible to predict a protein's binding residues without having any prior knowledge regarding its potential interaction partners. Evaluating the performance of cross-docking predictions using the area under the specificity-sensitivity ROC curve (AUC) leads to an AUC value of 0.77 for the complete benchmark (compared to the 0.5 AUC value obtained for random predictions). Furthermore, a new clustering analysis performed on the binding patches that are scattered on the protein surface show that their distribution and growth will depend on the protein's functional group. Finally, in several cases, the binding-site predictions resulting from the cross-docking simulations will lead to the identification of an alternate interface, which corresponds to the interaction with a biomolecular partner that is not included in the original benchmark. Proteins 2016; 84:1408-1421. © 2016 The Authors Proteins: Structure, Function, and Bioinformatics Published by Wiley Periodicals, Inc. © 2016 The Authors Proteins: Structure, Function, and Bioinformatics Published by Wiley Periodicals, Inc.

A hidden markov model derived structural alphabet for proteins.

PubMed

Camproux, A C; Gautier, R; Tufféry, P

2004-06-04

Understanding and predicting protein structures depends on the complexity and the accuracy of the models used to represent them. We have set up a hidden Markov model that discretizes protein backbone conformation as series of overlapping fragments (states) of four residues length. This approach learns simultaneously the geometry of the states and their connections. We obtain, using a statistical criterion, an optimal systematic decomposition of the conformational variability of the protein peptidic chain in 27 states with strong connection logic. This result is stable over different protein sets. Our model fits well the previous knowledge related to protein architecture organisation and seems able to grab some subtle details of protein organisation, such as helix sub-level organisation schemes. Taking into account the dependence between the states results in a description of local protein structure of low complexity. On an average, the model makes use of only 8.3 states among 27 to describe each position of a protein structure. Although we use short fragments, the learning process on entire protein conformations captures the logic of the assembly on a larger scale. Using such a model, the structure of proteins can be reconstructed with an average accuracy close to 1.1A root-mean-square deviation and for a complexity of only 3. Finally, we also observe that sequence specificity increases with the number of states of the structural alphabet. Such models can constitute a very relevant approach to the analysis of protein architecture in particular for protein structure prediction.

Prediction of the translocon-mediated membrane insertion free energies of protein sequences.

PubMed

Park, Yungki; Helms, Volkhard

2008-05-15

Helical membrane proteins (HMPs) play crucial roles in a variety of cellular processes. Unlike water-soluble proteins, HMPs need not only to fold but also get inserted into the membrane to be fully functional. This process of membrane insertion is mediated by the translocon complex. Thus, it is of great interest to develop computational methods for predicting the translocon-mediated membrane insertion free energies of protein sequences. We have developed Membrane Insertion (MINS), a novel sequence-based computational method for predicting the membrane insertion free energies of protein sequences. A benchmark test gives a correlation coefficient of 0.74 between predicted and observed free energies for 357 known cases, which corresponds to a mean unsigned error of 0.41 kcal/mol. These results are significantly better than those obtained by traditional hydropathy analysis. Moreover, the ability of MINS to reasonably predict membrane insertion free energies of protein sequences allows for effective identification of transmembrane (TM) segments. Subsequently, MINS was applied to predict the membrane insertion free energies of 316 TM segments found in known structures. An in-depth analysis of the predicted free energies reveals a number of interesting findings about the biogenesis and structural stability of HMPs. A web server for MINS is available at http://service.bioinformatik.uni-saarland.de/mins

Prediction of beta-turns at over 80% accuracy based on an ensemble of predicted secondary structures and multiple alignments.

PubMed

Zheng, Ce; Kurgan, Lukasz

2008-10-10

beta-turn is a secondary protein structure type that plays significant role in protein folding, stability, and molecular recognition. To date, several methods for prediction of beta-turns from protein sequences were developed, but they are characterized by relatively poor prediction quality. The novelty of the proposed sequence-based beta-turn predictor stems from the usage of a window based information extracted from four predicted three-state secondary structures, which together with a selected set of position specific scoring matrix (PSSM) values serve as an input to the support vector machine (SVM) predictor. We show that (1) all four predicted secondary structures are useful; (2) the most useful information extracted from the predicted secondary structure includes the structure of the predicted residue, secondary structure content in a window around the predicted residue, and features that indicate whether the predicted residue is inside a secondary structure segment; (3) the PSSM values of Asn, Asp, Gly, Ile, Leu, Met, Pro, and Val were among the top ranked features, which corroborates with recent studies. The Asn, Asp, Gly, and Pro indicate potential beta-turns, while the remaining four amino acids are useful to predict non-beta-turns. Empirical evaluation using three nonredundant datasets shows favorable Q total, Q predicted and MCC values when compared with over a dozen of modern competing methods. Our method is the first to break the 80% Q total barrier and achieves Q total = 80.9%, MCC = 0.47, and Q predicted higher by over 6% when compared with the second best method. We use feature selection to reduce the dimensionality of the feature vector used as the input for the proposed prediction method. The applied feature set is smaller by 86, 62 and 37% when compared with the second and two third-best (with respect to MCC) competing methods, respectively. Experiments show that the proposed method constitutes an improvement over the competing prediction methods. The proposed prediction model can better discriminate between beta-turns and non-beta-turns due to obtaining lower numbers of false positive predictions. The prediction model and datasets are freely available at http://biomine.ece.ualberta.ca/BTNpred/BTNpred.html.

System and methods for predicting transmembrane domains in membrane proteins and mining the genome for recognizing G-protein coupled receptors

DOEpatents

Trabanino, Rene J; Vaidehi, Nagarajan; Hall, Spencer E; Goddard, William A; Floriano, Wely

2013-02-05

The invention provides computer-implemented methods and apparatus implementing a hierarchical protocol using multiscale molecular dynamics and molecular modeling methods to predict the presence of transmembrane regions in proteins, such as G-Protein Coupled Receptors (GPCR), and protein structural models generated according to the protocol. The protocol features a coarse grain sampling method, such as hydrophobicity analysis, to provide a fast and accurate procedure for predicting transmembrane regions. Methods and apparatus of the invention are useful to screen protein or polynucleotide databases for encoded proteins with transmembrane regions, such as GPCRs.

Protein-Protein Interface Predictions by Data-Driven Methods: A Review

PubMed Central

Xue, Li C; Dobbs, Drena; Bonvin, Alexandre M.J.J.; Honavar, Vasant

2015-01-01

Reliably pinpointing which specific amino acid residues form the interface(s) between a protein and its binding partner(s) is critical for understanding the structural and physicochemical determinants of protein recognition and binding affinity, and has wide applications in modeling and validating protein interactions predicted by high-throughput methods, in engineering proteins, and in prioritizing drug targets. Here, we review the basic concepts, principles and recent advances in computational approaches to the analysis and prediction of protein-protein interfaces. We point out caveats for objectively evaluating interface predictors, and discuss various applications of data-driven interface predictors for improving energy model-driven protein-protein docking. Finally, we stress the importance of exploiting binding partner information in reliably predicting interfaces and highlight recent advances in this emerging direction. PMID:26460190

A benchmark testing ground for integrating homology modeling and protein docking.

PubMed

Bohnuud, Tanggis; Luo, Lingqi; Wodak, Shoshana J; Bonvin, Alexandre M J J; Weng, Zhiping; Vajda, Sandor; Schueler-Furman, Ora; Kozakov, Dima

2017-01-01

Protein docking procedures carry out the task of predicting the structure of a protein-protein complex starting from the known structures of the individual protein components. More often than not, however, the structure of one or both components is not known, but can be derived by homology modeling on the basis of known structures of related proteins deposited in the Protein Data Bank (PDB). Thus, the problem is to develop methods that optimally integrate homology modeling and docking with the goal of predicting the structure of a complex directly from the amino acid sequences of its component proteins. One possibility is to use the best available homology modeling and docking methods. However, the models built for the individual subunits often differ to a significant degree from the bound conformation in the complex, often much more so than the differences observed between free and bound structures of the same protein, and therefore additional conformational adjustments, both at the backbone and side chain levels need to be modeled to achieve an accurate docking prediction. In particular, even homology models of overall good accuracy frequently include localized errors that unfavorably impact docking results. The predicted reliability of the different regions in the model can also serve as a useful input for the docking calculations. Here we present a benchmark dataset that should help to explore and solve combined modeling and docking problems. This dataset comprises a subset of the experimentally solved 'target' complexes from the widely used Docking Benchmark from the Weng Lab (excluding antibody-antigen complexes). This subset is extended to include the structures from the PDB related to those of the individual components of each complex, and hence represent potential templates for investigating and benchmarking integrated homology modeling and docking approaches. Template sets can be dynamically customized by specifying ranges in sequence similarity and in PDB release dates, or using other filtering options, such as excluding sets of specific structures from the template list. Multiple sequence alignments, as well as structural alignments of the templates to their corresponding subunits in the target are also provided. The resource is accessible online or can be downloaded at http://cluspro.org/benchmark, and is updated on a weekly basis in synchrony with new PDB releases. Proteins 2016; 85:10-16. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

From Structure to Function: A Comprehensive Compendium of Tools to Unveil Protein Domains and Understand Their Role in Cytokinesis.

PubMed

Rincon, Sergio A; Paoletti, Anne

2016-01-01

Unveiling the function of a novel protein is a challenging task that requires careful experimental design. Yeast cytokinesis is a conserved process that involves modular structural and regulatory proteins. For such proteins, an important step is to identify their domains and structural organization. Here we briefly discuss a collection of methods commonly used for sequence alignment and prediction of protein structure that represent powerful tools for the identification homologous domains and design of structure-function approaches to test experimentally the function of multi-domain proteins such as those implicated in yeast cytokinesis.

Conservation of coevolving protein interfaces bridges prokaryote-eukaryote homologies in the twilight zone.

PubMed

Rodriguez-Rivas, Juan; Marsili, Simone; Juan, David; Valencia, Alfonso

2016-12-27

Protein-protein interactions are fundamental for the proper functioning of the cell. As a result, protein interaction surfaces are subject to strong evolutionary constraints. Recent developments have shown that residue coevolution provides accurate predictions of heterodimeric protein interfaces from sequence information. So far these approaches have been limited to the analysis of families of prokaryotic complexes for which large multiple sequence alignments of homologous sequences can be compiled. We explore the hypothesis that coevolution points to structurally conserved contacts at protein-protein interfaces, which can be reliably projected to homologous complexes with distantly related sequences. We introduce a domain-centered protocol to study the interplay between residue coevolution and structural conservation of protein-protein interfaces. We show that sequence-based coevolutionary analysis systematically identifies residue contacts at prokaryotic interfaces that are structurally conserved at the interface of their eukaryotic counterparts. In turn, this allows the prediction of conserved contacts at eukaryotic protein-protein interfaces with high confidence using solely mutational patterns extracted from prokaryotic genomes. Even in the context of high divergence in sequence (the twilight zone), where standard homology modeling of protein complexes is unreliable, our approach provides sequence-based accurate information about specific details of protein interactions at the residue level. Selected examples of the application of prokaryotic coevolutionary analysis to the prediction of eukaryotic interfaces further illustrate the potential of this approach.

New in protein structure and function annotation: hotspots, single nucleotide polymorphisms and the 'Deep Web'.

PubMed

Bromberg, Yana; Yachdav, Guy; Ofran, Yanay; Schneider, Reinhard; Rost, Burkhard

2009-05-01

The rapidly increasing quantity of protein sequence data continues to widen the gap between available sequences and annotations. Comparative modeling suggests some aspects of the 3D structures of approximately half of all known proteins; homology- and network-based inferences annotate some aspect of function for a similar fraction of the proteome. For most known protein sequences, however, there is detailed knowledge about neither their function nor their structure. Comprehensive efforts towards the expert curation of sequence annotations have failed to meet the demand of the rapidly increasing number of available sequences. Only the automated prediction of protein function in the absence of homology can close the gap between available sequences and annotations in the foreseeable future. This review focuses on two novel methods for automated annotation, and briefly presents an outlook on how modern web software may revolutionize the field of protein sequence annotation. First, predictions of protein binding sites and functional hotspots, and the evolution of these into the most successful type of prediction of protein function from sequence will be discussed. Second, a new tool, comprehensive in silico mutagenesis, which contributes important novel predictions of function and at the same time prepares for the onset of the next sequencing revolution, will be described. While these two new sub-fields of protein prediction represent the breakthroughs that have been achieved methodologically, it will then be argued that a different development might further change the way biomedical researchers benefit from annotations: modern web software can connect the worldwide web in any browser with the 'Deep Web' (ie, proprietary data resources). The availability of this direct connection, and the resulting access to a wealth of data, may impact drug discovery and development more than any existing method that contributes to protein annotation.

A functional role of Rv1738 in Mycobacterium tuberculosis persistence suggested by racemic protein crystallography

DOE Office of Scientific and Technical Information (OSTI.GOV)

Bunker, Richard D.; Mandal, Kalyaneswar; Bashiri, Ghader

Racemic protein crystallography was used to determine the X-ray structure of the predicted Mycobacterium tuberculosis protein Rv1738, which had been completely recalcitrant to crystallization in its natural L-form. Native chemical ligation was used to synthesize both L-protein and D-protein enantiomers of Rv1738. Crystallization of the racemic {D-protein + L-protein} mixture was immediately successful. The resulting crystals diffracted to high resolution and also enabled facile structure determination because of the quantized phases of the data from centrosymmetric crystals. The X-ray structure of Rv1738 revealed striking similarity with bacterial hibernation factors, despite minimal sequence similarity. As a result, we predict that Rv1738,more » which is highly up-regulated in conditions that mimic the onset of persistence, helps trigger dormancy by association with the bacterial ribosome.« less

A functional role of Rv1738 in Mycobacterium tuberculosis persistence suggested by racemic protein crystallography

DOE PAGES

Bunker, Richard D.; Mandal, Kalyaneswar; Bashiri, Ghader; ...

2015-04-07

Racemic protein crystallography was used to determine the X-ray structure of the predicted Mycobacterium tuberculosis protein Rv1738, which had been completely recalcitrant to crystallization in its natural L-form. Native chemical ligation was used to synthesize both L-protein and D-protein enantiomers of Rv1738. Crystallization of the racemic {D-protein + L-protein} mixture was immediately successful. The resulting crystals diffracted to high resolution and also enabled facile structure determination because of the quantized phases of the data from centrosymmetric crystals. The X-ray structure of Rv1738 revealed striking similarity with bacterial hibernation factors, despite minimal sequence similarity. As a result, we predict that Rv1738,more » which is highly up-regulated in conditions that mimic the onset of persistence, helps trigger dormancy by association with the bacterial ribosome.« less

Crystal structure of the toxin Msmeg_6760, the structural homolog of Mycobacterium tuberculosis Rv2035, a novel type II toxin involved in the hypoxic response

DOE Office of Scientific and Technical Information (OSTI.GOV)

Bajaj, R. Alexandra; Arbing, Mark A.; Shin, Annie

The structure of Msmeg_6760, a protein of unknown function, has been determined. Biochemical and bioinformatics analyses determined that Msmeg_6760 interacts with a protein encoded in the same operon, Msmeg_6762, and predicted that the operon is a toxin–antitoxin (TA) system. Structural comparison of Msmeg_6760 with proteins of known function suggests that Msmeg_6760 binds a hydrophobic ligand in a buried cavity lined by large hydrophobic residues. Access to this cavity could be controlled by a gate–latch mechanism. The function of the Msmeg_6760 toxin is unknown, but structure-based predictions revealed that Msmeg_6760 and Msmeg_6762 are homologous to Rv2034 and Rv2035, a predicted novelmore » TA system involved inMycobacterium tuberculosislatency during macrophage infection. The Msmeg_6760 toxin fold has not been previously described for bacterial toxins and its unique structural features suggest that toxin activation is likely to be mediated by a novel mechanism.« less

A cross docking pipeline for improving pose prediction and virtual screening performance

NASA Astrophysics Data System (ADS)

Kumar, Ashutosh; Zhang, Kam Y. J.

2018-01-01

Pose prediction and virtual screening performance of a molecular docking method depend on the choice of protein structures used for docking. Multiple structures for a target protein are often used to take into account the receptor flexibility and problems associated with a single receptor structure. However, the use of multiple receptor structures is computationally expensive when docking a large library of small molecules. Here, we propose a new cross-docking pipeline suitable to dock a large library of molecules while taking advantage of multiple target protein structures. Our method involves the selection of a suitable receptor for each ligand in a screening library utilizing ligand 3D shape similarity with crystallographic ligands. We have prospectively evaluated our method in D3R Grand Challenge 2 and demonstrated that our cross-docking pipeline can achieve similar or better performance than using either single or multiple-receptor structures. Moreover, our method displayed not only decent pose prediction performance but also better virtual screening performance over several other methods.

SVM-PB-Pred: SVM based protein block prediction method using sequence profiles and secondary structures.

PubMed

Suresh, V; Parthasarathy, S

2014-01-01

We developed a support vector machine based web server called SVM-PB-Pred, to predict the Protein Block for any given amino acid sequence. The input features of SVM-PB-Pred include i) sequence profiles (PSSM) and ii) actual secondary structures (SS) from DSSP method or predicted secondary structures from NPS@ and GOR4 methods. There were three combined input features PSSM+SS(DSSP), PSSM+SS(NPS@) and PSSM+SS(GOR4) used to test and train the SVM models. Similarly, four datasets RS90, DB433, LI1264 and SP1577 were used to develop the SVM models. These four SVM models developed were tested using three different benchmarking tests namely; (i) self consistency, (ii) seven fold cross validation test and (iii) independent case test. The maximum possible prediction accuracy of ~70% was observed in self consistency test for the SVM models of both LI1264 and SP1577 datasets, where PSSM+SS(DSSP) input features was used to test. The prediction accuracies were reduced to ~53% for PSSM+SS(NPS@) and ~43% for PSSM+SS(GOR4) in independent case test, for the SVM models of above two same datasets. Using our method, it is possible to predict the protein block letters for any query protein sequence with ~53% accuracy, when the SP1577 dataset and predicted secondary structure from NPS@ server were used. The SVM-PB-Pred server can be freely accessed through http://bioinfo.bdu.ac.in/~svmpbpred.

Benchmark data sets for structure-based computational target prediction.

PubMed

Schomburg, Karen T; Rarey, Matthias

2014-08-25

Structure-based computational target prediction methods identify potential targets for a bioactive compound. Methods based on protein-ligand docking so far face many challenges, where the greatest probably is the ranking of true targets in a large data set of protein structures. Currently, no standard data sets for evaluation exist, rendering comparison and demonstration of improvements of methods cumbersome. Therefore, we propose two data sets and evaluation strategies for a meaningful evaluation of new target prediction methods, i.e., a small data set consisting of three target classes for detailed proof-of-concept and selectivity studies and a large data set consisting of 7992 protein structures and 72 drug-like ligands allowing statistical evaluation with performance metrics on a drug-like chemical space. Both data sets are built from openly available resources, and any information needed to perform the described experiments is reported. We describe the composition of the data sets, the setup of screening experiments, and the evaluation strategy. Performance metrics capable to measure the early recognition of enrichments like AUC, BEDROC, and NSLR are proposed. We apply a sequence-based target prediction method to the large data set to analyze its content of nontrivial evaluation cases. The proposed data sets are used for method evaluation of our new inverse screening method iRAISE. The small data set reveals the method's capability and limitations to selectively distinguish between rather similar protein structures. The large data set simulates real target identification scenarios. iRAISE achieves in 55% excellent or good enrichment a median AUC of 0.67 and RMSDs below 2.0 Å for 74% and was able to predict the first true target in 59 out of 72 cases in the top 2% of the protein data set of about 8000 structures.

A Feature and Algorithm Selection Method for Improving the Prediction of Protein Structural Class.

PubMed

Ni, Qianwu; Chen, Lei

2017-01-01

Correct prediction of protein structural class is beneficial to investigation on protein functions, regulations and interactions. In recent years, several computational methods have been proposed in this regard. However, based on various features, it is still a great challenge to select proper classification algorithm and extract essential features to participate in classification. In this study, a feature and algorithm selection method was presented for improving the accuracy of protein structural class prediction. The amino acid compositions and physiochemical features were adopted to represent features and thirty-eight machine learning algorithms collected in Weka were employed. All features were first analyzed by a feature selection method, minimum redundancy maximum relevance (mRMR), producing a feature list. Then, several feature sets were constructed by adding features in the list one by one. For each feature set, thirtyeight algorithms were executed on a dataset, in which proteins were represented by features in the set. The predicted classes yielded by these algorithms and true class of each protein were collected to construct a dataset, which were analyzed by mRMR method, yielding an algorithm list. From the algorithm list, the algorithm was taken one by one to build an ensemble prediction model. Finally, we selected the ensemble prediction model with the best performance as the optimal ensemble prediction model. Experimental results indicate that the constructed model is much superior to models using single algorithm and other models that only adopt feature selection procedure or algorithm selection procedure. The feature selection procedure or algorithm selection procedure are really helpful for building an ensemble prediction model that can yield a better performance. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

Thermodynamic database for proteins: features and applications.

PubMed

Gromiha, M Michael; Sarai, Akinori

2010-01-01

We have developed a thermodynamic database for proteins and mutants, ProTherm, which is a collection of a large number of thermodynamic data on protein stability along with the sequence and structure information, experimental methods and conditions, and literature information. This is a valuable resource for understanding/predicting the stability of proteins, and it can be accessible at http://www.gibk26.bse.kyutech.ac.jp/jouhou/Protherm/protherm.html . ProTherm has several features including various search, display, and sorting options and visualization tools. We have analyzed the data in ProTherm to examine the relationship among thermodynamics, structure, and function of proteins. We describe the progress on the development of methods for understanding/predicting protein stability, such as (i) relationship between the stability of protein mutants and amino acid properties, (ii) average assignment method, (iii) empirical energy functions, (iv) torsion, distance, and contact potentials, and (v) machine learning techniques. The list of online resources for predicting protein stability has also been provided.

PconsFold: improved contact predictions improve protein models.

PubMed

Michel, Mirco; Hayat, Sikander; Skwark, Marcin J; Sander, Chris; Marks, Debora S; Elofsson, Arne

2014-09-01

Recently it has been shown that the quality of protein contact prediction from evolutionary information can be improved significantly if direct and indirect information is separated. Given sufficiently large protein families, the contact predictions contain sufficient information to predict the structure of many protein families. However, since the first studies contact prediction methods have improved. Here, we ask how much the final models are improved if improved contact predictions are used. In a small benchmark of 15 proteins, we show that the TM-scores of top-ranked models are improved by on average 33% using PconsFold compared with the original version of EVfold. In a larger benchmark, we find that the quality is improved with 15-30% when using PconsC in comparison with earlier contact prediction methods. Further, using Rosetta instead of CNS does not significantly improve global model accuracy, but the chemistry of models generated with Rosetta is improved. PconsFold is a fully automated pipeline for ab initio protein structure prediction based on evolutionary information. PconsFold is based on PconsC contact prediction and uses the Rosetta folding protocol. Due to its modularity, the contact prediction tool can be easily exchanged. The source code of PconsFold is available on GitHub at https://www.github.com/ElofssonLab/pcons-fold under the MIT license. PconsC is available from http://c.pcons.net/. Supplementary data are available at Bioinformatics online. © The Author 2014. Published by Oxford University Press.

Protein Structure Prediction with Evolutionary Algorithms

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hart, W.E.; Krasnogor, N.; Pelta, D.A.

1999-02-08

Evolutionary algorithms have been successfully applied to a variety of molecular structure prediction problems. In this paper we reconsider the design of genetic algorithms that have been applied to a simple protein structure prediction problem. Our analysis considers the impact of several algorithmic factors for this problem: the confirmational representation, the energy formulation and the way in which infeasible conformations are penalized, Further we empirically evaluated the impact of these factors on a small set of polymer sequences. Our analysis leads to specific recommendations for both GAs as well as other heuristic methods for solving PSP on the HP model.

Novel proteases from the genome of the carnivorous plant Drosera capensis: structural prediction and comparative analysis

PubMed Central

Butts, Carter T.; Bierma, Jan C.; Martin, Rachel W.

2016-01-01

In his 1875 monograph on insectivorous plants, Darwin described the feeding reactions of Drosera flypaper traps and predicted that their secretions contained a “ferment” similar to mammalian pepsin, an aspartic protease. Here we report a high-quality draft genome sequence for the cape sundew, Drosera capensis, the first genome of a carnivorous plant from order Caryophyllales, which also includes the Venus flytrap (Dionaea) and the tropical pitcher plants (Nepenthes). This species was selected in part for its hardiness and ease of cultivation, making it an excellent model organism for further investigations of plant carnivory. Analysis of predicted protein sequences yields genes encoding proteases homologous to those found in other plants, some of which display sequence and structural features that suggest novel functionalities. Because the sequence similarity to proteins of known structure is in most cases too low for traditional homology modeling, 3D structures of representative proteases are predicted using comparative modeling with all-atom refinement. Although the overall folds and active residues for these proteins are conserved, we find structural and sequence differences consistent with a diversity of substrate recognition patterns. Finally, we predict differences in substrate specificities using in silico experiments, providing targets for structure/function studies of novel enzymes with biological and technological significance. PMID:27353064

Predicting protein structures with a multiplayer online game.

PubMed

Cooper, Seth; Khatib, Firas; Treuille, Adrien; Barbero, Janos; Lee, Jeehyung; Beenen, Michael; Leaver-Fay, Andrew; Baker, David; Popović, Zoran; Players, Foldit

2010-08-05

People exert large amounts of problem-solving effort playing computer games. Simple image- and text-recognition tasks have been successfully 'crowd-sourced' through games, but it is not clear if more complex scientific problems can be solved with human-directed computing. Protein structure prediction is one such problem: locating the biologically relevant native conformation of a protein is a formidable computational challenge given the very large size of the search space. Here we describe Foldit, a multiplayer online game that engages non-scientists in solving hard prediction problems. Foldit players interact with protein structures using direct manipulation tools and user-friendly versions of algorithms from the Rosetta structure prediction methodology, while they compete and collaborate to optimize the computed energy. We show that top-ranked Foldit players excel at solving challenging structure refinement problems in which substantial backbone rearrangements are necessary to achieve the burial of hydrophobic residues. Players working collaboratively develop a rich assortment of new strategies and algorithms; unlike computational approaches, they explore not only the conformational space but also the space of possible search strategies. The integration of human visual problem-solving and strategy development capabilities with traditional computational algorithms through interactive multiplayer games is a powerful new approach to solving computationally-limited scientific problems.

Robust enzyme design: bioinformatic tools for improved protein stability.

PubMed

Suplatov, Dmitry; Voevodin, Vladimir; Švedas, Vytas

2015-03-01

The ability of proteins and enzymes to maintain a functionally active conformation under adverse environmental conditions is an important feature of biocatalysts, vaccines, and biopharmaceutical proteins. From an evolutionary perspective, robust stability of proteins improves their biological fitness and allows for further optimization. Viewed from an industrial perspective, enzyme stability is crucial for the practical application of enzymes under the required reaction conditions. In this review, we analyze bioinformatic-driven strategies that are used to predict structural changes that can be applied to wild type proteins in order to produce more stable variants. The most commonly employed techniques can be classified into stochastic approaches, empirical or systematic rational design strategies, and design of chimeric proteins. We conclude that bioinformatic analysis can be efficiently used to study large protein superfamilies systematically as well as to predict particular structural changes which increase enzyme stability. Evolution has created a diversity of protein properties that are encoded in genomic sequences and structural data. Bioinformatics has the power to uncover this evolutionary code and provide a reproducible selection of hotspots - key residues to be mutated in order to produce more stable and functionally diverse proteins and enzymes. Further development of systematic bioinformatic procedures is needed to organize and analyze sequences and structures of proteins within large superfamilies and to link them to function, as well as to provide knowledge-based predictions for experimental evaluation. Copyright © 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Protein Structure Determination using Metagenome sequence data

PubMed Central

Ovchinnikov, Sergey; Park, Hahnbeom; Varghese, Neha; Huang, Po-Ssu; Pavlopoulos, Georgios A.; Kim, David E.; Kamisetty, Hetunandan; Kyrpides, Nikos C.; Baker, David

2017-01-01

Despite decades of work by structural biologists, there are still ~5200 protein families with unknown structure outside the range of comparative modeling. We show that Rosetta structure prediction guided by residue-residue contacts inferred from evolutionary information can accurately model proteins that belong to large families, and that metagenome sequence data more than triples the number of protein families with sufficient sequences for accurate modeling. We then integrate metagenome data, contact based structure matching and Rosetta structure calculations to generate models for 614 protein families with currently unknown structures; 206 are membrane proteins and 137 have folds not represented in the PDB. This approach provides the representative models for large protein families originally envisioned as the goal of the protein structure initiative at a fraction of the cost. PMID:28104891

Integration of Structural Dynamics and Molecular Evolution via Protein Interaction Networks: A New Era in Genomic Medicine

PubMed Central

Kumar, Avishek; Butler, Brandon M.; Kumar, Sudhir; Ozkan, S. Banu

2016-01-01

Summary Sequencing technologies are revealing many new non-synonymous single nucleotide variants (nsSNVs) in each personal exome. To assess their functional impacts, comparative genomics is frequently employed to predict if they are benign or not. However, evolutionary analysis alone is insufficient, because it misdiagnoses many disease-associated nsSNVs, such as those at positions involved in protein interfaces, and because evolutionary predictions do not provide mechanistic insights into functional change or loss. Structural analyses can aid in overcoming both of these problems by incorporating conformational dynamics and allostery in nSNV diagnosis. Finally, protein-protein interaction networks using systems-level methodologies shed light onto disease etiology and pathogenesis. Bridging these network approaches with structurally resolved protein interactions and dynamics will advance genomic medicine. PMID:26684487

Mapping monomeric threading to protein-protein structure prediction.

PubMed

Guerler, Aysam; Govindarajoo, Brandon; Zhang, Yang

2013-03-25

The key step of template-based protein-protein structure prediction is the recognition of complexes from experimental structure libraries that have similar quaternary fold. Maintaining two monomer and dimer structure libraries is however laborious, and inappropriate library construction can degrade template recognition coverage. We propose a novel strategy SPRING to identify complexes by mapping monomeric threading alignments to protein-protein interactions based on the original oligomer entries in the PDB, which does not rely on library construction and increases the efficiency and quality of complex template recognitions. SPRING is tested on 1838 nonhomologous protein complexes which can recognize correct quaternary template structures with a TM score >0.5 in 1115 cases after excluding homologous proteins. The average TM score of the first model is 60% and 17% higher than that by HHsearch and COTH, respectively, while the number of targets with an interface RMSD <2.5 Å by SPRING is 134% and 167% higher than these competing methods. SPRING is controlled with ZDOCK on 77 docking benchmark proteins. Although the relative performance of SPRING and ZDOCK depends on the level of homology filters, a combination of the two methods can result in a significantly higher model quality than ZDOCK at all homology thresholds. These data demonstrate a new efficient approach to quaternary structure recognition that is ready to use for genome-scale modeling of protein-protein interactions due to the high speed and accuracy.

Prediction of beta-turns at over 80% accuracy based on an ensemble of predicted secondary structures and multiple alignments

PubMed Central

Zheng, Ce; Kurgan, Lukasz

2008-01-01

Background β-turn is a secondary protein structure type that plays significant role in protein folding, stability, and molecular recognition. To date, several methods for prediction of β-turns from protein sequences were developed, but they are characterized by relatively poor prediction quality. The novelty of the proposed sequence-based β-turn predictor stems from the usage of a window based information extracted from four predicted three-state secondary structures, which together with a selected set of position specific scoring matrix (PSSM) values serve as an input to the support vector machine (SVM) predictor. Results We show that (1) all four predicted secondary structures are useful; (2) the most useful information extracted from the predicted secondary structure includes the structure of the predicted residue, secondary structure content in a window around the predicted residue, and features that indicate whether the predicted residue is inside a secondary structure segment; (3) the PSSM values of Asn, Asp, Gly, Ile, Leu, Met, Pro, and Val were among the top ranked features, which corroborates with recent studies. The Asn, Asp, Gly, and Pro indicate potential β-turns, while the remaining four amino acids are useful to predict non-β-turns. Empirical evaluation using three nonredundant datasets shows favorable Qtotal, Qpredicted and MCC values when compared with over a dozen of modern competing methods. Our method is the first to break the 80% Qtotal barrier and achieves Qtotal = 80.9%, MCC = 0.47, and Qpredicted higher by over 6% when compared with the second best method. We use feature selection to reduce the dimensionality of the feature vector used as the input for the proposed prediction method. The applied feature set is smaller by 86, 62 and 37% when compared with the second and two third-best (with respect to MCC) competing methods, respectively. Conclusion Experiments show that the proposed method constitutes an improvement over the competing prediction methods. The proposed prediction model can better discriminate between β-turns and non-β-turns due to obtaining lower numbers of false positive predictions. The prediction model and datasets are freely available at . PMID:18847492

NeEMO: a method using residue interaction networks to improve prediction of protein stability upon mutation.

PubMed

Giollo, Manuel; Martin, Alberto J M; Walsh, Ian; Ferrari, Carlo; Tosatto, Silvio C E

2014-01-01

The rapid growth of un-annotated missense variants poses challenges requiring novel strategies for their interpretation. From the thermodynamic point of view, amino acid changes can lead to a change in the internal energy of a protein and induce structural rearrangements. This is of great relevance for the study of diseases and protein design, justifying the development of prediction methods for variant-induced stability changes. Here we propose NeEMO, a tool for the evaluation of stability changes using an effective representation of proteins based on residue interaction networks (RINs). RINs are used to extract useful features describing interactions of the mutant amino acid with its structural environment. Benchmarking shows NeEMO to be very effective, allowing reliable predictions in different parts of the protein such as β-strands and buried residues. Validation on a previously published independent dataset shows that NeEMO has a Pearson correlation coefficient of 0.77 and a standard error of 1 Kcal/mol, outperforming nine recent methods. The NeEMO web server can be freely accessed from URL: http://protein.bio.unipd.it/neemo/. NeEMO offers an innovative and reliable tool for the annotation of amino acid changes. A key contribution are RINs, which can be used for modeling proteins and their interactions effectively. Interestingly, the approach is very general, and can motivate the development of a new family of RIN-based protein structure analyzers. NeEMO may suggest innovative strategies for bioinformatics tools beyond protein stability prediction.

AGGRESCAN3D (A3D): server for prediction of aggregation properties of protein structures.

PubMed

Zambrano, Rafael; Jamroz, Michal; Szczasiuk, Agata; Pujols, Jordi; Kmiecik, Sebastian; Ventura, Salvador

2015-07-01

Protein aggregation underlies an increasing number of disorders and constitutes a major bottleneck in the development of therapeutic proteins. Our present understanding on the molecular determinants of protein aggregation has crystalized in a series of predictive algorithms to identify aggregation-prone sites. A majority of these methods rely only on sequence. Therefore, they find difficulties to predict the aggregation properties of folded globular proteins, where aggregation-prone sites are often not contiguous in sequence or buried inside the native structure. The AGGRESCAN3D (A3D) server overcomes these limitations by taking into account the protein structure and the experimental aggregation propensity scale from the well-established AGGRESCAN method. Using the A3D server, the identified aggregation-prone residues can be virtually mutated to design variants with increased solubility, or to test the impact of pathogenic mutations. Additionally, A3D server enables to take into account the dynamic fluctuations of protein structure in solution, which may influence aggregation propensity. This is possible in A3D Dynamic Mode that exploits the CABS-flex approach for the fast simulations of flexibility of globular proteins. The A3D server can be accessed at http://biocomp.chem.uw.edu.pl/A3D/. © The Author(s) 2015. Published by Oxford University Press on behalf of Nucleic Acids Research.

Prediction of Protein Configurational Entropy (Popcoen).

PubMed

Goethe, Martin; Gleixner, Jan; Fita, Ignacio; Rubi, J Miguel

2018-03-13

A knowledge-based method for configurational entropy prediction of proteins is presented; this methodology is extremely fast, compared to previous approaches, because it does not involve any type of configurational sampling. Instead, the configurational entropy of a query fold is estimated by evaluating an artificial neural network, which was trained on molecular-dynamics simulations of ∼1000 proteins. The predicted entropy can be incorporated into a large class of protein software based on cost-function minimization/evaluation, in which configurational entropy is currently neglected for performance reasons. Software of this type is used for all major protein tasks such as structure predictions, proteins design, NMR and X-ray refinement, docking, and mutation effect predictions. Integrating the predicted entropy can yield a significant accuracy increase as we show exemplarily for native-state identification with the prominent protein software FoldX. The method has been termed Popcoen for Prediction of Protein Configurational Entropy. An implementation is freely available at http://fmc.ub.edu/popcoen/ .

BetaSCPWeb: side-chain prediction for protein structures using Voronoi diagrams and geometry prioritization

PubMed Central

Ryu, Joonghyun; Lee, Mokwon; Cha, Jehyun; Laskowski, Roman A.; Ryu, Seong Eon; Kim, Deok-Soo

2016-01-01

Many applications, such as protein design, homology modeling, flexible docking, etc. require the prediction of a protein's optimal side-chain conformations from just its amino acid sequence and backbone structure. Side-chain prediction (SCP) is an NP-hard energy minimization problem. Here, we present BetaSCPWeb which efficiently computes a conformation close to optimal using a geometry-prioritization method based on the Voronoi diagram of spherical atoms. Its outputs are visual, textual and PDB file format. The web server is free and open to all users at http://voronoi.hanyang.ac.kr/betascpweb with no login requirement. PMID:27151195

How Structure Defines Affinity in Protein-Protein Interactions

PubMed Central

Erijman, Ariel; Rosenthal, Eran; Shifman, Julia M.

2014-01-01

Protein-protein interactions (PPI) in nature are conveyed by a multitude of binding modes involving various surfaces, secondary structure elements and intermolecular interactions. This diversity results in PPI binding affinities that span more than nine orders of magnitude. Several early studies attempted to correlate PPI binding affinities to various structure-derived features with limited success. The growing number of high-resolution structures, the appearance of more precise methods for measuring binding affinities and the development of new computational algorithms enable more thorough investigations in this direction. Here, we use a large dataset of PPI structures with the documented binding affinities to calculate a number of structure-based features that could potentially define binding energetics. We explore how well each calculated biophysical feature alone correlates with binding affinity and determine the features that could be used to distinguish between high-, medium- and low- affinity PPIs. Furthermore, we test how various combinations of features could be applied to predict binding affinity and observe a slow improvement in correlation as more features are incorporated into the equation. In addition, we observe a considerable improvement in predictions if we exclude from our analysis low-resolution and NMR structures, revealing the importance of capturing exact intermolecular interactions in our calculations. Our analysis should facilitate prediction of new interactions on the genome scale, better characterization of signaling networks and design of novel binding partners for various target proteins. PMID:25329579

The dual role of fragments in fragment-assembly methods for de novo protein structure prediction

PubMed Central

Handl, Julia; Knowles, Joshua; Vernon, Robert; Baker, David; Lovell, Simon C.

2013-01-01

In fragment-assembly techniques for protein structure prediction, models of protein structure are assembled from fragments of known protein structures. This process is typically guided by a knowledge-based energy function and uses a heuristic optimization method. The fragments play two important roles in this process: they define the set of structural parameters available, and they also assume the role of the main variation operators that are used by the optimiser. Previous analysis has typically focused on the first of these roles. In particular, the relationship between local amino acid sequence and local protein structure has been studied by a range of authors. The correlation between the two has been shown to vary with the window length considered, and the results of these analyses have informed directly the choice of fragment length in state-of-the-art prediction techniques. Here, we focus on the second role of fragments and aim to determine the effect of fragment length from an optimization perspective. We use theoretical analyses to reveal how the size and structure of the search space changes as a function of insertion length. Furthermore, empirical analyses are used to explore additional ways in which the size of the fragment insertion influences the search both in a simulation model and for the fragment-assembly technique, Rosetta. PMID:22095594

Biomacromolecular quantitative structure-activity relationship (BioQSAR): a proof-of-concept study on the modeling, prediction and interpretation of protein-protein binding affinity.

PubMed

Zhou, Peng; Wang, Congcong; Tian, Feifei; Ren, Yanrong; Yang, Chao; Huang, Jian

2013-01-01

Quantitative structure-activity relationship (QSAR), a regression modeling methodology that establishes statistical correlation between structure feature and apparent behavior for a series of congeneric molecules quantitatively, has been widely used to evaluate the activity, toxicity and property of various small-molecule compounds such as drugs, toxicants and surfactants. However, it is surprising to see that such useful technique has only very limited applications to biomacromolecules, albeit the solved 3D atom-resolution structures of proteins, nucleic acids and their complexes have accumulated rapidly in past decades. Here, we present a proof-of-concept paradigm for the modeling, prediction and interpretation of the binding affinity of 144 sequence-nonredundant, structure-available and affinity-known protein complexes (Kastritis et al. Protein Sci 20:482-491, 2011) using a biomacromolecular QSAR (BioQSAR) scheme. We demonstrate that the modeling performance and predictive power of BioQSAR are comparable to or even better than that of traditional knowledge-based strategies, mechanism-type methods and empirical scoring algorithms, while BioQSAR possesses certain additional features compared to the traditional methods, such as adaptability, interpretability, deep-validation and high-efficiency. The BioQSAR scheme could be readily modified to infer the biological behavior and functions of other biomacromolecules, if their X-ray crystal structures, NMR conformation assemblies or computationally modeled structures are available.

Predicting the tolerated sequences for proteins and protein interfaces using RosettaBackrub flexible backbone design.

PubMed

Smith, Colin A; Kortemme, Tanja

2011-01-01

Predicting the set of sequences that are tolerated by a protein or protein interface, while maintaining a desired function, is useful for characterizing protein interaction specificity and for computationally designing sequence libraries to engineer proteins with new functions. Here we provide a general method, a detailed set of protocols, and several benchmarks and analyses for estimating tolerated sequences using flexible backbone protein design implemented in the Rosetta molecular modeling software suite. The input to the method is at least one experimentally determined three-dimensional protein structure or high-quality model. The starting structure(s) are expanded or refined into a conformational ensemble using Monte Carlo simulations consisting of backrub backbone and side chain moves in Rosetta. The method then uses a combination of simulated annealing and genetic algorithm optimization methods to enrich for low-energy sequences for the individual members of the ensemble. To emphasize certain functional requirements (e.g. forming a binding interface), interactions between and within parts of the structure (e.g. domains) can be reweighted in the scoring function. Results from each backbone structure are merged together to create a single estimate for the tolerated sequence space. We provide an extensive description of the protocol and its parameters, all source code, example analysis scripts and three tests applying this method to finding sequences predicted to stabilize proteins or protein interfaces. The generality of this method makes many other applications possible, for example stabilizing interactions with small molecules, DNA, or RNA. Through the use of within-domain reweighting and/or multistate design, it may also be possible to use this method to find sequences that stabilize particular protein conformations or binding interactions over others.

AUTO-MUTE 2.0: A Portable Framework with Enhanced Capabilities for Predicting Protein Functional Consequences upon Mutation.

PubMed

Masso, Majid; Vaisman, Iosif I

2014-01-01

The AUTO-MUTE 2.0 stand-alone software package includes a collection of programs for predicting functional changes to proteins upon single residue substitutions, developed by combining structure-based features with trained statistical learning models. Three of the predictors evaluate changes to protein stability upon mutation, each complementing a distinct experimental approach. Two additional classifiers are available, one for predicting activity changes due to residue replacements and the other for determining the disease potential of mutations associated with nonsynonymous single nucleotide polymorphisms (nsSNPs) in human proteins. These five command-line driven tools, as well as all the supporting programs, complement those that run our AUTO-MUTE web-based server. Nevertheless, all the codes have been rewritten and substantially altered for the new portable software, and they incorporate several new features based on user feedback. Included among these upgrades is the ability to perform three highly requested tasks: to run "big data" batch jobs; to generate predictions using modified protein data bank (PDB) structures, and unpublished personal models prepared using standard PDB file formatting; and to utilize NMR structure files that contain multiple models.

Mapping of ligand-binding cavities in proteins.

PubMed

Andersson, C David; Chen, Brian Y; Linusson, Anna

2010-05-01

The complex interactions between proteins and small organic molecules (ligands) are intensively studied because they play key roles in biological processes and drug activities. Here, we present a novel approach to characterize and map the ligand-binding cavities of proteins without direct geometric comparison of structures, based on Principal Component Analysis of cavity properties (related mainly to size, polarity, and charge). This approach can provide valuable information on the similarities and dissimilarities, of binding cavities due to mutations, between-species differences and flexibility upon ligand-binding. The presented results show that information on ligand-binding cavity variations can complement information on protein similarity obtained from sequence comparisons. The predictive aspect of the method is exemplified by successful predictions of serine proteases that were not included in the model construction. The presented strategy to compare ligand-binding cavities of related and unrelated proteins has many potential applications within protein and medicinal chemistry, for example in the characterization and mapping of "orphan structures", selection of protein structures for docking studies in structure-based design, and identification of proteins for selectivity screens in drug design programs. 2009 Wiley-Liss, Inc.

Random close packing in protein cores

NASA Astrophysics Data System (ADS)

Gaines, Jennifer C.; Smith, W. Wendell; Regan, Lynne; O'Hern, Corey S.

2016-03-01

Shortly after the determination of the first protein x-ray crystal structures, researchers analyzed their cores and reported packing fractions ϕ ≈0.75 , a value that is similar to close packing of equal-sized spheres. A limitation of these analyses was the use of extended atom models, rather than the more physically accurate explicit hydrogen model. The validity of the explicit hydrogen model was proved in our previous studies by its ability to predict the side chain dihedral angle distributions observed in proteins. In contrast, the extended atom model is not able to recapitulate the side chain dihedral angle distributions, and gives rise to large atomic clashes at side chain dihedral angle combinations that are highly probable in protein crystal structures. Here, we employ the explicit hydrogen model to calculate the packing fraction of the cores of over 200 high-resolution protein structures. We find that these protein cores have ϕ ≈0.56 , which is similar to results obtained from simulations of random packings of individual amino acids. This result provides a deeper understanding of the physical basis of protein structure that will enable predictions of the effects of amino acid mutations to protein cores and interfaces of known structure.

Random close packing in protein cores.

PubMed

Gaines, Jennifer C; Smith, W Wendell; Regan, Lynne; O'Hern, Corey S

2016-03-01

Shortly after the determination of the first protein x-ray crystal structures, researchers analyzed their cores and reported packing fractions ϕ ≈ 0.75, a value that is similar to close packing of equal-sized spheres. A limitation of these analyses was the use of extended atom models, rather than the more physically accurate explicit hydrogen model. The validity of the explicit hydrogen model was proved in our previous studies by its ability to predict the side chain dihedral angle distributions observed in proteins. In contrast, the extended atom model is not able to recapitulate the side chain dihedral angle distributions, and gives rise to large atomic clashes at side chain dihedral angle combinations that are highly probable in protein crystal structures. Here, we employ the explicit hydrogen model to calculate the packing fraction of the cores of over 200 high-resolution protein structures. We find that these protein cores have ϕ ≈ 0.56, which is similar to results obtained from simulations of random packings of individual amino acids. This result provides a deeper understanding of the physical basis of protein structure that will enable predictions of the effects of amino acid mutations to protein cores and interfaces of known structure.

fRMSDPred: Predicting Local RMSD Between Structural Fragments Using Sequence Information

DTIC Science & Technology

2007-04-04

machine learning approaches for estimating the RMSD value of a pair of protein fragments. These estimated fragment-level RMSD values can be used to construct the alignment, assess the quality of an alignment, and identify high-quality alignment segments. We present algorithms to solve this fragment-level RMSD prediction problem using a supervised learning framework based on support vector regression and classification that incorporates protein profiles, predicted secondary structure, effective information encoding schemes, and novel second-order pairwise exponential kernel

Improved method for predicting protein fold patterns with ensemble classifiers.

PubMed

Chen, W; Liu, X; Huang, Y; Jiang, Y; Zou, Q; Lin, C

2012-01-27

Protein folding is recognized as a critical problem in the field of biophysics in the 21st century. Predicting protein-folding patterns is challenging due to the complex structure of proteins. In an attempt to solve this problem, we employed ensemble classifiers to improve prediction accuracy. In our experiments, 188-dimensional features were extracted based on the composition and physical-chemical property of proteins and 20-dimensional features were selected using a coupled position-specific scoring matrix. Compared with traditional prediction methods, these methods were superior in terms of prediction accuracy. The 188-dimensional feature-based method achieved 71.2% accuracy in five cross-validations. The accuracy rose to 77% when we used a 20-dimensional feature vector. These methods were used on recent data, with 54.2% accuracy. Source codes and dataset, together with web server and software tools for prediction, are available at: http://datamining.xmu.edu.cn/main/~cwc/ProteinPredict.html.

Ab Initio Protein Structure Prediction Using Chunk-TASSER

PubMed Central

Zhou, Hongyi; Skolnick, Jeffrey

2007-01-01

We have developed an ab initio protein structure prediction method called chunk-TASSER that uses ab initio folded supersecondary structure chunks of a given target as well as threading templates for obtaining contact potentials and distance restraints. The predicted chunks, selected on the basis of a new fragment comparison method, are folded by a fragment insertion method. Full-length models are built and refined by the TASSER methodology, which searches conformational space via parallel hyperbolic Monte Carlo. We employ an optimized reduced force field that includes knowledge-based statistical potentials and restraints derived from the chunks as well as threading templates. The method is tested on a dataset of 425 hard target proteins ≤250 amino acids in length. The average TM-scores of the best of top five models per target are 0.266, 0.336, and 0.362 by the threading algorithm SP3, original TASSER and chunk-TASSER, respectively. For a subset of 80 proteins with predicted α-helix content ≥50%, these averages are 0.284, 0.356, and 0.403, respectively. The percentages of proteins with the best of top five models having TM-score ≥0.4 (a statistically significant threshold for structural similarity) are 3.76, 20.94, and 28.94% by SP3, TASSER, and chunk-TASSER, respectively, overall, while for the subset of 80 predominantly helical proteins, these percentages are 2.50, 23.75, and 41.25%. Thus, chunk-TASSER shows a significant improvement over TASSER for modeling hard targets where no good template can be identified. We also tested chunk-TASSER on 21 medium/hard targets <200 amino-acids-long from CASP7. Chunk-TASSER is ∼11% (10%) better than TASSER for the total TM-score of the first (best of top five) models. Chunk-TASSER is fully automated and can be used in proteome scale protein structure prediction. PMID:17496016

Comprehensive inventory of protein complexes in the Protein Data Bank from consistent classification of interfaces.

PubMed

Bordner, Andrew J; Gorin, Andrey A

2008-05-12

Protein-protein interactions are ubiquitous and essential for all cellular processes. High-resolution X-ray crystallographic structures of protein complexes can reveal the details of their function and provide a basis for many computational and experimental approaches. Differentiation between biological and non-biological contacts and reconstruction of the intact complex is a challenging computational problem. A successful solution can provide additional insights into the fundamental principles of biological recognition and reduce errors in many algorithms and databases utilizing interaction information extracted from the Protein Data Bank (PDB). We have developed a method for identifying protein complexes in the PDB X-ray structures by a four step procedure: (1) comprehensively collecting all protein-protein interfaces; (2) clustering similar protein-protein interfaces together; (3) estimating the probability that each cluster is relevant based on a diverse set of properties; and (4) combining these scores for each PDB entry in order to predict the complex structure. The resulting clusters of biologically relevant interfaces provide a reliable catalog of evolutionary conserved protein-protein interactions. These interfaces, as well as the predicted protein complexes, are available from the Protein Interface Server (PInS) website (see Availability and requirements section). Our method demonstrates an almost two-fold reduction of the annotation error rate as evaluated on a large benchmark set of complexes validated from the literature. We also estimate relative contributions of each interface property to the accurate discrimination of biologically relevant interfaces and discuss possible directions for further improving the prediction method.

Efficient Relaxation of Protein-Protein Interfaces by Discrete Molecular Dynamics Simulations.

PubMed

Emperador, Agusti; Solernou, Albert; Sfriso, Pedro; Pons, Carles; Gelpi, Josep Lluis; Fernandez-Recio, Juan; Orozco, Modesto

2013-02-12

Protein-protein interactions are responsible for the transfer of information inside the cell and represent one of the most interesting research fields in structural biology. Unfortunately, after decades of intense research, experimental approaches still have difficulties in providing 3D structures for the hundreds of thousands of interactions formed between the different proteins in a living organism. The use of theoretical approaches like docking aims to complement experimental efforts to represent the structure of the protein interactome. However, we cannot ignore that current methods have limitations due to problems of sampling of the protein-protein conformational space and the lack of accuracy of available force fields. Cases that are especially difficult for prediction are those in which complex formation implies a non-negligible change in the conformation of the interacting proteins, i.e., those cases where protein flexibility plays a key role in protein-protein docking. In this work, we present a new approach to treat flexibility in docking by global structural relaxation based on ultrafast discrete molecular dynamics. On a standard benchmark of protein complexes, the method provides a general improvement over the results obtained by rigid docking. The method is especially efficient in cases with large conformational changes upon binding, in which structure relaxation with discrete molecular dynamics leads to a predictive success rate double that obtained with state-of-the-art rigid-body docking.

Discriminative structural approaches for enzyme active-site prediction.

PubMed

Kato, Tsuyoshi; Nagano, Nozomi

2011-02-15

Predicting enzyme active-sites in proteins is an important issue not only for protein sciences but also for a variety of practical applications such as drug design. Because enzyme reaction mechanisms are based on the local structures of enzyme active-sites, various template-based methods that compare local structures in proteins have been developed to date. In comparing such local sites, a simple measurement, RMSD, has been used so far. This paper introduces new machine learning algorithms that refine the similarity/deviation for comparison of local structures. The similarity/deviation is applied to two types of applications, single template analysis and multiple template analysis. In the single template analysis, a single template is used as a query to search proteins for active sites, whereas a protein structure is examined as a query to discover the possible active-sites using a set of templates in the multiple template analysis. This paper experimentally illustrates that the machine learning algorithms effectively improve the similarity/deviation measurements for both the analyses.

Support Vector Machines Trained with Evolutionary Algorithms Employing Kernel Adatron for Large Scale Classification of Protein Structures.

PubMed

Arana-Daniel, Nancy; Gallegos, Alberto A; López-Franco, Carlos; Alanís, Alma Y; Morales, Jacob; López-Franco, Adriana

2016-01-01

With the increasing power of computers, the amount of data that can be processed in small periods of time has grown exponentially, as has the importance of classifying large-scale data efficiently. Support vector machines have shown good results classifying large amounts of high-dimensional data, such as data generated by protein structure prediction, spam recognition, medical diagnosis, optical character recognition and text classification, etc. Most state of the art approaches for large-scale learning use traditional optimization methods, such as quadratic programming or gradient descent, which makes the use of evolutionary algorithms for training support vector machines an area to be explored. The present paper proposes an approach that is simple to implement based on evolutionary algorithms and Kernel-Adatron for solving large-scale classification problems, focusing on protein structure prediction. The functional properties of proteins depend upon their three-dimensional structures. Knowing the structures of proteins is crucial for biology and can lead to improvements in areas such as medicine, agriculture and biofuels.

Protein classification using probabilistic chain graphs and the Gene Ontology structure.

PubMed

Carroll, Steven; Pavlovic, Vladimir

2006-08-01

Probabilistic graphical models have been developed in the past for the task of protein classification. In many cases, classifications obtained from the Gene Ontology have been used to validate these models. In this work we directly incorporate the structure of the Gene Ontology into the graphical representation for protein classification. We present a method in which each protein is represented by a replicate of the Gene Ontology structure, effectively modeling each protein in its own 'annotation space'. Proteins are also connected to one another according to different measures of functional similarity, after which belief propagation is run to make predictions at all ontology terms. The proposed method was evaluated on a set of 4879 proteins from the Saccharomyces Genome Database whose interactions were also recorded in the GRID project. Results indicate that direct utilization of the Gene Ontology improves predictive ability, outperforming traditional models that do not take advantage of dependencies among functional terms. Average increase in accuracy (precision) of positive and negative term predictions of 27.8% (2.0%) over three different similarity measures and three subontologies was observed. C/C++/Perl implementation is available from authors upon request.

Robustness of atomistic Gō models in predicting native-like folding intermediates

NASA Astrophysics Data System (ADS)

Estácio, S. G.; Fernandes, C. S.; Krobath, H.; Faísca, P. F. N.; Shakhnovich, E. I.

2012-08-01

Gō models are exceedingly popular tools in computer simulations of protein folding. These models are native-centric, i.e., they are directly constructed from the protein's native structure. Therefore, it is important to understand up to which extent the atomistic details of the native structure dictate the folding behavior exhibited by Gō models. Here we address this challenge by performing exhaustive discrete molecular dynamics simulations of a Gō potential combined with a full atomistic protein representation. In particular, we investigate the robustness of this particular type of Gō models in predicting the existence of intermediate states in protein folding. We focus on the N47G mutational form of the Spc-SH3 folding domain (x-ray structure) and compare its folding pathway with that of alternative native structures produced in silico. Our methodological strategy comprises equilibrium folding simulations, structural clustering, and principal component analysis.

Predicted secondary structure similarity in the absence of primary amino acid sequence homology: hepatitis B virus open reading frames.

PubMed Central

Schaeffer, E; Sninsky, J J

1984-01-01

Proteins that are related evolutionarily may have diverged at the level of primary amino acid sequence while maintaining similar secondary structures. Computer analysis has been used to compare the open reading frames of the hepatitis B virus to those of the woodchuck hepatitis virus at the level of amino acid sequence, and to predict the relative hydrophilic character and the secondary structure of putative polypeptides. Similarity is seen at the levels of relative hydrophilicity and secondary structure, in the absence of sequence homology. These data reinforce the proposal that these open reading frames encode viral proteins. Computer analysis of this type can be more generally used to establish structural similarities between proteins that do not share obvious sequence homology as well as to assess whether an open reading frame is fortuitous or codes for a protein. PMID:6585835

Deciphering the Preference and Predicting the Viability of Circular Permutations in Proteins

PubMed Central

Liu, Yen-Yi; Wang, Li-Fen; Hwang, Jenn-Kang; Lyu, Ping-Chiang

2012-01-01

Circular permutation (CP) refers to situations in which the termini of a protein are relocated to other positions in the structure. CP occurs naturally and has been artificially created to study protein function, stability and folding. Recently CP is increasingly applied to engineer enzyme structure and function, and to create bifunctional fusion proteins unachievable by tandem fusion. CP is a complicated and expensive technique. An intrinsic difficulty in its application lies in the fact that not every position in a protein is amenable for creating a viable permutant. To examine the preferences of CP and develop CP viability prediction methods, we carried out comprehensive analyses of the sequence, structural, and dynamical properties of known CP sites using a variety of statistics and simulation methods, such as the bootstrap aggregating, permutation test and molecular dynamics simulations. CP particularly favors Gly, Pro, Asp and Asn. Positions preferred by CP lie within coils, loops, turns, and at residues that are exposed to solvent, weakly hydrogen-bonded, environmentally unpacked, or flexible. Disfavored positions include Cys, bulky hydrophobic residues, and residues located within helices or near the protein's core. These results fostered the development of an effective viable CP site prediction system, which combined four machine learning methods, e.g., artificial neural networks, the support vector machine, a random forest, and a hierarchical feature integration procedure developed in this work. As assessed by using the hydrofolate reductase dataset as the independent evaluation dataset, this prediction system achieved an AUC of 0.9. Large-scale predictions have been performed for nine thousand representative protein structures; several new potential applications of CP were thus identified. Many unreported preferences of CP are revealed in this study. The developed system is the best CP viability prediction method currently available. This work will facilitate the application of CP in research and biotechnology. PMID:22359629

Partial unfolding and refolding for structure refinement: A unified approach of geometric simulations and molecular dynamics.

PubMed

Kumar, Avishek; Campitelli, Paul; Thorpe, M F; Ozkan, S Banu

2015-12-01

The most successful protein structure prediction methods to date have been template-based modeling (TBM) or homology modeling, which predicts protein structure based on experimental structures. These high accuracy predictions sometimes retain structural errors due to incorrect templates or a lack of accurate templates in the case of low sequence similarity, making these structures inadequate in drug-design studies or molecular dynamics simulations. We have developed a new physics based approach to the protein refinement problem by mimicking the mechanism of chaperons that rehabilitate misfolded proteins. The template structure is unfolded by selectively (targeted) pulling on different portions of the protein using the geometric based technique FRODA, and then refolded using hierarchically restrained replica exchange molecular dynamics simulations (hr-REMD). FRODA unfolding is used to create a diverse set of topologies for surveying near native-like structures from a template and to provide a set of persistent contacts to be employed during re-folding. We have tested our approach on 13 previous CASP targets and observed that this method of folding an ensemble of partially unfolded structures, through the hierarchical addition of contact restraints (that is, first local and then nonlocal interactions), leads to a refolding of the structure along with refinement in most cases (12/13). Although this approach yields refined models through advancement in sampling, the task of blind selection of the best refined models still needs to be solved. Overall, the method can be useful for improved sampling for low resolution models where certain of the portions of the structure are incorrectly modeled. © 2015 Wiley Periodicals, Inc.

Template-Based Modeling of Protein-RNA Interactions.

PubMed

Zheng, Jinfang; Kundrotas, Petras J; Vakser, Ilya A; Liu, Shiyong

2016-09-01

Protein-RNA complexes formed by specific recognition between RNA and RNA-binding proteins play an important role in biological processes. More than a thousand of such proteins in human are curated and many novel RNA-binding proteins are to be discovered. Due to limitations of experimental approaches, computational techniques are needed for characterization of protein-RNA interactions. Although much progress has been made, adequate methodologies reliably providing atomic resolution structural details are still lacking. Although protein-RNA free docking approaches proved to be useful, in general, the template-based approaches provide higher quality of predictions. Templates are key to building a high quality model. Sequence/structure relationships were studied based on a representative set of binary protein-RNA complexes from PDB. Several approaches were tested for pairwise target/template alignment. The analysis revealed a transition point between random and correct binding modes. The results showed that structural alignment is better than sequence alignment in identifying good templates, suitable for generating protein-RNA complexes close to the native structure, and outperforms free docking, successfully predicting complexes where the free docking fails, including cases of significant conformational change upon binding. A template-based protein-RNA interaction modeling protocol PRIME was developed and benchmarked on a representative set of complexes.

Predicting domain-domain interaction based on domain profiles with feature selection and support vector machines

PubMed Central

2010-01-01

Background Protein-protein interaction (PPI) plays essential roles in cellular functions. The cost, time and other limitations associated with the current experimental methods have motivated the development of computational methods for predicting PPIs. As protein interactions generally occur via domains instead of the whole molecules, predicting domain-domain interaction (DDI) is an important step toward PPI prediction. Computational methods developed so far have utilized information from various sources at different levels, from primary sequences, to molecular structures, to evolutionary profiles. Results In this paper, we propose a computational method to predict DDI using support vector machines (SVMs), based on domains represented as interaction profile hidden Markov models (ipHMM) where interacting residues in domains are explicitly modeled according to the three dimensional structural information available at the Protein Data Bank (PDB). Features about the domains are extracted first as the Fisher scores derived from the ipHMM and then selected using singular value decomposition (SVD). Domain pairs are represented by concatenating their selected feature vectors, and classified by a support vector machine trained on these feature vectors. The method is tested by leave-one-out cross validation experiments with a set of interacting protein pairs adopted from the 3DID database. The prediction accuracy has shown significant improvement as compared to InterPreTS (Interaction Prediction through Tertiary Structure), an existing method for PPI prediction that also uses the sequences and complexes of known 3D structure. Conclusions We show that domain-domain interaction prediction can be significantly enhanced by exploiting information inherent in the domain profiles via feature selection based on Fisher scores, singular value decomposition and supervised learning based on support vector machines. Datasets and source code are freely available on the web at http://liao.cis.udel.edu/pub/svdsvm. Implemented in Matlab and supported on Linux and MS Windows. PMID:21034480

A deep learning framework for modeling structural features of RNA-binding protein targets

PubMed Central

Zhang, Sai; Zhou, Jingtian; Hu, Hailin; Gong, Haipeng; Chen, Ligong; Cheng, Chao; Zeng, Jianyang

2016-01-01

RNA-binding proteins (RBPs) play important roles in the post-transcriptional control of RNAs. Identifying RBP binding sites and characterizing RBP binding preferences are key steps toward understanding the basic mechanisms of the post-transcriptional gene regulation. Though numerous computational methods have been developed for modeling RBP binding preferences, discovering a complete structural representation of the RBP targets by integrating their available structural features in all three dimensions is still a challenging task. In this paper, we develop a general and flexible deep learning framework for modeling structural binding preferences and predicting binding sites of RBPs, which takes (predicted) RNA tertiary structural information into account for the first time. Our framework constructs a unified representation that characterizes the structural specificities of RBP targets in all three dimensions, which can be further used to predict novel candidate binding sites and discover potential binding motifs. Through testing on the real CLIP-seq datasets, we have demonstrated that our deep learning framework can automatically extract effective hidden structural features from the encoded raw sequence and structural profiles, and predict accurate RBP binding sites. In addition, we have conducted the first study to show that integrating the additional RNA tertiary structural features can improve the model performance in predicting RBP binding sites, especially for the polypyrimidine tract-binding protein (PTB), which also provides a new evidence to support the view that RBPs may own specific tertiary structural binding preferences. In particular, the tests on the internal ribosome entry site (IRES) segments yield satisfiable results with experimental support from the literature and further demonstrate the necessity of incorporating RNA tertiary structural information into the prediction model. The source code of our approach can be found in https://github.com/thucombio/deepnet-rbp. PMID:26467480

Structural protein descriptors in 1-dimension and their sequence-based predictions.

PubMed

Kurgan, Lukasz; Disfani, Fatemeh Miri

2011-09-01

The last few decades observed an increasing interest in development and application of 1-dimensional (1D) descriptors of protein structure. These descriptors project 3D structural features onto 1D strings of residue-wise structural assignments. They cover a wide-range of structural aspects including conformation of the backbone, burying depth/solvent exposure and flexibility of residues, and inter-chain residue-residue contacts. We perform first-of-its-kind comprehensive comparative review of the existing 1D structural descriptors. We define, review and categorize ten structural descriptors and we also describe, summarize and contrast over eighty computational models that are used to predict these descriptors from the protein sequences. We show that the majority of the recent sequence-based predictors utilize machine learning models, with the most popular being neural networks, support vector machines, hidden Markov models, and support vector and linear regressions. These methods provide high-throughput predictions and most of them are accessible to a non-expert user via web servers and/or stand-alone software packages. We empirically evaluate several recent sequence-based predictors of secondary structure, disorder, and solvent accessibility descriptors using a benchmark set based on CASP8 targets. Our analysis shows that the secondary structure can be predicted with over 80% accuracy and segment overlap (SOV), disorder with over 0.9 AUC, 0.6 Matthews Correlation Coefficient (MCC), and 75% SOV, and relative solvent accessibility with PCC of 0.7 and MCC of 0.6 (0.86 when homology is used). We demonstrate that the secondary structure predicted from sequence without the use of homology modeling is as good as the structure extracted from the 3D folds predicted by top-performing template-based methods.

Soft Computing Methods for Disulfide Connectivity Prediction.

PubMed

Márquez-Chamorro, Alfonso E; Aguilar-Ruiz, Jesús S

2015-01-01

The problem of protein structure prediction (PSP) is one of the main challenges in structural bioinformatics. To tackle this problem, PSP can be divided into several subproblems. One of these subproblems is the prediction of disulfide bonds. The disulfide connectivity prediction problem consists in identifying which nonadjacent cysteines would be cross-linked from all possible candidates. Determining the disulfide bond connectivity between the cysteines of a protein is desirable as a previous step of the 3D PSP, as the protein conformational search space is highly reduced. The most representative soft computing approaches for the disulfide bonds connectivity prediction problem of the last decade are summarized in this paper. Certain aspects, such as the different methodologies based on soft computing approaches (artificial neural network or support vector machine) or features of the algorithms, are used for the classification of these methods.

Application of long-range order to predict unfolding rates of two-state proteins.

PubMed

Harihar, B; Selvaraj, S

2011-03-01

Predicting the experimental unfolding rates of two-state proteins and models describing the unfolding rates of these proteins is quite limited because of the complexity present in the unfolding mechanism and the lack of experimental unfolding data compared with folding data. In this work, 25 two-state proteins characterized by Maxwell et al. (Protein Sci 2005;14:602–616) using a consensus set of experimental conditions were taken, and the parameter long-range order (LRO) derived from their three-dimensional structures were related with their experimental unfolding rates ln(k(u)). From the total data set of 30 proteins used by Maxwell et al. (Protein Sci 2005;14:602–616), five slow-unfolding proteins with very low unfolding rates were considered to be outliers and were not included in our data set. Except all beta structural class, LRO of both the all-alpha and mixed-class proteins showed a strong inverse correlation of r = -0.99 and -0.88, respectively, with experimental ln(k(u)). LRO shows a correlation of -0.62 with experimental ln(k(u)) for all-beta proteins. For predicting the unfolding rates, a simple statistical method has been used and linear regression equations were developed for individual structural classes of proteins using LRO, and the results obtained showed a better agreement with experimental results. Copyright © 2010 Wiley-Liss, Inc.

Update of the ATTRACT force field for the prediction of protein-protein binding affinity.

PubMed

Chéron, Jean-Baptiste; Zacharias, Martin; Antonczak, Serge; Fiorucci, Sébastien

2017-06-05

Determining the protein-protein interactions is still a major challenge for molecular biology. Docking protocols has come of age in predicting the structure of macromolecular complexes. However, they still lack accuracy to estimate the binding affinities, the thermodynamic quantity that drives the formation of a complex. Here, an updated version of the protein-protein ATTRACT force field aiming at predicting experimental binding affinities is reported. It has been designed on a dataset of 218 protein-protein complexes. The correlation between the experimental and predicted affinities reaches 0.6, outperforming most of the available protocols. Focusing on a subset of rigid and flexible complexes, the performance raises to 0.76 and 0.69, respectively. © 2017 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.

Interactome INSIDER: a structural interactome browser for genomic studies.

PubMed

Meyer, Michael J; Beltrán, Juan Felipe; Liang, Siqi; Fragoza, Robert; Rumack, Aaron; Liang, Jin; Wei, Xiaomu; Yu, Haiyuan

2018-01-01

We present Interactome INSIDER, a tool to link genomic variant information with structural protein-protein interactomes. Underlying this tool is the application of machine learning to predict protein interaction interfaces for 185,957 protein interactions with previously unresolved interfaces in human and seven model organisms, including the entire experimentally determined human binary interactome. Predicted interfaces exhibit functional properties similar to those of known interfaces, including enrichment for disease mutations and recurrent cancer mutations. Through 2,164 de novo mutagenesis experiments, we show that mutations of predicted and known interface residues disrupt interactions at a similar rate and much more frequently than mutations outside of predicted interfaces. To spur functional genomic studies, Interactome INSIDER (http://interactomeinsider.yulab.org) enables users to identify whether variants or disease mutations are enriched in known and predicted interaction interfaces at various resolutions. Users may explore known population variants, disease mutations, and somatic cancer mutations, or they may upload their own set of mutations for this purpose.

Improved hybrid optimization algorithm for 3D protein structure prediction.

PubMed

Zhou, Changjun; Hou, Caixia; Wei, Xiaopeng; Zhang, Qiang

2014-07-01

A new improved hybrid optimization algorithm - PGATS algorithm, which is based on toy off-lattice model, is presented for dealing with three-dimensional protein structure prediction problems. The algorithm combines the particle swarm optimization (PSO), genetic algorithm (GA), and tabu search (TS) algorithms. Otherwise, we also take some different improved strategies. The factor of stochastic disturbance is joined in the particle swarm optimization to improve the search ability; the operations of crossover and mutation that are in the genetic algorithm are changed to a kind of random liner method; at last tabu search algorithm is improved by appending a mutation operator. Through the combination of a variety of strategies and algorithms, the protein structure prediction (PSP) in a 3D off-lattice model is achieved. The PSP problem is an NP-hard problem, but the problem can be attributed to a global optimization problem of multi-extremum and multi-parameters. This is the theoretical principle of the hybrid optimization algorithm that is proposed in this paper. The algorithm combines local search and global search, which overcomes the shortcoming of a single algorithm, giving full play to the advantage of each algorithm. In the current universal standard sequences, Fibonacci sequences and real protein sequences are certified. Experiments show that the proposed new method outperforms single algorithms on the accuracy of calculating the protein sequence energy value, which is proved to be an effective way to predict the structure of proteins.

Computational-based structural, functional and phylogenetic analysis of Enterobacter phytases.

PubMed

Pramanik, Krishnendu; Kundu, Shreyasi; Banerjee, Sandipan; Ghosh, Pallab Kumar; Maiti, Tushar Kanti

2018-06-01

Myo-inositol hexakisphosphate phosphohydrolases (i.e., phytases) are known to be a very important enzyme responsible for solubilization of insoluble phosphates. In the present study, Enterobacter phytases have characterized by different phylogenetic, structural and functional parameters using some standard bio-computational tools. Results showed that majority of the Enterobacter phytases are acidic in nature as most of the isoelectric points were under 7.0. The aliphatic indices predicted for the selected proteins were below 40 indicating their thermostable nature. The average molecular weight of the proteins was 48 kDa. The lower values of GRAVY of the said proteins implied that they have better interactions with water. Secondary structure prediction revealed that alpha-helical content was highest among the other forms such as sheets, coils, etc. Moreover, the predicted 3D structure of Enterobacter phytases divulged that the proteins consisted of four monomeric polypeptide chains i.e., it was a tetrameric protein. The predicted tertiary model of E. aerogenes (A0A0M3HCJ2) was deposited in Protein Model Database (Acc. No.: PM0080561) for further utilization after a thorough quality check from QMEAN and SAVES server. Functional analysis supported their classification as histidine acid phosphatases. Besides, multiple sequence alignment revealed that "DG-DP-LG" was the most highly conserved residues within the Enterobacter phytases. Thus, the present study will be useful in selecting suitable phytase-producing microbe exclusively for using in the animal food industry as a food additive.

Toward structure prediction of cyclic peptides.

PubMed

Yu, Hongtao; Lin, Yu-Shan

2015-02-14

Cyclic peptides are a promising class of molecules that can be used to target specific protein-protein interactions. A computational method to accurately predict their structures would substantially advance the development of cyclic peptides as modulators of protein-protein interactions. Here, we develop a computational method that integrates bias-exchange metadynamics simulations, a Boltzmann reweighting scheme, dihedral principal component analysis and a modified density peak-based cluster analysis to provide a converged structural description for cyclic peptides. Using this method, we evaluate the performance of a number of popular protein force fields on a model cyclic peptide. All the tested force fields seem to over-stabilize the α-helix and PPII/β regions in the Ramachandran plot, commonly populated by linear peptides and proteins. Our findings suggest that re-parameterization of a force field that well describes the full Ramachandran plot is necessary to accurately model cyclic peptides.

Prediction of beta-turns in proteins using the first-order Markov models.

PubMed

Lin, Thy-Hou; Wang, Ging-Ming; Wang, Yen-Tseng

2002-01-01

We present a method based on the first-order Markov models for predicting simple beta-turns and loops containing multiple turns in proteins. Sequences of 338 proteins in a database are divided using the published turn criteria into the following three regions, namely, the turn, the boundary, and the nonturn ones. A transition probability matrix is constructed for either the turn or the nonturn region using the weighted transition probabilities computed for dipeptides identified from each region. There are two such matrices constructed for the boundary region since the transition probabilities for dipeptides immediately preceding or following a turn are different. The window used for scanning a protein sequence from amino (N-) to carboxyl (C-) terminal is a hexapeptide since the transition probability computed for a turn tetrapeptide is capped at both the N- and C- termini with a boundary transition probability indexed respectively from the two boundary transition matrices. A sum of the averaged product of the transition probabilities of all the hexapeptides involving each residue is computed. This is then weighted with a probability computed from assuming that all the hexapeptides are from the nonturn region to give the final prediction quantity. Both simple beta-turns and loops containing multiple turns in a protein are then identified by the rising of the prediction quantity computed. The performance of the prediction scheme or the percentage (%) of correct prediction is evaluated through computation of Matthews correlation coefficients for each protein predicted. It is found that the prediction method is capable of giving prediction results with better correlation between the percent of correct prediction and the Matthews correlation coefficients for a group of test proteins as compared with those predicted using some secondary structural prediction methods. The prediction accuracy for about 40% of proteins in the database or 50% of proteins in the test set is better than 70%. Such a percentage for the test set is reduced to 30 if the structures of all the proteins in the set are treated as unknown.

Local Geometry and Evolutionary Conservation of Protein Surfaces Reveal the Multiple Recognition Patches in Protein-Protein Interactions

PubMed Central

Laine, Elodie; Carbone, Alessandra

2015-01-01

Protein-protein interactions (PPIs) are essential to all biological processes and they represent increasingly important therapeutic targets. Here, we present a new method for accurately predicting protein-protein interfaces, understanding their properties, origins and binding to multiple partners. Contrary to machine learning approaches, our method combines in a rational and very straightforward way three sequence- and structure-based descriptors of protein residues: evolutionary conservation, physico-chemical properties and local geometry. The implemented strategy yields very precise predictions for a wide range of protein-protein interfaces and discriminates them from small-molecule binding sites. Beyond its predictive power, the approach permits to dissect interaction surfaces and unravel their complexity. We show how the analysis of the predicted patches can foster new strategies for PPIs modulation and interaction surface redesign. The approach is implemented in JET2, an automated tool based on the Joint Evolutionary Trees (JET) method for sequence-based protein interface prediction. JET2 is freely available at www.lcqb.upmc.fr/JET2. PMID:26690684

SAAMBE: Webserver to Predict the Charge of Binding Free Energy Caused by Amino Acids Mutations.

PubMed

Petukh, Marharyta; Dai, Luogeng; Alexov, Emil

2016-04-12

Predicting the effect of amino acid substitutions on protein-protein affinity (typically evaluated via the change of protein binding free energy) is important for both understanding the disease-causing mechanism of missense mutations and guiding protein engineering. In addition, researchers are also interested in understanding which energy components are mostly affected by the mutation and how the mutation affects the overall structure of the corresponding protein. Here we report a webserver, the Single Amino Acid Mutation based change in Binding free Energy (SAAMBE) webserver, which addresses the demand for tools for predicting the change of protein binding free energy. SAAMBE is an easy to use webserver, which only requires that a coordinate file be inputted and the user is provided with various, but easy to navigate, options. The user specifies the mutation position, wild type residue and type of mutation to be made. The server predicts the binding free energy change, the changes of the corresponding energy components and provides the energy minimized 3D structure of the wild type and mutant proteins for download. The SAAMBE protocol performance was tested by benchmarking the predictions against over 1300 experimentally determined changes of binding free energy and a Pearson correlation coefficient of 0.62 was obtained. How the predictions can be used for discriminating disease-causing from harmless mutations is discussed. The webserver can be accessed via http://compbio.clemson.edu/saambe_webserver/.

Vivek S. Bharadwaj | NREL

Science.gov Websites

Interests Vivek's interests broadly span across protein structure and dynamics, reaction mechanisms, and energetics and kinetics from first principles Protein structure prediction and docking Education PhD structure on the fumarate addition mechanism - a gas-phase ab initio study," Physical Chemistry

Improving the accuracy of protein stability predictions with multistate design using a variety of backbone ensembles.

PubMed

Davey, James A; Chica, Roberto A

2014-05-01

Multistate computational protein design (MSD) with backbone ensembles approximating conformational flexibility can predict higher quality sequences than single-state design with a single fixed backbone. However, it is currently unclear what characteristics of backbone ensembles are required for the accurate prediction of protein sequence stability. In this study, we aimed to improve the accuracy of protein stability predictions made with MSD by using a variety of backbone ensembles to recapitulate the experimentally measured stability of 85 Streptococcal protein G domain β1 sequences. Ensembles tested here include an NMR ensemble as well as those generated by molecular dynamics (MD) simulations, by Backrub motions, and by PertMin, a new method that we developed involving the perturbation of atomic coordinates followed by energy minimization. MSD with the PertMin ensembles resulted in the most accurate predictions by providing the highest number of stable sequences in the top 25, and by correctly binning sequences as stable or unstable with the highest success rate (≈90%) and the lowest number of false positives. The performance of PertMin ensembles is due to the fact that their members closely resemble the input crystal structure and have low potential energy. Conversely, the NMR ensemble as well as those generated by MD simulations at 500 or 1000 K reduced prediction accuracy due to their low structural similarity to the crystal structure. The ensembles tested herein thus represent on- or off-target models of the native protein fold and could be used in future studies to design for desired properties other than stability. Copyright © 2013 Wiley Periodicals, Inc.

iDBPs: a web server for the identification of DNA binding proteins

PubMed Central

Nimrod, Guy; Schushan, Maya; Szilágyi, András; Leslie, Christina; Ben-Tal, Nir

2010-01-01

Summary: The iDBPs server uses the three-dimensional (3D) structure of a query protein to predict whether it binds DNA. First, the algorithm predicts the functional region of the protein based on its evolutionary profile; the assumption is that large clusters of conserved residues are good markers of functional regions. Next, various characteristics of the predicted functional region as well as global features of the protein are calculated, such as the average surface electrostatic potential, the dipole moment and cluster-based amino acid conservation patterns. Finally, a random forests classifier is used to predict whether the query protein is likely to bind DNA and to estimate the prediction confidence. We have trained and tested the classifier on various datasets and shown that it outperformed related methods. On a dataset that reflects the fraction of DNA binding proteins (DBPs) in a proteome, the area under the ROC curve was 0.90. The application of the server to an updated version of the N-Func database, which contains proteins of unknown function with solved 3D-structure, suggested new putative DBPs for experimental studies. Availability: http://idbps.tau.ac.il/ Contact: NirB@tauex.tau.ac.il Supplementary information: Supplementary data are available at Bioinformatics online. PMID:20089514

Integration of structural dynamics and molecular evolution via protein interaction networks: a new era in genomic medicine.

PubMed

Kumar, Avishek; Butler, Brandon M; Kumar, Sudhir; Ozkan, S Banu

2015-12-01

Sequencing technologies are revealing many new non-synonymous single nucleotide variants (nsSNVs) in each personal exome. To assess their functional impacts, comparative genomics is frequently employed to predict if they are benign or not. However, evolutionary analysis alone is insufficient, because it misdiagnoses many disease-associated nsSNVs, such as those at positions involved in protein interfaces, and because evolutionary predictions do not provide mechanistic insights into functional change or loss. Structural analyses can aid in overcoming both of these problems by incorporating conformational dynamics and allostery in nSNV diagnosis. Finally, protein-protein interaction networks using systems-level methodologies shed light onto disease etiology and pathogenesis. Bridging these network approaches with structurally resolved protein interactions and dynamics will advance genomic medicine. Copyright © 2015 Elsevier Ltd. All rights reserved.

Molecular Simulation-Based Structural Prediction of Protein Complexes in Mass Spectrometry: The Human Insulin Dimer

PubMed Central

Li, Jinyu; Rossetti, Giulia; Dreyer, Jens; Raugei, Simone; Ippoliti, Emiliano; Lüscher, Bernhard; Carloni, Paolo

2014-01-01

Protein electrospray ionization (ESI) mass spectrometry (MS)-based techniques are widely used to provide insight into structural proteomics under the assumption that non-covalent protein complexes being transferred into the gas phase preserve basically the same intermolecular interactions as in solution. Here we investigate the applicability of this assumption by extending our previous structural prediction protocol for single proteins in ESI-MS to protein complexes. We apply our protocol to the human insulin dimer (hIns2) as a test case. Our calculations reproduce the main charge and the collision cross section (CCS) measured in ESI-MS experiments. Molecular dynamics simulations for 0.075 ms show that the complex maximizes intermolecular non-bonded interactions relative to the structure in water, without affecting the cross section. The overall gas-phase structure of hIns2 does exhibit differences with the one in aqueous solution, not inferable from a comparison with calculated CCS. Hence, care should be exerted when interpreting ESI-MS proteomics data based solely on NMR and/or X-ray structural information. PMID:25210764

Structure prediction, expression, and antigenicity of c-terminal of GRP78.

PubMed

Aghamollaei, Hossein; Mousavi Gargari, Seyed Latif; Ghanei, Mostafa; Rasaee, Mohamad Javad; Amani, Jafar; Bakherad, Hamid; Farnoosh, Gholamreza

2017-01-01

Glucose-regulated protein 78 (GRP78) is a typical endoplasmic reticulum luminal chaperone having a main role in the activation of the unfolded protein response. Because of hypoxia and nutrient deprivation in the tumor microenvironment, expression of GRP78 in these cells becomes higher than the native cells, which makes it a suitable candidate for cancer targeting. Suppression of survival signals by antibody production against C-terminal domain of GR78 (CGRP) can induce apoptosis of cancer cells. The aim of this study was in silico analysis, recombinant production, and characterization of CGRP in Escherichia coli. Structural prediction of CGRP by bioinformatics tools was done and the construct containing optimized sequence was transferred to E. coli T7 shuffle. Expression was induced by isopropyl-β-d-thiogalactoside, and recombinant protein was purified by Ni-NTA agarose resin. The content of secondary structures was obtained by circular dichroism (CD) spectrum. CGRP immunogenicity was evaluated from the immunized mouse sera. SDS-PAGE analysis showed CGRP expression in E. coli. CD spectrum also confirmed prediction of structures by bioinformatics tools. The enzyme-linked immunosorbent assay using sera from immunized mice revealed CGRP as a good immunogen. The results obtained in this study showed that the structure of truncated CGRP is very similar to its structure in the whole protein context. This protein can be used in cancer researches. © 2015 International Union of Biochemistry and Molecular Biology, Inc.

Amino Acid Distribution Rules Predict Protein Fold: Protein Grammar for Beta-Strand Sandwich-Like Structures

PubMed Central

Kister, Alexander

2015-01-01

We present an alternative approach to protein 3D folding prediction based on determination of rules that specify distribution of “favorable” residues, that are mainly responsible for a given fold formation, and “unfavorable” residues, that are incompatible with that fold, in polypeptide sequences. The process of determining favorable and unfavorable residues is iterative. The starting assumptions are based on the general principles of protein structure formation as well as structural features peculiar to a protein fold under investigation. The initial assumptions are tested one-by-one for a set of all known proteins with a given structure. The assumption is accepted as a “rule of amino acid distribution” for the protein fold if it holds true for all, or near all, structures. If the assumption is not accepted as a rule, it can be modified to better fit the data and then tested again in the next step of the iterative search algorithm, or rejected. We determined the set of amino acid distribution rules for a large group of beta sandwich-like proteins characterized by a specific arrangement of strands in two beta sheets. It was shown that this set of rules is highly sensitive (~90%) and very specific (~99%) for identifying sequences of proteins with specified beta sandwich fold structure. The advantage of the proposed approach is that it does not require that query proteins have a high degree of homology to proteins with known structure. So long as the query protein satisfies residue distribution rules, it can be confidently assigned to its respective protein fold. Another advantage of our approach is that it allows for a better understanding of which residues play an essential role in protein fold formation. It may, therefore, facilitate rational protein engineering design. PMID:25625198

PredPPCrys: accurate prediction of sequence cloning, protein production, purification and crystallization propensity from protein sequences using multi-step heterogeneous feature fusion and selection.

PubMed

Wang, Huilin; Wang, Mingjun; Tan, Hao; Li, Yuan; Zhang, Ziding; Song, Jiangning

2014-01-01

X-ray crystallography is the primary approach to solve the three-dimensional structure of a protein. However, a major bottleneck of this method is the failure of multi-step experimental procedures to yield diffraction-quality crystals, including sequence cloning, protein material production, purification, crystallization and ultimately, structural determination. Accordingly, prediction of the propensity of a protein to successfully undergo these experimental procedures based on the protein sequence may help narrow down laborious experimental efforts and facilitate target selection. A number of bioinformatics methods based on protein sequence information have been developed for this purpose. However, our knowledge on the important determinants of propensity for a protein sequence to produce high diffraction-quality crystals remains largely incomplete. In practice, most of the existing methods display poorer performance when evaluated on larger and updated datasets. To address this problem, we constructed an up-to-date dataset as the benchmark, and subsequently developed a new approach termed 'PredPPCrys' using the support vector machine (SVM). Using a comprehensive set of multifaceted sequence-derived features in combination with a novel multi-step feature selection strategy, we identified and characterized the relative importance and contribution of each feature type to the prediction performance of five individual experimental steps required for successful crystallization. The resulting optimal candidate features were used as inputs to build the first-level SVM predictor (PredPPCrys I). Next, prediction outputs of PredPPCrys I were used as the input to build second-level SVM classifiers (PredPPCrys II), which led to significantly enhanced prediction performance. Benchmarking experiments indicated that our PredPPCrys method outperforms most existing procedures on both up-to-date and previous datasets. In addition, the predicted crystallization targets of currently non-crystallizable proteins were provided as compendium data, which are anticipated to facilitate target selection and design for the worldwide structural genomics consortium. PredPPCrys is freely available at http://www.structbioinfor.org/PredPPCrys.

Predicting structural classes of proteins by incorporating their global and local physicochemical and conformational properties into general Chou's PseAAC.

PubMed

Contreras-Torres, Ernesto

2018-06-02

In this study, I introduce novel global and local 0D-protein descriptors based on a statistical quantity named Total Sum of Squares (TSS). This quantity represents the sum of the squares differences of amino acid properties from the arithmetic mean property. As an extension, the amino acid-types and amino acid-groups formalisms are used for describing zones of interest in proteins. To assess the effectiveness of the proposed descriptors, a Nearest Neighbor model for predicting the major four protein structural classes was built. This model has a success rate of 98.53% on the jackknife cross-validation test; this performance being superior to other reported methods despite the simplicity of the predictor. Additionally, this predictor has an average success rate of 98.35% in different cross-validation tests performed. A value of 0.98 for the Kappa statistic clearly discriminates this model from a random predictor. The results obtained by the Nearest Neighbor model demonstrated the ability of the proposed descriptors not only to reflect relevant biochemical information related to the structural classes of proteins but also to allow appropriate interpretability. It can thus be expected that the current method may play a supplementary role to other existing approaches for protein structural class prediction and other protein attributes. Copyright © 2018 Elsevier Ltd. All rights reserved.

Active sites prediction and binding analysis E1-E2 protein human papillomavirus with biphenylsulfonacetic acid

NASA Astrophysics Data System (ADS)

Iryani, I.; Amelia, F.; Iswendi, I.

2018-04-01

Cervix cancer triggered by Human papillomavirus infection is the second cause to woman death in worldwide. The binding site of E1-E2 protein of HPV 16 is not known from a 3-D structure yet, so in this study we address this issue to study the structure of E1-E2 protein from Human papillomavirus type 16 and to find its potential binding sites using biphenylsulfonacetic acid as inhibitor. Swiss model was used for 3D structure prediction and PDB: 2V9P (E1 protein) and 2NNU (E2 protein) having 52.32% and 100% identity respectively was selected as a template. The 3D model structure developed of E1 and E2 in the core and allowed regions were 99.2% and 99.5%. The ligand binding sites were predicted using online server meta pocket 2.0 and MOE 2009.10 was used for docking. E1-and E2 protein of HPV-16 has three potential binding site that can interact with the inhibitors. The Docking biphenylsulfonacetic acid using these binding sites shows that ligand interact with the protein through hydrogen bonds on Lys 403, Arg 410, His 551 in the first pocket, on Tyr 32, Leu 99 in the second pocket, and Lys 558m Lys 517 in the third pocket.

G23D: Online tool for mapping and visualization of genomic variants on 3D protein structures.

PubMed

Solomon, Oz; Kunik, Vered; Simon, Amos; Kol, Nitzan; Barel, Ortal; Lev, Atar; Amariglio, Ninette; Somech, Raz; Rechavi, Gidi; Eyal, Eran

2016-08-26

Evaluation of the possible implications of genomic variants is an increasingly important task in the current high throughput sequencing era. Structural information however is still not routinely exploited during this evaluation process. The main reasons can be attributed to the partial structural coverage of the human proteome and the lack of tools which conveniently convert genomic positions, which are the frequent output of genomic pipelines, to proteins and structure coordinates. We present G23D, a tool for conversion of human genomic coordinates to protein coordinates and protein structures. G23D allows mapping of genomic positions/variants on evolutionary related (and not only identical) protein three dimensional (3D) structures as well as on theoretical models. By doing so it significantly extends the space of variants for which structural insight is feasible. To facilitate interpretation of the variant consequence, pathogenic variants, functional sites and polymorphism sites are displayed on protein sequence and structure diagrams alongside the input variants. G23D also provides modeling of the mutant structure, analysis of intra-protein contacts and instant access to functional predictions and predictions of thermo-stability changes. G23D is available at http://www.sheba-cancer.org.il/G23D . G23D extends the fraction of variants for which structural analysis is applicable and provides better and faster accessibility for structural data to biologists and geneticists who routinely work with genomic information.

Prediction of Water Binding to Protein Hydration Sites with a Discrete, Semiexplicit Solvent Model.

PubMed

Setny, Piotr

2015-12-08

Buried water molecules are ubiquitous in protein structures and are found at the interface of most protein-ligand complexes. Determining their distribution and thermodynamic effect is a challenging yet important task, of great of practical value for the modeling of biomolecular structures and their interactions. In this study, we present a novel method aimed at the prediction of buried water molecules in protein structures and estimation of their binding free energies. It is based on a semiexplicit, discrete solvation model, which we previously introduced in the context of small molecule hydration. The method is applicable to all macromolecular structures described by a standard all-atom force field, and predicts complete solvent distribution within a single run with modest computational cost. We demonstrate that it indicates positions of buried hydration sites, including those filled by more than one water molecule, and accurately differentiates them from sterically accessible to water but void regions. The obtained estimates of water binding free energies are in fair agreement with reference results determined with the double decoupling method.

Predicting helix–helix interactions from residue contacts in membrane proteins

PubMed Central

Lo, Allan; Chiu, Yi-Yuan; Rødland, Einar Andreas; Lyu, Ping-Chiang; Sung, Ting-Yi; Hsu, Wen-Lian

2009-01-01

Motivation: Helix–helix interactions play a critical role in the structure assembly, stability and function of membrane proteins. On the molecular level, the interactions are mediated by one or more residue contacts. Although previous studies focused on helix-packing patterns and sequence motifs, few of them developed methods specifically for contact prediction. Results: We present a new hierarchical framework for contact prediction, with an application in membrane proteins. The hierarchical scheme consists of two levels: in the first level, contact residues are predicted from the sequence and their pairing relationships are further predicted in the second level. Statistical analyses on contact propensities are combined with other sequence and structural information for training the support vector machine classifiers. Evaluated on 52 protein chains using leave-one-out cross validation (LOOCV) and an independent test set of 14 protein chains, the two-level approach consistently improves the conventional direct approach in prediction accuracy, with 80% reduction of input for prediction. Furthermore, the predicted contacts are then used to infer interactions between pairs of helices. When at least three predicted contacts are required for an inferred interaction, the accuracy, sensitivity and specificity are 56%, 40% and 89%, respectively. Our results demonstrate that a hierarchical framework can be applied to eliminate false positives (FP) while reducing computational complexity in predicting contacts. Together with the estimated contact propensities, this method can be used to gain insights into helix-packing in membrane proteins. Availability: http://bio-cluster.iis.sinica.edu.tw/TMhit/ Contact: tsung@iis.sinica.edu.tw Supplementary information:Supplementary data are available at Bioinformatics online. PMID:19244388

Exploiting protein flexibility to predict the location of allosteric sites

PubMed Central

2012-01-01

Background Allostery is one of the most powerful and common ways of regulation of protein activity. However, for most allosteric proteins identified to date the mechanistic details of allosteric modulation are not yet well understood. Uncovering common mechanistic patterns underlying allostery would allow not only a better academic understanding of the phenomena, but it would also streamline the design of novel therapeutic solutions. This relatively unexplored therapeutic potential and the putative advantages of allosteric drugs over classical active-site inhibitors fuel the attention allosteric-drug research is receiving at present. A first step to harness the regulatory potential and versatility of allosteric sites, in the context of drug-discovery and design, would be to detect or predict their presence and location. In this article, we describe a simple computational approach, based on the effect allosteric ligands exert on protein flexibility upon binding, to predict the existence and position of allosteric sites on a given protein structure. Results By querying the literature and a recently available database of allosteric sites, we gathered 213 allosteric proteins with structural information that we further filtered into a non-redundant set of 91 proteins. We performed normal-mode analysis and observed significant changes in protein flexibility upon allosteric-ligand binding in 70% of the cases. These results agree with the current view that allosteric mechanisms are in many cases governed by changes in protein dynamics caused by ligand binding. Furthermore, we implemented an approach that achieves 65% positive predictive value in identifying allosteric sites within the set of predicted cavities of a protein (stricter parameters set, 0.22 sensitivity), by combining the current analysis on dynamics with previous results on structural conservation of allosteric sites. We also analyzed four biological examples in detail, revealing that this simple coarse-grained methodology is able to capture the effects triggered by allosteric ligands already described in the literature. Conclusions We introduce a simple computational approach to predict the presence and position of allosteric sites in a protein based on the analysis of changes in protein normal modes upon the binding of a coarse-grained ligand at predicted cavities. Its performance has been demonstrated using a newly curated non-redundant set of 91 proteins with reported allosteric properties. The software developed in this work is available upon request from the authors. PMID:23095452

Exploiting protein flexibility to predict the location of allosteric sites.

PubMed

Panjkovich, Alejandro; Daura, Xavier

2012-10-25

Allostery is one of the most powerful and common ways of regulation of protein activity. However, for most allosteric proteins identified to date the mechanistic details of allosteric modulation are not yet well understood. Uncovering common mechanistic patterns underlying allostery would allow not only a better academic understanding of the phenomena, but it would also streamline the design of novel therapeutic solutions. This relatively unexplored therapeutic potential and the putative advantages of allosteric drugs over classical active-site inhibitors fuel the attention allosteric-drug research is receiving at present. A first step to harness the regulatory potential and versatility of allosteric sites, in the context of drug-discovery and design, would be to detect or predict their presence and location. In this article, we describe a simple computational approach, based on the effect allosteric ligands exert on protein flexibility upon binding, to predict the existence and position of allosteric sites on a given protein structure. By querying the literature and a recently available database of allosteric sites, we gathered 213 allosteric proteins with structural information that we further filtered into a non-redundant set of 91 proteins. We performed normal-mode analysis and observed significant changes in protein flexibility upon allosteric-ligand binding in 70% of the cases. These results agree with the current view that allosteric mechanisms are in many cases governed by changes in protein dynamics caused by ligand binding. Furthermore, we implemented an approach that achieves 65% positive predictive value in identifying allosteric sites within the set of predicted cavities of a protein (stricter parameters set, 0.22 sensitivity), by combining the current analysis on dynamics with previous results on structural conservation of allosteric sites. We also analyzed four biological examples in detail, revealing that this simple coarse-grained methodology is able to capture the effects triggered by allosteric ligands already described in the literature. We introduce a simple computational approach to predict the presence and position of allosteric sites in a protein based on the analysis of changes in protein normal modes upon the binding of a coarse-grained ligand at predicted cavities. Its performance has been demonstrated using a newly curated non-redundant set of 91 proteins with reported allosteric properties. The software developed in this work is available upon request from the authors.

regSNPs-splicing: a tool for prioritizing synonymous single-nucleotide substitution.

PubMed

Zhang, Xinjun; Li, Meng; Lin, Hai; Rao, Xi; Feng, Weixing; Yang, Yuedong; Mort, Matthew; Cooper, David N; Wang, Yue; Wang, Yadong; Wells, Clark; Zhou, Yaoqi; Liu, Yunlong

2017-09-01

While synonymous single-nucleotide variants (sSNVs) have largely been unstudied, since they do not alter protein sequence, mounting evidence suggests that they may affect RNA conformation, splicing, and the stability of nascent-mRNAs to promote various diseases. Accurately prioritizing deleterious sSNVs from a pool of neutral ones can significantly improve our ability of selecting functional genetic variants identified from various genome-sequencing projects, and, therefore, advance our understanding of disease etiology. In this study, we develop a computational algorithm to prioritize sSNVs based on their impact on mRNA splicing and protein function. In addition to genomic features that potentially affect splicing regulation, our proposed algorithm also includes dozens structural features that characterize the functions of alternatively spliced exons on protein function. Our systematical evaluation on thousands of sSNVs suggests that several structural features, including intrinsic disorder protein scores, solvent accessible surface areas, protein secondary structures, and known and predicted protein family domains, show significant differences between disease-causing and neutral sSNVs. Our result suggests that the protein structure features offer an added dimension of information while distinguishing disease-causing and neutral synonymous variants. The inclusion of structural features increases the predictive accuracy for functional sSNV prioritization.

Investigating the Structural Compaction of Biomolecules Upon Transition to the Gas-Phase Using ESI-TWIMS-MS.

PubMed

Devine, Paul W A; Fisher, Henry C; Calabrese, Antonio N; Whelan, Fiona; Higazi, Daniel R; Potts, Jennifer R; Lowe, David C; Radford, Sheena E; Ashcroft, Alison E

2017-09-01

Collision cross-section (CCS) measurements obtained from ion mobility spectrometry-mass spectrometry (IMS-MS) analyses often provide useful information concerning a protein's size and shape and can be complemented by modeling procedures. However, there have been some concerns about the extent to which certain proteins maintain a native-like conformation during the gas-phase analysis, especially proteins with dynamic or extended regions. Here we have measured the CCSs of a range of biomolecules including non-globular proteins and RNAs of different sequence, size, and stability. Using traveling wave IMS-MS, we show that for the proteins studied, the measured CCS deviates significantly from predicted CCS values based upon currently available structures. The results presented indicate that these proteins collapse to different extents varying on their elongated structures upon transition into the gas-phase. Comparing two RNAs of similar mass but different solution structures, we show that these biomolecules may also be susceptible to gas-phase compaction. Together, the results suggest that caution is needed when predicting structural models based on CCS data for RNAs as well as proteins with non-globular folds. Graphical Abstract ᅟ.

Capturing non-local interactions by long short-term memory bidirectional recurrent neural networks for improving prediction of protein secondary structure, backbone angles, contact numbers and solvent accessibility.

PubMed

Heffernan, Rhys; Yang, Yuedong; Paliwal, Kuldip; Zhou, Yaoqi

2017-09-15

The accuracy of predicting protein local and global structural properties such as secondary structure and solvent accessible surface area has been stagnant for many years because of the challenge of accounting for non-local interactions between amino acid residues that are close in three-dimensional structural space but far from each other in their sequence positions. All existing machine-learning techniques relied on a sliding window of 10-20 amino acid residues to capture some 'short to intermediate' non-local interactions. Here, we employed Long Short-Term Memory (LSTM) Bidirectional Recurrent Neural Networks (BRNNs) which are capable of capturing long range interactions without using a window. We showed that the application of LSTM-BRNN to the prediction of protein structural properties makes the most significant improvement for residues with the most long-range contacts (|i-j| >19) over a previous window-based, deep-learning method SPIDER2. Capturing long-range interactions allows the accuracy of three-state secondary structure prediction to reach 84% and the correlation coefficient between predicted and actual solvent accessible surface areas to reach 0.80, plus a reduction of 5%, 10%, 5% and 10% in the mean absolute error for backbone ϕ , ψ , θ and τ angles, respectively, from SPIDER2. More significantly, 27% of 182724 40-residue models directly constructed from predicted C α atom-based θ and τ have similar structures to their corresponding native structures (6Å RMSD or less), which is 3% better than models built by ϕ and ψ angles. We expect the method to be useful for assisting protein structure and function prediction. The method is available as a SPIDER3 server and standalone package at http://sparks-lab.org . yaoqi.zhou@griffith.edu.au or yuedong.yang@griffith.edu.au. Supplementary data are available at Bioinformatics online. © The Author (2017). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com

A Survey of Computational Intelligence Techniques in Protein Function Prediction

PubMed Central

Tiwari, Arvind Kumar; Srivastava, Rajeev

2014-01-01

During the past, there was a massive growth of knowledge of unknown proteins with the advancement of high throughput microarray technologies. Protein function prediction is the most challenging problem in bioinformatics. In the past, the homology based approaches were used to predict the protein function, but they failed when a new protein was different from the previous one. Therefore, to alleviate the problems associated with homology based traditional approaches, numerous computational intelligence techniques have been proposed in the recent past. This paper presents a state-of-the-art comprehensive review of various computational intelligence techniques for protein function predictions using sequence, structure, protein-protein interaction network, and gene expression data used in wide areas of applications such as prediction of DNA and RNA binding sites, subcellular localization, enzyme functions, signal peptides, catalytic residues, nuclear/G-protein coupled receptors, membrane proteins, and pathway analysis from gene expression datasets. This paper also summarizes the result obtained by many researchers to solve these problems by using computational intelligence techniques with appropriate datasets to improve the prediction performance. The summary shows that ensemble classifiers and integration of multiple heterogeneous data are useful for protein function prediction. PMID:25574395

Analysis of Free Modeling Predictions by RBO Aleph in CASP11

PubMed Central

Mabrouk, Mahmoud; Werner, Tim; Schneider, Michael; Putz, Ines; Brock, Oliver

2015-01-01

The CASP experiment is a biannual benchmark for assessing protein structure prediction methods. In CASP11, RBO Aleph ranked as one of the top-performing automated servers in the free modeling category. This category consists of targets for which structural templates are not easily retrievable. We analyze the performance of RBO Aleph and show that its success in CASP was a result of its ab initio structure prediction protocol. A detailed analysis of this protocol demonstrates that two components unique to our method greatly contributed to prediction quality: residue–residue contact prediction by EPC-map and contact–guided conformational space search by model-based search (MBS). Interestingly, our analysis also points to a possible fundamental problem in evaluating the performance of protein structure prediction methods: Improvements in components of the method do not necessarily lead to improvements of the entire method. This points to the fact that these components interact in ways that are poorly understood. This problem, if indeed true, represents a significant obstacle to community-wide progress. PMID:26492194

Prediction of hot regions in protein-protein interaction by combining density-based incremental clustering with feature-based classification.

PubMed

Hu, Jing; Zhang, Xiaolong; Liu, Xiaoming; Tang, Jinshan

2015-06-01

Discovering hot regions in protein-protein interaction is important for drug and protein design, while experimental identification of hot regions is a time-consuming and labor-intensive effort; thus, the development of predictive models can be very helpful. In hot region prediction research, some models are based on structure information, and others are based on a protein interaction network. However, the prediction accuracy of these methods can still be improved. In this paper, a new method is proposed for hot region prediction, which combines density-based incremental clustering with feature-based classification. The method uses density-based incremental clustering to obtain rough hot regions, and uses feature-based classification to remove the non-hot spot residues from the rough hot regions. Experimental results show that the proposed method significantly improves the prediction performance of hot regions. Copyright © 2015 Elsevier Ltd. All rights reserved.

PSPP: A Protein Structure Prediction Pipeline for Computing Clusters

DTIC Science & Technology

2009-07-01

Evanseck JD, et al. (1998) All-atom empirical potential for molecular modeling and dynamics studies of proteins. Journal of Physical Chemistry B 102...dimensional (3-D) protein structures are critical for the understanding of molecular mechanisms of living systems. Traditionally, X-ray crystallography...disordered proteins are often responsible for molecular recognition, molecular assembly, protein modifica- tion, and entropic chain activities in organisms [26

Proteome-wide Prediction of Self-interacting Proteins Based on Multiple Properties*

PubMed Central

Liu, Zhongyang; Guo, Feifei; Zhang, Jiyang; Wang, Jian; Lu, Liang; Li, Dong; He, Fuchu

2013-01-01

Self-interacting proteins, whose two or more copies can interact with each other, play important roles in cellular functions and the evolution of protein interaction networks (PINs). Knowing whether a protein can self-interact can contribute to and sometimes is crucial for the elucidation of its functions. Previous related research has mainly focused on the structures and functions of specific self-interacting proteins, whereas knowledge on their overall properties is limited. Meanwhile, the two current most common high throughput protein interaction assays have limited ability to detect self-interactions because of biological artifacts and design limitations, whereas the bioinformatic prediction method of self-interacting proteins is lacking. This study aims to systematically study and predict self-interacting proteins from an overall perspective. We find that compared with other proteins the self-interacting proteins in the structural aspect contain more domains; in the evolutionary aspect they tend to be conserved and ancient; in the functional aspect they are significantly enriched with enzyme genes, housekeeping genes, and drug targets, and in the topological aspect tend to occupy important positions in PINs. Furthermore, based on these features, after feature selection, we use logistic regression to integrate six representative features, including Gene Ontology term, domain, paralogous interactor, enzyme, model organism self-interacting protein, and betweenness centrality in the PIN, to develop a proteome-wide prediction model of self-interacting proteins. Using 5-fold cross-validation and an independent test, this model shows good performance. Finally, the prediction model is developed into a user-friendly web service SLIPPER (SeLf-Interacting Protein PrEdictoR). Users may submit a list of proteins, and then SLIPPER will return the probability_scores measuring their possibility to be self-interacting proteins and various related annotation information. This work helps us understand the role self-interacting proteins play in cellular functions from an overall perspective, and the constructed prediction model may contribute to the high throughput finding of self-interacting proteins and provide clues for elucidating their functions. PMID:23422585

Quantification of the transferability of a designed protein specificity switch reveals extensive epistasis in molecular recognition

DOE PAGES

Melero, Cristina; Ollikainen, Noah; Harwood, Ian; ...

2014-10-13

Re-engineering protein–protein recognition is an important route to dissecting and controlling complex interaction networks. Experimental approaches have used the strategy of “second-site suppressors,” where a functional interaction is inferred between two proteins if a mutation in one protein can be compensated by a mutation in the second. Mimicking this strategy, computational design has been applied successfully to change protein recognition specificity by predicting such sets of compensatory mutations in protein–protein interfaces. To extend this approach, it would be advantageous to be able to “transplant” existing engineered and experimentally validated specificity changes to other homologous protein–protein complexes. Here, we test thismore » strategy by designing a pair of mutations that modulates peptide recognition specificity in the Syntrophin PDZ domain, confirming the designed interaction biochemically and structurally, and then transplanting the mutations into the context of five related PDZ domain–peptide complexes. We find a wide range of energetic effects of identical mutations in structurally similar positions, revealing a dramatic context dependence (epistasis) of designed mutations in homologous protein–protein interactions. To better understand the structural basis of this context dependence, we apply a structure-based computational model that recapitulates these energetic effects and we use this model to make and validate forward predictions. The context dependence of these mutations is captured by computational predictions, our results both highlight the considerable difficulties in designing protein–protein interactions and provide challenging benchmark cases for the development of improved protein modeling and design methods that accurately account for the context.« less

Quantification of the transferability of a designed protein specificity switch reveals extensive epistasis in molecular recognition

DOE Office of Scientific and Technical Information (OSTI.GOV)

Melero, Cristina; Ollikainen, Noah; Harwood, Ian

Re-engineering protein–protein recognition is an important route to dissecting and controlling complex interaction networks. Experimental approaches have used the strategy of “second-site suppressors,” where a functional interaction is inferred between two proteins if a mutation in one protein can be compensated by a mutation in the second. Mimicking this strategy, computational design has been applied successfully to change protein recognition specificity by predicting such sets of compensatory mutations in protein–protein interfaces. To extend this approach, it would be advantageous to be able to “transplant” existing engineered and experimentally validated specificity changes to other homologous protein–protein complexes. Here, we test thismore » strategy by designing a pair of mutations that modulates peptide recognition specificity in the Syntrophin PDZ domain, confirming the designed interaction biochemically and structurally, and then transplanting the mutations into the context of five related PDZ domain–peptide complexes. We find a wide range of energetic effects of identical mutations in structurally similar positions, revealing a dramatic context dependence (epistasis) of designed mutations in homologous protein–protein interactions. To better understand the structural basis of this context dependence, we apply a structure-based computational model that recapitulates these energetic effects and we use this model to make and validate forward predictions. The context dependence of these mutations is captured by computational predictions, our results both highlight the considerable difficulties in designing protein–protein interactions and provide challenging benchmark cases for the development of improved protein modeling and design methods that accurately account for the context.« less

CATH-Gene3D: Generation of the Resource and Its Use in Obtaining Structural and Functional Annotations for Protein Sequences.

PubMed

Dawson, Natalie L; Sillitoe, Ian; Lees, Jonathan G; Lam, Su Datt; Orengo, Christine A

2017-01-01

This chapter describes the generation of the data in the CATH-Gene3D online resource and how it can be used to study protein domains and their evolutionary relationships. Methods will be presented for: comparing protein structures, recognizing homologs, predicting domain structures within protein sequences, and subclassifying superfamilies into functionally pure families, together with a guide on using the webpages.

Extending RosettaDock with water, sugar, and pH for prediction of complex structures and affinities for CAPRI rounds 20-27.

PubMed

Kilambi, Krishna Praneeth; Pacella, Michael S; Xu, Jianqing; Labonte, Jason W; Porter, Justin R; Muthu, Pravin; Drew, Kevin; Kuroda, Daisuke; Schueler-Furman, Ora; Bonneau, Richard; Gray, Jeffrey J

2013-12-01

Rounds 20-27 of the Critical Assessment of PRotein Interactions (CAPRI) provided a testing platform for computational methods designed to address a wide range of challenges. The diverse targets drove the creation of and new combinations of computational tools. In this study, RosettaDock and other novel Rosetta protocols were used to successfully predict four of the 10 blind targets. For example, for DNase domain of Colicin E2-Im2 immunity protein, RosettaDock and RosettaLigand were used to predict the positions of water molecules at the interface, recovering 46% of the native water-mediated contacts. For α-repeat Rep4-Rep2 and g-type lysozyme-PliG inhibitor complexes, homology models were built and standard and pH-sensitive docking algorithms were used to generate structures with interface RMSD values of 3.3 Å and 2.0 Å, respectively. A novel flexible sugar-protein docking protocol was also developed and used for structure prediction of the BT4661-heparin-like saccharide complex, recovering 71% of the native contacts. Challenges remain in the generation of accurate homology models for protein mutants and sampling during global docking. On proteins designed to bind influenza hemagglutinin, only about half of the mutations were identified that affect binding (T55: 54%; T56: 48%). The prediction of the structure of the xylanase complex involving homology modeling and multidomain docking pushed the limits of global conformational sampling and did not result in any successful prediction. The diversity of problems at hand requires computational algorithms to be versatile; the recent additions to the Rosetta suite expand the capabilities to encompass more biologically realistic docking problems. Copyright © 2013 Wiley Periodicals, Inc.

X-ray scattering data and structural genomics

NASA Astrophysics Data System (ADS)

Doniach, Sebastian

2003-03-01

High throughput structural genomics has the ambitious goal of determining the structure of all, or a very large number of protein folds using the high-resolution techniques of protein crystallography and NMR. However, the program is facing significant bottlenecks in reaching this goal, which include problems of protein expression and crystallization. In this talk, some preliminary results on how the low-resolution technique of small-angle X-ray solution scattering (SAXS) can help ameliorate some of these bottlenecks will be presented. One of the most significant bottlenecks arises from the difficulty of crystallizing integral membrane proteins, where only a handful of structures are available compared to thousands of structures for soluble proteins. By 3-dimensional reconstruction from SAXS data, the size and shape of detergent-solubilized integral membrane proteins can be characterized. This information can then be used to classify membrane proteins which constitute some 25% of all genomes. SAXS may also be used to study the dependence of interparticle interference scattering on solvent conditions so that regions of the protein solution phase diagram which favor crystallization can be elucidated. As a further application, SAXS may be used to provide physical constraints on computational methods for protein structure prediction based on primary sequence information. This in turn can help in identifying structural homologs of a given protein, which can then give clues to its function. D. Walther, F. Cohen and S. Doniach. "Reconstruction of low resolution three-dimensional density maps from one-dimensional small angle x-ray scattering data for biomolecules." J. Appl. Cryst. 33(2):350-363 (2000). Protein structure prediction constrained by solution X-ray scattering data and structural homology identification Zheng WJ, Doniach S JOURNAL OF MOLECULAR BIOLOGY , v. 316(#1) pp. 173-187 FEB 8, 2002

BetaSCPWeb: side-chain prediction for protein structures using Voronoi diagrams and geometry prioritization.

PubMed

Ryu, Joonghyun; Lee, Mokwon; Cha, Jehyun; Laskowski, Roman A; Ryu, Seong Eon; Kim, Deok-Soo

2016-07-08

Many applications, such as protein design, homology modeling, flexible docking, etc. require the prediction of a protein's optimal side-chain conformations from just its amino acid sequence and backbone structure. Side-chain prediction (SCP) is an NP-hard energy minimization problem. Here, we present BetaSCPWeb which efficiently computes a conformation close to optimal using a geometry-prioritization method based on the Voronoi diagram of spherical atoms. Its outputs are visual, textual and PDB file format. The web server is free and open to all users at http://voronoi.hanyang.ac.kr/betascpweb with no login requirement. © The Author(s) 2016. Published by Oxford University Press on behalf of Nucleic Acids Research.

Geometrical analysis of Cys-Cys bridges in proteins and their prediction from incomplete structural information

NASA Technical Reports Server (NTRS)

Goldblum, A.; Rein, R.

1987-01-01

Analysis of C-alpha atom positions from cysteines involved in disulphide bridges in protein crystals shows that their geometric characteristics are unique with respect to other Cys-Cys, non-bridging pairs. They may be used for predicting disulphide connections in incompletely determined protein structures, such as low resolution crystallography or theoretical folding experiments. The basic unit for analysis and prediction is the 3 x 3 distance matrix for Cx positions of residues (i - 1), Cys(i), (i +1) with (j - 1), Cys(j), (j + 1). In each of its columns, row and diagonal vector--outer distances are larger than the central distance. This analysis is compared with some analytical models.

Predicting the Effect of Mutations on Protein-Protein Binding Interactions through Structure-Based Interface Profiles

PubMed Central

Brender, Jeffrey R.; Zhang, Yang

2015-01-01

The formation of protein-protein complexes is essential for proteins to perform their physiological functions in the cell. Mutations that prevent the proper formation of the correct complexes can have serious consequences for the associated cellular processes. Since experimental determination of protein-protein binding affinity remains difficult when performed on a large scale, computational methods for predicting the consequences of mutations on binding affinity are highly desirable. We show that a scoring function based on interface structure profiles collected from analogous protein-protein interactions in the PDB is a powerful predictor of protein binding affinity changes upon mutation. As a standalone feature, the differences between the interface profile score of the mutant and wild-type proteins has an accuracy equivalent to the best all-atom potentials, despite being two orders of magnitude faster once the profile has been constructed. Due to its unique sensitivity in collecting the evolutionary profiles of analogous binding interactions and the high speed of calculation, the interface profile score has additional advantages as a complementary feature to combine with physics-based potentials for improving the accuracy of composite scoring approaches. By incorporating the sequence-derived and residue-level coarse-grained potentials with the interface structure profile score, a composite model was constructed through the random forest training, which generates a Pearson correlation coefficient >0.8 between the predicted and observed binding free-energy changes upon mutation. This accuracy is comparable to, or outperforms in most cases, the current best methods, but does not require high-resolution full-atomic models of the mutant structures. The binding interface profiling approach should find useful application in human-disease mutation recognition and protein interface design studies. PMID:26506533