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.

Genome-Wide Identification of Regulatory Sequences Undergoing Accelerated Evolution in the Human Genome

PubMed Central

Dong, Xinran; Wang, Xiao; Zhang, Feng; Tian, Weidong

2016-01-01

Accelerated evolution of regulatory sequence can alter the expression pattern of target genes, and cause phenotypic changes. In this study, we used DNase I hypersensitive sites (DHSs) to annotate putative regulatory sequences in the human genome, and conducted a genome-wide analysis of the effects of accelerated evolution on regulatory sequences. Working under the assumption that local ancient repeat elements of DHSs are under neutral evolution, we discovered that ∼0.44% of DHSs are under accelerated evolution (ace-DHSs). We found that ace-DHSs tend to be more active than background DHSs, and are strongly associated with epigenetic marks of active transcription. The target genes of ace-DHSs are significantly enriched in neuron-related functions, and their expression levels are positively selected in the human brain. Thus, these lines of evidences strongly suggest that accelerated evolution on regulatory sequences plays important role in the evolution of human-specific phenotypes. PMID:27401230

The identification of complete domains within protein sequences using accurate E-values for semi-global alignment

PubMed Central

Kann, Maricel G.; Sheetlin, Sergey L.; Park, Yonil; Bryant, Stephen H.; Spouge, John L.

2007-01-01

The sequencing of complete genomes has created a pressing need for automated annotation of gene function. Because domains are the basic units of protein function and evolution, a gene can be annotated from a domain database by aligning domains to the corresponding protein sequence. Ideally, complete domains are aligned to protein subsequences, in a ‘semi-global alignment’. Local alignment, which aligns pieces of domains to subsequences, is common in high-throughput annotation applications, however. It is a mature technique, with the heuristics and accurate E-values required for screening large databases and evaluating the screening results. Hidden Markov models (HMMs) provide an alternative theoretical framework for semi-global alignment, but their use is limited because they lack heuristic acceleration and accurate E-values. Our new tool, GLOBAL, overcomes some limitations of previous semi-global HMMs: it has accurate E-values and the possibility of the heuristic acceleration required for high-throughput applications. Moreover, according to a standard of truth based on protein structure, two semi-global HMM alignment tools (GLOBAL and HMMer) had comparable performance in identifying complete domains, but distinctly outperformed two tools based on local alignment. When searching for complete protein domains, therefore, GLOBAL avoids disadvantages commonly associated with HMMs, yet maintains their superior retrieval performance. PMID:17596268

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.

Establishing homologies in protein sequences

NASA Technical Reports Server (NTRS)

Dayhoff, M. O.; Barker, W. C.; Hunt, L. T.

1983-01-01

Computer-based statistical techniques used to determine homologies between proteins occurring in different species are reviewed. The technique is based on comparison of two protein sequences, either by relating all segments of a given length in one sequence to all segments of the second or by finding the best alignment of the two sequences. Approaches discussed include selection using printed tabulations, identification of very similar sequences, and computer searches of a database. The use of the SEARCH, RELATE, and ALIGN programs (Dayhoff, 1979) is explained; sample data are presented in graphs, diagrams, and tables and the construction of scoring matrices is considered.

Genome-Wide Identification of Regulatory Sequences Undergoing Accelerated Evolution in the Human Genome.

PubMed

Dong, Xinran; Wang, Xiao; Zhang, Feng; Tian, Weidong

2016-10-01

Accelerated evolution of regulatory sequence can alter the expression pattern of target genes, and cause phenotypic changes. In this study, we used DNase I hypersensitive sites (DHSs) to annotate putative regulatory sequences in the human genome, and conducted a genome-wide analysis of the effects of accelerated evolution on regulatory sequences. Working under the assumption that local ancient repeat elements of DHSs are under neutral evolution, we discovered that ∼0.44% of DHSs are under accelerated evolution (ace-DHSs). We found that ace-DHSs tend to be more active than background DHSs, and are strongly associated with epigenetic marks of active transcription. The target genes of ace-DHSs are significantly enriched in neuron-related functions, and their expression levels are positively selected in the human brain. Thus, these lines of evidences strongly suggest that accelerated evolution on regulatory sequences plays important role in the evolution of human-specific phenotypes. © The Author 2016. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution.

Protein Sequencing with Tandem Mass Spectrometry

NASA Astrophysics Data System (ADS)

Ziady, Assem G.; Kinter, Michael

The recent introduction of electrospray ionization techniques that are suitable for peptides and whole proteins has allowed for the design of mass spectrometric protocols that provide accurate sequence information for proteins. The advantages gained by these approaches over traditional Edman Degradation sequencing include faster analysis and femtomole, sometimes attomole, sensitivity. The ability to efficiently identify proteins has allowed investigators to conduct studies on their differential expression or modification in response to various treatments or disease states. In this chapter, we discuss the use of electrospray tandem mass spectrometry, a technique whereby protein-derived peptides are subjected to fragmentation in the gas phase, revealing sequence information for the protein. This powerful technique has been instrumental for the study of proteins and markers associated with various disorders, including heart disease, cancer, and cystic fibrosis. We use the study of protein expression in cystic fibrosis as an example.

The limits of protein sequence comparison?

PubMed Central

Pearson, William R; Sierk, Michael L

2010-01-01

Modern sequence alignment algorithms are used routinely to identify homologous proteins, proteins that share a common ancestor. Homologous proteins always share similar structures and often have similar functions. Over the past 20 years, sequence comparison has become both more sensitive, largely because of profile-based methods, and more reliable, because of more accurate statistical estimates. As sequence and structure databases become larger, and comparison methods become more powerful, reliable statistical estimates will become even more important for distinguishing similarities that are due to homology from those that are due to analogy (convergence). The newest sequence alignment methods are more sensitive than older methods, but more accurate statistical estimates are needed for their full power to be realized. PMID:15919194

Rosetta stone method for detecting protein function and protein-protein interactions from genome sequences

DOEpatents

Eisenberg, David; Marcotte, Edward M.; Pellegrini, Matteo; Thompson, Michael J.; Yeates, Todd O.

2002-10-15

A computational method system, and computer program are provided for inferring functional links from genome sequences. One method is based on the observation that some pairs of proteins A' and B' have homologs in another organism fused into a single protein chain AB. A trans-genome comparison of sequences can reveal these AB sequences, which are Rosetta Stone sequences because they decipher an interaction between A' and B. Another method compares the genomic sequence of two or more organisms to create a phylogenetic profile for each protein indicating its presence or absence across all the genomes. The profile provides information regarding functional links between different families of proteins. In yet another method a combination of the above two methods is used to predict functional links.

MALDI Top-Down sequencing: calling N- and C-terminal protein sequences with high confidence and speed.

PubMed

Suckau, Detlev; Resemann, Anja

2009-12-01

The ability to match Top-Down protein sequencing (TDS) results by MALDI-TOF to protein sequences by classical protein database searching was evaluated in this work. Resulting from these analyses were the protein identity, the simultaneous assignment of the N- and C-termini and protein sequences of up to 70 residues from either terminus. In combination with de novo sequencing using the MALDI-TDS data, even fusion proteins were assigned and the detailed sequence around the fusion site was elucidated. MALDI-TDS allowed to efficiently match protein sequences quickly and to validate recombinant protein structures-in particular, protein termini-on the level of undigested proteins.

Analysis of sequence repeats of proteins in the PDB.

PubMed

Mary Rajathei, David; Selvaraj, Samuel

2013-12-01

Internal repeats in protein sequences play a significant role in the evolution of protein structure and function. Applications of different bioinformatics tools help in the identification and characterization of these repeats. In the present study, we analyzed sequence repeats in a non-redundant set of proteins available in the Protein Data Bank (PDB). We used RADAR for detecting internal repeats in a protein, PDBeFOLD for assessing structural similarity, PDBsum for finding functional involvement and Pfam for domain assignment of the repeats in a protein. Through the analysis of sequence repeats, we found that identity of the sequence repeats falls in the range of 20-40% and, the superimposed structures of the most of the sequence repeats maintain similar overall folding. Analysis sequence repeats at the functional level reveals that most of the sequence repeats are involved in the function of the protein through functionally involved residues in the repeat regions. We also found that sequence repeats in single and two domain proteins often contained conserved sequence motifs for the function of the domain. Copyright © 2013 Elsevier Ltd. All rights reserved.

Metagenome assembly through clustering of next-generation sequencing data using protein sequences.

PubMed

Sim, Mikang; Kim, Jaebum

2015-02-01

The study of environmental microbial communities, called metagenomics, has gained a lot of attention because of the recent advances in next-generation sequencing (NGS) technologies. Microbes play a critical role in changing their environments, and the mode of their effect can be solved by investigating metagenomes. However, the difficulty of metagenomes, such as the combination of multiple microbes and different species abundance, makes metagenome assembly tasks more challenging. In this paper, we developed a new metagenome assembly method by utilizing protein sequences, in addition to the NGS read sequences. Our method (i) builds read clusters by using mapping information against available protein sequences, and (ii) creates contig sequences by finding consensus sequences through probabilistic choices from the read clusters. By using simulated NGS read sequences from real microbial genome sequences, we evaluated our method in comparison with four existing assembly programs. We found that our method could generate relatively long and accurate metagenome assemblies, indicating that the idea of using protein sequences, as a guide for the assembly, is promising. Copyright © 2015 Elsevier B.V. All rights reserved.

Isolation from genomic DNA of sequences binding specific regulatory proteins by the acceleration of protein electrophoretic mobility upon DNA binding.

PubMed

Subrahmanyam, S; Cronan, J E

1999-01-21

We report an efficient and flexible in vitro method for the isolation of genomic DNA sequences that are the binding targets of a given DNA binding protein. This method takes advantage of the fact that binding of a protein to a DNA molecule generally increases the rate of migration of the protein in nondenaturing gel electrophoresis. By the use of a radioactively labeled DNA-binding protein and nonradioactive DNA coupled with PCR amplification from gel slices, we show that specific binding sites can be isolated from Escherichia coli genomic DNA. We have applied this method to isolate a binding site for FadR, a global regulator of fatty acid metabolism in E. coli. We have also isolated a second binding site for BirA, the biotin operon repressor/biotin ligase, from the E. coli genome that has a very low binding efficiency compared with the bio operator region.

Use of designed sequences in protein structure recognition.

PubMed

Kumar, Gayatri; Mudgal, Richa; Srinivasan, Narayanaswamy; Sandhya, Sankaran

2018-05-09

Knowledge of the protein structure is a pre-requisite for improved understanding of molecular function. The gap in the sequence-structure space has increased in the post-genomic era. Grouping related protein sequences into families can aid in narrowing the gap. In the Pfam database, structure description is provided for part or full-length proteins of 7726 families. For the remaining 52% of the families, information on 3-D structure is not yet available. We use the computationally designed sequences that are intermediately related to two protein domain families, which are already known to share the same fold. These strategically designed sequences enable detection of distant relationships and here, we have employed them for the purpose of structure recognition of protein families of yet unknown structure. We first measured the success rate of our approach using a dataset of protein families of known fold and achieved a success rate of 88%. Next, for 1392 families of yet unknown structure, we made structural assignments for part/full length of the proteins. Fold association for 423 domains of unknown function (DUFs) are provided as a step towards functional annotation. The results indicate that knowledge-based filling of gaps in protein sequence space is a lucrative approach for structure recognition. Such sequences assist in traversal through protein sequence space and effectively function as 'linkers', where natural linkers between distant proteins are unavailable. This article was reviewed by Oliviero Carugo, Christine Orengo and Srikrishna Subramanian.

MIPS: a database for genomes and protein sequences.

PubMed Central

Mewes, H W; Heumann, K; Kaps, A; Mayer, K; Pfeiffer, F; Stocker, S; Frishman, D

1999-01-01

The Munich Information Center for Protein Sequences (MIPS-GSF), Martinsried near Munich, Germany, develops and maintains genome oriented databases. It is commonplace that the amount of sequence data available increases rapidly, but not the capacity of qualified manual annotation at the sequence databases. Therefore, our strategy aims to cope with the data stream by the comprehensive application of analysis tools to sequences of complete genomes, the systematic classification of protein sequences and the active support of sequence analysis and functional genomics projects. This report describes the systematic and up-to-date analysis of genomes (PEDANT), a comprehensive database of the yeast genome (MYGD), a database reflecting the progress in sequencing the Arabidopsis thaliana genome (MATD), the database of assembled, annotated human EST clusters (MEST), and the collection of protein sequence data within the framework of the PIR-International Protein Sequence Database (described elsewhere in this volume). MIPS provides access through its WWW server (http://www.mips.biochem.mpg.de) to a spectrum of generic databases, including the above mentioned as well as a database of protein families (PROTFAM), the MITOP database, and the all-against-all FASTA database. PMID:9847138

Shotgun protein sequencing: assembly of peptide tandem mass spectra from mixtures of modified proteins.

PubMed

Bandeira, Nuno; Clauser, Karl R; Pevzner, Pavel A

2007-07-01

Despite significant advances in the identification of known proteins, the analysis of unknown proteins by MS/MS still remains a challenging open problem. Although Klaus Biemann recognized the potential of MS/MS for sequencing of unknown proteins in the 1980s, low throughput Edman degradation followed by cloning still remains the main method to sequence unknown proteins. The automated interpretation of MS/MS spectra has been limited by a focus on individual spectra and has not capitalized on the information contained in spectra of overlapping peptides. Indeed the powerful shotgun DNA sequencing strategies have not been extended to automated protein sequencing. We demonstrate, for the first time, the feasibility of automated shotgun protein sequencing of protein mixtures by utilizing MS/MS spectra of overlapping and possibly modified peptides generated via multiple proteases of different specificities. We validate this approach by generating highly accurate de novo reconstructions of multiple regions of various proteins in western diamondback rattlesnake venom. We further argue that shotgun protein sequencing has the potential to overcome the limitations of current protein sequencing approaches and thus catalyze the otherwise impractical applications of proteomics methodologies in studies of unknown proteins.

Neutrality and evolvability of designed protein sequences

NASA Astrophysics Data System (ADS)

Bhattacherjee, Arnab; Biswas, Parbati

2010-07-01

The effect of foldability on protein’s evolvability is analyzed by a two-prong approach consisting of a self-consistent mean-field theory and Monte Carlo simulations. Theory and simulation models representing protein sequences with binary patterning of amino acid residues compatible with a particular foldability criteria are used. This generalized foldability criterion is derived using the high temperature cumulant expansion approximating the free energy of folding. The effect of cumulative point mutations on these designed proteins is studied under neutral condition. The robustness, protein’s ability to tolerate random point mutations is determined with a selective pressure of stability (ΔΔG) for the theory designed sequences, which are found to be more robust than that of Monte Carlo and mean-field-biased Monte Carlo generated sequences. The results show that this foldability criterion selects viable protein sequences more effectively compared to the Monte Carlo method, which has a marked effect on how the selective pressure shapes the evolutionary sequence space. These observations may impact de novo sequence design and its applications in protein engineering.

Sequence Complexity of Amyloidogenic Regions in Intrinsically Disordered Human Proteins

PubMed Central

Das, Swagata; Pal, Uttam; Das, Supriya; Bagga, Khyati; Roy, Anupam; Mrigwani, Arpita; Maiti, Nakul C.

2014-01-01

An amyloidogenic region (AR) in a protein sequence plays a significant role in protein aggregation and amyloid formation. We have investigated the sequence complexity of AR that is present in intrinsically disordered human proteins. More than 80% human proteins in the disordered protein databases (DisProt+IDEAL) contained one or more ARs. With decrease of protein disorder, AR content in the protein sequence was decreased. A probability density distribution analysis and discrete analysis of AR sequences showed that ∼8% residue in a protein sequence was in AR and the region was in average 8 residues long. The residues in the AR were high in sequence complexity and it seldom overlapped with low complexity regions (LCR), which was largely abundant in disorder proteins. The sequences in the AR showed mixed conformational adaptability towards α-helix, β-sheet/strand and coil conformations. PMID:24594841

Predicting Protein-Protein Interactions by Combing Various Sequence-Derived.

PubMed

Zhao, Xiao-Wei; Ma, Zhi-Qiang; Yin, Ming-Hao

2011-09-20

Knowledge of protein-protein interactions (PPIs) plays an important role in constructing protein interaction networks and understanding the general machineries of biological systems. In this study, a new method is proposed to predict PPIs using a comprehensive set of 930 features based only on sequence information, these features measure the interactions between residues a certain distant apart in the protein sequences from different aspects. To achieve better performance, the principal component analysis (PCA) is first employed to obtain an optimized feature subset. Then, the resulting 67-dimensional feature vectors are fed to Support Vector Machine (SVM). Experimental results on Drosophila melanogaster and Helicobater pylori datasets show that our method is very promising to predict PPIs and may at least be a useful supplement tool to existing methods.

Sequencing proteins with transverse ionic transport in nanochannels.

PubMed

Boynton, Paul; Di Ventra, Massimiliano

2016-05-03

De novo protein sequencing is essential for understanding cellular processes that govern the function of living organisms and all sequence modifications that occur after a protein has been constructed from its corresponding DNA code. By obtaining the order of the amino acids that compose a given protein one can then determine both its secondary and tertiary structures through structure prediction, which is used to create models for protein aggregation diseases such as Alzheimer's Disease. Here, we propose a new technique for de novo protein sequencing that involves translocating a polypeptide through a synthetic nanochannel and measuring the ionic current of each amino acid through an intersecting perpendicular nanochannel. We find that the distribution of ionic currents for each of the 20 proteinogenic amino acids encoded by eukaryotic genes is statistically distinct, showing this technique's potential for de novo protein sequencing.

``Sequence space soup'' of proteins and copolymers

NASA Astrophysics Data System (ADS)

Chan, Hue Sun; Dill, Ken A.

1991-09-01

To study the protein folding problem, we use exhaustive computer enumeration to explore ``sequence space soup,'' an imaginary solution containing the ``native'' conformations (i.e., of lowest free energy) under folding conditions, of every possible copolymer sequence. The model is of short self-avoiding chains of hydrophobic (H) and polar (P) monomers configured on the two-dimensional square lattice. By exhaustive enumeration, we identify all native structures for every possible sequence. We find that random sequences of H/P copolymers will bear striking resemblance to known proteins: Most sequences under folding conditions will be approximately as compact as known proteins, will have considerable amounts of secondary structure, and it is most probable that an arbitrary sequence will fold to a number of lowest free energy conformations that is of order one. In these respects, this simple model shows that proteinlike behavior should arise simply in copolymers in which one monomer type is highly solvent averse. It suggests that the structures and uniquenesses of native proteins are not consequences of having 20 different monomer types, or of unique properties of amino acid monomers with regard to special packing or interactions, and thus that simple copolymers might be designable to collapse to proteinlike structures and properties. A good strategy for designing a sequence to have a minimum possible number of native states is to strategically insert many P monomers. Thus known proteins may be marginally stable due to a balance: More H residues stabilize the desired native state, but more P residues prevent simultaneous stabilization of undesired native states.

Computationally mapping sequence space to understand evolutionary protein engineering.

PubMed

Armstrong, Kathryn A; Tidor, Bruce

2008-01-01

Evolutionary protein engineering has been dramatically successful, producing a wide variety of new proteins with altered stability, binding affinity, and enzymatic activity. However, the success of such procedures is often unreliable, and the impact of the choice of protein, engineering goal, and evolutionary procedure is not well understood. We have created a framework for understanding aspects of the protein engineering process by computationally mapping regions of feasible sequence space for three small proteins using structure-based design protocols. We then tested the ability of different evolutionary search strategies to explore these sequence spaces. The results point to a non-intuitive relationship between the error-prone PCR mutation rate and the number of rounds of replication. The evolutionary relationships among feasible sequences reveal hub-like sequences that serve as particularly fruitful starting sequences for evolutionary search. Moreover, genetic recombination procedures were examined, and tradeoffs relating sequence diversity and search efficiency were identified. This framework allows us to consider the impact of protein structure on the allowed sequence space and therefore on the challenges that each protein presents to error-prone PCR and genetic recombination procedures.

Protein Sequence Classification with Improved Extreme Learning Machine Algorithms

PubMed Central

2014-01-01

Precisely classifying a protein sequence from a large biological protein sequences database plays an important role for developing competitive pharmacological products. Comparing the unseen sequence with all the identified protein sequences and returning the category index with the highest similarity scored protein, conventional methods are usually time-consuming. Therefore, it is urgent and necessary to build an efficient protein sequence classification system. In this paper, we study the performance of protein sequence classification using SLFNs. The recent efficient extreme learning machine (ELM) and its invariants are utilized as the training algorithms. The optimal pruned ELM is first employed for protein sequence classification in this paper. To further enhance the performance, the ensemble based SLFNs structure is constructed where multiple SLFNs with the same number of hidden nodes and the same activation function are used as ensembles. For each ensemble, the same training algorithm is adopted. The final category index is derived using the majority voting method. Two approaches, namely, the basic ELM and the OP-ELM, are adopted for the ensemble based SLFNs. The performance is analyzed and compared with several existing methods using datasets obtained from the Protein Information Resource center. The experimental results show the priority of the proposed algorithms. PMID:24795876

Systematic Analysis of Long Noncoding RNAs in the Senescence-accelerated Mouse Prone 8 Brain Using RNA Sequencing.

PubMed

Zhang, Shuai; Qin, Chunxia; Cao, Guoqiong; Xin, Wenfeng; Feng, Chengqiang; Zhang, Wensheng

2016-08-02

Long noncoding RNAs (lncRNAs) may play an important role in Alzheimer's disease (AD) pathogenesis. However, despite considerable research in this area, the comprehensive and systematic understanding of lncRNAs in AD is still limited. The emergence of RNA sequencing provides a predictor and has incomparable advantage compared with other methods, including microarray. In this study, we identified lncRNAs in a 7-month-old mouse brain through deep RNA sequencing using the senescence-accelerated mouse prone 8 (SAMP8) and senescence-accelerated mouse resistant 1 (SAMR1) models. A total of 599,985,802 clean reads and 23,334 lncRNA transcripts were obtained. Then, we identified 97 significantly upregulated and 114 significantly downregulated lncRNA transcripts from all cases in SAMP8 mice relative to SAMR1 mice. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes analyses revealed that these significantly dysregulated lncRNAs were involved in regulating the development of AD from various angles, such as nerve growth factor term (GO: 1990089), mitogen-activated protein kinase signaling pathway, and AD pathway. Furthermore, the most probable AD-associated lncRNAs were predicted and listed in detail. Our study provided the systematic dissection of lncRNA profiling in SAMP8 mouse brain and accelerated the development of lncRNA biomarkers in AD. These attracting biomarkers could provide significant insights into AD therapy in the future.

Sequence space and the ongoing expansion of the protein universe.

PubMed

Povolotskaya, Inna S; Kondrashov, Fyodor A

2010-06-17

The need to maintain the structural and functional integrity of an evolving protein severely restricts the repertoire of acceptable amino-acid substitutions. However, it is not known whether these restrictions impose a global limit on how far homologous protein sequences can diverge from each other. Here we explore the limits of protein evolution using sequence divergence data. We formulate a computational approach to study the rate of divergence of distant protein sequences and measure this rate for ancient proteins, those that were present in the last universal common ancestor. We show that ancient proteins are still diverging from each other, indicating an ongoing expansion of the protein sequence universe. The slow rate of this divergence is imposed by the sparseness of functional protein sequences in sequence space and the ruggedness of the protein fitness landscape: approximately 98 per cent of sites cannot accept an amino-acid substitution at any given moment but a vast majority of all sites may eventually be permitted to evolve when other, compensatory, changes occur. Thus, approximately 3.5 x 10(9) yr has not been enough to reach the limit of divergent evolution of proteins, and for most proteins the limit of sequence similarity imposed by common function may not exceed that of random sequences.

Dissecting the relationship between protein structure and sequence variation

NASA Astrophysics Data System (ADS)

Shahmoradi, Amir; Wilke, Claus; Wilke Lab Team

2015-03-01

Over the past decade several independent works have shown that some structural properties of proteins are capable of predicting protein evolution. The strength and significance of these structure-sequence relations, however, appear to vary widely among different proteins, with absolute correlation strengths ranging from 0 . 1 to 0 . 8 . Here we present the results from a comprehensive search for the potential biophysical and structural determinants of protein evolution by studying more than 200 structural and evolutionary properties in a dataset of 209 monomeric enzymes. We discuss the main protein characteristics responsible for the general patterns of protein evolution, and identify sequence divergence as the main determinant of the strengths of virtually all structure-evolution relationships, explaining ~ 10 - 30 % of observed variation in sequence-structure relations. In addition to sequence divergence, we identify several protein structural properties that are moderately but significantly coupled with the strength of sequence-structure relations. In particular, proteins with more homogeneous back-bone hydrogen bond energies, large fractions of helical secondary structures and low fraction of beta sheets tend to have the strongest sequence-structure relation. BEACON-NSF center for the study of evolution in action.

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

Can natural proteins designed with 'inverted' peptide sequences adopt native-like protein folds?

PubMed

Sridhar, Settu; Guruprasad, Kunchur

2014-01-01

We have carried out a systematic computational analysis on a representative dataset of proteins of known three-dimensional structure, in order to evaluate whether it would possible to 'swap' certain short peptide sequences in naturally occurring proteins with their corresponding 'inverted' peptides and generate 'artificial' proteins that are predicted to retain native-like protein fold. The analysis of 3,967 representative proteins from the Protein Data Bank revealed 102,677 unique identical inverted peptide sequence pairs that vary in sequence length between 5-12 and 18 amino acid residues. Our analysis illustrates with examples that such 'artificial' proteins may be generated by identifying peptides with 'similar structural environment' and by using comparative protein modeling and validation studies. Our analysis suggests that natural proteins may be tolerant to accommodating such peptides.

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

Protein 3D Structure Computed from Evolutionary Sequence Variation

PubMed Central

Sheridan, Robert; Hopf, Thomas A.; Pagnani, Andrea; Zecchina, Riccardo; Sander, Chris

2011-01-01

The evolutionary trajectory of a protein through sequence space is constrained by its function. Collections of sequence homologs record the outcomes of millions of evolutionary experiments in which the protein evolves according to these constraints. Deciphering the evolutionary record held in these sequences and exploiting it for predictive and engineering purposes presents a formidable challenge. The potential benefit of solving this challenge is amplified by the advent of inexpensive high-throughput genomic sequencing. In this paper we ask whether we can infer evolutionary constraints from a set of sequence homologs of a protein. The challenge is to distinguish true co-evolution couplings from the noisy set of observed correlations. We address this challenge using a maximum entropy model of the protein sequence, constrained by the statistics of the multiple sequence alignment, to infer residue pair couplings. Surprisingly, we find that the strength of these inferred couplings is an excellent predictor of residue-residue proximity in folded structures. Indeed, the top-scoring residue couplings are sufficiently accurate and well-distributed to define the 3D protein fold with remarkable accuracy. We quantify this observation by computing, from sequence alone, all-atom 3D structures of fifteen test proteins from different fold classes, ranging in size from 50 to 260 residues., including a G-protein coupled receptor. These blinded inferences are de novo, i.e., they do not use homology modeling or sequence-similar fragments from known structures. The co-evolution signals provide sufficient information to determine accurate 3D protein structure to 2.7–4.8 Å Cα-RMSD error relative to the observed structure, over at least two-thirds of the protein (method called EVfold, details at http://EVfold.org). This discovery provides insight into essential interactions constraining protein evolution and will facilitate a comprehensive survey of the universe of protein

Dynamics of domain coverage of the protein sequence universe.

PubMed

Rekapalli, Bhanu; Wuichet, Kristin; Peterson, Gregory D; Zhulin, Igor B

2012-11-16

The currently known protein sequence space consists of millions of sequences in public databases and is rapidly expanding. Assigning sequences to families leads to a better understanding of protein function and the nature of the protein universe. However, a large portion of the current protein space remains unassigned and is referred to as its "dark matter". Here we suggest that true size of "dark matter" is much larger than stated by current definitions. We propose an approach to reducing the size of "dark matter" by identifying and subtracting regions in protein sequences that are not likely to contain any domain. Recent improvements in computational domain modeling result in a decrease, albeit slowly, in the relative size of "dark matter"; however, its absolute size increases substantially with the growth of sequence data.

Visualizing and Clustering Protein Similarity Networks: Sequences, Structures, and Functions.

PubMed

Mai, Te-Lun; Hu, Geng-Ming; Chen, Chi-Ming

2016-07-01

Research in the recent decade has demonstrated the usefulness of protein network knowledge in furthering the study of molecular evolution of proteins, understanding the robustness of cells to perturbation, and annotating new protein functions. In this study, we aimed to provide a general clustering approach to visualize the sequence-structure-function relationship of protein networks, and investigate possible causes for inconsistency in the protein classifications based on sequences, structures, and functions. Such visualization of protein networks could facilitate our understanding of the overall relationship among proteins and help researchers comprehend various protein databases. As a demonstration, we clustered 1437 enzymes by their sequences and structures using the minimum span clustering (MSC) method. The general structure of this protein network was delineated at two clustering resolutions, and the second level MSC clustering was found to be highly similar to existing enzyme classifications. The clustering of these enzymes based on sequence, structure, and function information is consistent with each other. For proteases, the Jaccard's similarity coefficient is 0.86 between sequence and function classifications, 0.82 between sequence and structure classifications, and 0.78 between structure and function classifications. From our clustering results, we discussed possible examples of divergent evolution and convergent evolution of enzymes. Our clustering approach provides a panoramic view of the sequence-structure-function network of proteins, helps visualize the relation between related proteins intuitively, and is useful in predicting the structure and function of newly determined protein sequences.

CAFE: aCcelerated Alignment-FrEe sequence analysis.

PubMed

Lu, Yang Young; Tang, Kujin; Ren, Jie; Fuhrman, Jed A; Waterman, Michael S; Sun, Fengzhu

2017-07-03

Alignment-free genome and metagenome comparisons are increasingly important with the development of next generation sequencing (NGS) technologies. Recently developed state-of-the-art k-mer based alignment-free dissimilarity measures including CVTree, $d_2^*$ and $d_2^S$ are more computationally expensive than measures based solely on the k-mer frequencies. Here, we report a standalone software, aCcelerated Alignment-FrEe sequence analysis (CAFE), for efficient calculation of 28 alignment-free dissimilarity measures. CAFE allows for both assembled genome sequences and unassembled NGS shotgun reads as input, and wraps the output in a standard PHYLIP format. In downstream analyses, CAFE can also be used to visualize the pairwise dissimilarity measures, including dendrograms, heatmap, principal coordinate analysis and network display. CAFE serves as a general k-mer based alignment-free analysis platform for studying the relationships among genomes and metagenomes, and is freely available at https://github.com/younglululu/CAFE. © The Author(s) 2017. Published by Oxford University Press on behalf of Nucleic Acids Research.

GuiTope: an application for mapping random-sequence peptides to protein sequences.

PubMed

Halperin, Rebecca F; Stafford, Phillip; Emery, Jack S; Navalkar, Krupa Arun; Johnston, Stephen Albert

2012-01-03

Random-sequence peptide libraries are a commonly used tool to identify novel ligands for binding antibodies, other proteins, and small molecules. It is often of interest to compare the selected peptide sequences to the natural protein binding partners to infer the exact binding site or the importance of particular residues. The ability to search a set of sequences for similarity to a set of peptides may sometimes enable the prediction of an antibody epitope or a novel binding partner. We have developed a software application designed specifically for this task. GuiTope provides a graphical user interface for aligning peptide sequences to protein sequences. All alignment parameters are accessible to the user including the ability to specify the amino acid frequency in the peptide library; these frequencies often differ significantly from those assumed by popular alignment programs. It also includes a novel feature to align di-peptide inversions, which we have found improves the accuracy of antibody epitope prediction from peptide microarray data and shows utility in analyzing phage display datasets. Finally, GuiTope can randomly select peptides from a given library to estimate a null distribution of scores and calculate statistical significance. GuiTope provides a convenient method for comparing selected peptide sequences to protein sequences, including flexible alignment parameters, novel alignment features, ability to search a database, and statistical significance of results. The software is available as an executable (for PC) at http://www.immunosignature.com/software and ongoing updates and source code will be available at sourceforge.net.

Dynamics of domain coverage of the protein sequence universe

PubMed Central

2012-01-01

Background The currently known protein sequence space consists of millions of sequences in public databases and is rapidly expanding. Assigning sequences to families leads to a better understanding of protein function and the nature of the protein universe. However, a large portion of the current protein space remains unassigned and is referred to as its “dark matter”. Results Here we suggest that true size of “dark matter” is much larger than stated by current definitions. We propose an approach to reducing the size of “dark matter” by identifying and subtracting regions in protein sequences that are not likely to contain any domain. Conclusions Recent improvements in computational domain modeling result in a decrease, albeit slowly, in the relative size of “dark matter”; however, its absolute size increases substantially with the growth of sequence data. PMID:23157439

Sequence-similar, structure-dissimilar protein pairs in the PDB.

PubMed

Kosloff, Mickey; Kolodny, Rachel

2008-05-01

It is often assumed that in the Protein Data Bank (PDB), two proteins with similar sequences will also have similar structures. Accordingly, it has proved useful to develop subsets of the PDB from which "redundant" structures have been removed, based on a sequence-based criterion for similarity. Similarly, when predicting protein structure using homology modeling, if a template structure for modeling a target sequence is selected by sequence alone, this implicitly assumes that all sequence-similar templates are equivalent. Here, we show that this assumption is often not correct and that standard approaches to create subsets of the PDB can lead to the loss of structurally and functionally important information. We have carried out sequence-based structural superpositions and geometry-based structural alignments of a large number of protein pairs to determine the extent to which sequence similarity ensures structural similarity. We find many examples where two proteins that are similar in sequence have structures that differ significantly from one another. The source of the structural differences usually has a functional basis. The number of such proteins pairs that are identified and the magnitude of the dissimilarity depend on the approach that is used to calculate the differences; in particular sequence-based structure superpositioning will identify a larger number of structurally dissimilar pairs than geometry-based structural alignments. When two sequences can be aligned in a statistically meaningful way, sequence-based structural superpositioning provides a meaningful measure of structural differences. This approach and geometry-based structure alignments reveal somewhat different information and one or the other might be preferable in a given application. Our results suggest that in some cases, notably homology modeling, the common use of nonredundant datasets, culled from the PDB based on sequence, may mask important structural and functional information. We

MIPS: a database for genomes and protein sequences.

PubMed

Mewes, H W; Frishman, D; Güldener, U; Mannhaupt, G; Mayer, K; Mokrejs, M; Morgenstern, B; Münsterkötter, M; Rudd, S; Weil, B

2002-01-01

The Munich Information Center for Protein Sequences (MIPS-GSF, Neuherberg, Germany) continues to provide genome-related information in a systematic way. MIPS supports both national and European sequencing and functional analysis projects, develops and maintains automatically generated and manually annotated genome-specific databases, develops systematic classification schemes for the functional annotation of protein sequences, and provides tools for the comprehensive analysis of protein sequences. This report updates the information on the yeast genome (CYGD), the Neurospora crassa genome (MNCDB), the databases for the comprehensive set of genomes (PEDANT genomes), the database of annotated human EST clusters (HIB), the database of complete cDNAs from the DHGP (German Human Genome Project), as well as the project specific databases for the GABI (Genome Analysis in Plants) and HNB (Helmholtz-Netzwerk Bioinformatik) networks. The Arabidospsis thaliana database (MATDB), the database of mitochondrial proteins (MITOP) and our contribution to the PIR International Protein Sequence Database have been described elsewhere [Schoof et al. (2002) Nucleic Acids Res., 30, 91-93; Scharfe et al. (2000) Nucleic Acids Res., 28, 155-158; Barker et al. (2001) Nucleic Acids Res., 29, 29-32]. All databases described, the protein analysis tools provided and the detailed descriptions of our projects can be accessed through the MIPS World Wide Web server (http://mips.gsf.de).

CAMELOT: A machine learning approach for coarse-grained simulations of aggregation of block-copolymeric protein sequences

PubMed Central

Ruff, Kiersten M.; Harmon, Tyler S.; Pappu, Rohit V.

2015-01-01

We report the development and deployment of a coarse-graining method that is well suited for computer simulations of aggregation and phase separation of protein sequences with block-copolymeric architectures. Our algorithm, named CAMELOT for Coarse-grained simulations Aided by MachinE Learning Optimization and Training, leverages information from converged all atom simulations that is used to determine a suitable resolution and parameterize the coarse-grained model. To parameterize a system-specific coarse-grained model, we use a combination of Boltzmann inversion, non-linear regression, and a Gaussian process Bayesian optimization approach. The accuracy of the coarse-grained model is demonstrated through direct comparisons to results from all atom simulations. We demonstrate the utility of our coarse-graining approach using the block-copolymeric sequence from the exon 1 encoded sequence of the huntingtin protein. This sequence comprises of 17 residues from the N-terminal end of huntingtin (N17) followed by a polyglutamine (polyQ) tract. Simulations based on the CAMELOT approach are used to show that the adsorption and unfolding of the wild type N17 and its sequence variants on the surface of polyQ tracts engender a patchy colloid like architecture that promotes the formation of linear aggregates. These results provide a plausible explanation for experimental observations, which show that N17 accelerates the formation of linear aggregates in block-copolymeric N17-polyQ sequences. The CAMELOT approach is versatile and is generalizable for simulating the aggregation and phase behavior of a range of block-copolymeric protein sequences. PMID:26723608

Coarse-grained sequences for protein folding and design

PubMed Central

Brown, Scott; Fawzi, Nicolas J.; Head-Gordon, Teresa

2003-01-01

We present the results of sequence design on our off-lattice minimalist model in which no specification of native-state tertiary contacts is needed. We start with a sequence that adopts a target topology and build on it through sequence mutation to produce new sequences that comprise distinct members within a target fold class. In this work, we use the α/β ubiquitin fold class and design two new sequences that, when characterized through folding simulations, reproduce the differences in folding mechanism seen experimentally for proteins L and G. The primary implication of this work is that patterning of hydrophobic and hydrophilic residues is the physical origin for the success of relative contact-order descriptions of folding, and that these physics-based potentials provide a predictive connection between free energy landscapes and amino acid sequence (the original protein folding problem). We present results of the sequence mapping from a 20- to the three-letter code for determining a sequence that folds into the WW domain topology to illustrate future extensions to protein design. PMID:12963815

Coarse-grained sequences for protein folding and design.

PubMed

Brown, Scott; Fawzi, Nicolas J; Head-Gordon, Teresa

2003-09-16

We present the results of sequence design on our off-lattice minimalist model in which no specification of native-state tertiary contacts is needed. We start with a sequence that adopts a target topology and build on it through sequence mutation to produce new sequences that comprise distinct members within a target fold class. In this work, we use the alpha/beta ubiquitin fold class and design two new sequences that, when characterized through folding simulations, reproduce the differences in folding mechanism seen experimentally for proteins L and G. The primary implication of this work is that patterning of hydrophobic and hydrophilic residues is the physical origin for the success of relative contact-order descriptions of folding, and that these physics-based potentials provide a predictive connection between free energy landscapes and amino acid sequence (the original protein folding problem). We present results of the sequence mapping from a 20- to the three-letter code for determining a sequence that folds into the WW domain topology to illustrate future extensions to protein design.

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

Tuning calcite morphology and growth acceleration by a rational design of highly stable protein-mimetics

NASA Astrophysics Data System (ADS)

Chen, Chun-Long; Qi, Jiahui; Tao, Jinhui; Zuckermann, Ronald N.; Deyoreo, James J.

2014-09-01

In nature, proteins play a significant role in biomineral formation. One of the ultimate goals of bioinspired materials science is to develop highly stable synthetic molecules that mimic the function of these natural proteins by controlling crystal formation. Here, we demonstrate that both the morphology and the degree of acceleration or inhibition observed during growth of calcite in the presence of peptoids can be rationally tuned by balancing the electrostatic and hydrophobic interactions, with hydrophobic interactions playing the dominant role. While either strong electrostatic or hydrophobic interactions inhibit growth and reduces expression of the {104} faces, correlations between peptoid-crystal binding energies and observed changes in calcite growth indicate moderate electrostatic interactions allow peptoids to weakly adsorb while moderate hydrophobic interactions cause disruption of surface-adsorbed water layers, leading to growth acceleration with retained expression of the {104} faces. This study provides fundamental principles for designing peptoids as crystallization promoters, and offers a straightforward screening method based on macroscopic crystal morphology. Because peptoids are sequence-specific, highly stable, and easily synthesized, peptoid-enhanced crystallization offers a broad range of potential applications.

Tuning calcite morphology and growth acceleration by a rational design of highly stable protein-mimetics

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

Chen, Chunlong; Qi, Jiahui; Tao, Jinhui

2014-09-05

In nature, proteins play a significant role in biomineral formation. One of the ultimate goals of bioinspired materials science is to develop highly stable synthetic molecules that mimic the function of these natural proteins by controlling crystal formation. Here, we demonstrate that both the morphology and the degree of acceleration or inhibition observed during growth of calcite in the presence of peptoids can be rationally tuned by balancing the electrostatic interactions (EI) and hydrophobic interactions (HI), with HI playing the dominant role. While either strong EI or HI inhibit growth and suppress (104) face expression, correlations between peptoid-crystal binding energiesmore » and observed changes in calcite growth indicate moderate EI allow peptoids to weakly adsorb while moderate HI cause disruption of surface-adsorbed water layers, leading to growth acceleration with retained expression of (104) faces. This study provides fundamental principles for designing peptoids as crystallization promoters, and offers a straightforward screening method based on macroscopic crystal morphology. Because peptoids are sequence-specific, highly stable, and easily synthesized, peptoid-enhanced crystallization offers a broad range of potential applications.« less

Tuning calcite morphology and growth acceleration by a rational design of highly stable protein-mimetics

PubMed Central

Chen, Chun-Long; Qi, Jiahui; Tao, Jinhui; Zuckermann, Ronald N.; DeYoreo, James J.

2014-01-01

In nature, proteins play a significant role in biomineral formation. One of the ultimate goals of bioinspired materials science is to develop highly stable synthetic molecules that mimic the function of these natural proteins by controlling crystal formation. Here, we demonstrate that both the morphology and the degree of acceleration or inhibition observed during growth of calcite in the presence of peptoids can be rationally tuned by balancing the electrostatic and hydrophobic interactions, with hydrophobic interactions playing the dominant role. While either strong electrostatic or hydrophobic interactions inhibit growth and reduces expression of the {104} faces, correlations between peptoid-crystal binding energies and observed changes in calcite growth indicate moderate electrostatic interactions allow peptoids to weakly adsorb while moderate hydrophobic interactions cause disruption of surface-adsorbed water layers, leading to growth acceleration with retained expression of the {104} faces. This study provides fundamental principles for designing peptoids as crystallization promoters, and offers a straightforward screening method based on macroscopic crystal morphology. Because peptoids are sequence-specific, highly stable, and easily synthesized, peptoid-enhanced crystallization offers a broad range of potential applications. PMID:25189418

Gene Unprediction with Spurio: A tool to identify spurious protein sequences.

PubMed

Höps, Wolfram; Jeffryes, Matt; Bateman, Alex

2018-01-01

We now have access to the sequences of tens of millions of proteins. These protein sequences are essential for modern molecular biology and computational biology. The vast majority of protein sequences are derived from gene prediction tools and have no experimental supporting evidence for their translation.  Despite the increasing accuracy of gene prediction tools there likely exists a large number of spurious protein predictions in the sequence databases.  We have developed the Spurio tool to help identify spurious protein predictions in prokaryotes.  Spurio searches the query protein sequence against a prokaryotic nucleotide database using tblastn and identifies homologous sequences. The tblastn matches are used to score the query sequence's likelihood of being a spurious protein prediction using a Gaussian process model. The most informative feature is the appearance of stop codons within the presumed translation of homologous DNA sequences. Benchmarking shows that the Spurio tool is able to distinguish spurious from true proteins. However, transposon proteins are prone to be predicted as spurious because of the frequency of degraded homologs found in the DNA sequence databases. Our initial experiments suggest that less than 1% of the proteins in the UniProtKB sequence database are likely to be spurious and that Spurio is able to identify over 60 times more spurious proteins than the AntiFam resource. The Spurio software and source code is available under an MIT license at the following URL: https://bitbucket.org/bateman-group/spurio.

Structure and Sequence Search on Aptamer-Protein Docking

NASA Astrophysics Data System (ADS)

Xiao, Jiajie; Bonin, Keith; Guthold, Martin; Salsbury, Freddie

2015-03-01

Interactions between proteins and deoxyribonucleic acid (DNA) play a significant role in the living systems, especially through gene regulation. However, short nucleic acids sequences (aptamers) with specific binding affinity to specific proteins exhibit clinical potential as therapeutics. Our capillary and gel electrophoresis selection experiments show that specific sequences of aptamers can be selected that bind specific proteins. Computationally, given the experimentally-determined structure and sequence of a thrombin-binding aptamer, we can successfully dock the aptamer onto thrombin in agreement with experimental structures of the complex. In order to further study the conformational flexibility of this thrombin-binding aptamer and to potentially develop a predictive computational model of aptamer-binding, we use GPU-enabled molecular dynamics simulations to both examine the conformational flexibility of the aptamer in the absence of binding to thrombin, and to determine our ability to fold an aptamer. This study should help further de-novo predictions of aptamer sequences by enabling the study of structural and sequence-dependent effects on aptamer-protein docking specificity.

AlignMe—a membrane protein sequence alignment web server

PubMed Central

Stamm, Marcus; Staritzbichler, René; Khafizov, Kamil; Forrest, Lucy R.

2014-01-01

We present a web server for pair-wise alignment of membrane protein sequences, using the program AlignMe. The server makes available two operational modes of AlignMe: (i) sequence to sequence alignment, taking two sequences in fasta format as input, combining information about each sequence from multiple sources and producing a pair-wise alignment (PW mode); and (ii) alignment of two multiple sequence alignments to create family-averaged hydropathy profile alignments (HP mode). For the PW sequence alignment mode, four different optimized parameter sets are provided, each suited to pairs of sequences with a specific similarity level. These settings utilize different types of inputs: (position-specific) substitution matrices, secondary structure predictions and transmembrane propensities from transmembrane predictions or hydrophobicity scales. In the second (HP) mode, each input multiple sequence alignment is converted into a hydrophobicity profile averaged over the provided set of sequence homologs; the two profiles are then aligned. The HP mode enables qualitative comparison of transmembrane topologies (and therefore potentially of 3D folds) of two membrane proteins, which can be useful if the proteins have low sequence similarity. In summary, the AlignMe web server provides user-friendly access to a set of tools for analysis and comparison of membrane protein sequences. Access is available at http://www.bioinfo.mpg.de/AlignMe PMID:24753425

MIPS: a database for genomes and protein sequences

PubMed Central

Mewes, H. W.; Frishman, D.; Güldener, U.; Mannhaupt, G.; Mayer, K.; Mokrejs, M.; Morgenstern, B.; Münsterkötter, M.; Rudd, S.; Weil, B.

2002-01-01

The Munich Information Center for Protein Sequences (MIPS-GSF, Neuherberg, Germany) continues to provide genome-related information in a systematic way. MIPS supports both national and European sequencing and functional analysis projects, develops and maintains automatically generated and manually annotated genome-specific databases, develops systematic classification schemes for the functional annotation of protein sequences, and provides tools for the comprehensive analysis of protein sequences. This report updates the information on the yeast genome (CYGD), the Neurospora crassa genome (MNCDB), the databases for the comprehensive set of genomes (PEDANT genomes), the database of annotated human EST clusters (HIB), the database of complete cDNAs from the DHGP (German Human Genome Project), as well as the project specific databases for the GABI (Genome Analysis in Plants) and HNB (Helmholtz–Netzwerk Bioinformatik) networks. The Arabidospsis thaliana database (MATDB), the database of mitochondrial proteins (MITOP) and our contribution to the PIR International Protein Sequence Database have been described elsewhere [Schoof et al. (2002) Nucleic Acids Res., 30, 91–93; Scharfe et al. (2000) Nucleic Acids Res., 28, 155–158; Barker et al. (2001) Nucleic Acids Res., 29, 29–32]. All databases described, the protein analysis tools provided and the detailed descriptions of our projects can be accessed through the MIPS World Wide Web server (http://mips.gsf.de). PMID:11752246

MIPS: a database for protein sequences and complete genomes.

PubMed Central

Mewes, H W; Hani, J; Pfeiffer, F; Frishman, D

1998-01-01

The MIPS group [Munich Information Center for Protein Sequences of the German National Center for Environment and Health (GSF)] at the Max-Planck-Institute for Biochemistry, Martinsried near Munich, Germany, is involved in a number of data collection activities, including a comprehensive database of the yeast genome, a database reflecting the progress in sequencing the Arabidopsis thaliana genome, the systematic analysis of other small genomes and the collection of protein sequence data within the framework of the PIR-International Protein Sequence Database (described elsewhere in this volume). Through its WWW server (http://www.mips.biochem.mpg.de ) MIPS provides access to a variety of generic databases, including a database of protein families as well as automatically generated data by the systematic application of sequence analysis algorithms. The yeast genome sequence and its related information was also compiled on CD-ROM to provide dynamic interactive access to the 16 chromosomes of the first eukaryotic genome unraveled. PMID:9399795

Attomole quantitation of protein separations with accelerator mass spectrometry

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

Vogel, J S; Grant, P G; Buccholz, B A

2000-12-15

Quantification of specific proteins depends on separation by chromatography or electrophoresis followed by chemical detection schemes such as staining and fluorophore adhesion. Chemical exchange of short-lived isotopes, particularly sulfur, is also prevalent despite the inconveniences of counting radioactivity. Physical methods based on isotopic and elemental analyses offer highly sensitive protein quantitation that has linear response over wide dynamic ranges and is independent of protein conformation. Accelerator mass spectrometry quantifies long-lived isotopes such as 14C to sub-attomole sensitivity. We quantified protein interactions with small molecules such as toxins, vitamins, and natural biochemicals at precisions of 1-5% . Micro-proton-induced-xray-emission quantifies elemental abundancesmore » in separated metalloprotein samples to nanogram amounts and is capable of quantifying phosphorylated loci in gels. Accelerator-based quantitation is a possible tool for quantifying the genome translation into proteome.« less

Evaluating the protein coding potential of exonized transposable element sequences

PubMed Central

Piriyapongsa, Jittima; Rutledge, Mark T; Patel, Sanil; Borodovsky, Mark; Jordan, I King

2007-01-01

Background Transposable element (TE) sequences, once thought to be merely selfish or parasitic members of the genomic community, have been shown to contribute a wide variety of functional sequences to their host genomes. Analysis of complete genome sequences have turned up numerous cases where TE sequences have been incorporated as exons into mRNAs, and it is widely assumed that such 'exonized' TEs encode protein sequences. However, the extent to which TE-derived sequences actually encode proteins is unknown and a matter of some controversy. We have tried to address this outstanding issue from two perspectives: i-by evaluating ascertainment biases related to the search methods used to uncover TE-derived protein coding sequences (CDS) and ii-through a probabilistic codon-frequency based analysis of the protein coding potential of TE-derived exons. Results We compared the ability of three classes of sequence similarity search methods to detect TE-derived sequences among data sets of experimentally characterized proteins: 1-a profile-based hidden Markov model (HMM) approach, 2-BLAST methods and 3-RepeatMasker. Profile based methods are more sensitive and more selective than the other methods evaluated. However, the application of profile-based search methods to the detection of TE-derived sequences among well-curated experimentally characterized protein data sets did not turn up many more cases than had been previously detected and nowhere near as many cases as recent genome-wide searches have. We observed that the different search methods used were complementary in the sense that they yielded largely non-overlapping sets of hits and differed in their ability to recover known cases of TE-derived CDS. The probabilistic analysis of TE-derived exon sequences indicates that these sequences have low protein coding potential on average. In particular, non-autonomous TEs that do not encode protein sequences, such as Alu elements, are frequently exonized but unlikely to

Correlation between protein sequence similarity and x-ray diffraction quality in the protein data bank.

PubMed

Lu, Hui-Meng; Yin, Da-Chuan; Ye, Ya-Jing; Luo, Hui-Min; Geng, Li-Qiang; Li, Hai-Sheng; Guo, Wei-Hong; Shang, Peng

2009-01-01

As the most widely utilized technique to determine the 3-dimensional structure of protein molecules, X-ray crystallography can provide structure of the highest resolution among the developed techniques. The resolution obtained via X-ray crystallography is known to be influenced by many factors, such as the crystal quality, diffraction techniques, and X-ray sources, etc. In this paper, the authors found that the protein sequence could also be one of the factors. We extracted information of the resolution and the sequence of proteins from the Protein Data Bank (PDB), classified the proteins into different clusters according to the sequence similarity, and statistically analyzed the relationship between the sequence similarity and the best resolution obtained. The results showed that there was a pronounced correlation between the sequence similarity and the obtained resolution. These results indicate that protein structure itself is one variable that may affect resolution when X-ray crystallography is used.

CUDA compatible GPU cards as efficient hardware accelerators for Smith-Waterman sequence alignment

PubMed Central

Manavski, Svetlin A; Valle, Giorgio

2008-01-01

Background Searching for similarities in protein and DNA databases has become a routine procedure in Molecular Biology. The Smith-Waterman algorithm has been available for more than 25 years. It is based on a dynamic programming approach that explores all the possible alignments between two sequences; as a result it returns the optimal local alignment. Unfortunately, the computational cost is very high, requiring a number of operations proportional to the product of the length of two sequences. Furthermore, the exponential growth of protein and DNA databases makes the Smith-Waterman algorithm unrealistic for searching similarities in large sets of sequences. For these reasons heuristic approaches such as those implemented in FASTA and BLAST tend to be preferred, allowing faster execution times at the cost of reduced sensitivity. The main motivation of our work is to exploit the huge computational power of commonly available graphic cards, to develop high performance solutions for sequence alignment. Results In this paper we present what we believe is the fastest solution of the exact Smith-Waterman algorithm running on commodity hardware. It is implemented in the recently released CUDA programming environment by NVidia. CUDA allows direct access to the hardware primitives of the last-generation Graphics Processing Units (GPU) G80. Speeds of more than 3.5 GCUPS (Giga Cell Updates Per Second) are achieved on a workstation running two GeForce 8800 GTX. Exhaustive tests have been done to compare our implementation to SSEARCH and BLAST, running on a 3 GHz Intel Pentium IV processor. Our solution was also compared to a recently published GPU implementation and to a Single Instruction Multiple Data (SIMD) solution. These tests show that our implementation performs from 2 to 30 times faster than any other previous attempt available on commodity hardware. Conclusions The results show that graphic cards are now sufficiently advanced to be used as efficient hardware

SIBIS: a Bayesian model for inconsistent protein sequence estimation.

PubMed

Khenoussi, Walyd; Vanhoutrève, Renaud; Poch, Olivier; Thompson, Julie D

2014-09-01

The prediction of protein coding genes is a major challenge that depends on the quality of genome sequencing, the accuracy of the model used to elucidate the exonic structure of the genes and the complexity of the gene splicing process leading to different protein variants. As a consequence, today's protein databases contain a huge amount of inconsistency, due to both natural variants and sequence prediction errors. We have developed a new method, called SIBIS, to detect such inconsistencies based on the evolutionary information in multiple sequence alignments. A Bayesian framework, combined with Dirichlet mixture models, is used to estimate the probability of observing specific amino acids and to detect inconsistent or erroneous sequence segments. We evaluated the performance of SIBIS on a reference set of protein sequences with experimentally validated errors and showed that the sensitivity is significantly higher than previous methods, with only a small loss of specificity. We also assessed a large set of human sequences from the UniProt database and found evidence of inconsistency in 48% of the previously uncharacterized sequences. We conclude that the integration of quality control methods like SIBIS in automatic analysis pipelines will be critical for the robust inference of structural, functional and phylogenetic information from these sequences. Source code, implemented in C on a linux system, and the datasets of protein sequences are freely available for download at http://www.lbgi.fr/∼julie/SIBIS. © The Author 2014. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

How Many Protein Sequences Fold to a Given Structure? A Coevolutionary Analysis.

PubMed

Tian, Pengfei; Best, Robert B

2017-10-17

Quantifying the relationship between protein sequence and structure is key to understanding the protein universe. A fundamental measure of this relationship is the total number of amino acid sequences that can fold to a target protein structure, known as the "sequence capacity," which has been suggested as a proxy for how designable a given protein fold is. Although sequence capacity has been extensively studied using lattice models and theory, numerical estimates for real protein structures are currently lacking. In this work, we have quantitatively estimated the sequence capacity of 10 proteins with a variety of different structures using a statistical model based on residue-residue co-evolution to capture the variation of sequences from the same protein family. Remarkably, we find that even for the smallest protein folds, such as the WW domain, the number of foldable sequences is extremely large, exceeding the Avogadro constant. In agreement with earlier theoretical work, the calculated sequence capacity is positively correlated with the size of the protein, or better, the density of contacts. This allows the absolute sequence capacity of a given protein to be approximately predicted from its structure. On the other hand, the relative sequence capacity, i.e., normalized by the total number of possible sequences, is an extremely tiny number and is strongly anti-correlated with the protein length. Thus, although there may be more foldable sequences for larger proteins, it will be much harder to find them. Lastly, we have correlated the evolutionary age of proteins in the CATH database with their sequence capacity as predicted by our model. The results suggest a trade-off between the opposing requirements of high designability and the likelihood of a novel fold emerging by chance. Published by Elsevier Inc.

SW#db: GPU-Accelerated Exact Sequence Similarity Database Search.

PubMed

Korpar, Matija; Šošić, Martin; Blažeka, Dino; Šikić, Mile

2015-01-01

In recent years we have witnessed a growth in sequencing yield, the number of samples sequenced, and as a result-the growth of publicly maintained sequence databases. The increase of data present all around has put high requirements on protein similarity search algorithms with two ever-opposite goals: how to keep the running times acceptable while maintaining a high-enough level of sensitivity. The most time consuming step of similarity search are the local alignments between query and database sequences. This step is usually performed using exact local alignment algorithms such as Smith-Waterman. Due to its quadratic time complexity, alignments of a query to the whole database are usually too slow. Therefore, the majority of the protein similarity search methods prior to doing the exact local alignment apply heuristics to reduce the number of possible candidate sequences in the database. However, there is still a need for the alignment of a query sequence to a reduced database. In this paper we present the SW#db tool and a library for fast exact similarity search. Although its running times, as a standalone tool, are comparable to the running times of BLAST, it is primarily intended to be used for exact local alignment phase in which the database of sequences has already been reduced. It uses both GPU and CPU parallelization and was 4-5 times faster than SSEARCH, 6-25 times faster than CUDASW++ and more than 20 times faster than SSW at the time of writing, using multiple queries on Swiss-prot and Uniref90 databases.

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.

Identification of Sequence Specificity of 5-Methylcytosine Oxidation by Tet1 Protein with High-Throughput Sequencing.

PubMed

Kizaki, Seiichiro; Chandran, Anandhakumar; Sugiyama, Hiroshi

2016-03-02

Tet (ten-eleven translocation) family proteins have the ability to oxidize 5-methylcytosine (mC) to 5-hydroxymethylcytosine (hmC), 5-formylcytosine (fC), and 5-carboxycytosine (caC). However, the oxidation reaction of Tet is not understood completely. Evaluation of genomic-level epigenetic changes by Tet protein requires unbiased identification of the highly selective oxidation sites. In this study, we used high-throughput sequencing to investigate the sequence specificity of mC oxidation by Tet1. A 6.6×10(4) -member mC-containing random DNA-sequence library was constructed. The library was subjected to Tet-reactive pulldown followed by high-throughput sequencing. Analysis of the obtained sequence data identified the Tet1-reactive sequences. We identified mCpG as a highly reactive sequence of Tet1 protein. © 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Adaptive compressive learning for prediction of protein-protein interactions from primary sequence.

PubMed

Zhang, Ya-Nan; Pan, Xiao-Yong; Huang, Yan; Shen, Hong-Bin

2011-08-21

Protein-protein interactions (PPIs) play an important role in biological processes. Although much effort has been devoted to the identification of novel PPIs by integrating experimental biological knowledge, there are still many difficulties because of lacking enough protein structural and functional information. It is highly desired to develop methods based only on amino acid sequences for predicting PPIs. However, sequence-based predictors are often struggling with the high-dimensionality causing over-fitting and high computational complexity problems, as well as the redundancy of sequential feature vectors. In this paper, a novel computational approach based on compressed sensing theory is proposed to predict yeast Saccharomyces cerevisiae PPIs from primary sequence and has achieved promising results. The key advantage of the proposed compressed sensing algorithm is that it can compress the original high-dimensional protein sequential feature vector into a much lower but more condensed space taking the sparsity property of the original signal into account. What makes compressed sensing much more attractive in protein sequence analysis is its compressed signal can be reconstructed from far fewer measurements than what is usually considered necessary in traditional Nyquist sampling theory. Experimental results demonstrate that proposed compressed sensing method is powerful for analyzing noisy biological data and reducing redundancy in feature vectors. The proposed method represents a new strategy of dealing with high-dimensional protein discrete model and has great potentiality to be extended to deal with many other complicated biological systems. Copyright © 2011 Elsevier Ltd. All rights reserved.

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.

Sequence signatures of allosteric proteins towards rational design.

PubMed

Namboodiri, Saritha; Verma, Chandra; Dhar, Pawan K; Giuliani, Alessandro; Nair, Achuthsankar S

2010-12-01

Allostery is the phenomenon of changes in the structure and activity of proteins that appear as a consequence of ligand binding at sites other than the active site. Studying mechanistic basis of allostery leading to protein design with predetermined functional endpoints is an important unmet need of synthetic biology. Here, we screened the amino acid sequence landscape in search of sequence-signatures of allostery using Recurrence Quantitative Analysis (RQA) method. A characteristic vector, comprised of 10 features extracted from RQA was defined for amino acid sequences. Using Principal Component Analysis, four factors were found to be important determinants of allosteric behavior. Our sequence-based predictor method shows 82.6% accuracy, 85.7% sensitivity and 77.9% specificity with the current dataset. Further, we show that Laminarity-Mean-hydrophobicity representing repeated hydrophobic patches is the most crucial indicator of allostery. To our best knowledge this is the first report that describes sequence determinants of allostery based on hydrophobicity. As an outcome of these findings, we plan to explore possibility of inducing allostery in proteins.

Molecular Design of Performance Proteins With Repetitive Sequences

NASA Astrophysics Data System (ADS)

Vendrely, Charlotte; Ackerschott, Christian; Römer, Lin; Scheibel, Thomas

Most performance proteins responsible for the mechanical stability of cells and organisms reveal highly repetitive sequences. Mimicking such performance proteins is of high interest for the design of nanostructured biomaterials. In this article, flagelliform silk is exemplary introduced to describe a general principle for designing genes of repetitive performance proteins for recombinant expression in Escherichia coli . In the first step, repeating amino acid sequence motifs are reversely transcripted into DNA cassettes, which can in a second step be seamlessly ligated, yielding a designed gene. Recombinant expression thereof leads to proteins mimicking the natural ones. The recombinant proteins can be assembled into nanostructured materials in a controlled manner, allowing their use in several applications.

Increasing Sequence Diversity with Flexible Backbone Protein Design: The Complete Redesign of a Protein Hydrophobic Core

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

Murphy, Grant S.; Mills, Jeffrey L.; Miley, Michael J.

2015-10-15

Protein design tests our understanding of protein stability and structure. Successful design methods should allow the exploration of sequence space not found in nature. However, when redesigning naturally occurring protein structures, most fixed backbone design algorithms return amino acid sequences that share strong sequence identity with wild-type sequences, especially in the protein core. This behavior places a restriction on functional space that can be explored and is not consistent with observations from nature, where sequences of low identity have similar structures. Here, we allow backbone flexibility during design to mutate every position in the core (38 residues) of a four-helixmore » bundle protein. Only small perturbations to the backbone, 12 {angstrom}, were needed to entirely mutate the core. The redesigned protein, DRNN, is exceptionally stable (melting point >140C). An NMR and X-ray crystal structure show that the side chains and backbone were accurately modeled (all-atom RMSD = 1.3 {angstrom}).« less

NCBI Reference Sequence (RefSeq): a curated non-redundant sequence database of genomes, transcripts and proteins

PubMed Central

Pruitt, Kim D.; Tatusova, Tatiana; Maglott, Donna R.

2005-01-01

The National Center for Biotechnology Information (NCBI) Reference Sequence (RefSeq) database (http://www.ncbi.nlm.nih.gov/RefSeq/) provides a non-redundant collection of sequences representing genomic data, transcripts and proteins. Although the goal is to provide a comprehensive dataset representing the complete sequence information for any given species, the database pragmatically includes sequence data that are currently publicly available in the archival databases. The database incorporates data from over 2400 organisms and includes over one million proteins representing significant taxonomic diversity spanning prokaryotes, eukaryotes and viruses. Nucleotide and protein sequences are explicitly linked, and the sequences are linked to other resources including the NCBI Map Viewer and Gene. Sequences are annotated to include coding regions, conserved domains, variation, references, names, database cross-references, and other features using a combined approach of collaboration and other input from the scientific community, automated annotation, propagation from GenBank and curation by NCBI staff. PMID:15608248

Molecular sled sequences are common in mammalian proteins.

PubMed

Xiong, Kan; Blainey, Paul C

2016-03-18

Recent work revealed a new class of molecular machines called molecular sleds, which are small basic molecules that bind and slide along DNA with the ability to carry cargo along DNA. Here, we performed biochemical and single-molecule flow stretching assays to investigate the basis of sliding activity in molecular sleds. In particular, we identified the functional core of pVIc, the first molecular sled characterized; peptide functional groups that control sliding activity; and propose a model for the sliding activity of molecular sleds. We also observed widespread DNA binding and sliding activity among basic polypeptide sequences that implicate mammalian nuclear localization sequences and many cell penetrating peptides as molecular sleds. These basic protein motifs exhibit weak but physiologically relevant sequence-nonspecific DNA affinity. Our findings indicate that many mammalian proteins contain molecular sled sequences and suggest the possibility that substantial undiscovered sliding activity exists among nuclear mammalian proteins. © The Author(s) 2016. Published by Oxford University Press on behalf of Nucleic Acids Research.

Adaptive Local Realignment of Protein Sequences.

PubMed

DeBlasio, Dan; Kececioglu, John

2018-06-11

While mutation rates can vary markedly over the residues of a protein, multiple sequence alignment tools typically use the same values for their scoring-function parameters across a protein's entire length. We present a new approach, called adaptive local realignment, that in contrast automatically adapts to the diversity of mutation rates along protein sequences. This builds upon a recent technique known as parameter advising, which finds global parameter settings for an aligner, to now adaptively find local settings. Our approach in essence identifies local regions with low estimated accuracy, constructs a set of candidate realignments using a carefully-chosen collection of parameter settings, and replaces the region if a realignment has higher estimated accuracy. This new method of local parameter advising, when combined with prior methods for global advising, boosts alignment accuracy as much as 26% over the best default setting on hard-to-align protein benchmarks, and by 6.4% over global advising alone. Adaptive local realignment has been implemented within the Opal aligner using the Facet accuracy estimator.

Evolution of EF-hand calcium-modulated proteins. III. Exon sequences confirm most dendrograms based on protein sequences: calmodulin dendrograms show significant lack of parallelism

NASA Technical Reports Server (NTRS)

Nakayama, S.; Kretsinger, R. H.

1993-01-01

In the first report in this series we presented dendrograms based on 152 individual proteins of the EF-hand family. In the second we used sequences from 228 proteins, containing 835 domains, and showed that eight of the 29 subfamilies are congruent and that the EF-hand domains of the remaining 21 subfamilies have diverse evolutionary histories. In this study we have computed dendrograms within and among the EF-hand subfamilies using the encoding DNA sequences. In most instances the dendrograms based on protein and on DNA sequences are very similar. Significant differences between protein and DNA trees for calmodulin remain unexplained. In our fourth report we evaluate the sequences and the distribution of introns within the EF-hand family and conclude that exon shuffling did not play a significant role in its evolution.

Deep sequencing methods for protein engineering and design.

PubMed

Wrenbeck, Emily E; Faber, Matthew S; Whitehead, Timothy A

2017-08-01

The advent of next-generation sequencing (NGS) has revolutionized protein science, and the development of complementary methods enabling NGS-driven protein engineering have followed. In general, these experiments address the functional consequences of thousands of protein variants in a massively parallel manner using genotype-phenotype linked high-throughput functional screens followed by DNA counting via deep sequencing. We highlight the use of information rich datasets to engineer protein molecular recognition. Examples include the creation of multiple dual-affinity Fabs targeting structurally dissimilar epitopes and engineering of a broad germline-targeted anti-HIV-1 immunogen. Additionally, we highlight the generation of enzyme fitness landscapes for conducting fundamental studies of protein behavior and evolution. We conclude with discussion of technological advances. Copyright © 2016 Elsevier Ltd. All rights reserved.

CAFE: aCcelerated Alignment-FrEe sequence analysis

PubMed Central

Lu, Yang Young; Tang, Kujin; Ren, Jie; Fuhrman, Jed A.; Waterman, Michael S.

2017-01-01

Abstract Alignment-free genome and metagenome comparisons are increasingly important with the development of next generation sequencing (NGS) technologies. Recently developed state-of-the-art k-mer based alignment-free dissimilarity measures including CVTree, \\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{upgreek} \\usepackage{mathrsfs} \\setlength{\\oddsidemargin}{-69pt} \\begin{document} }{}$d_2^*$\\end{document} and \\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{upgreek} \\usepackage{mathrsfs} \\setlength{\\oddsidemargin}{-69pt} \\begin{document} }{}$d_2^S$\\end{document} are more computationally expensive than measures based solely on the k-mer frequencies. Here, we report a standalone software, aCcelerated Alignment-FrEe sequence analysis (CAFE), for efficient calculation of 28 alignment-free dissimilarity measures. CAFE allows for both assembled genome sequences and unassembled NGS shotgun reads as input, and wraps the output in a standard PHYLIP format. In downstream analyses, CAFE can also be used to visualize the pairwise dissimilarity measures, including dendrograms, heatmap, principal coordinate analysis and network display. CAFE serves as a general k-mer based alignment-free analysis platform for studying the relationships among genomes and metagenomes, and is freely available at https://github.com/younglululu/CAFE. PMID:28472388

MIPS: a database for protein sequences, homology data and yeast genome information.

PubMed Central

Mewes, H W; Albermann, K; Heumann, K; Liebl, S; Pfeiffer, F

1997-01-01

The MIPS group (Martinsried Institute for Protein Sequences) at the Max-Planck-Institute for Biochemistry, Martinsried near Munich, Germany, collects, processes and distributes protein sequence data within the framework of the tripartite association of the PIR-International Protein Sequence Database (,). MIPS contributes nearly 50% of the data input to the PIR-International Protein Sequence Database. The database is distributed on CD-ROM together with PATCHX, an exhaustive supplement of unique, unverified protein sequences from external sources compiled by MIPS. Through its WWW server (http://www.mips.biochem.mpg.de/ ) MIPS permits internet access to sequence databases, homology data and to yeast genome information. (i) Sequence similarity results from the FASTA program () are stored in the FASTA database for all proteins from PIR-International and PATCHX. The database is dynamically maintained and permits instant access to FASTA results. (ii) Starting with FASTA database queries, proteins have been classified into families and superfamilies (PROT-FAM). (iii) The HPT (hashed position tree) data structure () developed at MIPS is a new approach for rapid sequence and pattern searching. (iv) MIPS provides access to the sequence and annotation of the complete yeast genome (), the functional classification of yeast genes (FunCat) and its graphical display, the 'Genome Browser' (). A CD-ROM based on the JAVA programming language providing dynamic interactive access to the yeast genome and the related protein sequences has been compiled and is available on request. PMID:9016498

Determinants of the rate of protein sequence evolution

PubMed Central

Zhang, Jianzhi; Yang, Jian-Rong

2015-01-01

The rate and mechanism of protein sequence evolution have been central questions in evolutionary biology since the 1960s. Although the rate of protein sequence evolution depends primarily on the level of functional constraint, exactly what constitutes functional constraint has remained unclear. The increasing availability of genomic data has allowed for much needed empirical examinations on the nature of functional constraint. These studies found that the evolutionary rate of a protein is predominantly influenced by its expression level rather than functional importance. A combination of theoretical and empirical analyses have identified multiple mechanisms behind these observations and demonstrated a prominent role that selection against errors in molecular and cellular processes plays in protein evolution. PMID:26055156

Mining for class-specific motifs in protein sequence classification

PubMed Central

2013-01-01

Background In protein sequence classification, identification of the sequence motifs or n-grams that can precisely discriminate between classes is a more interesting scientific question than the classification itself. A number of classification methods aim at accurate classification but fail to explain which sequence features indeed contribute to the accuracy. We hypothesize that sequences in lower denominations (n-grams) can be used to explore the sequence landscape and to identify class-specific motifs that discriminate between classes during classification. Discriminative n-grams are short peptide sequences that are highly frequent in one class but are either minimally present or absent in other classes. In this study, we present a new substitution-based scoring function for identifying discriminative n-grams that are highly specific to a class. Results We present a scoring function based on discriminative n-grams that can effectively discriminate between classes. The scoring function, initially, harvests the entire set of 4- to 8-grams from the protein sequences of different classes in the dataset. Similar n-grams of the same size are combined to form new n-grams, where the similarity is defined by positive amino acid substitution scores in the BLOSUM62 matrix. Substitution has resulted in a large increase in the number of discriminatory n-grams harvested. Due to the unbalanced nature of the dataset, the frequencies of the n-grams are normalized using a dampening factor, which gives more weightage to the n-grams that appear in fewer classes and vice-versa. After the n-grams are normalized, the scoring function identifies discriminative 4- to 8-grams for each class that are frequent enough to be above a selection threshold. By mapping these discriminative n-grams back to the protein sequences, we obtained contiguous n-grams that represent short class-specific motifs in protein sequences. Our method fared well compared to an existing motif finding method known as

Single-molecule protein sequencing through fingerprinting: computational assessment

NASA Astrophysics Data System (ADS)

Yao, Yao; Docter, Margreet; van Ginkel, Jetty; de Ridder, Dick; Joo, Chirlmin

2015-10-01

Proteins are vital in all biological systems as they constitute the main structural and functional components of cells. Recent advances in mass spectrometry have brought the promise of complete proteomics by helping draft the human proteome. Yet, this commonly used protein sequencing technique has fundamental limitations in sensitivity. Here we propose a method for single-molecule (SM) protein sequencing. A major challenge lies in the fact that proteins are composed of 20 different amino acids, which demands 20 molecular reporters. We computationally demonstrate that it suffices to measure only two types of amino acids to identify proteins and suggest an experimental scheme using SM fluorescence. When achieved, this highly sensitive approach will result in a paradigm shift in proteomics, with major impact in the biological and medical sciences.

Self-organized neural maps of human protein sequences.

PubMed Central

Ferrán, E. A.; Pflugfelder, B.; Ferrara, P.

1994-01-01

We have recently described a method based on artificial neural networks to cluster protein sequences into families. The network was trained with Kohonen's unsupervised learning algorithm using, as inputs, the matrix patterns derived from the dipeptide composition of the proteins. We present here a large-scale application of that method to classify the 1,758 human protein sequences stored in the SwissProt database (release 19.0), whose lengths are greater than 50 amino acids. In the final 2-dimensional topologically ordered map of 15 x 15 neurons, proteins belonging to known families were associated with the same neuron or with neighboring ones. Also, as an attempt to reduce the time-consuming learning procedure, we compared 2 learning protocols: one of 500 epochs (100 SUN CPU-hours [CPU-h]), and another one of 30 epochs (6.7 CPU-h). A further reduction of learning-computing time, by a factor of about 3.3, with similar protein clustering results, was achieved using a matrix of 11 x 11 components to represent the sequences. Although network training is time consuming, the classification of a new protein in the final ordered map is very fast (14.6 CPU-seconds). We also show a comparison between the artificial neural network approach and conventional methods of biosequence analysis. PMID:8019421

High-Resolution Sequence-Function Mapping of Full-Length Proteins

PubMed Central

Kowalsky, Caitlin A.; Klesmith, Justin R.; Stapleton, James A.; Kelly, Vince; Reichkitzer, Nolan; Whitehead, Timothy A.

2015-01-01

Comprehensive sequence-function mapping involves detailing the fitness contribution of every possible single mutation to a gene by comparing the abundance of each library variant before and after selection for the phenotype of interest. Deep sequencing of library DNA allows frequency reconstruction for tens of thousands of variants in a single experiment, yet short read lengths of current sequencers makes it challenging to probe genes encoding full-length proteins. Here we extend the scope of sequence-function maps to entire protein sequences with a modular, universal sequence tiling method. We demonstrate the approach with both growth-based selections and FACS screening, offer parameters and best practices that simplify design of experiments, and present analytical solutions to normalize data across independent selections. Using this protocol, sequence-function maps covering full sequences can be obtained in four to six weeks. Best practices introduced in this manuscript are fully compatible with, and complementary to, other recently published sequence-function mapping protocols. PMID:25790064

Integrating mRNA and Protein Sequencing Enables the Detection and Quantitative Profiling of Natural Protein Sequence Variants of Populus trichocarpa.

PubMed

Abraham, Paul E; Wang, Xiaojing; Ranjan, Priya; Nookaew, Intawat; Zhang, Bing; Tuskan, Gerald A; Hettich, Robert L

2015-12-04

Next-generation sequencing has transformed the ability to link genotypes to phenotypes and facilitates the dissection of genetic contribution to complex traits. However, it is challenging to link genetic variants with the perturbed functional effects on proteins encoded by such genes. Here we show how RNA sequencing can be exploited to construct genotype-specific protein sequence databases to assess natural variation in proteins, providing information about the molecular toolbox driving cellular processes. For this study, we used two natural genotypes selected from a recent genome-wide association study of Populus trichocarpa, an obligate outcrosser with tremendous phenotypic variation across the natural population. This strategy allowed us to comprehensively catalogue proteins containing single amino acid polymorphisms (SAAPs), as well as insertions and deletions. We profiled the frequency of 128 types of naturally occurring amino acid substitutions, including both expected (neutral) and unexpected (non-neutral) SAAPs, with a subset occurring in regions of the genome having strong polymorphism patterns consistent with recent positive and/or divergent selection. By zeroing in on the molecular signatures of these important regions that might have previously been uncharacterized, we now provide a high-resolution molecular inventory that should improve accessibility and subsequent identification of natural protein variants in future genotype-to-phenotype studies.

A Novel Cylindrical Representation for Characterizing Intrinsic Properties of Protein Sequences.

PubMed

Yu, Jia-Feng; Dou, Xiang-Hua; Wang, Hong-Bo; Sun, Xiao; Zhao, Hui-Ying; Wang, Ji-Hua

2015-06-22

The composition and sequence order of amino acid residues are the two most important characteristics to describe a protein sequence. Graphical representations facilitate visualization of biological sequences and produce biologically useful numerical descriptors. In this paper, we propose a novel cylindrical representation by placing the 20 amino acid residue types in a circle and sequence positions along the z axis. This representation allows visualization of the composition and sequence order of amino acids at the same time. Ten numerical descriptors and one weighted numerical descriptor have been developed to quantitatively describe intrinsic properties of protein sequences on the basis of the cylindrical model. Their applications to similarity/dissimilarity analysis of nine ND5 proteins indicated that these numerical descriptors are more effective than several classical numerical matrices. Thus, the cylindrical representation obtained here provides a new useful tool for visualizing and charactering protein sequences. An online server is available at http://biophy.dzu.edu.cn:8080/CNumD/input.jsp .

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.

Biophysical and structural considerations for protein sequence evolution

PubMed Central

2011-01-01

Background Protein sequence evolution is constrained by the biophysics of folding and function, causing interdependence between interacting sites in the sequence. However, current site-independent models of sequence evolutions do not take this into account. Recent attempts to integrate the influence of structure and biophysics into phylogenetic models via statistical/informational approaches have not resulted in expected improvements in model performance. This suggests that further innovations are needed for progress in this field. Results Here we develop a coarse-grained physics-based model of protein folding and binding function, and compare it to a popular informational model. We find that both models violate the assumption of the native sequence being close to a thermodynamic optimum, causing directional selection away from the native state. Sampling and simulation show that the physics-based model is more specific for fold-defining interactions that vary less among residue type. The informational model diffuses further in sequence space with fewer barriers and tends to provide less support for an invariant sites model, although amino acid substitutions are generally conservative. Both approaches produce sequences with natural features like dN/dS < 1 and gamma-distributed rates across sites. Conclusions Simple coarse-grained models of protein folding can describe some natural features of evolving proteins but are currently not accurate enough to use in evolutionary inference. This is partly due to improper packing of the hydrophobic core. We suggest possible improvements on the representation of structure, folding energy, and binding function, as regards both native and non-native conformations, and describe a large number of possible applications for such a model. PMID:22171550

Meta sequence analysis of human blood peptides and their parent proteins.

PubMed

Bowden, Peter; Pendrak, Voitek; Zhu, Peihong; Marshall, John G

2010-04-18

Sequence analysis of the blood peptides and their qualities will be key to understanding the mechanisms that contribute to error in LC-ESI-MS/MS. Analysis of peptides and their proteins at the level of sequences is much more direct and informative than the comparison of disparate accession numbers. A portable database of all blood peptide and protein sequences with descriptor fields and gene ontology terms might be useful for designing immunological or MRM assays from human blood. The results of twelve studies of human blood peptides and/or proteins identified by LC-MS/MS and correlated against a disparate array of genetic libraries were parsed and matched to proteins from the human ENSEMBL, SwissProt and RefSeq databases by SQL. The reported peptide and protein sequences were organized into an SQL database with full protein sequences and up to five unique peptides in order of prevalence along with the peptide count for each protein. Structured query language or BLAST was used to acquire descriptive information in current databases. Sampling error at the level of peptides is the largest source of disparity between groups. Chi Square analysis of peptide to protein distributions confirmed the significant agreement between groups on identified proteins. Copyright 2010. Published by Elsevier B.V.

Acceleration through passive destabilization: protein folding in a weak hydrophobic environment

NASA Astrophysics Data System (ADS)

Jewett, Andrew; Baumketner, Andrij; Shea, Joan-Emma

2004-03-01

The GroEL chaperonin is a biomolecule which assists the folding of an extremely diverse range of proteins in Eubacteria. Some proteins undergo many rounds of ATP-regulated binding and dissociation from GroEL/ES before folding. It has been proposed that transient stress from ATP-regulated binding and release from GroEL/ES frees frustrated proteins from misfolded conformations. However recent evidence suggests that chaperonin-accelerated protein folding can take place entirely within a mutated GroEL+ES cavity that is unable to open and release the protein. Using molecular dynamics, we demonstrate that static confinement within a weakly hydrophobic (attractive) cavity (similar to the interior of the cavity formed by the GroEL+ES complex) is sufficient to significantly accelerate the folding of a highly frustrated protein-like heteropolymer. Our frustrated molecule benifits kinetically from a static hydrophobic environment that destabilizes misfolded conformations. This may shed light on the mechanisms used by other chaperones which do not depend on ATP.

Inverse statistical physics of protein sequences: a key issues review.

PubMed

Cocco, Simona; Feinauer, Christoph; Figliuzzi, Matteo; Monasson, Rémi; Weigt, Martin

2018-03-01

In the course of evolution, proteins undergo important changes in their amino acid sequences, while their three-dimensional folded structure and their biological function remain remarkably conserved. Thanks to modern sequencing techniques, sequence data accumulate at unprecedented pace. This provides large sets of so-called homologous, i.e. evolutionarily related protein sequences, to which methods of inverse statistical physics can be applied. Using sequence data as the basis for the inference of Boltzmann distributions from samples of microscopic configurations or observables, it is possible to extract information about evolutionary constraints and thus protein function and structure. Here we give an overview over some biologically important questions, and how statistical-mechanics inspired modeling approaches can help to answer them. Finally, we discuss some open questions, which we expect to be addressed over the next years.

Inverse statistical physics of protein sequences: a key issues review

NASA Astrophysics Data System (ADS)

Cocco, Simona; Feinauer, Christoph; Figliuzzi, Matteo; Monasson, Rémi; Weigt, Martin

2018-03-01

In the course of evolution, proteins undergo important changes in their amino acid sequences, while their three-dimensional folded structure and their biological function remain remarkably conserved. Thanks to modern sequencing techniques, sequence data accumulate at unprecedented pace. This provides large sets of so-called homologous, i.e. evolutionarily related protein sequences, to which methods of inverse statistical physics can be applied. Using sequence data as the basis for the inference of Boltzmann distributions from samples of microscopic configurations or observables, it is possible to extract information about evolutionary constraints and thus protein function and structure. Here we give an overview over some biologically important questions, and how statistical-mechanics inspired modeling approaches can help to answer them. Finally, we discuss some open questions, which we expect to be addressed over the next years.

GASP: Gapped Ancestral Sequence Prediction for proteins

PubMed Central

Edwards, Richard J; Shields, Denis C

2004-01-01

Background The prediction of ancestral protein sequences from multiple sequence alignments is useful for many bioinformatics analyses. Predicting ancestral sequences is not a simple procedure and relies on accurate alignments and phylogenies. Several algorithms exist based on Maximum Parsimony or Maximum Likelihood methods but many current implementations are unable to process residues with gaps, which may represent insertion/deletion (indel) events or sequence fragments. Results Here we present a new algorithm, GASP (Gapped Ancestral Sequence Prediction), for predicting ancestral sequences from phylogenetic trees and the corresponding multiple sequence alignments. Alignments may be of any size and contain gaps. GASP first assigns the positions of gaps in the phylogeny before using a likelihood-based approach centred on amino acid substitution matrices to assign ancestral amino acids. Important outgroup information is used by first working down from the tips of the tree to the root, using descendant data only to assign probabilities, and then working back up from the root to the tips using descendant and outgroup data to make predictions. GASP was tested on a number of simulated datasets based on real phylogenies. Prediction accuracy for ungapped data was similar to three alternative algorithms tested, with GASP performing better in some cases and worse in others. Adding simple insertions and deletions to the simulated data did not have a detrimental effect on GASP accuracy. Conclusions GASP (Gapped Ancestral Sequence Prediction) will predict ancestral sequences from multiple protein alignments of any size. Although not as accurate in all cases as some of the more sophisticated maximum likelihood approaches, it can process a wide range of input phylogenies and will predict ancestral sequences for gapped and ungapped residues alike. PMID:15350199

Rapid Identification of Sequences for Orphan Enzymes to Power Accurate Protein Annotation

PubMed Central

Ojha, Sunil; Watson, Douglas S.; Bomar, Martha G.; Galande, Amit K.; Shearer, Alexander G.

2013-01-01

The power of genome sequencing depends on the ability to understand what those genes and their proteins products actually do. The automated methods used to assign functions to putative proteins in newly sequenced organisms are limited by the size of our library of proteins with both known function and sequence. Unfortunately this library grows slowly, lagging well behind the rapid increase in novel protein sequences produced by modern genome sequencing methods. One potential source for rapidly expanding this functional library is the “back catalog” of enzymology – “orphan enzymes,” those enzymes that have been characterized and yet lack any associated sequence. There are hundreds of orphan enzymes in the Enzyme Commission (EC) database alone. In this study, we demonstrate how this orphan enzyme “back catalog” is a fertile source for rapidly advancing the state of protein annotation. Starting from three orphan enzyme samples, we applied mass-spectrometry based analysis and computational methods (including sequence similarity networks, sequence and structural alignments, and operon context analysis) to rapidly identify the specific sequence for each orphan while avoiding the most time- and labor-intensive aspects of typical sequence identifications. We then used these three new sequences to more accurately predict the catalytic function of 385 previously uncharacterized or misannotated proteins. We expect that this kind of rapid sequence identification could be efficiently applied on a larger scale to make enzymology’s “back catalog” another powerful tool to drive accurate genome annotation. PMID:24386392

Rapid identification of sequences for orphan enzymes to power accurate protein annotation.

PubMed

Ramkissoon, Kevin R; Miller, Jennifer K; Ojha, Sunil; Watson, Douglas S; Bomar, Martha G; Galande, Amit K; Shearer, Alexander G

2013-01-01

The power of genome sequencing depends on the ability to understand what those genes and their proteins products actually do. The automated methods used to assign functions to putative proteins in newly sequenced organisms are limited by the size of our library of proteins with both known function and sequence. Unfortunately this library grows slowly, lagging well behind the rapid increase in novel protein sequences produced by modern genome sequencing methods. One potential source for rapidly expanding this functional library is the "back catalog" of enzymology--"orphan enzymes," those enzymes that have been characterized and yet lack any associated sequence. There are hundreds of orphan enzymes in the Enzyme Commission (EC) database alone. In this study, we demonstrate how this orphan enzyme "back catalog" is a fertile source for rapidly advancing the state of protein annotation. Starting from three orphan enzyme samples, we applied mass-spectrometry based analysis and computational methods (including sequence similarity networks, sequence and structural alignments, and operon context analysis) to rapidly identify the specific sequence for each orphan while avoiding the most time- and labor-intensive aspects of typical sequence identifications. We then used these three new sequences to more accurately predict the catalytic function of 385 previously uncharacterized or misannotated proteins. We expect that this kind of rapid sequence identification could be efficiently applied on a larger scale to make enzymology's "back catalog" another powerful tool to drive accurate genome annotation.

A novel method based on new adaptive LVQ neural network for predicting protein-protein interactions from protein sequences.

PubMed

Yousef, Abdulaziz; Moghadam Charkari, Nasrollah

2013-11-07

Protein-Protein interaction (PPI) is one of the most important data in understanding the cellular processes. Many interesting methods have been proposed in order to predict PPIs. However, the methods which are based on the sequence of proteins as a prior knowledge are more universal. In this paper, a sequence-based, fast, and adaptive PPI prediction method is introduced to assign two proteins to an interaction class (yes, no). First, in order to improve the presentation of the sequences, twelve physicochemical properties of amino acid have been used by different representation methods to transform the sequence of protein pairs into different feature vectors. Then, for speeding up the learning process and reducing the effect of noise PPI data, principal component analysis (PCA) is carried out as a proper feature extraction algorithm. Finally, a new and adaptive Learning Vector Quantization (LVQ) predictor is designed to deal with different models of datasets that are classified into balanced and imbalanced datasets. The accuracy of 93.88%, 90.03%, and 89.72% has been found on S. cerevisiae, H. pylori, and independent datasets, respectively. The results of various experiments indicate the efficiency and validity of the method. © 2013 Published by Elsevier Ltd.

Accelerated molecular dynamics simulations of protein folding.

PubMed

Miao, Yinglong; Feixas, Ferran; Eun, Changsun; McCammon, J Andrew

2015-07-30

Folding of four fast-folding proteins, including chignolin, Trp-cage, villin headpiece and WW domain, was simulated via accelerated molecular dynamics (aMD). In comparison with hundred-of-microsecond timescale conventional molecular dynamics (cMD) simulations performed on the Anton supercomputer, aMD captured complete folding of the four proteins in significantly shorter simulation time. The folded protein conformations were found within 0.2-2.1 Å of the native NMR or X-ray crystal structures. Free energy profiles calculated through improved reweighting of the aMD simulations using cumulant expansion to the second-order are in good agreement with those obtained from cMD simulations. This allows us to identify distinct conformational states (e.g., unfolded and intermediate) other than the native structure and the protein folding energy barriers. Detailed analysis of protein secondary structures and local key residue interactions provided important insights into the protein folding pathways. Furthermore, the selections of force fields and aMD simulation parameters are discussed in detail. Our work shows usefulness and accuracy of aMD in studying protein folding, providing basic references in using aMD in future protein-folding studies. © 2015 Wiley Periodicals, Inc.

Comparative analysis of the prion protein gene sequences in African lion.

PubMed

Wu, Chang-De; Pang, Wan-Yong; Zhao, De-Ming

2006-10-01

The prion protein gene of African lion (Panthera Leo) was first cloned and polymorphisms screened. The results suggest that the prion protein gene of eight African lions is highly homogenous. The amino acid sequences of the prion protein (PrP) of all samples tested were identical. Four single nucleotide polymorphisms (C42T, C81A, C420T, T600C) in the prion protein gene (Prnp) of African lion were found, but no amino acid substitutions. Sequence analysis showed that the higher homology is observed to felis catus AF003087 (96.7%) and to sheep number M31313.1 (96.2%) Genbank accessed. With respect to all the mammalian prion protein sequences compared, the African lion prion protein sequence has three amino acid substitutions. The homology might in turn affect the potential intermolecular interactions critical for cross species transmission of prion disease.

Protein Sequence Annotation Tool (PSAT): A centralized web-based meta-server for high-throughput sequence annotations

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

Leung, Elo; Huang, Amy; Cadag, Eithon

In this study, we introduce the Protein Sequence Annotation Tool (PSAT), a web-based, sequence annotation meta-server for performing integrated, high-throughput, genome-wide sequence analyses. Our goals in building PSAT were to (1) create an extensible platform for integration of multiple sequence-based bioinformatics tools, (2) enable functional annotations and enzyme predictions over large input protein fasta data sets, and (3) provide a web interface for convenient execution of the tools. In this paper, we demonstrate the utility of PSAT by annotating the predicted peptide gene products of Herbaspirillum sp. strain RV1423, importing the results of PSAT into EC2KEGG, and using the resultingmore » functional comparisons to identify a putative catabolic pathway, thereby distinguishing RV1423 from a well annotated Herbaspirillum species. This analysis demonstrates that high-throughput enzyme predictions, provided by PSAT processing, can be used to identify metabolic potential in an otherwise poorly annotated genome. Lastly, PSAT is a meta server that combines the results from several sequence-based annotation and function prediction codes, and is available at http://psat.llnl.gov/psat/. PSAT stands apart from other sequencebased genome annotation systems in providing a high-throughput platform for rapid de novo enzyme predictions and sequence annotations over large input protein sequence data sets in FASTA. PSAT is most appropriately applied in annotation of large protein FASTA sets that may or may not be associated with a single genome.« less

Protein Sequence Annotation Tool (PSAT): A centralized web-based meta-server for high-throughput sequence annotations

DOE PAGES

Leung, Elo; Huang, Amy; Cadag, Eithon; ...

2016-01-20

In this study, we introduce the Protein Sequence Annotation Tool (PSAT), a web-based, sequence annotation meta-server for performing integrated, high-throughput, genome-wide sequence analyses. Our goals in building PSAT were to (1) create an extensible platform for integration of multiple sequence-based bioinformatics tools, (2) enable functional annotations and enzyme predictions over large input protein fasta data sets, and (3) provide a web interface for convenient execution of the tools. In this paper, we demonstrate the utility of PSAT by annotating the predicted peptide gene products of Herbaspirillum sp. strain RV1423, importing the results of PSAT into EC2KEGG, and using the resultingmore » functional comparisons to identify a putative catabolic pathway, thereby distinguishing RV1423 from a well annotated Herbaspirillum species. This analysis demonstrates that high-throughput enzyme predictions, provided by PSAT processing, can be used to identify metabolic potential in an otherwise poorly annotated genome. Lastly, PSAT is a meta server that combines the results from several sequence-based annotation and function prediction codes, and is available at http://psat.llnl.gov/psat/. PSAT stands apart from other sequencebased genome annotation systems in providing a high-throughput platform for rapid de novo enzyme predictions and sequence annotations over large input protein sequence data sets in FASTA. PSAT is most appropriately applied in annotation of large protein FASTA sets that may or may not be associated with a single genome.« less

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

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

Elman RNN based classification of proteins sequences on account of their mutual information.

PubMed

Mishra, Pooja; Nath Pandey, Paras

2012-10-21

In the present work we have employed the method of estimating residue correlation within the protein sequences, by using the mutual information (MI) of adjacent residues, based on structural and solvent accessibility properties of amino acids. The long range correlation between nonadjacent residues is improved by constructing a mutual information vector (MIV) for a single protein sequence, like this each protein sequence is associated with its corresponding MIVs. These MIVs are given to Elman RNN to obtain the classification of protein sequences. The modeling power of MIV was shown to be significantly better, giving a new approach towards alignment free classification of protein sequences. We also conclude that sequence structural and solvent accessible property based MIVs are better predictor. Copyright © 2012 Elsevier Ltd. All rights reserved.

Predicting membrane protein types by fusing composite protein sequence features into pseudo amino acid composition.

PubMed

Hayat, Maqsood; Khan, Asifullah

2011-02-21

Membrane proteins are vital type of proteins that serve as channels, receptors, and energy transducers in a cell. Prediction of membrane protein types is an important research area in bioinformatics. Knowledge of membrane protein types provides some valuable information for predicting novel example of the membrane protein types. However, classification of membrane protein types can be both time consuming and susceptible to errors due to the inherent similarity of membrane protein types. In this paper, neural networks based membrane protein type prediction system is proposed. Composite protein sequence representation (CPSR) is used to extract the features of a protein sequence, which includes seven feature sets; amino acid composition, sequence length, 2 gram exchange group frequency, hydrophobic group, electronic group, sum of hydrophobicity, and R-group. Principal component analysis is then employed to reduce the dimensionality of the feature vector. The probabilistic neural network (PNN), generalized regression neural network, and support vector machine (SVM) are used as classifiers. A high success rate of 86.01% is obtained using SVM for the jackknife test. In case of independent dataset test, PNN yields the highest accuracy of 95.73%. These classifiers exhibit improved performance using other performance measures such as sensitivity, specificity, Mathew's correlation coefficient, and F-measure. The experimental results show that the prediction performance of the proposed scheme for classifying membrane protein types is the best reported, so far. This performance improvement may largely be credited to the learning capabilities of neural networks and the composite feature extraction strategy, which exploits seven different properties of protein sequences. The proposed Mem-Predictor can be accessed at http://111.68.99.218/Mem-Predictor. Copyright © 2010 Elsevier Ltd. All rights reserved.

Sequence patterns mediating functions of disordered proteins.

PubMed

Exarchos, Konstantinos P; Kourou, Konstantina; Exarchos, Themis P; Papaloukas, Costas; Karamouzis, Michalis V; Fotiadis, Dimitrios I

2015-01-01

Disordered proteins lack specific 3D structure in their native state and have been implicated with numerous cellular functions as well as with the induction of severe diseases, e.g., cardiovascular and neurodegenerative diseases as well as diabetes. Due to their conformational flexibility they are often found to interact with a multitude of protein molecules; this one-to-many interaction which is vital for their versatile functioning involves short consensus protein sequences, which are normally detected using slow and cumbersome experimental procedures. In this work we exploit information from disorder-oriented protein interaction networks focused specifically on humans, in order to assemble, by means of overrepresentation, a set of sequence patterns that mediate the functioning of disordered proteins; hence, we are able to identify how a single protein achieves such functional promiscuity. Next, we study the sequential characteristics of the extracted patterns, which exhibit a striking preference towards a very limited subset of amino acids; specifically, residues leucine, glutamic acid, and serine are particularly frequent among the extracted patterns, and we also observe a nontrivial propensity towards alanine and glycine. Furthermore, based on the extracted patterns we set off to infer potential functional implications in order to verify our findings and potentially further extrapolate our knowledge regarding the functioning of disordered proteins. We observe that the extracted patterns are primarily involved with regulation, binding and posttranslational modifications, which constitute the most prominent functions of disordered proteins.

Sequence Determines Degree of Knottedness in a Coarse-Grained Protein Model

NASA Astrophysics Data System (ADS)

Wüst, Thomas; Reith, Daniel; Virnau, Peter

2015-01-01

Knots are abundant in globular homopolymers but rare in globular proteins. To shed new light on this long-standing conundrum, we study the influence of sequence on the formation of knots in proteins under native conditions within the framework of the hydrophobic-polar lattice protein model. By employing large-scale Wang-Landau simulations combined with suitable Monte Carlo trial moves we show that even though knots are still abundant on average, sequence introduces large variability in the degree of self-entanglements. Moreover, we are able to design sequences which are either almost always or almost never knotted. Our findings serve as proof of concept that the introduction of just one additional degree of freedom per monomer (in our case sequence) facilitates evolution towards a protein universe in which knots are rare.

Quantiprot - a Python package for quantitative analysis of protein sequences.

PubMed

Konopka, Bogumił M; Marciniak, Marta; Dyrka, Witold

2017-07-17

The field of protein sequence analysis is dominated by tools rooted in substitution matrices and alignments. A complementary approach is provided by methods of quantitative characterization. A major advantage of the approach is that quantitative properties defines a multidimensional solution space, where sequences can be related to each other and differences can be meaningfully interpreted. Quantiprot is a software package in Python, which provides a simple and consistent interface to multiple methods for quantitative characterization of protein sequences. The package can be used to calculate dozens of characteristics directly from sequences or using physico-chemical properties of amino acids. Besides basic measures, Quantiprot performs quantitative analysis of recurrence and determinism in the sequence, calculates distribution of n-grams and computes the Zipf's law coefficient. We propose three main fields of application of the Quantiprot package. First, quantitative characteristics can be used in alignment-free similarity searches, and in clustering of large and/or divergent sequence sets. Second, a feature space defined by quantitative properties can be used in comparative studies of protein families and organisms. Third, the feature space can be used for evaluating generative models, where large number of sequences generated by the model can be compared to actually observed sequences.

Deciphering mRNA Sequence Determinants of Protein Production Rate

NASA Astrophysics Data System (ADS)

Szavits-Nossan, Juraj; Ciandrini, Luca; Romano, M. Carmen

2018-03-01

One of the greatest challenges in biophysical models of translation is to identify coding sequence features that affect the rate of translation and therefore the overall protein production in the cell. We propose an analytic method to solve a translation model based on the inhomogeneous totally asymmetric simple exclusion process, which allows us to unveil simple design principles of nucleotide sequences determining protein production rates. Our solution shows an excellent agreement when compared to numerical genome-wide simulations of S. cerevisiae transcript sequences and predicts that the first 10 codons, which is the ribosome footprint length on the mRNA, together with the value of the initiation rate, are the main determinants of protein production rate under physiological conditions. Finally, we interpret the obtained analytic results based on the evolutionary role of the codons' choice for regulating translation rates and ribosome densities.

MutaBind estimates and interprets the effects of sequence variants on protein-protein interactions.

PubMed

Li, Minghui; Simonetti, Franco L; Goncearenco, Alexander; Panchenko, Anna R

2016-07-08

Proteins engage in highly selective interactions with their macromolecular partners. Sequence variants that alter protein binding affinity may cause significant perturbations or complete abolishment of function, potentially leading to diseases. There exists a persistent need to develop a mechanistic understanding of impacts of variants on proteins. To address this need we introduce a new computational method MutaBind to evaluate the effects of sequence variants and disease mutations on protein interactions and calculate the quantitative changes in binding affinity. The MutaBind method uses molecular mechanics force fields, statistical potentials and fast side-chain optimization algorithms. The MutaBind server maps mutations on a structural protein complex, calculates the associated changes in binding affinity, determines the deleterious effect of a mutation, estimates the confidence of this prediction and produces a mutant structural model for download. MutaBind can be applied to a large number of problems, including determination of potential driver mutations in cancer and other diseases, elucidation of the effects of sequence variants on protein fitness in evolution and protein design. MutaBind is available at http://www.ncbi.nlm.nih.gov/projects/mutabind/. Published by Oxford University Press on behalf of Nucleic Acids Research 2016. This work is written by (a) US Government employee(s) and is in the public domain in the US.

The C Terminus of Formin FMNL3 Accelerates Actin Polymerization and Contains a WH2 Domain-like Sequence That Binds Both Monomers and Filament Barbed Ends*

PubMed Central

Heimsath, Ernest G.; Higgs, Henry N.

2012-01-01

Formin proteins are actin assembly factors that accelerate filament nucleation then remain on the elongating barbed end and modulate filament elongation. The formin homology 2 (FH2) domain is central to these activities, but recent work has suggested that additional sequences enhance FH2 domain function. Here we show that the C-terminal 76 amino acids of the formin FMNL3 have a dramatic effect on the ability of the FH2 domain to accelerate actin assembly. This C-terminal region contains a WASp homology 2 (WH2)-like sequence that binds actin monomers in a manner that is competitive with other WH2 domains and with profilin. In addition, the C terminus binds filament barbed ends. As a monomer, the FMNL3 C terminus inhibits actin polymerization and slows barbed end elongation with moderate affinity. As a dimer, the C terminus accelerates actin polymerization from monomers and displays high affinity inhibition of barbed end elongation. These properties are not common to all formin C termini, as those of mDia1 and INF2 do not behave similarly. Interestingly, mutation of two aliphatic residues, which blocks high affinity actin binding by the WH2-like sequence, has no effect on the ability of the C terminus to enhance FH2-mediated polymerization. However, mutation of three successive basic residues at the C terminus of the WH2-like sequence compromises polymerization enhancement. These results illustrate that the C termini of formins are highly diverse in their interactions with actin. PMID:22094460

Structure-related statistical singularities along protein sequences: a correlation study.

PubMed

Colafranceschi, Mauro; Colosimo, Alfredo; Zbilut, Joseph P; Uversky, Vladimir N; Giuliani, Alessandro

2005-01-01

A data set composed of 1141 proteins representative of all eukaryotic protein sequences in the Swiss-Prot Protein Knowledge base was coded by seven physicochemical properties of amino acid residues. The resulting numerical profiles were submitted to correlation analysis after the application of a linear (simple mean) and a nonlinear (Recurrence Quantification Analysis, RQA) filter. The main RQA variables, Recurrence and Determinism, were subsequently analyzed by Principal Component Analysis. The RQA descriptors showed that (i) within protein sequences is embedded specific information neither present in the codes nor in the amino acid composition and (ii) the most sensitive code for detecting ordered recurrent (deterministic) patterns of residues in protein sequences is the Miyazawa-Jernigan hydrophobicity scale. The most deterministic proteins in terms of autocorrelation properties of primary structures were found (i) to be involved in protein-protein and protein-DNA interactions and (ii) to display a significantly higher proportion of structural disorder with respect to the average data set. A study of the scaling behavior of the average determinism with the setting parameters of RQA (embedding dimension and radius) allows for the identification of patterns of minimal length (six residues) as possible markers of zones specifically prone to inter- and intramolecular interactions.

On the relationship between residue structural environment and sequence conservation in proteins.

PubMed

Liu, Jen-Wei; Lin, Jau-Ji; Cheng, Chih-Wen; Lin, Yu-Feng; Hwang, Jenn-Kang; Huang, Tsun-Tsao

2017-09-01

Residues that are crucial to protein function or structure are usually evolutionarily conserved. To identify the important residues in protein, sequence conservation is estimated, and current methods rely upon the unbiased collection of homologous sequences. Surprisingly, our previous studies have shown that the sequence conservation is closely correlated with the weighted contact number (WCN), a measure of packing density for residue's structural environment, calculated only based on the C α positions of a protein structure. Moreover, studies have shown that sequence conservation is correlated with environment-related structural properties calculated based on different protein substructures, such as a protein's all atoms, backbone atoms, side-chain atoms, or side-chain centroid. To know whether the C α atomic positions are adequate to show the relationship between residue environment and sequence conservation or not, here we compared C α atoms with other substructures in their contributions to the sequence conservation. Our results show that C α positions are substantially equivalent to the other substructures in calculations of various measures of residue environment. As a result, the overlapping contributions between C α atoms and the other substructures are high, yielding similar structure-conservation relationship. Take the WCN as an example, the average overlapping contribution to sequence conservation is 87% between C α and all-atom substructures. These results indicate that only C α atoms of a protein structure could reflect sequence conservation at the residue level. © 2017 Wiley Periodicals, Inc.

Avoidance of truncated proteins from unintended ribosome binding sites within heterologous protein coding sequences.

PubMed

Whitaker, Weston R; Lee, Hanson; Arkin, Adam P; Dueber, John E

2015-03-20

Genetic sequences ported into non-native hosts for synthetic biology applications can gain unexpected properties. In this study, we explored sequences functioning as ribosome binding sites (RBSs) within protein coding DNA sequences (CDSs) that cause internal translation, resulting in truncated proteins. Genome-wide prediction of bacterial RBSs, based on biophysical calculations employed by the RBS calculator, suggests a selection against internal RBSs within CDSs in Escherichia coli, but not those in Saccharomyces cerevisiae. Based on these calculations, silent mutations aimed at removing internal RBSs can effectively reduce truncation products from internal translation. However, a solution for complete elimination of internal translation initiation is not always feasible due to constraints of available coding sequences. Fluorescence assays and Western blot analysis showed that in genes with internal RBSs, increasing the strength of the intended upstream RBS had little influence on the internal translation strength. Another strategy to minimize truncated products from an internal RBS is to increase the relative strength of the upstream RBS with a concomitant reduction in promoter strength to achieve the same protein expression level. Unfortunately, lower transcription levels result in increased noise at the single cell level due to stochasticity in gene expression. At the low expression regimes desired for many synthetic biology applications, this problem becomes particularly pronounced. We found that balancing promoter strengths and upstream RBS strengths to intermediate levels can achieve the target protein concentration while avoiding both excessive noise and truncated protein.

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

Conservation of Shannon's redundancy for proteins. [information theory applied to amino acid sequences

NASA Technical Reports Server (NTRS)

Gatlin, L. L.

1974-01-01

Concepts of information theory are applied to examine various proteins in terms of their redundancy in natural originators such as animals and plants. The Monte Carlo method is used to derive information parameters for random protein sequences. Real protein sequence parameters are compared with the standard parameters of protein sequences having a specific length. The tendency of a chain to contain some amino acids more frequently than others and the tendency of a chain to contain certain amino acid pairs more frequently than other pairs are used as randomness measures of individual protein sequences. Non-periodic proteins are generally found to have random Shannon redundancies except in cases of constraints due to short chain length and genetic codes. Redundant characteristics of highly periodic proteins are discussed. A degree of periodicity parameter is derived.

Traveling wave linear accelerator with RF power flow outside of accelerating cavities

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

Dolgashev, Valery A.

A high power RF traveling wave accelerator structure includes a symmetric RF feed, an input matching cell coupled to the symmetric RF feed, a sequence of regular accelerating cavities coupled to the input matching cell at an input beam pipe end of the sequence, one or more waveguides parallel to and coupled to the sequence of regular accelerating cavities, an output matching cell coupled to the sequence of regular accelerating cavities at an output beam pipe end of the sequence, and output waveguide circuit or RF loads coupled to the output matching cell. Each of the regular accelerating cavities hasmore » a nose cone that cuts off field propagating into the beam pipe and therefore all power flows in a traveling wave along the structure in the waveguide.« less

Mining new crystal protein genes from Bacillus thuringiensis on the basis of mixed plasmid-enriched genome sequencing and a computational pipeline.

PubMed

Ye, Weixing; Zhu, Lei; Liu, Yingying; Crickmore, Neil; Peng, Donghai; Ruan, Lifang; Sun, Ming

2012-07-01

We have designed a high-throughput system for the identification of novel crystal protein genes (cry) from Bacillus thuringiensis strains. The system was developed with two goals: (i) to acquire the mixed plasmid-enriched genomic sequence of B. thuringiensis using next-generation sequencing biotechnology, and (ii) to identify cry genes with a computational pipeline (using BtToxin_scanner). In our pipeline method, we employed three different kinds of well-developed prediction methods, BLAST, hidden Markov model (HMM), and support vector machine (SVM), to predict the presence of Cry toxin genes. The pipeline proved to be fast (average speed, 1.02 Mb/min for proteins and open reading frames [ORFs] and 1.80 Mb/min for nucleotide sequences), sensitive (it detected 40% more protein toxin genes than a keyword extraction method using genomic sequences downloaded from GenBank), and highly specific. Twenty-one strains from our laboratory's collection were selected based on their plasmid pattern and/or crystal morphology. The plasmid-enriched genomic DNA was extracted from these strains and mixed for Illumina sequencing. The sequencing data were de novo assembled, and a total of 113 candidate cry sequences were identified using the computational pipeline. Twenty-seven candidate sequences were selected on the basis of their low level of sequence identity to known cry genes, and eight full-length genes were obtained with PCR. Finally, three new cry-type genes (primary ranks) and five cry holotypes, which were designated cry8Ac1, cry7Ha1, cry21Ca1, cry32Fa1, and cry21Da1 by the B. thuringiensis Toxin Nomenclature Committee, were identified. The system described here is both efficient and cost-effective and can greatly accelerate the discovery of novel cry genes.

Terminal sequence importance of de novo proteins from binary-patterned library: stable artificial proteins with 11- or 12-amino acid alphabet.

PubMed

Okura, Hiromichi; Takahashi, Tsuyoshi; Mihara, Hisakazu

2012-06-01

Successful approaches of de novo protein design suggest a great potential to create novel structural folds and to understand natural rules of protein folding. For these purposes, smaller and simpler de novo proteins have been developed. Here, we constructed smaller proteins by removing the terminal sequences from stable de novo vTAJ proteins and compared stabilities between mutant and original proteins. vTAJ proteins were screened from an α3β3 binary-patterned library which was designed with polar/ nonpolar periodicities of α-helix and β-sheet. vTAJ proteins have the additional terminal sequences due to the method of constructing the genetically repeated library sequences. By removing the parts of the sequences, we successfully obtained the stable smaller de novo protein mutants with fewer amino acid alphabets than the originals. However, these mutants showed the differences on ANS binding properties and stabilities against denaturant and pH change. The terminal sequences, which were designed just as flexible linkers not as secondary structure units, sufficiently affected these physicochemical details. This study showed implications for adjusting protein stabilities by designing N- and C-terminal sequences.

PredPPCrys: Accurate Prediction of Sequence Cloning, Protein Production, Purification and Crystallization Propensity from Protein Sequences Using Multi-Step Heterogeneous Feature Fusion and Selection

PubMed Central

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

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

Microwave-assisted acid and base hydrolysis of intact proteins containing disulfide bonds for protein sequence analysis by mass spectrometry.

PubMed

Reiz, Bela; Li, Liang

2010-09-01

Controlled hydrolysis of proteins to generate peptide ladders combined with mass spectrometric analysis of the resultant peptides can be used for protein sequencing. In this paper, two methods of improving the microwave-assisted protein hydrolysis process are described to enable rapid sequencing of proteins containing disulfide bonds and increase sequence coverage, respectively. It was demonstrated that proteins containing disulfide bonds could be sequenced by MS analysis by first performing hydrolysis for less than 2 min, followed by 1 h of reduction to release the peptides originally linked by disulfide bonds. It was shown that a strong base could be used as a catalyst for microwave-assisted protein hydrolysis, producing complementary sequence information to that generated by microwave-assisted acid hydrolysis. However, using either acid or base hydrolysis, amide bond breakages in small regions of the polypeptide chains of the model proteins (e.g., cytochrome c and lysozyme) were not detected. Dynamic light scattering measurement of the proteins solubilized in an acid or base indicated that protein-protein interaction or aggregation was not the cause of the failure to hydrolyze certain amide bonds. It was speculated that there were some unknown local structures that might play a role in preventing an acid or base from reacting with the peptide bonds therein. 2010 American Society for Mass Spectrometry. Published by Elsevier Inc. All rights reserved.

Sequence-based prediction of protein-binding sites in DNA: comparative study of two SVM models.

PubMed

Park, Byungkyu; Im, Jinyong; Tuvshinjargal, Narankhuu; Lee, Wook; Han, Kyungsook

2014-11-01

As many structures of protein-DNA complexes have been known in the past years, several computational methods have been developed to predict DNA-binding sites in proteins. However, its inverse problem (i.e., predicting protein-binding sites in DNA) has received much less attention. One of the reasons is that the differences between the interaction propensities of nucleotides are much smaller than those between amino acids. Another reason is that DNA exhibits less diverse sequence patterns than protein. Therefore, predicting protein-binding DNA nucleotides is much harder than predicting DNA-binding amino acids. We computed the interaction propensity (IP) of nucleotide triplets with amino acids using an extensive dataset of protein-DNA complexes, and developed two support vector machine (SVM) models that predict protein-binding nucleotides from sequence data alone. One SVM model predicts protein-binding nucleotides using DNA sequence data alone, and the other SVM model predicts protein-binding nucleotides using both DNA and protein sequences. In a 10-fold cross-validation with 1519 DNA sequences, the SVM model that uses DNA sequence data only predicted protein-binding nucleotides with an accuracy of 67.0%, an F-measure of 67.1%, and a Matthews correlation coefficient (MCC) of 0.340. With an independent dataset of 181 DNAs that were not used in training, it achieved an accuracy of 66.2%, an F-measure 66.3% and a MCC of 0.324. Another SVM model that uses both DNA and protein sequences achieved an accuracy of 69.6%, an F-measure of 69.6%, and a MCC of 0.383 in a 10-fold cross-validation with 1519 DNA sequences and 859 protein sequences. With an independent dataset of 181 DNAs and 143 proteins, it showed an accuracy of 67.3%, an F-measure of 66.5% and a MCC of 0.329. Both in cross-validation and independent testing, the second SVM model that used both DNA and protein sequence data showed better performance than the first model that used DNA sequence data. To the best of

Protein sequences from mastodon and Tyrannosaurus rex revealed by mass spectrometry.

PubMed

Asara, John M; Schweitzer, Mary H; Freimark, Lisa M; Phillips, Matthew; Cantley, Lewis C

2007-04-13

Fossilized bones from extinct taxa harbor the potential for obtaining protein or DNA sequences that could reveal evolutionary links to extant species. We used mass spectrometry to obtain protein sequences from bones of a 160,000- to 600,000-year-old extinct mastodon (Mammut americanum) and a 68-million-year-old dinosaur (Tyrannosaurus rex). The presence of T. rex sequences indicates that their peptide bonds were remarkably stable. Mass spectrometry can thus be used to determine unique sequences from ancient organisms from peptide fragmentation patterns, a valuable tool to study the evolution and adaptation of ancient taxa from which genomic sequences are unlikely to be obtained.

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".

Protein sequences bound to mineral surfaces persist into deep time

PubMed Central

Demarchi, Beatrice; Hall, Shaun; Roncal-Herrero, Teresa; Freeman, Colin L; Woolley, Jos; Crisp, Molly K; Wilson, Julie; Fotakis, Anna; Fischer, Roman; Kessler, Benedikt M; Rakownikow Jersie-Christensen, Rosa; Olsen, Jesper V; Haile, James; Thomas, Jessica; Marean, Curtis W; Parkington, John; Presslee, Samantha; Lee-Thorp, Julia; Ditchfield, Peter; Hamilton, Jacqueline F; Ward, Martyn W; Wang, Chunting Michelle; Shaw, Marvin D; Harrison, Terry; Domínguez-Rodrigo, Manuel; MacPhee, Ross DE; Kwekason, Amandus; Ecker, Michaela; Kolska Horwitz, Liora; Chazan, Michael; Kröger, Roland; Thomas-Oates, Jane; Harding, John H; Cappellini, Enrico; Penkman, Kirsty; Collins, Matthew J

2016-01-01

Proteins persist longer in the fossil record than DNA, but the longevity, survival mechanisms and substrates remain contested. Here, we demonstrate the role of mineral binding in preserving the protein sequence in ostrich (Struthionidae) eggshell, including from the palaeontological sites of Laetoli (3.8 Ma) and Olduvai Gorge (1.3 Ma) in Tanzania. By tracking protein diagenesis back in time we find consistent patterns of preservation, demonstrating authenticity of the surviving sequences. Molecular dynamics simulations of struthiocalcin-1 and -2, the dominant proteins within the eggshell, reveal that distinct domains bind to the mineral surface. It is the domain with the strongest calculated binding energy to the calcite surface that is selectively preserved. Thermal age calculations demonstrate that the Laetoli and Olduvai peptides are 50 times older than any previously authenticated sequence (equivalent to ~16 Ma at a constant 10°C). DOI: http://dx.doi.org/10.7554/eLife.17092.001 PMID:27668515

Sequence- and Interactome-Based Prediction of Viral Protein Hotspots Targeting Host Proteins: A Case Study for HIV Nef

PubMed Central

Sarmady, Mahdi; Dampier, William; Tozeren, Aydin

2011-01-01

Virus proteins alter protein pathways of the host toward the synthesis of viral particles by breaking and making edges via binding to host proteins. In this study, we developed a computational approach to predict viral sequence hotspots for binding to host proteins based on sequences of viral and host proteins and literature-curated virus-host protein interactome data. We use a motif discovery algorithm repeatedly on collections of sequences of viral proteins and immediate binding partners of their host targets and choose only those motifs that are conserved on viral sequences and highly statistically enriched among binding partners of virus protein targeted host proteins. Our results match experimental data on binding sites of Nef to host proteins such as MAPK1, VAV1, LCK, HCK, HLA-A, CD4, FYN, and GNB2L1 with high statistical significance but is a poor predictor of Nef binding sites on highly flexible, hoop-like regions. Predicted hotspots recapture CD8 cell epitopes of HIV Nef highlighting their importance in modulating virus-host interactions. Host proteins potentially targeted or outcompeted by Nef appear crowding the T cell receptor, natural killer cell mediated cytotoxicity, and neurotrophin signaling pathways. Scanning of HIV Nef motifs on multiple alignments of hepatitis C protein NS5A produces results consistent with literature, indicating the potential value of the hotspot discovery in advancing our understanding of virus-host crosstalk. PMID:21738584

Reference System of DNA and Protein Sequences on CD-ROM

NASA Astrophysics Data System (ADS)

Nasu, Hisanori; Ito, Toshiaki

DNASIS-DBREF31 is a database for DNA and Protein sequences in the form of optical Compact Disk (CD) ROM, developed and commercialized by Hitachi Software Engineering Co., Ltd. Both nucleic acid base sequences and protein amino acid sequences can be retrieved from a single CD-ROM. Existing database is offered in the form of on-line service, floppy disks, or magnetic tape, all of which have some problems or other, such as usability or storage capacity. DNASIS-DBREF31 newly adopt a CD-ROM as a database device to realize a mass storage and personal use of the database.

SIMAP--a comprehensive database of pre-calculated protein sequence similarities, domains, annotations and clusters.

PubMed

Rattei, Thomas; Tischler, Patrick; Götz, Stefan; Jehl, Marc-André; Hoser, Jonathan; Arnold, Roland; Conesa, Ana; Mewes, Hans-Werner

2010-01-01

The prediction of protein function as well as the reconstruction of evolutionary genesis employing sequence comparison at large is still the most powerful tool in sequence analysis. Due to the exponential growth of the number of known protein sequences and the subsequent quadratic growth of the similarity matrix, the computation of the Similarity Matrix of Proteins (SIMAP) becomes a computational intensive task. The SIMAP database provides a comprehensive and up-to-date pre-calculation of the protein sequence similarity matrix, sequence-based features and sequence clusters. As of September 2009, SIMAP covers 48 million proteins and more than 23 million non-redundant sequences. Novel features of SIMAP include the expansion of the sequence space by including databases such as ENSEMBL as well as the integration of metagenomes based on their consistent processing and annotation. Furthermore, protein function predictions by Blast2GO are pre-calculated for all sequences in SIMAP and the data access and query functions have been improved. SIMAP assists biologists to query the up-to-date sequence space systematically and facilitates large-scale downstream projects in computational biology. Access to SIMAP is freely provided through the web portal for individuals (http://mips.gsf.de/simap/) and for programmatic access through DAS (http://webclu.bio.wzw.tum.de/das/) and Web-Service (http://mips.gsf.de/webservices/services/SimapService2.0?wsdl).

De novo protein sequencing by combining top-down and bottom-up tandem mass spectra.

PubMed

Liu, Xiaowen; Dekker, Lennard J M; Wu, Si; Vanduijn, Martijn M; Luider, Theo M; Tolić, Nikola; Kou, Qiang; Dvorkin, Mikhail; Alexandrova, Sonya; Vyatkina, Kira; Paša-Tolić, Ljiljana; Pevzner, Pavel A

2014-07-03

There are two approaches for de novo protein sequencing: Edman degradation and mass spectrometry (MS). Existing MS-based methods characterize a novel protein by assembling tandem mass spectra of overlapping peptides generated from multiple proteolytic digestions of the protein. Because each tandem mass spectrum covers only a short peptide of the target protein, the key to high coverage protein sequencing is to find spectral pairs from overlapping peptides in order to assemble tandem mass spectra to long ones. However, overlapping regions of peptides may be too short to be confidently identified. High-resolution mass spectrometers have become accessible to many laboratories. These mass spectrometers are capable of analyzing molecules of large mass values, boosting the development of top-down MS. Top-down tandem mass spectra cover whole proteins. However, top-down tandem mass spectra, even combined, rarely provide full ion fragmentation coverage of a protein. We propose an algorithm, TBNovo, for de novo protein sequencing by combining top-down and bottom-up MS. In TBNovo, a top-down tandem mass spectrum is utilized as a scaffold, and bottom-up tandem mass spectra are aligned to the scaffold to increase sequence coverage. Experiments on data sets of two proteins showed that TBNovo achieved high sequence coverage and high sequence accuracy.

Comparative analysis of ribosomal protein L5 sequences from bacteria of the genus Thermus.

PubMed

Jahn, O; Hartmann, R K; Boeckh, T; Erdmann, V A

1991-06-01

The genes for the ribosomal 5S rRNA binding protein L5 have been cloned from three extremely thermophilic eubacteria, Thermus flavus, Thermus thermophilus HB8 and Thermus aquaticus (Jahn et al, submitted). Genes for protein L5 from the three Thermus strains display 95% G/C in third positions of codons. Amino acid sequences deduced from the DNA sequence were shown to be identical for T flavus and T thermophilus, although the corresponding DNA sequences differed by two T to C transitions in the T thermophilus gene. Protein L5 sequences from T flavus and T thermophilus are 95% homologous to L5 from T aquaticus and 56.5% homologous to the corresponding E coli sequence. The lowest degrees of homology were found between the T flavus/T thermophilus L5 proteins and those of yeast L16 (27.5%), Halobacterium marismortui (34.0%) and Methanococcus vannielii (36.6%). From sequence comparison it becomes clear that thermostability of Thermus L5 proteins is achieved by an increase in hydrophobic interactions and/or by restriction of steric flexibility due to the introduction of amino acids with branched aliphatic side chains such as leucine. Alignment of the nine protein sequences equivalent to Thermus L5 proteins led to identification of a conserved internal segment, rich in acidic amino acids, which shows homology to subsequences of E coli L18 and L25. The occurrence of conserved sequence elements in 5S rRNA binding proteins and ribosomal proteins in general is discussed in terms of evolution and function.

PEPSI-Dock: a detailed data-driven protein-protein interaction potential accelerated by polar Fourier correlation.

PubMed

Neveu, Emilie; Ritchie, David W; Popov, Petr; Grudinin, Sergei

2016-09-01

Docking prediction algorithms aim to find the native conformation of a complex of proteins from knowledge of their unbound structures. They rely on a combination of sampling and scoring methods, adapted to different scales. Polynomial Expansion of Protein Structures and Interactions for Docking (PEPSI-Dock) improves the accuracy of the first stage of the docking pipeline, which will sharpen up the final predictions. Indeed, PEPSI-Dock benefits from the precision of a very detailed data-driven model of the binding free energy used with a global and exhaustive rigid-body search space. As well as being accurate, our computations are among the fastest by virtue of the sparse representation of the pre-computed potentials and FFT-accelerated sampling techniques. Overall, this is the first demonstration of a FFT-accelerated docking method coupled with an arbitrary-shaped distance-dependent interaction potential. First, we present a novel learning process to compute data-driven distant-dependent pairwise potentials, adapted from our previous method used for rescoring of putative protein-protein binding poses. The potential coefficients are learned by combining machine-learning techniques with physically interpretable descriptors. Then, we describe the integration of the deduced potentials into a FFT-accelerated spherical sampling provided by the Hex library. Overall, on a training set of 163 heterodimers, PEPSI-Dock achieves a success rate of 91% mid-quality predictions in the top-10 solutions. On a subset of the protein docking benchmark v5, it achieves 44.4% mid-quality predictions in the top-10 solutions when starting from bound structures and 20.5% when starting from unbound structures. The method runs in 5-15 min on a modern laptop and can easily be extended to other types of interactions. https://team.inria.fr/nano-d/software/PEPSI-Dock sergei.grudinin@inria.fr. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e

Protein sequencing via nanopore based devices: a nanofluidics perspective

NASA Astrophysics Data System (ADS)

Chinappi, Mauro; Cecconi, Fabio

2018-05-01

Proteins perform a huge number of central functions in living organisms, thus all the new techniques allowing their precise, fast and accurate characterization at single-molecule level certainly represent a burst in proteomics with important biomedical impact. In this review, we describe the recent progresses in the developing of nanopore based devices for protein sequencing. We start with a critical analysis of the main technical requirements for nanopore protein sequencing, summarizing some ideas and methodologies that have recently appeared in the literature. In the last sections, we focus on the physical modelling of the transport phenomena occurring in nanopore based devices. The multiscale nature of the problem is discussed and, in this respect, some of the main possible computational approaches are illustrated.

Investigation of the protein osteocalcin of Camelops hesternus: Sequence, structure and phylogenetic implications

NASA Astrophysics Data System (ADS)

Humpula, James F.; Ostrom, Peggy H.; Gandhi, Hasand; Strahler, John R.; Walker, Angela K.; Stafford, Thomas W.; Smith, James J.; Voorhies, Michael R.; George Corner, R.; Andrews, Phillip C.

2007-12-01

Ancient DNA sequences offer an extraordinary opportunity to unravel the evolutionary history of ancient organisms. Protein sequences offer another reservoir of genetic information that has recently become tractable through the application of mass spectrometric techniques. The extent to which ancient protein sequences resolve phylogenetic relationships, however, has not been explored. We determined the osteocalcin amino acid sequence from the bone of an extinct Camelid (21 ka, Camelops hesternus) excavated from Isleta Cave, New Mexico and three bones of extant camelids: bactrian camel ( Camelus bactrianus); dromedary camel ( Camelus dromedarius) and guanaco ( Llama guanacoe) for a diagenetic and phylogenetic assessment. There was no difference in sequence among the four taxa. Structural attributes observed in both modern and ancient osteocalcin include a post-translation modification, Hyp 9, deamidation of Gln 35 and Gln 39, and oxidation of Met 36. Carbamylation of the N-terminus in ancient osteocalcin may result in blockage and explain previous difficulties in sequencing ancient proteins via Edman degradation. A phylogenetic analysis using osteocalcin sequences of 25 vertebrate taxa was conducted to explore osteocalcin protein evolution and the utility of osteocalcin sequences for delineating phylogenetic relationships. The maximum likelihood tree closely reflected generally recognized taxonomic relationships. For example, maximum likelihood analysis recovered rodents, birds and, within hominins, the Homo-Pan-Gorilla trichotomy. Within Artiodactyla, character state analysis showed that a substitution of Pro 4 for His 4 defines the Capra-Ovis clade within Artiodactyla. Homoplasy in our analysis indicated that osteocalcin evolution is not a perfect indicator of species evolution. Limited sequence availability prevented assigning functional significance to sequence changes. Our preliminary analysis of osteocalcin evolution represents an initial step towards a

UET: a database of evolutionarily-predicted functional determinants of protein sequences that cluster as functional sites in protein structures.

PubMed

Lua, Rhonald C; Wilson, Stephen J; Konecki, Daniel M; Wilkins, Angela D; Venner, Eric; Morgan, Daniel H; Lichtarge, Olivier

2016-01-04

The structure and function of proteins underlie most aspects of biology and their mutational perturbations often cause disease. To identify the molecular determinants of function as well as targets for drugs, it is central to characterize the important residues and how they cluster to form functional sites. The Evolutionary Trace (ET) achieves this by ranking the functional and structural importance of the protein sequence positions. ET uses evolutionary distances to estimate functional distances and correlates genotype variations with those in the fitness phenotype. Thus, ET ranks are worse for sequence positions that vary among evolutionarily closer homologs but better for positions that vary mostly among distant homologs. This approach identifies functional determinants, predicts function, guides the mutational redesign of functional and allosteric specificity, and interprets the action of coding sequence variations in proteins, people and populations. Now, the UET database offers pre-computed ET analyses for the protein structure databank, and on-the-fly analysis of any protein sequence. A web interface retrieves ET rankings of sequence positions and maps results to a structure to identify functionally important regions. This UET database integrates several ways of viewing the results on the protein sequence or structure and can be found at http://mammoth.bcm.tmc.edu/uet/. © The Author(s) 2015. Published by Oxford University Press on behalf of Nucleic Acids Research.

How the Sequence of a Gene Specifies Structural Symmetry in Proteins

PubMed Central

Shen, Xiaojuan; Huang, Tongcheng; Wang, Guanyu; Li, Guanglin

2015-01-01

Internal symmetry is commonly observed in the majority of fundamental protein folds. Meanwhile, sufficient evidence suggests that nascent polypeptide chains of proteins have the potential to start the co-translational folding process and this process allows mRNA to contain additional information on protein structure. In this paper, we study the relationship between gene sequences and protein structures from the viewpoint of symmetry to explore how gene sequences code for structural symmetry in proteins. We found that, for a set of two-fold symmetric proteins from left-handed beta-helix fold, intragenic symmetry always exists in their corresponding gene sequences. Meanwhile, codon usage bias and local mRNA structure might be involved in modulating translation speed for the formation of structural symmetry: a major decrease of local codon usage bias in the middle of the codon sequence can be identified as a common feature; and major or consecutive decreases in local mRNA folding energy near the boundaries of the symmetric substructures can also be observed. The results suggest that gene duplication and fusion may be an evolutionarily conserved process for this protein fold. In addition, the usage of rare codons and the formation of higher order of secondary structure near the boundaries of symmetric substructures might have coevolved as conserved mechanisms to slow down translation elongation and to facilitate effective folding of symmetric substructures. These findings provide valuable insights into our understanding of the mechanisms of translation and its evolution, as well as the design of proteins via symmetric modules. PMID:26641668

Adhesive proteins of stalked and acorn barnacles display homology with low sequence similarities.

PubMed

Jonker, Jaimie-Leigh; Abram, Florence; Pires, Elisabete; Varela Coelho, Ana; Grunwald, Ingo; Power, Anne Marie

2014-01-01

Barnacle adhesion underwater is an important phenomenon to understand for the prevention of biofouling and potential biotechnological innovations, yet so far, identifying what makes barnacle glue proteins 'sticky' has proved elusive. Examination of a broad range of species within the barnacles may be instructive to identify conserved adhesive domains. We add to extensive information from the acorn barnacles (order Sessilia) by providing the first protein analysis of a stalked barnacle adhesive, Lepas anatifera (order Lepadiformes). It was possible to separate the L. anatifera adhesive into at least 10 protein bands using SDS-PAGE. Intense bands were present at approximately 30, 70, 90 and 110 kilodaltons (kDa). Mass spectrometry for protein identification was followed by de novo sequencing which detected 52 peptides of 7-16 amino acids in length. None of the peptides matched published or unpublished transcriptome sequences, but some amino acid sequence similarity was apparent between L. anatifera and closely-related Dosima fascicularis. Antibodies against two acorn barnacle proteins (ab-cp-52k and ab-cp-68k) showed cross-reactivity in the adhesive glands of L. anatifera. We also analysed the similarity of adhesive proteins across several barnacle taxa, including Pollicipes pollicipes (a stalked barnacle in the order Scalpelliformes). Sequence alignment of published expressed sequence tags clearly indicated that P. pollicipes possesses homologues for the 19 kDa and 100 kDa proteins in acorn barnacles. Homology aside, sequence similarity in amino acid and gene sequences tended to decline as taxonomic distance increased, with minimum similarities of 18-26%, depending on the gene. The results indicate that some adhesive proteins (e.g. 100 kDa) are more conserved within barnacles than others (20 kDa).

Transiently disordered tails accelerate folding of globular proteins.

PubMed

Mallik, Saurav; Ray, Tanaya; Kundu, Sudip

2017-07-01

Numerous biological proteins exhibit intrinsic disorder at their termini, which are associated with multifarious functional roles. Here, we show the surprising result that an increased percentage of terminal short transiently disordered regions with enhanced flexibility (TstDREF) is associated with accelerated folding rates of globular proteins. Evolutionary conservation of predicted disorder at TstDREFs and drastic alteration of folding rates upon point-mutations suggest critical regulatory role(s) of TstDREFs in shaping the folding kinetics. TstDREFs are associated with long-range intramolecular interactions and the percentage of native secondary structural elements physically contacted by TstDREFs exhibit another surprising positive correlation with folding kinetics. These results allow us to infer probable molecular mechanisms behind the TstDREF-mediated regulation of folding kinetics that challenge protein biochemists to assess by direct experimental testing. © 2017 Federation of European Biochemical Societies.

Beta.-glucosidase coding sequences and protein from orpinomyces PC-2

DOEpatents

Li, Xin-Liang; Ljungdahl, Lars G.; Chen, Huizhong; Ximenes, Eduardo A.

2001-02-06

Provided is a novel .beta.-glucosidase from Orpinomyces sp. PC2, nucleotide sequences encoding the mature protein and the precursor protein, and methods for recombinant production of this .beta.-glucosidase.

Protein sequence comparison based on K-string dictionary.

PubMed

Yu, Chenglong; He, Rong L; Yau, Stephen S-T

2013-10-25

The current K-string-based protein sequence comparisons require large amounts of computer memory because the dimension of the protein vector representation grows exponentially with K. In this paper, we propose a novel concept, the "K-string dictionary", to solve this high-dimensional problem. It allows us to use a much lower dimensional K-string-based frequency or probability vector to represent a protein, and thus significantly reduce the computer memory requirements for their implementation. Furthermore, based on this new concept, we use Singular Value Decomposition to analyze real protein datasets, and the improved protein vector representation allows us to obtain accurate gene trees. © 2013.

Characterization of DNA-protein interactions using high-throughput sequencing data from pulldown experiments

NASA Astrophysics Data System (ADS)

Moreland, Blythe; Oman, Kenji; Curfman, John; Yan, Pearlly; Bundschuh, Ralf

Methyl-binding domain (MBD) protein pulldown experiments have been a valuable tool in measuring the levels of methylated CpG dinucleotides. Due to the frequent use of this technique, high-throughput sequencing data sets are available that allow a detailed quantitative characterization of the underlying interaction between methylated DNA and MBD proteins. Analyzing such data sets, we first found that two such proteins cannot bind closer to each other than 2 bp, consistent with structural models of the DNA-protein interaction. Second, the large amount of sequencing data allowed us to find rather weak but nevertheless clearly statistically significant sequence preferences for several bases around the required CpG. These results demonstrate that pulldown sequencing is a high-precision tool in characterizing DNA-protein interactions. This material is based upon work supported by the National Science Foundation under Grant No. DMR-1410172.

An ensemble approach for large-scale identification of protein-protein interactions using the alignments of multiple sequences

PubMed Central

Wang, Lei; You, Zhu-Hong; Chen, Xing; Li, Jian-Qiang; Yan, Xin; Zhang, Wei; Huang, Yu-An

2017-01-01

Protein–Protein Interactions (PPI) is not only the critical component of various biological processes in cells, but also the key to understand the mechanisms leading to healthy and diseased states in organisms. However, it is time-consuming and cost-intensive to identify the interactions among proteins using biological experiments. Hence, how to develop a more efficient computational method rapidly became an attractive topic in the post-genomic era. In this paper, we propose a novel method for inference of protein-protein interactions from protein amino acids sequences only. Specifically, protein amino acids sequence is firstly transformed into Position-Specific Scoring Matrix (PSSM) generated by multiple sequences alignments; then the Pseudo PSSM is used to extract feature descriptors. Finally, ensemble Rotation Forest (RF) learning system is trained to predict and recognize PPIs based solely on protein sequence feature. When performed the proposed method on the three benchmark data sets (Yeast, H. pylori, and independent dataset) for predicting PPIs, our method can achieve good average accuracies of 98.38%, 89.75%, and 96.25%, respectively. In order to further evaluate the prediction performance, we also compare the proposed method with other methods using same benchmark data sets. The experiment results demonstrate that the proposed method consistently outperforms other state-of-the-art method. Therefore, our method is effective and robust and can be taken as a useful tool in exploring and discovering new relationships between proteins. A web server is made publicly available at the URL http://202.119.201.126:8888/PsePSSM/ for academic use. PMID:28029645

Improving pairwise comparison of protein sequences with domain co-occurrence

PubMed Central

Gascuel, Olivier

2018-01-01

Comparing and aligning protein sequences is an essential task in bioinformatics. More specifically, local alignment tools like BLAST are widely used for identifying conserved protein sub-sequences, which likely correspond to protein domains or functional motifs. However, to limit the number of false positives, these tools are used with stringent sequence-similarity thresholds and hence can miss several hits, especially for species that are phylogenetically distant from reference organisms. A solution to this problem is then to integrate additional contextual information to the procedure. Here, we propose to use domain co-occurrence to increase the sensitivity of pairwise sequence comparisons. Domain co-occurrence is a strong feature of proteins, since most protein domains tend to appear with a limited number of other domains on the same protein. We propose a method to take this information into account in a typical BLAST analysis and to construct new domain families on the basis of these results. We used Plasmodium falciparum as a case study to evaluate our method. The experimental findings showed an increase of 14% of the number of significant BLAST hits and an increase of 25% of the proteome area that can be covered with a domain. Our method identified 2240 new domains for which, in most cases, no model of the Pfam database could be linked. Moreover, our study of the quality of the new domains in terms of alignment and physicochemical properties show that they are close to that of standard Pfam domains. Source code of the proposed approach and supplementary data are available at: https://gite.lirmm.fr/menichelli/pairwise-comparison-with-cooccurrence PMID:29293498

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

Top-down analysis of protein samples by de novo sequencing techniques

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

Vyatkina, Kira; Wu, Si; Dekker, Lennard J. M.

MOTIVATION: Recent technological advances have made high-resolution mass spectrometers affordable to many laboratories, thus boosting rapid development of top-down mass spectrometry, and implying a need in efficient methods for analyzing this kind of data. RESULTS: We describe a method for analysis of protein samples from top-down tandem mass spectrometry data, which capitalizes on de novo sequencing of fragments of the proteins present in the sample. Our algorithm takes as input a set of de novo amino acid strings derived from the given mass spectra using the recently proposed Twister approach, and combines them into aggregated strings endowed with offsets. Themore » former typically constitute accurate sequence fragments of sufficiently well-represented proteins from the sample being analyzed, while the latter indicate their location in the protein sequence, and also bear information on post-translational modifications and fragmentation patterns.« less

Draft versus finished sequence data for DNA and protein diagnostic signature development

PubMed Central

Gardner, Shea N.; Lam, Marisa W.; Smith, Jason R.; Torres, Clinton L.; Slezak, Tom R.

2005-01-01

Sequencing pathogen genomes is costly, demanding careful allocation of limited sequencing resources. We built a computational Sequencing Analysis Pipeline (SAP) to guide decisions regarding the amount of genomic sequencing necessary to develop high-quality diagnostic DNA and protein signatures. SAP uses simulations to estimate the number of target genomes and close phylogenetic relatives (near neighbors or NNs) to sequence. We use SAP to assess whether draft data are sufficient or finished sequencing is required using Marburg and variola virus sequences. Simulations indicate that intermediate to high-quality draft with error rates of 10−3–10−5 (∼8× coverage) of target organisms is suitable for DNA signature prediction. Low-quality draft with error rates of ∼1% (3× to 6× coverage) of target isolates is inadequate for DNA signature prediction, although low-quality draft of NNs is sufficient, as long as the target genomes are of high quality. For protein signature prediction, sequencing errors in target genomes substantially reduce the detection of amino acid sequence conservation, even if the draft is of high quality. In summary, high-quality draft of target and low-quality draft of NNs appears to be a cost-effective investment for DNA signature prediction, but may lead to underestimation of predicted protein signatures. PMID:16243783

Adhesive Proteins of Stalked and Acorn Barnacles Display Homology with Low Sequence Similarities

PubMed Central

Jonker, Jaimie-Leigh; Abram, Florence; Pires, Elisabete; Varela Coelho, Ana; Grunwald, Ingo; Power, Anne Marie

2014-01-01

Barnacle adhesion underwater is an important phenomenon to understand for the prevention of biofouling and potential biotechnological innovations, yet so far, identifying what makes barnacle glue proteins ‘sticky’ has proved elusive. Examination of a broad range of species within the barnacles may be instructive to identify conserved adhesive domains. We add to extensive information from the acorn barnacles (order Sessilia) by providing the first protein analysis of a stalked barnacle adhesive, Lepas anatifera (order Lepadiformes). It was possible to separate the L. anatifera adhesive into at least 10 protein bands using SDS-PAGE. Intense bands were present at approximately 30, 70, 90 and 110 kilodaltons (kDa). Mass spectrometry for protein identification was followed by de novo sequencing which detected 52 peptides of 7–16 amino acids in length. None of the peptides matched published or unpublished transcriptome sequences, but some amino acid sequence similarity was apparent between L. anatifera and closely-related Dosima fascicularis. Antibodies against two acorn barnacle proteins (ab-cp-52k and ab-cp-68k) showed cross-reactivity in the adhesive glands of L. anatifera. We also analysed the similarity of adhesive proteins across several barnacle taxa, including Pollicipes pollicipes (a stalked barnacle in the order Scalpelliformes). Sequence alignment of published expressed sequence tags clearly indicated that P. pollicipes possesses homologues for the 19 kDa and 100 kDa proteins in acorn barnacles. Homology aside, sequence similarity in amino acid and gene sequences tended to decline as taxonomic distance increased, with minimum similarities of 18–26%, depending on the gene. The results indicate that some adhesive proteins (e.g. 100 kDa) are more conserved within barnacles than others (20 kDa). PMID:25295513

Analyzing radial acceleration with a smartphone acceleration sensor

NASA Astrophysics Data System (ADS)

Vogt, Patrik; Kuhn, Jochen

2013-03-01

This paper continues the sequence of experiments using the acceleration sensor of smartphones (for description of the function and the use of the acceleration sensor, see Ref. 1) within this column, in this case for analyzing the radial acceleration.

Relationships between residue Voronoi volume and sequence conservation in proteins.

PubMed

Liu, Jen-Wei; Cheng, Chih-Wen; Lin, Yu-Feng; Chen, Shao-Yu; Hwang, Jenn-Kang; Yen, Shih-Chung

2018-02-01

Functional and biophysical constraints can cause different levels of sequence conservation in proteins. Previously, structural properties, e.g., relative solvent accessibility (RSA) and packing density of the weighted contact number (WCN), have been found to be related to protein sequence conservation (CS). The Voronoi volume has recently been recognized as a new structural property of the local protein structural environment reflecting CS. However, for surface residues, it is sensitive to water molecules surrounding the protein structure. Herein, we present a simple structural determinant termed the relative space of Voronoi volume (RSV); it uses the Voronoi volume and the van der Waals volume of particular residues to quantify the local structural environment. RSV (range, 0-1) is defined as (Voronoi volume-van der Waals volume)/Voronoi volume of the target residue. The concept of RSV describes the extent of available space for every protein residue. RSV and Voronoi profiles with and without water molecules (RSVw, RSV, VOw, and VO) were compared for 554 non-homologous proteins. RSV (without water) showed better Pearson's correlations with CS than did RSVw, VO, or VOw values. The mean correlation coefficient between RSV and CS was 0.51, which is comparable to the correlation between RSA and CS (0.49) and that between WCN and CS (0.56). RSV is a robust structural descriptor with and without water molecules and can quantitatively reflect evolutionary information in a single protein structure. Therefore, it may represent a practical structural determinant to study protein sequence, structure, and function relationships. Copyright © 2017 Elsevier B.V. All rights reserved.

Extracting features from protein sequences to improve deep extreme learning machine for protein fold recognition.

PubMed

Ibrahim, Wisam; Abadeh, Mohammad Saniee

2017-05-21

Protein fold recognition is an important problem in bioinformatics to predict three-dimensional structure of a protein. One of the most challenging tasks in protein fold recognition problem is the extraction of efficient features from the amino-acid sequences to obtain better classifiers. In this paper, we have proposed six descriptors to extract features from protein sequences. These descriptors are applied in the first stage of a three-stage framework PCA-DELM-LDA to extract feature vectors from the amino-acid sequences. Principal Component Analysis PCA has been implemented to reduce the number of extracted features. The extracted feature vectors have been used with original features to improve the performance of the Deep Extreme Learning Machine DELM in the second stage. Four new features have been extracted from the second stage and used in the third stage by Linear Discriminant Analysis LDA to classify the instances into 27 folds. The proposed framework is implemented on the independent and combined feature sets in SCOP datasets. The experimental results show that extracted feature vectors in the first stage could improve the performance of DELM in extracting new useful features in second stage. Copyright © 2017 Elsevier Ltd. All rights reserved.

Use of signal sequences as an in situ removable sequence element to stimulate protein synthesis in cell-free extracts

PubMed Central

Ahn, Jin-Ho; Hwang, Mi-Yeon; Lee, Kyung-Ho; Choi, Cha-Yong; Kim, Dong-Myung

2007-01-01

This study developed a method to boost the expression of recombinant proteins in a cell-free protein synthesis system without leaving additional amino acid residues. It was found that the nucleotide sequences of the signal peptides serve as an efficient downstream box to stimulate protein synthesis when they were fused upstream of the target genes. The extent of stimulation was critically affected by the identity of the second codons of the signal sequences. Moreover, the yield of the synthesized protein was enhanced by as much as 10 times in the presence of an optimal second codon. The signal peptides were in situ cleaved and the target proteins were produced in their native sizes by carrying out the cell-free synthesis reactions in the presence of Triton X-100, most likely through the activation of signal peptidase in the S30 extract. The amplification of the template DNA and the addition of the signal sequences were accomplished by PCR. Hence, elevated levels of recombinant proteins were generated within several hours. PMID:17185295

Cube - an online tool for comparison and contrasting of protein sequences.

PubMed

Zhang, Zong Hong; Khoo, Aik Aun; Mihalek, Ivana

2013-01-01

When comparing sequences of similar proteins, two kinds of questions can be asked, and the related two kinds of inference made. First, one may ask to what degree they are similar, and then, how they differ. In the first case one may tentatively conclude that the conserved elements common to all sequences are of central and common importance to the protein's function. In the latter case the regions of specialization may be discriminative of the function or binding partners across subfamilies of related proteins. Experimental efforts - mutagenesis or pharmacological intervention - can then be pointed in either direction, depending on the context of the study. Cube simplifies this process for users that already have their favorite sets of sequences, and helps them collate the information by visualization of the conservation and specialization scores on the sequence and on the structure, and by spreadsheet tabulation. All information can be visualized on the spot, or downloaded for reference and later inspection. http://eopsf.org/cube.

Efficient Feature Selection and Classification of Protein Sequence Data in Bioinformatics

PubMed Central

Faye, Ibrahima; Samir, Brahim Belhaouari; Md Said, Abas

2014-01-01

Bioinformatics has been an emerging area of research for the last three decades. The ultimate aims of bioinformatics were to store and manage the biological data, and develop and analyze computational tools to enhance their understanding. The size of data accumulated under various sequencing projects is increasing exponentially, which presents difficulties for the experimental methods. To reduce the gap between newly sequenced protein and proteins with known functions, many computational techniques involving classification and clustering algorithms were proposed in the past. The classification of protein sequences into existing superfamilies is helpful in predicting the structure and function of large amount of newly discovered proteins. The existing classification results are unsatisfactory due to a huge size of features obtained through various feature encoding methods. In this work, a statistical metric-based feature selection technique has been proposed in order to reduce the size of the extracted feature vector. The proposed method of protein classification shows significant improvement in terms of performance measure metrics: accuracy, sensitivity, specificity, recall, F-measure, and so forth. PMID:25045727

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

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

N-Terminal Amino Acid Sequence Determination of Proteins by N-Terminal Dimethyl Labeling: Pitfalls and Advantages When Compared with Edman Degradation Sequence Analysis.

PubMed

Chang, Elizabeth; Pourmal, Sergei; Zhou, Chun; Kumar, Rupesh; Teplova, Marianna; Pavletich, Nikola P; Marians, Kenneth J; Erdjument-Bromage, Hediye

2016-07-01

In recent history, alternative approaches to Edman sequencing have been investigated, and to this end, the Association of Biomolecular Resource Facilities (ABRF) Protein Sequencing Research Group (PSRG) initiated studies in 2014 and 2015, looking into bottom-up and top-down N-terminal (Nt) dimethyl derivatization of standard quantities of intact proteins with the aim to determine Nt sequence information. We have expanded this initiative and used low picomole amounts of myoglobin to determine the efficiency of Nt-dimethylation. Application of this approach on protein domains, generated by limited proteolysis of overexpressed proteins, confirms that it is a universal labeling technique and is very sensitive when compared with Edman sequencing. Finally, we compared Edman sequencing and Nt-dimethylation of the same polypeptide fragments; results confirm that there is agreement in the identity of the Nt amino acid sequence between these 2 methods.

Learning cellular sorting pathways using protein interactions and sequence motifs.

PubMed

Lin, Tien-Ho; Bar-Joseph, Ziv; Murphy, Robert F

2011-11-01

Proper subcellular localization is critical for proteins to perform their roles in cellular functions. Proteins are transported by different cellular sorting pathways, some of which take a protein through several intermediate locations until reaching its final destination. The pathway a protein is transported through is determined by carrier proteins that bind to specific sequence motifs. In this article, we present a new method that integrates protein interaction and sequence motif data to model how proteins are sorted through these sorting pathways. We use a hidden Markov model (HMM) to represent protein sorting pathways. The model is able to determine intermediate sorting states and to assign carrier proteins and motifs to the sorting pathways. In simulation studies, we show that the method can accurately recover an underlying sorting model. Using data for yeast, we show that our model leads to accurate prediction of subcellular localization. We also show that the pathways learned by our model recover many known sorting pathways and correctly assign proteins to the path they utilize. The learned model identified new pathways and their putative carriers and motifs and these may represent novel protein sorting mechanisms. Supplementary results and software implementation are available from http://murphylab.web.cmu.edu/software/2010_RECOMB_pathways/.

Learning Cellular Sorting Pathways Using Protein Interactions and Sequence Motifs

PubMed Central

Lin, Tien-Ho; Bar-Joseph, Ziv

2011-01-01

Abstract Proper subcellular localization is critical for proteins to perform their roles in cellular functions. Proteins are transported by different cellular sorting pathways, some of which take a protein through several intermediate locations until reaching its final destination. The pathway a protein is transported through is determined by carrier proteins that bind to specific sequence motifs. In this article, we present a new method that integrates protein interaction and sequence motif data to model how proteins are sorted through these sorting pathways. We use a hidden Markov model (HMM) to represent protein sorting pathways. The model is able to determine intermediate sorting states and to assign carrier proteins and motifs to the sorting pathways. In simulation studies, we show that the method can accurately recover an underlying sorting model. Using data for yeast, we show that our model leads to accurate prediction of subcellular localization. We also show that the pathways learned by our model recover many known sorting pathways and correctly assign proteins to the path they utilize. The learned model identified new pathways and their putative carriers and motifs and these may represent novel protein sorting mechanisms. Supplementary results and software implementation are available from http://murphylab.web.cmu.edu/software/2010_RECOMB_pathways/. PMID:21999284

FPGA-based protein sequence alignment : A review

NASA Astrophysics Data System (ADS)

Isa, Mohd. Nazrin Md.; Muhsen, Ku Noor Dhaniah Ku; Saiful Nurdin, Dayana; Ahmad, Muhammad Imran; Anuar Zainol Murad, Sohiful; Nizam Mohyar, Shaiful; Harun, Azizi; Hussin, Razaidi

2017-11-01

Sequence alignment have been optimized using several techniques in order to accelerate the computation time to obtain the optimal score by implementing DP-based algorithm into hardware such as FPGA-based platform. During hardware implementation, there will be performance challenges such as the frequent memory access and highly data dependent in computation process. Therefore, investigation in processing element (PE) configuration where involves more on memory access in load or access the data (substitution matrix, query sequence character) and the PE configuration time will be the main focus in this paper. There are various approaches to enhance the PE configuration performance that have been done in previous works such as by using serial configuration chain and parallel configuration chain i.e. the configuration data will be loaded into each PEs sequentially and simultaneously respectively. Some researchers have proven that the performance using parallel configuration chain has optimized both the configuration time and area.

Integrating mRNA and protein sequencing enables the detection and quantitative profiling of natural protein sequence variants of Populus trichocarpa

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

Abraham, Paul E.; Wang, Xiaojing; Ranjan, Priya

The availability of next-generation sequencing technologies has rapidly transformed our ability to link genotypes to phenotypes, and as such, promises to facilitate the dissection of genetic contribution to complex traits. Although discoveries of genetic associations will further our understanding of biology, once candidate variants have been identified, investigators are faced with the challenge of characterizing the functional effects on proteins encoded by such genes. Here we show how next-generation RNA sequencing data can be exploited to construct genotype-specific protein sequence databases, which provide a clearer picture of the molecular toolbox underlying cellular and organismal processes and their variation in amore » natural population. For this study, we used two individual genotypes (DENA-17-3 and VNDL-27-4) from a recent genome wide association (GWA) study of Populus trichocarpa, an obligate outcrosser that exhibits tremendous phenotypic variation across the natural population. This strategy allowed us to comprehensively catalogue proteins containing single amino acid polymorphisms (SAAPs) and insertions and deletions (INDELS). Based on large-scale identification of SAAPs, we profiled the frequency of 128 types of naturally occurring amino acid substitutions, with a subset of SAAPs occurring in regions of the genome having strong polymorphism patterns consistent with recent positive and/or divergent selection. In addition, we were able to explore the diploid landscape of Populus at the proteome-level, allowing the characterization of heterozygous variants.« less

Integrating mRNA and protein sequencing enables the detection and quantitative profiling of natural protein sequence variants of Populus trichocarpa

DOE PAGES

Abraham, Paul E.; Wang, Xiaojing; Ranjan, Priya; ...

2015-10-20

The availability of next-generation sequencing technologies has rapidly transformed our ability to link genotypes to phenotypes, and as such, promises to facilitate the dissection of genetic contribution to complex traits. Although discoveries of genetic associations will further our understanding of biology, once candidate variants have been identified, investigators are faced with the challenge of characterizing the functional effects on proteins encoded by such genes. Here we show how next-generation RNA sequencing data can be exploited to construct genotype-specific protein sequence databases, which provide a clearer picture of the molecular toolbox underlying cellular and organismal processes and their variation in amore » natural population. For this study, we used two individual genotypes (DENA-17-3 and VNDL-27-4) from a recent genome wide association (GWA) study of Populus trichocarpa, an obligate outcrosser that exhibits tremendous phenotypic variation across the natural population. This strategy allowed us to comprehensively catalogue proteins containing single amino acid polymorphisms (SAAPs) and insertions and deletions (INDELS). Based on large-scale identification of SAAPs, we profiled the frequency of 128 types of naturally occurring amino acid substitutions, with a subset of SAAPs occurring in regions of the genome having strong polymorphism patterns consistent with recent positive and/or divergent selection. In addition, we were able to explore the diploid landscape of Populus at the proteome-level, allowing the characterization of heterozygous variants.« less

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.

GOLabeler: Improving Sequence-based Large-scale Protein Function Prediction by Learning to Rank.

PubMed

You, Ronghui; Zhang, Zihan; Xiong, Yi; Sun, Fengzhu; Mamitsuka, Hiroshi; Zhu, Shanfeng

2018-03-07

Gene Ontology (GO) has been widely used to annotate functions of proteins and understand their biological roles. Currently only <1% of more than 70 million proteins in UniProtKB have experimental GO annotations, implying the strong necessity of automated function prediction (AFP) of proteins, where AFP is a hard multilabel classification problem due to one protein with a diverse number of GO terms. Most of these proteins have only sequences as input information, indicating the importance of sequence-based AFP (SAFP: sequences are the only input). Furthermore homology-based SAFP tools are competitive in AFP competitions, while they do not necessarily work well for so-called difficult proteins, which have <60% sequence identity to proteins with annotations already. Thus the vital and challenging problem now is how to develop a method for SAFP, particularly for difficult proteins. The key of this method is to extract not only homology information but also diverse, deep- rooted information/evidence from sequence inputs and integrate them into a predictor in a both effective and efficient manner. We propose GOLabeler, which integrates five component classifiers, trained from different features, including GO term frequency, sequence alignment, amino acid trigram, domains and motifs, and biophysical properties, etc., in the framework of learning to rank (LTR), a paradigm of machine learning, especially powerful for multilabel classification. The empirical results obtained by examining GOLabeler extensively and thoroughly by using large-scale datasets revealed numerous favorable aspects of GOLabeler, including significant performance advantage over state-of-the-art AFP methods. http://datamining-iip.fudan.edu.cn/golabeler. zhusf@fudan.edu.cn. Supplementary data are available at Bioinformatics online.

Investigating Correlation between Protein Sequence Similarity and Semantic Similarity Using Gene Ontology Annotations.

PubMed

Ikram, Najmul; Qadir, Muhammad Abdul; Afzal, Muhammad Tanvir

2018-01-01

Sequence similarity is a commonly used measure to compare proteins. With the increasing use of ontologies, semantic (function) similarity is getting importance. The correlation between these measures has been applied in the evaluation of new semantic similarity methods, and in protein function prediction. In this research, we investigate the relationship between the two similarity methods. The results suggest absence of a strong correlation between sequence and semantic similarities. There is a large number of proteins with low sequence similarity and high semantic similarity. We observe that Pearson's correlation coefficient is not sufficient to explain the nature of this relationship. Interestingly, the term semantic similarity values above 0 and below 1 do not seem to play a role in improving the correlation. That is, the correlation coefficient depends only on the number of common GO terms in proteins under comparison, and the semantic similarity measurement method does not influence it. Semantic similarity and sequence similarity have a distinct behavior. These findings are of significant effect for future works on protein comparison, and will help understand the semantic similarity between proteins in a better way.

Determination of the sequences of protein-derived peptides and peptide mixtures by mass spectrometry

PubMed Central

Morris, Howard R.; Williams, Dudley H.; Ambler, Richard P.

1971-01-01

Micro-quantities of protein-derived peptides have been converted into N-acetylated permethyl derivatives, and their sequences determined by low-resolution mass spectrometry without prior knowledge of their amino acid compositions or lengths. A new strategy is suggested for the mass spectrometric sequencing of oligopeptides or proteins, involving gel filtration of protein hydrolysates and subsequent sequence analysis of peptide mixtures. Finally, results are given that demonstrate for the first time the use of mass spectrometry for the analysis of a protein-derived peptide mixture, again without prior knowledge of the protein or components within the mixture. PMID:5158904

Protein sequences clustering of herpes virus by using Tribe Markov clustering (Tribe-MCL)

NASA Astrophysics Data System (ADS)

Bustamam, A.; Siswantining, T.; Febriyani, N. L.; Novitasari, I. D.; Cahyaningrum, R. D.

2017-07-01

The herpes virus can be found anywhere and one of the important characteristics is its ability to cause acute and chronic infection at certain times so as a result of the infection allows severe complications occurred. The herpes virus is composed of DNA containing protein and wrapped by glycoproteins. In this work, the Herpes viruses family is classified and analyzed by clustering their protein-sequence using Tribe Markov Clustering (Tribe-MCL) algorithm. Tribe-MCL is an efficient clustering method based on the theory of Markov chains, to classify protein families from protein sequences using pre-computed sequence similarity information. We implement the Tribe-MCL algorithm using an open source program of R. We select 24 protein sequences of Herpes virus obtained from NCBI database. The dataset consists of three types of glycoprotein B, F, and H. Each type has eight herpes virus that infected humans. Based on our simulation using different inflation factor r=1.5, 2, 3 we find a various number of the clusters results. The greater the inflation factor the greater the number of their clusters. Each protein will grouped together in the same type of protein.

CUDASW++ 3.0: accelerating Smith-Waterman protein database search by coupling CPU and GPU SIMD instructions.

PubMed

Liu, Yongchao; Wirawan, Adrianto; Schmidt, Bertil

2013-04-04

The maximal sensitivity for local alignments makes the Smith-Waterman algorithm a popular choice for protein sequence database search based on pairwise alignment. However, the algorithm is compute-intensive due to a quadratic time complexity. Corresponding runtimes are further compounded by the rapid growth of sequence databases. We present CUDASW++ 3.0, a fast Smith-Waterman protein database search algorithm, which couples CPU and GPU SIMD instructions and carries out concurrent CPU and GPU computations. For the CPU computation, this algorithm employs SSE-based vector execution units as accelerators. For the GPU computation, we have investigated for the first time a GPU SIMD parallelization, which employs CUDA PTX SIMD video instructions to gain more data parallelism beyond the SIMT execution model. Moreover, sequence alignment workloads are automatically distributed over CPUs and GPUs based on their respective compute capabilities. Evaluation on the Swiss-Prot database shows that CUDASW++ 3.0 gains a performance improvement over CUDASW++ 2.0 up to 2.9 and 3.2, with a maximum performance of 119.0 and 185.6 GCUPS, on a single-GPU GeForce GTX 680 and a dual-GPU GeForce GTX 690 graphics card, respectively. In addition, our algorithm has demonstrated significant speedups over other top-performing tools: SWIPE and BLAST+. CUDASW++ 3.0 is written in CUDA C++ and PTX assembly languages, targeting GPUs based on the Kepler architecture. This algorithm obtains significant speedups over its predecessor: CUDASW++ 2.0, by benefiting from the use of CPU and GPU SIMD instructions as well as the concurrent execution on CPUs and GPUs. The source code and the simulated data are available at http://cudasw.sourceforge.net.

Application of 2D graphic representation of protein sequence based on Huffman tree method.

PubMed

Qi, Zhao-Hui; Feng, Jun; Qi, Xiao-Qin; Li, Ling

2012-05-01

Based on Huffman tree method, we propose a new 2D graphic representation of protein sequence. This representation can completely avoid loss of information in the transfer of data from a protein sequence to its graphic representation. The method consists of two parts. One is about the 0-1 codes of 20 amino acids by Huffman tree with amino acid frequency. The amino acid frequency is defined as the statistical number of an amino acid in the analyzed protein sequences. The other is about the 2D graphic representation of protein sequence based on the 0-1 codes. Then the applications of the method on ten ND5 genes and seven Escherichia coli strains are presented in detail. The results show that the proposed model may provide us with some new sights to understand the evolution patterns determined from protein sequences and complete genomes. Copyright © 2012 Elsevier Ltd. All rights reserved.

Knowledge-based computational intelligence development for predicting protein secondary structures from sequences.

PubMed

Shen, Hong-Bin; Yi, Dong-Liang; Yao, Li-Xiu; Yang, Jie; Chou, Kuo-Chen

2008-10-01

In the postgenomic age, with the avalanche of protein sequences generated and relatively slow progress in determining their structures by experiments, it is important to develop automated methods to predict the structure of a protein from its sequence. The membrane proteins are a special group in the protein family that accounts for approximately 30% of all proteins; however, solved membrane protein structures only represent less than 1% of known protein structures to date. Although a great success has been achieved for developing computational intelligence techniques to predict secondary structures in both globular and membrane proteins, there is still much challenging work in this regard. In this review article, we firstly summarize the recent progress of automation methodology development in predicting protein secondary structures, especially in membrane proteins; we will then give some future directions in this research field.

Exploring the sequence-structure protein landscape in the glycosyltransferase family

PubMed Central

Zhang, Ziding; Kochhar, Sunil; Grigorov, Martin

2003-01-01

To understand the molecular basis of glycosyltransferases’ (GTFs) catalytic mechanism, extensive structural information is required. Here, fold recognition methods were employed to assign 3D protein shapes (folds) to the currently known GTF sequences, available in public databases such as GenBank and Swissprot. First, GTF sequences were retrieved and classified into clusters, based on sequence similarity only. Intracluster sequence similarity was chosen sufficiently high to ensure that the same fold is found within a given cluster. Then, a representative sequence from each cluster was selected to compose a subset of GTF sequences. The members of this reduced set were processed by three different fold recognition methods: 3D-PSSM, FUGUE, and GeneFold. Finally, the results from different fold recognition methods were analyzed and compared to sequence-similarity search methods (i.e., BLAST and PSI-BLAST). It was established that the folds of about 70% of all currently known GTF sequences can be confidently assigned by fold recognition methods, a value which is higher than the fold identification rate based on sequence comparison alone (48% for BLAST and 64% for PSI-BLAST). The identified folds were submitted to 3D clustering, and we found that most of the GTF sequences adopt the typical GTF A or GTF B folds. Our results indicate a lack of evidence that new GTF folds (i.e., folds other than GTF A and B) exist. Based on cases where fold identification was not possible, we suggest several sequences as the most promising targets for a structural genomics initiative focused on the GTF protein family. PMID:14500887

Sequence composition and environment effects on residue fluctuations in protein structures

NASA Astrophysics Data System (ADS)

Ruvinsky, Anatoly M.; Vakser, Ilya A.

2010-10-01

Structure fluctuations in proteins affect a broad range of cell phenomena, including stability of proteins and their fragments, allosteric transitions, and energy transfer. This study presents a statistical-thermodynamic analysis of relationship between the sequence composition and the distribution of residue fluctuations in protein-protein complexes. A one-node-per-residue elastic network model accounting for the nonhomogeneous protein mass distribution and the interatomic interactions through the renormalized inter-residue potential is developed. Two factors, a protein mass distribution and a residue environment, were found to determine the scale of residue fluctuations. Surface residues undergo larger fluctuations than core residues in agreement with experimental observations. Ranking residues over the normalized scale of fluctuations yields a distinct classification of amino acids into three groups: (i) highly fluctuating-Gly, Ala, Ser, Pro, and Asp, (ii) moderately fluctuating-Thr, Asn, Gln, Lys, Glu, Arg, Val, and Cys, and (iii) weakly fluctuating-Ile, Leu, Met, Phe, Tyr, Trp, and His. The structural instability in proteins possibly relates to the high content of the highly fluctuating residues and a deficiency of the weakly fluctuating residues in irregular secondary structure elements (loops), chameleon sequences, and disordered proteins. Strong correlation between residue fluctuations and the sequence composition of protein loops supports this hypothesis. Comparing fluctuations of binding site residues (interface residues) with other surface residues shows that, on average, the interface is more rigid than the rest of the protein surface and Gly, Ala, Ser, Cys, Leu, and Trp have a propensity to form more stable docking patches on the interface. The findings have broad implications for understanding mechanisms of protein association and stability of protein structures.

Sequence Determinants of Compaction in Intrinsically Disordered Proteins

PubMed Central

Marsh, Joseph A.; Forman-Kay, Julie D.

2010-01-01

Abstract Intrinsically disordered proteins (IDPs), which lack folded structure and are disordered under nondenaturing conditions, have been shown to perform important functions in a large number of cellular processes. These proteins have interesting structural properties that deviate from the random-coil-like behavior exhibited by chemically denatured proteins. In particular, IDPs are often observed to exhibit significant compaction. In this study, we have analyzed the hydrodynamic radii of a number of IDPs to investigate the sequence determinants of this compaction. Net charge and proline content are observed to be strongly correlated with increased hydrodynamic radii, suggesting that these are the dominant contributors to compaction. Hydrophobicity and secondary structure, on the other hand, appear to have negligible effects on compaction, which implies that the determinants of structure in folded and intrinsically disordered proteins are profoundly different. Finally, we observe that polyhistidine tags seem to increase IDP compaction, which suggests that these tags have significant perturbing effects and thus should be removed before any structural characterizations of IDPs. Using the relationships observed in this analysis, we have developed a sequence-based predictor of hydrodynamic radius for IDPs that shows substantial improvement over a simple model based upon chain length alone. PMID:20483348

PROFESS: a PROtein Function, Evolution, Structure and Sequence database

PubMed Central

Triplet, Thomas; Shortridge, Matthew D.; Griep, Mark A.; Stark, Jaime L.; Powers, Robert; Revesz, Peter

2010-01-01

The proliferation of biological databases and the easy access enabled by the Internet is having a beneficial impact on biological sciences and transforming the way research is conducted. There are ∼1100 molecular biology databases dispersed throughout the Internet. To assist in the functional, structural and evolutionary analysis of the abundant number of novel proteins continually identified from whole-genome sequencing, we introduce the PROFESS (PROtein Function, Evolution, Structure and Sequence) database. Our database is designed to be versatile and expandable and will not confine analysis to a pre-existing set of data relationships. A fundamental component of this approach is the development of an intuitive query system that incorporates a variety of similarity functions capable of generating data relationships not conceived during the creation of the database. The utility of PROFESS is demonstrated by the analysis of the structural drift of homologous proteins and the identification of potential pancreatic cancer therapeutic targets based on the observation of protein–protein interaction networks. Database URL: http://cse.unl.edu/∼profess/ PMID:20624718

Integrated analysis of RNA-binding protein complexes using in vitro selection and high-throughput sequencing and sequence specificity landscapes (SEQRS).

PubMed

Lou, Tzu-Fang; Weidmann, Chase A; Killingsworth, Jordan; Tanaka Hall, Traci M; Goldstrohm, Aaron C; Campbell, Zachary T

2017-04-15

RNA-binding proteins (RBPs) collaborate to control virtually every aspect of RNA function. Tremendous progress has been made in the area of global assessment of RBP specificity using next-generation sequencing approaches both in vivo and in vitro. Understanding how protein-protein interactions enable precise combinatorial regulation of RNA remains a significant problem. Addressing this challenge requires tools that can quantitatively determine the specificities of both individual proteins and multimeric complexes in an unbiased and comprehensive way. One approach utilizes in vitro selection, high-throughput sequencing, and sequence-specificity landscapes (SEQRS). We outline a SEQRS experiment focused on obtaining the specificity of a multi-protein complex between Drosophila RBPs Pumilio (Pum) and Nanos (Nos). We discuss the necessary controls in this type of experiment and examine how the resulting data can be complemented with structural and cell-based reporter assays. Additionally, SEQRS data can be integrated with functional genomics data to uncover biological function. Finally, we propose extensions of the technique that will enhance our understanding of multi-protein regulatory complexes assembled onto RNA. Copyright © 2016 Elsevier Inc. All rights reserved.

Fluctuation Flooding Method (FFM) for accelerating conformational transitions of proteins.

PubMed

Harada, Ryuhei; Takano, Yu; Shigeta, Yasuteru

2014-03-28

A powerful conformational sampling method for accelerating structural transitions of proteins, "Fluctuation Flooding Method (FFM)," is proposed. In FFM, cycles of the following steps enhance the transitions: (i) extractions of largely fluctuating snapshots along anisotropic modes obtained from trajectories of multiple independent molecular dynamics (MD) simulations and (ii) conformational re-sampling of the snapshots via re-generations of initial velocities when re-starting MD simulations. In an application to bacteriophage T4 lysozyme, FFM successfully accelerated the open-closed transition with the 6 ns simulation starting solely from the open state, although the 1-μs canonical MD simulation failed to sample such a rare event.

Fluctuation Flooding Method (FFM) for accelerating conformational transitions of proteins

NASA Astrophysics Data System (ADS)

Harada, Ryuhei; Takano, Yu; Shigeta, Yasuteru

2014-03-01

A powerful conformational sampling method for accelerating structural transitions of proteins, "Fluctuation Flooding Method (FFM)," is proposed. In FFM, cycles of the following steps enhance the transitions: (i) extractions of largely fluctuating snapshots along anisotropic modes obtained from trajectories of multiple independent molecular dynamics (MD) simulations and (ii) conformational re-sampling of the snapshots via re-generations of initial velocities when re-starting MD simulations. In an application to bacteriophage T4 lysozyme, FFM successfully accelerated the open-closed transition with the 6 ns simulation starting solely from the open state, although the 1-μs canonical MD simulation failed to sample such a rare event.

Algorithm to find distant repeats in a single protein sequence

PubMed Central

Banerjee, Nirjhar; Sarani, Rangarajan; Ranjani, Chellamuthu Vasuki; Sowmiya, Govindaraj; Michael, Daliah; Balakrishnan, Narayanasamy; Sekar, Kanagaraj

2008-01-01

Distant repeats in protein sequence play an important role in various aspects of protein analysis. A keen analysis of the distant repeats would enable to establish a firm relation of the repeats with respect to their function and three-dimensional structure during the evolutionary process. Further, it enlightens the diversity of duplication during the evolution. To this end, an algorithm has been developed to find all distant repeats in a protein sequence. The scores from Point Accepted Mutation (PAM) matrix has been deployed for the identification of amino acid substitutions while detecting the distant repeats. Due to the biological importance of distant repeats, the proposed algorithm will be of importance to structural biologists, molecular biologists, biochemists and researchers involved in phylogenetic and evolutionary studies. PMID:19052663

Sequence and structural implications of a bovine corneal keratan sulfate proteoglycan core protein. Protein 37B represents bovine lumican and proteins 37A and 25 are unique

NASA Technical Reports Server (NTRS)

Funderburgh, J. L.; Funderburgh, M. L.; Brown, S. J.; Vergnes, J. P.; Hassell, J. R.; Mann, M. M.; Conrad, G. W.; Spooner, B. S. (Principal Investigator)

1993-01-01

Amino acid sequence from tryptic peptides of three different bovine corneal keratan sulfate proteoglycan (KSPG) core proteins (designated 37A, 37B, and 25) showed similarities to the sequence of a chicken KSPG core protein lumican. Bovine lumican cDNA was isolated from a bovine corneal expression library by screening with chicken lumican cDNA. The bovine cDNA codes for a 342-amino acid protein, M(r) 38,712, containing amino acid sequences identified in the 37B KSPG core protein. The bovine lumican is 68% identical to chicken lumican, with an 83% identity excluding the N-terminal 40 amino acids. Location of 6 cysteine and 4 consensus N-glycosylation sites in the bovine sequence were identical to those in chicken lumican. Bovine lumican had about 50% identity to bovine fibromodulin and 20% identity to bovine decorin and biglycan. About two-thirds of the lumican protein consists of a series of 10 amino acid leucine-rich repeats that occur in regions of calculated high beta-hydrophobic moment, suggesting that the leucine-rich repeats contribute to beta-sheet formation in these proteins. Sequences obtained from 37A and 25 core proteins were absent in bovine lumican, thus predicting a unique primary structure and separate mRNA for each of the three bovine KSPG core proteins.

Streamlining workflow and automation to accelerate laboratory scale protein production.

PubMed

Konczal, Jennifer; Gray, Christopher H

2017-05-01

Protein production facilities are often required to produce diverse arrays of proteins for demanding methodologies including crystallography, NMR, ITC and other reagent intensive techniques. It is common for these teams to find themselves a bottleneck in the pipeline of ambitious projects. This pressure to deliver has resulted in the evolution of many novel methods to increase capacity and throughput at all stages in the pipeline for generation of recombinant proteins. This review aims to describe current and emerging options to accelerate the success of protein production in Escherichia coli. We emphasize technologies that have been evaluated and implemented in our laboratory, including innovative molecular biology and expression vectors, small-scale expression screening strategies and the automation of parallel and multidimensional chromatography. Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.

Comparative characterization of random-sequence proteins consisting of 5, 12, and 20 kinds of amino acids

PubMed Central

Tanaka, Junko; Doi, Nobuhide; Takashima, Hideaki; Yanagawa, Hiroshi

2010-01-01

Screening of functional proteins from a random-sequence library has been used to evolve novel proteins in the field of evolutionary protein engineering. However, random-sequence proteins consisting of the 20 natural amino acids tend to aggregate, and the occurrence rate of functional proteins in a random-sequence library is low. From the viewpoint of the origin of life, it has been proposed that primordial proteins consisted of a limited set of amino acids that could have been abundantly formed early during chemical evolution. We have previously found that members of a random-sequence protein library constructed with five primitive amino acids show high solubility (Doi et al., Protein Eng Des Sel 2005;18:279–284). Although such a library is expected to be appropriate for finding functional proteins, the functionality may be limited, because they have no positively charged amino acid. Here, we constructed three libraries of 120-amino acid, random-sequence proteins using alphabets of 5, 12, and 20 amino acids by preselection using mRNA display (to eliminate sequences containing stop codons and frameshifts) and characterized and compared the structural properties of random-sequence proteins arbitrarily chosen from these libraries. We found that random-sequence proteins constructed with the 12-member alphabet (including five primitive amino acids and positively charged amino acids) have higher solubility than those constructed with the 20-member alphabet, though other biophysical properties are very similar in the two libraries. Thus, a library of moderate complexity constructed from 12 amino acids may be a more appropriate resource for functional screening than one constructed from 20 amino acids. PMID:20162614

DROIDS 1.20: A GUI-Based Pipeline for GPU-Accelerated Comparative Protein Dynamics.

PubMed

Babbitt, Gregory A; Mortensen, Jamie S; Coppola, Erin E; Adams, Lily E; Liao, Justin K

2018-03-13

Traditional informatics in comparative genomics work only with static representations of biomolecules (i.e., sequence and structure), thereby ignoring the molecular dynamics (MD) of proteins that define function in the cell. A comparative approach applied to MD would connect this very short timescale process, defined in femtoseconds, to one of the longest in the universe: molecular evolution measured in millions of years. Here, we leverage advances in graphics-processing-unit-accelerated MD simulation software to develop a comparative method of MD analysis and visualization that can be applied to any two homologous Protein Data Bank structures. Our open-source pipeline, DROIDS (Detecting Relative Outlier Impacts in Dynamic Simulations), works in conjunction with existing molecular modeling software to convert any Linux gaming personal computer into a "comparative computational microscope" for observing the biophysical effects of mutations and other chemical changes in proteins. DROIDS implements structural alignment and Benjamini-Hochberg-corrected Kolmogorov-Smirnov statistics to compare nanosecond-scale atom bond fluctuations on the protein backbone, color mapping the significant differences identified in protein MD with single-amino-acid resolution. DROIDS is simple to use, incorporating graphical user interface control for Amber16 MD simulations, cpptraj analysis, and the final statistical and visual representations in R graphics and UCSF Chimera. We demonstrate that DROIDS can be utilized to visually investigate molecular evolution and disease-related functional changes in MD due to genetic mutation and epigenetic modification. DROIDS can also be used to potentially investigate binding interactions of pharmaceuticals, toxins, or other biomolecules in a functional evolutionary context as well. Copyright © 2018 Biophysical Society. Published by Elsevier Inc. All rights reserved.

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

A 3D sequence-independent representation of the protein data bank.

PubMed

Fischer, D; Tsai, C J; Nussinov, R; Wolfson, H

1995-10-01

Here we address the following questions. How many structurally different entries are there in the Protein Data Bank (PDB)? How do the proteins populate the structural universe? To investigate these questions a structurally non-redundant set of representative entries was selected from the PDB. Construction of such a dataset is not trivial: (i) the considerable size of the PDB requires a large number of comparisons (there were more than 3250 structures of protein chains available in May 1994); (ii) the PDB is highly redundant, containing many structurally similar entries, not necessarily with significant sequence homology, and (iii) there is no clear-cut definition of structural similarity. The latter depend on the criteria and methods used. Here, we analyze structural similarity ignoring protein topology. To date, representative sets have been selected either by hand, by sequence comparison techniques which ignore the three-dimensional (3D) structures of the proteins or by using sequence comparisons followed by linear structural comparison (i.e. the topology, or the sequential order of the chains, is enforced in the structural comparison). Here we describe a 3D sequence-independent automated and efficient method to obtain a representative set of protein molecules from the PDB which contains all unique structures and which is structurally non-redundant. The method has two novel features. The first is the use of strictly structural criteria in the selection process without taking into account the sequence information. To this end we employ a fast structural comparison algorithm which requires on average approximately 2 s per pairwise comparison on a workstation. The second novel feature is the iterative application of a heuristic clustering algorithm that greatly reduces the number of comparisons required. We obtain a representative set of 220 chains with resolution better than 3.0 A, or 268 chains including lower resolution entries, NMR entries and models. The

Protein evolution analysis of S-hydroxynitrile lyase by complete sequence design utilizing the INTMSAlign software.

PubMed

Nakano, Shogo; Asano, Yasuhisa

2015-02-03

Development of software and methods for design of complete sequences of functional proteins could contribute to studies of protein engineering and protein evolution. To this end, we developed the INTMSAlign software, and used it to design functional proteins and evaluate their usefulness. The software could assign both consensus and correlation residues of target proteins. We generated three protein sequences with S-selective hydroxynitrile lyase (S-HNL) activity, which we call designed S-HNLs; these proteins folded as efficiently as the native S-HNL. Sequence and biochemical analysis of the designed S-HNLs suggested that accumulation of neutral mutations occurs during the process of S-HNLs evolution from a low-activity form to a high-activity (native) form. Taken together, our results demonstrate that our software and the associated methods could be applied not only to design of complete sequences, but also to predictions of protein evolution, especially within families such as esterases and S-HNLs.

Protein evolution analysis of S-hydroxynitrile lyase by complete sequence design utilizing the INTMSAlign software

NASA Astrophysics Data System (ADS)

Nakano, Shogo; Asano, Yasuhisa

2015-02-01

Development of software and methods for design of complete sequences of functional proteins could contribute to studies of protein engineering and protein evolution. To this end, we developed the INTMSAlign software, and used it to design functional proteins and evaluate their usefulness. The software could assign both consensus and correlation residues of target proteins. We generated three protein sequences with S-selective hydroxynitrile lyase (S-HNL) activity, which we call designed S-HNLs; these proteins folded as efficiently as the native S-HNL. Sequence and biochemical analysis of the designed S-HNLs suggested that accumulation of neutral mutations occurs during the process of S-HNLs evolution from a low-activity form to a high-activity (native) form. Taken together, our results demonstrate that our software and the associated methods could be applied not only to design of complete sequences, but also to predictions of protein evolution, especially within families such as esterases and S-HNLs.

C-Terminal DxD-Containing Sequences within Paramyxovirus Nucleocapsid Proteins Determine Matrix Protein Compatibility and Can Direct Foreign Proteins into Budding Particles

PubMed Central

Ray, Greeshma; Schmitt, Phuong Tieu

2016-01-01

ABSTRACT Paramyxovirus particles are formed by a budding process coordinated by viral matrix (M) proteins. M proteins coalesce at sites underlying infected cell membranes and induce other viral components, including viral glycoproteins and viral ribonucleoprotein complexes (vRNPs), to assemble at these locations from which particles bud. M proteins interact with the nucleocapsid (NP or N) components of vRNPs, and these interactions enable production of infectious, genome-containing virions. For the paramyxoviruses parainfluenza virus 5 (PIV5) and mumps virus, M-NP interaction also contributes to efficient production of virus-like particles (VLPs) in transfected cells. A DLD sequence near the C-terminal end of PIV5 NP protein was previously found to be necessary for M-NP interaction and efficient VLP production. Here, we demonstrate that 15-residue-long, DLD-containing sequences derived from either the PIV5 or Nipah virus nucleocapsid protein C-terminal ends are sufficient to direct packaging of a foreign protein, Renilla luciferase, into budding VLPs. Mumps virus NP protein harbors DWD in place of the DLD sequence found in PIV5 NP protein, and consequently, PIV5 NP protein is incompatible with mumps virus M protein. A single amino acid change converting DLD to DWD within PIV5 NP protein induced compatibility between these proteins and allowed efficient production of mumps VLPs. Our data suggest a model in which paramyxoviruses share an overall common strategy for directing M-NP interactions but with important variations contained within DLD-like sequences that play key roles in defining M/NP protein compatibilities. IMPORTANCE Paramyxoviruses are responsible for a wide range of diseases that affect both humans and animals. Paramyxovirus pathogens include measles virus, mumps virus, human respiratory syncytial virus, and the zoonotic paramyxoviruses Nipah virus and Hendra virus. Infectivity of paramyxovirus particles depends on matrix-nucleocapsid protein

C-Terminal DxD-Containing Sequences within Paramyxovirus Nucleocapsid Proteins Determine Matrix Protein Compatibility and Can Direct Foreign Proteins into Budding Particles.

PubMed

Ray, Greeshma; Schmitt, Phuong Tieu; Schmitt, Anthony P

2016-01-20

Paramyxovirus particles are formed by a budding process coordinated by viral matrix (M) proteins. M proteins coalesce at sites underlying infected cell membranes and induce other viral components, including viral glycoproteins and viral ribonucleoprotein complexes (vRNPs), to assemble at these locations from which particles bud. M proteins interact with the nucleocapsid (NP or N) components of vRNPs, and these interactions enable production of infectious, genome-containing virions. For the paramyxoviruses parainfluenza virus 5 (PIV5) and mumps virus, M-NP interaction also contributes to efficient production of virus-like particles (VLPs) in transfected cells. A DLD sequence near the C-terminal end of PIV5 NP protein was previously found to be necessary for M-NP interaction and efficient VLP production. Here, we demonstrate that 15-residue-long, DLD-containing sequences derived from either the PIV5 or Nipah virus nucleocapsid protein C-terminal ends are sufficient to direct packaging of a foreign protein, Renilla luciferase, into budding VLPs. Mumps virus NP protein harbors DWD in place of the DLD sequence found in PIV5 NP protein, and consequently, PIV5 NP protein is incompatible with mumps virus M protein. A single amino acid change converting DLD to DWD within PIV5 NP protein induced compatibility between these proteins and allowed efficient production of mumps VLPs. Our data suggest a model in which paramyxoviruses share an overall common strategy for directing M-NP interactions but with important variations contained within DLD-like sequences that play key roles in defining M/NP protein compatibilities. Paramyxoviruses are responsible for a wide range of diseases that affect both humans and animals. Paramyxovirus pathogens include measles virus, mumps virus, human respiratory syncytial virus, and the zoonotic paramyxoviruses Nipah virus and Hendra virus. Infectivity of paramyxovirus particles depends on matrix-nucleocapsid protein interactions which enable

Sequence charge decoration dictates coil-globule transition in intrinsically disordered proteins.

PubMed

Firman, Taylor; Ghosh, Kingshuk

2018-03-28

We present an analytical theory to compute conformations of heteropolymers-applicable to describe disordered proteins-as a function of temperature and charge sequence. The theory describes coil-globule transition for a given protein sequence when temperature is varied and has been benchmarked against the all-atom Monte Carlo simulation (using CAMPARI) of intrinsically disordered proteins (IDPs). In addition, the model quantitatively shows how subtle alterations of charge placement in the primary sequence-while maintaining the same charge composition-can lead to significant changes in conformation, even as drastic as a coil (swelled above a purely random coil) to globule (collapsed below a random coil) and vice versa. The theory provides insights on how to control (enhance or suppress) these changes by tuning the temperature (or solution condition) and charge decoration. As an application, we predict the distribution of conformations (at room temperature) of all naturally occurring IDPs in the DisProt database and notice significant size variation even among IDPs with a similar composition of positive and negative charges. Based on this, we provide a new diagram-of-states delineating the sequence-conformation relation for proteins in the DisProt database. Next, we study the effect of post-translational modification, e.g., phosphorylation, on IDP conformations. Modifications as little as two-site phosphorylation can significantly alter the size of an IDP with everything else being constant (temperature, salt concentration, etc.). However, not all possible modification sites have the same effect on protein conformations; there are certain "hot spots" that can cause maximal change in conformation. The location of these "hot spots" in the parent sequence can readily be identified by using a sequence charge decoration metric originally introduced by Sawle and Ghosh. The ability of our model to predict conformations (both expanded and collapsed states) of IDPs at a high

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

Sequence heuristics to encode phase behaviour in intrinsically disordered protein polymers

PubMed Central

Quiroz, Felipe García; Chilkoti, Ashutosh

2015-01-01

Proteins and synthetic polymers that undergo aqueous phase transitions mediate self-assembly in nature and in man-made material systems. Yet little is known about how the phase behaviour of a protein is encoded in its amino acid sequence. Here, by synthesizing intrinsically disordered, repeat proteins to test motifs that we hypothesized would encode phase behaviour, we show that the proteins can be designed to exhibit tunable lower or upper critical solution temperature (LCST and UCST, respectively) transitions in physiological solutions. We also show that mutation of key residues at the repeat level abolishes phase behaviour or encodes an orthogonal transition. Furthermore, we provide heuristics to identify, at the proteome level, proteins that might exhibit phase behaviour and to design novel protein polymers consisting of biologically active peptide repeats that exhibit LCST or UCST transitions. These findings set the foundation for the prediction and encoding of phase behaviour at the sequence level. PMID:26390327

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

Different evolutionary patterns of SNPs between domains and unassigned regions in human protein-coding sequences.

PubMed

Pang, Erli; Wu, Xiaomei; Lin, Kui

2016-06-01

Protein evolution plays an important role in the evolution of each genome. Because of their functional nature, in general, most of their parts or sites are differently constrained selectively, particularly by purifying selection. Most previous studies on protein evolution considered individual proteins in their entirety or compared protein-coding sequences with non-coding sequences. Less attention has been paid to the evolution of different parts within each protein of a given genome. To this end, based on PfamA annotation of all human proteins, each protein sequence can be split into two parts: domains or unassigned regions. Using this rationale, single nucleotide polymorphisms (SNPs) in protein-coding sequences from the 1000 Genomes Project were mapped according to two classifications: SNPs occurring within protein domains and those within unassigned regions. With these classifications, we found: the density of synonymous SNPs within domains is significantly greater than that of synonymous SNPs within unassigned regions; however, the density of non-synonymous SNPs shows the opposite pattern. We also found there are signatures of purifying selection on both the domain and unassigned regions. Furthermore, the selective strength on domains is significantly greater than that on unassigned regions. In addition, among all of the human protein sequences, there are 117 PfamA domains in which no SNPs are found. Our results highlight an important aspect of protein domains and may contribute to our understanding of protein evolution.

Prediction of glutathionylation sites in proteins using minimal sequence information and their experimental validation.

PubMed

Pal, Debojyoti; Sharma, Deepak; Kumar, Mukesh; Sandur, Santosh K

2016-09-01

S-glutathionylation of proteins plays an important role in various biological processes and is known to be protective modification during oxidative stress. Since, experimental detection of S-glutathionylation is labor intensive and time consuming, bioinformatics based approach is a viable alternative. Available methods require relatively longer sequence information, which may prevent prediction if sequence information is incomplete. Here, we present a model to predict glutathionylation sites from pentapeptide sequences. It is based upon differential association of amino acids with glutathionylated and non-glutathionylated cysteines from a database of experimentally verified sequences. This data was used to calculate position dependent F-scores, which measure how a particular amino acid at a particular position may affect the likelihood of glutathionylation event. Glutathionylation-score (G-score), indicating propensity of a sequence to undergo glutathionylation, was calculated using position-dependent F-scores for each amino-acid. Cut-off values were used for prediction. Our model returned an accuracy of 58% with Matthew's correlation-coefficient (MCC) value of 0.165. On an independent dataset, our model outperformed the currently available model, in spite of needing much less sequence information. Pentapeptide motifs having high abundance among glutathionylated proteins were identified. A list of potential glutathionylation hotspot sequences were obtained by assigning G-scores and subsequent Protein-BLAST analysis revealed a total of 254 putative glutathionable proteins, a number of which were already known to be glutathionylated. Our model predicted glutathionylation sites in 93.93% of experimentally verified glutathionylated proteins. Outcome of this study may assist in discovering novel glutathionylation sites and finding candidate proteins for glutathionylation.

Biophysical models of protein evolution: Understanding the patterns of evolutionary sequence divergence

PubMed Central

Echave, Julian; Wilke, Claus O.

2018-01-01

For decades, rates of protein evolution have been interpreted in terms of the vague concept of “functional importance”. Slowly evolving proteins or sites within proteins were assumed to be more functionally important and thus subject to stronger selection pressure. More recently, biophysical models of protein evolution, which combine evolutionary theory with protein biophysics, have completely revolutionized our view of the forces that shape sequence divergence. Slowly evolving proteins have been found to evolve slowly because of selection against toxic misfolding and misinteractions, linking their rate of evolution primarily to their abundance. Similarly, most slowly evolving sites in proteins are not directly involved in function, but mutating them has large impacts on protein structure and stability. Here, we review the studies of the emergent field of biophysical protein evolution that have shaped our current understanding of sequence divergence patterns. We also propose future research directions to develop this nascent field. PMID:28301766

Sequence specificity of single-stranded DNA-binding proteins: a novel DNA microarray approach

PubMed Central

Morgan, Hugh P.; Estibeiro, Peter; Wear, Martin A.; Max, Klaas E.A.; Heinemann, Udo; Cubeddu, Liza; Gallagher, Maurice P.; Sadler, Peter J.; Walkinshaw, Malcolm D.

2007-01-01

We have developed a novel DNA microarray-based approach for identification of the sequence-specificity of single-stranded nucleic-acid-binding proteins (SNABPs). For verification, we have shown that the major cold shock protein (CspB) from Bacillus subtilis binds with high affinity to pyrimidine-rich sequences, with a binding preference for the consensus sequence, 5′-GTCTTTG/T-3′. The sequence was modelled onto the known structure of CspB and a cytosine-binding pocket was identified, which explains the strong preference for a cytosine base at position 3. This microarray method offers a rapid high-throughput approach for determining the specificity and strength of ss DNA–protein interactions. Further screening of this newly emerging family of transcription factors will help provide an insight into their cellular function. PMID:17488853

Dr. Sanger's Apprentice: A Computer-Aided Instruction to Protein Sequencing.

ERIC Educational Resources Information Center

Schmidt, Thomas G.; Place, Allen R.

1985-01-01

Modeled after the program "Mastermind," this program teaches students the art of protein sequencing. The program (written in Turbo Pascal for the IBM PC, requiring 128K, a graphics adapter, and an 8070 mathematics coprocessor) generates a polypeptide whose sequence and length can be user-defined (for practice) or computer-generated (for…

Protein sequence annotation in the genome era: the annotation concept of SWISS-PROT+TREMBL.

PubMed

Apweiler, R; Gateau, A; Contrino, S; Martin, M J; Junker, V; O'Donovan, C; Lang, F; Mitaritonna, N; Kappus, S; Bairoch, A

1997-01-01

SWISS-PROT is a curated protein sequence database which strives to provide a high level of annotation, a minimal level of redundancy and high level of integration with other databases. Ongoing genome sequencing projects have dramatically increased the number of protein sequences to be incorporated into SWISS-PROT. Since we do not want to dilute the quality standards of SWISS-PROT by incorporating sequences without proper sequence analysis and annotation, we cannot speed up the incorporation of new incoming data indefinitely. However, as we also want to make the sequences available as fast as possible, we introduced TREMBL (TRanslation of EMBL nucleotide sequence database), a supplement to SWISS-PROT. TREMBL consists of computer-annotated entries in SWISS-PROT format derived from the translation of all coding sequences (CDS) in the EMBL nucleotide sequence database, except for CDS already included in SWISS-PROT. While TREMBL is already of immense value, its computer-generated annotation does not match the quality of SWISS-PROTs. The main difference is in the protein functional information attached to sequences. With this in mind, we are dedicating substantial effort to develop and apply computer methods to enhance the functional information attached to TREMBL entries.

Pattern similarity study of functional sites in protein sequences: lysozymes and cystatins

PubMed Central

Nakai, Shuryo; Li-Chan, Eunice CY; Dou, Jinglie

2005-01-01

Background Although it is generally agreed that topography is more conserved than sequences, proteins sharing the same fold can have different functions, while there are protein families with low sequence similarity. An alternative method for profile analysis of characteristic conserved positions of the motifs within the 3D structures may be needed for functional annotation of protein sequences. Using the approach of quantitative structure-activity relationships (QSAR), we have proposed a new algorithm for postulating functional mechanisms on the basis of pattern similarity and average of property values of side-chains in segments within sequences. This approach was used to search for functional sites of proteins belonging to the lysozyme and cystatin families. Results Hydrophobicity and β-turn propensity of reference segments with 3–7 residues were used for the homology similarity search (HSS) for active sites. Hydrogen bonding was used as the side-chain property for searching the binding sites of lysozymes. The profiles of similarity constants and average values of these parameters as functions of their positions in the sequences could identify both active and substrate binding sites of the lysozyme of Streptomyces coelicolor, which has been reported as a new fold enzyme (Cellosyl). The same approach was successfully applied to cystatins, especially for postulating the mechanisms of amyloidosis of human cystatin C as well as human lysozyme. Conclusion Pattern similarity and average index values of structure-related properties of side chains in short segments of three residues or longer were, for the first time, successfully applied for predicting functional sites in sequences. This new approach may be applicable to studying functional sites in un-annotated proteins, for which complete 3D structures are not yet available. PMID:15904486

Understanding sequence similarity and framework analysis between centromere proteins using computational biology.

PubMed

Doss, C George Priya; Chakrabarty, Chiranjib; Debajyoti, C; Debottam, S

2014-11-01

Certain mysteries pointing toward their recruitment pathways, cell cycle regulation mechanisms, spindle checkpoint assembly, and chromosome segregation process are considered the centre of attraction in cancer research. In modern times, with the established databases, ranges of computational platforms have provided a platform to examine almost all the physiological and biochemical evidences in disease-associated phenotypes. Using existing computational methods, we have utilized the amino acid residues to understand the similarity within the evolutionary variance of different associated centromere proteins. This study related to sequence similarity, protein-protein networking, co-expression analysis, and evolutionary trajectory of centromere proteins will speed up the understanding about centromere biology and will create a road map for upcoming researchers who are initiating their work of clinical sequencing using centromere proteins.

A method for partitioning the information contained in a protein sequence between its structure and function.

PubMed

Possenti, Andrea; Vendruscolo, Michele; Camilloni, Carlo; Tiana, Guido

2018-05-23

Proteins employ the information stored in the genetic code and translated into their sequences to carry out well-defined functions in the cellular environment. The possibility to encode for such functions is controlled by the balance between the amount of information supplied by the sequence and that left after that the protein has folded into its structure. We study the amount of information necessary to specify the protein structure, providing an estimate that keeps into account the thermodynamic properties of protein folding. We thus show that the information remaining in the protein sequence after encoding for its structure (the 'information gap') is very close to what needed to encode for its function and interactions. Then, by predicting the information gap directly from the protein sequence, we show that it may be possible to use these insights from information theory to discriminate between ordered and disordered proteins, to identify unknown functions, and to optimize artificially-designed protein sequences. This article is protected by copyright. All rights reserved. © 2018 Wiley Periodicals, Inc.

Special AT-rich sequence binding protein 1 promotes tumor growth and metastasis of esophageal squamous cell carcinoma.

PubMed

Ma, Jun; Wu, Kaiming; Zhao, Zhenxian; Miao, Rong; Xu, Zhe

2017-03-01

Esophageal squamous cell carcinoma is one of the most aggressive malignancies worldwide. Special AT-rich sequence binding protein 1 is a nuclear matrix attachment region binding protein which participates in higher order chromatin organization and tissue-specific gene expression. However, the role of special AT-rich sequence binding protein 1 in esophageal squamous cell carcinoma remains unknown. In this study, western blot and quantitative real-time polymerase chain reaction analysis were performed to identify differentially expressed special AT-rich sequence binding protein 1 in a series of esophageal squamous cell carcinoma tissue samples. The effects of special AT-rich sequence binding protein 1 silencing by two short-hairpin RNAs on cell proliferation, migration, and invasion were assessed by the CCK-8 assay and transwell assays in esophageal squamous cell carcinoma in vitro. Special AT-rich sequence binding protein 1 was significantly upregulated in esophageal squamous cell carcinoma tissue samples and cell lines. Silencing of special AT-rich sequence binding protein 1 inhibited the proliferation of KYSE450 and EC9706 cells which have a relatively high level of special AT-rich sequence binding protein 1, and the ability of migration and invasion of KYSE450 and EC9706 cells was distinctly suppressed. Special AT-rich sequence binding protein 1 could be a potential target for the treatment of esophageal squamous cell carcinoma and inhibition of special AT-rich sequence binding protein 1 may provide a new strategy for the prevention of esophageal squamous cell carcinoma invasion and metastasis.

Synthetic signal sequences that enable efficient secretory protein production in the yeast Kluyveromyces marxianus.

PubMed

Yarimizu, Tohru; Nakamura, Mikiko; Hoshida, Hisashi; Akada, Rinji

2015-02-14

Targeting of cellular proteins to the extracellular environment is directed by a secretory signal sequence located at the N-terminus of a secretory protein. These signal sequences usually contain an N-terminal basic amino acid followed by a stretch containing hydrophobic residues, although no consensus signal sequence has been identified. In this study, simple modeling of signal sequences was attempted using Gaussia princeps secretory luciferase (GLuc) in the yeast Kluyveromyces marxianus, which allowed comprehensive recombinant gene construction to substitute synthetic signal sequences. Mutational analysis of the GLuc signal sequence revealed that the GLuc hydrophobic peptide length was lower limit for effective secretion and that the N-terminal basic residue was indispensable. Deletion of the 16th Glu caused enhanced levels of secreted protein, suggesting that this hydrophilic residue defined the boundary of a hydrophobic peptide stretch. Consequently, we redesigned this domain as a repeat of a single hydrophobic amino acid between the N-terminal Lys and C-terminal Glu. Stretches consisting of Phe, Leu, Ile, or Met were effective for secretion but the number of residues affected secretory activity. A stretch containing sixteen consecutive methionine residues (M16) showed the highest activity; the M16 sequence was therefore utilized for the secretory production of human leukemia inhibitory factor protein in yeast, resulting in enhanced secreted protein yield. We present a new concept for the provision of secretory signal sequence ability in the yeast K. marxianus, determined by the number of residues of a single hydrophobic residue located between N-terminal basic and C-terminal acidic amino acid boundaries.

Identification and application of self-binding zipper-like sequences in SARS-CoV spike protein.

PubMed

Zhang, Si Min; Liao, Ying; Neo, Tuan Ling; Lu, Yanning; Liu, Ding Xiang; Vahlne, Anders; Tam, James P

2018-05-22

Self-binding peptides containing zipper-like sequences, such as the Leu/Ile zipper sequence within the coiled coil regions of proteins and the cross-β spine steric zippers within the amyloid-like fibrils, could bind to the protein-of-origin through homophilic sequence-specific zipper motifs. These self-binding sequences represent opportunities for the development of biochemical tools and/or therapeutics. Here, we report on the identification of a putative self-binding β-zipper-forming peptide within the severe acute respiratory syndrome-associated coronavirus spike (S) protein and its application in viral detection. Peptide array scanning of overlapping peptides covering the entire length of S protein identified 34 putative self-binding peptides of six clusters, five of which contained octapeptide core consensus sequences. The Cluster I consensus octapeptide sequence GINITNFR was predicted by the Eisenberg's 3D profile method to have high amyloid-like fibrillation potential through steric β-zipper formation. Peptide C6 containing the Cluster I consensus sequence was shown to oligomerize and form amyloid-like fibrils. Taking advantage of this, C6 was further applied to detect the S protein expression in vitro by fluorescence staining. Meanwhile, the coiled-coil-forming Leu/Ile heptad repeat sequences within the S protein were under-represented during peptide array scanning, in agreement with that long peptide lengths were required to attain high helix-mediated interaction avidity. The data suggest that short β-zipper-like self-binding peptides within the S protein could be identified through combining the peptide scanning and predictive methods, and could be exploited as biochemical detection reagents for viral infection. Copyright © 2018. Published by Elsevier Ltd.

Combining protein sequence, structure, and dynamics: A novel approach for functional evolution analysis of PAS domain superfamily.

PubMed

Dong, Zheng; Zhou, Hongyu; Tao, Peng

2018-02-01

PAS domains are widespread in archaea, bacteria, and eukaryota, and play important roles in various functions. In this study, we aim to explore functional evolutionary relationship among proteins in the PAS domain superfamily in view of the sequence-structure-dynamics-function relationship. We collected protein sequences and crystal structure data from RCSB Protein Data Bank of the PAS domain superfamily belonging to three biological functions (nucleotide binding, photoreceptor activity, and transferase activity). Protein sequences were aligned and then used to select sequence-conserved residues and build phylogenetic tree. Three-dimensional structure alignment was also applied to obtain structure-conserved residues. The protein dynamics were analyzed using elastic network model (ENM) and validated by molecular dynamics (MD) simulation. The result showed that the proteins with same function could be grouped by sequence similarity, and proteins in different functional groups displayed statistically significant difference in their vibrational patterns. Interestingly, in all three functional groups, conserved amino acid residues identified by sequence and structure conservation analysis generally have a lower fluctuation than other residues. In addition, the fluctuation of conserved residues in each biological function group was strongly correlated with the corresponding biological function. This research suggested a direct connection in which the protein sequences were related to various functions through structural dynamics. This is a new attempt to delineate functional evolution of proteins using the integrated information of sequence, structure, and dynamics. © 2017 The Protein Society.

Top-down analysis of protein samples by de novo sequencing techniques.

PubMed

Vyatkina, Kira; Wu, Si; Dekker, Lennard J M; VanDuijn, Martijn M; Liu, Xiaowen; Tolić, Nikola; Luider, Theo M; Paša-Tolić, Ljiljana; Pevzner, Pavel A

2016-09-15

Recent technological advances have made high-resolution mass spectrometers affordable to many laboratories, thus boosting rapid development of top-down mass spectrometry, and implying a need in efficient methods for analyzing this kind of data. We describe a method for analysis of protein samples from top-down tandem mass spectrometry data, which capitalizes on de novo sequencing of fragments of the proteins present in the sample. Our algorithm takes as input a set of de novo amino acid strings derived from the given mass spectra using the recently proposed Twister approach, and combines them into aggregated strings endowed with offsets. The former typically constitute accurate sequence fragments of sufficiently well-represented proteins from the sample being analyzed, while the latter indicate their location in the protein sequence, and also bear information on post-translational modifications and fragmentation patterns. Freely available on the web at http://bioinf.spbau.ru/en/twister vyatkina@spbau.ru or ppevzner@ucsd.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.

Sequence diagrams and the presentation of structural and evolutionary relationships among proteins.

PubMed

Thomas, B R

1975-01-01

Protein sequences mapped on two-dimensional diagrams show characteristic patterns that should be of value in visualising sequence information and in distinguishing simpler structures. A convenient map form for comparative purposes is the alpha-helix diagram with aminoacid distribution analogous to the surface of an alpha-helix oriented so that an alpha-helix structure corresponds on the diagram to a vertical band 3.6 residues wide. The sequence diagram for an alpha-keratin, high-sulphur protein suggests a new form of polypeptide helix based on a repeating unit of five which may be an important component of alpha-keratin fibres.

The LINE-1 DNA sequences in four mammalian orders predict proteins that conserve homologies to retrovirus proteins.

PubMed Central

Fanning, T; Singer, M

1987-01-01

Recent work suggests that one or more members of the highly repeated LINE-1 (L1) DNA family found in all mammals may encode one or more proteins. Here we report the sequence of a portion of an L1 cloned from the domestic cat (Felis catus). These data permit comparison of the L1 sequences in four mammalian orders (Carnivore, Lagomorph, Rodent and Primate) and the comparison supports the suggested coding potential. In two separate, noncontiguous regions in the carboxy terminal half of the proteins predicted from the DNA sequences, there are several strongly conserved segments. In one region, these share homology with known or suspected reverse transcriptases, as described by others in rodents and primates. In the second region, closer to the carboxy terminus, the strongly conserved segments are over 90% homologous among the four orders. One of the latter segments is cysteine rich and resembles the putative metal binding domains of nucleic acid binding proteins, including those of TFIIIA and retroviruses. PMID:3562227

A plasma membrane sucrose-binding protein that mediates sucrose uptake shares structural and sequence similarity with seed storage proteins but remains functionally distinct.

PubMed

Overvoorde, P J; Chao, W S; Grimes, H D

1997-06-20

Photoaffinity labeling of a soybean cotyledon membrane fraction identified a sucrose-binding protein (SBP). Subsequent studies have shown that the SBP is a unique plasma membrane protein that mediates the linear uptake of sucrose in the presence of up to 30 mM external sucrose when ectopically expressed in yeast. Analysis of the SBP-deduced amino acid sequence indicates it lacks sequence similarity with other known transport proteins. Data presented here, however, indicate that the SBP shares significant sequence and structural homology with the vicilin-like seed storage proteins that organize into homotrimers. These similarities include a repeated sequence that forms the basis of the reiterated domain structure characteristic of the vicilin-like protein family. In addition, analytical ultracentrifugation and nonreducing SDS-polyacrylamide gel electrophoresis demonstrate that the SBP appears to be organized into oligomeric complexes with a Mr indicative of the existence of SBP homotrimers and homodimers. The structural similarity shared by the SBP and vicilin-like proteins provides a novel framework to explore the mechanistic basis of SBP-mediated sucrose uptake. Expression of the maize Glb protein (a vicilin-like protein closely related to the SBP) in yeast demonstrates that a closely related vicilin-like protein is unable to mediate sucrose uptake. Thus, despite sequence and structural similarities shared by the SBP and the vicilin-like protein family, the SBP is functionally divergent from other members of this group.

A scalable double-barcode sequencing platform for characterization of dynamic protein-protein interactions.

PubMed

Schlecht, Ulrich; Liu, Zhimin; Blundell, Jamie R; St Onge, Robert P; Levy, Sasha F

2017-05-25

Several large-scale efforts have systematically catalogued protein-protein interactions (PPIs) of a cell in a single environment. However, little is known about how the protein interactome changes across environmental perturbations. Current technologies, which assay one PPI at a time, are too low throughput to make it practical to study protein interactome dynamics. Here, we develop a highly parallel protein-protein interaction sequencing (PPiSeq) platform that uses a novel double barcoding system in conjunction with the dihydrofolate reductase protein-fragment complementation assay in Saccharomyces cerevisiae. PPiSeq detects PPIs at a rate that is on par with current assays and, in contrast with current methods, quantitatively scores PPIs with enough accuracy and sensitivity to detect changes across environments. Both PPI scoring and the bulk of strain construction can be performed with cell pools, making the assay scalable and easily reproduced across environments. PPiSeq is therefore a powerful new tool for large-scale investigations of dynamic PPIs.

Heterologous mitochondrial targeting sequences can deliver functional proteins into mitochondria.

PubMed

Marcus, Dana; Lichtenstein, Michal; Cohen, Natali; Hadad, Rita; Erlich-Hadad, Tal; Greif, Hagar; Lorberboum-Galski, Haya

2016-12-01

Mitochondrial Targeting Sequences (MTSs) are responsible for trafficking nuclear-encoded proteins into mitochondria. Once entering the mitochondria, the MTS is recognized and cleaved off. Some MTSs are long and undergo two-step processing, as in the case of the human frataxin (FXN) protein (80aa), implicated in Friedreich's ataxia (FA). Therefore, we chose the FXN protein to examine whether nuclear-encoded mitochondrial proteins can efficiently be targeted via a heterologous MTS (hMTS) and deliver a functional protein into mitochondria. We examined three hMTSs; that of citrate synthase (cs), lipoamide deydrogenase (LAD) and C6ORF66 (ORF), as classically MTS sequences, known to be removed by one-step processing, to deliver FXN into mitochondria, in the form of fusion proteins. We demonstrate that using hMTSs for delivering FXN results in the production of 4-5-fold larger amounts of the fusion proteins, and at 4-5-fold higher concentrations. Moreover, hMTSs delivered a functional FXN protein into the mitochondria even more efficiently than the native MTSfxn, as evidenced by the rescue of FA patients' cells from oxidative stress; demonstrating a 18%-54% increase in cell survival; and a 13%-33% increase in ATP levels, as compared to the fusion protein carrying the native MTS. One fusion protein with MTScs increased aconitase activity within patients' cells, by 400-fold. The implications form our studies are of vast importance for both basic and translational research of mitochondrial proteins as any mitochondrial protein can be delivered efficiently by an hMTS. Moreover, effective targeting of functional proteins is important for restoration of mitochondrial function and treatment of related disorders. Copyright © 2016 Elsevier Ltd. All rights reserved.

Combining phage display with de novo protein sequencing for reverse engineering of monoclonal antibodies

PubMed Central

Rickert, Keith W.; Grinberg, Luba; Woods, Robert M.; Wilson, Susan; Bowen, Michael A.; Baca, Manuel

2016-01-01

ABSTRACT The enormous diversity created by gene recombination and somatic hypermutation makes de novo protein sequencing of monoclonal antibodies a uniquely challenging problem. Modern mass spectrometry-based sequencing will rarely, if ever, provide a single unambiguous sequence for the variable domains. A more likely outcome is computation of an ensemble of highly similar sequences that can satisfy the experimental data. This outcome can result in the need for empirical testing of many candidate sequences, sometimes iteratively, to identity one which can replicate the activity of the parental antibody. Here we describe an improved approach to antibody protein sequencing by using phage display technology to generate a combinatorial library of sequences that satisfy the mass spectrometry data, and selecting for functional candidates that bind antigen. This approach was used to reverse engineer 2 commercially-obtained monoclonal antibodies against murine CD137. Proteomic data enabled us to assign the majority of the variable domain sequences, with the exception of 3–5% of the sequence located within or adjacent to complementarity-determining regions. To efficiently resolve the sequence in these regions, small phage-displayed libraries were generated and subjected to antigen binding selection. Following enrichment of antigen-binding clones, 2 clones were selected for each antibody and recombinantly expressed as antigen-binding fragments (Fabs). In both cases, the reverse-engineered Fabs exhibited identical antigen binding affinity, within error, as Fabs produced from the commercial IgGs. This combination of proteomic and protein engineering techniques provides a useful approach to simplifying the technically challenging process of reverse engineering monoclonal antibodies from protein material. PMID:26852694

Combining phage display with de novo protein sequencing for reverse engineering of monoclonal antibodies.

PubMed

Rickert, Keith W; Grinberg, Luba; Woods, Robert M; Wilson, Susan; Bowen, Michael A; Baca, Manuel

2016-01-01

The enormous diversity created by gene recombination and somatic hypermutation makes de novo protein sequencing of monoclonal antibodies a uniquely challenging problem. Modern mass spectrometry-based sequencing will rarely, if ever, provide a single unambiguous sequence for the variable domains. A more likely outcome is computation of an ensemble of highly similar sequences that can satisfy the experimental data. This outcome can result in the need for empirical testing of many candidate sequences, sometimes iteratively, to identity one which can replicate the activity of the parental antibody. Here we describe an improved approach to antibody protein sequencing by using phage display technology to generate a combinatorial library of sequences that satisfy the mass spectrometry data, and selecting for functional candidates that bind antigen. This approach was used to reverse engineer 2 commercially-obtained monoclonal antibodies against murine CD137. Proteomic data enabled us to assign the majority of the variable domain sequences, with the exception of 3-5% of the sequence located within or adjacent to complementarity-determining regions. To efficiently resolve the sequence in these regions, small phage-displayed libraries were generated and subjected to antigen binding selection. Following enrichment of antigen-binding clones, 2 clones were selected for each antibody and recombinantly expressed as antigen-binding fragments (Fabs). In both cases, the reverse-engineered Fabs exhibited identical antigen binding affinity, within error, as Fabs produced from the commercial IgGs. This combination of proteomic and protein engineering techniques provides a useful approach to simplifying the technically challenging process of reverse engineering monoclonal antibodies from protein material.

OPAL: prediction of MoRF regions in intrinsically disordered protein sequences.

PubMed

Sharma, Ronesh; Raicar, Gaurav; Tsunoda, Tatsuhiko; Patil, Ashwini; Sharma, Alok

2018-06-01

Intrinsically disordered proteins lack stable 3-dimensional structure and play a crucial role in performing various biological functions. Key to their biological function are the molecular recognition features (MoRFs) located within long disordered regions. Computationally identifying these MoRFs from disordered protein sequences is a challenging task. In this study, we present a new MoRF predictor, OPAL, to identify MoRFs in disordered protein sequences. OPAL utilizes two independent sources of information computed using different component predictors. The scores are processed and combined using common averaging method. The first score is computed using a component MoRF predictor which utilizes composition and sequence similarity of MoRF and non-MoRF regions to detect MoRFs. The second score is calculated using half-sphere exposure (HSE), solvent accessible surface area (ASA) and backbone angle information of the disordered protein sequence, using information from the amino acid properties of flanks surrounding the MoRFs to distinguish MoRF and non-MoRF residues. OPAL is evaluated using test sets that were previously used to evaluate MoRF predictors, MoRFpred, MoRFchibi and MoRFchibi-web. The results demonstrate that OPAL outperforms all the available MoRF predictors and is the most accurate predictor available for MoRF prediction. It is available at http://www.alok-ai-lab.com/tools/opal/. ashwini@hgc.jp or alok.sharma@griffith.edu.au. Supplementary data are available at Bioinformatics online.

EST-PAC a web package for EST annotation and protein sequence prediction

PubMed Central

Strahm, Yvan; Powell, David; Lefèvre, Christophe

2006-01-01

With the decreasing cost of DNA sequencing technology and the vast diversity of biological resources, researchers increasingly face the basic challenge of annotating a larger number of expressed sequences tags (EST) from a variety of species. This typically consists of a series of repetitive tasks, which should be automated and easy to use. The results of these annotation tasks need to be stored and organized in a consistent way. All these operations should be self-installing, platform independent, easy to customize and amenable to using distributed bioinformatics resources available on the Internet. In order to address these issues, we present EST-PAC a web oriented multi-platform software package for expressed sequences tag (EST) annotation. EST-PAC provides a solution for the administration of EST and protein sequence annotations accessible through a web interface. Three aspects of EST annotation are automated: 1) searching local or remote biological databases for sequence similarities using Blast services, 2) predicting protein coding sequence from EST data and, 3) annotating predicted protein sequences with functional domain predictions. In practice, EST-PAC integrates the BLASTALL suite, EST-Scan2 and HMMER in a relational database system accessible through a simple web interface. EST-PAC also takes advantage of the relational database to allow consistent storage, powerful queries of results and, management of the annotation process. The system allows users to customize annotation strategies and provides an open-source data-management environment for research and education in bioinformatics. PMID:17147782

CDSbank: taxonomy-aware extraction, selection, renaming and formatting of protein-coding DNA or amino acid sequences.

PubMed

Hazes, Bart

2014-02-28

Protein-coding DNA sequences and their corresponding amino acid sequences are routinely used to study relationships between sequence, structure, function, and evolution. The rapidly growing size of sequence databases increases the power of such comparative analyses but it makes it more challenging to prepare high quality sequence data sets with control over redundancy, quality, completeness, formatting, and labeling. Software tools for some individual steps in this process exist but manual intervention remains a common and time consuming necessity. CDSbank is a database that stores both the protein-coding DNA sequence (CDS) and amino acid sequence for each protein annotated in Genbank. CDSbank also stores Genbank feature annotation, a flag to indicate incomplete 5' and 3' ends, full taxonomic data, and a heuristic to rank the scientific interest of each species. This rich information allows fully automated data set preparation with a level of sophistication that aims to meet or exceed manual processing. Defaults ensure ease of use for typical scenarios while allowing great flexibility when needed. Access is via a free web server at http://hazeslab.med.ualberta.ca/CDSbank/. CDSbank presents a user-friendly web server to download, filter, format, and name large sequence data sets. Common usage scenarios can be accessed via pre-programmed default choices, while optional sections give full control over the processing pipeline. Particular strengths are: extract protein-coding DNA sequences just as easily as amino acid sequences, full access to taxonomy for labeling and filtering, awareness of incomplete sequences, and the ability to take one protein sequence and extract all synonymous CDS or identical protein sequences in other species. Finally, CDSbank can also create labeled property files to, for instance, annotate or re-label phylogenetic trees.

Mass spectrometry-based protein identification by integrating de novo sequencing with database searching.

PubMed

Wang, Penghao; Wilson, Susan R

2013-01-01

Mass spectrometry-based protein identification is a very challenging task. The main identification approaches include de novo sequencing and database searching. Both approaches have shortcomings, so an integrative approach has been developed. The integrative approach firstly infers partial peptide sequences, known as tags, directly from tandem spectra through de novo sequencing, and then puts these sequences into a database search to see if a close peptide match can be found. However the current implementation of this integrative approach has several limitations. Firstly, simplistic de novo sequencing is applied and only very short sequence tags are used. Secondly, most integrative methods apply an algorithm similar to BLAST to search for exact sequence matches and do not accommodate sequence errors well. Thirdly, by applying these methods the integrated de novo sequencing makes a limited contribution to the scoring model which is still largely based on database searching. We have developed a new integrative protein identification method which can integrate de novo sequencing more efficiently into database searching. Evaluated on large real datasets, our method outperforms popular identification methods.

Amino acid sequences of ribosomal proteins S11 from Bacillus stearothermophilus and S19 from Halobacterium marismortui. Comparison of the ribosomal protein S11 family.

PubMed

Kimura, M; Kimura, J; Hatakeyama, T

1988-11-21

The complete amino acid sequences of ribosomal proteins S11 from the Gram-positive eubacterium Bacillus stearothermophilus and of S19 from the archaebacterium Halobacterium marismortui have been determined. A search for homologous sequences of these proteins revealed that they belong to the ribosomal protein S11 family. Homologous proteins have previously been sequenced from Escherichia coli as well as from chloroplast, yeast and mammalian ribosomes. A pairwise comparison of the amino acid sequences showed that Bacillus protein S11 shares 68% identical residues with S11 from Escherichia coli and a slightly lower homology (52%) with the homologous chloroplast protein. The halophilic protein S19 is more related to the eukaryotic (45-49%) than to the eubacterial counterparts (35%).

Efficient use of unlabeled data for protein sequence classification: a comparative study

PubMed Central

Kuksa, Pavel; Huang, Pai-Hsi; Pavlovic, Vladimir

2009-01-01

Background Recent studies in computational primary protein sequence analysis have leveraged the power of unlabeled data. For example, predictive models based on string kernels trained on sequences known to belong to particular folds or superfamilies, the so-called labeled data set, can attain significantly improved accuracy if this data is supplemented with protein sequences that lack any class tags–the unlabeled data. In this study, we present a principled and biologically motivated computational framework that more effectively exploits the unlabeled data by only using the sequence regions that are more likely to be biologically relevant for better prediction accuracy. As overly-represented sequences in large uncurated databases may bias the estimation of computational models that rely on unlabeled data, we also propose a method to remove this bias and improve performance of the resulting classifiers. Results Combined with state-of-the-art string kernels, our proposed computational framework achieves very accurate semi-supervised protein remote fold and homology detection on three large unlabeled databases. It outperforms current state-of-the-art methods and exhibits significant reduction in running time. Conclusion The unlabeled sequences used under the semi-supervised setting resemble the unpolished gemstones; when used as-is, they may carry unnecessary features and hence compromise the classification accuracy but once cut and polished, they improve the accuracy of the classifiers considerably. PMID:19426450

Efficient use of unlabeled data for protein sequence classification: a comparative study.

PubMed

Kuksa, Pavel; Huang, Pai-Hsi; Pavlovic, Vladimir

2009-04-29

Recent studies in computational primary protein sequence analysis have leveraged the power of unlabeled data. For example, predictive models based on string kernels trained on sequences known to belong to particular folds or superfamilies, the so-called labeled data set, can attain significantly improved accuracy if this data is supplemented with protein sequences that lack any class tags-the unlabeled data. In this study, we present a principled and biologically motivated computational framework that more effectively exploits the unlabeled data by only using the sequence regions that are more likely to be biologically relevant for better prediction accuracy. As overly-represented sequences in large uncurated databases may bias the estimation of computational models that rely on unlabeled data, we also propose a method to remove this bias and improve performance of the resulting classifiers. Combined with state-of-the-art string kernels, our proposed computational framework achieves very accurate semi-supervised protein remote fold and homology detection on three large unlabeled databases. It outperforms current state-of-the-art methods and exhibits significant reduction in running time. The unlabeled sequences used under the semi-supervised setting resemble the unpolished gemstones; when used as-is, they may carry unnecessary features and hence compromise the classification accuracy but once cut and polished, they improve the accuracy of the classifiers considerably.

TRDistiller: a rapid filter for enrichment of sequence datasets with proteins containing tandem repeats.

PubMed

Richard, François D; Kajava, Andrey V

2014-06-01

The dramatic growth of sequencing data evokes an urgent need to improve bioinformatics tools for large-scale proteome analysis. Over the last two decades, the foremost efforts of computer scientists were devoted to proteins with aperiodic sequences having globular 3D structures. However, a large portion of proteins contain periodic sequences representing arrays of repeats that are directly adjacent to each other (so called tandem repeats or TRs). These proteins frequently fold into elongated fibrous structures carrying different fundamental functions. Algorithms specific to the analysis of these regions are urgently required since the conventional approaches developed for globular domains have had limited success when applied to the TR regions. The protein TRs are frequently not perfect, containing a number of mutations, and some of them cannot be easily identified. To detect such "hidden" repeats several algorithms have been developed. However, the most sensitive among them are time-consuming and, therefore, inappropriate for large scale proteome analysis. To speed up the TR detection we developed a rapid filter that is based on the comparison of composition and order of short strings in the adjacent sequence motifs. Tests show that our filter discards up to 22.5% of proteins which are known to be without TRs while keeping almost all (99.2%) TR-containing sequences. Thus, we are able to decrease the size of the initial sequence dataset enriching it with TR-containing proteins which allows a faster subsequent TR detection by other methods. The program is available upon request. Copyright © 2014 Elsevier Inc. 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

Sequence-Based Prediction of RNA-Binding Residues in Proteins.

PubMed

Walia, Rasna R; El-Manzalawy, Yasser; Honavar, Vasant G; Dobbs, Drena

2017-01-01

Identifying individual residues in the interfaces of protein-RNA complexes is important for understanding the molecular determinants of protein-RNA recognition and has many potential applications. Recent technical advances have led to several high-throughput experimental methods for identifying partners in protein-RNA complexes, but determining RNA-binding residues in proteins is still expensive and time-consuming. This chapter focuses on available computational methods for identifying which amino acids in an RNA-binding protein participate directly in contacting RNA. Step-by-step protocols for using three different web-based servers to predict RNA-binding residues are described. In addition, currently available web servers and software tools for predicting RNA-binding sites, as well as databases that contain valuable information about known protein-RNA complexes, RNA-binding motifs in proteins, and protein-binding recognition sites in RNA are provided. We emphasize sequence-based methods that can reliably identify interfacial residues without the requirement for structural information regarding either the RNA-binding protein or its RNA partner.

Nuclear accumulation of epidermal growth factor receptor and acceleration of G1/S stage by Epstein-Barr-encoded oncoprotein latent membrane protein 1

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

Tao Yongguang; Song Xing; Deng Xiyun

Epstein-Barr virus (EBV)-encoded latent membrane protein 1 (LMP1) is considered to be the major oncogenic protein of EBV-encoded proteins and has always been the core of the oncogenic mechanism of EBV. Advanced studies on nuclear translocation of the epidermal growth factor receptor (EGFR) family have greatly improved our knowledge of the biological function of cell surface receptors. In this study, we used the Tet-on LMP1 HNE2 cell line as a cell model, which is a dual-stable LMP1-integrated nasopharyngeal carcinoma (NPC) cell line and the expression of LMP1 which could be regulated by the Tet system. We found that LMP1 couldmore » regulate the nuclear accumulation of EGFR in a dose-dependent manner quantitatively and qualitatively. We also demonstrated that the nuclear localization sequence of EGFR played some roles in the location of the protein within the nucleus under LMP1 regulation and EGFR in the nucleus could bind to the promoters of cyclinD1 and cyclinE, respectively. We further demonstrated that EGFR is involved in the acceleration of the G1/S phase transition by LMP1 through binding to cyclinD1 and cyclinE directly. These findings provided a novel view that the acceleration of LMP1 on the G1/S transition via the nuclear accumulation of EGFR was critical in the process of nasopharyngeal carcinoma.« less

STAT1:DNA sequence-dependent binding modulation by phosphorylation, protein:protein interactions and small-molecule inhibition

PubMed Central

Bonham, Andrew J.; Wenta, Nikola; Osslund, Leah M.; Prussin, Aaron J.; Vinkemeier, Uwe; Reich, Norbert O.

2013-01-01

The DNA-binding specificity and affinity of the dimeric human transcription factor (TF) STAT1, were assessed by total internal reflectance fluorescence protein-binding microarrays (TIRF-PBM) to evaluate the effects of protein phosphorylation, higher-order polymerization and small-molecule inhibition. Active, phosphorylated STAT1 showed binding preferences consistent with prior characterization, whereas unphosphorylated STAT1 showed a weak-binding preference for one-half of the GAS consensus site, consistent with recent models of STAT1 structure and function in response to phosphorylation. This altered-binding preference was further tested by use of the inhibitor LLL3, which we show to disrupt STAT1 binding in a sequence-dependent fashion. To determine if this sequence-dependence is specific to STAT1 and not a general feature of human TF biology, the TF Myc/Max was analysed and tested with the inhibitor Mycro3. Myc/Max inhibition by Mycro3 is sequence independent, suggesting that the sequence-dependent inhibition of STAT1 may be specific to this system and a useful target for future inhibitor design. PMID:23180800

Characterization of tannase protein sequences of bacteria and fungi: an in silico study.

PubMed

Banerjee, Amrita; Jana, Arijit; Pati, Bikash R; Mondal, Keshab C; Das Mohapatra, Pradeep K

2012-04-01

The tannase protein sequences of 149 bacteria and 36 fungi were retrieved from NCBI database. Among them only 77 bacterial and 31 fungal tannase sequences were taken which have different amino acid compositions. These sequences were analysed for different physical and chemical properties, superfamily search, multiple sequence alignment, phylogenetic tree construction and motif finding to find out the functional motif and the evolutionary relationship among them. The superfamily search for these tannase exposed the occurrence of proline iminopeptidase-like, biotin biosynthesis protein BioH, O-acetyltransferase, carboxylesterase/thioesterase 1, carbon-carbon bond hydrolase, haloperoxidase, prolyl oligopeptidase, C-terminal domain and mycobacterial antigens families and alpha/beta hydrolase superfamily. Some bacterial and fungal sequence showed similarity with different families individually. The multiple sequence alignment of these tannase protein sequences showed conserved regions at different stretches with maximum homology from amino acid residues 389-469 and 482-523 which could be used for designing degenerate primers or probes specific for tannase producing bacterial and fungal species. Phylogenetic tree showed two different clusters; one has only bacteria and another have both fungi and bacteria showing some relationship between these different genera. Although in second cluster near about all fungal species were found together in a corner which indicates the sequence level similarity among fungal genera. The distributions of fourteen motifs analysis revealed Motif 1 with a signature amino acid sequence of 29 amino acids, i.e. GCSTGGREALKQAQRWPHDYDGIIANNPA, was uniformly observed in 83.3 % of studied tannase sequences representing its participation with the structure and enzymatic function.

Accelerating Information Retrieval from Profile Hidden Markov Model Databases.

PubMed

Tamimi, Ahmad; Ashhab, Yaqoub; Tamimi, Hashem

2016-01-01

Profile Hidden Markov Model (Profile-HMM) is an efficient statistical approach to represent protein families. Currently, several databases maintain valuable protein sequence information as profile-HMMs. There is an increasing interest to improve the efficiency of searching Profile-HMM databases to detect sequence-profile or profile-profile homology. However, most efforts to enhance searching efficiency have been focusing on improving the alignment algorithms. Although the performance of these algorithms is fairly acceptable, the growing size of these databases, as well as the increasing demand for using batch query searching approach, are strong motivations that call for further enhancement of information retrieval from profile-HMM databases. This work presents a heuristic method to accelerate the current profile-HMM homology searching approaches. The method works by cluster-based remodeling of the database to reduce the search space, rather than focusing on the alignment algorithms. Using different clustering techniques, 4284 TIGRFAMs profiles were clustered based on their similarities. A representative for each cluster was assigned. To enhance sensitivity, we proposed an extended step that allows overlapping among clusters. A validation benchmark of 6000 randomly selected protein sequences was used to query the clustered profiles. To evaluate the efficiency of our approach, speed and recall values were measured and compared with the sequential search approach. Using hierarchical, k-means, and connected component clustering techniques followed by the extended overlapping step, we obtained an average reduction in time of 41%, and an average recall of 96%. Our results demonstrate that representation of profile-HMMs using a clustering-based approach can significantly accelerate data retrieval from profile-HMM databases.

DNA Multiple Sequence Alignment Guided by Protein Domains: The MSA-PAD 2.0 Method.

PubMed

Balech, Bachir; Monaco, Alfonso; Perniola, Michele; Santamaria, Monica; Donvito, Giacinto; Vicario, Saverio; Maggi, Giorgio; Pesole, Graziano

2018-01-01

Multiple sequence alignment (MSA) is a fundamental component in many DNA sequence analyses including metagenomics studies and phylogeny inference. When guided by protein profiles, DNA multiple alignments assume a higher precision and robustness. Here we present details of the use of the upgraded version of MSA-PAD (2.0), which is a DNA multiple sequence alignment framework able to align DNA sequences coding for single/multiple protein domains guided by PFAM or user-defined annotations. MSA-PAD has two alignment strategies, called "Gene" and "Genome," accounting for coding domains order and genomic rearrangements, respectively. Novel options were added to the present version, where the MSA can be guided by protein profiles provided by the user. This allows MSA-PAD 2.0 to run faster and to add custom protein profiles sometimes not present in PFAM database according to the user's interest. MSA-PAD 2.0 is currently freely available as a Web application at https://recasgateway.cloud.ba.infn.it/ .

Automated sequence-specific protein NMR assignment using the memetic algorithm MATCH.

PubMed

Volk, Jochen; Herrmann, Torsten; Wüthrich, Kurt

2008-07-01

MATCH (Memetic Algorithm and Combinatorial Optimization Heuristics) is a new memetic algorithm for automated sequence-specific polypeptide backbone NMR assignment of proteins. MATCH employs local optimization for tracing partial sequence-specific assignments within a global, population-based search environment, where the simultaneous application of local and global optimization heuristics guarantees high efficiency and robustness. MATCH thus makes combined use of the two predominant concepts in use for automated NMR assignment of proteins. Dynamic transition and inherent mutation are new techniques that enable automatic adaptation to variable quality of the experimental input data. The concept of dynamic transition is incorporated in all major building blocks of the algorithm, where it enables switching between local and global optimization heuristics at any time during the assignment process. Inherent mutation restricts the intrinsically required randomness of the evolutionary algorithm to those regions of the conformation space that are compatible with the experimental input data. Using intact and artificially deteriorated APSY-NMR input data of proteins, MATCH performed sequence-specific resonance assignment with high efficiency and robustness.

An Internal Signal Sequence Directs Intramembrane Proteolysis of a Cellular Immunoglobulin Domain Protein*S⃞

PubMed Central

Robakis, Thalia; Bak, Beata; Lin, Shu-huei; Bernard, Daniel J.; Scheiffele, Peter

2008-01-01

Precursor proteolysis is a crucial mechanism for regulating protein structure and function. Signal peptidase (SP) is an enzyme with a well defined role in cleaving N-terminal signal sequences but no demonstrated function in the proteolysis of cellular precursor proteins. We provide evidence that SP mediates intraprotein cleavage of IgSF1, a large cellular Ig domain protein that is processed into two separate Ig domain proteins. In addition, our results suggest the involvement of signal peptide peptidase (SPP), an intramembrane protease, which acts on substrates that have been previously cleaved by SP. We show that IgSF1 is processed through sequential proteolysis by SP and SPP. Cleavage is directed by an internal signal sequence and generates two separate Ig domain proteins from a polytopic precursor. Our findings suggest that SP and SPP function are not restricted to N-terminal signal sequence cleavage but also contribute to the processing of cellular transmembrane proteins. PMID:18981173

Progressive structure-based alignment of homologous proteins: Adopting sequence comparison strategies.

PubMed

Joseph, Agnel Praveen; Srinivasan, Narayanaswamy; de Brevern, Alexandre G

2012-09-01

Comparison of multiple protein structures has a broad range of applications in the analysis of protein structure, function and evolution. Multiple structure alignment tools (MSTAs) are necessary to obtain a simultaneous comparison of a family of related folds. In this study, we have developed a method for multiple structure comparison largely based on sequence alignment techniques. A widely used Structural Alphabet named Protein Blocks (PBs) was used to transform the information on 3D protein backbone conformation as a 1D sequence string. A progressive alignment strategy similar to CLUSTALW was adopted for multiple PB sequence alignment (mulPBA). Highly similar stretches identified by the pairwise alignments are given higher weights during the alignment. The residue equivalences from PB based alignments are used to obtain a three dimensional fit of the structures followed by an iterative refinement of the structural superposition. Systematic comparisons using benchmark datasets of MSTAs underlines that the alignment quality is better than MULTIPROT, MUSTANG and the alignments in HOMSTRAD, in more than 85% of the cases. Comparison with other rigid-body and flexible MSTAs also indicate that mulPBA alignments are superior to most of the rigid-body MSTAs and highly comparable to the flexible alignment methods. Copyright © 2012 Elsevier Masson SAS. All rights reserved.

Novel numerical and graphical representation of DNA sequences and proteins.

PubMed

Randić, M; Novic, M; Vikić-Topić, D; Plavsić, D

2006-12-01

We have introduced novel numerical and graphical representations of DNA, which offer a simple and unique characterization of DNA sequences. The numerical representation of a DNA sequence is given as a sequence of real numbers derived from a unique graphical representation of the standard genetic code. There is no loss of information on the primary structure of a DNA sequence associated with this numerical representation. The novel representations are illustrated with the coding sequences of the first exon of beta-globin gene of half a dozen species in addition to human. The method can be extended to proteins as is exemplified by humanin, a 24-aa peptide that has recently been identified as a specific inhibitor of neuronal cell death induced by familial Alzheimer's disease mutant genes.

DNA-binding proteins from marine bacteria expand the known sequence diversity of TALE-like repeats

PubMed Central

de Lange, Orlando; Wolf, Christina; Thiel, Philipp; Krüger, Jens; Kleusch, Christian; Kohlbacher, Oliver; Lahaye, Thomas

2015-01-01

Transcription Activator-Like Effectors (TALEs) of Xanthomonas bacteria are programmable DNA binding proteins with unprecedented target specificity. Comparative studies into TALE repeat structure and function are hindered by the limited sequence variation among TALE repeats. More sequence-diverse TALE-like proteins are known from Ralstonia solanacearum (RipTALs) and Burkholderia rhizoxinica (Bats), but RipTAL and Bat repeats are conserved with those of TALEs around the DNA-binding residue. We study two novel marine-organism TALE-like proteins (MOrTL1 and MOrTL2), the first to date of non-terrestrial origin. We have assessed their DNA-binding properties and modelled repeat structures. We found that repeats from these proteins mediate sequence specific DNA binding conforming to the TALE code, despite low sequence similarity to TALE repeats, and with novel residues around the BSR. However, MOrTL1 repeats show greater sequence discriminating power than MOrTL2 repeats. Sequence alignments show that there are only three residues conserved between repeats of all TALE-like proteins including the two new additions. This conserved motif could prove useful as an identifier for future TALE-likes. Additionally, comparing MOrTL repeats with those of other TALE-likes suggests a common evolutionary origin for the TALEs, RipTALs and Bats. PMID:26481363

The N-terminal sequence of ribosomal protein L10 from the archaebacterium Halobacterium marismortui and its relationship to eubacterial protein L6 and other ribosomal proteins.

PubMed

Dijk, J; van den Broek, R; Nasiulas, G; Beck, A; Reinhardt, R; Wittmann-Liebold, B

1987-08-01

The amino-terminal sequence of ribosomal protein L10 from Halobacterium marismortui has been determined up to residue 54, using both a liquid- and a gas-phase sequenator. The two sequences are in good agreement. The protein is clearly homologous to protein HcuL10 from the related strain Halobacterium cutirubrum. Furthermore, a weaker but distinct homology to ribosomal protein L6 from Escherichia coli and Bacillus stearothermophilus can be detected. In addition to 7 identical amino acids in the first 36 residues in all four sequences a number of conservative replacements occurs, of mainly hydrophobic amino acids. In this common region the pattern of conserved amino acids suggests the presence of a beta-alpha fold as it occurs in ribosomal proteins L12 and L30. Furthermore, several potential cases of homology to other ribosomal components of the three ur-kingdoms have been found.

The bioinformatics of nucleotide sequence coding for proteins requiring metal coenzymes and proteins embedded with metals

NASA Astrophysics Data System (ADS)

Tremberger, G.; Dehipawala, Sunil; Cheung, E.; Holden, T.; Sullivan, R.; Nguyen, A.; Lieberman, D.; Cheung, T.

2015-09-01

All metallo-proteins need post-translation metal incorporation. In fact, the isotope ratio of Fe, Cu, and Zn in physiology and oncology have emerged as an important tool. The nickel containing F430 is the prosthetic group of the enzyme methyl coenzyme M reductase which catalyzes the release of methane in the final step of methano-genesis, a prime energy metabolism candidate for life exploration space mission in the solar system. The 3.5 Gyr early life sulfite reductase as a life switch energy metabolism had Fe-Mo clusters. The nitrogenase for nitrogen fixation 3 billion years ago had Mo. The early life arsenite oxidase needed for anoxygenic photosynthesis energy metabolism 2.8 billion years ago had Mo and Fe. The selection pressure in metal incorporation inside a protein would be quantifiable in terms of the related nucleotide sequence complexity with fractal dimension and entropy values. Simulation model showed that the studied metal-required energy metabolism sequences had at least ten times more selection pressure relatively in comparison to the horizontal transferred sequences in Mealybug, guided by the outcome histogram of the correlation R-sq values. The metal energy metabolism sequence group was compared to the circadian clock KaiC sequence group using magnesium atomic level bond shifting mechanism in the protein, and the simulation model would suggest a much higher selection pressure for the energy life switch sequence group. The possibility of using Kepler 444 as an example of ancient life in Galaxy with the associated exoplanets has been proposed and is further discussed in this report. Examples of arsenic metal bonding shift probed by Synchrotron-based X-ray spectroscopy data and Zn controlled FOXP2 regulated pathways in human and chimp brain studied tissue samples are studied in relationship to the sequence bioinformatics. The analysis results suggest that relatively large metal bonding shift amount is associated with low probability correlation R

A theoretical method to compute sequence dependent configurational properties in charged polymers and proteins.

PubMed

Sawle, Lucas; Ghosh, Kingshuk

2015-08-28

A general formalism to compute configurational properties of proteins and other heteropolymers with an arbitrary sequence of charges and non-uniform excluded volume interaction is presented. A variational approach is utilized to predict average distance between any two monomers in the chain. The presented analytical model, for the first time, explicitly incorporates the role of sequence charge distribution to determine relative sizes between two sequences that vary not only in total charge composition but also in charge decoration (even when charge composition is fixed). Furthermore, the formalism is general enough to allow variation in excluded volume interactions between two monomers. Model predictions are benchmarked against the all-atom Monte Carlo studies of Das and Pappu [Proc. Natl. Acad. Sci. U. S. A. 110, 13392 (2013)] for 30 different synthetic sequences of polyampholytes. These sequences possess an equal number of glutamic acid (E) and lysine (K) residues but differ in the patterning within the sequence. Without any fit parameter, the model captures the strong sequence dependence of the simulated values of the radius of gyration with a correlation coefficient of R(2) = 0.9. The model is then applied to real proteins to compare the unfolded state dimensions of 540 orthologous pairs of thermophilic and mesophilic proteins. The excluded volume parameters are assumed similar under denatured conditions, and only electrostatic effects encoded in the sequence are accounted for. With these assumptions, thermophilic proteins are found-with high statistical significance-to have more compact disordered ensemble compared to their mesophilic counterparts. The method presented here, due to its analytical nature, is capable of making such high throughput analysis of multiple proteins and will have broad applications in proteomic studies as well as in other heteropolymeric systems.

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

Detection and sequence/structure mapping of biophysical constraints to protein variation in saturated mutational libraries and protein sequence alignments with a dedicated server.

PubMed

Abriata, Luciano A; Bovigny, Christophe; Dal Peraro, Matteo

2016-06-17

Protein variability can now be studied by measuring high-resolution tolerance-to-substitution maps and fitness landscapes in saturated mutational libraries. But these rich and expensive datasets are typically interpreted coarsely, restricting detailed analyses to positions of extremely high or low variability or dubbed important beforehand based on existing knowledge about active sites, interaction surfaces, (de)stabilizing mutations, etc. Our new webserver PsychoProt (freely available without registration at http://psychoprot.epfl.ch or at http://lucianoabriata.altervista.org/psychoprot/index.html ) helps to detect, quantify, and sequence/structure map the biophysical and biochemical traits that shape amino acid preferences throughout a protein as determined by deep-sequencing of saturated mutational libraries or from large alignments of naturally occurring variants. We exemplify how PsychoProt helps to (i) unveil protein structure-function relationships from experiments and from alignments that are consistent with structures according to coevolution analysis, (ii) recall global information about structural and functional features and identify hitherto unknown constraints to variation in alignments, and (iii) point at different sources of variation among related experimental datasets or between experimental and alignment-based data. Remarkably, metabolic costs of the amino acids pose strong constraints to variability at protein surfaces in nature but not in the laboratory. This and other differences call for caution when extrapolating results from in vitro experiments to natural scenarios in, for example, studies of protein evolution. We show through examples how PsychoProt can be a useful tool for the broad communities of structural biology and molecular evolution, particularly for studies about protein modeling, evolution and design.

Global investigation of protein-protein interactions in yeast Saccharomyces cerevisiae using re-occurring short polypeptide sequences.

PubMed

Pitre, S; North, C; Alamgir, M; Jessulat, M; Chan, A; Luo, X; Green, J R; Dumontier, M; Dehne, F; Golshani, A

2008-08-01

Protein-protein interaction (PPI) maps provide insight into cellular biology and have received considerable attention in the post-genomic era. While large-scale experimental approaches have generated large collections of experimentally determined PPIs, technical limitations preclude certain PPIs from detection. Recently, we demonstrated that yeast PPIs can be computationally predicted using re-occurring short polypeptide sequences between known interacting protein pairs. However, the computational requirements and low specificity made this method unsuitable for large-scale investigations. Here, we report an improved approach, which exhibits a specificity of approximately 99.95% and executes 16,000 times faster. Importantly, we report the first all-to-all sequence-based computational screen of PPIs in yeast, Saccharomyces cerevisiae in which we identify 29,589 high confidence interactions of approximately 2 x 10(7) possible pairs. Of these, 14,438 PPIs have not been previously reported and may represent novel interactions. In particular, these results reveal a richer set of membrane protein interactions, not readily amenable to experimental investigations. From the novel PPIs, a novel putative protein complex comprised largely of membrane proteins was revealed. In addition, two novel gene functions were predicted and experimentally confirmed to affect the efficiency of non-homologous end-joining, providing further support for the usefulness of the identified PPIs in biological investigations.

Tardigrade workbench: comparing stress-related proteins, sequence-similar and functional protein clusters as well as RNA elements in tardigrades.

PubMed

Förster, Frank; Liang, Chunguang; Shkumatov, Alexander; Beisser, Daniela; Engelmann, Julia C; Schnölzer, Martina; Frohme, Marcus; Müller, Tobias; Schill, Ralph O; Dandekar, Thomas

2009-10-12

Tardigrades represent an animal phylum with extraordinary resistance to environmental stress. To gain insights into their stress-specific adaptation potential, major clusters of related and similar proteins are identified, as well as specific functional clusters delineated comparing all tardigrades and individual species (Milnesium tardigradum, Hypsibius dujardini, Echiniscus testudo, Tulinus stephaniae, Richtersius coronifer) and functional elements in tardigrade mRNAs are analysed. We find that 39.3% of the total sequences clustered in 58 clusters of more than 20 proteins. Among these are ten tardigrade specific as well as a number of stress-specific protein clusters. Tardigrade-specific functional adaptations include strong protein, DNA- and redox protection, maintenance and protein recycling. Specific regulatory elements regulate tardigrade mRNA stability such as lox P DICE elements whereas 14 other RNA elements of higher eukaryotes are not found. Further features of tardigrade specific adaption are rapidly identified by sequence and/or pattern search on the web-tool tardigrade analyzer http://waterbear.bioapps.biozentrum.uni-wuerzburg.de. The work-bench offers nucleotide pattern analysis for promotor and regulatory element detection (tardigrade specific; nrdb) as well as rapid COG search for function assignments including species-specific repositories of all analysed data. Different protein clusters and regulatory elements implicated in tardigrade stress adaptations are analysed including unpublished tardigrade sequences.

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.

Preparation of protein samples for mass spectrometry and N-terminal sequencing.

PubMed

Glenn, Gary

2014-01-01

The preparation of protein samples for mass spectrometry and N-terminal sequencing is a key step in successfully identifying proteins. Mass spectrometry is a very sensitive technique, and as such, samples must be prepared carefully since they can be subject to contamination of the sample (e.g., due to incomplete subcellular fractionation or purification of a multiprotein complex), overwhelming of the sample by highly abundant proteins, and contamination from skin or hair (keratin can be a very common hit). One goal of sample preparation for mass spec is to reduce the complexity of the sample - in the example presented here, mitochondria are purified, solubilized, and fractionated by sucrose density gradient sedimentation prior to preparative 1D SDS-PAGE. It is important to verify the purity and integrity of the sample so that you can have confidence in the hits obtained. More protein is needed for N-terminal sequencing and ideally it should be purified to a single band when run on an SDS-polyacrylamide gel. The example presented here involves stably expressing a tagged protein in HEK293 cells and then isolating the protein by affinity purification and SDS-PAGE. © 2014 Elsevier Inc. All rights reserved.

Protein location prediction using atomic composition and global features of the amino acid sequence

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

Cherian, Betsy Sheena, E-mail: betsy.skb@gmail.com; Nair, Achuthsankar S.

2010-01-22

Subcellular location of protein is constructive information in determining its function, screening for drug candidates, vaccine design, annotation of gene products and in selecting relevant proteins for further studies. Computational prediction of subcellular localization deals with predicting the location of a protein from its amino acid sequence. For a computational localization prediction method to be more accurate, it should exploit all possible relevant biological features that contribute to the subcellular localization. In this work, we extracted the biological features from the full length protein sequence to incorporate more biological information. A new biological feature, distribution of atomic composition is effectivelymore » used with, multiple physiochemical properties, amino acid composition, three part amino acid composition, and sequence similarity for predicting the subcellular location of the protein. Support Vector Machines are designed for four modules and prediction is made by a weighted voting system. Our system makes prediction with an accuracy of 100, 82.47, 88.81 for self-consistency test, jackknife test and independent data test respectively. Our results provide evidence that the prediction based on the biological features derived from the full length amino acid sequence gives better accuracy than those derived from N-terminal alone. Considering the features as a distribution within the entire sequence will bring out underlying property distribution to a greater detail to enhance the prediction accuracy.« less

SSMART: Sequence-structure motif identification for RNA-binding proteins.

PubMed

Munteanu, Alina; Mukherjee, Neelanjan; Ohler, Uwe

2018-06-11

RNA-binding proteins (RBPs) regulate every aspect of RNA metabolism and function. There are hundreds of RBPs encoded in the eukaryotic genomes, and each recognize its RNA targets through a specific mixture of RNA sequence and structure properties. For most RBPs, however, only a primary sequence motif has been determined, while the structure of the binding sites is uncharacterized. We developed SSMART, an RNA motif finder that simultaneously models the primary sequence and the structural properties of the RNA targets sites. The sequence-structure motifs are represented as consensus strings over a degenerate alphabet, extending the IUPAC codes for nucleotides to account for secondary structure preferences. Evaluation on synthetic data showed that SSMART is able to recover both sequence and structure motifs implanted into 3'UTR-like sequences, for various degrees of structured/unstructured binding sites. In addition, we successfully used SSMART on high-throughput in vivo and in vitro data, showing that we not only recover the known sequence motif, but also gain insight into the structural preferences of the RBP. Availability: SSMART is freely available at https://ohlerlab.mdc-berlin.de/software/SSMART_137/. Supplementary data are available at Bioinformatics online.

Diverse nucleotide compositions and sequence fluctuation in Rubisco protein genes

NASA Astrophysics Data System (ADS)

Holden, Todd; Dehipawala, S.; Cheung, E.; Bienaime, R.; Ye, J.; Tremberger, G., Jr.; Schneider, P.; Lieberman, D.; Cheung, T.

2011-10-01

The Rubisco protein-enzyme is arguably the most abundance protein on Earth. The biology dogma of transcription and translation necessitates the study of the Rubisco genes and Rubisco-like genes in various species. Stronger correlation of fractal dimension of the atomic number fluctuation along a DNA sequence with Shannon entropy has been observed in the studied Rubisco-like gene sequences, suggesting a more diverse evolutionary pressure and constraints in the Rubisco sequences. The strategy of using metal for structural stabilization appears to be an ancient mechanism, with data from the porphobilinogen deaminase gene in Capsaspora owczarzaki and Monosiga brevicollis. Using the chi-square distance probability, our analysis supports the conjecture that the more ancient Rubisco-like sequence in Microcystis aeruginosa would have experienced very different evolutionary pressure and bio-chemical constraint as compared to Bordetella bronchiseptica, the two microbes occupying either end of the correlation graph. Our exploratory study would indicate that high fractal dimension Rubisco sequence would support high carbon dioxide rate via the Michaelis- Menten coefficient; with implication for the control of the whooping cough pathogen Bordetella bronchiseptica, a microbe containing a high fractal dimension Rubisco-like sequence (2.07). Using the internal comparison of chi-square distance probability for 16S rRNA (~ E-22) versus radiation repair Rec-A gene (~ E-05) in high GC content Deinococcus radiodurans, our analysis supports the conjecture that high GC content microbes containing Rubisco-like sequence are likely to include an extra-terrestrial origin, relative to Deinococcus radiodurans. Similar photosynthesis process that could utilize host star radiation would not compete with radiation resistant process from the biology dogma perspective in environments such as Mars and exoplanets.

Synthetic biology for the directed evolution of protein biocatalysts: navigating sequence space intelligently

PubMed Central

Currin, Andrew; Swainston, Neil; Day, Philip J.

2015-01-01

The amino acid sequence of a protein affects both its structure and its function. Thus, the ability to modify the sequence, and hence the structure and activity, of individual proteins in a systematic way, opens up many opportunities, both scientifically and (as we focus on here) for exploitation in biocatalysis. Modern methods of synthetic biology, whereby increasingly large sequences of DNA can be synthesised de novo, allow an unprecedented ability to engineer proteins with novel functions. However, the number of possible proteins is far too large to test individually, so we need means for navigating the ‘search space’ of possible protein sequences efficiently and reliably in order to find desirable activities and other properties. Enzymologists distinguish binding (K d) and catalytic (k cat) steps. In a similar way, judicious strategies have blended design (for binding, specificity and active site modelling) with the more empirical methods of classical directed evolution (DE) for improving k cat (where natural evolution rarely seeks the highest values), especially with regard to residues distant from the active site and where the functional linkages underpinning enzyme dynamics are both unknown and hard to predict. Epistasis (where the ‘best’ amino acid at one site depends on that or those at others) is a notable feature of directed evolution. The aim of this review is to highlight some of the approaches that are being developed to allow us to use directed evolution to improve enzyme properties, often dramatically. We note that directed evolution differs in a number of ways from natural evolution, including in particular the available mechanisms and the likely selection pressures. Thus, we stress the opportunities afforded by techniques that enable one to map sequence to (structure and) activity in silico, as an effective means of modelling and exploring protein landscapes. Because known landscapes may be assessed and reasoned about as a whole

DLocalMotif: a discriminative approach for discovering local motifs in protein sequences.

PubMed

Mehdi, Ahmed M; Sehgal, Muhammad Shoaib B; Kobe, Bostjan; Bailey, Timothy L; Bodén, Mikael

2013-01-01

Local motifs are patterns of DNA or protein sequences that occur within a sequence interval relative to a biologically defined anchor or landmark. Current protein motif discovery methods do not adequately consider such constraints to identify biologically significant motifs that are only weakly over-represented but spatially confined. Using negatives, i.e. sequences known to not contain a local motif, can further increase the specificity of their discovery. This article introduces the method DLocalMotif that makes use of positional information and negative data for local motif discovery in protein sequences. DLocalMotif combines three scoring functions, measuring degrees of motif over-representation, entropy and spatial confinement, specifically designed to discriminatively exploit the availability of negative data. The method is shown to outperform current methods that use only a subset of these motif characteristics. We apply the method to several biological datasets. The analysis of peroxisomal targeting signals uncovers several novel motifs that occur immediately upstream of the dominant peroxisomal targeting signal-1 signal. The analysis of proline-tyrosine nuclear localization signals uncovers multiple novel motifs that overlap with C2H2 zinc finger domains. We also evaluate the method on classical nuclear localization signals and endoplasmic reticulum retention signals and find that DLocalMotif successfully recovers biologically relevant sequence properties. http://bioinf.scmb.uq.edu.au/dlocalmotif/

Electrostatically Accelerated Encounter and Folding for Facile Recognition of Intrinsically Disordered Proteins

PubMed Central

Ganguly, Debabani; Zhang, Weihong; Chen, Jianhan

2013-01-01

Achieving facile specific recognition is essential for intrinsically disordered proteins (IDPs) that are involved in cellular signaling and regulation. Consideration of the physical time scales of protein folding and diffusion-limited protein-protein encounter has suggested that the frequent requirement of protein folding for specific IDP recognition could lead to kinetic bottlenecks. How IDPs overcome such potential kinetic bottlenecks to viably function in signaling and regulation in general is poorly understood. Our recent computational and experimental study of cell-cycle regulator p27 (Ganguly et al., J. Mol. Biol. (2012)) demonstrated that long-range electrostatic forces exerted on enriched charges of IDPs could accelerate protein-protein encounter via “electrostatic steering” and at the same time promote “folding-competent” encounter topologies to enhance the efficiency of IDP folding upon encounter. Here, we further investigated the coupled binding and folding mechanisms and the roles of electrostatic forces in the formation of three IDP complexes with more complex folded topologies. The surface electrostatic potentials of these complexes lack prominent features like those observed for the p27/Cdk2/cyclin A complex to directly suggest the ability of electrostatic forces to facilitate folding upon encounter. Nonetheless, similar electrostatically accelerated encounter and folding mechanisms were consistently predicted for all three complexes using topology-based coarse-grained simulations. Together with our previous analysis of charge distributions in known IDP complexes, our results support a prevalent role of electrostatic interactions in promoting efficient coupled binding and folding for facile specific recognition. These results also suggest that there is likely a co-evolution of IDP folded topology, charge characteristics, and coupled binding and folding mechanisms, driven at least partially by the need to achieve fast association kinetics for

DNA-binding proteins from marine bacteria expand the known sequence diversity of TALE-like repeats.

PubMed

de Lange, Orlando; Wolf, Christina; Thiel, Philipp; Krüger, Jens; Kleusch, Christian; Kohlbacher, Oliver; Lahaye, Thomas

2015-11-16

Transcription Activator-Like Effectors (TALEs) of Xanthomonas bacteria are programmable DNA binding proteins with unprecedented target specificity. Comparative studies into TALE repeat structure and function are hindered by the limited sequence variation among TALE repeats. More sequence-diverse TALE-like proteins are known from Ralstonia solanacearum (RipTALs) and Burkholderia rhizoxinica (Bats), but RipTAL and Bat repeats are conserved with those of TALEs around the DNA-binding residue. We study two novel marine-organism TALE-like proteins (MOrTL1 and MOrTL2), the first to date of non-terrestrial origin. We have assessed their DNA-binding properties and modelled repeat structures. We found that repeats from these proteins mediate sequence specific DNA binding conforming to the TALE code, despite low sequence similarity to TALE repeats, and with novel residues around the BSR. However, MOrTL1 repeats show greater sequence discriminating power than MOrTL2 repeats. Sequence alignments show that there are only three residues conserved between repeats of all TALE-like proteins including the two new additions. This conserved motif could prove useful as an identifier for future TALE-likes. Additionally, comparing MOrTL repeats with those of other TALE-likes suggests a common evolutionary origin for the TALEs, RipTALs and Bats. © The Author(s) 2015. Published by Oxford University Press on behalf of Nucleic Acids Research.

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

Cloning and sequence analysis of a cDNA clone coding for the mouse GM2 activator protein.

PubMed Central

Bellachioma, G; Stirling, J L; Orlacchio, A; Beccari, T

1993-01-01

A cDNA (1.1 kb) containing the complete coding sequence for the mouse GM2 activator protein was isolated from a mouse macrophage library using a cDNA for the human protein as a probe. There was a single ATG located 12 bp from the 5' end of the cDNA clone followed by an open reading frame of 579 bp. Northern blot analysis of mouse macrophage RNA showed that there was a single band with a mobility corresponding to a size of 2.3 kb. We deduce from this that the mouse mRNA, in common with the mRNA for the human GM2 activator protein, has a long 3' untranslated sequence of approx. 1.7 kb. Alignment of the mouse and human deduced amino acid sequences showed 68% identity overall and 75% identity for the sequence on the C-terminal side of the first 31 residues, which in the human GM2 activator protein contains the signal peptide. Hydropathicity plots showed great similarity between the mouse and human sequences even in regions of low sequence similarity. There is a single N-glycosylation site in the mouse GM2 activator protein sequence (Asn151-Phe-Thr) which differs in its location from the single site reported in the human GM2 activator protein sequence (Asn63-Val-Thr). Images Figure 1 PMID:7689829

Controlling protein molecular dynamics: How to accelerate folding while preserving the native state

NASA Astrophysics Data System (ADS)

Jensen, Christian H.; Nerukh, Dmitry; Glen, Robert C.

2008-12-01

The dynamics of peptides and proteins generated by classical molecular dynamics (MD) is described by using a Markov model. The model is built by clustering the trajectory into conformational states and estimating transition probabilities between the states. Assuming that it is possible to influence the dynamics of the system by varying simulation parameters, we show how to use the Markov model to determine the parameter values that preserve the folded state of the protein and at the same time, reduce the folding time in the simulation. We investigate this by applying the method to two systems. The first system is an imaginary peptide described by given transition probabilities with a total folding time of 1μs. We find that only small changes in the transition probabilities are needed to accelerate (or decelerate) the folding. This implies that folding times for slowly folding peptides and proteins calculated using MD cannot be meaningfully compared to experimental results. The second system is a four residue peptide valine-proline-alanine-leucine in water. We control the dynamics of the transitions by varying the temperature and the atom masses. The simulation results show that it is possible to find the combinations of parameter values that accelerate the dynamics and at the same time preserve the native state of the peptide. A method for accelerating larger systems without performing simulations for the whole folding process is outlined.

Nucleotide sequence of the gene encoding the nitrogenase iron protein of Thiobacillus ferrooxidans

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

Pretorius, I.M.; Rawlings, D.E.; O'Neill, E.G.

1987-01-01

The DNA sequence was determined for the cloned Thiobacillus ferrooxidans nifH and part of the nifD genes. The DNA chains were radiolabeled with (..cap alpha..-/sup 32/P)dCTP (3000 Ci/mmol) or (..cap alpha..-/sup 35/S)dCTP (400 Ci/mmol). A putative T. ferrooxidans nifH promoter was identified whose sequences showed perfect consensus with those of the Klebsiella pneumoniae nif promoter. Two putative consensus upstream activator sequences were also identified. The amino acid sequence was deduced from the DNA sequence. In a comparison of nifH DNA sequences from T. ferrooxidans and eight other nitrogen-fixing microbes, a Rhizobium sp. isolated from Parasponia andersonii showed the greatest homologymore » (74%) and Clostridium pasteurianum (nifH1) showed the least homology (54%). In the comparison of the amino acid sequences of the Fe proteins, the Rhizobium sp. and Rhizobium japonicum showed the greatest homology (both 86%) and C. pasteurianum (nifH1 gene product) demonstrated the least homology (56%) to the T. ferrooxidans Fe protein.« less

Bacterial-like PPP protein phosphatases: novel sequence alterations in pathogenic eukaryotes and peculiar features of bacterial sequence similarity.

PubMed

Kerk, David; Uhrig, R Glen; Moorhead, Greg B

2013-01-01

Reversible phosphorylation is a widespread modification affecting the great majority of eukaryotic cellular proteins, and whose effects influence nearly every cellular function. Protein phosphatases are increasingly recognized as exquisitely regulated contributors to these changes. The PPP (phosphoprotein phosphatase) family comprises enzymes, which catalyze dephosphorylation at serine and threonine residues. Nearly a decade ago, "bacterial-like" enzymes were recognized with similarity to proteins from various bacterial sources: SLPs (Shewanella-like phosphatases), RLPHs (Rhizobiales-like phosphatases), and ALPHs (ApaH-like phosphatases). A recent article from our laboratory appearing in Plant Physiology characterizes their extensive organismal distribution, abundance in plant species, predicted subcellular localization, motif organization, and sequence evolution. One salient observation is the distinct evolutionary trajectory followed by SLP genes and proteins in photosynthetic eukaryotes vs. animal and plant pathogens derived from photosynthetic ancestors. We present here a closer look at sequence data that emphasizes the distinctiveness of pathogen SLP proteins and that suggests that they might represent novel drug targets. A second observation in our original report was the high degree of similarity between the bacterial-like PPPs of eukaryotes and closely related proteins of the "eukaryotic-like" phyla Myxococcales and Planctomycetes. We here reflect on the possible implications of these observations and their importance for future research.

G protein-coupled odorant receptors: From sequence to structure.

PubMed

de March, Claire A; Kim, Soo-Kyung; Antonczak, Serge; Goddard, William A; Golebiowski, Jérôme

2015-09-01

Odorant receptors (ORs) are the largest subfamily within class A G protein-coupled receptors (GPCRs). No experimental structural data of any OR is available to date and atomic-level insights are likely to be obtained by means of molecular modeling. In this article, we critically align sequences of ORs with those GPCRs for which a structure is available. Here, an alignment consistent with available site-directed mutagenesis data on various ORs is proposed. Using this alignment, the choice of the template is deemed rather minor for identifying residues that constitute the wall of the binding cavity or those involved in G protein recognition. © 2015 The Protein Society.

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.

The C-Terminal Sequence of RhoB Directs Protein Degradation through an Endo-Lysosomal Pathway

PubMed Central

Ramos, Irene; Herrera, Mónica; Stamatakis, Konstantinos

2009-01-01

Background Protein degradation is essential for cell homeostasis. Targeting of proteins for degradation is often achieved by specific protein sequences or posttranslational modifications such as ubiquitination. Methodology/Principal Findings By using biochemical and genetic tools we have monitored the localization and degradation of endogenous and chimeric proteins in live primary cells by confocal microscopy and ultra-structural analysis. Here we identify an eight amino acid sequence from the C-terminus of the short-lived GTPase RhoB that directs the rapid degradation of both RhoB and chimeric proteins bearing this sequence through a lysosomal pathway. Elucidation of the RhoB degradation pathway unveils a mechanism dependent on protein isoprenylation and palmitoylation that involves sorting of the protein into multivesicular bodies, mediated by the ESCRT machinery. Moreover, RhoB sorting is regulated by late endosome specific lipid dynamics and is altered in human genetic lipid traffic disease. Conclusions/Significance Our findings characterize a short-lived cytosolic protein that is degraded through a lysosomal pathway. In addition, we define a novel motif for protein sorting and rapid degradation, which allows controlling protein levels by means of clinically used drugs. PMID:19956591

ssHMM: extracting intuitive sequence-structure motifs from high-throughput RNA-binding protein data

PubMed Central

Krestel, Ralf; Ohler, Uwe; Vingron, Martin; Marsico, Annalisa

2017-01-01

Abstract RNA-binding proteins (RBPs) play an important role in RNA post-transcriptional regulation and recognize target RNAs via sequence-structure motifs. The extent to which RNA structure influences protein binding in the presence or absence of a sequence motif is still poorly understood. Existing RNA motif finders either take the structure of the RNA only partially into account, or employ models which are not directly interpretable as sequence-structure motifs. We developed ssHMM, an RNA motif finder based on a hidden Markov model (HMM) and Gibbs sampling which fully captures the relationship between RNA sequence and secondary structure preference of a given RBP. Compared to previous methods which output separate logos for sequence and structure, it directly produces a combined sequence-structure motif when trained on a large set of sequences. ssHMM’s model is visualized intuitively as a graph and facilitates biological interpretation. ssHMM can be used to find novel bona fide sequence-structure motifs of uncharacterized RBPs, such as the one presented here for the YY1 protein. ssHMM reaches a high motif recovery rate on synthetic data, it recovers known RBP motifs from CLIP-Seq data, and scales linearly on the input size, being considerably faster than MEMERIS and RNAcontext on large datasets while being on par with GraphProt. It is freely available on Github and as a Docker image. PMID:28977546

Myelin protein zero gene sequencing diagnoses Charcot-Marie-Tooth Type 1B disease

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

Su, Y.; Zhang, H.; Madrid, R.

1994-09-01

Charcot-Marie-Tooth disease (CMT), the most common genetic neuropathy, affects about 1 in 2600 people in Norway and is found worldwide. CMT Type 1 (CMT1) has slow nerve conduction with demyelinated Schwann cells. Autosomal dominant CMT Type 1B (CMT1B) results from mutations in the myelin protein zero gene which directs the synthesis of more than half of all Schwann cell protein. This gene was mapped to the chromosome 1q22-1q23.1 borderline by fluorescence in situ hybridization. The first 7 of 7 reported CMT1B mutations are unique. Thus the most effective means to identify CMT1B mutations in at-risk family members and fetuses ismore » to sequence the entire coding sequence in dominant or sporadic CMT patients without the CMT1A duplication. Of the 19 primers used in 16 pars to uniquely amplify the entire MPZ coding sequence, 6 primer pairs were used to amplify and sequence the 6 exons. The DyeDeoxy Terminator cycle sequencing method used with four different color fluorescent lables was superior to manual sequencing because it sequences more bases unambiguously from extracted genomic DNA samples within 24 hours. This protocol was used to test 28 CMT and Dejerine-Sottas patients without CMT1A gene duplication. Sequencing MPZ gene-specific amplified fragments identified 9 polymorphic sites within the 6 exons that encode the 248 amino acid MPZ protein. The large number of major CMT1B mutations identified by single strand sequencing are being verified by reverse strand sequencing and when possible, by restriction enzyme analysis. This protocol can be used to distringuish CMT1B patients from othre CMT phenotypes and to determine the CMT1B status of relatives both presymptomatically and prenatally.« less

DeepGO: predicting protein functions from sequence and interactions using a deep ontology-aware classifier.

PubMed

Kulmanov, Maxat; Khan, Mohammed Asif; Hoehndorf, Robert; Wren, Jonathan

2018-02-15

A large number of protein sequences are becoming available through the application of novel high-throughput sequencing technologies. Experimental functional characterization of these proteins is time-consuming and expensive, and is often only done rigorously for few selected model organisms. Computational function prediction approaches have been suggested to fill this gap. The functions of proteins are classified using the Gene Ontology (GO), which contains over 40 000 classes. Additionally, proteins have multiple functions, making function prediction a large-scale, multi-class, multi-label problem. We have developed a novel method to predict protein function from sequence. We use deep learning to learn features from protein sequences as well as a cross-species protein-protein interaction network. Our approach specifically outputs information in the structure of the GO and utilizes the dependencies between GO classes as background information to construct a deep learning model. We evaluate our method using the standards established by the Computational Assessment of Function Annotation (CAFA) and demonstrate a significant improvement over baseline methods such as BLAST, in particular for predicting cellular locations. Web server: http://deepgo.bio2vec.net, Source code: https://github.com/bio-ontology-research-group/deepgo. robert.hoehndorf@kaust.edu.sa. Supplementary data are available at Bioinformatics online. © The Author(s) 2017. Published by Oxford University Press.

muBLASTP: database-indexed protein sequence search on multicore CPUs.

PubMed

Zhang, Jing; Misra, Sanchit; Wang, Hao; Feng, Wu-Chun

2016-11-04

The Basic Local Alignment Search Tool (BLAST) is a fundamental program in the life sciences that searches databases for sequences that are most similar to a query sequence. Currently, the BLAST algorithm utilizes a query-indexed approach. Although many approaches suggest that sequence search with a database index can achieve much higher throughput (e.g., BLAT, SSAHA, and CAFE), they cannot deliver the same level of sensitivity as the query-indexed BLAST, i.e., NCBI BLAST, or they can only support nucleotide sequence search, e.g., MegaBLAST. Due to different challenges and characteristics between query indexing and database indexing, the existing techniques for query-indexed search cannot be used into database indexed search. muBLASTP, a novel database-indexed BLAST for protein sequence search, delivers identical hits returned to NCBI BLAST. On Intel Haswell multicore CPUs, for a single query, the single-threaded muBLASTP achieves up to a 4.41-fold speedup for alignment stages, and up to a 1.75-fold end-to-end speedup over single-threaded NCBI BLAST. For a batch of queries, the multithreaded muBLASTP achieves up to a 5.7-fold speedups for alignment stages, and up to a 4.56-fold end-to-end speedup over multithreaded NCBI BLAST. With a newly designed index structure for protein database and associated optimizations in BLASTP algorithm, we re-factored BLASTP algorithm for modern multicore processors that achieves much higher throughput with acceptable memory footprint for the database index.

Tardigrade workbench: comparing stress-related proteins, sequence-similar and functional protein clusters as well as RNA elements in tardigrades

PubMed Central

2009-01-01

Background Tardigrades represent an animal phylum with extraordinary resistance to environmental stress. Results To gain insights into their stress-specific adaptation potential, major clusters of related and similar proteins are identified, as well as specific functional clusters delineated comparing all tardigrades and individual species (Milnesium tardigradum, Hypsibius dujardini, Echiniscus testudo, Tulinus stephaniae, Richtersius coronifer) and functional elements in tardigrade mRNAs are analysed. We find that 39.3% of the total sequences clustered in 58 clusters of more than 20 proteins. Among these are ten tardigrade specific as well as a number of stress-specific protein clusters. Tardigrade-specific functional adaptations include strong protein, DNA- and redox protection, maintenance and protein recycling. Specific regulatory elements regulate tardigrade mRNA stability such as lox P DICE elements whereas 14 other RNA elements of higher eukaryotes are not found. Further features of tardigrade specific adaption are rapidly identified by sequence and/or pattern search on the web-tool tardigrade analyzer http://waterbear.bioapps.biozentrum.uni-wuerzburg.de. The work-bench offers nucleotide pattern analysis for promotor and regulatory element detection (tardigrade specific; nrdb) as well as rapid COG search for function assignments including species-specific repositories of all analysed data. Conclusion Different protein clusters and regulatory elements implicated in tardigrade stress adaptations are analysed including unpublished tardigrade sequences. PMID:19821996

Selection of a platinum-binding sequence in a loop of a four-helix bundle protein.

PubMed

Yagi, Sota; Akanuma, Satoshi; Kaji, Asumi; Niiro, Hiroya; Akiyama, Hayato; Uchida, Tatsuya; Yamagishi, Akihiko

2018-02-01

Protein-metal hybrids are functional materials with various industrial applications. For example, a redox enzyme immobilized on a platinum electrode is a key component of some biofuel cells and biosensors. To create these hybrid materials, protein molecules are bound to metal surfaces. Here, we report the selection of a novel platinum-binding sequence in a loop of a four-helix bundle protein, the Lac repressor four-helix protein (LARFH), an artificial protein in which four identical α-helices are connected via three identical loops. We created a genetic library in which the Ser-Gly-Gln-Gly-Gly-Ser sequence within the first inter-helical loop of LARFH was semi-randomly mutated. The library was then subjected to selection for platinum-binding affinity by using the T7 phage display method. The majority of the selected variants contained the Tyr-Lys-Arg-Gly-Tyr-Lys (YKRGYK) sequence in their randomized segment. We characterized the platinum-binding properties of mutant LARFH by using quartz crystal microbalance analysis. Mutant LARFH seemed to interact with platinum through its loop containing the YKRGYK sequence, as judged by the estimated exclusive area occupied by a single molecule. Furthermore, a 10-residue peptide containing the YKRGYK sequence bound to platinum with reasonably high affinity and basic side chains in the peptide were crucial in mediating this interaction. In conclusion, we have identified an amino acid sequence, YKRGYK, in the loop of a helix-loop-helix motif that shows high platinum-binding affinity. This sequence could be grafted into loops of other polypeptides as an approach to immobilize proteins on platinum electrodes for use as biosensors among other applications. Copyright © 2017 The Society for Biotechnology, Japan. Published by Elsevier B.V. All rights reserved.

Isolation and N-terminal sequencing of a novel cadmium-binding protein from Boletus edulis

NASA Astrophysics Data System (ADS)

Collin-Hansen, C.; Andersen, R. A.; Steinnes, E.

2003-05-01

A Cd-binding protein was isolated from the popular edible mushroom Boletus edulis, which is a hyperaccumulator of both Cd and Hg. Wild-growing samples of B. edulis were collected from soils rich in Cd. Cd radiotracer was added to the crude protein preparation obtained from ethanol precipitation of heat-treated cytosol. Proteins were then further separated in two consecutive steps; gel filtration and anion exchange chromatography. In both steps the Cd radiotracer profile showed only one distinct peak, which corresponded well with the profiles of endogenous Cd obtained by atomic absorption spectrophotometry (AAS). Concentrations of the essential elements Cu and Zn were low in the protein fractions high in Cd. N-terminal sequencing performed on the Cd-binding protein fractions revealed a protein with a novel amino acid sequence, which contained aromatic amino acids as well as proline. Both the N-terminal sequencing and spectrofluorimetric analysis with EDTA and ABD-F (4-aminosulfonyl-7-fluoro-2, 1, 3-benzoxadiazole) failed to detect cysteine in the Cd-binding fractions. These findings conclude that the novel protein does not belong to the metallothionein family. The results suggest a role for the protein in Cd transport and storage, and they are of importance in view of toxicology and food chemistry, but also for environmental protection.

Sequence charge decoration dictates coil-globule transition in intrinsically disordered proteins

NASA Astrophysics Data System (ADS)

Firman, Taylor; Ghosh, Kingshuk

2018-03-01

We present an analytical theory to compute conformations of heteropolymers—applicable to describe disordered proteins—as a function of temperature and charge sequence. The theory describes coil-globule transition for a given protein sequence when temperature is varied and has been benchmarked against the all-atom Monte Carlo simulation (using CAMPARI) of intrinsically disordered proteins (IDPs). In addition, the model quantitatively shows how subtle alterations of charge placement in the primary sequence—while maintaining the same charge composition—can lead to significant changes in conformation, even as drastic as a coil (swelled above a purely random coil) to globule (collapsed below a random coil) and vice versa. The theory provides insights on how to control (enhance or suppress) these changes by tuning the temperature (or solution condition) and charge decoration. As an application, we predict the distribution of conformations (at room temperature) of all naturally occurring IDPs in the DisProt database and notice significant size variation even among IDPs with a similar composition of positive and negative charges. Based on this, we provide a new diagram-of-states delineating the sequence-conformation relation for proteins in the DisProt database. Next, we study the effect of post-translational modification, e.g., phosphorylation, on IDP conformations. Modifications as little as two-site phosphorylation can significantly alter the size of an IDP with everything else being constant (temperature, salt concentration, etc.). However, not all possible modification sites have the same effect on protein conformations; there are certain "hot spots" that can cause maximal change in conformation. The location of these "hot spots" in the parent sequence can readily be identified by using a sequence charge decoration metric originally introduced by Sawle and Ghosh. The ability of our model to predict conformations (both expanded and collapsed states) of IDPs at

Confirmation of the "protein-traffic-hypothesis" and the "protein-localization-hypothesis" using the diabetes-mellitus-type-1-knock-in and transgenic-murine-models and the trepitope sequences.

PubMed

Arneth, Borros

2012-10-01

As possible mechanisms to explain the emergence of autoimmune diseases, the current author has suggested in earlier papers two new pathways: the "protein localization hypothesis" and the "protein traffic hypothesis". The "protein localization hypothesis" states that an autoimmune disease develops if a protein accumulates in a previously unoccupied compartment, that did not previously contain that protein. Similarly, the "protein traffic hypothesis" states that a sudden error within the transport of a certain protein leads to the emergence of an autoimmune disease. The current article discusses the usefulness of the different commercially available transgenic murine models of diabetes mellitus type 1 to confirm the aforementioned hypotheses. This discussion shows that several transgenic murine models of diabetes mellitus type 1 are in-line and confirm the aforementioned hypotheses. Furthermore, these hypotheses are additionally inline with the occurrence of several newly discovered protein sequences, the so-called trepitope sequences. These sequences modulate the immune response to certain proteins. The current study analyzed to what extent the hypotheses are supported by the occurrence of these new sequences. Thereby the occurrence of the trepitope sequences provides additional evidence supporting the aforementioned hypotheses. Both the "protein localization hypothesis" and the "protein traffic hypothesis" have the potential to lead to new causal therapy concepts. The "protein localization hypothesis" and the "protein traffic hypothesis" provide conceptional explanations for the diabetes mouse models as well as for the newly discovered trepitope sequences. Copyright © 2012 Elsevier Ltd. All rights reserved.

The DynaMine webserver: predicting protein dynamics from sequence.

PubMed

Cilia, Elisa; Pancsa, Rita; Tompa, Peter; Lenaerts, Tom; Vranken, Wim F

2014-07-01

Protein dynamics are important for understanding protein function. Unfortunately, accurate protein dynamics information is difficult to obtain: here we present the DynaMine webserver, which provides predictions for the fast backbone movements of proteins directly from their amino-acid sequence. DynaMine rapidly produces a profile describing the statistical potential for such movements at residue-level resolution. The predicted values have meaning on an absolute scale and go beyond the traditional binary classification of residues as ordered or disordered, thus allowing for direct dynamics comparisons between protein regions. Through this webserver, we provide molecular biologists with an efficient and easy to use tool for predicting the dynamical characteristics of any protein of interest, even in the absence of experimental observations. The prediction results are visualized and can be directly downloaded. The DynaMine webserver, including instructive examples describing the meaning of the profiles, is available at http://dynamine.ibsquare.be. © The Author(s) 2014. Published by Oxford University Press on behalf of Nucleic Acids Research.

A statistical physics perspective on alignment-independent protein sequence comparison.

PubMed

Chattopadhyay, Amit K; Nasiev, Diar; Flower, Darren R

2015-08-01

Within bioinformatics, the textual alignment of amino acid sequences has long dominated the determination of similarity between proteins, with all that implies for shared structure, function and evolutionary descent. Despite the relative success of modern-day sequence alignment algorithms, so-called alignment-free approaches offer a complementary means of determining and expressing similarity, with potential benefits in certain key applications, such as regression analysis of protein structure-function studies, where alignment-base similarity has performed poorly. Here, we offer a fresh, statistical physics-based perspective focusing on the question of alignment-free comparison, in the process adapting results from 'first passage probability distribution' to summarize statistics of ensemble averaged amino acid propensity values. In this article, we introduce and elaborate this approach. © The Author 2015. Published by Oxford University Press.

Successful Recovery of Nuclear Protein-Coding Genes from Small Insects in Museums Using Illumina Sequencing.

PubMed

Kanda, Kojun; Pflug, James M; Sproul, John S; Dasenko, Mark A; Maddison, David R

2015-01-01

In this paper we explore high-throughput Illumina sequencing of nuclear protein-coding, ribosomal, and mitochondrial genes in small, dried insects stored in natural history collections. We sequenced one tenebrionid beetle and 12 carabid beetles ranging in size from 3.7 to 9.7 mm in length that have been stored in various museums for 4 to 84 years. Although we chose a number of old, small specimens for which we expected low sequence recovery, we successfully recovered at least some low-copy nuclear protein-coding genes from all specimens. For example, in one 56-year-old beetle, 4.4 mm in length, our de novo assembly recovered about 63% of approximately 41,900 nucleotides in a target suite of 67 nuclear protein-coding gene fragments, and 70% using a reference-based assembly. Even in the least successfully sequenced carabid specimen, reference-based assembly yielded fragments that were at least 50% of the target length for 34 of 67 nuclear protein-coding gene fragments. Exploration of alternative references for reference-based assembly revealed few signs of bias created by the reference. For all specimens we recovered almost complete copies of ribosomal and mitochondrial genes. We verified the general accuracy of the sequences through comparisons with sequences obtained from PCR and Sanger sequencing, including of conspecific, fresh specimens, and through phylogenetic analysis that tested the placement of sequences in predicted regions. A few possible inaccuracies in the sequences were detected, but these rarely affected the phylogenetic placement of the samples. Although our sample sizes are low, an exploratory regression study suggests that the dominant factor in predicting success at recovering nuclear protein-coding genes is a high number of Illumina reads, with success at PCR of COI and killing by immersion in ethanol being secondary factors; in analyses of only high-read samples, the primary significant explanatory variable was body length, with small beetles

Successful Recovery of Nuclear Protein-Coding Genes from Small Insects in Museums Using Illumina Sequencing

PubMed Central

Dasenko, Mark A.

2015-01-01

In this paper we explore high-throughput Illumina sequencing of nuclear protein-coding, ribosomal, and mitochondrial genes in small, dried insects stored in natural history collections. We sequenced one tenebrionid beetle and 12 carabid beetles ranging in size from 3.7 to 9.7 mm in length that have been stored in various museums for 4 to 84 years. Although we chose a number of old, small specimens for which we expected low sequence recovery, we successfully recovered at least some low-copy nuclear protein-coding genes from all specimens. For example, in one 56-year-old beetle, 4.4 mm in length, our de novo assembly recovered about 63% of approximately 41,900 nucleotides in a target suite of 67 nuclear protein-coding gene fragments, and 70% using a reference-based assembly. Even in the least successfully sequenced carabid specimen, reference-based assembly yielded fragments that were at least 50% of the target length for 34 of 67 nuclear protein-coding gene fragments. Exploration of alternative references for reference-based assembly revealed few signs of bias created by the reference. For all specimens we recovered almost complete copies of ribosomal and mitochondrial genes. We verified the general accuracy of the sequences through comparisons with sequences obtained from PCR and Sanger sequencing, including of conspecific, fresh specimens, and through phylogenetic analysis that tested the placement of sequences in predicted regions. A few possible inaccuracies in the sequences were detected, but these rarely affected the phylogenetic placement of the samples. Although our sample sizes are low, an exploratory regression study suggests that the dominant factor in predicting success at recovering nuclear protein-coding genes is a high number of Illumina reads, with success at PCR of COI and killing by immersion in ethanol being secondary factors; in analyses of only high-read samples, the primary significant explanatory variable was body length, with small beetles

Toward rules relating zinc finger protein sequences and DNA binding site preferences.

PubMed

Desjarlais, J R; Berg, J M

1992-08-15

Zinc finger proteins of the Cys2-His2 type consist of tandem arrays of domains, where each domain appears to contact three adjacent base pairs of DNA through three key residues. We have designed and prepared a series of variants of the central zinc finger within the DNA binding domain of Sp1 by using information from an analysis of a large data base of zinc finger protein sequences. Through systematic variations at two of the three contact positions (underlined), relatively specific recognition of sequences of the form 5'-GGGGN(G or T)GGG-3' has been achieved. These results provide the basis for rules that may develop into a code that will allow the design of zinc finger proteins with preselected DNA site specificity.

Protein model discrimination using mutational sensitivity derived from deep sequencing.

PubMed

Adkar, Bharat V; Tripathi, Arti; Sahoo, Anusmita; Bajaj, Kanika; Goswami, Devrishi; Chakrabarti, Purbani; Swarnkar, Mohit K; Gokhale, Rajesh S; Varadarajan, Raghavan

2012-02-08

A major bottleneck in protein structure prediction is the selection of correct models from a pool of decoys. Relative activities of ∼1,200 individual single-site mutants in a saturation library of the bacterial toxin CcdB were estimated by determining their relative populations using deep sequencing. This phenotypic information was used to define an empirical score for each residue (RankScore), which correlated with the residue depth, and identify active-site residues. Using these correlations, ∼98% of correct models of CcdB (RMSD ≤ 4Å) were identified from a large set of decoys. The model-discrimination methodology was further validated on eleven different monomeric proteins using simulated RankScore values. The methodology is also a rapid, accurate way to obtain relative activities of each mutant in a large pool and derive sequence-structure-function relationships without protein isolation or characterization. It can be applied to any system in which mutational effects can be monitored by a phenotypic readout. Copyright © 2012 Elsevier Ltd. All rights reserved.

Indel PDB: a database of structural insertions and deletions derived from sequence alignments of closely related proteins.

PubMed

Hsing, Michael; Cherkasov, Artem

2008-06-25

Insertions and deletions (indels) represent a common type of sequence variations, which are less studied and pose many important biological questions. Recent research has shown that the presence of sizable indels in protein sequences may be indicative of protein essentiality and their role in protein interaction networks. Examples of utilization of indels for structure-based drug design have also been recently demonstrated. Nonetheless many structural and functional characteristics of indels remain less researched or unknown. We have created a web-based resource, Indel PDB, representing a structural database of insertions/deletions identified from the sequence alignments of highly similar proteins found in the Protein Data Bank (PDB). Indel PDB utilized large amounts of available structural information to characterize 1-, 2- and 3-dimensional features of indel sites. Indel PDB contains 117,266 non-redundant indel sites extracted from 11,294 indel-containing proteins. Unlike loop databases, Indel PDB features more indel sequences with secondary structures including alpha-helices and beta-sheets in addition to loops. The insertion fragments have been characterized by their sequences, lengths, locations, secondary structure composition, solvent accessibility, protein domain association and three dimensional structures. By utilizing the data available in Indel PDB, we have studied and presented here several sequence and structural features of indels. We anticipate that Indel PDB will not only enable future functional studies of indels, but will also assist protein modeling efforts and identification of indel-directed drug binding sites.

Mapping a nucleolar targeting sequence of an RNA binding nucleolar protein, Nop25

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

Fujiwara, Takashi; Suzuki, Shunji; Kanno, Motoko

2006-06-10

Nop25 is a putative RNA binding nucleolar protein associated with rRNA transcription. The present study was undertaken to determine the mechanism of Nop25 localization in the nucleolus. Deletion experiments of Nop25 amino acid sequence showed Nop25 to contain a nuclear targeting sequence in the N-terminal and a nucleolar targeting sequence in the C-terminal. By expressing derivative peptides from the C-terminal as GFP-fusion proteins in the cells, a lysine and arginine residue-enriched peptide (KRKHPRRAQDSTKKPPSATRTSKTQRRRR) allowed a GFP-fusion protein to be transported and fully retained in the nucleolus. When the peptide was fused with cMyc epitope and expressed in the cells, amore » cMyc epitope was then detected in the nucleolus. Nop25 did not localize in the nucleolus by deletion of the peptide from Nop25. Furthermore, deletion of a subdomain (KRKHPRRAQ) in the peptide or amino acid substitution of lysine and arginine residues in the subdomain resulted in the loss of Nop25 nucleolar localization. These results suggest that the lysine and arginine residue-enriched peptide is the most prominent nucleolar targeting sequence of Nop25 and that the long stretch of basic residues might play an important role in the nucleolar localization of Nop25. Although Nop25 contained putative SUMOylation, phosphorylation and glycosylation sites, the amino acid substitution in these sites had no effect on the nucleolar localization, thus suggesting that these post-translational modifications did not contribute to the localization of Nop25 in the nucleolus. The treatment of the cells, which expressed a GFP-fusion protein with a nucleolar targeting sequence of Nop25, with RNase A resulted in a complete dislocation of the protein from the nucleolus. These data suggested that the nucleolar targeting sequence might therefore play an important role in the binding of Nop25 to RNA molecules and that the RNA binding of Nop25 might be essential for the nucleolar localization of Nop25.« less

Identification of a nuclear localization sequence in the polyomavirus capsid protein VP2

NASA Technical Reports Server (NTRS)

Chang, D.; Haynes, J. I. 2nd; Brady, J. N.; Consigli, R. A.; Spooner, B. S. (Principal Investigator)

1992-01-01

A nuclear localization signal (NLS) has been identified in the C-terminal (Glu307-Glu-Asp-Gly-Pro-Gln-Lys-Lys-Lys-Arg-Arg-Leu318) amino acid sequence of the polyomavirus minor capsid protein VP2. The importance of this amino acid sequence for nuclear transport of newly synthesized VP2 was demonstrated by a genetic "subtractive" study using the constructs pSG5VP2 (expressing full-length VP2) and pSG5 delta 3VP2 (expressing truncated VP2, lacking amino acids Glu307-Leu318). These constructs were transfected into COS-7 cells, and the intracellular localization of the VP2 protein was determined by indirect immunofluorescence. These studies revealed that the full-length VP2 was localized in the nucleus, while the truncated VP2 protein was localized in the cytoplasm and not transported to the nucleus. A biochemical "additive" approach was also used to determine whether this sequence could target nonnuclear proteins to the nucleus. A synthetic peptide identical to VP2 amino acids Glu307-Leu318 was cross-linked to the nonnuclear proteins bovine serum albumin (BSA) or immunoglobulin G (IgG). The conjugates were then labeled with fluorescein isothiocyanate and microinjected into the cytoplasm of NIH 3T6 cells. Both conjugates localized in the nucleus of the microinjected cells, whereas unconjugated BSA and IgG remained in the cytoplasm. Taken together, these genetic subtractive and biochemical additive approaches have identified the C-terminal sequence of polyoma-virus VP2 (containing amino acids Glu307-Leu318) as the NLS of this protein.

Computational Framework for Prediction of Peptide Sequences That May Mediate Multiple Protein Interactions in Cancer-Associated Hub Proteins.

PubMed

Sarkar, Debasree; Patra, Piya; Ghosh, Abhirupa; Saha, Sudipto

2016-01-01

A considerable proportion of protein-protein interactions (PPIs) in the cell are estimated to be mediated by very short peptide segments that approximately conform to specific sequence patterns known as linear motifs (LMs), often present in the disordered regions in the eukaryotic proteins. These peptides have been found to interact with low affinity and are able bind to multiple interactors, thus playing an important role in the PPI networks involving date hubs. In this work, PPI data and de novo motif identification based method (MEME) were used to identify such peptides in three cancer-associated hub proteins-MYC, APC and MDM2. The peptides corresponding to the significant LMs identified for each hub protein were aligned, the overlapping regions across these peptides being termed as overlapping linear peptides (OLPs). These OLPs were thus predicted to be responsible for multiple PPIs of the corresponding hub proteins and a scoring system was developed to rank them. We predicted six OLPs in MYC and five OLPs in MDM2 that scored higher than OLP predictions from randomly generated protein sets. Two OLP sequences from the C-terminal of MYC were predicted to bind with FBXW7, component of an E3 ubiquitin-protein ligase complex involved in proteasomal degradation of MYC. Similarly, we identified peptides in the C-terminal of MDM2 interacting with FKBP3, which has a specific role in auto-ubiquitinylation of MDM2. The peptide sequences predicted in MYC and MDM2 look promising for designing orthosteric inhibitors against possible disease-associated PPIs. Since these OLPs can interact with other proteins as well, these inhibitors should be specific to the targeted interactor to prevent undesired side-effects. This computational framework has been designed to predict and rank the peptide regions that may mediate multiple PPIs and can be applied to other disease-associated date hub proteins for prediction of novel therapeutic targets of small molecule PPI modulators.

Amino acid sequence of the smaller basic protein from rat brain myelin

PubMed Central

Dunkley, Peter R.; Carnegie, Patrick R.

1974-01-01

1. The complete amino acid sequence of the smaller basic protein from rat brain myelin was determined. This protein differs from myelin basic proteins of other species in having a deletion of a polypeptide of 40 amino acid residues from the centre of the molecule. 2. A detailed comparison is made of the constant and variable regions in a group of myelin basic proteins from six species. 3. An arginine residue in the rat protein was found to be partially methylated. The ratio of methylated to unmethylated arginine at this position differed from that found for the human basic protein. 4. Three tryptic peptides were isolated in more than one form. The differences between the two forms of each peptide are discussed in relation to the electrophoretic heterogeneity of myelin basic proteins, which is known to occur at alkaline pH values. 5. Detailed evidence for the amino acid sequence of the protein has been deposited as Supplementary Publication SUP 50029 at the British Library (Lending Division) (formerly the National Lending Library for Science and Technology), Boston Spa, Yorks. LS23 7BQ, U.K., from whom copies may be obtained on the terms given in Biochem. J. (1973) 131, 5. PMID:4141893

"De-novo" amino acid sequence elucidation of protein G'e by combined "top-down" and "bottom-up" mass spectrometry.

PubMed

Yefremova, Yelena; Al-Majdoub, Mahmoud; Opuni, Kwabena F M; Koy, Cornelia; Cui, Weidong; Yan, Yuetian; Gross, Michael L; Glocker, Michael O

2015-03-01

Mass spectrometric de-novo sequencing was applied to review the amino acid sequence of a commercially available recombinant protein G´ with great scientific and economic importance. Substantial deviations to the published amino acid sequence (Uniprot Q54181) were found by the presence of 46 additional amino acids at the N-terminus, including a so-called "His-tag" as well as an N-terminal partial α-N-gluconoylation and α-N-phosphogluconoylation, respectively. The unexpected amino acid sequence of the commercial protein G' comprised 241 amino acids and resulted in a molecular mass of 25,998.9 ± 0.2 Da for the unmodified protein. Due to the higher mass that is caused by its extended amino acid sequence compared with the original protein G' (185 amino acids), we named this protein "protein G'e." By means of mass spectrometric peptide mapping, the suggested amino acid sequence, as well as the N-terminal partial α-N-gluconoylations, was confirmed with 100% sequence coverage. After the protein G'e sequence was determined, we were able to determine the expression vector pET-28b from Novagen with the Xho I restriction enzyme cleavage site as the best option that was used for cloning and expressing the recombinant protein G'e in E. coli. A dissociation constant (K(d)) value of 9.4 nM for protein G'e was determined thermophoretically, showing that the N-terminal flanking sequence extension did not cause significant changes in the binding affinity to immunoglobulins.

PDNAsite: Identification of DNA-binding Site from Protein Sequence by Incorporating Spatial and Sequence Context

PubMed Central

Zhou, Jiyun; Xu, Ruifeng; He, Yulan; Lu, Qin; Wang, Hongpeng; Kong, Bing

2016-01-01

Protein-DNA interactions are involved in many fundamental biological processes essential for cellular function. Most of the existing computational approaches employed only the sequence context of the target residue for its prediction. In the present study, for each target residue, we applied both the spatial context and the sequence context to construct the feature space. Subsequently, Latent Semantic Analysis (LSA) was applied to remove the redundancies in the feature space. Finally, a predictor (PDNAsite) was developed through the integration of the support vector machines (SVM) classifier and ensemble learning. Results on the PDNA-62 and the PDNA-224 datasets demonstrate that features extracted from spatial context provide more information than those from sequence context and the combination of them gives more performance gain. An analysis of the number of binding sites in the spatial context of the target site indicates that the interactions between binding sites next to each other are important for protein-DNA recognition and their binding ability. The comparison between our proposed PDNAsite method and the existing methods indicate that PDNAsite outperforms most of the existing methods and is a useful tool for DNA-binding site identification. A web-server of our predictor (http://hlt.hitsz.edu.cn:8080/PDNAsite/) is made available for free public accessible to the biological research community. PMID:27282833

Large-scale analysis of intrinsic disorder flavors and associated functions in the protein sequence universe.

PubMed

Necci, Marco; Piovesan, Damiano; Tosatto, Silvio C E

2016-12-01

Intrinsic disorder (ID) in proteins has been extensively described for the last decade; a large-scale classification of ID in proteins is mostly missing. Here, we provide an extensive analysis of ID in the protein universe on the UniProt database derived from sequence-based predictions in MobiDB. Almost half the sequences contain an ID region of at least five residues. About 9% of proteins have a long ID region of over 20 residues which are more abundant in Eukaryotic organisms and most frequently cover less than 20% of the sequence. A small subset of about 67,000 (out of over 80 million) proteins is fully disordered and mostly found in Viruses. Most proteins have only one ID, with short ID evenly distributed along the sequence and long ID overrepresented in the center. The charged residue composition of Das and Pappu was used to classify ID proteins by structural propensities and corresponding functional enrichment. Swollen Coils seem to be used mainly as structural components and in biosynthesis in both Prokaryotes and Eukaryotes. In Bacteria, they are confined in the nucleoid and in Viruses provide DNA binding function. Coils & Hairpins seem to be specialized in ribosome binding and methylation activities. Globules & Tadpoles bind antigens in Eukaryotes but are involved in killing other organisms and cytolysis in Bacteria. The Undefined class is used by Bacteria to bind toxic substances and mediate transport and movement between and within organisms in Viruses. Fully disordered proteins behave similarly, but are enriched for glycine residues and extracellular structures. © 2016 The Protein Society.

Nucleotide sequence analysis of the gene encoding the Deinococcus radiodurans surface protein, derived amino acid sequence, and complementary protein chemical studies

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

Peters, J.; Peters, M.; Lottspeich, F.

1987-11-01

The complete nucleotide sequence of the gene encoding the surface (hexagonally packed intermediate (HPI))-layer polypeptide of Deinococcus radiodurans Sark was determined and found to encode a polypeptide of 1036 amino acids. Amino acid sequence analysis of about 30% of the residues revealed that the mature polypeptide consists of at least 978 amino acids. The N terminus was blocked to Edman degradation. The results of proteolytic modification of the HPI layer in situ and M/sub r/ estimations of the HPI polypeptide expressed in Escherichia coli indicated that there is a leader sequence. The N-terminal region contained a very high percentage (29%)more » of threonine and serine, including a cluster of nine consecutive serine or threonine residues, whereas a stretch near the C terminus was extremely rich in aromatic amino acids (29%). The protein contained at least two disulfide bridges, as well as tightly bound reducing sugars and fatty acids.« less

Predicting protein-protein interactions by combing various sequence- derived features into the general form of Chou's Pseudo amino acid composition.

PubMed

Zhao, Xiao-Wei; Ma, Zhi-Qiang; Yin, Ming-Hao

2012-05-01

Knowledge of protein-protein interactions (PPIs) plays an important role in constructing protein interaction networks and understanding the general machineries of biological systems. In this study, a new method is proposed to predict PPIs using a comprehensive set of 930 features based only on sequence information, these features measure the interactions between residues a certain distant apart in the protein sequences from different aspects. To achieve better performance, the principal component analysis (PCA) is first employed to obtain an optimized feature subset. Then, the resulting 67-dimensional feature vectors are fed to Support Vector Machine (SVM). Experimental results on Drosophila melanogaster and Helicobater pylori datasets show that our method is very promising to predict PPIs and may at least be a useful supplement tool to existing methods.

RStrucFam: a web server to associate structure and cognate RNA for RNA-binding proteins from sequence information.

PubMed

Ghosh, Pritha; Mathew, Oommen K; Sowdhamini, Ramanathan

2016-10-07

RNA-binding proteins (RBPs) interact with their cognate RNA(s) to form large biomolecular assemblies. They are versatile in their functionality and are involved in a myriad of processes inside the cell. RBPs with similar structural features and common biological functions are grouped together into families and superfamilies. It will be useful to obtain an early understanding and association of RNA-binding property of sequences of gene products. Here, we report a web server, RStrucFam, to predict the structure, type of cognate RNA(s) and function(s) of proteins, where possible, from mere sequence information. The web server employs Hidden Markov Model scan (hmmscan) to enable association to a back-end database of structural and sequence families. The database (HMMRBP) comprises of 437 HMMs of RBP families of known structure that have been generated using structure-based sequence alignments and 746 sequence-centric RBP family HMMs. The input protein sequence is associated with structural or sequence domain families, if structure or sequence signatures exist. In case of association of the protein with a family of known structures, output features like, multiple structure-based sequence alignment (MSSA) of the query with all others members of that family is provided. Further, cognate RNA partner(s) for that protein, Gene Ontology (GO) annotations, if any and a homology model of the protein can be obtained. The users can also browse through the database for details pertaining to each family, protein or RNA and their related information based on keyword search or RNA motif search. RStrucFam is a web server that exploits structurally conserved features of RBPs, derived from known family members and imprinted in mathematical profiles, to predict putative RBPs from sequence information. Proteins that fail to associate with such structure-centric families are further queried against the sequence-centric RBP family HMMs in the HMMRBP database. Further, all other essential

Domain fusion analysis by applying relational algebra to protein sequence and domain databases.

PubMed

Truong, Kevin; Ikura, Mitsuhiko

2003-05-06

Domain fusion analysis is a useful method to predict functionally linked proteins that may be involved in direct protein-protein interactions or in the same metabolic or signaling pathway. As separate domain databases like BLOCKS, PROSITE, Pfam, SMART, PRINTS-S, ProDom, TIGRFAMs, and amalgamated domain databases like InterPro continue to grow in size and quality, a computational method to perform domain fusion analysis that leverages on these efforts will become increasingly powerful. This paper proposes a computational method employing relational algebra to find domain fusions in protein sequence databases. The feasibility of this method was illustrated on the SWISS-PROT+TrEMBL sequence database using domain predictions from the Pfam HMM (hidden Markov model) database. We identified 235 and 189 putative functionally linked protein partners in H. sapiens and S. cerevisiae, respectively. From scientific literature, we were able to confirm many of these functional linkages, while the remainder offer testable experimental hypothesis. Results can be viewed at http://calcium.uhnres.utoronto.ca/pi. As the analysis can be computed quickly on any relational database that supports standard SQL (structured query language), it can be dynamically updated along with the sequence and domain databases, thereby improving the quality of predictions over time.

Domain fusion analysis by applying relational algebra to protein sequence and domain databases

PubMed Central

Truong, Kevin; Ikura, Mitsuhiko

2003-01-01

Background Domain fusion analysis is a useful method to predict functionally linked proteins that may be involved in direct protein-protein interactions or in the same metabolic or signaling pathway. As separate domain databases like BLOCKS, PROSITE, Pfam, SMART, PRINTS-S, ProDom, TIGRFAMs, and amalgamated domain databases like InterPro continue to grow in size and quality, a computational method to perform domain fusion analysis that leverages on these efforts will become increasingly powerful. Results This paper proposes a computational method employing relational algebra to find domain fusions in protein sequence databases. The feasibility of this method was illustrated on the SWISS-PROT+TrEMBL sequence database using domain predictions from the Pfam HMM (hidden Markov model) database. We identified 235 and 189 putative functionally linked protein partners in H. sapiens and S. cerevisiae, respectively. From scientific literature, we were able to confirm many of these functional linkages, while the remainder offer testable experimental hypothesis. Results can be viewed at . Conclusion As the analysis can be computed quickly on any relational database that supports standard SQL (structured query language), it can be dynamically updated along with the sequence and domain databases, thereby improving the quality of predictions over time. PMID:12734020

[Molecular cloning and characterization of a novel Clonorchis sinensis antigenic protein containing tandem repeat sequences].

PubMed

Liu, Qian; Xu, Xue-Nian; Zhou, Yan; Cheng, Na; Dong, Yu-Ting; Zheng, Hua-Jun; Zhu, Yong-Qiang; Zhu, Yong-Qiang

2013-08-01

To find and clone new antigen genes from the lambda-ZAP cDNA expression library of adult Clonorchis sinensis, and determine the immunological characteristics of the recombinant proteins. The cDNA expression library of adult C. sinensis was screened by pooled sera of clonorchiasis patients. The sequences of the positive phage clones were compared with the sequences in EST database, and the full-length sequence of the gene (Cs22 gene) was obtained by RT-PCR. cDNA fragments containing 2 and 3 times tandem repeat sequences were generated by jumping PCR. The sequence encoding the mature peptide or the tandem repeat sequence was respectively cloned into the prokaryotic expression vector pET28a (+), and then transformed into E. coli Rosetta DE3 cells for expression. The recombinant proteins (rCs22-2r, rCs22-3r, rCs22M-2r, and rCs22M-3r) were purified by His-bind-resin (Ni-NTA) affinity chromatography. The immunogenicity of rCs22-2r and rCs22-3r was identified by ELISA. To evaluate the immunological diagnostic value of rCs22-2r and rCs22-3r, serum samples from 35 clonorchiasis patients, 31 healthy individuals, 15 schistosomiasis patients, 15 paragonimiasis westermani patients and 13 cysticercosis patients were examined by ELISA. To locate antigenic determinants, the pooled sera of clonorchiasis patients and healthy persons were analyzed for specific antibodies by ELISA with recombinant protein rCs22M-2r and rCs22M-3r containing the tandem repeat sequences. The full-length sequence of Cs22 antigen gene of C. sinensis was obtained. It contained 13 times tandem repeat sequences of EQQDGDEEGMGGDGGRGKEKGKVEGEDGAGEQKEQA. Bioinformatics analysis indicated that the protein (Cs22) belonged to GPI-anchored proteins family. The recombinant proteins rCs22-2r and rCs22-3r showed a certain level of immunogenicity. The positive rate by ELISA coated with the purified PrCs22-2r and PrCs22-3r for sera of clonorchiasis patients both were 45.7% (16/35), and 3.2% (1/31) for those of healthy

A Sequence-Dependent DNA Condensation Induced by Prion Protein

PubMed Central

2018-01-01

Different studies indicated that the prion protein induces hybridization of complementary DNA strands. Cell culture studies showed that the scrapie isoform of prion protein remained bound with the chromosome. In present work, we used an oxazole dye, YOYO, as a reporter to quantitative characterization of the DNA condensation by prion protein. We observe that the prion protein induces greater fluorescence quenching of YOYO intercalated in DNA containing only GC bases compared to the DNA containing four bases whereas the effect of dye bound to DNA containing only AT bases is marginal. DNA-condensing biological polyamines are less effective than prion protein in quenching of DNA-bound YOYO fluorescence. The prion protein induces marginal quenching of fluorescence of the dye bound to oligonucleotides, which are resistant to condensation. The ultrastructural studies with electron microscope also validate the biophysical data. The GC bases of the target DNA are probably responsible for increased condensation in the presence of prion protein. To our knowledge, this is the first report of a human cellular protein inducing a sequence-dependent DNA condensation. The increased condensation of GC-rich DNA by prion protein may suggest a biological function of the prion protein and a role in its pathogenesis. PMID:29657864

A Sequence-Dependent DNA Condensation Induced by Prion Protein.

PubMed

Bera, Alakesh; Biring, Sajal

2018-01-01

Different studies indicated that the prion protein induces hybridization of complementary DNA strands. Cell culture studies showed that the scrapie isoform of prion protein remained bound with the chromosome. In present work, we used an oxazole dye, YOYO, as a reporter to quantitative characterization of the DNA condensation by prion protein. We observe that the prion protein induces greater fluorescence quenching of YOYO intercalated in DNA containing only GC bases compared to the DNA containing four bases whereas the effect of dye bound to DNA containing only AT bases is marginal. DNA-condensing biological polyamines are less effective than prion protein in quenching of DNA-bound YOYO fluorescence. The prion protein induces marginal quenching of fluorescence of the dye bound to oligonucleotides, which are resistant to condensation. The ultrastructural studies with electron microscope also validate the biophysical data. The GC bases of the target DNA are probably responsible for increased condensation in the presence of prion protein. To our knowledge, this is the first report of a human cellular protein inducing a sequence-dependent DNA condensation. The increased condensation of GC-rich DNA by prion protein may suggest a biological function of the prion protein and a role in its pathogenesis.

Generic accelerated sequence alignment in SeqAn using vectorization and multi-threading.

PubMed

Rahn, René; Budach, Stefan; Costanza, Pascal; Ehrhardt, Marcel; Hancox, Jonny; Reinert, Knut

2018-05-03

Pairwise sequence alignment is undoubtedly a central tool in many bioinformatics analyses. In this paper, we present a generically accelerated module for pairwise sequence alignments applicable for a broad range of applications. In our module, we unified the standard dynamic programming kernel used for pairwise sequence alignments and extended it with a generalized inter-sequence vectorization layout, such that many alignments can be computed simultaneously by exploiting SIMD (Single Instruction Multiple Data) instructions of modern processors. We then extended the module by adding two layers of thread-level parallelization, where we a) distribute many independent alignments on multiple threads and b) inherently parallelize a single alignment computation using a work stealing approach producing a dynamic wavefront progressing along the minor diagonal. We evaluated our alignment vectorization and parallelization on different processors, including the newest Intel® Xeon® (Skylake) and Intel® Xeon Phi™ (KNL) processors, and use cases. The instruction set AVX512-BW (Byte and Word), available on Skylake processors, can genuinely improve the performance of vectorized alignments. We could run single alignments 1600 times faster on the Xeon Phi™ and 1400 times faster on the Xeon® than executing them with our previous sequential alignment module. The module is programmed in C++ using the SeqAn (Reinert et al., 2017) library and distributed with version 2.4. under the BSD license. We support SSE4, AVX2, AVX512 instructions and included UME::SIMD, a SIMD-instruction wrapper library, to extend our module for further instruction sets. We thoroughly test all alignment components with all major C++ compilers on various platforms. rene.rahn@fu-berlin.de.

Sequence-Based Prediction of RNA-Binding Residues in Proteins

PubMed Central

Walia, Rasna R.; EL-Manzalawy, Yasser; Honavar, Vasant G.; Dobbs, Drena

2017-01-01

Identifying individual residues in the interfaces of protein–RNA complexes is important for understanding the molecular determinants of protein–RNA recognition and has many potential applications. Recent technical advances have led to several high-throughput experimental methods for identifying partners in protein–RNA complexes, but determining RNA-binding residues in proteins is still expensive and time-consuming. This chapter focuses on available computational methods for identifying which amino acids in an RNA-binding protein participate directly in contacting RNA. Step-by-step protocols for using three different web-based servers to predict RNA-binding residues are described. In addition, currently available web servers and software tools for predicting RNA-binding sites, as well as databases that contain valuable information about known protein–RNA complexes, RNA-binding motifs in proteins, and protein-binding recognition sites in RNA are provided. We emphasize sequence-based methods that can reliably identify interfacial residues without the requirement for structural information regarding either the RNA-binding protein or its RNA partner. PMID:27787829

The hypervariable region 1 protein of hepatitis C virus broadly reactive with sera of patients with chronic hepatitis C has a similar amino acid sequence with the consensus sequence.

PubMed

Watanabe, K; Yoshioka, K; Ito, H; Ishigami, M; Takagi, K; Utsunomiya, S; Kobayashi, M; Kishimoto, H; Yano, M; Kakumu, S

1999-11-10

Hypervariable region 1 (HVR1) proteins of hepatitis C virus (HCV) have been reported to react broadly with sera of patients with HCV infection. However, the variability of the broad reactivity of individual HVR1 proteins has not been elucidated. We assessed the reactivity of 25 different HVR1 proteins (genotype 1b) with sera of 81 patients with HCV infection (genotype 1b) by Western blot. HVR1 proteins reacted with 2-60 sera. The number of sera reactive with each HVR1 protein significantly correlated with the number of amino acid residues identical to the consensus sequence defined by Puntoriero et al. (G. Puntoriero, A. Lahm, S. Zucchelli, B. B. Ercole, R. Tafi, M. Penzzanera, M. U. Mondelli, R. Cortese, A. Tramontano, G. Galfre', and A. Nicosia. 1998. EMBO J. 17, 3521-3533. ) (r = 0.561, P < 0.005). The most widely reactive HVR1 protein, 12-22, had a sequence similar to the consensus sequence. The peptide with C-terminal 13-amino-acids sequence of HVR1 protein 12-22 (NH2-CSFTSLFTPGPSQK) was injected into rabbits as an immunogen. The rabbit immune sera reacted with 9 of 25 HVR1 proteins of genotype 1b including HVR1 protein 12-22 and with 3 of 12 proteins of genotype 2a. These results indicate that the HVR1 protein broadly reactive with patients' sera has a sequence similar to the consensus sequence, can induce broadly reactive sera, and could be one of the candidate immunogens in a prophylactic vaccine against HCV. Copyright 1999 Academic Press.

Rapid detection, classification and accurate alignment of up to a million or more related protein sequences.

PubMed

Neuwald, Andrew F

2009-08-01

The patterns of sequence similarity and divergence present within functionally diverse, evolutionarily related proteins contain implicit information about corresponding biochemical similarities and differences. A first step toward accessing such information is to statistically analyze these patterns, which, in turn, requires that one first identify and accurately align a very large set of protein sequences. Ideally, the set should include many distantly related, functionally divergent subgroups. Because it is extremely difficult, if not impossible for fully automated methods to align such sequences correctly, researchers often resort to manual curation based on detailed structural and biochemical information. However, multiply-aligning vast numbers of sequences in this way is clearly impractical. This problem is addressed using Multiply-Aligned Profiles for Global Alignment of Protein Sequences (MAPGAPS). The MAPGAPS program uses a set of multiply-aligned profiles both as a query to detect and classify related sequences and as a template to multiply-align the sequences. It relies on Karlin-Altschul statistics for sensitivity and on PSI-BLAST (and other) heuristics for speed. Using as input a carefully curated multiple-profile alignment for P-loop GTPases, MAPGAPS correctly aligned weakly conserved sequence motifs within 33 distantly related GTPases of known structure. By comparison, the sequence- and structurally based alignment methods hmmalign and PROMALS3D misaligned at least 11 and 23 of these regions, respectively. When applied to a dataset of 65 million protein sequences, MAPGAPS identified, classified and aligned (with comparable accuracy) nearly half a million putative P-loop GTPase sequences. A C++ implementation of MAPGAPS is available at http://mapgaps.igs.umaryland.edu. Supplementary data are available at Bioinformatics online.

A novel class of plant-specific zinc-dependent DNA-binding protein that binds to A/T-rich DNA sequences

PubMed Central

Nagano, Yukio; Furuhashi, Hirofumi; Inaba, Takehito; Sasaki, Yukiko

2001-01-01

Complementary DNA encoding a DNA-binding protein, designated PLATZ1 (plant AT-rich sequence- and zinc-binding protein 1), was isolated from peas. The amino acid sequence of the protein is similar to those of other uncharacterized proteins predicted from the genome sequences of higher plants. However, no paralogous sequences have been found outside the plant kingdom. Multiple alignments among these paralogous proteins show that several cysteine and histidine residues are invariant, suggesting that these proteins are a novel class of zinc-dependent DNA-binding proteins with two distantly located regions, C-x2-H-x11-C-x2-C-x(4–5)-C-x2-C-x(3–7)-H-x2-H and C-x2-C-x(10–11)-C-x3-C. In an electrophoretic mobility shift assay, the zinc chelator 1,10-o-phenanthroline inhibited DNA binding, and two distant zinc-binding regions were required for DNA binding. A protein blot with 65ZnCl2 showed that both regions are required for zinc-binding activity. The PLATZ1 protein non-specifically binds to A/T-rich sequences, including the upstream region of the pea GTPase pra2 and plastocyanin petE genes. Expression of the PLATZ1 repressed those of the reporter constructs containing the coding sequence of luciferase gene driven by the cauliflower mosaic virus (CaMV) 35S90 promoter fused to the tandem repeat of the A/T-rich sequences. These results indicate that PLATZ1 is a novel class of plant-specific zinc-dependent DNA-binding protein responsible for A/T-rich sequence-mediated transcriptional repression. PMID:11600698

Analysis of sequencing data for probing RNA secondary structures and protein-RNA binding in studying posttranscriptional regulations.

PubMed

Hu, Xihao; Wu, Yang; Lu, Zhi John; Yip, Kevin Y

2016-11-01

High-throughput sequencing has been used to study posttranscriptional regulations, where the identification of protein-RNA binding is a major and fast-developing sub-area, which is in turn benefited by the sequencing methods for whole-transcriptome probing of RNA secondary structures. In the study of RNA secondary structures using high-throughput sequencing, bases are modified or cleaved according to their structural features, which alter the resulting composition of sequencing reads. In the study of protein-RNA binding, methods have been proposed to immuno-precipitate (IP) protein-bound RNA transcripts in vitro or in vivo By sequencing these transcripts, the protein-RNA interactions and the binding locations can be identified. For both types of data, read counts are affected by a combination of confounding factors, including expression levels of transcripts, sequence biases, mapping errors and the probing or IP efficiency of the experimental protocols. Careful processing of the sequencing data and proper extraction of important features are fundamentally important to a successful analysis. Here we review and compare different experimental methods for probing RNA secondary structures and binding sites of RNA-binding proteins (RBPs), and the computational methods proposed for analyzing the corresponding sequencing data. We suggest how these two types of data should be integrated to study the structural properties of RBP binding sites as a systematic way to better understand posttranscriptional regulations. © The Author 2015. Published by Oxford University Press. For Permissions, please email: journals.permissions@oup.com.

Sequence walkers: a graphical method to display how binding proteins interact with DNA or RNA sequences | Center for Cancer Research

Cancer.gov

A graphical method is presented for displaying how binding proteins and other macromolecules interact with individual bases of nucleotide sequences. Characters representing the sequence are either oriented normally and placed above a line indicating favorable contact, or upside-down and placed below the line indicating unfavorable contact. The positive or negative height of

ORFer--retrieval of protein sequences and open reading frames from GenBank and storage into relational databases or text files.

PubMed

Büssow, Konrad; Hoffmann, Steve; Sievert, Volker

2002-12-19

Functional genomics involves the parallel experimentation with large sets of proteins. This requires management of large sets of open reading frames as a prerequisite of the cloning and recombinant expression of these proteins. A Java program was developed for retrieval of protein and nucleic acid sequences and annotations from NCBI GenBank, using the XML sequence format. Annotations retrieved by ORFer include sequence name, organism and also the completeness of the sequence. The program has a graphical user interface, although it can be used in a non-interactive mode. For protein sequences, the program also extracts the open reading frame sequence, if available, and checks its correct translation. ORFer accepts user input in the form of single or lists of GenBank GI identifiers or accession numbers. It can be used to extract complete sets of open reading frames and protein sequences from any kind of GenBank sequence entry, including complete genomes or chromosomes. Sequences are either stored with their features in a relational database or can be exported as text files in Fasta or tabulator delimited format. The ORFer program is freely available at http://www.proteinstrukturfabrik.de/orfer. The ORFer program allows for fast retrieval of DNA sequences, protein sequences and their open reading frames and sequence annotations from GenBank. Furthermore, storage of sequences and features in a relational database is supported. Such a database can supplement a laboratory information system (LIMS) with appropriate sequence information.

Recurring sequence-structure motifs in (βα)8-barrel proteins and experimental optimization of a chimeric protein designed based on such motifs.

PubMed

Wang, Jichao; Zhang, Tongchuan; Liu, Ruicun; Song, Meilin; Wang, Juncheng; Hong, Jiong; Chen, Quan; Liu, Haiyan

2017-02-01

An interesting way of generating novel artificial proteins is to combine sequence motifs from natural proteins, mimicking the evolutionary path suggested by natural proteins comprising recurring motifs. We analyzed the βα and αβ modules of TIM barrel proteins by structure alignment-based sequence clustering. A number of preferred motifs were identified. A chimeric TIM was designed by using recurring elements as mutually compatible interfaces. The foldability of the designed TIM protein was then significantly improved by six rounds of directed evolution. The melting temperature has been improved by more than 20°C. A variety of characteristics suggested that the resulting protein is well-folded. Our analysis provided a library of peptide motifs that is potentially useful for different protein engineering studies. The protein engineering strategy of using recurring motifs as interfaces to connect partial natural proteins may be applied to other protein folds. Copyright © 2016 Elsevier B.V. All rights reserved.

Whole Wiskott‑Aldrich syndrome protein gene deletion identified by high throughput sequencing.

PubMed

He, Xiangling; Zou, Runying; Zhang, Bing; You, Yalan; Yang, Yang; Tian, Xin

2017-11-01

Wiskott‑Aldrich syndrome (WAS) is a rare X‑linked recessive immunodeficiency disorder, characterized by thrombocytopenia, small platelets, eczema and recurrent infections associated with increased risk of autoimmunity and malignancy disorders. Mutations in the WAS protein (WASP) gene are responsible for WAS. To date, WASP mutations, including missense/nonsense, splicing, small deletions, small insertions, gross deletions, and gross insertions have been identified in patients with WAS. In addition, WASP‑interacting proteins are suspected in patients with clinical features of WAS, in whom the WASP gene sequence and mRNA levels are normal. The present study aimed to investigate the application of next generation sequencing in definitive diagnosis and clinical therapy for WAS. A 5 month‑old child with WAS who displayed symptoms of thrombocytopenia was examined. Whole exome sequence analysis of genomic DNA showed that the coverage and depth of WASP were extremely low. Quantitative polymerase chain reaction indicated total WASP gene deletion in the proband. In conclusion, high throughput sequencing is useful for the verification of WAS on the genetic profile, and has implications for family planning guidance and establishment of clinical programs.

Neural network and SVM classifiers accurately predict lipid binding proteins, irrespective of sequence homology.

PubMed

Bakhtiarizadeh, Mohammad Reza; Moradi-Shahrbabak, Mohammad; Ebrahimi, Mansour; Ebrahimie, Esmaeil

2014-09-07

Due to the central roles of lipid binding proteins (LBPs) in many biological processes, sequence based identification of LBPs is of great interest. The major challenge is that LBPs are diverse in sequence, structure, and function which results in low accuracy of sequence homology based methods. Therefore, there is a need for developing alternative functional prediction methods irrespective of sequence similarity. To identify LBPs from non-LBPs, the performances of support vector machine (SVM) and neural network were compared in this study. Comprehensive protein features and various techniques were employed to create datasets. Five-fold cross-validation (CV) and independent evaluation (IE) tests were used to assess the validity of the two methods. The results indicated that SVM outperforms neural network. SVM achieved 89.28% (CV) and 89.55% (IE) overall accuracy in identification of LBPs from non-LBPs and 92.06% (CV) and 92.90% (IE) (in average) for classification of different LBPs classes. Increasing the number and the range of extracted protein features as well as optimization of the SVM parameters significantly increased the efficiency of LBPs class prediction in comparison to the only previous report in this field. Altogether, the results showed that the SVM algorithm can be run on broad, computationally calculated protein features and offers a promising tool in detection of LBPs classes. The proposed approach has the potential to integrate and improve the common sequence alignment based methods. Copyright © 2014 Elsevier Ltd. All rights reserved.

Comprehensive analysis of RNA-protein interactions by high-throughput sequencing-RNA affinity profiling.

PubMed

Tome, Jacob M; Ozer, Abdullah; Pagano, John M; Gheba, Dan; Schroth, Gary P; Lis, John T

2014-06-01

RNA-protein interactions play critical roles in gene regulation, but methods to quantitatively analyze these interactions at a large scale are lacking. We have developed a high-throughput sequencing-RNA affinity profiling (HiTS-RAP) assay by adapting a high-throughput DNA sequencer to quantify the binding of fluorescently labeled protein to millions of RNAs anchored to sequenced cDNA templates. Using HiTS-RAP, we measured the affinity of mutagenized libraries of GFP-binding and NELF-E-binding aptamers to their respective targets and identified critical regions of interaction. Mutations additively affected the affinity of the NELF-E-binding aptamer, whose interaction depended mainly on a single-stranded RNA motif, but not that of the GFP aptamer, whose interaction depended primarily on secondary structure.

The Papillomavirus Episteme: a central resource for papillomavirus sequence data and analysis.

PubMed

Van Doorslaer, Koenraad; Tan, Qina; Xirasagar, Sandhya; Bandaru, Sandya; Gopalan, Vivek; Mohamoud, Yasmin; Huyen, Yentram; McBride, Alison A

2013-01-01

The goal of the Papillomavirus Episteme (PaVE) is to provide an integrated resource for the analysis of papillomavirus (PV) genome sequences and related information. The PaVE is a freely accessible, web-based tool (http://pave.niaid.nih.gov) created around a relational database, which enables storage, analysis and exchange of sequence information. From a design perspective, the PaVE adopts an Open Source software approach and stresses the integration and reuse of existing tools. Reference PV genome sequences have been extracted from publicly available databases and reannotated using a custom-created tool. To date, the PaVE contains 241 annotated PV genomes, 2245 genes and regions, 2004 protein sequences and 47 protein structures, which users can explore, analyze or download. The PaVE provides scientists with the data and tools needed to accelerate scientific progress for the study and treatment of diseases caused by PVs.

Evol and ProDy for bridging protein sequence evolution and structural dynamics

PubMed Central

Mao, Wenzhi; Liu, Ying; Chennubhotla, Chakra; Lezon, Timothy R.; Bahar, Ivet

2014-01-01

Correlations between sequence evolution and structural dynamics are of utmost importance in understanding the molecular mechanisms of function and their evolution. We have integrated Evol, a new package for fast and efficient comparative analysis of evolutionary patterns and conformational dynamics, into ProDy, a computational toolbox designed for inferring protein dynamics from experimental and theoretical data. Using information-theoretic approaches, Evol coanalyzes conservation and coevolution profiles extracted from multiple sequence alignments of protein families with their inferred dynamics. Availability and implementation: ProDy and Evol are open-source and freely available under MIT License from http://prody.csb.pitt.edu/. Contact: bahar@pitt.edu PMID:24849577

An Ensemble Method to Distinguish Bacteriophage Virion from Non-Virion Proteins Based on Protein Sequence Characteristics.

PubMed

Zhang, Lina; Zhang, Chengjin; Gao, Rui; Yang, Runtao

2015-09-09

Bacteriophage virion proteins and non-virion proteins have distinct functions in biological processes, such as specificity determination for host bacteria, bacteriophage replication and transcription. Accurate identification of bacteriophage virion proteins from bacteriophage protein sequences is significant to understand the complex virulence mechanism in host bacteria and the influence of bacteriophages on the development of antibacterial drugs. In this study, an ensemble method for bacteriophage virion protein prediction from bacteriophage protein sequences is put forward with hybrid feature spaces incorporating CTD (composition, transition and distribution), bi-profile Bayes, PseAAC (pseudo-amino acid composition) and PSSM (position-specific scoring matrix). When performing on the training dataset 10-fold cross-validation, the presented method achieves a satisfactory prediction result with a sensitivity of 0.870, a specificity of 0.830, an accuracy of 0.850 and Matthew's correlation coefficient (MCC) of 0.701, respectively. To evaluate the prediction performance objectively, an independent testing dataset is used to evaluate the proposed method. Encouragingly, our proposed method performs better than previous studies with a sensitivity of 0.853, a specificity of 0.815, an accuracy of 0.831 and MCC of 0.662 on the independent testing dataset. These results suggest that the proposed method can be a potential candidate for bacteriophage virion protein prediction, which may provide a useful tool to find novel antibacterial drugs and to understand the relationship between bacteriophage and host bacteria. For the convenience of the vast majority of experimental Int. J. Mol. Sci. 2015, 16,21735 scientists, a user-friendly and publicly-accessible web-server for the proposed ensemble method is established.

KinFin: Software for Taxon-Aware Analysis of Clustered Protein Sequences.

PubMed

Laetsch, Dominik R; Blaxter, Mark L

2017-10-05

The field of comparative genomics is concerned with the study of similarities and differences between the information encoded in the genomes of organisms. A common approach is to define gene families by clustering protein sequences based on sequence similarity, and analyze protein cluster presence and absence in different species groups as a guide to biology. Due to the high dimensionality of these data, downstream analysis of protein clusters inferred from large numbers of species, or species with many genes, is nontrivial, and few solutions exist for transparent, reproducible, and customizable analyses. We present KinFin, a streamlined software solution capable of integrating data from common file formats and delivering aggregative annotation of protein clusters. KinFin delivers analyses based on systematic taxonomy of the species analyzed, or on user-defined, groupings of taxa, for example, sets based on attributes such as life history traits, organismal phenotypes, or competing phylogenetic hypotheses. Results are reported through graphical and detailed text output files. We illustrate the utility of the KinFin pipeline by addressing questions regarding the biology of filarial nematodes, which include parasites of veterinary and medical importance. We resolve the phylogenetic relationships between the species and explore functional annotation of proteins in clusters in key lineages and between custom taxon sets, identifying gene families of interest. KinFin can easily be integrated into existing comparative genomic workflows, and promotes transparent and reproducible analysis of clustered protein data. Copyright © 2017 Laetsch and Blaxter.

RNA regulators responding to ribosomal protein S15 are frequent in sequence space

PubMed Central

Slinger, Betty L.; Meyer, Michelle M.

2016-01-01

There are several natural examples of distinct RNA structures that interact with the same ligand to regulate the expression of homologous genes in different organisms. One essential question regarding this phenomenon is whether such RNA regulators are the result of convergent or divergent evolution. Are the RNAs derived from some common ancestor and diverged to the point where we cannot identify the similarity, or have multiple solutions to the same biological problem arisen independently? A key variable in assessing these alternatives is how frequently such regulators arise within sequence space. Ribosomal protein S15 is autogenously regulated via an RNA regulator in many bacterial species; four apparently distinct regulators have been functionally validated in different bacterial phyla. Here, we explore how frequently such regulators arise within a partially randomized sequence population. We find many RNAs that interact specifically with ribosomal protein S15 from Geobacillus kaustophilus with biologically relevant dissociation constants. Furthermore, of the six sequences we characterize, four show regulatory activity in an Escherichia coli reporter assay. Subsequent footprinting and mutagenesis analysis indicates that protein binding proximal to regulatory features such as the Shine–Dalgarno sequence is sufficient to enable regulation, suggesting that regulation in response to S15 is relatively easily acquired. PMID:27580716

Poliovirus replication proteins: RNA sequence encoding P3-1b and the sites of proteolytic processing

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

Semler, B.L.; Anderson, C.W.; Kitamura, N.

1981-06-01

A partial amino-terminal amino acid sequence of each of the major proteins encoded by the replicase region of the poliovirus genome has been determined. A comparison of this sequence information with the amino acid sequence predicted from the RNA sequence that has been determined for the 3' region of the poliovirus genome has allowed us to locate precisely the proteolytic cleavage sites at which the initial polyprotein is processed to create the poliovirus products P3-1b (NCVP1b), P3-2 (NCVP2), P3-4b (NCVP4b), and P3-7c (NCVP7c). For each of these products, as well as for the small genome-linked protein VPg, proteolytic cleavage occursmore » between a glutamine and a glycine residue to create the amino terminus of each protein. This result suggests that a single proteinase may be responsible for all of these cleavages. The sequence data also allow the precise positioning of the genome-linked protein VPg within the precursor P3-1b just proximal to the amino terminus of polypeptide P3-2.« less

Nucleotide sequence of the L1 ribosomal protein gene of Xenopus laevis: remarkable sequence homology among introns.

PubMed Central

Loreni, F; Ruberti, I; Bozzoni, I; Pierandrei-Amaldi, P; Amaldi, F

1985-01-01

Ribosomal protein L1 is encoded by two genes in Xenopus laevis. The comparison of two cDNA sequences shows that the two L1 gene copies (L1a and L1b) have diverged in many silent sites and very few substitution sites; moreover a small duplication occurred at the very end of the coding region of the L1b gene which thus codes for a product five amino acids longer than that coded by L1a. Quantitatively the divergence between the two L1 genes confirms that a whole genome duplication took place in Xenopus laevis approximately 30 million years ago. A genomic fragment containing one of the two L1 gene copies (L1a), with its nine introns and flanking regions, has been completely sequenced. The 5' end of this gene has been mapped within a 20-pyridimine stretch as already found for other vertebrate ribosomal protein genes. Four of the nine introns have a 60-nucleotide sequence with 80% homology; within this region some boxes, one of which is 16 nucleotides long, are 100% homologous among the four introns. This feature of L1a gene introns is interesting since we have previously shown that the activity of this gene is regulated at a post-transcriptional level and it involves the block of the normal splicing of some intron sequences. Images Fig. 3. Fig. 5. PMID:3841512

Isolation and characterization of adrenoleukodystrophy protein (ALDP) related sequences in the human genome

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

Geraghty, M.T.; Stetten, G.; Kearns, W.

1994-09-01

X-linked adrenoleukodystrophy (ALD) is a disorder of peroxisomal {beta}-oxidation of very long chain fatty acids. It presents either as progressive dementia in childhood or as progressive paraparesis in later years. Adrenal insufficiency occurs in both phenotypes. The gene of the ALD protein has been mapped to Xq28 and has recently been cloned and characterized. The ALD protein has significant homology to the peroxisomal membrane protein, PMP70 and belongs to the ATP binding cassette superfamily of transporters. We screened a human genomic library with an ALDP cDNA and isolated 5 different but highly similar clones containing sequences corresponding to the 3{prime}more » end of the ALDP gene. Comparison of the sequences over the region corresponding to exon 9 through the 3{prime} end of the ALDP gene reveals {approximately}96% nucleotide identity in both exonic and intronic regions. Splice sites and open reading frames are maintained. Using both FISH and human-rodent DNA mapping panels, we positively assign these ALDP-related sequences to chromosomes 2, 16 and 22, and provisionally to 1 and 20. Southern blot of primate DNA probed with a partial ALDP cDNA (exon 2-10) shows that expansion of ALDP-related sequences occurred in higher primates (chimp, gorilla and human). Although Northern blots show multiple ALDP-hybridizing transcripts in certain tissues, we have no evidence to date for expression of these ALDP-related sequences. In conclusion, our data show there has been an unusual and recent dispersal to multiple chromosomes of structural gene sequences related to the ALDP gene. The functional significance of these sequences remains to be determined but their existence complicates PCR and mutation analysis of the ALDP gene.« less

A combined de novo protein sequencing and cDNA library approach to the venomic analysis of Chinese spider Araneus ventricosus.

PubMed

Duan, Zhigui; Cao, Rui; Jiang, Liping; Liang, Songping

2013-01-14

In past years, spider venoms have attracted increasing attention due to their extraordinary chemical and pharmacological diversity. The recently popularized proteomic method highly improved our ability to analyze the proteins in the venom. However, the lack of information about isolated venom proteins sequences dramatically limits the ability to confidently identify venom proteins. In the present paper, the venom from Araneus ventricosus was analyzed using two complementary approaches: 2-DE/Shotgun-LC-MS/MS coupled to MASCOT search and 2-DE/Shotgun-LC-MS/MS coupled to manual de novo sequencing followed by local venom protein database (LVPD) search. The LVPD was constructed with toxin-like protein sequences obtained from the analysis of cDNA library from A. ventricosus venom glands. Our results indicate that a total of 130 toxin-like protein sequences were unambiguously identified by manual de novo sequencing coupled to LVPD search, accounting for 86.67% of all toxin-like proteins in LVPD. Thus manual de novo sequencing coupled to LVPD search was proved an extremely effective approach for the analysis of venom proteins. In addition, the approach displays impeccable advantage in validating mutant positions of isoforms from the same toxin-like family. Intriguingly, methyl esterifcation of glutamic acid was discovered for the first time in animal venom proteins by manual de novo sequencing. Crown Copyright © 2012. Published by Elsevier B.V. All rights reserved.

Thermal adaptation analyzed by comparison of protein sequences from mesophilic and extremely thermophilic Methanococcus species

NASA Technical Reports Server (NTRS)

Haney, P. J.; Badger, J. H.; Buldak, G. L.; Reich, C. I.; Woese, C. R.; Olsen, G. J.

1999-01-01

The genome sequence of the extremely thermophilic archaeon Methanococcus jannaschii provides a wealth of data on proteins from a thermophile. In this paper, sequences of 115 proteins from M. jannaschii are compared with their homologs from mesophilic Methanococcus species. Although the growth temperatures of the mesophiles are about 50 degrees C below that of M. jannaschii, their genomic G+C contents are nearly identical. The properties most correlated with the proteins of the thermophile include higher residue volume, higher residue hydrophobicity, more charged amino acids (especially Glu, Arg, and Lys), and fewer uncharged polar residues (Ser, Thr, Asn, and Gln). These are recurring themes, with all trends applying to 83-92% of the proteins for which complete sequences were available. Nearly all of the amino acid replacements most significantly correlated with the temperature change are the same relatively conservative changes observed in all proteins, but in the case of the mesophile/thermophile comparison there is a directional bias. We identify 26 specific pairs of amino acids with a statistically significant (P < 0.01) preferred direction of replacement.

Hidden Markov models incorporating fuzzy measures and integrals for protein sequence identification and alignment.

PubMed

Bidargaddi, Niranjan P; Chetty, Madhu; Kamruzzaman, Joarder

2008-06-01

Profile hidden Markov models (HMMs) based on classical HMMs have been widely applied for protein sequence identification. The formulation of the forward and backward variables in profile HMMs is made under statistical independence assumption of the probability theory. We propose a fuzzy profile HMM to overcome the limitations of that assumption and to achieve an improved alignment for protein sequences belonging to a given family. The proposed model fuzzifies the forward and backward variables by incorporating Sugeno fuzzy measures and Choquet integrals, thus further extends the generalized HMM. Based on the fuzzified forward and backward variables, we propose a fuzzy Baum-Welch parameter estimation algorithm for profiles. The strong correlations and the sequence preference involved in the protein structures make this fuzzy architecture based model as a suitable candidate for building profiles of a given family, since the fuzzy set can handle uncertainties better than classical methods.

From protein sequence to dynamics and disorder with DynaMine.

PubMed

Cilia, Elisa; Pancsa, Rita; Tompa, Peter; Lenaerts, Tom; Vranken, Wim F

2013-01-01

Protein function and dynamics are closely related; however, accurate dynamics information is difficult to obtain. Here based on a carefully assembled data set derived from experimental data for proteins in solution, we quantify backbone dynamics properties on the amino-acid level and develop DynaMine--a fast, high-quality predictor of protein backbone dynamics. DynaMine uses only protein sequence information as input and shows great potential in distinguishing regions of different structural organization, such as folded domains, disordered linkers, molten globules and pre-structured binding motifs of different sizes. It also identifies disordered regions within proteins with an accuracy comparable to the most sophisticated existing predictors, without depending on prior disorder knowledge or three-dimensional structural information. DynaMine provides molecular biologists with an important new method that grasps the dynamical characteristics of any protein of interest, as we show here for human p53 and E1A from human adenovirus 5.

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

CROSS-DISCIPLINARY PHYSICS AND RELATED AREAS OF SCIENCE AND TECHNOLOGY: Chaos game representation walk model for the protein sequences

NASA Astrophysics Data System (ADS)

Gao, Jie; Jiang, Li-Li; Xu, Zhen-Yuan

2009-10-01

A new chaos game representation of protein sequences based on the detailed hydrophobic-hydrophilic (HP) model has been proposed by Yu et al (Physica A 337 (2004) 171). A CGR-walk model is proposed based on the new CGR coordinates for the protein sequences from complete genomes in the present paper. The new CGR coordinates based on the detailed HP model are converted into a time series, and a long-memory ARFIMA(p, d, q) model is introduced into the protein sequence analysis. This model is applied to simulating real CGR-walk sequence data of twelve protein sequences. Remarkably long-range correlations are uncovered in the data and the results obtained from these models are reasonably consistent with those available from the ARFIMA(p, d, q) model.

Sequence preservation of osteocalcin protein and mitochondrial DNA in bison bones older than 55 ka

NASA Astrophysics Data System (ADS)

Nielsen-Marsh, Christina M.; Ostrom, Peggy H.; Gandhi, Hasand; Shapiro, Beth; Cooper, Alan; Hauschka, Peter V.; Collins, Matthew J.

2002-12-01

We report the first complete sequences of the protein osteocalcin from small amounts (20 mg) of two bison bone (Bison priscus) dated to older than 55.6 ka and older than 58.9 ka. Osteocalcin was purified using new gravity columns (never exposed to protein) followed by microbore reversed-phase high-performance liquid chromatography. Sequencing of osteocalcin employed two methods of matrix-assisted laser desorption ionization mass spectrometry (MALDI-MS): peptide mass mapping (PMM) and post-source decay (PSD). The PMM shows that ancient and modern bison osteocalcin have the same mass to charge (m/z) distribution, indicating an identical protein sequence and absence of diagenetic products. This was confirmed by PSD of the m/z 2066 tryptic peptide (residues 1 19); the mass spectra from ancient and modern peptides were identical. The 129 mass unit difference in the molecular ion between cow (Bos taurus) and bison is caused by a single amino-acid substitution between the taxa (Trp in cow is replaced by Gly in bison at residue 5). Bison mitochondrial control region DNA sequences were obtained from the older than 55.6 ka fossil. These results suggest that DNA and protein sequences can be used to directly investigate molecular phylogenies over a considerable time period, the absolute limit of which is yet to be determined.

Sequence, Structure, and Context Preferences of Human RNA Binding Proteins.

PubMed

Dominguez, Daniel; Freese, Peter; Alexis, Maria S; Su, Amanda; Hochman, Myles; Palden, Tsultrim; Bazile, Cassandra; Lambert, Nicole J; Van Nostrand, Eric L; Pratt, Gabriel A; Yeo, Gene W; Graveley, Brenton R; Burge, Christopher B

2018-06-07

RNA binding proteins (RBPs) orchestrate the production, processing, and function of mRNAs. Here, we present the affinity landscapes of 78 human RBPs using an unbiased assay that determines the sequence, structure, and context preferences of these proteins in vitro by deep sequencing of bound RNAs. These data enable construction of "RNA maps" of RBP activity without requiring crosslinking-based assays. We found an unexpectedly low diversity of RNA motifs, implying frequent convergence of binding specificity toward a relatively small set of RNA motifs, many with low compositional complexity. Offsetting this trend, however, we observed extensive preferences for contextual features distinct from short linear RNA motifs, including spaced "bipartite" motifs, biased flanking nucleotide composition, and bias away from or toward RNA structure. Our results emphasize the importance of contextual features in RNA recognition, which likely enable targeting of distinct subsets of transcripts by different RBPs that recognize the same linear motif. Copyright © 2018 The Authors. Published by Elsevier Inc. All rights reserved.

PFP: Automated prediction of gene ontology functional annotations with confidence scores using protein sequence data.

PubMed

Hawkins, Troy; Chitale, Meghana; Luban, Stanislav; Kihara, Daisuke

2009-02-15

Protein function prediction is a central problem in bioinformatics, increasing in importance recently due to the rapid accumulation of biological data awaiting interpretation. Sequence data represents the bulk of this new stock and is the obvious target for consideration as input, as newly sequenced organisms often lack any other type of biological characterization. We have previously introduced PFP (Protein Function Prediction) as our sequence-based predictor of Gene Ontology (GO) functional terms. PFP interprets the results of a PSI-BLAST search by extracting and scoring individual functional attributes, searching a wide range of E-value sequence matches, and utilizing conventional data mining techniques to fill in missing information. We have shown it to be effective in predicting both specific and low-resolution functional attributes when sufficient data is unavailable. Here we describe (1) significant improvements to the PFP infrastructure, including the addition of prediction significance and confidence scores, (2) a thorough benchmark of performance and comparisons to other related prediction methods, and (3) applications of PFP predictions to genome-scale data. We applied PFP predictions to uncharacterized protein sequences from 15 organisms. Among these sequences, 60-90% could be annotated with a GO molecular function term at high confidence (>or=80%). We also applied our predictions to the protein-protein interaction network of the Malaria plasmodium (Plasmodium falciparum). High confidence GO biological process predictions (>or=90%) from PFP increased the number of fully enriched interactions in this dataset from 23% of interactions to 94%. Our benchmark comparison shows significant performance improvement of PFP relative to GOtcha, InterProScan, and PSI-BLAST predictions. This is consistent with the performance of PFP as the overall best predictor in both the AFP-SIG '05 and CASP7 function (FN) assessments. PFP is available as a web service at http

Understanding the mechanisms of protein-DNA interactions

NASA Astrophysics Data System (ADS)

Lavery, Richard

2004-03-01

Structural, biochemical and thermodynamic data on protein-DNA interactions show that specific recognition cannot be reduced to a simple set of binary interactions between the partners (such as hydrogen bonds, ion pairs or steric contacts). The mechanical properties of the partners also play a role and, in the case of DNA, variations in both conformation and flexibility as a function of base sequence can be a significant factor in guiding a protein to the correct binding site. All-atom molecular modeling offers a means of analyzing the role of different binding mechanisms within protein-DNA complexes of known structure. This however requires estimating the binding strengths for the full range of sequences with which a given protein can interact. Since this number grows exponentially with the length of the binding site it is necessary to find a method to accelerate the calculations. We have achieved this by using a multi-copy approach (ADAPT) which allows us to build a DNA fragment with a variable base sequence. The results obtained with this method correlate well with experimental consensus binding sequences. They enable us to show that indirect recognition mechanisms involving the sequence dependent properties of DNA play a significant role in many complexes. This approach also offers a means of predicting protein binding sites on the basis of binding energies, which is complementary to conventional lexical techniques.

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.

Variability of the protein sequences of lcrV between epidemic and atypical rhamnose-positive strains of Yersinia pestis.

PubMed

Anisimov, Andrey P; Panfertsev, Evgeniy A; Svetoch, Tat'yana E; Dentovskaya, Svetlana V

2007-01-01

Sequencing of lcrV genes and comparison of the deduced amino acid sequences from ten Y. pestis strains belonging mostly to the group of atypical rhamnose-positive isolates (non-pestis subspecies or pestoides group) showed that the LcrV proteins analyzed could be classified into five sequence types. This classification was based on major amino acid polymorphisms among LcrV proteins in the four "hot points" of the protein sequences. Some additional minor polymorphisms were found throughout these sequence types. The "hot points" corresponded to amino acids 18 (Lys --> Asn), 72 (Lys --> Arg), 273 (Cys --> Ser), and 324-326 (Ser-Gly-Lys --> Arg) in the LcrV sequence of the reference Y. pestis strain CO92. One possible explanation for polymorphism in amino acid sequences of LcrV among different strains is that strain-specific variation resulted from adaptation of the plague pathogen to different rodent and lagomorph hosts.

Evol and ProDy for bridging protein sequence evolution and structural dynamics.

PubMed

Bakan, Ahmet; Dutta, Anindita; Mao, Wenzhi; Liu, Ying; Chennubhotla, Chakra; Lezon, Timothy R; Bahar, Ivet

2014-09-15

Correlations between sequence evolution and structural dynamics are of utmost importance in understanding the molecular mechanisms of function and their evolution. We have integrated Evol, a new package for fast and efficient comparative analysis of evolutionary patterns and conformational dynamics, into ProDy, a computational toolbox designed for inferring protein dynamics from experimental and theoretical data. Using information-theoretic approaches, Evol coanalyzes conservation and coevolution profiles extracted from multiple sequence alignments of protein families with their inferred dynamics. ProDy and Evol are open-source and freely available under MIT License from http://prody.csb.pitt.edu/. © The Author 2014. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Artificial seismic acceleration

USGS Publications Warehouse

Felzer, Karen R.; Page, Morgan T.; Michael, Andrew J.

2015-01-01

In their 2013 paper, Bouchon, Durand, Marsan, Karabulut, 3 and Schmittbuhl (BDMKS) claim to see significant accelerating seismicity before M 6.5 interplate mainshocks, but not before intraplate mainshocks, reflecting a preparatory process before large events. We concur with the finding of BDMKS that their interplate dataset has significantly more fore- shocks than their intraplate dataset; however, we disagree that the foreshocks are predictive of large events in particular. Acceleration in stacked foreshock sequences has been seen before and has been explained by the cascade model, in which earthquakes occasionally trigger aftershocks larger than themselves4. In this model, the time lags between the smaller mainshocks and larger aftershocks follow the inverse power law common to all aftershock sequences, creating an apparent acceleration when stacked (see Supplementary Information).

Apple Macintosh programs for nucleic and protein sequence analyses.

PubMed Central

Bellon, B

1988-01-01

This paper describes a package of programs for handling and analyzing nucleic acid and protein sequences using the Apple Macintosh microcomputer. There are three important features of these programs: first, because of the now classical Macintosh interface the programs can be easily used by persons with little or no computer experience. Second, it is possible to save all the data, written in an editable scrolling text window or drawn in a graphic window, as files that can be directly used either as word processing documents or as picture documents. Third, sequences can be easily exchanged with any other computer. The package is composed of thirteen programs, written in Pascal programming language. PMID:2832832

Assignment of protein sequences to existing domain and family classification systems: Pfam and the PDB.

PubMed

Xu, Qifang; Dunbrack, Roland L

2012-11-01

Automating the assignment of existing domain and protein family classifications to new sets of sequences is an important task. Current methods often miss assignments because remote relationships fail to achieve statistical significance. Some assignments are not as long as the actual domain definitions because local alignment methods often cut alignments short. Long insertions in query sequences often erroneously result in two copies of the domain assigned to the query. Divergent repeat sequences in proteins are often missed. We have developed a multilevel procedure to produce nearly complete assignments of protein families of an existing classification system to a large set of sequences. We apply this to the task of assigning Pfam domains to sequences and structures in the Protein Data Bank (PDB). We found that HHsearch alignments frequently scored more remotely related Pfams in Pfam clans higher than closely related Pfams, thus, leading to erroneous assignment at the Pfam family level. A greedy algorithm allowing for partial overlaps was, thus, applied first to sequence/HMM alignments, then HMM-HMM alignments and then structure alignments, taking care to join partial alignments split by large insertions into single-domain assignments. Additional assignment of repeat Pfams with weaker E-values was allowed after stronger assignments of the repeat HMM. Our database of assignments, presented in a database called PDBfam, contains Pfams for 99.4% of chains >50 residues. The Pfam assignment data in PDBfam are available at http://dunbrack2.fccc.edu/ProtCid/PDBfam, which can be searched by PDB codes and Pfam identifiers. They will be updated regularly.

Accelerating Aerobic Sludge Granulation by Adding Dry Sewage Sludge Micropowder in Sequencing Batch Reactors

PubMed Central

Li, Jun; Liu, Jun; Wang, Danjun; Chen, Tao; Ma, Ting; Wang, Zhihong; Zhuo, Weilong

2015-01-01

Micropowder (20–250 µm) made from ground dry waste sludge from a municipal sewage treatment plant was added in a sequencing batch reactor (R2), which was fed by synthetic wastewater with acetate as carbon source. Compared with the traditional SBR (R1), aerobic sludge granulation time was shortened 15 days in R2. Furthermore, filamentous bacteria in bulking sludge were controlled to accelerate aerobic granulation and form large granules. Correspondingly, the SVI decreased from 225 mL/g to 37 mL/g. X-ray Fluorescence (XRF) analysis demonstrated that Al and Si from the micropowder were accumulated in granules. A mechanism hypotheses for the acceleration of aerobic granulation by adding dry sludge micropowder is proposed: added micropowder acts as nuclei to induce bacterial attachment; dissolved matters from the micropowder increase abruptly the organic load for starved sludge to control overgrown filamentous bacteria as a framework for aggregation; increased friction from the movement of micropowder forces the filaments which extend outwards to shrink for shaping granules. PMID:26308025

Visualization of protein sequence features using JavaScript and SVG with pViz.js.

PubMed

Mukhyala, Kiran; Masselot, Alexandre

2014-12-01

pViz.js is a visualization library for displaying protein sequence features in a Web browser. By simply providing a sequence and the locations of its features, this lightweight, yet versatile, JavaScript library renders an interactive view of the protein features. Interactive exploration of protein sequence features over the Web is a common need in Bioinformatics. Although many Web sites have developed viewers to display these features, their implementations are usually focused on data from a specific source or use case. Some of these viewers can be adapted to fit other use cases but are not designed to be reusable. pViz makes it easy to display features as boxes aligned to a protein sequence with zooming functionality but also includes predefined renderings for secondary structure and post-translational modifications. The library is designed to further customize this view. We demonstrate such applications of pViz using two examples: a proteomic data visualization tool with an embedded viewer for displaying features on protein structure, and a tool to visualize the results of the variant_effect_predictor tool from Ensembl. pViz.js is a JavaScript library, available on github at https://github.com/Genentech/pviz. This site includes examples and functional applications, installation instructions and usage documentation. A Readme file, which explains how to use pViz with examples, is available as Supplementary Material A. © The Author 2014. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Sequence fingerprints distinguish erroneous from correct predictions of intrinsically disordered protein regions.

PubMed

Saravanan, Konda Mani; Dunker, A Keith; Krishnaswamy, Sankaran

2017-12-27

More than 60 prediction methods for intrinsically disordered proteins (IDPs) have been developed over the years, many of which are accessible on the World Wide Web. Nearly, all of these predictors give balanced accuracies in the ~65%-~80% range. Since predictors are not perfect, further studies are required to uncover the role of amino acid residues in native IDP as compared to predicted IDP regions. In the present work, we make use of sequences of 100% predicted IDP regions, false positive disorder predictions, and experimentally determined IDP regions to distinguish the characteristics of native versus predicted IDP regions. A higher occurrence of asparagine is observed in sequences of native IDP regions but not in sequences of false positive predictions of IDP regions. The occurrences of certain combinations of amino acids at the pentapeptide level provide a distinguishing feature in the IDPs with respect to globular proteins. The distinguishing features presented in this paper provide insights into the sequence fingerprints of amino acid residues in experimentally determined as compared to predicted IDP regions. These observations and additional work along these lines should enable the development of improvements in the accuracy of disorder prediction algorithm.

G protein-coupled odorant receptors: From sequence to structure

PubMed Central

de March, Claire A; Kim, Soo-Kyung; Antonczak, Serge; Goddard, William A; Golebiowski, Jérôme

2015-01-01

Odorant receptors (ORs) are the largest subfamily within class A G protein-coupled receptors (GPCRs). No experimental structural data of any OR is available to date and atomic-level insights are likely to be obtained by means of molecular modeling. In this article, we critically align sequences of ORs with those GPCRs for which a structure is available. Here, an alignment consistent with available site-directed mutagenesis data on various ORs is proposed. Using this alignment, the choice of the template is deemed rather minor for identifying residues that constitute the wall of the binding cavity or those involved in G protein recognition. PMID:26044705

Implication of the cause of differences in 3D structures of proteins with high sequence identity based on analyses of amino acid sequences and 3D structures.

PubMed

Matsuoka, Masanari; Sugita, Masatake; Kikuchi, Takeshi

2014-09-18

Proteins that share a high sequence homology while exhibiting drastically different 3D structures are investigated in this study. Recently, artificial proteins related to the sequences of the GA and IgG binding GB domains of human serum albumin have been designed. These artificial proteins, referred to as GA and GB, share 98% amino acid sequence identity but exhibit different 3D structures, namely, a 3α bundle versus a 4β + α structure. Discriminating between their 3D structures based on their amino acid sequences is a very difficult problem. In the present work, in addition to using bioinformatics techniques, an analysis based on inter-residue average distance statistics is used to address this problem. It was hard to distinguish which structure a given sequence would take only with the results of ordinary analyses like BLAST and conservation analyses. However, in addition to these analyses, with the analysis based on the inter-residue average distance statistics and our sequence tendency analysis, we could infer which part would play an important role in its structural formation. The results suggest possible determinants of the different 3D structures for sequences with high sequence identity. The possibility of discriminating between the 3D structures based on the given sequences is also discussed.