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Sample records for peptides prediction methods

  1. Method for predicting peptide detection in mass spectrometry

    DOEpatents

    Kangas, Lars [West Richland, WA; Smith, Richard D [Richland, WA; Petritis, Konstantinos [Richland, WA

    2010-07-13

    A method of predicting whether a peptide present in a biological sample will be detected by analysis with a mass spectrometer. The method uses at least one mass spectrometer to perform repeated analysis of a sample containing peptides from proteins with known amino acids. The method then generates a data set of peptides identified as contained within the sample by the repeated analysis. The method then calculates the probability that a specific peptide in the data set was detected in the repeated analysis. The method then creates a plurality of vectors, where each vector has a plurality of dimensions, and each dimension represents a property of one or more of the amino acids present in each peptide and adjacent peptides in the data set. Using these vectors, the method then generates an algorithm from the plurality of vectors and the calculated probabilities that specific peptides in the data set were detected in the repeated analysis. The algorithm is thus capable of calculating the probability that a hypothetical peptide represented as a vector will be detected by a mass spectrometry based proteomic platform, given that the peptide is present in a sample introduced into a mass spectrometer.

  2. Improved methods for classification, prediction, and design of antimicrobial peptides.

    PubMed

    Wang, Guangshun

    2015-01-01

    Peptides with diverse amino acid sequences, structures, and functions are essential players in biological systems. The construction of well-annotated databases not only facilitates effective information management, search, and mining but also lays the foundation for developing and testing new peptide algorithms and machines. The antimicrobial peptide database (APD) is an original construction in terms of both database design and peptide entries. The host defense antimicrobial peptides (AMPs) registered in the APD cover the five kingdoms (bacteria, protists, fungi, plants, and animals) or three domains of life (bacteria, archaea, and eukaryota). This comprehensive database ( http://aps.unmc.edu/AP ) provides useful information on peptide discovery timeline, nomenclature, classification, glossary, calculation tools, and statistics. The APD enables effective search, prediction, and design of peptides with antibacterial, antiviral, antifungal, antiparasitic, insecticidal, spermicidal, anticancer activities, chemotactic, immune modulation, or antioxidative properties. A universal classification scheme is proposed herein to unify innate immunity peptides from a variety of biological sources. As an improvement, the upgraded APD makes predictions based on the database-defined parameter space and provides a list of the sequences most similar to natural AMPs. In addition, the powerful pipeline design of the database search engine laid a solid basis for designing novel antimicrobials to combat resistant superbugs, viruses, fungi, or parasites. This comprehensive AMP database is a useful tool for both research and education. PMID:25555720

  3. Improved Methods for Classification, Prediction and Design of Antimicrobial Peptides

    PubMed Central

    Wang, Guangshun

    2015-01-01

    Peptides with diverse amino acid sequences, structures and functions are essential players in biological systems. The construction of well-annotated databases not only facilitates effective information management, search and mining, but also lays the foundation for developing and testing new peptide algorithms and machines. The antimicrobial peptide database (APD) is an original construction in terms of both database design and peptide entries. The host defense antimicrobial peptides (AMPs) registered in the APD cover the five kingdoms (bacteria, protists, fungi, plants, and animals) or three domains of life (bacteria, archaea, and eukaryota). This comprehensive database (http://aps.unmc.edu/AP) provides useful information on peptide discovery timeline, nomenclature, classification, glossary, calculation tools, and statistics. The APD enables effective search, prediction, and design of peptides with antibacterial, antiviral, antifungal, antiparasitic, insecticidal, spermicidal, anticancer activities, chemotactic, immune modulation, or anti-oxidative properties. A universal classification scheme is proposed herein to unify innate immunity peptides from a variety of biological sources. As an improvement, the upgraded APD makes predictions based on the database-defined parameter space and provides a list of the sequences most similar to natural AMPs. In addition, the powerful pipeline design of the database search engine laid a solid basis for designing novel antimicrobials to combat resistant superbugs, viruses, fungi or parasites. This comprehensive AMP database is a useful tool for both research and education. PMID:25555720

  4. Method for enhanced accuracy in predicting peptides using liquid separations or chromatography

    DOEpatents

    Kangas, Lars J.; Auberry, Kenneth J.; Anderson, Gordon A.; Smith, Richard D.

    2006-11-14

    A method for predicting the elution time of a peptide in chromatographic and electrophoretic separations by first providing a data set of known elution times of known peptides, then creating a plurality of vectors, each vector having a plurality of dimensions, and each dimension representing the elution time of amino acids present in each of these known peptides from the data set. The elution time of any protein is then be predicted by first creating a vector by assigning dimensional values for the elution time of amino acids of at least one hypothetical peptide and then calculating a predicted elution time for the vector by performing a multivariate regression of the dimensional values of the hypothetical peptide using the dimensional values of the known peptides. Preferably, the multivariate regression is accomplished by the use of an artificial neural network and the elution times are first normalized using a transfer function.

  5. A model-based method for the prediction of the isotopic distribution of peptides.

    PubMed

    Valkenborg, Dirk; Jansen, Ivy; Burzykowski, Tomasz

    2008-05-01

    The process of monoisotopic mass determination, i.e., nomination of the correct peak of an isotopically resolved group of peptide peaks as a monoisotopic peak, requires prior information about the isotopic distribution of the peptide. This points immediately to the difficulty of monoisotopic mass determination, whereas a single mass spectrum does not contain information about the atomic composition of a peptide and therefore the isotopic distribution of the peptide remains unknown. To solve this problem a technique is required, which is able to estimate the isotopic distribution given the information of a single mass spectrum. Senko et al. calculated the average isotopic distribution for any mass peptide via the multinomial expansion (Yergey 1983), using a scaled version of the average amino acid Averagine (Senko et al. 1995). Another method, introduced by Breen et al., approximates the result of the multinomial expansion by a Poisson model (Breen et al. 2000). Although both methods perform well, they have their specific limitations. In this manuscript, we propose an alternative method for the prediction of the isotopic distribution based on a model for consecutive ratios of peaks from the isotopic distribution, similar in spirit to the approach introduced by Gay et al. (1999). The presented method is computationally simple and accurate in predicting the expected isotopic distribution. Further, we extend our method to estimate the isotopic distribution of sulphur-containing peptides. This is important because the naturally occurring isotopes of sulphur have an impact on the isotopic distribution of a peptide. PMID:18325782

  6. A fast method for large-scale de novo peptide and miniprotein structure prediction.

    PubMed

    Maupetit, Julien; Derreumaux, Philippe; Tufféry, Pierre

    2010-03-01

    Although peptides have many biological and biomedical implications, an accurate method predicting their equilibrium structural ensembles from amino acid sequences and suitable for large-scale experiments is still missing. We introduce a new approach-PEP-FOLD-to the de novo prediction of peptides and miniproteins. It first predicts, in the terms of a Hidden Markov Model-derived structural alphabet, a limited number of local conformations at each position of the structure. It then performs their assembly using a greedy procedure driven by a coarse-grained energy score. On a benchmark of 52 peptides with 9-23 amino acids, PEP-FOLD generates lowest-energy conformations within 2.8 and 2.3 A Calpha root-mean-square deviation from the full nuclear magnetic resonance structures (NMR) and the NMR rigid cores, respectively, outperforming previous approaches. For 13 miniproteins with 27-49 amino acids, PEP-FOLD reaches an accuracy of 3.6 and 4.6 A Calpha root-mean-square deviation for the most-native and lowest-energy conformations, using the nonflexible regions identified by NMR. PEP-FOLD simulations are fast-a few minutes only-opening therefore, the door to in silico large-scale rational design of new bioactive peptides and miniproteins. PMID:19569182

  7. A Consensus Method for the Prediction of ‘Aggregation-Prone’ Peptides in Globular Proteins

    PubMed Central

    Tsolis, Antonios C.; Papandreou, Nikos C.; Iconomidou, Vassiliki A.; Hamodrakas, Stavros J.

    2013-01-01

    The purpose of this work was to construct a consensus prediction algorithm of ‘aggregation-prone’ peptides in globular proteins, combining existing tools. This allows comparison of the different algorithms and the production of more objective and accurate results. Eleven (11) individual methods are combined and produce AMYLPRED2, a publicly, freely available web tool to academic users (http://biophysics.biol.uoa.gr/AMYLPRED2), for the consensus prediction of amyloidogenic determinants/‘aggregation-prone’ peptides in proteins, from sequence alone. The performance of AMYLPRED2 indicates that it functions better than individual aggregation-prediction algorithms, as perhaps expected. AMYLPRED2 is a useful tool for identifying amyloid-forming regions in proteins that are associated with several conformational diseases, called amyloidoses, such as Altzheimer's, Parkinson's, prion diseases and type II diabetes. It may also be useful for understanding the properties of protein folding and misfolding and for helping to the control of protein aggregation/solubility in biotechnology (recombinant proteins forming bacterial inclusion bodies) and biotherapeutics (monoclonal antibodies and biopharmaceutical proteins). PMID:23326595

  8. MHC2MIL: a novel multiple instance learning based method for MHC-II peptide binding prediction by considering peptide flanking region and residue positions

    PubMed Central

    2014-01-01

    Background Computational prediction of major histocompatibility complex class II (MHC-II) binding peptides can assist researchers in understanding the mechanism of immune systems and developing peptide based vaccines. Although many computational methods have been proposed, the performance of these methods are far from satisfactory. The difficulty of MHC-II peptide binding prediction comes mainly from the large length variation of binding peptides. Methods We develop a novel multiple instance learning based method called MHC2MIL, in order to predict MHC-II binding peptides. We deem each peptide in MHC2MIL as a bag, and some substrings of the peptide as the instances in the bag. Unlike previous multiple instance learning based methods that consider only instances of fixed length 9 (9 amino acids), MHC2MIL is able to deal with instances of both lengths of 9 and 11 (11 amino acids), simultaneously. As such, MHC2MIL incorporates important information in the peptide flanking region. For measuring the distances between different instances, furthermore, MHC2MIL explicitly highlights the amino acids in some important positions. Results Experimental results on a benchmark dataset have shown that, the performance of MHC2MIL is significantly improved by considering the instances of both 9 and 11 amino acids, as well as by emphasizing amino acids at key positions in the instance. The results are consistent with those reported in the literature on MHC-II peptide binding. In addition to five important positions (1, 4, 6, 7 and 9) for HLA(human leukocyte antigen, the name of MHC in Humans) DR peptide binding, we also find that position 2 may play some roles in the binding process. By using 5-fold cross validation on the benchmark dataset, MHC2MIL outperforms two state-of-the-art methods of MHC2SK and NN-align with being statistically significant, on 12 HLA DP and DQ molecules. In addition, it achieves comparable performance with MHC2SK and NN-align on 14 HLA DR molecules. MHC2MIL

  9. In Vitro and In Vivo Activities of Antimicrobial Peptides Developed Using an Amino Acid-Based Activity Prediction Method

    PubMed Central

    Wu, Xiaozhe; Wang, Zhenling; Li, Xiaolu; Fan, Yingzi; He, Gu; Wan, Yang; Yu, Chaoheng; Tang, Jianying; Li, Meng; Zhang, Xian; Zhang, Hailong; Xiang, Rong; Pan, Ying; Liu, Yan; Lu, Lian

    2014-01-01

    To design and discover new antimicrobial peptides (AMPs) with high levels of antimicrobial activity, a number of machine-learning methods and prediction methods have been developed. Here, we present a new prediction method that can identify novel AMPs that are highly similar in sequence to known peptides but offer improved antimicrobial activity along with lower host cytotoxicity. Using previously generated AMP amino acid substitution data, we developed an amino acid activity contribution matrix that contained an activity contribution value for each amino acid in each position of the model peptide. A series of AMPs were designed with this method. After evaluating the antimicrobial activities of these novel AMPs against both Gram-positive and Gram-negative bacterial strains, DP7 was chosen for further analysis. Compared to the parent peptide HH2, this novel AMP showed broad-spectrum, improved antimicrobial activity, and in a cytotoxicity assay it showed lower toxicity against human cells. The in vivo antimicrobial activity of DP7 was tested in a Staphylococcus aureus infection murine model. When inoculated and treated via intraperitoneal injection, DP7 reduced the bacterial load in the peritoneal lavage solution. Electron microscope imaging and the results indicated disruption of the S. aureus outer membrane by DP7. Our new prediction method can therefore be employed to identify AMPs possessing minor amino acid differences with improved antimicrobial activities, potentially increasing the therapeutic agents available to combat multidrug-resistant infections. PMID:24982064

  10. Molecular dynamics methods to predict peptide locations in membranes: LAH4 as a stringent test case.

    PubMed

    Farrotti, A; Bocchinfuso, G; Palleschi, A; Rosato, N; Salnikov, E S; Voievoda, N; Bechinger, B; Stella, L

    2015-02-01

    Determining the structure of membrane-active peptides inside lipid bilayers is essential to understand their mechanism of action. Molecular dynamics simulations can easily provide atomistic details, but need experimental validation. We assessed the reliability of self-assembling (or "minimum-bias") and potential of mean force (PMF) approaches, using all-atom (AA) and coarse-grained (CG) force-fields. The LAH4 peptide was selected as a stringent test case, since it is known to attain different orientations depending on the protonation state of its four histidine residues. In all simulations the histidine side-chains inserted in the membrane when neutral, while they interacted with phospholipid headgroups in their charged state. This led to transmembrane orientations for neutral-His LAH4 in all minimum-bias AA simulations and in most CG trajectories. By contrast, the charged-His peptide stabilized membrane defects in AA simulations, whereas it was located at the membrane surface in some CG trajectories, and interacted with both lipid leaflets in others. This behavior is consistent with the higher antimicrobial activity and membrane-permeabilizing behavior of the charged-His LAH4. In addition, good agreement with solid-state NMR orientational data was observed in AA simulations. PMF calculations correctly predicted a higher membrane affinity for the neutral-His peptide. Interestingly, the structures and relative populations of PMF local free-energy minima corresponded to those determined in the less computationally demanding minimum-bias simulations. These data provide an indication about the possible membrane-perturbation mechanism of the charged-His LAH4 peptide: by interacting with lipid headgroups of both leaflets through its cationic side-chains, it could favor membrane defects and facilitate translocation across the bilayer. PMID:25445672

  11. Predicting protein-peptide interactions from scratch

    NASA Astrophysics Data System (ADS)

    Yan, Chengfei; Xu, Xianjin; Zou, Xiaoqin; Zou lab Team

    Protein-peptide interactions play an important role in many cellular processes. The ability to predict protein-peptide complex structures is valuable for mechanistic investigation and therapeutic development. Due to the high flexibility of peptides and lack of templates for homologous modeling, predicting protein-peptide complex structures is extremely challenging. Recently, we have developed a novel docking framework for protein-peptide structure prediction. Specifically, given the sequence of a peptide and a 3D structure of the protein, initial conformations of the peptide are built through protein threading. Then, the peptide is globally and flexibly docked onto the protein using a novel iterative approach. Finally, the sampled modes are scored and ranked by a statistical potential-based energy scoring function that was derived for protein-peptide interactions from statistical mechanics principles. Our docking methodology has been tested on the Peptidb database and compared with other protein-peptide docking methods. Systematic analysis shows significantly improved results compared to the performances of the existing methods. Our method is computationally efficient and suitable for large-scale applications. Nsf CAREER Award 0953839 (XZ) NIH R01GM109980 (XZ).

  12. [A new peptide retention time prediction method for mass spectrometry based proteomic analysis by a serial and parallel support vector machine model].

    PubMed

    Zhang, Jiyang; Zhang, Daibing; Zhang, Wei; Xie, Hongwei

    2012-09-01

    The online reversed-phase liquid chromatography (RPLC) contributes a lot for the large scale mass spectrometry based protein identification in proteomics. Retention time (RT) as an important evidence can be used to distinguish the false positive/true positive peptide identifications. Because of the nonlinear concentration curve of organic phase in the whole range of run time and the interactions among peptides, the sequence based RT prediction of peptides has low accuracy and is difficult to generalize in practice, and thus is less effective in the validation of peptide identifications. A serial and parallel support vector machine (SP-SVM) method was proposed to characterize the nonlinear effect of organic phase concentration and the interactions among peptides. The SP-SVM contains a support vector regression (SVR) only for model training (named as p-SVR) and 4 SVM models (named as C-SVM, 1-SVR, s-SVR and n-SVR) for the RT prediction. After distinguishing the peptide chromatographic behavior by C-SVM, 1-SVR and s-SVR were used to predict the peptide RT specifically to improve the accuracy. Then the peptide RT was normalized by n-SVR to characterize the peptide interactions. The prediction accuracy was improved significantly by applying this method to the processing of the complex sample dataset. The coefficient of the determination between predictive and experimental RTs reaches 0. 95, the prediction error range was less than 20% of the total LC run time for more than 95% cases, and less than 10% of the total LC run time for more than 70% cases. The performance of this model reaches the best of known so far. More important, the SP-SVM method provides a framework to take into account the interactions among peptides in chromatographic separation, and its performance can be improved further by introducing new data processing and experiment strategy. PMID:23285964

  13. Additive method for the prediction of protein-peptide binding affinity. Application to the MHC class I molecule HLA-A*0201.

    PubMed

    Doytchinova, Irini A; Blythe, Martin J; Flower, Darren R

    2002-01-01

    A method has been developed for prediction of binding affinities between proteins and peptides. We exemplify the method through its application to binding predictions of peptides with affinity to major histocompatibility complex class I molecule HLA-A*0201. The method is named "additive" because it is based on the assumption that the binding affinity of a peptide could be presented as a sum of the contributions of the amino acids at each position and the interactions between them. The amino acid contributions and the contributions of the interactions between adjacent side chains and every second side chain were derived using a partial least squares (PLS) statistical methodology using a training set of 420 experimental IC50 values. The predictive power of the method was assessed using rigorous cross-validation and using an independent test set of 89 peptides. The mean value of the residuals between the experimental and predicted pIC50 values was 0.508 for this test set. The additive method was implemented in a program for rapid T-cell epitope search. It is universal and can be applied to any peptide-protein interaction where binding data is known. PMID:12645903

  14. On the hydrophobicity of peptides: Comparing empirical predictions of peptide log P values.

    PubMed

    Thompson, Sarah J; Hattotuwagama, Channa K; Holliday, John D; Flower, Darren R

    2006-01-01

    Peptides are of great therapeutic potential as vaccines and drugs. Knowledge of physicochemical descriptors, including the partition coefficient logP, is useful for the development of predictive Quantitative Structure-Activity Relationships (QSARs). We have investigated the accuracy of available programs for the prediction of logP values for peptides with known experimental values obtained from the literature. Eight prediction programs were tested, of which seven programs were fragment-based methods: XLogP, LogKow, PLogP, ACDLogP, AlogP, Interactive Analysis's LogP and MlogP; and one program used a whole molecule approach: QikProp. The predictive accuracy of the programs was assessed using r(2) values, with ALogP being the most effective (r( 2) = 0.822) and MLogP the least (r(2) = 0.090). We also examined three distinct types of peptide structure: blocked, unblocked, and cyclic. For each study (all peptides, blocked, unblocked and cyclic peptides) the performance of programs rated from best to worse is as follows: all peptides - ALogP, QikProp, PLogP, XLogP, IALogP, LogKow, ACDLogP, and MlogP; blocked peptides - PLogP, XLogP, ACDLogP, IALogP, LogKow, QikProp, ALogP, and MLogP; unblocked peptides - QikProp, IALogP, ALogP, ACDLogP, MLogP, XLogP, LogKow and PLogP; cyclic peptides - LogKow, ALogP, XLogP, MLogP, QikProp, ACDLogP, IALogP. In summary, all programs gave better predictions for blocked peptides, while, in general, logP values for cyclic peptides were under-predicted and those of unblocked peptides were over-predicted. PMID:17597897

  15. Prediction of cell-penetrating peptides with feature selection techniques.

    PubMed

    Tang, Hua; Su, Zhen-Dong; Wei, Huan-Huan; Chen, Wei; Lin, Hao

    2016-08-12

    Cell-penetrating peptides are a group of peptides which can transport different types of cargo molecules such as drugs across plasma membrane and have been applied in the treatment of various diseases. Thus, the accurate prediction of cell-penetrating peptides with bioinformatics methods will accelerate the development of drug delivery systems. The study aims to develop a powerful model to accurately identify cell-penetrating peptides. At first, the peptides were translated into a set of vectors with the same dimension by using dipeptide compositions. Secondly, the Analysis of Variance-based technique was used to reduce the dimension of the vector and explore the optimized features. Finally, the support vector machine was utilized to discriminate cell-penetrating peptides from non-cell-penetrating peptides. The five-fold cross-validated results showed that our proposed method could achieve an overall prediction accuracy of 83.6%. Based on the proposed model, we constructed a free webserver called C2Pred (http://lin.uestc.edu.cn/server/C2Pred). PMID:27291150

  16. Prediction of cell penetrating peptides by support vector machines.

    PubMed

    Sanders, William S; Johnston, C Ian; Bridges, Susan M; Burgess, Shane C; Willeford, Kenneth O

    2011-07-01

    Cell penetrating peptides (CPPs) are those peptides that can transverse cell membranes to enter cells. Once inside the cell, different CPPs can localize to different cellular components and perform different roles. Some generate pore-forming complexes resulting in the destruction of cells while others localize to various organelles. Use of machine learning methods to predict potential new CPPs will enable more rapid screening for applications such as drug delivery. We have investigated the influence of the composition of training datasets on the ability to classify peptides as cell penetrating using support vector machines (SVMs). We identified 111 known CPPs and 34 known non-penetrating peptides from the literature and commercial vendors and used several approaches to build training data sets for the classifiers. Features were calculated from the datasets using a set of basic biochemical properties combined with features from the literature determined to be relevant in the prediction of CPPs. Our results using different training datasets confirm the importance of a balanced training set with approximately equal number of positive and negative examples. The SVM based classifiers have greater classification accuracy than previously reported methods for the prediction of CPPs, and because they use primary biochemical properties of the peptides as features, these classifiers provide insight into the properties needed for cell-penetration. To confirm our SVM classifications, a subset of peptides classified as either penetrating or non-penetrating was selected for synthesis and experimental validation. Of the synthesized peptides predicted to be CPPs, 100% of these peptides were shown to be penetrating. PMID:21779156

  17. Prediction of Antibacterial Activity from Physicochemical Properties of Antimicrobial Peptides

    PubMed Central

    Melo, Manuel N.; Ferre, Rafael; Feliu, Lídia; Bardají, Eduard; Planas, Marta; Castanho, Miguel A. R. B.

    2011-01-01

    Consensus is gathering that antimicrobial peptides that exert their antibacterial action at the membrane level must reach a local concentration threshold to become active. Studies of peptide interaction with model membranes do identify such disruptive thresholds but demonstrations of the possible correlation of these with the in vivo onset of activity have only recently been proposed. In addition, such thresholds observed in model membranes occur at local peptide concentrations close to full membrane coverage. In this work we fully develop an interaction model of antimicrobial peptides with biological membranes; by exploring the consequences of the underlying partition formalism we arrive at a relationship that provides antibacterial activity prediction from two biophysical parameters: the affinity of the peptide to the membrane and the critical bound peptide to lipid ratio. A straightforward and robust method to implement this relationship, with potential application to high-throughput screening approaches, is presented and tested. In addition, disruptive thresholds in model membranes and the onset of antibacterial peptide activity are shown to occur over the same range of locally bound peptide concentrations (10 to 100 mM), which conciliates the two types of observations. PMID:22194847

  18. Peptides and methods against diabetes

    DOEpatents

    Albertini, Richard J.; Falta, Michael T.

    2000-01-01

    This invention relates to methods of preventing or reducing the severity of diabetes. In one embodiment, the method involves administering to the individual a peptide having substantially the sequence of a on-conserved region sequence of a T cell receptor present on the surface of T cells mediating diabetes or a fragment thereof, wherein the peptide or fragment is capable of causing an effect on the immune system to regulate the T cells. In particular, the T cell receptor has the V.beta. regional V.beta.6 or V.beta.14. In another embodiment, the method involves gene therapy. The invention also relates to methods of diagnosing diabetes by determining the presence of diabetes predominant T cell receptors.

  19. Prediction of Biofilm Inhibiting Peptides: An In silico Approach

    PubMed Central

    Gupta, Sudheer; Sharma, Ashok K.; Jaiswal, Shubham K.; Sharma, Vineet K.

    2016-01-01

    Approximately 75% of microbial infections found in humans are caused by microbial biofilms. These biofilms are resistant to host immune system and most of the currently available antibiotics. Small peptides are extensively studied for their role as anti-microbial peptides, however, only a limited studies have shown their potential as inhibitors of biofilm. Therefore, to develop a unique computational method aimed at the prediction of biofilm inhibiting peptides, the experimentally validated biofilm inhibiting peptides sequences were used to extract sequence based features and to identify unique sequence motifs. Biofilm inhibiting peptides were observed to be abundant in positively charged and aromatic amino acids, and also showed selective abundance of some dipeptides and sequence motifs. These individual sequence based features were utilized to construct Support Vector Machine-based prediction models and additionally by including sequence motifs information, the hybrid models were constructed. Using 10-fold cross validation, the hybrid model displayed the accuracy and Matthews Correlation Coefficient (MCC) of 97.83% and 0.87, respectively. On the validation dataset, the hybrid model showed the accuracy and MCC value of 97.19% and 0.84, respectively. The validated model and other tools developed for the prediction of biofilm inhibiting peptides are available freely as web server at http://metagenomics.iiserb.ac.in/biofin/ and http://metabiosys.iiserb.ac.in/biofin/. PMID:27379078

  20. Epitope prediction algorithms for peptide-based vaccine design.

    PubMed

    Florea, Liliana; Halldórsson, Bjarni; Kohlbacher, Oliver; Schwartz, Russell; Hoffman, Stephen; Istrail, Sorin

    2003-01-01

    Peptide-based vaccines, in which small peptides derived from target proteins (eptiopes) are used to provoke an immune reaction, have attracted considerable attention recently as a potential means both of treating infectious diseases and promoting the destruction of cancerous cells by a patient's own immune system. With the availability of large sequence databases and computers fast enough for rapid processing of large numbers of peptides, computer aided design of peptide-based vaccines has emerged as a promising approach to screening among billions of possible immune-active peptides to find those likely to provoke an immune response to a particular cell type. In this paper, we describe the development of three novel classes of methods for the prediction problem. We present a quadratic programming approach that can be trained on quantitative as well as qualitative data. The second method uses linear programming to counteract the fact that our training data contains mostly positive examples. The third class of methods uses sequence profiles obtained by clustering known epitopes to score candidate peptides. By integrating these methods, using a simple voting heuristic, we achieve improved accuracy over the state of the art. PMID:16826643

  1. Improved peptide elution time prediction for reversed-phase liquid chromatography-MS by incorporating peptide sequence information

    SciTech Connect

    Petritis, Konstantinos; Kangas, Lars J.; Yan, Bo; Monroe, Matthew E.; Strittmatter, Eric F.; Qian, Weijun; Adkins, Joshua N.; Moore, Ronald J.; Xu, Ying; Lipton, Mary S.; Camp, David G.; Smith, Richard D.

    2006-07-15

    We describe an improved artificial neural network (ANN)-based method for predicting peptide retention times in reversed phase liquid chromatography. In addition to the peptide amino acid composition, this study investigated several other peptide descriptors to improve the predictive capability, such as peptide length, sequence, hydrophobicity and hydrophobic moment, and nearest neighbor amino acid, as well as peptide predicted structural configurations (i.e., helix, sheet, coil). An ANN architecture that consisted of 1052 input nodes, 24 hidden nodes, and 1 output node was used to fully consider the amino acid residue sequence in each peptide. The network was trained using {approx}345,000 non-redundant peptides identified from a total of 12,059 LC-MS/MS analyses of more than 20 different organisms, and the predictive capability of the model was tested using 1303 confidently identified peptides that were not included in the training set. The model demonstrated an average elution time precision of {approx}1.5% and was able to distinguish among isomeric peptides based upon the inclusion of peptide sequence information. The prediction power represents a significant improvement over our earlier report (Petritis et al., Anal. Chem. 2003, 75, 1039-1048) and other previously reported models.

  2. PQuad: Visualization of Predicted Peptides and Proteins

    SciTech Connect

    Havre, Susan L.; Singhal, Mudita; Payne, Deborah A.; Webb-Robertson, Bobbie-Jo M.

    2004-10-10

    New high-throughput proteomic techniques generate data faster than biologist and bioinformaticists can analyze it. Yet, hidden within this massive and complex data are answers to basic questions about how cells function to support life or respond to disease. Now biologists can take a global or systems approach studying not one or two proteins at a time but whole proteomes comprising all the proteins in a cell. However, the tremendous size and complexity of the high-throughput experiment data make it difficult to process and interpret. Visualization provides powerful analysis capabilities for such enormous and complex data. In this paper, we introduce a novel interactive visualization, PQuad (Peptide Permutation and Protein Prediction), designed for the visual analysis of peptides (protein fragments) identified from high-throughput data. PQuad depicts the experiment peptides in the context of their parent protein and DNA, thereby integrating proteomic and genomic information. A wrapped line metaphor is applied across key resolutions of the data, from a compressed view of an entire chromosome to the actual nucleotide sequence. PQuad provides a difference visualization for comparing peptides from different experimental conditions. We describe the requirements for such a visual analysis tool, the design decisions, and the novel aspects of PQuad.

  3. Peptide mass fingerprinting peak intensity prediction: extracting knowledge from spectra.

    PubMed

    Gay, Steven; Binz, Pierre-Alain; Hochstrasser, Denis F; Appel, Ron D

    2002-10-01

    Matrix-assisted laser desorption/ionization-time of flight mass spectrometry has become a valuable tool in proteomics. With the increasing acquisition rate of mass spectrometers, one of the major issues is the development of accurate, efficient and automatic peptide mass fingerprinting (PMF) identification tools. Current tools are mostly based on counting the number of experimental peptide masses matching with theoretical masses. Almost all of them use additional criteria such as isoelectric point, molecular weight, PTMs, taxonomy or enzymatic cleavage rules to enhance prediction performance. However, these identification tools seldom use peak intensities as parameter as there is currently no model predicting the intensities based on the physicochemical properties of peptides. In this work, we used standard datamining methods such as classification and regression methods to find correlations between peak intensities and the properties of the peptides composing a PMF spectrum. These methods were applied on a dataset comprising a series of PMF experiments involving 157 proteins. We found that the C4.5 method gave the more informative results for the classification task (prediction of the presence or absence of a peptide in a spectra) and M5' for the regression methods (prediction of the normalized intensity of a peptide peak). The C4.5 result correctly classified 88% of the theoretical peaks; whereas the M5' peak intensities had a correlation coefficient of 0.6743 with the experimental peak intensities. These methods enabled us to obtain decision and model trees that can be directly used for prediction and identification of PMF results. The work performed permitted to lay the foundations of a method to analyze factors influencing the peak intensity of PMF spectra. A simple extension of this analysis could lead to improve the accuracy of the results by using a larger dataset. Additional peptide characteristics or even PMF experimental parameters can also be taken into

  4. Predicting protein-ligand and protein-peptide interfaces

    NASA Astrophysics Data System (ADS)

    Bertolazzi, Paola; Guerra, Concettina; Liuzzi, Giampaolo

    2014-06-01

    The paper deals with the identification of binding sites and concentrates on interactions involving small interfaces. In particular we focus our attention on two major interface types, namely protein-ligand and protein-peptide interfaces. As concerns protein-ligand binding site prediction, we classify the most interesting methods and approaches into four main categories: (a) shape-based methods, (b) alignment-based methods, (c) graph-theoretic approaches and (d) machine learning methods. Class (a) encompasses those methods which employ, in some way, geometric information about the protein surface. Methods falling into class (b) address the prediction problem as an alignment problem, i.e. finding protein-ligand atom pairs that occupy spatially equivalent positions. Graph theoretic approaches, class (c), are mainly based on the definition of a particular graph, known as the protein contact graph, and then apply some sophisticated methods from graph theory to discover subgraphs or score similarities for uncovering functional sites. The last class (d) contains those methods that are based on the learn-from-examples paradigm and that are able to take advantage of the large amount of data available on known protein-ligand pairs. As for protein-peptide interfaces, due to the often disordered nature of the regions involved in binding, shape similarity is no longer a determining factor. Then, in geometry-based methods, geometry is accounted for by providing the relative position of the atoms surrounding the peptide residues in known structures. Finally, also for protein-peptide interfaces, we present a classification of some successful machine learning methods. Indeed, they can be categorized in the way adopted to construct the learning examples. In particular, we envisage three main methods: distance functions, structure and potentials and structure alignment.

  5. A computational model for predicting fusion peptide of retroviruses.

    PubMed

    Wu, Sijia; Han, Jiuqiang; Liu, Ruiling; Liu, Jun; Lv, Hongqiang

    2016-04-01

    As a pivotal domain within envelope protein, fusion peptide (FP) plays a crucial role in pathogenicity and therapeutic intervention. Taken into account the limited FP annotations in NCBI database and absence of FP prediction software, it is urgent and desirable to develop a bioinformatics tool to predict new putative FPs (np-FPs) in retroviruses. In this work, a sequence-based FP model was proposed by combining Hidden Markov Method with similarity comparison. The classification accuracies are 91.97% and 92.31% corresponding to 10-fold and leave-one-out cross-validation. After scanning sequences without FP annotations, this model discovered 53,946 np-FPs. The statistical results on FPs or np-FPs reveal that FP is a conserved and hydrophobic domain. The FP software programmed for windows environment is available at https://sourceforge.net/projects/fptool/files/?source=navbar. PMID:26963379

  6. Prediction and verification of novel peptide targets of protein tyrosine phosphatase 1B.

    PubMed

    Li, Xun; Köhn, Maja

    2016-08-01

    Phosphotyrosine peptides are useful starting points for inhibitor design and for the search for protein tyrosine phosphatase (PTP) phosphoprotein substrates. To identify novel phosphopeptide substrates of PTP1B, we developed a computational prediction protocol based on a virtual library of protein sequences with known phosphotyrosine sites. To these we applied sequence-based methods, biologically meaningful filters and molecular docking. Five peptides were selected for biochemical testing of their potential as PTP1B substrates. All five peptides were equally good substrates for PTP1B compared to a known peptide substrate whereas appropriate control peptides were not recognized, showing that our protocol can be used to identify novel peptide substrates of PTP1B. PMID:27025565

  7. Improved nonlinear prediction method

    NASA Astrophysics Data System (ADS)

    Adenan, Nur Hamiza; Md Noorani, Mohd Salmi

    2014-06-01

    The analysis and prediction of time series data have been addressed by researchers. Many techniques have been developed to be applied in various areas, such as weather forecasting, financial markets and hydrological phenomena involving data that are contaminated by noise. Therefore, various techniques to improve the method have been introduced to analyze and predict time series data. In respect of the importance of analysis and the accuracy of the prediction result, a study was undertaken to test the effectiveness of the improved nonlinear prediction method for data that contain noise. The improved nonlinear prediction method involves the formation of composite serial data based on the successive differences of the time series. Then, the phase space reconstruction was performed on the composite data (one-dimensional) to reconstruct a number of space dimensions. Finally the local linear approximation method was employed to make a prediction based on the phase space. This improved method was tested with data series Logistics that contain 0%, 5%, 10%, 20% and 30% of noise. The results show that by using the improved method, the predictions were found to be in close agreement with the observed ones. The correlation coefficient was close to one when the improved method was applied on data with up to 10% noise. Thus, an improvement to analyze data with noise without involving any noise reduction method was introduced to predict the time series data.

  8. Prediction of MHC binding peptides and epitopes from alfalfa mosaic virus.

    PubMed

    Gomase, Virendra S; Kale, Karbhari V; Chikhale, Nandkishor J; Changbhale, Smruti S

    2007-08-01

    Peptide fragments from alfalfa mosaic virus involved multiple antigenic components directing and empowering the immune system to protect the host from infection. MHC molecules are cell surface proteins, which take active part in host immune reactions and involvement of MHC class-I & II in response to almost all antigens. Coat protein of alfalfa mosaic virus contains 221 aa residues. Analysis found five MHC ligands in coat protein as 64-LSSFNGLGV-72; 86- RILEEDLIY-94; 96-MVFSITPSY-104; 100- ITPSYAGTF-108; 110- LTDDVTTED-118; having rescaled binding affinity and c-terminal cleavage affinity more than 0.5. The predicted binding affinity is normalized by the 1% fractil. The MHC peptide binding is predicted using neural networks trained on c-terminals of known epitopes. In analysis predicted MHC/peptide binding is a log transformed value related to the IC50 values in nM units. Total numbers of peptides found are 213. Predicted MHC binding regions act like red flags for antigen specific and generate immune response against the parent antigen. So a small fragment of antigen can induce immune response against whole antigen. This theme is implemented in designing subunit and synthetic peptide vaccines. The sequence analysis method allows potential drug targets to identify active sites against plant diseases. The method integrates prediction of peptide MHC class I binding; proteosomal c-terminal cleavage and TAP transport efficiency. PMID:17691913

  9. Coupling in silico and in vitro analysis of peptide-MHC binding: a bioinformatic approach enabling prediction of superbinding peptides and anchorless epitopes.

    PubMed

    Doytchinova, Irini A; Walshe, Valerie A; Jones, Nicola A; Gloster, Simone E; Borrow, Persephone; Flower, Darren R

    2004-06-15

    The ability to define and manipulate the interaction of peptides with MHC molecules has immense immunological utility, with applications in epitope identification, vaccine design, and immunomodulation. However, the methods currently available for prediction of peptide-MHC binding are far from ideal. We recently described the application of a bioinformatic prediction method based on quantitative structure-affinity relationship methods to peptide-MHC binding. In this study we demonstrate the predictivity and utility of this approach. We determined the binding affinities of a set of 90 nonamer peptides for the MHC class I allele HLA-A*0201 using an in-house, FACS-based, MHC stabilization assay, and from these data we derived an additive quantitative structure-affinity relationship model for peptide interaction with the HLA-A*0201 molecule. Using this model we then designed a series of high affinity HLA-A2-binding peptides. Experimental analysis revealed that all these peptides showed high binding affinities to the HLA-A*0201 molecule, significantly higher than the highest previously recorded. In addition, by the use of systematic substitution at principal anchor positions 2 and 9, we showed that high binding peptides are tolerant to a wide range of nonpreferred amino acids. Our results support a model in which the affinity of peptide binding to MHC is determined by the interactions of amino acids at multiple positions with the MHC molecule and may be enhanced by enthalpic cooperativity between these component interactions. PMID:15187128

  10. Prediction of a stable associated liquid of short amyloidogenic peptides.

    PubMed

    Luiken, Jurriaan A; Bolhuis, Peter G

    2015-04-28

    Amyloid fibril formation is believed to be a nucleation-controlled process. Depending on the nature of peptide sequence, fibril nucleation can occur in one step, straight from a dilute solution, or in multiple steps via oligomers or disordered aggregates. What determines this process is poorly understood. Since the fibril formation kinetics is driven by thermodynamic forces, knowledge of the phase behavior is crucial. Here, we investigated the phase behavior of three short peptide sequences of varying side-chain hydrophobicity. Replica exchange molecular dynamics simulations of a mid-resolution model indicate that the weakly hydrophobic peptide forms fibrils directly from solution, whereas the most hydrophobic peptide forms a dense liquid phase before crystallizing into ordered fibrils at low temperatures. For the medium hydrophobic peptide we found evidence of a novel additional transition to a liquid phase consisting of clusters of aligned peptides, implying a three-step nucleation process. We tested the robustness of this prediction by applying Wertheim's theory and statistical associating fluid theory to a hard-sphere model dressed with isotropic and anisotropic attractions. We found that the ratio of interaction strengths strongly affects the phase behavior, and under certain conditions indeed gives rise to a stable polymerized liquid phase. The peptide clusters in the associated liquid tend to be slow and long-lived, which may give the oligomer droplet more time to act as a toxic oligomer, before turning into a fibril. PMID:25804723

  11. Membrane Protein Prediction Methods

    PubMed Central

    Punta, Marco; Forrest, Lucy R.; Bigelow, Henry; Kernytsky, Andrew; Liu, Jinfeng; Rost, Burkhard

    2007-01-01

    We survey computational approaches that tackle membrane protein structure and function prediction. While describing the main ideas that have led to the development of the most relevant and novel methods, we also discuss pitfalls, provide practical hints and highlight the challenges that remain. The methods covered include: sequence alignment, motif search, functional residue identification, transmembrane segment and protein topology predictions, homology and ab initio modeling. Overall, predictions of functional and structural features of membrane proteins are improving, although progress is hampered by the limited amount of high-resolution experimental information available. While predictions of transmembrane segments and protein topology rank among the most accurate methods in computational biology, more attention and effort will be required in the future to ameliorate database search, homology and ab initio modeling. PMID:17367718

  12. Method of identity analyte-binding peptides

    DOEpatents

    Kauvar, L.M.

    1990-10-16

    A method for affinity chromatography or adsorption of a designated analyte utilizes a paralog as the affinity partner. The immobilized paralog can be used in purification or analysis of the analyte; the paralog can also be used as a substitute for antibody in an immunoassay. The paralog is identified by screening candidate peptide sequences of 4--20 amino acids for specific affinity to the analyte. 5 figs.

  13. Method of identity analyte-binding peptides

    DOEpatents

    Kauvar, Lawrence M.

    1990-01-01

    A method for affinity chromatography or adsorption of a designated analyte utilizes a paralog as the affinity partner. The immobilized paralog can be used in purification or analysis of the analyte; the paralog can also be used as a substitute for antibody in an immunoassay. The paralog is identified by screening candidate peptide sequences of 4-20 amino acids for specific affinity to the analyte.

  14. Computer-based prediction of mitochondria-targeting peptides.

    PubMed

    Martelli, Pier Luigi; Savojardo, Castrense; Fariselli, Piero; Tasco, Gianluca; Casadio, Rita

    2015-01-01

    Computational methods are invaluable when protein sequences, directly derived from genomic data, need functional and structural annotation. Subcellular localization is a feature necessary for understanding the protein role and the compartment where the mature protein is active and very difficult to characterize experimentally. Mitochondrial proteins encoded on the cytosolic ribosomes carry specific patterns in the precursor sequence from where it is possible to recognize a peptide targeting the protein to its final destination. Here we discuss to which extent it is feasible to develop computational methods for detecting mitochondrial targeting peptides in the precursor sequences and benchmark our and other methods on the human mitochondrial proteins endowed with experimentally characterized targeting peptides. Furthermore, we illustrate our newly implemented web server and its usage on the whole human proteome in order to infer mitochondrial targeting peptides, their cleavage sites, and whether the targeting peptide regions contain or not arginine-rich recurrent motifs. By this, we add some other 2,800 human proteins to the 124 ones already experimentally annotated with a mitochondrial targeting peptide. PMID:25631024

  15. Development of Antimicrobial Peptide Prediction Tool for Aquaculture Industries.

    PubMed

    Gautam, Aditi; Sharma, Asuda; Jaiswal, Sarika; Fatma, Samar; Arora, Vasu; Iquebal, M A; Nandi, S; Sundaray, J K; Jayasankar, P; Rai, Anil; Kumar, Dinesh

    2016-09-01

    Microbial diseases in fish, plant, animal and human are rising constantly; thus, discovery of their antidote is imperative. The use of antibiotic in aquaculture further compounds the problem by development of resistance and consequent consumer health risk by bio-magnification. Antimicrobial peptides (AMPs) have been highly promising as natural alternative to chemical antibiotics. Though AMPs are molecules of innate immune defense of all advance eukaryotic organisms, fish being heavily dependent on their innate immune defense has been a good source of AMPs with much wider applicability. Machine learning-based prediction method using wet laboratory-validated fish AMP can accelerate the AMP discovery using available fish genomic and proteomic data. Earlier AMP prediction servers are based on multi-phyla/species data, and we report here the world's first AMP prediction server in fishes. It is freely accessible at http://webapp.cabgrid.res.in/fishamp/ . A total of 151 AMPs related to fish collected from various databases and published literature were taken for this study. For model development and prediction, N-terminus residues, C-terminus residues and full sequences were considered. Best models were with kernels polynomial-2, linear and radial basis function with accuracy of 97, 99 and 97 %, respectively. We found that performance of support vector machine-based models is superior to artificial neural network. This in silico approach can drastically reduce the time and cost of AMP discovery. This accelerated discovery of lead AMP molecules having potential wider applications in diverse area like fish and human health as substitute of antibiotics, immunomodulator, antitumor, vaccine adjuvant and inactivator, and also for packaged food can be of much importance for industries. PMID:27141850

  16. Peptide motif analysis predicts alphaviruses as triggers for rheumatoid arthritis.

    PubMed

    Hogeboom, Charissa

    2015-12-01

    Rheumatoid arthritis (RA) develops in response to both genetic and environmental factors. The strongest genetic determinant is HLA-DR, where polymorphisms within the P4 and P6 binding pockets confer elevated risk. However, low disease concordance across monozygotic twin pairs underscores the importance of an environmental factor, probably infectious. The goal of this investigation was to predict the microorganism most likely to interact with HLA-DR to trigger RA under the molecular mimicry hypothesis. A set of 185 structural proteins from viruses or intracellular bacteria was scanned for regions of sequence homology with a collagen peptide that binds preferentially to DR4; candidates were then evaluated against a motif required for T cell cross-reactivity. The plausibility of the predicted agent was evaluated by comparison of microbial prevalence patterns to epidemiological characteristics of RA. Peptides from alphavirus capsid proteins provided the closest fit. Variations in the P6 position suggest that the HLA binding preference may vary by species, with Ross River virus, Chikungunya virus, and Mayaro virus peptides binding preferentially to DR4, and peptides from Sindbis/Ockelbo virus showing stronger affinity to DR1. The predicted HLA preference is supported by epidemiological studies of post-infection chronic arthralgia. Parallels between the cytokine profiles of RA and chronic alphavirus infection are discussed. PMID:26476978

  17. Prediction of peptides binding to MHC class I and II alleles by temporal motif mining

    PubMed Central

    2013-01-01

    Background MHC (Major Histocompatibility Complex) is a key player in the immune response of most vertebrates. The computational prediction of whether a given antigenic peptide will bind to a specific MHC allele is important in the development of vaccines for emerging pathogens, the creation of possibilities for controlling immune response, and for the applications of immunotherapy. One of the problems that make this computational prediction difficult is the detection of the binding core region in peptides, coupled with the presence of bulges and loops causing variations in the total sequence length. Most machine learning methods require the sequences to be of the same length to successfully discover the binding motifs, ignoring the length variance in both motif mining and prediction steps. In order to overcome this limitation, we propose the use of time-based motif mining methods that work position-independently. Results The prediction method was tested on a benchmark set of 28 different alleles for MHC class I and 27 different alleles for MHC class II. The obtained results are comparable to the state of the art methods for both MHC classes, surpassing the published results for some alleles. The average prediction AUC values are 0.897 for class I, and 0.858 for class II. Conclusions Temporal motif mining using partial periodic patterns can capture information about the sequences well enough to predict the binding of the peptides and is comparable to state of the art methods in the literature. Unlike neural networks or matrix based predictors, our proposed method does not depend on peptide length and can work with both short and long fragments. This advantage allows better use of the available training data and the prediction of peptides of uncommon lengths. PMID:23368521

  18. Prediction of Surface and pH-Specific Binding of Peptides to Metal and Oxide Nanoparticles

    NASA Astrophysics Data System (ADS)

    Heinz, Hendrik; Lin, Tzu-Jen; Emami, Fateme Sadat; Ramezani-Dakhel, Hadi; Naik, Rajesh; Knecht, Marc; Perry, Carole C.; Huang, Yu

    2015-03-01

    The mechanism of specific peptide adsorption onto metallic and oxidic nanostructures has been elucidated in atomic resolution using novel force fields and surface models in comparison to measurements. As an example, variations in peptide adsorption on Pd and Pt nanoparticles depending on shape, size, and location of peptides on specific bounding facets are explained. Accurate computational predictions of reaction rates in C-C coupling reactions using particle models derived from HE-XRD and PDF data illustrate the utility of computational methods for the rational design of new catalysts. On oxidic nanoparticles such as silica and apatites, it is revealed how changes in pH lead to similarity scores of attracted peptides lower than 20%, supported by appropriate model surfaces and data from adsorption isotherms. The results demonstrate how new computational methods can support the design of nanoparticle carriers for drug release and the understanding of calcification mechanisms in the human body.

  19. Quantitative prediction of peptide binding to HLA-DP1 protein.

    PubMed

    Ivanov, Stefan; Dimitrov, Ivan; Doytchinova, Irini

    2013-01-01

    The exogenous proteins are processed by the host antigen-processing cells. Peptidic fragments of them are presented on the cell surface bound to the major hystocompatibility complex (MHC) molecules class II and recognized by the CD4+ T lymphocytes. The MHC binding is considered as the crucial prerequisite for T-cell recognition. Only peptides able to form stable complexes with the MHC proteins are recognized by the T-cells. These peptides are known as T-cell epitopes. All T-cell epitopes are MHC binders, but not all MHC binders are T-cell epitopes. The T-cell epitope prediction is one of the main priorities of immunoinformatics. In the present study, three chemometric techniques are combined to derive a model for in silico prediction of peptide binding to the human MHC class II protein HLA-DP1. The structures of a set of known peptide binders are described by amino acid z-descriptors. Data are processed by an iterative self-consisted algorithm using the method of partial least squares, and a quantitative matrix (QM) for peptide binding prediction to HLA-DP1 is derived. The QM is validated by two sets of proteins and showed an average accuracy of 86 percent. PMID:24091413

  20. Prediction of Nucleotide Binding Peptides Using Star Graph Topological Indices.

    PubMed

    Liu, Yong; Munteanu, Cristian R; Fernández Blanco, Enrique; Tan, Zhiliang; Santos Del Riego, Antonino; Pazos, Alejandro

    2015-11-01

    The nucleotide binding proteins are involved in many important cellular processes, such as transmission of genetic information or energy transfer and storage. Therefore, the screening of new peptides for this biological function is an important research topic. The current study proposes a mixed methodology to obtain the first classification model that is able to predict new nucleotide binding peptides, using only the amino acid sequence. Thus, the methodology uses a Star graph molecular descriptor of the peptide sequences and the Machine Learning technique for the best classifier. The best model represents a Random Forest classifier based on two features of the embedded and non-embedded graphs. The performance of the model is excellent, considering similar models in the field, with an Area Under the Receiver Operating Characteristic Curve (AUROC) value of 0.938 and true positive rate (TPR) of 0.886 (test subset). The prediction of new nucleotide binding peptides with this model could be useful for drug target studies in drug development. PMID:27491034

  1. Generalized Pattern Search Algorithm for Peptide Structure Prediction

    PubMed Central

    Nicosia, Giuseppe; Stracquadanio, Giovanni

    2008-01-01

    Finding the near-native structure of a protein is one of the most important open problems in structural biology and biological physics. The problem becomes dramatically more difficult when a given protein has no regular secondary structure or it does not show a fold similar to structures already known. This situation occurs frequently when we need to predict the tertiary structure of small molecules, called peptides. In this research work, we propose a new ab initio algorithm, the generalized pattern search algorithm, based on the well-known class of Search-and-Poll algorithms. We performed an extensive set of simulations over a well-known set of 44 peptides to investigate the robustness and reliability of the proposed algorithm, and we compared the peptide conformation with a state-of-the-art algorithm for peptide structure prediction known as PEPstr. In particular, we tested the algorithm on the instances proposed by the originators of PEPstr, to validate the proposed algorithm; the experimental results confirm that the generalized pattern search algorithm outperforms PEPstr by 21.17% in terms of average root mean-square deviation, RMSD Cα. PMID:18487293

  2. Basophile: Accurate Fragment Charge State Prediction Improves Peptide Identification Rates

    DOE PAGESBeta

    Wang, Dong; Dasari, Surendra; Chambers, Matthew C.; Holman, Jerry D.; Chen, Kan; Liebler, Daniel; Orton, Daniel J.; Purvine, Samuel O.; Monroe, Matthew E.; Chung, Chang Y.; et al

    2013-03-07

    In shotgun proteomics, database search algorithms rely on fragmentation models to predict fragment ions that should be observed for a given peptide sequence. The most widely used strategy (Naive model) is oversimplified, cleaving all peptide bonds with equal probability to produce fragments of all charges below that of the precursor ion. More accurate models, based on fragmentation simulation, are too computationally intensive for on-the-fly use in database search algorithms. We have created an ordinal-regression-based model called Basophile that takes fragment size and basic residue distribution into account when determining the charge retention during CID/higher-energy collision induced dissociation (HCD) of chargedmore » peptides. This model improves the accuracy of predictions by reducing the number of unnecessary fragments that are routinely predicted for highly-charged precursors. Basophile increased the identification rates by 26% (on average) over the Naive model, when analyzing triply-charged precursors from ion trap data. Basophile achieves simplicity and speed by solving the prediction problem with an ordinal regression equation, which can be incorporated into any database search software for shotgun proteomic identification.« less

  3. Basophile: Accurate Fragment Charge State Prediction Improves Peptide Identification Rates

    SciTech Connect

    Wang, Dong; Dasari, Surendra; Chambers, Matthew C.; Holman, Jerry D.; Chen, Kan; Liebler, Daniel; Orton, Daniel J.; Purvine, Samuel O.; Monroe, Matthew E.; Chung, Chang Y.; Rose, Kristie L.; Tabb, David L.

    2013-03-07

    In shotgun proteomics, database search algorithms rely on fragmentation models to predict fragment ions that should be observed for a given peptide sequence. The most widely used strategy (Naive model) is oversimplified, cleaving all peptide bonds with equal probability to produce fragments of all charges below that of the precursor ion. More accurate models, based on fragmentation simulation, are too computationally intensive for on-the-fly use in database search algorithms. We have created an ordinal-regression-based model called Basophile that takes fragment size and basic residue distribution into account when determining the charge retention during CID/higher-energy collision induced dissociation (HCD) of charged peptides. This model improves the accuracy of predictions by reducing the number of unnecessary fragments that are routinely predicted for highly-charged precursors. Basophile increased the identification rates by 26% (on average) over the Naive model, when analyzing triply-charged precursors from ion trap data. Basophile achieves simplicity and speed by solving the prediction problem with an ordinal regression equation, which can be incorporated into any database search software for shotgun proteomic identification.

  4. Predictive chromatography of peptides and proteins as a complementary tool for proteomics.

    PubMed

    Tarasova, Irina A; Masselon, Christophe D; Gorshkov, Alexander V; Gorshkov, Mikhail V

    2016-08-01

    In the last couple of decades, considerable effort has been focused on developing methods for quantitative and qualitative proteome characterization. The method of choice in this characterization is mass spectrometry used in combination with sample separation. One of the most widely used separation techniques at the front end of a mass spectrometer is high performance liquid chromatography (HPLC). A unique feature of HPLC is its specificity to the amino acid sequence of separated peptides and proteins. This specificity may provide additional information about the peptides or proteins under study which is complementary to the mass spectrometry data. The value of this information for proteomics has been recognized in the past few decades, which has stimulated significant effort in the development and implementation of computational and theoretical models for the prediction of peptide retention time for a given sequence. Here we review the advances in this area and the utility of predicted retention times for proteomic applications. PMID:27419248

  5. Sequence-based prediction of protein-peptide binding sites using support vector machine.

    PubMed

    Taherzadeh, Ghazaleh; Yang, Yuedong; Zhang, Tuo; Liew, Alan Wee-Chung; Zhou, Yaoqi

    2016-05-15

    Protein-peptide interactions are essential for all cellular processes including DNA repair, replication, gene-expression, and metabolism. As most protein-peptide interactions are uncharacterized, it is cost effective to investigate them computationally as the first step. All existing approaches for predicting protein-peptide binding sites, however, are based on protein structures despite the fact that the structures for most proteins are not yet solved. This article proposes the first machine-learning method called SPRINT to make Sequence-based prediction of Protein-peptide Residue-level Interactions. SPRINT yields a robust and consistent performance for 10-fold cross validations and independent test. The most important feature is evolution-generated sequence profiles. For the test set (1056 binding and non-binding residues), it yields a Matthews' Correlation Coefficient of 0.326 with a sensitivity of 64% and a specificity of 68%. This sequence-based technique shows comparable or more accurate than structure-based methods for peptide-binding site prediction. SPRINT is available as an online server at: http://sparks-lab.org/. © 2016 Wiley Periodicals, Inc. PMID:26833816

  6. Predicting binding within disordered protein regions to structurally characterised peptide-binding domains.

    PubMed

    Khan, Waqasuddin; Duffy, Fergal; Pollastri, Gianluca; Shields, Denis C; Mooney, Catherine

    2013-01-01

    Disordered regions of proteins often bind to structured domains, mediating interactions within and between proteins. However, it is difficult to identify a priori the short disordered regions involved in binding. We set out to determine if docking such peptide regions to peptide binding domains would assist in these predictions.We assembled a redundancy reduced dataset of SLiM (Short Linear Motif) containing proteins from the ELM database. We selected 84 sequences which had an associated PDB structures showing the SLiM bound to a protein receptor, where the SLiM was found within a 50 residue region of the protein sequence which was predicted to be disordered. First, we investigated the Vina docking scores of overlapping tripeptides from the 50 residue SLiM containing disordered regions of the protein sequence to the corresponding PDB domain. We found only weak discrimination of docking scores between peptides involved in binding and adjacent non-binding peptides in this context (AUC 0.58).Next, we trained a bidirectional recurrent neural network (BRNN) using as input the protein sequence, predicted secondary structure, Vina docking score and predicted disorder score. The results were very promising (AUC 0.72) showing that multiple sources of information can be combined to produce results which are clearly superior to any single source.We conclude that the Vina docking score alone has only modest power to define the location of a peptide within a larger protein region known to contain it. However, combining this information with other knowledge (using machine learning methods) clearly improves the identification of peptide binding regions within a protein sequence. This approach combining docking with machine learning is primarily a predictor of binding to peptide-binding sites, and is not intended as a predictor of specificity of binding to particular receptors. PMID:24019881

  7. Downstream prediction using a nonlinear prediction method

    NASA Astrophysics Data System (ADS)

    Adenan, N. H.; Noorani, M. S. M.

    2013-11-01

    The estimation of river flow is significantly related to the impact of urban hydrology, as this could provide information to solve important problems, such as flooding downstream. The nonlinear prediction method has been employed for analysis of four years of daily river flow data for the Langat River at Kajang, Malaysia, which is located in a downstream area. The nonlinear prediction method involves two steps; namely, the reconstruction of phase space and prediction. The reconstruction of phase space involves reconstruction from a single variable to the m-dimensional phase space in which the dimension m is based on optimal values from two methods: the correlation dimension method (Model I) and false nearest neighbour(s) (Model II). The selection of an appropriate method for selecting a combination of preliminary parameters, such as m, is important to provide an accurate prediction. From our investigation, we gather that via manipulation of the appropriate parameters for the reconstruction of the phase space, Model II provides better prediction results. In particular, we have used Model II together with the local linear prediction method to achieve the prediction results for the downstream area with a high correlation coefficient. In summary, the results show that Langat River in Kajang is chaotic, and, therefore, predictable using the nonlinear prediction method. Thus, the analysis and prediction of river flow in this area can provide river flow information to the proper authorities for the construction of flood control, particularly for the downstream area.

  8. Predictive spark timing method

    SciTech Connect

    Tang, D.L.; Chang, M.F.; Sultan, M.C.

    1990-01-09

    This patent describes a method of determining spark time in a spark timing system of an internal combustion engine having a plurality of cylinders and a spark period for each cylinder in which a spark occurs. It comprises: generating at least one crankshaft position reference pulse for each spark firing event, the reference pulse nearest the next spark being set to occur within a same cylinder event as the next spark; measuring at least two reference periods between recent reference pulses; calculating the spark timing synchronously with crankshaft position by performing the calculation upon receipt of the reference pulse nearest the next spark; predicting the engine speed for the next spark period from at least two reference periods including the most recent reference period; and based on the predicted speed, calculating a spark time measured from the the reference pulse nearest the next spark.

  9. Accurate Structure Prediction and Conformational Analysis of Cyclic Peptides with Residue-Specific Force Fields.

    PubMed

    Geng, Hao; Jiang, Fan; Wu, Yun-Dong

    2016-05-19

    Cyclic peptides (CPs) are promising candidates for drugs, chemical biology tools, and self-assembling nanomaterials. However, the development of reliable and accurate computational methods for their structure prediction has been challenging. Here, 20 all-trans CPs of 5-12 residues selected from Cambridge Structure Database have been simulated using replica-exchange molecular dynamics with four different force fields. Our recently developed residue-specific force fields RSFF1 and RSFF2 can correctly identify the crystal-like conformations of more than half CPs as the most populated conformation. The RSFF2 performs the best, which consistently predicts the crystal structures of 17 out of 20 CPs with rmsd < 1.1 Å. We also compared the backbone (ϕ, ψ) sampling of residues in CPs with those in short linear peptides and in globular proteins. In general, unlike linear peptides, CPs have local conformational free energies and entropies quite similar to globular proteins. PMID:27128113

  10. Prediction of Peptide and Protein Propensity for Amyloid Formation

    PubMed Central

    Família, Carlos; Dennison, Sarah R.; Quintas, Alexandre; Phoenix, David A.

    2015-01-01

    Understanding which peptides and proteins have the potential to undergo amyloid formation and what driving forces are responsible for amyloid-like fiber formation and stabilization remains limited. This is mainly because proteins that can undergo structural changes, which lead to amyloid formation, are quite diverse and share no obvious sequence or structural homology, despite the structural similarity found in the fibrils. To address these issues, a novel approach based on recursive feature selection and feed-forward neural networks was undertaken to identify key features highly correlated with the self-assembly problem. This approach allowed the identification of seven physicochemical and biochemical properties of the amino acids highly associated with the self-assembly of peptides and proteins into amyloid-like fibrils (normalized frequency of β-sheet, normalized frequency of β-sheet from LG, weights for β-sheet at the window position of 1, isoelectric point, atom-based hydrophobic moment, helix termination parameter at position j+1 and ΔG° values for peptides extrapolated in 0 M urea). Moreover, these features enabled the development of a new predictor (available at http://cran.r-project.org/web/packages/appnn/index.html) capable of accurately and reliably predicting the amyloidogenic propensity from the polypeptide sequence alone with a prediction accuracy of 84.9 % against an external validation dataset of sequences with experimental in vitro, evidence of amyloid formation. PMID:26241652

  11. Clinical predictive circulating peptides in rectal cancer patients treated with neoadjuvant chemoradiotherapy.

    PubMed

    Crotti, Sara; Enzo, Maria Vittoria; Bedin, Chiara; Pucciarelli, Salvatore; Maretto, Isacco; Del Bianco, Paola; Traldi, Pietro; Tasciotti, Ennio; Ferrari, Mauro; Rizzolio, Flavio; Toffoli, Giuseppe; Giordano, Antonio; Nitti, Donato; Agostini, Marco

    2015-08-01

    Preoperative chemoradiotherapy is worldwide accepted as a standard treatment for locally advanced rectal cancer. Current standard of treatment includes administration of ionizing radiation for 45-50.4 Gy in 25-28 fractions associated with 5-fluorouracil administration during radiation therapy. Unfortunately, 40% of patients have a poor or absent response and novel predictive biomarkers are demanding. For the first time, we apply a novel peptidomic methodology and analysis in rectal cancer patients treated with preoperative chemoradiotherapy. Circulating peptides (Molecular Weight <3 kDa) have been harvested from patients' plasma (n = 33) using nanoporous silica chip and analyzed by Matrix-Assisted Laser Desorption/Ionization-Time of Flight mass spectrometer. Peptides fingerprint has been compared between responders and non-responders. Random Forest classification selected three peptides at m/z 1082.552, 1098.537, and 1104.538 that were able to correctly discriminate between responders (n = 16) and non-responders (n = 17) before therapy (T0) providing an overall accuracy of 86% and an area under the receiver operating characteristic (ROC) curve of 0.92. In conclusion, the nanoporous silica chip coupled to mass spectrometry method was found to be a realistic method for plasma-based peptide analysis and we provide the first list of predictive circulating biomarker peptides in rectal cancer patients underwent preoperative chemoradiotherapy. PMID:25522009

  12. Motor degradation prediction methods

    SciTech Connect

    Arnold, J.R.; Kelly, J.F.; Delzingaro, M.J.

    1996-12-01

    Motor Operated Valve (MOV) squirrel cage AC motor rotors are susceptible to degradation under certain conditions. Premature failure can result due to high humidity/temperature environments, high running load conditions, extended periods at locked rotor conditions (i.e. > 15 seconds) or exceeding the motor`s duty cycle by frequent starts or multiple valve stroking. Exposure to high heat and moisture due to packing leaks, pressure seal ring leakage or other causes can significantly accelerate the degradation. ComEd and Liberty Technologies have worked together to provide and validate a non-intrusive method using motor power diagnostics to evaluate MOV rotor condition and predict failure. These techniques have provided a quick, low radiation dose method to evaluate inaccessible motors, identify degradation and allow scheduled replacement of motors prior to catastrophic failures.

  13. sNebula, a network-based algorithm to predict binding between human leukocyte antigens and peptides

    PubMed Central

    Luo, Heng; Ye, Hao; Ng, Hui Wen; Sakkiah, Sugunadevi; Mendrick, Donna L.; Hong, Huixiao

    2016-01-01

    Understanding the binding between human leukocyte antigens (HLAs) and peptides is important to understand the functioning of the immune system. Since it is time-consuming and costly to measure the binding between large numbers of HLAs and peptides, computational methods including machine learning models and network approaches have been developed to predict HLA-peptide binding. However, there are several limitations for the existing methods. We developed a network-based algorithm called sNebula to address these limitations. We curated qualitative Class I HLA-peptide binding data and demonstrated the prediction performance of sNebula on this dataset using leave-one-out cross-validation and five-fold cross-validations. This algorithm can predict not only peptides of different lengths and different types of HLAs, but also the peptides or HLAs that have no existing binding data. We believe sNebula is an effective method to predict HLA-peptide binding and thus improve our understanding of the immune system. PMID:27558848

  14. sNebula, a network-based algorithm to predict binding between human leukocyte antigens and peptides.

    PubMed

    Luo, Heng; Ye, Hao; Ng, Hui Wen; Sakkiah, Sugunadevi; Mendrick, Donna L; Hong, Huixiao

    2016-01-01

    Understanding the binding between human leukocyte antigens (HLAs) and peptides is important to understand the functioning of the immune system. Since it is time-consuming and costly to measure the binding between large numbers of HLAs and peptides, computational methods including machine learning models and network approaches have been developed to predict HLA-peptide binding. However, there are several limitations for the existing methods. We developed a network-based algorithm called sNebula to address these limitations. We curated qualitative Class I HLA-peptide binding data and demonstrated the prediction performance of sNebula on this dataset using leave-one-out cross-validation and five-fold cross-validations. This algorithm can predict not only peptides of different lengths and different types of HLAs, but also the peptides or HLAs that have no existing binding data. We believe sNebula is an effective method to predict HLA-peptide binding and thus improve our understanding of the immune system. PMID:27558848

  15. Quantitative online prediction of peptide binding to the major histocompatibility complex.

    PubMed

    Hattotuwagama, Channa K; Guan, Pingping; Doytchinova, Irini A; Zygouri, Christianna; Flower, Darren R

    2004-01-01

    With its implications for vaccine discovery, the accurate prediction of T cell epitopes is one of the key aspirations of computational vaccinology. We have developed a robust multivariate statistical method, based on partial least squares, for the quantitative prediction of peptide binding to major histocompatibility complexes (MHC), the principal checkpoint on the antigen presentation pathway. As a service to the immunobiology community, we have made a Perl implementation of the method available via a World Wide Web server. We call this server MHCPred. Access to the server is freely available from the URL: http://www.jenner.ac.uk/MHCPred. We have exemplified our method with a model for peptides binding to the common human MHC molecule HLA-B*3501. PMID:14629978

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

    PubMed Central

    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. PMID

  17. How frequently are predicted peptides actually recognized by CD8 cells?

    PubMed

    Moldovan, Ioana; Targoni, Oleg; Zhang, Wenji; Sundararaman, Srividya; Lehmann, Paul V

    2016-07-01

    Detection of antigen-specific CD8 cells frequently relies on the use of peptides that are predicted to bind to HLA Class I molecules or have been shown to induce immune responses. There is extensive knowledge on individual HLA alleles' peptide-binding requirements, and immunogenic peptides for many antigens have been defined. The 32 individual peptides that comprise the CEF peptide pool represent such well-defined peptide determinants for Cytomegalo-, Epstein-barr-, and Influenza virus. We tested the accuracy of these peptide recognition predictions on 42 healthy human donors that have been high-resolution HLA-typed. According to the predictions, 241 recall responses should have been detected in these donors. Actual testing showed that 36 (15 %) of the predicted CD8 cell responses occurred in the high frequency range, 41 (17 %) in mid-frequencies, and 45 (19 %) were at the detection limit. In 119 instances (49 %), the predicted peptides were not targeted by CD8 cells detectably. The individual CEF peptides were recognized in an unpredicted fashion in 57 test cases. Moreover, the frequency of CD8 cells responding to a single peptide did not reflect on the number of CD8 cells targeting other determinants on the same antigen. Thus, reliance on one or a few predicted peptides provides a rather inaccurate assessment of antigen-specific CD8 cell immunity, strongly arguing for the use of peptide pools for immune monitoring. PMID:27108305

  18. Predicting Peptide Structures in Native Proteins from Physical Simulations of Fragments

    PubMed Central

    Voelz, Vincent A.; Shell, M. Scott; Dill, Ken A.

    2009-01-01

    It has long been proposed that much of the information encoding how a protein folds is contained locally in the peptide chain. Here we present a large-scale simulation study designed to examine the extent to which conformations of peptide fragments in water predict native conformations in proteins. We perform replica exchange molecular dynamics (REMD) simulations of 872 8-mer, 12-mer, and 16-mer peptide fragments from 13 proteins using the AMBER 96 force field and the OBC implicit solvent model. To analyze the simulations, we compute various contact-based metrics, such as contact probability, and then apply Bayesian classifier methods to infer which metastable contacts are likely to be native vs. non-native. We find that a simple measure, the observed contact probability, is largely more predictive of a peptide's native structure in the protein than combinations of metrics or multi-body components. Our best classification model is a logistic regression model that can achieve up to 63% correct classifications for 8-mers, 71% for 12-mers, and 76% for 16-mers. We validate these results on fragments of a protein outside our training set. We conclude that local structure provides information to solve some but not all of the conformational search problem. These results help improve our understanding of folding mechanisms, and have implications for improving physics-based conformational sampling and structure prediction using all-atom molecular simulations. PMID:19197352

  19. Method for synthesizing peptides with saccharide linked enzyme polymer conjugates

    DOEpatents

    Callstrom, M.R.; Bednarski, M.D.; Gruber, P.R.

    1997-06-17

    A method is disclosed for synthesizing peptides using water soluble enzyme polymer conjugates. The method comprises catalyzing the peptide synthesis with enzyme which has been covalently bonded to a polymer through at least three linkers which linkers have three or more hydroxyl groups. The enzyme is conjugated at lysines or arginines. 19 figs.

  20. Method for synthesizing peptides with saccharide linked enzyme polymer conjugates

    DOEpatents

    Callstrom, Matthew R.; Bednarski, Mark D.; Gruber, Patrick R.

    1997-01-01

    A method is disclosed for synthesizing peptides using water soluble enzyme polymer conjugates. The method comprises catalyzing the peptide synthesis with enzyme which has been covalently bonded to a polymer through at least three linkers which linkers have three or more hydroxyl groups. The enzyme is conjugated at lysines or arginines.

  1. Prediction of antimicrobial peptides based on the adaptive neuro-fuzzy inference system application.

    PubMed

    Fernandes, Fabiano C; Rigden, Daniel J; Franco, Octavio L

    2012-01-01

    Antimicrobial peptides (AMPs) are widely distributed defense molecules and represent a promising alternative for solving the problem of antibiotic resistance. Nevertheless, the experimental time required to screen putative AMPs makes computational simulations based on peptide sequence analysis and/or molecular modeling extremely attractive. Artificial intelligence methods acting as simulation and prediction tools are of great importance in helping to efficiently discover and design novel AMPs. In the present study, state-of-the-art published outcomes using different prediction methods and databases were compared to an adaptive neuro-fuzzy inference system (ANFIS) model. Data from our study showed that ANFIS obtained an accuracy of 96.7% and a Matthew's Correlation Coefficient (MCC) of0.936, which proved it to be an efficient model for pattern recognition in antimicrobial peptide prediction. Furthermore, a lower number of input parameters were needed for the ANFIS model, improving the speed and ease of prediction. In summary, due to the fuzzy nature ofAMP physicochemical properties, the ANFIS approach presented here can provide an efficient solution for screening putative AMP sequences and for exploration of properties characteristic of AMPs. PMID:23193592

  2. Analysis and prediction of affinity of TAP binding peptides using cascade SVM.

    PubMed

    Bhasin, Manoj; Raghava, G P S

    2004-03-01

    The generation of cytotoxic T lymphocyte (CTL) epitopes from an antigenic sequence involves number of intracellular processes, including production of peptide fragments by proteasome and transport of peptides to endoplasmic reticulum through transporter associated with antigen processing (TAP). In this study, 409 peptides that bind to human TAP transporter with varying affinity were analyzed to explore the selectivity and specificity of TAP transporter. The abundance of each amino acid from P1 to P9 positions in high-, intermediate-, and low-affinity TAP binders were examined. The rules for predicting TAP binding regions in an antigenic sequence were derived from the above analysis. The quantitative matrix was generated on the basis of contribution of each position and residue in binding affinity. The correlation of r = 0.65 was obtained between experimentally determined and predicted binding affinity by using a quantitative matrix. Further a support vector machine (SVM)-based method has been developed to model the TAP binding affinity of peptides. The correlation (r = 0.80) was obtained between the predicted and experimental measured values by using sequence-based SVM. The reliability of prediction was further improved by cascade SVM that uses features of amino acids along with sequence. An extremely good correlation (r = 0.88) was obtained between measured and predicted values, when the cascade SVM-based method was evaluated through jackknife testing. A Web service, TAPPred (http://www.imtech.res.in/raghava/tappred/ or http://bioinformatics.uams.edu/mirror/tappred/), has been developed based on this approach. PMID:14978300

  3. Optimization of reversed-phase chromatography methods for peptide analytics.

    PubMed

    Khalaf, Rushd; Baur, Daniel; Pfister, David

    2015-12-18

    The analytical description and quantification of peptide solutions is an essential part in the quality control of peptide production processes and in peptide mapping techniques. Traditionally, an important tool is analytical reversed phase liquid chromatography. In this work, we develop a model-based tool to find optimal analytical conditions in a clear, efficient and robust manner. The model, based on the Van't Hoff equation, the linear solvent strength correlation, and an analytical solution of the mass balance on a chromatographic column describing peptide retention in gradient conditions is used to optimize the analytical scale separation between components in a peptide mixture. The proposed tool is then applied in the design of analytical reversed phase liquid chromatography methods of five different peptide mixtures. PMID:26620597

  4. New horizons in mouse immunoinformatics: reliable in silico prediction of mouse class I histocompatibility major complex peptide binding affinity.

    PubMed

    Hattotuwagama, Channa K; Guan, Pingping; Doytchinova, Irini A; Flower, Darren R

    2004-11-21

    Quantitative structure-activity relationship (QSAR) analysis is a main cornerstone of modern informatic disciplines. Predictive computational models, based on QSAR technology, of peptide-major histocompatibility complex (MHC) binding affinity have now become a vital component of modern day computational immunovaccinology. Historically, such approaches have been built around semi-qualitative, classification methods, but these are now giving way to quantitative regression methods. The additive method, an established immunoinformatics technique for the quantitative prediction of peptide-protein affinity, was used here to identify the sequence dependence of peptide binding specificity for three mouse class I MHC alleles: H2-D(b), H2-K(b) and H2-K(k). As we show, in terms of reliability the resulting models represent a significant advance on existing methods. They can be used for the accurate prediction of T-cell epitopes and are freely available online ( http://www.jenner.ac.uk/MHCPred). PMID:15534705

  5. Prediction method abstracts

    SciTech Connect

    1994-12-31

    This conference was held December 4--8, 1994 in Asilomar, California. The purpose of this meeting was to provide a forum for exchange of state-of-the-art information concerning the prediction of protein structure. Attention if focused on the following: comparative modeling; sequence to fold assignment; and ab initio folding.

  6. CS-AMPPred: An Updated SVM Model for Antimicrobial Activity Prediction in Cysteine-Stabilized Peptides

    PubMed Central

    Porto, William F.; Pires, Állan S.; Franco, Octavio L.

    2012-01-01

    The antimicrobial peptides (AMP) have been proposed as an alternative to control resistant pathogens. However, due to multifunctional properties of several AMP classes, until now there has been no way to perform efficient AMP identification, except through in vitro and in vivo tests. Nevertheless, an indication of activity can be provided by prediction methods. In order to contribute to the AMP prediction field, the CS-AMPPred (Cysteine-Stabilized Antimicrobial Peptides Predictor) is presented here, consisting of an updated version of the Support Vector Machine (SVM) model for antimicrobial activity prediction in cysteine-stabilized peptides. The CS-AMPPred is based on five sequence descriptors: indexes of (i) α-helix and (ii) loop formation; and averages of (iii) net charge, (iv) hydrophobicity and (v) flexibility. CS-AMPPred was based on 310 cysteine-stabilized AMPs and 310 sequences extracted from PDB. The polynomial kernel achieves the best accuracy on 5-fold cross validation (85.81%), while the radial and linear kernels achieve 84.19%. Testing in a blind data set, the polynomial and radial kernels achieve an accuracy of 90.00%, while the linear model achieves 89.33%. The three models reach higher accuracies than previously described methods. A standalone version of CS-AMPPred is available for download at and runs on any Linux machine. PMID:23240023

  7. Prediction of binding free energy for adsorption of antimicrobial peptide lactoferricin B on a POPC membrane

    NASA Astrophysics Data System (ADS)

    Vivcharuk, Victor; Tomberli, Bruno; Tolokh, Igor S.; Gray, C. G.

    2008-03-01

    Molecular dynamics (MD) simulations are used to study the interaction of a zwitterionic palmitoyl-oleoyl-phosphatidylcholine (POPC) bilayer with the cationic antimicrobial peptide bovine lactoferricin (LFCinB) in a 100 mM NaCl solution at 310 K. The interaction of LFCinB with POPC is used as a model system for studying the details of membrane-peptide interactions, with the peptide selected because of its antimicrobial nature. Seventy-two 3 ns MD simulations, with six orientations of LFCinB at 12 different distances from a POPC membrane, are carried out to determine the potential of mean force (PMF) or free energy profile for the peptide as a function of the distance between LFCinB and the membrane surface. To calculate the PMF for this relatively large system a new variant of constrained MD and thermodynamic integration is developed. A simplified method for relating the PMF to the LFCinB-membrane binding free energy is described and used to predict a free energy of adsorption (or binding) of -1.05±0.39kcal/mol , and corresponding maximum binding force of about 20 pN, for LFCinB-POPC. The contributions of the ions-LFCinB and the water-LFCinB interactions to the PMF are discussed. The method developed will be a useful starting point for future work simulating peptides interacting with charged membranes and interactions involved in the penetration of membranes, features necessary to understand in order to rationally design peptides as potential alternatives to traditional antibiotics.

  8. Evaluating protocols and analytical methods for peptide adsorption experiments.

    PubMed

    Fears, Kenan P; Petrovykh, Dmitri Y; Clark, Thomas D

    2013-12-01

    This paper evaluates analytical techniques that are relevant for performing reliable quantitative analysis of peptide adsorption on surfaces. Two salient problems are addressed: determining the solution concentrations of model GG-X-GG, X5, and X10 oligopeptides (G = glycine, X = a natural amino acid), and quantitative analysis of these peptides following adsorption on surfaces. To establish a uniform methodology for measuring peptide concentrations in water across the entire GG-X-GG and X n series, three methods were assessed: UV spectroscopy of peptides having a C-terminal tyrosine, the bicinchoninic acid (BCA) protein assay, and amino acid (AA) analysis. Due to shortcomings or caveats associated with each of the different methods, none were effective at measuring concentrations across the entire range of representative model peptides. In general, reliable measurements were within 30% of the nominal concentration based on the weight of as-received lyophilized peptide. In quantitative analysis of model peptides adsorbed on surfaces, X-ray photoelectron spectroscopy (XPS) data for a series of lysine-based peptides (GGKGG, K5, and K10) on Au substrates, and for controls incubated in buffer in the absence of peptides, suggested a significant presence of aliphatic carbon species. Detailed analysis indicated that this carbonaceous contamination adsorbed from the atmosphere after the peptide deposition. The inferred adventitious nature of the observed aliphatic carbon was supported by control experiments in which substrates were sputter-cleaned by Ar(+) ions under ultra-high vacuum (UHV) then re-exposed to ambient air. In contrast to carbon contamination, no adventitious nitrogen species were detected on the controls; therefore, the relative surface densities of irreversibly-adsorbed peptides were calculated by normalizing the N/Au ratios by the average number of nitrogen atoms per residue. PMID:24706133

  9. Predicting Peptide-Mediated Interactions on a Genome-Wide Scale

    PubMed Central

    Chen, T. Scott; Petrey, Donald; Garzon, Jose Ignacio; Honig, Barry

    2015-01-01

    We describe a method to predict protein-protein interactions (PPIs) formed between structured domains and short peptide motifs. We take an integrative approach based on consensus patterns of known motifs in databases, structures of domain-motif complexes from the PDB and various sources of non-structural evidence. We combine this set of clues using a Bayesian classifier that reports the likelihood of an interaction and obtain significantly improved prediction performance when compared to individual sources of evidence and to previously reported algorithms. Our Bayesian approach was integrated into PrePPI, a structure-based PPI prediction method that, so far, has been limited to interactions formed between two structured domains. Around 80,000 new domain-motif mediated interactions were predicted, thus enhancing PrePPI’s coverage of the human protein interactome. PMID:25938916

  10. Enhancing In Silico Protein-Based Vaccine Discovery for Eukaryotic Pathogens Using Predicted Peptide-MHC Binding and Peptide Conservation Scores

    PubMed Central

    Goodswen, Stephen J.; Kennedy, Paul J.; Ellis, John T.

    2014-01-01

    Given thousands of proteins constituting a eukaryotic pathogen, the principal objective for a high-throughput in silico vaccine discovery pipeline is to select those proteins worthy of laboratory validation. Accurate prediction of T-cell epitopes on protein antigens is one crucial piece of evidence that would aid in this selection. Prediction of peptides recognised by T-cell receptors have to date proved to be of insufficient accuracy. The in silico approach is consequently reliant on an indirect method, which involves the prediction of peptides binding to major histocompatibility complex (MHC) molecules. There is no guarantee nevertheless that predicted peptide-MHC complexes will be presented by antigen-presenting cells and/or recognised by cognate T-cell receptors. The aim of this study was to determine if predicted peptide-MHC binding scores could provide contributing evidence to establish a protein’s potential as a vaccine. Using T-Cell MHC class I binding prediction tools provided by the Immune Epitope Database and Analysis Resource, peptide binding affinity to 76 common MHC I alleles were predicted for 160 Toxoplasma gondii proteins: 75 taken from published studies represented proteins known or expected to induce T-cell immune responses and 85 considered less likely vaccine candidates. The results show there is no universal set of rules that can be applied directly to binding scores to distinguish a vaccine from a non-vaccine candidate. We present, however, two proposed strategies exploiting binding scores that provide supporting evidence that a protein is likely to induce a T-cell immune response–one using random forest (a machine learning algorithm) with a 72% sensitivity and 82.4% specificity and the other, using amino acid conservation scores with a 74.6% sensitivity and 70.5% specificity when applied to the 160 benchmark proteins. More importantly, the binding score strategies are valuable evidence contributors to the overall in silico vaccine

  11. Improved Methods for the Enrichment and Analysis of Glycated Peptides

    SciTech Connect

    Zhang, Qibin; Schepmoes, Athena A; Brock, Jonathan W; Wu, Si; Moore, Ronald J; Purvine, Samuel O; Baynes, John; Smith, Richard D; Metz, Thomas O

    2008-12-15

    Non-enzymatic glycation of tissue proteins has important implications in the development of complications of diabetes mellitus. Herein we report improved methods for the enrichment and analysis of glycated peptides using boronate affinity chromatography and electron transfer dissociation mass spectrometry, respectively. The enrichment of glycated peptides was improved by replacing an off-line desalting step with an on-line wash of column-bound glycated peptides using 50 mM ammonium acetate. The analysis of glycated peptides by MS/MS was improved by considering only higher charged (≥3) precursor-ions during data-dependent acquisition, which increased the number of glycated peptide identifications. Similarly, the use of supplemental collisional activation after electron transfer (ETcaD) resulted in more glycated peptide identifications when the MS survey scan was acquired with enhanced resolution. In general, acquiring ETD-MS/MS data at a normal MS survey scan rate, in conjunction with the rejection of both 1+ and 2+ precursor-ions, increased the number of identified glycated peptides relative to ETcaD or the enhanced MS survey scan rate. Finally, an evaluation of trypsin, Arg-C, and Lys-C showed that tryptic digestion of glycated proteins was comparable to digestion with Lys-C and that both were better than Arg-C in terms of the number glycated peptides identified by LC-MS/MS.

  12. Seizure Prediction: Methods

    PubMed Central

    Carney, Paul R.; Myers, Stephen; Geyer, James D.

    2011-01-01

    Epilepsy, one of the most common neurological diseases, affects over 50 million people worldwide. Epilepsy can have a broad spectrum of debilitating medical and social consequences. Although antiepileptic drugs have helped treat millions of patients, roughly a third of all patients have seizures that are refractory to pharmacological intervention. The evolution of our understanding of this dynamic disease leads to new treatment possibilities. There is great interest in the development of devices that incorporate algorithms capable of detecting early onset of seizures or even predicting them hours before they occur. The lead time provided by these new technologies will allow for new types of interventional treatment. In the near future, seizures may be detected and aborted before physical manifestations begin. In this chapter we discuss the algorithms that make these devices possible and how they have been implemented to date. We also compare and contrast these measures, and review their individual strengths and weaknesses. Finally, we illustrate how these techniques can be combined in a closed-loop seizure prevention system. PMID:22078526

  13. Prediction of calcite morphology from computational and experimental studies of mutations of a de novo-designed peptide.

    PubMed

    Schrier, Sarah B; Sayeg, Marianna K; Gray, Jeffrey J

    2011-09-20

    Many organisms use macromolecules, often proteins or peptides, to control the growth of inorganic crystals into complex materials. The ability to model peptide-mineral interactions accurately could allow for the design of novel peptides to produce materials with desired properties. Here, we tested a computational algorithm developed to predict the structure of peptides on mineral surfaces. Using this algorithm, we analyzed energetic and structural differences between a 16-residue peptide (bap4) designed to interact with a calcite growth plane and single- and double-point mutations of the charged residues. Currently, no experimental method is available to resolve the structures of proteins on solid surfaces, which precludes benchmarking for computational models. Therefore, to test the models, we chemically synthesized each peptide and analyzed its effects on calcite crystal growth. Whereas bap4 affected the crystal growth by producing heavily stepped corners and edges, point mutants had variable influences on morphology. Calculated residue-specific binding energies correlated with experimental observations; point mutations of residues predicted to be crucial to surface interactions produced morphologies most similar to unmodified calcite. These results suggest that peptide conformation plays a role in mineral interactions and that the computational model supplies valid energetic and structural data that can provide information about expected crystal morphology. PMID:21797243

  14. Predicting most probable conformations of a given peptide sequence in the random coil state.

    PubMed

    Bayrak, Cigdem Sevim; Erman, Burak

    2012-11-01

    In this work, we present a computational scheme for finding high probability conformations of peptides. The scheme calculates the probability of a given conformation of the given peptide sequence using the probability distribution of torsion states. Dependence of the states of a residue on the states of its first neighbors along the chain is considered. Prior probabilities of torsion states are obtained from a coil library. Posterior probabilities are calculated by the matrix multiplication Rotational Isomeric States Model of polymer theory. The conformation of a peptide with highest probability is determined by using a hidden Markov model Viterbi algorithm. First, the probability distribution of the torsion states of the residues is obtained. Using the highest probability torsion state, one can generate, step by step, states with lower probabilities. To validate the method, the highest probability state of residues in a given sequence is calculated and compared with probabilities obtained from the Coil Databank. Predictions based on the method are 32% better than predictions based on the most probable states of residues. The ensemble of "n" high probability conformations of a given protein is also determined using the Viterbi algorithm with multistep backtracking. PMID:22955874

  15. Pan-Specific Prediction of Peptide-MHC Class I Complex Stability, a Correlate of T Cell Immunogenicity.

    PubMed

    Rasmussen, Michael; Fenoy, Emilio; Harndahl, Mikkel; Kristensen, Anne Bregnballe; Nielsen, Ida Kallehauge; Nielsen, Morten; Buus, Søren

    2016-08-15

    Binding of peptides to MHC class I (MHC-I) molecules is the most selective event in the processing and presentation of Ags to CTL, and insights into the mechanisms that govern peptide-MHC-I binding should facilitate our understanding of CTL biology. Peptide-MHC-I interactions have traditionally been quantified by the strength of the interaction, that is, the binding affinity, yet it has been shown that the stability of the peptide-MHC-I complex is a better correlate of immunogenicity compared with binding affinity. In this study, we have experimentally analyzed peptide-MHC-I complex stability of a large panel of human MHC-I allotypes and generated a body of data sufficient to develop a neural network-based pan-specific predictor of peptide-MHC-I complex stability. Integrating the neural network predictors of peptide-MHC-I complex stability with state-of-the-art predictors of peptide-MHC-I binding is shown to significantly improve the prediction of CTL epitopes. The method is publicly available at http://www.cbs.dtu.dk/services/NetMHCstabpan. PMID:27402703

  16. Signal-3L: A 3-layer approach for predicting signal peptides.

    PubMed

    Shen, Hong-Bin; Chou, Kuo-Chen

    2007-11-16

    Functioning as an "address tag" that directs nascent proteins to their proper cellular and extracellular locations, signal peptides have become a crucial tool in finding new drugs or reprogramming cells for gene therapy. To effectively and timely use such a tool, however, the first important thing is to develop an automated method for rapidly and accurately identifying the signal peptide for a given nascent protein. With the avalanche of new protein sequences generated in the post-genomic era, the challenge has become even more urgent and critical. In this paper, we have developed a novel method for predicting signal peptide sequences and their cleavage sites in human, plant, animal, eukaryotic, Gram-positive, and Gram-negative protein sequences, respectively. The new predictor is called Signal-3L that consists of three prediction engines working, respectively, for the following three progressively deepening layers: (1) identifying a query protein as secretory or non-secretory by an ensemble classifier formed by fusing many individual OET-KNN (optimized evidence-theoretic K nearest neighbor) classifiers operated in various dimensions of PseAA (pseudo amino acid) composition spaces; (2) selecting a set of candidates for the possible signal peptide cleavage sites of a query secretory protein by a subsite-coupled discrimination algorithm; (3) determining the final cleavage site by fusing the global sequence alignment outcome for each of the aforementioned candidates through a voting system. Signal-3L is featured by high success prediction rates with short computational time, and hence is particularly useful for the analysis of large-scale datasets. Signal-3L is freely available as a web-server at http://chou.med.harvard.edu/bioinf/Signal-3L/ or http://202.120.37.186/bioinf/Signal-3L, where, to further support the demand of the related areas, the signal peptides identified by Signal-3L for all the protein entries in Swiss-Prot databank that do not have signal peptide

  17. Virtual Screening for Dipeptide Aggregation: Toward Predictive Tools for Peptide Self-Assembly.

    PubMed

    Frederix, Pim W J M; Ulijn, Rein V; Hunt, Neil T; Tuttle, Tell

    2011-10-01

    Several short peptide sequences are known to self-assemble into supramolecular nanostructures with interesting properties. In this study, coarse-grained molecular dynamics is employed to rapidly screen all 400 dipeptide combinations and predict their ability to aggregate as a potential precursor to their self-assembly. The simulation protocol and scoring method proposed allows a rapid determination of whether a given peptide sequence is likely to aggregate (an indicator for the ability to self-assemble) under aqueous conditions. Systems that show strong aggregation tendencies in the initial screening are selected for longer simulations, which result in good agreement with the known self-assembly or aggregation of dipeptides reported in the literature. Our extended simulations of the diphenylalanine system show that the coarse-grain model is able to reproduce salient features of nanoscale systems and provide insight into the self-assembly process for this system. PMID:23795243

  18. Virtual Screening for Dipeptide Aggregation: Toward Predictive Tools for Peptide Self-Assembly

    PubMed Central

    2011-01-01

    Several short peptide sequences are known to self-assemble into supramolecular nanostructures with interesting properties. In this study, coarse-grained molecular dynamics is employed to rapidly screen all 400 dipeptide combinations and predict their ability to aggregate as a potential precursor to their self-assembly. The simulation protocol and scoring method proposed allows a rapid determination of whether a given peptide sequence is likely to aggregate (an indicator for the ability to self-assemble) under aqueous conditions. Systems that show strong aggregation tendencies in the initial screening are selected for longer simulations, which result in good agreement with the known self-assembly or aggregation of dipeptides reported in the literature. Our extended simulations of the diphenylalanine system show that the coarse-grain model is able to reproduce salient features of nanoscale systems and provide insight into the self-assembly process for this system. PMID:23795243

  19. Prediction of Leymus arenarius (L.) antimicrobial peptides based on de novo transcriptome assembly.

    PubMed

    Slavokhotova, Anna A; Shelenkov, Andrey A; Odintsova, Tatyana I

    2015-10-01

    Leymus arenarius is a unique wild growing Poaceae plant exhibiting extreme tolerance to environmental conditions. In this study we for the first time performed whole-transcriptome sequencing of lymegrass seedlings using Illumina platform followed by de novo transcriptome assembly and functional annotation. Our goal was to identify transcripts encoding antimicrobial peptides (AMPs), one of the key components of plant innate immunity. Using the custom software developed for this study that predicted AMPs and classified them into families, we revealed more than 160 putative AMPs in lymegrass seedlings. We classified them into 7 families based on their cysteine motifs and sequence similarity. The families included defensins, thionins, hevein-like peptides, snakins, cyclotide, alfa-hairpinins and LTPs. This is the first communication about the presence of almost all known AMP families in trascriptomic data of a single plant species. Additionally, cysteine-rich peptides that potentially represent novel families of AMPs were revealed. We have confirmed by RT-PCR validation the presence of 30 transcripts encoding selected AMPs in lymegrass seedlings. In summary, the presented method of pAMP prediction developed by us can be applied for relatively fast and simple screening of novel components of plant immunity system and is well suited for whole-transcriptome or genome analysis of uncharacterized plants. PMID:26369913

  20. The TOPCONS web server for consensus prediction of membrane protein topology and signal peptides

    PubMed Central

    Tsirigos, Konstantinos D.; Peters, Christoph; Shu, Nanjiang; Käll, Lukas; Elofsson, Arne

    2015-01-01

    TOPCONS (http://topcons.net/) is a widely used web server for consensus prediction of membrane protein topology. We hereby present a major update to the server, with some substantial improvements, including the following: (i) TOPCONS can now efficiently separate signal peptides from transmembrane regions. (ii) The server can now differentiate more successfully between globular and membrane proteins. (iii) The server now is even slightly faster, although a much larger database is used to generate the multiple sequence alignments. For most proteins, the final prediction is produced in a matter of seconds. (iv) The user-friendly interface is retained, with the additional feature of submitting batch files and accessing the server programmatically using standard interfaces, making it thus ideal for proteome-wide analyses. Indicatively, the user can now scan the entire human proteome in a few days. (v) For proteins with homology to a known 3D structure, the homology-inferred topology is also displayed. (vi) Finally, the combination of methods currently implemented achieves an overall increase in performance by 4% as compared to the currently available best-scoring methods and TOPCONS is the only method that can identify signal peptides and still maintain a state-of-the-art performance in topology predictions. PMID:25969446

  1. New method of peptide cleavage based on Edman degradation.

    PubMed

    Bąchor, Remigiusz; Kluczyk, Alicja; Stefanowicz, Piotr; Szewczuk, Zbigniew

    2013-08-01

    A straightforward cleavage method for N- acylated peptides based on the phenylthiohydantoin (PTH) formation is presented. The procedure could be applied to acid-stable resins, such as TentaGel HL-NH[Formula: see text]. We designed a cleavable linker that consists of a lysine residue with the [Formula: see text]-amino group blocked by Boc, whereas the [Formula: see text]-amino group is used for peptide synthesis. After the peptide assembly is completed, the protecting groups in peptide side chains are removed using trifluoroacetic acid, thus liberating also the [Formula: see text]-amino group of the lysine in the linker. Then the reaction with phenyl isothiocyanate followed by acidolysis causes an efficient peptide release from the resin as a stable PTH derivative. Furthermore, the application of a fixed charge tag in the form of 2-(4-aza-1-azoniabicyclo[2.2.2]octylammonium)acetyl group increases ionization efficiency and reduces the detection limit, allowing ESI-MS/MS sequencing of peptides in the subfemtomolar range. The proposed strategy is compatible with standard conditions during one-bead-one-compound peptide library synthesis. The applicability of the developed strategy in combinatorial chemistry was confirmed using a small training library of [Formula: see text]-chymotrypsin substrates. PMID:23690169

  2. A Peptide-Based Method for 13C Metabolic Flux Analysis in Microbial Communities

    PubMed Central

    Ghosh, Amit; Nilmeier, Jerome; Weaver, Daniel; Adams, Paul D.; Keasling, Jay D.; Mukhopadhyay, Aindrila; Petzold, Christopher J.; Martín, Héctor García

    2014-01-01

    The study of intracellular metabolic fluxes and inter-species metabolite exchange for microbial communities is of crucial importance to understand and predict their behaviour. The most authoritative method of measuring intracellular fluxes, 13C Metabolic Flux Analysis (13C MFA), uses the labeling pattern obtained from metabolites (typically amino acids) during 13C labeling experiments to derive intracellular fluxes. However, these metabolite labeling patterns cannot easily be obtained for each of the members of the community. Here we propose a new type of 13C MFA that infers fluxes based on peptide labeling, instead of amino acid labeling. The advantage of this method resides in the fact that the peptide sequence can be used to identify the microbial species it originates from and, simultaneously, the peptide labeling can be used to infer intracellular metabolic fluxes. Peptide identity and labeling patterns can be obtained in a high-throughput manner from modern proteomics techniques. We show that, using this method, it is theoretically possible to recover intracellular metabolic fluxes in the same way as through the standard amino acid based 13C MFA, and quantify the amount of information lost as a consequence of using peptides instead of amino acids. We show that by using a relatively small number of peptides we can counter this information loss. We computationally tested this method with a well-characterized simple microbial community consisting of two species. PMID:25188426

  3. SMALL CYSTEINE-RICH PEPTIDES RESEMBLING ANTIMICROBIAL PEPTIDES HAVE BEEN UNDER-PREDICTED IN PLANTS

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Multicellular organisms produce small cysteine-rich anti-microbial peptides as an innate defense against pathogens. While defensins, a well-known class of such peptides, are common among eukaryotes, there are classes restricted to the plant kingdom. These include thionins, lipid transfer proteins,...

  4. Enrichment of phosphorylated peptides and proteins by selective precipitation methods.

    PubMed

    Rainer, Matthias; Bonn, Günther K

    2015-01-01

    Protein phosphorylation is one of the most prominent post-translational modifications involved in the regulation of cellular processes. Fundamental understanding of biological processes requires appropriate bioanalytical methods for selectively enriching phosphorylated peptides and proteins. Most of the commonly applied enrichment approaches include chromatographic materials including Fe(3+)-immobilized metal-ion affinity chromatography or metal oxides. In the last years, the introduction of several non-chromatographic isolation technologies has increasingly attracted the interest of many scientists. Such approaches are based on the selective precipitation of phosphorylated peptides and proteins by applying various metal cations. The excellent performance of precipitation-based enrichment methods can be explained by the absence of any stationary phase, resin or sorbent, which usually leads to unspecific binding. This review provides an overview of recently published methods for the selective precipitation of phosphorylated peptides and proteins. PMID:25587840

  5. MetaMHCpan, A Meta Approach for Pan-Specific MHC Peptide Binding Prediction.

    PubMed

    Xu, Yichang; Luo, Cheng; Mamitsuka, Hiroshi; Zhu, Shanfeng

    2016-01-01

    Recent computational approaches in bioinformatics can achieve high performance, by which they can be a powerful support for performing real biological experiments, making biologists pay more attention to bioinformatics than before. In immunology, predicting peptides which can bind to MHC alleles is an important task, being tackled by many computational approaches. However, this situation causes a serious problem for immunologists to select the appropriate method to be used in bioinformatics. To overcome this problem, we develop an ensemble prediction-based Web server, which we call MetaMHCpan, consisting of two parts: MetaMHCIpan and MetaMHCIIpan, for predicting peptides which can bind MHC-I and MHC-II, respectively. MetaMHCIpan and MetaMHCIIpan use two (MHC2SKpan and LApan) and four (TEPITOPEpan, MHC2SKpan, LApan, and MHC2MIL) existing predictors, respectively. MetaMHCpan is available at http://datamining-iip.fudan.edu.cn/MetaMHCpan/index.php/pages/view/info . PMID:27076335

  6. SABinder: A Web Service for Predicting Streptavidin-Binding Peptides

    PubMed Central

    Kang, Juanjuan; Ru, Beibei; Zhou, Peng

    2016-01-01

    Streptavidin is sometimes used as the intended target to screen phage-displayed combinatorial peptide libraries for streptavidin-binding peptides (SBPs). More often in the biopanning system, however, streptavidin is just a commonly used anchoring molecule that can efficiently capture the biotinylated target. In this case, SBPs creeping into the biopanning results are not desired binders but target-unrelated peptides (TUP). Taking them as intended binders may mislead subsequent studies. Therefore, it is important to find if a peptide is likely to be an SBP when streptavidin is either the intended target or just the anchoring molecule. In this paper, we describe an SVM-based ensemble predictor called SABinder. It is the first predictor for SBP. The model was built with the feature of optimized dipeptide composition. It was observed that 89.20% (MCC = 0.78; AUC = 0.93; permutation test, p < 0.001) of peptides were correctly classified. As a web server, SABinder is freely accessible. The tool provides a highly efficient way to exclude potential SBP when they are TUP or to facilitate identification of possibly new SBP when they are the desired binders. In either case, it will be helpful and can benefit related scientific community. PMID:27610387

  7. SABinder: A Web Service for Predicting Streptavidin-Binding Peptides.

    PubMed

    He, Bifang; Kang, Juanjuan; Ru, Beibei; Ding, Hui; Zhou, Peng; Huang, Jian

    2016-01-01

    Streptavidin is sometimes used as the intended target to screen phage-displayed combinatorial peptide libraries for streptavidin-binding peptides (SBPs). More often in the biopanning system, however, streptavidin is just a commonly used anchoring molecule that can efficiently capture the biotinylated target. In this case, SBPs creeping into the biopanning results are not desired binders but target-unrelated peptides (TUP). Taking them as intended binders may mislead subsequent studies. Therefore, it is important to find if a peptide is likely to be an SBP when streptavidin is either the intended target or just the anchoring molecule. In this paper, we describe an SVM-based ensemble predictor called SABinder. It is the first predictor for SBP. The model was built with the feature of optimized dipeptide composition. It was observed that 89.20% (MCC = 0.78; AUC = 0.93; permutation test, p < 0.001) of peptides were correctly classified. As a web server, SABinder is freely accessible. The tool provides a highly efficient way to exclude potential SBP when they are TUP or to facilitate identification of possibly new SBP when they are the desired binders. In either case, it will be helpful and can benefit related scientific community. PMID:27610387

  8. A Novel Peptide Binding Prediction Approach for HLA-DR Molecule Based on Sequence and Structural Information

    PubMed Central

    Li, Zhao; Zhao, Yilei; Pan, Gaofeng; Tang, Jijun; Guo, Fei

    2016-01-01

    MHC molecule plays a key role in immunology, and the molecule binding reaction with peptide is an important prerequisite for T cell immunity induced. MHC II molecules do not have conserved residues, so they appear as open grooves. As a consequence, this will increase the difficulty in predicting MHC II molecules binding peptides. In this paper, we aim to propose a novel prediction method for MHC II molecules binding peptides. First, we calculate sequence similarity and structural similarity between different MHC II molecules. Then, we reorder pseudosequences according to descending similarity values and use a weight calculation formula to calculate new pocket profiles. Finally, we use three scoring functions to predict binding cores and evaluate the accuracy of prediction to judge performance of each scoring function. In the experiment, we set a parameter α in the weight formula. By changing α value, we can observe different performances of each scoring function. We compare our method with the best function to some popular prediction methods and ultimately find that our method outperforms them in identifying binding cores of HLA-DR molecules. PMID:27340658

  9. MHC2SKpan: a novel kernel based approach for pan-specific MHC class II peptide binding prediction

    PubMed Central

    2013-01-01

    Background Computational methods for the prediction of Major Histocompatibility Complex (MHC) class II binding peptides play an important role in facilitating the understanding of immune recognition and the process of epitope discovery. To develop an effective computational method, we need to consider two important characteristics of the problem: (1) the length of binding peptides is highly flexible; and (2) MHC molecules are extremely polymorphic and for the vast majority of them there are no sufficient training data. Methods We develop a novel string kernel MHC2SK (MHC-II String Kernel) method to measure the similarities among peptides with variable lengths. By considering the distinct features of MHC-II peptide binding prediction problem, MHC2SK differs significantly from the recently developed kernel based method, GS (Generic String) kernel, in the way of computing similarities. Furthermore, we extend MHC2SK to MHC2SKpan for pan-specific MHC-II peptide binding prediction by leveraging the binding data of various MHC molecules. Results MHC2SK outperformed GS in allele specific prediction using a benchmark dataset, which demonstrates the effectiveness of MHC2SK. Furthermore, we evaluated the performance of MHC2SKpan using various benckmark data sets from several different perspectives: Leave-one-allele-out (LOO), 5-fold cross validation as well as independent data testing. MHC2SKpan has achieved comparable performance with NetMHCIIpan-2.0 and outperformed NetMHCIIpan-1.0, TEPITOPEpan and MultiRTA, being statistically significant. MHC2SKpan can be freely accessed at http://datamining-iip.fudan.edu.cn/service/MHC2SKpan/index.html. PMID:24564280

  10. Activity Prediction and Molecular Mechanism of Bovine Blood Derived Angiotensin I-Converting Enzyme Inhibitory Peptides

    PubMed Central

    Zhang, Ting; Nie, Shaoping; Liu, Boqun; Yu, Yiding; Zhang, Yan; Liu, Jingbo

    2015-01-01

    Development of angiotensin I-converting enzyme (ACE, EC 3.4.15.1) inhibitory peptides from food protein is under extensive research as alternative for the prevention of hypertension. However, it is difficult to identify peptides released from food sources. To accelerate the progress of peptide identification, a three layer back propagation neural network model was established to predict the ACE-inhibitory activity of pentapeptides derived from bovine hemoglobin by simulated enzyme digestion. The pentapeptide WTQRF has the best predicted value with experimental IC50 23.93 μM. The potential molecular mechanism of the WTQRF / ACE interaction was investigated by flexible docking. PMID:25768442

  11. Prediction of Scylla olivacea (Crustacea; Brachyura) peptide hormones using publicly accessible transcriptome shotgun assembly (TSA) sequences.

    PubMed

    Christie, Andrew E

    2016-05-01

    The aquaculture of crabs from the genus Scylla is of increasing economic importance for many Southeast Asian countries. Expansion of Scylla farming has led to increased efforts to understand the physiology and behavior of these crabs, and as such, there are growing molecular resources for them. Here, publicly accessible Scylla olivacea transcriptomic data were mined for putative peptide-encoding transcripts; the proteins deduced from the identified sequences were then used to predict the structures of mature peptide hormones. Forty-nine pre/preprohormone-encoding transcripts were identified, allowing for the prediction of 187 distinct mature peptides. The identified peptides included isoforms of adipokinetic hormone-corazonin-like peptide, allatostatin A, allatostatin B, allatostatin C, bursicon β, CCHamide, corazonin, crustacean cardioactive peptide, crustacean hyperglycemic hormone/molt-inhibiting hormone, diuretic hormone 31, eclosion hormone, FMRFamide-like peptide, HIGSLYRamide, insulin-like peptide, intocin, leucokinin, myosuppressin, neuroparsin, neuropeptide F, orcokinin, pigment dispersing hormone, pyrokinin, red pigment concentrating hormone, RYamide, short neuropeptide F, SIFamide and tachykinin-related peptide, all well-known neuropeptide families. Surprisingly, the tissue used to generate the transcriptome mined here is reported to be testis. Whether or not the testis samples had neural contamination is unknown. However, if the peptides are truly produced by this reproductive organ, it could have far reaching consequences for the study of crustacean endocrinology, particularly in the area of reproductive control. Regardless, this peptidome is the largest thus far predicted for any brachyuran (true crab) species, and will serve as a foundation for future studies of peptidergic control in members of the commercially important genus Scylla. PMID:26965954

  12. Simultaneous prediction of binding free energy and specificity for PDZ domain-peptide interactions

    PubMed Central

    Crivelli, Joseph J.; Lemmon, Gordon; Kaufmann, Kristian W.; Meiler, Jens

    2014-01-01

    Interactions between protein domains and linear peptides underlie many biological processes. Among these interactions, the recognition of C-terminal peptides by PDZ domains is one of the most ubiquitous. In this work, we present a mathematical model for PDZ domain-peptide interactions capable of predicting both affinity and specificity of binding based on x-ray crystal structures and comparative modeling with Rosetta. We developed our mathematical model using a large phage display dataset describing binding specificity for a wild type PDZ domain and 91 single mutants, as well as binding affinity data for a wild type PDZ domain binding to 28 different peptides. Structural refinement was carried out through several Rosetta protocols, the most accurate of which included flexible peptide docking and several iterations of side chain repacking and backbone minimization. Our findings emphasize the importance of backbone flexibility and the energetic contributions of side chain-side chain hydrogen bonds in accurately predicting interactions. We also determined that predicting PDZ domain-peptide interactions became increasingly challenging as the length of the peptide increased in the N-terminal direction. In the training dataset, predicted binding energies correlated with those derived through calorimetry and specificity switches introduced through single mutations at interface positions were recapitulated. In independent tests, our best performing protocol was capable of predicting dissociation constants well within one order of magnitude of the experimental values and specificity profiles at the level of accuracy of previous studies. To our knowledge, this approach represents the first integrated protocol for predicting both affinity and specificity for PDZ domain-peptide interactions. PMID:24305904

  13. Prediction of Antimicrobial Activity of Synthetic Peptides by a Decision Tree Model

    PubMed Central

    Lira, Felipe; Perez, Pedro S.; Baranauskas, José A.

    2013-01-01

    Antimicrobial resistance is a persistent problem in the public health sphere. However, recent attempts to find effective substitutes to combat infections have been directed at identifying natural antimicrobial peptides in order to circumvent resistance to commercial antibiotics. This study describes the development of synthetic peptides with antimicrobial activity, created in silico by site-directed mutation modeling using wild-type peptides as scaffolds for these mutations. Fragments of antimicrobial peptides were used for modeling with molecular modeling computational tools. To analyze these peptides, a decision tree model, which indicated the action range of peptides on the types of microorganisms on which they can exercise biological activity, was created. The decision tree model was processed using physicochemistry properties from known antimicrobial peptides available at the Antimicrobial Peptide Database (APD). The two most promising peptides were synthesized, and antimicrobial assays showed inhibitory activity against Gram-positive and Gram-negative bacteria. Colossomin C and colossomin D were the most inhibitory peptides at 5 μg/ml against Staphylococcus aureus and Escherichia coli. The methods described in this work and the results obtained are useful for the identification and development of new compounds with antimicrobial activity through the use of computational tools. PMID:23455341

  14. Unsupervised HLA Peptidome Deconvolution Improves Ligand Prediction Accuracy and Predicts Cooperative Effects in Peptide-HLA Interactions.

    PubMed

    Bassani-Sternberg, Michal; Gfeller, David

    2016-09-15

    Ag presentation on HLA molecules plays a central role in infectious diseases and tumor immunology. To date, large-scale identification of (neo-)Ags from DNA sequencing data has mainly relied on predictions. In parallel, mass spectrometry analysis of HLA peptidome is increasingly performed to directly detect peptides presented on HLA molecules. In this study, we use a novel unsupervised approach to assign mass spectrometry-based HLA peptidomics data to their cognate HLA molecules. We show that incorporation of deconvoluted HLA peptidomics data in ligand prediction algorithms can improve their accuracy for HLA alleles with few ligands in existing databases. The results of our computational analysis of large datasets of naturally processed HLA peptides, together with experimental validation and protein structure analysis, further reveal how HLA-binding motifs change with peptide length and predict new cooperative effects between distant residues in HLA-B07:02 ligands. PMID:27511729

  15. NetMHCstab - predicting stability of peptide-MHC-I complexes; impacts for cytotoxic T lymphocyte epitope discovery.

    PubMed

    Jørgensen, Kasper W; Rasmussen, Michael; Buus, Søren; Nielsen, Morten

    2014-01-01

    Major histocompatibility complex class I (MHC-I) molecules play an essential role in the cellular immune response, presenting peptides to cytotoxic T lymphocytes (CTLs) allowing the immune system to scrutinize ongoing intracellular production of proteins. In the early 1990s, immunogenicity and stability of the peptide-MHC-I (pMHC-I) complex were shown to be correlated. At that time, measuring stability was cumbersome and time consuming and only small data sets were analysed. Here, we investigate this fairly unexplored area on a large scale compared with earlier studies. A recent small-scale study demonstrated that pMHC-I complex stability was a better correlate of CTL immunogenicity than peptide-MHC-I affinity. We here extended this study and analysed a total of 5509 distinct peptide stability measurements covering 10 different HLA class I molecules. Artificial neural networks were used to construct stability predictors capable of predicting the half-life of the pMHC-I complex. These predictors were shown to predict T-cell epitopes and MHC ligands from SYFPEITHI and IEDB to form significantly more stable MHC-I complexes compared with affinity-matched non-epitopes. Combining the stability predictions with a state-of-the-art affinity predictions NetMHCcons significantly improved the performance for identification of T-cell epitopes and ligands. For the HLA alleles included in the study, we could identify distinct sub-motifs that differentiate between stable and unstable peptide binders and demonstrate that anchor positions in the N-terminal of the binding motif (primarily P2 and P3) play a critical role for the formation of stable pMHC-I complexes. A webserver implementing the method is available at www.cbs.dtu.dk/services/NetMHCstab. PMID:23927693

  16. Discovery of a latent calcineurin inhibitory peptide from its autoinhibitory domain by docking, dynamic simulation, and in vitro methods.

    PubMed

    Harish, B M; Saraswathi, R; Vinod, D; Devaraju, K S

    2016-05-01

    Autoinhibitory domain (AID) of calcineurin (CN) was discovered two decades ago. Fewer investigations are reported to find out shortest possible peptide from the AID for CN inhibition. Hence, this study has focused on screening of nearly 150 peptide fragments derived from the AID using in silico method. Therefore, we have employed docking studies, aiming to analyze the best pose of AID-derived peptides on CN active site. We also analyzed binding free energy (ΔG) of docked complex using molecular mechanics/generalized Born surface area (MM/GBSA). MM/GBSA predicts two short peptides P1 and P2 found to be lowest binding free energy. Two peptides exhibit better binding affinity with CN, suggests that the possible candidates for potential CN inhibition. Further, the stability of the docked complex was analyzed using molecular dynamic (MD) simulation. MD study shows that CNA:P2 is the most stable complex than CN A:P1 and CN A:AID. Besides, we have synthesized and purified P1 and P2 peptides over high performance liquid chromatography (HPLC) found to be 90.31% and 98.93% of purity, respectively. In addition, AID peptides were characterized over mass spectral analysis. Peptides were subjected to CN inhibitory assay using malachite green method. Where, P1 and P2 exhibit CN inhibition better than AID. In particular, shortest peptide P2 shows highest inhibitory activity than AID. Enzyme assay reveals CN inhibitory activity of P2 peptide is consistent within silico results. In silico and in vitro, results corroborated each other to confirm short peptide P2 can be used as a potential CN inhibitor. PMID:26111023

  17. Binding Site Prediction of Proteins with Organic Compounds or Peptides Using GALAXY Web Servers.

    PubMed

    Heo, Lim; Lee, Hasup; Baek, Minkyung; Seok, Chaok

    2016-01-01

    We introduce two GALAXY web servers called GalaxySite and GalaxyPepDock that predict protein complex structures with small organic compounds and peptides, respectively. GalaxySite predicts ligands that may bind the input protein and generates complex structures of the protein with the predicted ligands from the protein structure given as input or predicted from the input sequence. GalaxyPepDock takes a protein structure and a peptide sequence as input and predicts structures for the protein-peptide complex. Both GalaxySite and GalaxyPepDock rely on available experimentally resolved structures of protein-ligand complexes evolutionarily related to the target. With the continuously increasing size of the protein structure database, the probability of finding related proteins in the database is increasing. The servers further relax the complex structures to refine the structural aspects that are missing in the available structures or that are not compatible with the given protein by optimizing physicochemical interactions. GalaxyPepDock allows conformational change of the protein receptor induced by peptide binding. The atomistic interactions with ligands predicted by the GALAXY servers may offer important clues for designing new molecules or proteins with desired binding properties. PMID:27094284

  18. dPABBs: A Novel in silico Approach for Predicting and Designing Anti-biofilm Peptides

    PubMed Central

    Sharma, Arun; Gupta, Pooja; Kumar, Rakesh; Bhardwaj, Anshu

    2016-01-01

    Increasingly, biofilms are being recognised for their causative role in persistent infections (like cystic fibrosis, otitis media, diabetic foot ulcers) and nosocomial diseases (biofilm-infected vascular catheters, implants and prosthetics). Given the clinical relevance of biofilms and their recalcitrance to conventional antibiotics, it is imperative that alternative therapeutics are proactively sought. We have developed dPABBs, a web server that facilitates the prediction and design of anti-biofilm peptides. The six SVM and Weka models implemented on dPABBs were observed to identify anti-biofilm peptides on the basis of their whole amino acid composition, selected residue features and the positional preference of the residues (maximum accuracy, sensitivity, specificity and MCC of 95.24%, 92.50%, 97.73% and 0.91, respectively, on the training datasets). On the N-terminus, it was seen that either of the cationic polar residues, R and K, is present at all five positions in case of the anti-biofilm peptides, whereas in the QS peptides, the uncharged polar residue S is preponderant at the first (also anionic polar residues D, E), third and fifth positions. Positive predictions were also obtained for 29 FDA-approved peptide drugs and ten antimicrobial peptides in clinical development, indicating at their possible repurposing for anti-biofilm therapy. dPABBs is freely accessible on: http://ab-openlab.csir.res.in/abp/antibiofilm/. PMID:26912180

  19. dPABBs: A Novel in silico Approach for Predicting and Designing Anti-biofilm Peptides.

    PubMed

    Sharma, Arun; Gupta, Pooja; Kumar, Rakesh; Bhardwaj, Anshu

    2016-01-01

    Increasingly, biofilms are being recognised for their causative role in persistent infections (like cystic fibrosis, otitis media, diabetic foot ulcers) and nosocomial diseases (biofilm-infected vascular catheters, implants and prosthetics). Given the clinical relevance of biofilms and their recalcitrance to conventional antibiotics, it is imperative that alternative therapeutics are proactively sought. We have developed dPABBs, a web server that facilitates the prediction and design of anti-biofilm peptides. The six SVM and Weka models implemented on dPABBs were observed to identify anti-biofilm peptides on the basis of their whole amino acid composition, selected residue features and the positional preference of the residues (maximum accuracy, sensitivity, specificity and MCC of 95.24%, 92.50%, 97.73% and 0.91, respectively, on the training datasets). On the N-terminus, it was seen that either of the cationic polar residues, R and K, is present at all five positions in case of the anti-biofilm peptides, whereas in the QS peptides, the uncharged polar residue S is preponderant at the first (also anionic polar residues D, E), third and fifth positions. Positive predictions were also obtained for 29 FDA-approved peptide drugs and ten antimicrobial peptides in clinical development, indicating at their possible repurposing for anti-biofilm therapy. dPABBs is freely accessible on: http://ab-openlab.csir.res.in/abp/antibiofilm/. PMID:26912180

  20. Genome-Wide Prediction and Validation of Peptides That Bind Human Prosurvival Bcl-2 Proteins

    PubMed Central

    DeBartolo, Joe; Taipale, Mikko; Keating, Amy E.

    2014-01-01

    Programmed cell death is regulated by interactions between pro-apoptotic and prosurvival members of the Bcl-2 family. Pro-apoptotic family members contain a weakly conserved BH3 motif that can adopt an alpha-helical structure and bind to a groove on prosurvival partners Bcl-xL, Bcl-w, Bcl-2, Mcl-1 and Bfl-1. Peptides corresponding to roughly 13 reported BH3 motifs have been verified to bind in this manner. Due to their short lengths and low sequence conservation, BH3 motifs are not detected using standard sequence-based bioinformatics approaches. Thus, it is possible that many additional proteins harbor BH3-like sequences that can mediate interactions with the Bcl-2 family. In this work, we used structure-based and data-based Bcl-2 interaction models to find new BH3-like peptides in the human proteome. We used peptide SPOT arrays to test candidate peptides for interaction with one or more of the prosurvival proteins Bcl-xL, Bcl-w, Bcl-2, Mcl-1 and Bfl-1. For the 36 most promising array candidates, we quantified binding to all five human receptors using direct and competition binding assays in solution. All 36 peptides showed evidence of interaction with at least one prosurvival protein, and 22 peptides bound at least one prosurvival protein with a dissociation constant between 1 and 500 nM; many peptides had specificity profiles not previously observed. We also screened the full-length parent proteins of a subset of array-tested peptides for binding to Bcl-xL and Mcl-1. Finally, we used the peptide binding data, in conjunction with previously reported interactions, to assess the affinity and specificity prediction performance of different models. PMID:24967846

  1. MULTIPRED2: a computational system for large-scale identification of peptides predicted to bind to HLA supertypes and alleles.

    PubMed

    Zhang, Guang Lan; DeLuca, David S; Keskin, Derin B; Chitkushev, Lou; Zlateva, Tanya; Lund, Ole; Reinherz, Ellis L; Brusic, Vladimir

    2011-11-30

    MULTIPRED2 is a computational system for facile prediction of peptide binding to multiple alleles belonging to human leukocyte antigen (HLA) class I and class II DR molecules. It enables prediction of peptide binding to products of individual HLA alleles, combination of alleles, or HLA supertypes. NetMHCpan and NetMHCIIpan are used as prediction engines. The 13 HLA Class I supertypes are A1, A2, A3, A24, B7, B8, B27, B44, B58, B62, C1, and C4. The 13 HLA Class II DR supertypes are DR1, DR3, DR4, DR6, DR7, DR8, DR9, DR11, DR12, DR13, DR14, DR15, and DR16. In total, MULTIPRED2 enables prediction of peptide binding to 1077 variants representing 26 HLA supertypes. MULTIPRED2 has visualization modules for mapping promiscuous T-cell epitopes as well as those regions of high target concentration - referred to as T-cell epitope hotspots. Novel graphic representations are employed to display the predicted binding peptides and immunological hotspots in an intuitive manner and also to provide a global view of results as heat maps. Another function of MULTIPRED2, which has direct relevance to vaccine design, is the calculation of population coverage. Currently it calculates population coverage in five major groups in North America. MULTIPRED2 is an important tool to complement wet-lab experimental methods for identification of T-cell epitopes. It is available at http://cvc.dfci.harvard.edu/multipred2/. PMID:21130094

  2. Dissociation of POMC Peptides after Self-Injury Predicts Responses To Centrally Acting Opiate Blockers.

    ERIC Educational Resources Information Center

    Sandman, Curt A.; Hetrick, William; Taylor, Derek V.; Chicz-DeMet, Aleksandra

    1997-01-01

    This study investigated whether blood plasma levels of pro-opiomelanocortin-derived (POMC) peptides, beta-endorphin-like activity, adrenocorticotrophic hormone, and adrenal cortisol immediately after self injurious behavior (SIB) episodes predicted subsequent response to an opiate blocker in 10 patients with mental retardation. Results suggest…

  3. Utility and limitations of a peptide reactivity assay to predict fragrance allergens in vitro.

    PubMed

    Natsch, A; Gfeller, H; Rothaupt, M; Ellis, G

    2007-10-01

    A key step in the skin sensitization process is the formation of a covalent adduct between the skin sensitizer and endogenous proteins and/or peptides in the skin. A published peptide depletion assay was used to relate the in vitro reactivity of fragrance molecules to LLNA data. Using the classical assay, 22 of 28 tested moderate to strong sensitizers were positive. The prediction of weak sensitizers proved to be more difficult with only 50% of weak sensitizers giving a positive response, but for some compounds this could also be due to false-positive results from the LLNA. LC-MS analysis yielded the expected mass of the peptide adducts in several cases, whereas in other cases putative oxidation reactions led to adducts of unexpected molecular weight. Several moderately sensitizing aldehydes were correctly predicted by the depletion assay, but no adducts were found and the depletion appears to be due to an oxidation of the parent peptide catalyzed by the test compound. Finally, alternative test peptides derived from a physiological reactive protein with enhanced sensitivity for weak Michael acceptors were found, further increasing the sensitivity of the assay. PMID:17513083

  4. Semi-supervised prediction of SH2-peptide interactions from imbalanced high-throughput data.

    PubMed

    Kundu, Kousik; Costa, Fabrizio; Huber, Michael; Reth, Michael; Backofen, Rolf

    2013-01-01

    Src homology 2 (SH2) domains are the largest family of the peptide-recognition modules (PRMs) that bind to phosphotyrosine containing peptides. Knowledge about binding partners of SH2-domains is key for a deeper understanding of different cellular processes. Given the high binding specificity of SH2, in-silico ligand peptide prediction is of great interest. Currently however, only a few approaches have been published for the prediction of SH2-peptide interactions. Their main shortcomings range from limited coverage, to restrictive modeling assumptions (they are mainly based on position specific scoring matrices and do not take into consideration complex amino acids inter-dependencies) and high computational complexity. We propose a simple yet effective machine learning approach for a large set of known human SH2 domains. We used comprehensive data from micro-array and peptide-array experiments on 51 human SH2 domains. In order to deal with the high data imbalance problem and the high signal-to-noise ration, we casted the problem in a semi-supervised setting. We report competitive predictive performance w.r.t. state-of-the-art. Specifically we obtain 0.83 AUC ROC and 0.93 AUC PR in comparison to 0.71 AUC ROC and 0.87 AUC PR previously achieved by the position specific scoring matrices (PSSMs) based SMALI approach. Our work provides three main contributions. First, we showed that better models can be obtained when the information on the non-interacting peptides (negative examples) is also used. Second, we improve performance when considering high order correlations between the ligand positions employing regularization techniques to effectively avoid overfitting issues. Third, we developed an approach to tackle the data imbalance problem using a semi-supervised strategy. Finally, we performed a genome-wide prediction of human SH2-peptide binding, uncovering several findings of biological relevance. We make our models and genome-wide predictions, for all the 51 SH2

  5. Orientational landscapes of peptides in membranes: prediction of (2)H NMR couplings in a dynamic context.

    PubMed

    Esteban-Martín, Santi; Giménez, Diana; Fuertes, Gustavo; Salgado, Jesús

    2009-12-01

    Unlike soluble proteins, membrane polypeptides face an anisotropic milieu. This imposes restraints on their orientation and provides a reference that makes structure prediction tractable by minimalistic thermodynamic models. Here we use this framework to build orientational distributions of monomeric membrane-bound peptides and to predict their expected solid-state (2)H NMR quadrupolar couplings when labeled at specific side chain positions. Using a complete rigid-body sampling of configurations relative to an implicit lipid membrane, peptide free energy landscapes are calculated. This allows us to obtain probability distributions of the peptide tilt, azimuthal rotation, and depth of membrane insertion. The orientational distributions are broad and originate from an interplay among the three relevant rigid-body degrees of freedom, which allows population of multiple states in shallow free energy minima. Remarkably, only when the orientational distributions are taken into account do we obtain a close correlation between predicted (2)H NMR splittings and values measured in experiments. Such a good correlation is not seen with splittings calculated from single configurations, being either the averaged or the lowest free energy state, showing there are distributions, rather than single structures, that best define the peptide-membrane systems. Moreover, we propose that these distributions contribute to the understanding of the rigid-body dynamics of the system. PMID:19860438

  6. PEP-FOLD: an online resource for de novo peptide structure prediction

    PubMed Central

    Maupetit, Julien; Derreumaux, Philippe; Tuffery, Pierre

    2009-01-01

    Rational peptide design and large-scale prediction of peptide structure from sequence remain a challenge for chemical biologists. We present PEP-FOLD, an online service, aimed at de novo modelling of 3D conformations for peptides between 9 and 25 amino acids in aqueous solution. Using a hidden Markov model-derived structural alphabet (SA) of 27 four-residue letters, PEP-FOLD first predicts the SA letter profiles from the amino acid sequence and then assembles the predicted fragments by a greedy procedure driven by a modified version of the OPEP coarse-grained force field. Starting from an amino acid sequence, PEP-FOLD performs series of 50 simulations and returns the most representative conformations identified in terms of energy and population. Using a benchmark of 25 peptides with 9–23 amino acids, and considering the reproducibility of the runs, we find that, on average, PEP-FOLD locates lowest energy conformations differing by 2.6 Å Cα root mean square deviation from the full NMR structures. PEP-FOLD can be accessed at http://bioserv.rpbs.univ-paris-diderot.fr/PEP-FOLD PMID:19433514

  7. Machine learning based prediction for peptide drift times in ion mobility spectrometry

    PubMed Central

    Shah, Anuj R.; Agarwal, Khushbu; Baker, Erin S.; Singhal, Mudita; Mayampurath, Anoop M.; Ibrahim, Yehia M.; Kangas, Lars J.; Monroe, Matthew E.; Zhao, Rui; Belov, Mikhail E.; Anderson, Gordon A.; Smith, Richard D.

    2010-01-01

    Motivation: Ion mobility spectrometry (IMS) has gained significant traction over the past few years for rapid, high-resolution separations of analytes based upon gas-phase ion structure, with significant potential impacts in the field of proteomic analysis. IMS coupled with mass spectrometry (MS) affords multiple improvements over traditional proteomics techniques, such as in the elucidation of secondary structure information, identification of post-translational modifications, as well as higher identification rates with reduced experiment times. The high throughput nature of this technique benefits from accurate calculation of cross sections, mobilities and associated drift times of peptides, thereby enhancing downstream data analysis. Here, we present a model that uses physicochemical properties of peptides to accurately predict a peptide's drift time directly from its amino acid sequence. This model is used in conjunction with two mathematical techniques, a partial least squares regression and a support vector regression setting. Results: When tested on an experimentally created high confidence database of 8675 peptide sequences with measured drift times, both techniques statistically significantly outperform the intrinsic size parameters-based calculations, the currently held practice in the field, on all charge states (+2, +3 and +4). Availability: The software executable, imPredict, is available for download from http:/omics.pnl.gov/software/imPredict.php Contact: rds@pnl.gov Supplementary information: Supplementary data are available at Bioinformatics online. PMID:20495001

  8. The pH dependence of predictive models relating electrophoretic mobility to peptide chemico-physical properties in capillary zone electrophoresis.

    PubMed

    Castagnola, M; Rossetti, D V; Corda, M; Pellegrini, M; Misiti, F; Olianas, A; Giardina, B; Messana, I

    1998-10-01

    We applied best fitting procedures to capillary electrophoresis (CE) mobility values, measured at varying acidic pH, of a set of 21 peptides with a molecular mass ranging from about 350 to 1850 Da. This method allowed the contemporary measurements of C-terminus and carboxylic group of the side-chain of aspartic and glutamic acid dissociation constants and of peptide Stokes radius at different protonation stages. Stokes radius was related to peptide molecular mass M at the power of a fractional coefficient, and best correlation was found at pH 2.25, the fractional coefficient being equal to 0.68. This value is close to that proposed by R. E. Offord (Nature 1966, 211, 591-593), who suggested a proportionality between the polymer Stokes radius and M(2/3). The coefficient value decreases at higher pH, reaching a value of 0.58 at pH 4.25, corresponding to a mean peptide conformational transition towards more compact structures as a consequence of C-terminus dissociation. The measurement of the dissociation constants of each peptide allowed us to determine the percentage error on peptide charge predictions performed utilizing mean dissociation constants. Even for the charge, the best predictive performance is obtained at the most acidic edge of the range of the pH studied, mainly at pH 2.25. Conclusively, this study shows that the best performance of predictive models for peptide CE mobility is obtainable in the very acidic pH range (2.25-2.50) and in the absence of electroosmotic flow, and that a satisfactory predictive equation of peptide electrophoretic mobility (m2V(-1)s(-1) is given by mu = 85.4(Z/M(0.68))10(-8). PMID:9788308

  9. Methods and kits for predicting a response to an erythropoietic agent

    SciTech Connect

    Merchant, Michael L.; Klein, Jon B.; Brier, Michael E.; Gaweda, Adam E.

    2015-06-16

    Methods for predicting a response to an erythropoietic agent in a subject include providing a biological sample from the subject, and determining an amount in the sample of at least one peptide selected from the group consisting of SEQ ID NOS: 1-17. If there is a measurable difference in the amount of the at least one peptide in the sample, when compared to a control level of the same peptide, the subject is then predicted to have a good response or a poor response to the erythropoietic agent. Kits for predicting a response to an erythropoietic agent are further provided and include one or more antibodies, or fragments thereof, that specifically recognize a peptide of SEQ ID NOS: 1-17.

  10. Prediction of Antimicrobial Peptides Based on Sequence Alignment and Support Vector Machine-Pairwise Algorithm Utilizing LZ-Complexity

    PubMed Central

    Shahrudin, Shahriza

    2015-01-01

    This study concerns an attempt to establish a new method for predicting antimicrobial peptides (AMPs) which are important to the immune system. Recently, researchers are interested in designing alternative drugs based on AMPs because they have found that a large number of bacterial strains have become resistant to available antibiotics. However, researchers have encountered obstacles in the AMPs designing process as experiments to extract AMPs from protein sequences are costly and require a long set-up time. Therefore, a computational tool for AMPs prediction is needed to resolve this problem. In this study, an integrated algorithm is newly introduced to predict AMPs by integrating sequence alignment and support vector machine- (SVM-) LZ complexity pairwise algorithm. It was observed that, when all sequences in the training set are used, the sensitivity of the proposed algorithm is 95.28% in jackknife test and 87.59% in independent test, while the sensitivity obtained for jackknife test and independent test is 88.74% and 78.70%, respectively, when only the sequences that has less than 70% similarity are used. Applying the proposed algorithm may allow researchers to effectively predict AMPs from unknown protein peptide sequences with higher sensitivity. PMID:25802839

  11. Enabling the Discovery and Virtual Screening of Potent and Safe Antimicrobial Peptides. Simultaneous Prediction of Antibacterial Activity and Cytotoxicity.

    PubMed

    Kleandrova, Valeria V; Ruso, Juan M; Speck-Planche, Alejandro; Dias Soeiro Cordeiro, M Natália

    2016-08-01

    Antimicrobial peptides (AMPs) represent promising alternatives to fight against bacterial pathogens. However, cellular toxicity remains one of the main concerns in the early development of peptide-based drugs. This work introduces the first multitasking (mtk) computational model focused on performing simultaneous predictions of antibacterial activities, and cytotoxicities of peptides. The model was created from a data set containing 3592 cases, and it displayed accuracy higher than 96% for classifying/predicting peptides in both training and prediction (test) sets. The technique known as alanine scanning was computationally applied to illustrate the calculation of the quantitative contributions of the amino acids (in their respective positions of the sequence) to the biological effects of a defined peptide. A small library formed by 10 peptides was generated, where peptides were designed by considering the interpretations of the different descriptors in the mtk-computational model. All the peptides were predicted to exhibit high antibacterial activities against multiple bacterial strains, and low cytotoxicity against various cell types. The present mtk-computational model can be considered a very useful tool to support high throughput research for the discovery of potent and safe AMPs. PMID:27280735

  12. Incremental predictive value of natriuretic peptides for prognosis in the chronic stable heart failure population: a systematic review.

    PubMed

    Don-Wauchope, Andrew C; Santaguida, Pasqualina L; Oremus, Mark; McKelvie, Robert; Ali, Usman; Brown, Judy A; Bustamam, Amy; Sohel, Nazmul; Hill, Stephen A; Booth, Ronald A; Balion, Cynthia; Raina, Parminder

    2014-08-01

    The aim of this study was to determine whether measurement of natriuretic peptides independently adds incremental predictive value for mortality and morbidity in patients with chronic stable heart failure (CSHF). We electronically searched Medline®, Embase™, AMED, Cochrane Central Register of Controlled Trials, Cochrane Database of Systematic Reviews, and CINAHL from 1989 to June 2012. We also searched reference lists of included articles, systematic reviews, and the gray literature. Studies were screened for eligibility criteria and assessed for methodological quality. Data were extracted on study design, population demographics, assay cutpoints, prognostic risk prediction model covariates, statistical methods, outcomes, and results. One hundred and eighty-three studies were identified as prognostic in the systematic review. From these, 15 studies (all NT-proBNP) considered incremental predictive value in CSHF subjects. Follow-up varied from 12 to 37 months. All studies presented at least one estimate of incremental predictive value of NT-proBNP relative to the base prognostic model. Using discrimination or likelihood statistics, these studies consistently showed that NT-proBNP increased model performance. Three studies used re-classification and model validation computations to establish incremental predictive value; these studies showed less consistency with respect to added value. Although there were differences in the base risk prediction models, assay cutpoints, and lengths of follow-up, there was consistency in NT-proBNP adding incremental predictive value for prognostic models in chronic stable CSHF patients. The limitations in the literature suggest that studies designed to evaluate prognostic models should be undertaken to evaluate the incremental value of natriuretic peptide as a predictor of mortality and morbidity in CSHF. PMID:25120174

  13. Empirical methods for identifying specific peptide-protein interactions for smart reagent development

    NASA Astrophysics Data System (ADS)

    Kogot, Joshua M.; Sarkes, Deborah A.; Stratis-Cullum, Dimitra N.; Pellegrino, Paul M.

    2012-06-01

    The current state of the art in the development of antibody alternatives is fraught with difficulties including mass production, robustness, and overall cost of production. The isolation of synthetic alternatives using peptide libraries offers great potential for recognition elements that are more stable and have improved binding affinity and target specificity. Although recent advances in rapid and automated discovery and synthetic library engineering continue to show promise for this emerging science, there remains a critical need for an improved fundamental understanding of the mechanisms of recognition. To better understand the fundamental mechanisms of binding, it is critical to be able to accurately assess binding between peptide reagents and protein targets. The development of empirical methods to analyze peptide-protein interactions is often overlooked, since it is often assumed that peptides can easily substitute for antibodies in antibody-derived immunoassays. The physico-chemical difference between peptides and antibodies represents a major challenge for developing peptides in standard immunoassays as capture or detection reagents. Analysis of peptide presents a unique challenge since the peptide has to be soluble, must be capable of target recognition, and capable of ELISA plate or SPR chip binding. Incorporating a plate-binding, hydrophilic peptide fusion (PS-tag) improves both the solubility and plate binding capability in a direct peptide ELISA format. Secondly, a solution based methods, affinity capillary electrophoresis (ACE) method is presented as a solution-based, affinity determination method that can be used for determining both the association constants and binding kinetics.

  14. Diagnosis and Prediction of CKD Progression by Assessment of Urinary Peptides

    PubMed Central

    Schanstra, Joost P.; Alkhalaf, Alaa; Argiles, Angel; Bakker, Stephan J.L.; Beige, Joachim; Bilo, Henk J.G.; Chatzikyrkou, Christos; Dakna, Mohammed; Dawson, Jesse; Delles, Christian; Haller, Hermann; Haubitz, Marion; Husi, Holger; Jankowski, Joachim; Jerums, George; Kleefstra, Nanne; Kuznetsova, Tatiana; Maahs, David M.; Menne, Jan; Mullen, William; Ortiz, Alberto; Persson, Frederik; Rossing, Peter; Ruggenenti, Piero; Rychlik, Ivan; Serra, Andreas L.; Siwy, Justyna; Snell-Bergeon, Janet; Spasovski, Goce; Staessen, Jan A.; Vlahou, Antonia; Mischak, Harald; Vanholder, Raymond

    2015-01-01

    Progressive CKD is generally detected at a late stage by a sustained decline in eGFR and/or the presence of significant albuminuria. With the aim of early and improved risk stratification of patients with CKD, we studied urinary peptides in a large cross-sectional multicenter cohort of 1990 individuals, including 522 with follow-up data, using proteome analysis. We validated that a previously established multipeptide urinary biomarker classifier performed significantly better in detecting and predicting progression of CKD than the current clinical standard, urinary albumin. The classifier was also more sensitive for identifying patients with rapidly progressing CKD. Compared with the combination of baseline eGFR and albuminuria (area under the curve [AUC]=0.758), the addition of the multipeptide biomarker classifier significantly improved CKD risk prediction (AUC=0.831) as assessed by the net reclassification index (0.303±−0.065; P<0.001) and integrated discrimination improvement (0.058±0.014; P<0.001). Correlation of individual urinary peptides with CKD stage and progression showed that the peptides that associated with CKD, irrespective of CKD stage or CKD progression, were either fragments of the major circulating proteins, suggesting failure of the glomerular filtration barrier sieving properties, or different collagen fragments, suggesting accumulation of intrarenal extracellular matrix. Furthermore, protein fragments associated with progression of CKD originated mostly from proteins related to inflammation and tissue repair. Results of this study suggest that urinary proteome analysis might significantly improve the current state of the art of CKD detection and outcome prediction and that identification of the urinary peptides allows insight into various ongoing pathophysiologic processes in CKD. PMID:25589610

  15. Diagnosis and Prediction of CKD Progression by Assessment of Urinary Peptides.

    PubMed

    Schanstra, Joost P; Zürbig, Petra; Alkhalaf, Alaa; Argiles, Angel; Bakker, Stephan J L; Beige, Joachim; Bilo, Henk J G; Chatzikyrkou, Christos; Dakna, Mohammed; Dawson, Jesse; Delles, Christian; Haller, Hermann; Haubitz, Marion; Husi, Holger; Jankowski, Joachim; Jerums, George; Kleefstra, Nanne; Kuznetsova, Tatiana; Maahs, David M; Menne, Jan; Mullen, William; Ortiz, Alberto; Persson, Frederik; Rossing, Peter; Ruggenenti, Piero; Rychlik, Ivan; Serra, Andreas L; Siwy, Justyna; Snell-Bergeon, Janet; Spasovski, Goce; Staessen, Jan A; Vlahou, Antonia; Mischak, Harald; Vanholder, Raymond

    2015-08-01

    Progressive CKD is generally detected at a late stage by a sustained decline in eGFR and/or the presence of significant albuminuria. With the aim of early and improved risk stratification of patients with CKD, we studied urinary peptides in a large cross-sectional multicenter cohort of 1990 individuals, including 522 with follow-up data, using proteome analysis. We validated that a previously established multipeptide urinary biomarker classifier performed significantly better in detecting and predicting progression of CKD than the current clinical standard, urinary albumin. The classifier was also more sensitive for identifying patients with rapidly progressing CKD. Compared with the combination of baseline eGFR and albuminuria (area under the curve [AUC]=0.758), the addition of the multipeptide biomarker classifier significantly improved CKD risk prediction (AUC=0.831) as assessed by the net reclassification index (0.303±-0.065; P<0.001) and integrated discrimination improvement (0.058±0.014; P<0.001). Correlation of individual urinary peptides with CKD stage and progression showed that the peptides that associated with CKD, irrespective of CKD stage or CKD progression, were either fragments of the major circulating proteins, suggesting failure of the glomerular filtration barrier sieving properties, or different collagen fragments, suggesting accumulation of intrarenal extracellular matrix. Furthermore, protein fragments associated with progression of CKD originated mostly from proteins related to inflammation and tissue repair. Results of this study suggest that urinary proteome analysis might significantly improve the current state of the art of CKD detection and outcome prediction and that identification of the urinary peptides allows insight into various ongoing pathophysiologic processes in CKD. PMID:25589610

  16. Interim prediction method for jet noise

    NASA Technical Reports Server (NTRS)

    Stone, J. R.

    1974-01-01

    A method is provided for predicting jet noise for a wide range of nozzle geometries and operating conditions of interest for aircraft engines. Jet noise theory, data and existing prediction methods was reviewed, and based on this information a interim method of jet noise prediction is proposed. Problem areas are idenified where further research is needed to improve the prediction method. This method predicts only the noise generated by the exhaust jets mixing with the surrounding air and does not include other noises emanating from the engine exhaust, such as combustion and machinery noise generated inside the engine (i.e., core noise). It does, however, include thrust reverser noise. Prediction relations are provided for conical nozzles, plug nozzles, coaxial nozzles and slot nozzles.

  17. Clinical Value of Natriuretic Peptides in Predicting Time to Dialysis in Stage 4 and 5 Chronic Kidney Disease Patients

    PubMed Central

    Sundqvist, Sofia; Larson, Thomas; Cauliez, Bruno; Bauer, Fabrice; Dumont, Audrey; Le Roy, Frank; Hanoy, Mélanie; Fréguin-Bouilland, Caroline; Godin, Michel

    2016-01-01

    Background Anticipating the time to renal replacement therapy (RRT) in chronic kidney disease (CKD) patients is an important but challenging issue. Natriuretic peptides are biomarkers of ventricular dysfunction related to poor outcome in CKD. We comparatively investigated the value of B-type natriuretic peptide (BNP) and N-terminal pro-B-type natriuretic peptide (NT-proBNP) as prognostic markers for the risk of RRT in stage 4 and 5 CKD patients, and in foretelling all-cause mortality and major cardiovascular events within a 5-year follow-up period. Methods Baseline plasma BNP (Triage, Biosite) and NT-proBNP (Elecsys, Roche) were measured at inclusion. Forty-three patients were followed-up during 5 years. Kaplan-Meier analysis, with log-rank testing and hazard ratios (HR), were calculated to evaluate survival without RRT, cardiovascular events or mortality. The independent prognostic value of the biomarkers was estimated in separate Cox multivariate analysis, including estimated glomerular filtration rate (eGFR), creatininemia and comorbidities. Results During the first 12-month follow-up period, 16 patients started RRT. NT-proBNP concentration was higher in patients who reached endpoint (3221 ng/L vs 777 ng/L, p = 0.02). NT-proBNP concentration > 1345 ng/L proved significant predictive value on survival analysis for cardiovascular events (p = 0.04) and dialysis within 60 months follow-up (p = 0.008). BNP concentration > 140 ng/L was an independent predictor of RRT after 12 months follow-up (p<0.005), and of significant predictive value for initiation of dialysis within 60 months follow-up. Conclusions Our results indicate a prognostic value for BNP and NT-proBNP in predicting RRT in stage 4 and 5 CKD patients, regarding both short- and long-term periods. NT-proBNP also proved a value in predicting cardiovascular events. Natriuretic peptides could be useful predictive biomarkers for therapeutic guidance in CKD. PMID:27548064

  18. Protein Residue Contacts and Prediction Methods.

    PubMed

    Adhikari, Badri; Cheng, Jianlin

    2016-01-01

    In the field of computational structural proteomics, contact predictions have shown new prospects of solving the longstanding problem of ab initio protein structure prediction. In the last few years, application of deep learning algorithms and availability of large protein sequence databases, combined with improvement in methods that derive contacts from multiple sequence alignments, have shown a huge increase in the precision of contact prediction. In addition, these predicted contacts have also been used to build three-dimensional models from scratch.In this chapter, we briefly discuss many elements of protein residue-residue contacts and the methods available for prediction, focusing on a state-of-the-art contact prediction tool, DNcon. Illustrating with a case study, we describe how DNcon can be used to make ab initio contact predictions for a given protein sequence and discuss how the predicted contacts may be analyzed and evaluated. PMID:27115648

  19. Protein Residue Contacts and Prediction Methods

    PubMed Central

    Adhikari, Badri

    2016-01-01

    In the field of computational structural proteomics, contact predictions have shown new prospects of solving the longstanding problem of ab initio protein structure prediction. In the last few years, application of deep learning algorithms and availability of large protein sequence databases, combined with improvement in methods that derive contacts from multiple sequence alignments, have shown a huge increase in the precision of contact prediction. In addition, these predicted contacts have also been used to build three-dimensional models from scratch. In this chapter, we briefly discuss many elements of protein residue–residue contacts and the methods available for prediction, focusing on a state-of-the-art contact prediction tool, DNcon. Illustrating with a case study, we describe how DNcon can be used to make ab initio contact predictions for a given protein sequence and discuss how the predicted contacts may be analyzed and evaluated. PMID:27115648

  20. Methods to follow intracellular trafficking of cell-penetrating peptides.

    PubMed

    Pärnaste, Ly; Arukuusk, Piret; Zagato, Elisa; Braeckmans, Kevin; Langel, Ülo

    2016-01-01

    Cell-penetrating peptides (CPPs) are efficient vehicles to transport bioactive molecules into the cells. Despite numerous studies the exact mechanism by which CPPs facilitate delivery of cargo to its intracellular target is still debated. The current work presents methods that can be used for tracking CPP/pDNA complexes through endosomal transport and show the role of endosomal transport in the delivery of cargo. Separation of endosomal vesicles by differential centrifugation enables to pinpoint the localization of delivered cargo without labeling it and gives important quantitative information about pDNA trafficing in certain endosomal compartments. Single particle tracking (SPT) allows following individual CPP/cargo complex through endosomal path in live cells, using fluoresently labled cargo and green fluoresent protein expressing cells. These two different methods show similar results about tested NickFect/pDNA complexes intracellular trafficing. NF51 facilitates rapid internalization of complexes into the cells, prolongs their stay in early endosomes and promotes release to cytosol. NF1 is less capable to induce endosomal release and higher amount of complexes are routed to lysosomes for degradation. Our findings offer potential delivery vector for in vivo applications, NF51, where endosomal entrapment has been allayed. Furthermore, these methods are valuable tools to study other CPP-based delivery systems. PMID:26460120

  1. Cyanine-based probe\\tag-peptide pair fluorescence protein imaging and fluorescence protein imaging methods

    DOEpatents

    Mayer-Cumblidge, M. Uljana; Cao, Haishi

    2013-01-15

    A molecular probe comprises two arsenic atoms and at least one cyanine based moiety. A method of producing a molecular probe includes providing a molecule having a first formula, treating the molecule with HgOAc, and subsequently transmetallizing with AsCl.sub.3. The As is liganded to ethanedithiol to produce a probe having a second formula. A method of labeling a peptide includes providing a peptide comprising a tag sequence and contacting the peptide with a biarsenical molecular probe. A complex is formed comprising the tag sequence and the molecular probe. A method of studying a peptide includes providing a mixture containing a peptide comprising a peptide tag sequence, adding a biarsenical probe to the mixture, and monitoring the fluorescence of the mixture.

  2. Oriented Circular Dichroism: A Method to Characterize Membrane-Active Peptides in Oriented Lipid Bilayers.

    PubMed

    Bürck, Jochen; Wadhwani, Parvesh; Fanghänel, Susanne; Ulrich, Anne S

    2016-02-16

    The structures of membrane-bound polypeptides are intimately related to their functions and may change dramatically with the lipid environment. Circular dichroism (CD) is a rapid analytical method that requires relatively low amounts of material and no labeling. Conventional CD is routinely used to monitor the secondary structure of peptides and proteins in solution, for example, in the presence of ligands and other binding partners. In the case of membrane-active peptides and transmembrane proteins, these measurements can be applied to, and remain limited to, samples containing detergent micelles or small sonicated lipid vesicles. Such traditional CD analysis reveals only secondary structures. With the help of an oriented circular dichroism (OCD) setup, however, based on the preparation of macroscopically oriented lipid bilayers, it is possible to address the membrane alignment of a peptide in addition to its conformation. This approach has been mostly used for α-helical peptides so far, but other structural elements are conceivable as well. OCD analysis relies on Moffitt's theory, which predicts that the electronic transition dipole moments of the backbone amide bonds in helical polypeptides are polarized either parallel or perpendicular to the helix axis. The interaction of the electric field vector of the circularly polarized light with these transitions results in an OCD spectrum of a membrane-bound α-helical peptide, which exhibits a characteristic line shape and reflects the angle between the helix axis and the bilayer normal. For parallel alignment of a peptide helix with respect to the membrane surface (S-state), the corresponding "fingerprint" CD band around 208 nm will exhibit maximum negative amplitude. If the helix changes its alignment via an obliquely tilted (T-state) to a fully inserted transmembrane orientation (I-state), the ellipticity at 208 nm decreases and the value approaches zero due to the decreased interactions between the field and the

  3. Prediction of anticancer peptides against MCF-7 breast cancer cells from the peptidomes of Achatina fulica mucus fractions

    PubMed Central

    E-kobon, Teerasak; Thongararm, Pennapa; Roytrakul, Sittiruk; Meesuk, Ladda; Chumnanpuen, Pramote

    2015-01-01

    Several reports have shown antimicrobial and anticancer activities of mucous glycoproteins extracted from the giant African snail Achatina fulica. Anticancer properties of the snail mucous peptides remain incompletely revealed. The aim of this study was to predict anticancer peptides from A. fulica mucus. Two of HPLC-separated mucous fractions (F2 and F5) showed in vitro cytotoxicity against the breast cancer cell line (MCF-7) and normal epithelium cell line (Vero). According to the mass spectrometric analysis, 404 and 424 peptides from the F2 and F5 fractions were identified. Our comprehensive bioinformatics workflow predicted 16 putative cationic and amphipathic anticancer peptides with diverse structures from these two peptidome data. These peptides would be promising molecules for new anti-breast cancer drug development. PMID:26862373

  4. An effective and effecient peptide binding prediction approach for a broad set of HLA-DR molecules based on ordered weighted averaging of binding pocket profiles

    PubMed Central

    2013-01-01

    Background The immune system must detect a wide variety of microbial pathogens, such as viruses, bacteria, fungi and parasitic worms, to protect the host against disease. Antigenic peptides displayed by MHC II (class II Major Histocompatibility Complex) molecules is a pivotal process to activate CD4+ TH cells (Helper T cells). The activated TH cells can differentiate into effector cells which assist various cells in activating against pathogen invasion. Each MHC locus encodes a great number of allele variants. Yet this limited number of MHC molecules are required to display enormous number of antigenic peptides. Since the peptide binding measurements of MHC molecules by biochemical experiments are expensive, only a few of the MHC molecules have suffecient measured peptides. To perform accurate binding prediction for those MHC alleles without suffecient measured peptides, a number of computational algorithms were proposed in the last decades. Results Here, we propose a new MHC II binding prediction approach, OWA-PSSM, which is a significantly extended version of a well known method called TEPITOPE. The TEPITOPE method is able to perform prediction for only 50 MHC alleles, while OWA-PSSM is able to perform prediction for much more, up to 879 HLA-DR molecules. We evaluate the method on five benchmark datasets. The method is demonstrated to be the best one in identifying binding cores compared with several other popular state-of-the-art approaches. Meanwhile, the method performs comparably to the TEPITOPE and NetMHCIIpan2.0 approaches in identifying HLA-DR epitopes and ligands, and it performs significantly better than TEPITOPEpan in the identification of HLA-DR ligands and MultiRTA in identifying HLA-DR T cell epitopes. Conclusions The proposed approach OWA-PSSM is fast and robust in identifying ligands, epitopes and binding cores for up to 879 MHC II molecules. PMID:24565049

  5. Design and Evaluation of Antimalarial Peptides Derived from Prediction of Short Linear Motifs in Proteins Related to Erythrocyte Invasion

    PubMed Central

    Bianchin, Alessandra; Bell, Angus; Chubb, Anthony J.; Doolan, Nathalie; Leneghan, Darren; Stavropoulos, Ilias; Shields, Denis C.; Mooney, Catherine

    2015-01-01

    The purpose of this study was to investigate the blood stage of the malaria causing parasite, Plasmodium falciparum, to predict potential protein interactions between the parasite merozoite and the host erythrocyte and design peptides that could interrupt these predicted interactions. We screened the P. falciparum and human proteomes for computationally predicted short linear motifs (SLiMs) in cytoplasmic portions of transmembrane proteins that could play roles in the invasion of the erythrocyte by the merozoite, an essential step in malarial pathogenesis. We tested thirteen peptides predicted to contain SLiMs, twelve of them palmitoylated to enhance membrane targeting, and found three that blocked parasite growth in culture by inhibiting the initiation of new infections in erythrocytes. Scrambled peptides for two of the most promising peptides suggested that their activity may be reflective of amino acid properties, in particular, positive charge. However, one peptide showed effects which were stronger than those of scrambled peptides. This was derived from human red blood cell glycophorin-B. We concluded that proteome-wide computational screening of the intracellular regions of both host and pathogen adhesion proteins provides potential lead peptides for the development of anti-malarial compounds. PMID:26039561

  6. Histidine-containing peptide catalysts developed by a facile library screening method.

    PubMed

    Akagawa, Kengo; Sakai, Nobutaka; Kudo, Kazuaki

    2015-02-01

    Although peptide catalysts have a high potential for the use as organocatalysts, the optimization of peptide sequences is laborious and time-consuming. To address this issue, a facile screening method for finding efficient aminocatalysts from a peptide library has been developed. In the screening for the Michael addition of a malonate to an enal, a dye-labeled product is immobilized on resin-bound peptides through reductive amination to visualize active catalysts. This procedure allows for the monitoring of the reactivity of entire peptides without modifying the resin beads beforehand. Peptides containing histidine at an appropriate position were identified by this method. A novel function of the histidyl residue, which enhances the binding of a substrate to the catalyst by capturing an iminium intermediate, was indicated. PMID:25521645

  7. RAId_DbS: Method for Peptide ID using Database Search with Accurate Statistics

    NASA Astrophysics Data System (ADS)

    Alves, Gelio; Ogurtsov, Aleksey; Yu, Yi-Kuo

    2007-03-01

    The key to proteomics studies, essential in systems biology, is peptide identification. Under tandem mass spectrometry, each spectrum generated consists of a list of mass/charge peaks along with their intensities. Software analysis is then required to identify from the spectrum peptide candidates that best interpret the spectrum. The library search, which compares the spectral peaks against theoretical peaks generated by each peptide in a library, is among the most popular methods. This method, although robust, lacks good quantitative statistical underpinning. As we show, many library search algorithms suffer from statistical instability. The need for a better statistical basis prompted us to develop RAId_DbS. Taking into account the skewness in the peak intensity distribution while scoring peptides, RAId_DbS provides an accurate statistical significance assignment to each peptide candidate. RAId_DbS will be a valuable tool especially when one intends to identify proteins through peptide identifications.

  8. In silico prediction of peptide binding affinity to class I mouse major histocompatibility complexes: a comparative molecular similarity index analysis (CoMSIA) study.

    PubMed

    Hattotuwagama, Channa K; Doytchinova, Irini A; Flower, Darren R

    2005-01-01

    Current methods for the in silico identification of T cell epitopes (which form the basis of many vaccines, diagnostics, and reagents) rely on the accurate prediction of peptide-major histocompatibility complex (MHC) affinity. A three-dimensional quantitative structure-activity relationship (3D-QSAR) for the prediction of peptide binding to class I MHC molecules was established using the comparative molecular similarity index analysis (CoMSIA) method. Three MHC alleles were studied: H2-D(b), H2-K(b), and H2-K(k). Models were produced for each allele. Each model consisted of five physicochemical descriptors-steric bulk, electrostatic potentials, hydrophobic interactions, and hydrogen-bond donor and hydrogen-bond acceptor abilities. The models have an acceptable level of predictivity: cross-validation leave-one-out statistical terms q2 and SEP (standard error of prediction) ranged between 0.490 and 0.679 and between 0.525 and 0.889, respectively. The non-cross-validated statistical terms r2 and SEE (standard error of estimate) ranged between 0.913 and 0.979 and between 0.167 and 0.248, respectively. The use of coefficient contour maps, which indicate favored and disfavored areas for each position of the MHC-bound peptides, allowed the binding specificity of each allele to be identified, visualized, and understood. The present study demonstrates the effectiveness of CoMSIA as a method for studying peptide-MHC interactions. The peptides used in this study are available on the Internet (http://www.jenner.ac.uk/AntiJen). The partial least-squares method is available commercially in the SYBYL molecular modeling software package. PMID:16180918

  9. A method for the 32P labeling of peptides or peptide nucleic acid oligomers

    NASA Technical Reports Server (NTRS)

    Kozlov, I. A.; Nielsen, P. E.; Orgel, L. E.; Bada, J. L. (Principal Investigator)

    1998-01-01

    A novel approach to the radioactive labeling of peptides and PNA oligomers is described. It is based on the conjugation of a deoxynucleoside 3'-phosphate with the terminal amine of the substrate, followed by phosphorylation of the 5'-hydroxyl group of the nucleotide using T4 polynucleotide kinase and [gamma-32P]ATP.

  10. Bioinformatic prediction of arthropod/nematode-like peptides in non-arthropod, non-nematode members of the Ecdysozoa.

    PubMed

    Christie, Andrew E; Nolan, Daniel H; Garcia, Zachery A; McCoole, Matthew D; Harmon, Sarah M; Congdon-Jones, Benjamin; Ohno, Paul; Hartline, Niko; Congdon, Clare Bates; Baer, Kevin N; Lenz, Petra H

    2011-02-01

    The Onychophora, Priapulida and Tardigrada, along with the Arthropoda, Nematoda and several other small phyla, form the superphylum Ecdysozoa. Numerous peptidomic studies have been undertaken for both the arthropods and nematodes, resulting in the identification of many peptides from each group. In contrast, little is known about the peptides used as paracrines/hormones by species from the other ecdysozoan taxa. Here, transcriptome mining and bioinformatic peptide prediction were used to identify peptides in members of the Onychophora, Priapulida and Tardigrada, the only non-arthropod, non-nematode members of the Ecdysozoa for which there are publicly accessible expressed sequence tags (ESTs). The extant ESTs for each phylum were queried using 106 arthropod/nematode peptide precursors. Transcripts encoding calcitonin-like diuretic hormone and pigment-dispersing hormone (PDH) were identified for the onychophoran Peripatopsis sedgwicki, with transcripts encoding C-type allatostatin (C-AST) and FMRFamide-like peptide identified for the priapulid Priapulus caudatus. For the Tardigrada, transcripts encoding members of the A-type allatostatin, C-AST, insect kinin, orcokinin, PDH and tachykinin-related peptide families were identified, all but one from Hypsibius dujardini (the exception being a Milnesium tardigradum orcokinin-encoding transcript). The proteins deduced from these ESTs resulted in the prediction of 48 novel peptides, six onychophoran, eight priapulid and 34 tardigrade, which are the first described from these phyla. PMID:21074533

  11. A continuous peptide epitope reacting with pandemic influenza AH1N1 predicted by bioinformatic approaches.

    PubMed

    Carrillo-Vazquez, Jonathan P; Correa-Basurto, José; García-Machorro, Jazmin; Campos-Rodríguez, Rafael; Moreau, Violaine; Rosas-Trigueros, Jorge L; Reyes-López, Cesar A; Rojas-López, Marlon; Zamorano-Carrillo, Absalom

    2015-09-01

    Computational identification of potential epitopes with an immunogenic capacity challenges immunological research. Several methods show considerable success, and together with experimental studies, the efficiency of the algorithms to identify potential peptides with biological activity has improved. Herein, an epitope was designed by combining bioinformatics, docking, and molecular dynamics simulations. The hemagglutinin protein of the H1N1 influenza pandemic strain served as a template, owing to the interest of obtaining a scheme of immunization. Afterward, we performed enzyme-linked immunosorbent assay (ELISA) using the epitope to analyze if any antibodies in human sera before and after the influenza outbreak in 2009 recognize this peptide. Also, a plaque reduction neutralization test induced by virus-neutralizing antibodies and the IgG determination showed the biological activity of this computationally designed peptide. The results of the ELISAs demonstrated that the serum of both prepandemic and pandemic recognized the epitope. Moreover, the plaque reduction neutralization test evidenced the capacity of the designed peptide to neutralize influenza virus in Madin-Darby canine cells. PMID:25788327

  12. In Silico Prediction of Neuropeptides/Peptide Hormone Transcripts in the Cheilostome Bryozoan Bugula neritina

    PubMed Central

    Zhang, Gen; He, Li-Sheng; Qian, Pei-Yuan

    2016-01-01

    The bryozoan Bugula neritina has a biphasic life cycle that consists of a planktonic larval stage and a sessile juvenile/adult stage. The transition between these two stages is crucial for the development and recruitment of B. neritina. Metamorphosis in B. neritina is mediated by both the nervous system and the release of developmental signals. However, no research has been conducted to investigate the expression of neuropeptides (NP)/peptide hormones in B. neritina larvae. Here, we report a comprehensive study of the NP/peptide hormones in the marine bryozoan B. neritina based on in silico identification methods. We recovered 22 transcripts encompassing 11 NP/peptide hormone precursor transcript sequences. The transcript sequences of the 11 isolated NP precursors were validated by cDNA cloning using gene-specific primers. We also examined the expression of three peptide hormone precursor transcripts (BnFDSIG, BnILP1, BnGPB) in the coronate larvae of B. neritina, demonstrating their distinct expression patterns in the larvae. Overall, our findings serve as an important foundation for subsequent investigations of the peptidergic control of bryozoan larval behavior and settlement. PMID:27537380

  13. In Silico Prediction of Neuropeptides/Peptide Hormone Transcripts in the Cheilostome Bryozoan Bugula neritina.

    PubMed

    Wong, Yue Him; Yu, Li; Zhang, Gen; He, Li-Sheng; Qian, Pei-Yuan

    2016-01-01

    The bryozoan Bugula neritina has a biphasic life cycle that consists of a planktonic larval stage and a sessile juvenile/adult stage. The transition between these two stages is crucial for the development and recruitment of B. neritina. Metamorphosis in B. neritina is mediated by both the nervous system and the release of developmental signals. However, no research has been conducted to investigate the expression of neuropeptides (NP)/peptide hormones in B. neritina larvae. Here, we report a comprehensive study of the NP/peptide hormones in the marine bryozoan B. neritina based on in silico identification methods. We recovered 22 transcripts encompassing 11 NP/peptide hormone precursor transcript sequences. The transcript sequences of the 11 isolated NP precursors were validated by cDNA cloning using gene-specific primers. We also examined the expression of three peptide hormone precursor transcripts (BnFDSIG, BnILP1, BnGPB) in the coronate larvae of B. neritina, demonstrating their distinct expression patterns in the larvae. Overall, our findings serve as an important foundation for subsequent investigations of the peptidergic control of bryozoan larval behavior and settlement. PMID:27537380

  14. Analysis and Prediction of the Critical Regions of Antimicrobial Peptides Based on Conditional Random Fields

    PubMed Central

    Chang, Kuan Y.; Lin, Tung-pei; Shih, Ling-Yi; Wang, Chien-Kuo

    2015-01-01

    Antimicrobial peptides (AMPs) are potent drug candidates against microbes such as bacteria, fungi, parasites, and viruses. The size of AMPs ranges from less than ten to hundreds of amino acids. Often only a few amino acids or the critical regions of antimicrobial proteins matter the functionality. Accurately predicting the AMP critical regions could benefit the experimental designs. However, no extensive analyses have been done specifically on the AMP critical regions and computational modeling on them is either non-existent or settled to other problems. With a focus on the AMP critical regions, we thus develop a computational model AMPcore by introducing a state-of-the-art machine learning method, conditional random fields. We generate a comprehensive dataset of 798 AMPs cores and a low similarity dataset of 510 representative AMP cores. AMPcore could reach a maximal accuracy of 90% and 0.79 Matthew’s correlation coefficient on the comprehensive dataset and a maximal accuracy of 83% and 0.66 MCC on the low similarity dataset. Our analyses of AMP cores follow what we know about AMPs: High in glycine and lysine, but low in aspartic acid, glutamic acid, and methionine; the abundance of α-helical structures; the dominance of positive net charges; the peculiarity of amphipathicity. Two amphipathic sequence motifs within the AMP cores, an amphipathic α-helix and an amphipathic π-helix, are revealed. In addition, a short sequence motif at the N-terminal boundary of AMP cores is reported for the first time: arginine at the P(-1) coupling with glycine at the P1 of AMP cores occurs the most, which might link to microbial cell adhesion. PMID:25803302

  15. Prediction and preliminary screening of HLA-A*0201-restricted epitope peptides of human GPC3.

    PubMed

    Hu, P; Wei, Z; Li, R; Wu, D; Meng, Z

    2016-06-01

    In response to the limited therapeutic option for hepatocellular carcinoma (HCC), immunotherapy as a promising approach points out a new direction to the cure of tumours through specific recognition and elimination of tumour cells by the immunity-enhanced autologous immunocytes of patients. Few effective tumour antigens, however, are alternative in addition to alpha fetoprotein or tumour cell lysates. Recent studies have demonstrated that glypican-3 (GPC3) is not only a promising diagnostic marker, but also ideal therapeutic target to HCC. In this study, potential HLA-A*0201 GPC3 peptides were screened with three epitope prediction software, the binding affinity of 13 predicted epitopes with high scores was determined by T2 cells binding assay and four optimal epitopes were identified. This is the first study in which the optimal HLA-A*0201 GPC3 epitopes were screened from a large number of candidates predicted by three software. The optimized HLA-A*0201 GPC3 peptides will provide new epitope candidates for HCC immunotherapy. PMID:27102087

  16. Folding peptides and proteins with all-atom physics: methods and applications

    NASA Astrophysics Data System (ADS)

    Shell, M. Scott

    2008-03-01

    Computational methods offer powerful tools for investigating proteins and peptides at the molecular-level; however, it has proven challenging to reproduce the long time scale folding processes of these molecules at a level that is both faithful to the atomic driving forces and attainable with modern commodity cluster computing. Alternatively, the past decade has seen significant progress in using bioinformatics-based approaches to infer the three dimensional native structures of proteins, drawing upon extensive knowledge databases of known protein structures [1]. These methods work remarkably well when a homologous protein can be found to provide a structural template for a candidate sequence. However, in cases where homology to database proteins is low, where the folding pathway is of interest, or where conformational flexibility is substantial---as in many emerging protein and peptide technologies---bioinformatics methods perform poorly. There is therefore great interest in seeing purely physics-based approaches succeed. We discuss a purely physics-based, database-free folding method, relying on proper thermal sampling (replica exchange molecular dynamics) and molecular potential energy functions. In order to surmount the tremendous computational demands of all-atom folding simulations, our approach implements a conformational search strategy based on a putative protein folding mechanism called zipping and assembly [2-4]. That is, we explicitly seek out potential folding pathways inferred from short simulations, and iteratively pursue all such routes by coaxing a polypeptide chain along them. The method is called the Zipping and Assembly Method (ZAM) and it works in two parts: (1) the full polypeptide chain is broken into small fragments that are first simulated independently and then successively re-assembled into larger segments with further sampling, and (2) consistently stable structure in fragments is detected and locked into place, in order to avoid re

  17. Quantum chemical calculations predict biological function: the case of T cell receptor interaction with a peptide/MHC class I

    PubMed Central

    Antipas, Georgios S. E.; Germenis, Anastasios E.

    2015-01-01

    A combination of atomic correlation statistics and quantum chemical calculations are shown to predict biological function. In the present study, various antigenic peptide-Major Histocompatibility Complex (pMHC) ligands with near-identical stereochemistries, in complexation with the same T cell receptor (TCR), were found to consistently induce distinctly different quantum chemical behavior, directly dependent on the peptide's electron spin density and intrinsically expressed by the protonation state of the peptide's N-terminus. Furthermore, the cumulative coordination difference of any variant in respect to the native peptide was found to accurately reflect peptide biological function and immerges as the physical observable which is directly related to the immunological end-effect of pMHC-TCR interaction. PMID:25713797

  18. Computational Prediction of Immunodominant Epitopes on Outer Membrane Protein (Omp) H of Pasteurella multocida Toward Designing of a Peptide Vaccine.

    PubMed

    Ganguly, Bhaskar

    2016-01-01

    Contemporary vaccine design necessitates discrimination between the immunogenic and non-immunogenic components within a pathogen. To successfully target a humoral immune response, the vaccine antigen should contain not only B-cell epitopes but abounding Th-cell agretopes and MHC-II binding regions as well. No single computational method is available that allows the identification of such regions on antigens with good reliability. A consensus approach based on several prediction methods can be adopted to overcome this problem.Targeting the outer membrane protein (Omp) H as a candidate, a comprehensive work flow is described for the computational identification of immunodominant epitopes toward the designing of a peptide vaccine against Pasteurella multocida. PMID:27076289

  19. Improved pan-specific MHC class I peptide-binding predictions using a novel representation of the MHC-binding cleft environment.

    PubMed

    Carrasco Pro, S; Zimic, M; Nielsen, M

    2014-02-01

    Major histocompatibility complex (MHC) molecules play a key role in cell-mediated immune responses presenting bounded peptides for recognition by the immune system cells. Several in silico methods have been developed to predict the binding affinity of a given peptide to a specific MHC molecule. One of the current state-of-the-art methods for MHC class I is NetMHCpan, which has a core ingredient for the representation of the MHC class I molecule using a pseudo-sequence representation of the binding cleft amino acid environment. New and large MHC-peptide-binding data sets are constantly being made available, and also new structures of MHC class I molecules with a bound peptide have been published. In order to test if the NetMHCpan method can be improved by integrating this novel information, we created new pseudo-sequence definitions for the MHC-binding cleft environment from sequence and structural analyses of different MHC data sets including human leukocyte antigen (HLA), non-human primates (chimpanzee, macaque and gorilla) and other animal alleles (cattle, mouse and swine). From these constructs, we showed that by focusing on MHC sequence positions found to be polymorphic across the MHC molecules used to train the method, the NetMHCpan method achieved a significant increase in the predictive performance, in particular, of non-human MHCs. This study hence showed that an improved performance of MHC-binding methods can be achieved not only by the accumulation of more MHC-peptide-binding data but also by a refined definition of the MHC-binding environment including information from non-human species. PMID:24447175

  20. MHCPred: A server for quantitative prediction of peptide-MHC binding.

    PubMed

    Guan, Pingping; Doytchinova, Irini A; Zygouri, Christianna; Flower, Darren R

    2003-07-01

    Accurate T-cell epitope prediction is a principal objective of computational vaccinology. As a service to the immunology and vaccinology communities at large, we have implemented, as a server on the World Wide Web, a partial least squares-based multivariate statistical approach to the quantitative prediction of peptide binding to major histocom- patibility complexes (MHC), the key checkpoint on the antigen presentation pathway within adaptive cellular immunity. MHCPred implements robust statistical models for both Class I alleles (HLA-A*0101, HLA-A*0201, HLA-A*0202, HLA-A*0203, HLA-A*0206, HLA-A*0301, HLA-A*1101, HLA-A*3301, HLA-A*6801, HLA-A*6802 and HLA-B*3501) and Class II alleles (HLA-DRB*0401, HLA-DRB*0401 and HLA-DRB*0701). MHCPred is available from the URL: http://www.jenner.ac.uk/MHCPred. PMID:12824380

  1. Design of a predicted MHC restricted short peptide immunodiagnostic and vaccine candidate for Fowl adenovirus C in chicken infection

    PubMed Central

    Valdivia-Olarte, Hugo; Requena, David; Ramirez, Manuel; Saravia, Luis E; Izquierdo, Ray; Falconi-Agapito, Francesca; Zavaleta, Milagros; Best, Iván; Fernández-Díaz, Manolo; Zimic, Mirko

    2015-01-01

    Fowl adenoviruses (FAdVs) are the ethiologic agents of multiple pathologies in chicken. There are five different species of FAdVs grouped as FAdV-A, FAdV-B, FAdV-C, FAdV-D, and FAdV-E. It is of interest to develop immunodiagnostics and vaccine candidate for Peruvian FAdV-C in chicken infection using MHC restricted short peptide candidates. We sequenced the complete genome of one FAdV strain isolated from a chicken of a local farm. A total of 44 protein coding genes were identified in each genome. We sequenced twelve Cobb chicken MHC alleles from animals of different farms in the central coast of Peru, and subsequently determined three optimal human MHC-I and four optimal human MHC-II substitute alleles for MHC-peptide prediction. The potential MHC restricted short peptide epitope-like candidates were predicted using human specific (with determined suitable chicken substitutes) NetMHC MHC-peptide prediction model with web server features from all the FAdV genomes available. FAdV specific peptides with calculated binding values to known substituted chicken MHC-I and MHC-II were further filtered for diagnostics and potential vaccine epitopes. Promiscuity to the 3/4 optimal human MHC-I/II alleles and conservation among the available FAdV genomes was considered in this analysis. The localization on the surface of the protein was considered for class II predicted peptides. Thus, a set of class I and class II specific peptides from FAdV were reported in this study. Hence, a multiepitopic protein was built with these peptides, and subsequently tested to confirm the production of specific antibodies in chicken. PMID:26664030

  2. Structural load prediction methods for space payloads

    NASA Technical Reports Server (NTRS)

    Wada, B. K.

    1982-01-01

    The state of the art in structural loads prediction procedures for spacecraft is summarized. Three categories of prediction techniques delineated by cost, complexity, comprehensiveness, accuracy, and applications are outlined. The lowest cost method has been used for earth resources, communications, and weather satellites, the medium cost method for sun-synchronous orbits and the large space telescope, and the most expensive for planetary missions, comet rendezvous, and out-of-ecliptic orbits, all assuming Shuttle launch. The lowest cost method involves a mass-acceleration curve. A shock spectra technique predicts a least upper bound for loads. A recovered transient method analyzes the interface acceleration of two connected launch vehicles. The most accurate method devised thus far is a transient analysis of the total launch vehicle/payload dynamic system.

  3. Hierarchical Ensemble Methods for Protein Function Prediction

    PubMed Central

    2014-01-01

    Protein function prediction is a complex multiclass multilabel classification problem, characterized by multiple issues such as the incompleteness of the available annotations, the integration of multiple sources of high dimensional biomolecular data, the unbalance of several functional classes, and the difficulty of univocally determining negative examples. Moreover, the hierarchical relationships between functional classes that characterize both the Gene Ontology and FunCat taxonomies motivate the development of hierarchy-aware prediction methods that showed significantly better performances than hierarchical-unaware “flat” prediction methods. In this paper, we provide a comprehensive review of hierarchical methods for protein function prediction based on ensembles of learning machines. According to this general approach, a separate learning machine is trained to learn a specific functional term and then the resulting predictions are assembled in a “consensus” ensemble decision, taking into account the hierarchical relationships between classes. The main hierarchical ensemble methods proposed in the literature are discussed in the context of existing computational methods for protein function prediction, highlighting their characteristics, advantages, and limitations. Open problems of this exciting research area of computational biology are finally considered, outlining novel perspectives for future research. PMID:25937954

  4. B-type natriuretic peptide and C-reactive protein in the prediction of atrial fibrillation risk: the CHARGE-AF Consortium of community-based cohort studies

    PubMed Central

    Sinner, Moritz F.; Stepas, Katherine A.; Moser, Carlee B.; Krijthe, Bouwe P.; Aspelund, Thor; Sotoodehnia, Nona; Fontes, João D.; Janssens, A. Cecile J.W.; Kronmal, Richard A.; Magnani, Jared W.; Witteman, Jacqueline C.; Chamberlain, Alanna M.; Lubitz, Steven A.; Schnabel, Renate B.; Vasan, Ramachandran S.; Wang, Thomas J.; Agarwal, Sunil K.; McManus, David D.; Franco, Oscar H.; Yin, Xiaoyan; Larson, Martin G.; Burke, Gregory L.; Launer, Lenore J.; Hofman, Albert; Levy, Daniel; Gottdiener, John S.; Kääb, Stefan; Couper, David; Harris, Tamara B.; Astor, Brad C.; Ballantyne, Christie M.; Hoogeveen, Ron C.; Arai, Andrew E.; Soliman, Elsayed Z.; Ellinor, Patrick T.; Stricker, Bruno H.C.; Gudnason, Vilmundur; Heckbert, Susan R.; Pencina, Michael J.; Benjamin, Emelia J.; Alonso, Alvaro

    2014-01-01

    Aims B-type natriuretic peptide (BNP) and C-reactive protein (CRP) predict atrial fibrillation (AF) risk. However, their risk stratification abilities in the broad community remain uncertain. We sought to improve risk stratification for AF using biomarker information. Methods and results We ascertained AF incidence in 18 556 Whites and African Americans from the Atherosclerosis Risk in Communities Study (ARIC, n=10 675), Cardiovascular Health Study (CHS, n = 5043), and Framingham Heart Study (FHS, n = 2838), followed for 5 years (prediction horizon). We added BNP (ARIC/CHS: N-terminal pro-B-type natriuretic peptide; FHS: BNP), CRP, or both to a previously reported AF risk score, and assessed model calibration and predictive ability [C-statistic, integrated discrimination improvement (IDI), and net reclassification improvement (NRI)]. We replicated models in two independent European cohorts: Age, Gene/Environment Susceptibility Reykjavik Study (AGES), n = 4467; Rotterdam Study (RS), n = 3203. B-type natriuretic peptide and CRP were significantly associated with AF incidence (n = 1186): hazard ratio per 1-SD ln-transformed biomarker 1.66 [95% confidence interval (CI), 1.56–1.76], P < 0.0001 and 1.18 (95% CI, 1.11–1.25), P < 0.0001, respectively. Model calibration was sufficient (BNP, χ2 = 17.0; CRP, χ2 = 10.5; BNP and CRP, χ2 = 13.1). B-type natriuretic peptide improved the C-statistic from 0.765 to 0.790, yielded an IDI of 0.027 (95% CI, 0.022–0.032), a relative IDI of 41.5%, and a continuous NRI of 0.389 (95% CI, 0.322–0.455). The predictive ability of CRP was limited (C-statistic increment 0.003). B-type natriuretic peptide consistently improved prediction in AGES and RS. Conclusion B-type natriuretic peptide, not CRP, substantially improved AF risk prediction beyond clinical factors in an independently replicated, heterogeneous population. B-type natriuretic peptide may serve as a benchmark to evaluate novel putative AF risk biomarkers. PMID:25037055

  5. A graph kernel approach for alignment-free domain–peptide interaction prediction with an application to human SH3 domains

    PubMed Central

    Kundu, Kousik; Costa, Fabrizio; Backofen, Rolf

    2013-01-01

    Motivation: State-of-the-art experimental data for determining binding specificities of peptide recognition modules (PRMs) is obtained by high-throughput approaches like peptide arrays. Most prediction tools applicable to this kind of data are based on an initial multiple alignment of the peptide ligands. Building an initial alignment can be error-prone, especially in the case of the proline-rich peptides bound by the SH3 domains. Results: Here, we present a machine-learning approach based on an efficient graph-kernel technique to predict the specificity of a large set of 70 human SH3 domains, which are an important class of PRMs. The graph-kernel strategy allows us to (i) integrate several types of physico-chemical information for each amino acid, (ii) consider high-order correlations between these features and (iii) eliminate the need for an initial peptide alignment. We build specialized models for each human SH3 domain and achieve competitive predictive performance of 0.73 area under precision-recall curve, compared with 0.27 area under precision-recall curve for state-of-the-art methods based on position weight matrices. We show that better models can be obtained when we use information on the noninteracting peptides (negative examples), which is currently not used by the state-of-the art approaches based on position weight matrices. To this end, we analyze two strategies to identify subsets of high confidence negative data. The techniques introduced here are more general and hence can also be used for any other protein domains, which interact with short peptides (i.e. other PRMs). Availability: The program with the predictive models can be found at http://www.bioinf.uni-freiburg.de/Software/SH3PepInt/SH3PepInt.tar.gz. We also provide a genome-wide prediction for all 70 human SH3 domains, which can be found under http://www.bioinf.uni-freiburg.de/Software/SH3PepInt/Genome-Wide-Predictions.tar.gz. Contact: backofen@informatik.uni-freiburg.de Supplementary

  6. Novel method to identify the optimal antimicrobial peptide in a combination matrix, using anoplin as an example.

    PubMed

    Munk, J K; Ritz, C; Fliedner, F P; Frimodt-Møller, N; Hansen, P R

    2014-01-01

    Microbial resistance is an increasing health concern and a true danger to human well-being. A worldwide search for new compounds is ongoing, and antimicrobial peptides are promising lead candidates for tomorrow's antibiotics. The decapeptide anoplin (GLLKRIKTLL-NH2) is an especially interesting candidate because of its small size as well as its antimicrobial and nonhemolytic properties. Optimization of the properties of an antimicrobial peptide such as anoplin requires multidimensional searching in a complex chemical space. Typically, such optimization is performed by labor-intensive and costly trial-and-error methods. In this study, we show the benefit of fractional factorial design for identification of the optimal antimicrobial peptide in a combination matrix. We synthesized and analyzed a training set of 12 anoplin analogs, representative of 64 analogs in total. Using MIC, hemolysis, and high-performance liquid chromatography retention time data, we constructed analysis-of-variance models that describe the relationship between these properties and the structural characteristics of the analogs. We show that the mathematical models derived from the training set data can be used to predict the properties of other analogs in the chemical space. Hence, this method provides an efficient means of identification of the optimal peptide in the searched chemical space. PMID:24277042

  7. Self-assembling peptide amphiphiles and related methods for growth factor delivery

    SciTech Connect

    Stupp, Samuel I; Donners, Jack J.J.M.; Silva, Gabriel A; Behanna, Heather A; Anthony, Shawn G

    2013-11-12

    Amphiphilic peptide compounds comprising one or more epitope sequences for binding interaction with one or more corresponding growth factors, micellar assemblies of such compounds and related methods of use.

  8. Self-assembling peptide amphiphiles and related methods for growth factor delivery

    DOEpatents

    Stupp, Samuel I.; Donners, Jack J. J. M.; Silva, Gabriel A.; Behanna, Heather A.; Anthony, Shawn G.

    2009-06-09

    Amphiphilic peptide compounds comprising one or more epitope sequences for binding interaction with one or more corresponding growth factors, micellar assemblies of such compounds and related methods of use.

  9. Self-assembling peptide amphiphiles and related methods for growth factor delivery

    DOEpatents

    Stupp, Samuel I.; Donners, Jack J. J. M.; Silva, Gabriel A.; Behanna, Heather A.; Anthony, Shawn G.

    2012-03-20

    Amphiphilic peptide compounds comprising one or more epitope sequences for binding interaction with one or more corresponding growth factors, micellar assemblies of such compounds and related methods of use.

  10. Predicting Antitumor Activity of Peptides by Consensus of Regression Models Trained on a Small Data Sample

    PubMed Central

    Radman, Andreja; Gredičak, Matija; Kopriva, Ivica; Jerić, Ivanka

    2011-01-01

    Predicting antitumor activity of compounds using regression models trained on a small number of compounds with measured biological activity is an ill-posed inverse problem. Yet, it occurs very often within the academic community. To counteract, up to some extent, overfitting problems caused by a small training data, we propose to use consensus of six regression models for prediction of biological activity of virtual library of compounds. The QSAR descriptors of 22 compounds related to the opioid growth factor (OGF, Tyr-Gly-Gly-Phe-Met) with known antitumor activity were used to train regression models: the feed-forward artificial neural network, the k-nearest neighbor, sparseness constrained linear regression, the linear and nonlinear (with polynomial and Gaussian kernel) support vector machine. Regression models were applied on a virtual library of 429 compounds that resulted in six lists with candidate compounds ranked by predicted antitumor activity. The highly ranked candidate compounds were synthesized, characterized and tested for an antiproliferative activity. Some of prepared peptides showed more pronounced activity compared with the native OGF; however, they were less active than highly ranked compounds selected previously by the radial basis function support vector machine (RBF SVM) regression model. The ill-posedness of the related inverse problem causes unstable behavior of trained regression models on test data. These results point to high complexity of prediction based on the regression models trained on a small data sample. PMID:22272081

  11. Predicting antitumor activity of peptides by consensus of regression models trained on a small data sample.

    PubMed

    Radman, Andreja; Gredičak, Matija; Kopriva, Ivica; Jerić, Ivanka

    2011-01-01

    Predicting antitumor activity of compounds using regression models trained on a small number of compounds with measured biological activity is an ill-posed inverse problem. Yet, it occurs very often within the academic community. To counteract, up to some extent, overfitting problems caused by a small training data, we propose to use consensus of six regression models for prediction of biological activity of virtual library of compounds. The QSAR descriptors of 22 compounds related to the opioid growth factor (OGF, Tyr-Gly-Gly-Phe-Met) with known antitumor activity were used to train regression models: the feed-forward artificial neural network, the k-nearest neighbor, sparseness constrained linear regression, the linear and nonlinear (with polynomial and Gaussian kernel) support vector machine. Regression models were applied on a virtual library of 429 compounds that resulted in six lists with candidate compounds ranked by predicted antitumor activity. The highly ranked candidate compounds were synthesized, characterized and tested for an antiproliferative activity. Some of prepared peptides showed more pronounced activity compared with the native OGF; however, they were less active than highly ranked compounds selected previously by the radial basis function support vector machine (RBF SVM) regression model. The ill-posedness of the related inverse problem causes unstable behavior of trained regression models on test data. These results point to high complexity of prediction based on the regression models trained on a small data sample. PMID:22272081

  12. A peptide N-terminal protection strategy for comprehensive glycoproteome analysis using hydrazide chemistry based method

    PubMed Central

    Huang, Junfeng; Qin, Hongqiang; Sun, Zhen; Huang, Guang; Mao, Jiawei; Cheng, Kai; Zhang, Zhang; Wan, Hao; Yao, Yating; Dong, Jing; Zhu, Jun; Wang, Fangjun; Ye, Mingliang; Zou, Hanfa

    2015-01-01

    Enrichment of glycopeptides by hydrazide chemistry (HC) is a popular method for glycoproteomics analysis. However, possible side reactions of peptide backbones during the glycan oxidation in this method have not been comprehensively studied. Here, we developed a proteomics approach to locate such side reactions and found several types of the side reactions that could seriously compromise the performance of glycoproteomics analysis. Particularly, the HC method failed to identify N-terminal Ser/Thr glycopeptides because the oxidation of vicinal amino alcohol on these peptides generates aldehyde groups and after they are covalently coupled to HC beads, these peptides cannot be released by PNGase F for identification. To overcome this drawback, we apply a peptide N-terminal protection strategy in which primary amine groups on peptides are chemically blocked via dimethyl labeling, thus the vicinal amino alcohols on peptide N-termini are eliminated. Our results showed that this strategy successfully prevented the oxidation of peptide N-termini and significantly improved the coverage of glycoproteome. PMID:25959593

  13. A T-EOF Based Prediction Method.

    NASA Astrophysics Data System (ADS)

    Lee, Yung-An

    2002-01-01

    A new statistical time series prediction method based on temporal empirical orthogonal function (T-EOF) is introduced in this study. This method first applies singular spectrum analysis (SSA) to extract dominant T-EOFs from historical data. Then, the most recent data are projected onto an optimal subset of the T-EOFs to estimate the corresponding temporal principal components (T-PCs). Finally, a forecast is constructed from these T-EOFs and T-PCs. Results from forecast experiments on the El Niño sea surface temperature (SST) indices from 1993 to 2000 showed that this method consistently yielded better correlation skill than autoregressive models for a lead time longer than 6 months. Furthermore, the correlation skills of this method in predicting Niño-3 index remained above 0.5 for a lead time up to 36 months during this period. However, this method still encountered the `spring barrier' problem. Because the 1990s exhibited relatively weak spring barrier, these results indicate that the T-EOF based prediction method has certain extended forecasting capability in the period when the spring barrier is weak. They also suggest that the potential predictability of ENSO in a certain period may be longer than previously thought.

  14. The investigation of the secondary structures of various peptide sequences of β-casein by the multicanonical simulation method

    NASA Astrophysics Data System (ADS)

    Yaşar, F.; Çelik, S.; Köksel, H.

    2006-05-01

    The structural properties of Arginine-Glutamic acid-Leucine-Glutamic acid-Glutamic acid-Leucine-Asparagine-Valine-Proline-Glycine (RELEELNVPG, in one letter code), Glutamic acid-Glutamic acid-Glutamine-Glutamine-Glutamine-Threonine-Glutamic acid (EEQQQTE) and Glutamic acid-Aspartic acid-Glutamic acid-Leucine-Glutamine-Aspartic acid-Lysine-Isoleucine (EDELQDKI) peptide sequences of β-casein were studied by three-dimensional molecular modeling. In this work, the three-dimensional conformations of each peptide from their primary sequences were obtained by multicanonical simulations. With using major advantage of this simulation technique, Ramachandran plots were prepared and analysed to predict the relative occurrence probabilities of β-turn, γ-turn and helical structures. Structural predictions of these sequences of β-casein molecule indicate the presence of high level of helical structures and βIII-turns. The occurrence probabilities of inverse and classical β-turns were low. The probability of helical structure of each sequence significantly decreased when the temperature increased. Our results show these peptides have highly helical structure and better agreement with the results of spectroscopic techniques and other prediction methods.

  15. Model for Vaccine Design by Prediction of B-Epitopes of IEDB Given Perturbations in Peptide Sequence, In Vivo Process, Experimental Techniques, and Source or Host Organisms

    PubMed Central

    González-Díaz, Humberto; Pérez-Montoto, Lázaro G.; Ubeira, Florencio M.

    2014-01-01

    Perturbation methods add variation terms to a known experimental solution of one problem to approach a solution for a related problem without known exact solution. One problem of this type in immunology is the prediction of the possible action of epitope of one peptide after a perturbation or variation in the structure of a known peptide and/or other boundary conditions (host organism, biological process, and experimental assay). However, to the best of our knowledge, there are no reports of general-purpose perturbation models to solve this problem. In a recent work, we introduced a new quantitative structure-property relationship theory for the study of perturbations in complex biomolecular systems. In this work, we developed the first model able to classify more than 200,000 cases of perturbations with accuracy, sensitivity, and specificity >90% both in training and validation series. The perturbations include structural changes in >50000 peptides determined in experimental assays with boundary conditions involving >500 source organisms, >50 host organisms, >10 biological process, and >30 experimental techniques. The model may be useful for the prediction of new epitopes or the optimization of known peptides towards computational vaccine design. PMID:24741624

  16. Predicting the Structure-Activity Relationship of Hydroxyapatite-Binding Peptides by Enhanced-Sampling Molecular Simulation.

    PubMed

    Zhao, Weilong; Xu, Zhijun; Cui, Qiang; Sahai, Nita

    2016-07-12

    Understanding the molecular structural and energetic basis of the interactions between peptides and inorganic surfaces is critical to their applications in tissue engineering and biomimetic material synthesis. Despite recent experimental progresses in the identification and functionalization of hydroxyapatite (HAP)-binding peptides, the molecular mechanisms of their interactions with HAP surfaces are yet to be explored. In particular, the traditional method of molecular dynamics (MD) simulation suffers from insufficient sampling at the peptide-inorganic interface that renders the molecular-level observation dubious. Here we demonstrate that an integrated approach combining bioinformatics, MD, and metadynamics provides a powerful tool for investigating the structure-activity relationship of HAP-binding peptides. Four low charge density peptides, previously identified by phage display, have been considered. As revealed by bioinformatics and MD, the binding conformation of the peptides is controlled by both the sequence and the amino acid composition. It was found that formation of hydrogen bonds between lysine residue and phosphate ions on the surface dictates the binding of positively charged peptide to HAP. The binding affinities of the peptides to the surface are estimated by free energy calculation using parallel-tempering metadynamics, and the results compare favorably to measurements reported in previous experimental studies. The calculation suggests that the charge density of the peptide primarily controls the binding affinity to the surface, while the backbone secondary structure that may restrain side chain orientation toward the surface plays a minor role. We also report that the application of enhanced-sampling metadynamics effects a major advantage over the steered MD method by significantly improving the reliability of binding free energy calculation. In general, our novel integration of diverse sampling techniques should contribute to the rational

  17. GECluster: a novel protein complex prediction method

    PubMed Central

    Su, Lingtao; Liu, Guixia; Wang, Han; Tian, Yuan; Zhou, Zhihui; Han, Liang; Yan, Lun

    2014-01-01

    Identification of protein complexes is of great importance in the understanding of cellular organization and functions. Traditional computational protein complex prediction methods mainly rely on the topology of protein–protein interaction (PPI) networks but seldom take biological information of proteins (such as Gene Ontology (GO)) into consideration. Meanwhile, the environment relevant analysis of protein complex evolution has been poorly studied, partly due to the lack of high-precision protein complex datasets. In this paper, a combined PPI network is introduced to predict protein complexes which integrate both GO and expression value of relevant protein-coding genes. A novel protein complex prediction method GECluster (Gene Expression Cluster) was proposed based on a seed node expansion strategy, in which a combined PPI network was utilized. GECluster was applied to a training combined PPI network and it predicted more credible complexes than peer methods. The results indicate that using a combined PPI network can efficiently improve protein complex prediction accuracy. In order to study protein complex evolution within cells due to changes in the living environment surrounding cells, GECluster was applied to seven combined PPI networks constructed using the data of a test set including yeast response to stress throughout a wine fermentation process. Our results showed that with the rise of alcohol concentration, protein complexes within yeast cells gradually evolve from one state to another. Besides this, the number of core and attachment proteins within a protein complex both changed significantly. PMID:26019559

  18. A Method for Selective Enrichment and Analysis of Nitrotyrosine-Containing Peptides in Complex Proteome Samples

    SciTech Connect

    Zhang, Qibin; Qian, Weijun; Knyushko, Tanya V.; Clauss, Therese RW; Purvine, Samuel O.; Moore, Ronald J.; Sacksteder, Colette A.; Chin, Mark H.; Smith, Desmond J.; Camp, David G.; Bigelow, Diana J.; Smith, Richard D.

    2007-06-01

    Elevated levels of protein tyrosine nitration have been found in various neurodegenerative diseases and aging related pathologies; however, the lack of an efficient enrichment method has prevented the analysis of this important low level protein modification. We have developed an efficient method for specific enrichment of nitrotyrosine containing peptides that permits nitrotyrosine peptides and specific nitration sites to be unambiguously identified with LC-MS/MS. The method is based on the derivatization of nitrotyrosine into free sulfhydryl groups followed by high efficiency enrichment of sulfhydryl-containing peptides with thiopropyl sepharose beads. The derivatization process starts with acetylation with acetic anhydride to block all primary amines, followed by reduction of nitrotyrosine to aminotyrosine, then derivatization of aminotyrosine with N-Succinimidyl S-Acetylthioacetate (SATA), and finally deprotecting of S-acetyl on SATA to form free sulfhydryl groups. This method was evaluated using nitrotyrosine containing peptides, in-vitro nitrated human histone 1.2, and bovine serum albumin (BSA). 91% and 62% of the identified peptides from enriched histone and BSA samples were nitrotyrosine derivatized peptides, respectively, suggesting relative high specificity of the enrichment method. The application of this method to in-vitro nitrated mouse brain homogenate resulted in 35% of identified peptides containing nitrotyrosine (compared to only 5.9% observed from the global analysis of unenriched sample), and a total of 150 unique nitrated peptides covering 102 proteins were identified with a false discovery rate estimated at 3.3% from duplicate LC-MS/MS analyses of a single enriched sample.

  19. Predicting the composition of red wine blends using an array of multicomponent Peptide-based sensors.

    PubMed

    Ghanem, Eman; Hopfer, Helene; Navarro, Andrea; Ritzer, Maxwell S; Mahmood, Lina; Fredell, Morgan; Cubley, Ashley; Bolen, Jessica; Fattah, Rabia; Teasdale, Katherine; Lieu, Linh; Chua, Tedmund; Marini, Federico; Heymann, Hildegarde; Anslyn, Eric V

    2015-01-01

    Differential sensing using synthetic receptors as mimics of the mammalian senses of taste and smell is a powerful approach for the analysis of complex mixtures. Herein, we report on the effectiveness of a cross-reactive, supramolecular, peptide-based sensing array in differentiating and predicting the composition of red wine blends. Fifteen blends of Cabernet Sauvignon, Merlot and Cabernet Franc, in addition to the mono varietals, were used in this investigation. Linear Discriminant Analysis (LDA) showed a clear differentiation of blends based on tannin concentration and composition where certain mono varietals like Cabernet Sauvignon seemed to contribute less to the overall characteristics of the blend. Partial Least Squares (PLS) Regression and cross validation were used to build a predictive model for the responses of the receptors to eleven binary blends and the three mono varietals. The optimized model was later used to predict the percentage of each mono varietal in an independent test set composted of four tri-blends with a 15% average error. A partial least square regression model using the mouth-feel and taste descriptive sensory attributes of the wine blends revealed a strong correlation of the receptors to perceived astringency, which is indicative of selective binding to polyphenols in wine. PMID:26007178

  20. Novel hyperspectral prediction method and apparatus

    NASA Astrophysics Data System (ADS)

    Kemeny, Gabor J.; Crothers, Natalie A.; Groth, Gard A.; Speck, Kathy A.; Marbach, Ralf

    2009-05-01

    Both the power and the challenge of hyperspectral technologies is the very large amount of data produced by spectral cameras. While off-line methodologies allow the collection of gigabytes of data, extended data analysis sessions are required to convert the data into useful information. In contrast, real-time monitoring, such as on-line process control, requires that compression of spectral data and analysis occur at a sustained full camera data rate. Efficient, high-speed practical methods for calibration and prediction are therefore sought to optimize the value of hyperspectral imaging. A novel method of matched filtering known as science based multivariate calibration (SBC) was developed for hyperspectral calibration. Classical (MLR) and inverse (PLS, PCR) methods are combined by spectroscopically measuring the spectral "signal" and by statistically estimating the spectral "noise." The accuracy of the inverse model is thus combined with the easy interpretability of the classical model. The SBC method is optimized for hyperspectral data in the Hyper-CalTM software used for the present work. The prediction algorithms can then be downloaded into a dedicated FPGA based High-Speed Prediction EngineTM module. Spectral pretreatments and calibration coefficients are stored on interchangeable SD memory cards, and predicted compositions are produced on a USB interface at real-time camera output rates. Applications include minerals, pharmaceuticals, food processing and remote sensing.

  1. Prediction of the effect on antihyperglycaemic action of sitagliptin by plasma active form glucagon-like peptide-1

    PubMed Central

    Kushiyama, Akifumi; Kikuchi, Takako; Tanaka, Kentaro; Tahara, Tazu; Takao, Toshiko; Onishi, Yukiko; Yoshida, Yoko; Kawazu, Shoji; Iwamoto, Yasuhiko

    2016-01-01

    AIM: To investigate whether active glucagon-like peptide-1 (GLP-1) is a prediction Factor of Effect of sitagliptin on patients with type 2 diabetes mellitus (GLP-1 FEST:UMIN000010645). METHODS: Seventy-six patients with type 2 diabetes, who had insufficient glycemic control [Hemoglobin A1c (HbA1c) ≥ 7%] in spite of treatment with metformin and/or sulfonylurea, were included in the investigation. Patients were divided into three groups by tertiles of fasting plasma active GLP-1 level, before the administration of 50 mg sitagliptin. RESULTS: At baseline, body mass index, serum UA, insulin and HOMA-IR were higher in the high active GLP-1 group than in the other two groups. The high active GLP-1 group did not show any decline of HbA1c (7.6% ± 1.4% to 7.5% ± 1.5%), whereas the middle and low groups indicated significant decline of HbA1c (7.4 ± 0.7 to 6.8 ± 0.6 and 7.4 ± 1.2 to 6.9 ± 1.3, respectively) during six months. Only the low and middle groups showed a significant increment of active GLP-1, C-peptide level, a decreased log and proinsulin/insulin ratio after administration. In logistic analysis, the low or middle group is a significant explanatory variable for an HbA1c decrease of ≥ 0.5%, and its odds ratio is 4.5 (1.40-17.6) (P = 0.01) against the high active GLP-1 group. This remains independent when adjusted for HbA1c level before administration, patients’ medical history, medications, insulin secretion and insulin resistance. CONCLUSION: Plasma fasting active GLP-1 is an independent predictive marker for the efficacy of dipeptidyl peptidase 4 inhibitor sitagliptin. PMID:27326345

  2. Quantitative Evaluation of Peptide-Material Interactions by a Force Mapping Method: Guidelines for Surface Modification.

    PubMed

    Mochizuki, Masahito; Oguchi, Masahiro; Kim, Seong-Oh; Jackman, Joshua A; Ogawa, Tetsu; Lkhamsuren, Ganchimeg; Cho, Nam-Joon; Hayashi, Tomohiro

    2015-07-28

    Peptide coatings on material surfaces have demonstrated wide application across materials science and biotechnology, facilitating the development of nanobio interfaces through surface modification. A guiding motivation in the field is to engineer peptides with a high and selective binding affinity to target materials. Herein, we introduce a quantitative force mapping method in order to evaluate the binding affinity of peptides to various hydrophilic oxide materials by atomic force microscopy (AFM). Statistical analysis of adhesion forces and probabilities obtained on substrates with a materials contrast enabled us to simultaneously compare the peptide binding affinity to different materials. On the basis of the experimental results and corresponding theoretical analysis, we discuss the role of various interfacial forces in modulating the strength of peptide attachment to hydrophilic oxide solid supports as well as to gold. The results emphasize the precision and robustness of our approach to evaluating the adhesion strength of peptides to solid supports, thereby offering guidelines to improve the design and fabrication of peptide-coated materials. PMID:26125092

  3. Predicting abrasive wear with coupled Lagrangian methods

    NASA Astrophysics Data System (ADS)

    Beck, Florian; Eberhard, Peter

    2015-05-01

    In this paper, a mesh-less approach for the simulation of a fluid with particle loading and the prediction of abrasive wear is presented. We are using the smoothed particle hydrodynamics (SPH) method for modeling the fluid and the discrete element method (DEM) for the solid particles, which represent the loading of the fluid. These Lagrangian methods are used to describe heavily sloshing fluids with their free surfaces as well as the interface between the fluid and the solid particles accurately. A Reynolds-averaged Navier-Stokes equations model is applied for handling turbulences. We are predicting abrasive wear on the boundary geometry with two different wear models taking cutting and deformation mechanisms into account. The boundary geometry is discretized with special DEM particles. In doing so, it is possible to use the same particle type for both the calculation of the boundary conditions for the SPH method as well as the DEM and for predicting the abrasive wear. After a brief introduction to the SPH method and the DEM, the handling of the boundary and the coupling of the fluid and the solid particles are discussed. Then, the applied wear models are presented and the simulation scenarios are described. The first numerical experiment is the simulation of a fluid with loading which is sloshing inside a tank. The second numerical experiment is the simulation of the impact of a free jet with loading to a simplified pelton bucket. We are especially investigating the wear patterns inside the tank and the bucket.

  4. Predicting functional regulatory SNPs in the human antimicrobial peptide genes DEFB1 and CAMP in tuberculosis and HIV/AIDS.

    PubMed

    Flores Saiffe Farías, Adolfo; Jaime Herrera López, Enrique; Moreno Vázquez, Cristopher Jorge; Li, Wentian; Prado Montes de Oca, Ernesto

    2015-12-01

    Single nucleotide polymorphisms (SNPs) in transcription factor binding sites (TFBSs) within gene promoter region or enhancers can modify the transcription rate of genes related to complex diseases. These SNPs can be called regulatory SNPs (rSNPs). Data compiled from recent projects, such as the 1000 Genomes Project and ENCODE, has revealed essential information used to perform in silico prediction of the molecular and biological repercussions of SNPs within TFBS. However, most of these studies are very limited, as they only analyze SNPs in coding regions or when applied to promoters, and do not integrate essential biological data like TFBSs, expression profiles, pathway analysis, homotypic redundancy (number of TFBSs for the same TF in a region), chromatin accessibility and others, which could lead to a more accurate prediction. Our aim was to integrate different data in a biologically coherent method to analyze the proximal promoter regions of two antimicrobial peptide genes, DEFB1 and CAMP, that are associated with tuberculosis (TB) and HIV/AIDS. We predicted SNPs within the promoter regions that are more likely to interact with transcription factors (TFs). We also assessed the impact of homotypic redundancy using a novel approach called the homotypic redundancy weight factor (HWF). Our results identified 10 SNPs, which putatively modify the binding affinity of 24 TFs previously identified as related to TB and HIV/AIDS expression profiles (e.g. KLF5, CEBPA and NFKB1 for TB; FOXP2, BRCA1, CEBPB, CREB1, EBF1 and ZNF354C for HIV/AIDS; and RUNX2, HIF1A, JUN/AP-1, NR4A2, EGR1 for both diseases). Validating with the OregAnno database and cell-specific functional/non functional SNPs from additional 13 genes, our algorithm performed 53% sensitivity and 84.6% specificity to detect functional rSNPs using the DNAseI-HUP database. We are proposing our algorithm as a novel in silico method to detect true functional rSNPs in antimicrobial peptide genes. With further

  5. PEP-FOLD3: faster de novo structure prediction for linear peptides in solution and in complex.

    PubMed

    Lamiable, Alexis; Thévenet, Pierre; Rey, Julien; Vavrusa, Marek; Derreumaux, Philippe; Tufféry, Pierre

    2016-07-01

    Structure determination of linear peptides of 5-50 amino acids in aqueous solution and interacting with proteins is a key aspect in structural biology. PEP-FOLD3 is a novel computational framework, that allows both (i) de novo free or biased prediction for linear peptides between 5 and 50 amino acids, and (ii) the generation of native-like conformations of peptides interacting with a protein when the interaction site is known in advance. PEP-FOLD3 is fast, and usually returns solutions in a few minutes. Testing PEP-FOLD3 on 56 peptides in aqueous solution led to experimental-like conformations for 80% of the targets. Using a benchmark of 61 peptide-protein targets starting from the unbound form of the protein receptor, PEP-FOLD3 was able to generate peptide poses deviating on average by 3.3Å from the experimental conformation and return a native-like pose in the first 10 clusters for 52% of the targets. PEP-FOLD3 is available at http://bioserv.rpbs.univ-paris-diderot.fr/services/PEP-FOLD3. PMID:27131374

  6. BactPepDB: a database of predicted peptides from a exhaustive survey of complete prokaryote genomes

    PubMed Central

    Rey, Julien; Deschavanne, Patrick; Tuffery, Pierre

    2014-01-01

    With the recent progress in complete genome sequencing, mining the increasing amount of genomic information available should in theory provide the means to discover new classes of peptides. However, annotation pipelines often do not consider small reading frames likely to be expressed. BactPepDB, available online at http://bactpepdb.rpbs.univ-paris-diderot.fr, is a database that aims at providing an exhaustive re-annotation of all complete prokaryotic genomes—chromosomal and plasmid DNA—available in RefSeq for coding sequences ranging between 10 and 80 amino acids. The identified peptides are classified as (i) previously identified in RefSeq, (ii) entity-overlapping (intragenic) or intergenic, and (iii) potential pseudogenes—intergenic sequences corresponding to a portion of a previously annotated larger gene. Additional information is related to homologs within order, predicted signal sequence, transmembrane segments, disulfide bonds, secondary structure, and the existence of a related 3D structure in the Protein Databank. As a result, BactPepDB provides insights about candidate peptides, and provides information about their conservation, together with some of their expected biological/structural features. The BactPepDB interface allows to search for candidate peptides in the database, or to search for peptides similar to a query, according to the multiple properties predicted or related to genomic localization. Database URL: http://www.yeastgenome.org/ PMID:25377257

  7. B-Type Natriuretic Peptide Levels Predict Ventricular Arrhythmia Post Left Ventricular Assist Device Implantation.

    PubMed

    Hellman, Yaron; Malik, Adnan S; Lin, Hongbo; Shen, Changyu; Wang, I-Wen; Wozniak, Thomas C; Hashmi, Zubair A; Pickrell, Jeanette; Jani, Milena; Caccamo, Marco A; Gradus-Pizlo, Irmina; Hadi, Azam

    2015-12-01

    B-type natriuretic peptide (BNP) levels have been shown to predict ventricular arrhythmia (VA) and sudden death in patients with heart failure. We sought to determine whether BNP levels before left ventricular assist device (LVAD) implantation can predict VA post LVAD implantation in advanced heart failure patients. We conducted a retrospective study consisting of patients who underwent LVAD implantation in our institution during the period of May 2009-March 2013. The study was limited to patients receiving a HeartMate II or HeartWare LVAD. Acute myocardial infarction patients were excluded. We compared between the patients who developed VA within 15 days post LVAD implantation to the patients without VA. A total of 85 patients underwent LVAD implantation during the study period. Eleven patients were excluded (five acute MI, four without BNP measurements, and two discharged earlier than 13 days post LVAD implantation). The incidence of VA was 31%, with 91% ventricular tachycardia (VT) and 9% ventricular fibrillation. BNP remained the single most powerful predictor of VA even after adjustment for other borderline significant factors in a multivariate logistic regression model (P < 0.05). BNP levels are a strong predictor of VA post LVAD implantation, surpassing previously described risk factors such as age and VT in the past. PMID:25864448

  8. Inhibition of the Hantavirus Fusion Process by Predicted Domain III and Stem Peptides from Glycoprotein Gc

    PubMed Central

    Barriga, Gonzalo P.; Villalón-Letelier, Fernando; Márquez, Chantal L.; Bignon, Eduardo A.; Acuña, Rodrigo; Ross, Breyan H.; Monasterio, Octavio; Mardones, Gonzalo A.; Vidal, Simon E.; Tischler, Nicole D.

    2016-01-01

    Hantaviruses can cause hantavirus pulmonary syndrome or hemorrhagic fever with renal syndrome in humans. To enter cells, hantaviruses fuse their envelope membrane with host cell membranes. Previously, we have shown that the Gc envelope glycoprotein is the viral fusion protein sharing characteristics with class II fusion proteins. The ectodomain of class II fusion proteins is composed of three domains connected by a stem region to a transmembrane anchor in the viral envelope. These fusion proteins can be inhibited through exogenous fusion protein fragments spanning domain III (DIII) and the stem region. Such fragments are thought to interact with the core of the fusion protein trimer during the transition from its pre-fusion to its post-fusion conformation. Based on our previous homology model structure for Gc from Andes hantavirus (ANDV), here we predicted and generated recombinant DIII and stem peptides to test whether these fragments inhibit hantavirus membrane fusion and cell entry. Recombinant ANDV DIII was soluble, presented disulfide bridges and beta-sheet secondary structure, supporting the in silico model. Using DIII and the C-terminal part of the stem region, the infection of cells by ANDV was blocked up to 60% when fusion of ANDV occurred within the endosomal route, and up to 95% when fusion occurred with the plasma membrane. Furthermore, the fragments impaired ANDV glycoprotein-mediated cell-cell fusion, and cross-inhibited the fusion mediated by the glycoproteins from Puumala virus (PUUV). The Gc fragments interfered in ANDV cell entry by preventing membrane hemifusion and pore formation, retaining Gc in a non-resistant homotrimer stage, as described for DIII and stem peptide inhibitors of class II fusion proteins. Collectively, our results demonstrate that hantavirus Gc shares not only structural, but also mechanistic similarity with class II viral fusion proteins, and will hopefully help in developing novel therapeutic strategies against hantaviruses

  9. Inhibition of the Hantavirus Fusion Process by Predicted Domain III and Stem Peptides from Glycoprotein Gc.

    PubMed

    Barriga, Gonzalo P; Villalón-Letelier, Fernando; Márquez, Chantal L; Bignon, Eduardo A; Acuña, Rodrigo; Ross, Breyan H; Monasterio, Octavio; Mardones, Gonzalo A; Vidal, Simon E; Tischler, Nicole D

    2016-07-01

    Hantaviruses can cause hantavirus pulmonary syndrome or hemorrhagic fever with renal syndrome in humans. To enter cells, hantaviruses fuse their envelope membrane with host cell membranes. Previously, we have shown that the Gc envelope glycoprotein is the viral fusion protein sharing characteristics with class II fusion proteins. The ectodomain of class II fusion proteins is composed of three domains connected by a stem region to a transmembrane anchor in the viral envelope. These fusion proteins can be inhibited through exogenous fusion protein fragments spanning domain III (DIII) and the stem region. Such fragments are thought to interact with the core of the fusion protein trimer during the transition from its pre-fusion to its post-fusion conformation. Based on our previous homology model structure for Gc from Andes hantavirus (ANDV), here we predicted and generated recombinant DIII and stem peptides to test whether these fragments inhibit hantavirus membrane fusion and cell entry. Recombinant ANDV DIII was soluble, presented disulfide bridges and beta-sheet secondary structure, supporting the in silico model. Using DIII and the C-terminal part of the stem region, the infection of cells by ANDV was blocked up to 60% when fusion of ANDV occurred within the endosomal route, and up to 95% when fusion occurred with the plasma membrane. Furthermore, the fragments impaired ANDV glycoprotein-mediated cell-cell fusion, and cross-inhibited the fusion mediated by the glycoproteins from Puumala virus (PUUV). The Gc fragments interfered in ANDV cell entry by preventing membrane hemifusion and pore formation, retaining Gc in a non-resistant homotrimer stage, as described for DIII and stem peptide inhibitors of class II fusion proteins. Collectively, our results demonstrate that hantavirus Gc shares not only structural, but also mechanistic similarity with class II viral fusion proteins, and will hopefully help in developing novel therapeutic strategies against hantaviruses

  10. Machine learning methods for predictive proteomics.

    PubMed

    Barla, Annalisa; Jurman, Giuseppe; Riccadonna, Samantha; Merler, Stefano; Chierici, Marco; Furlanello, Cesare

    2008-03-01

    The search for predictive biomarkers of disease from high-throughput mass spectrometry (MS) data requires a complex analysis path. Preprocessing and machine-learning modules are pipelined, starting from raw spectra, to set up a predictive classifier based on a shortlist of candidate features. As a machine-learning problem, proteomic profiling on MS data needs caution like the microarray case. The risk of overfitting and of selection bias effects is pervasive: not only potential features easily outnumber samples by 10(3) times, but it is easy to neglect information-leakage effects during preprocessing from spectra to peaks. The aim of this review is to explain how to build a general purpose design analysis protocol (DAP) for predictive proteomic profiling: we show how to limit leakage due to parameter tuning and how to organize classification and ranking on large numbers of replicate versions of the original data to avoid selection bias. The DAP can be used with alternative components, i.e. with different preprocessing methods (peak clustering or wavelet based), classifiers e.g. Support Vector Machine (SVM) or feature ranking methods (recursive feature elimination or I-Relief). A procedure for assessing stability and predictive value of the resulting biomarkers' list is also provided. The approach is exemplified with experiments on synthetic datasets (from the Cromwell MS simulator) and with publicly available datasets from cancer studies. PMID:18310105

  11. Method for the synthesis of highly pure vaccines using the lipid core peptide system.

    PubMed

    Moyle, Peter M; Olive, Colleen; Good, Michael F; Toth, Istvan

    2006-12-01

    Traditional vaccines consisting of whole attenuated microorganisms, killed microorganisms, or microbial components, administered with an adjuvant (e.g. alum), have been proved to be extremely successful. However, to develop new vaccines, or to improve upon current vaccines, new vaccine development techniques are required. Peptide vaccines offer the capacity to administer only the minimal microbial components necessary to elicit appropriate immune responses, minimizing the risk of vaccination associated adverse effects, and focusing the immune response toward important antigens. Peptide vaccines, however, are generally poorly immunogenic, necessitating administration with powerful, and potentially toxic adjuvants. The attachment of lipids to peptide antigens has been demonstrated as a potentially safe method for adjuvanting peptide epitopes. The lipid core peptide (LCP) system, which incorporates a lipidic adjuvant, carrier, and peptide epitopes into a single molecular entity, has been demonstrated to boost immunogenicity of attached peptide epitopes without the need for additional adjuvants. The synthesis of LCP systems normally yields a product that cannot be purified to homogeneity. The current study describes the development of methods for the synthesis of highly pure LCP analogs using native chemical ligation. Because of the highly lipophilic nature of the LCP lipid adjuvant, difficulties (e.g. poor solubility) were experienced with the ligation reactions. The addition of organic solvents to the ligation buffer solubilized lipidic species, but did not result in successful ligation reactions. In comparison, the addition of approximately 1% (w/v) sodium dodecyl sulfate (SDS) proved successful, enabling the synthesis of two highly pure, tri-epitopic Streptococcus pyogenes LCP analogs. Subcutaneous immunization of B10.BR (H-2(k)) mice with one of these vaccines, without the addition of any adjuvant, elicited high levels of systemic IgG antibodies against each of

  12. Secondary structure of short β-peptides as the chiral expression of monomeric building units: a rational and predictive model.

    PubMed

    Gorrea, Esther; Pohl, Gábor; Nolis, Pau; Celis, Sergio; Burusco, Kepa K; Branchadell, Vicenç; Perczel, András; Ortuño, Rosa M

    2012-11-01

    Chirality of the monomeric residues controls and determines the prevalent folding of small oligopeptides (from di- to tetramers) composed of 2-aminocyclobutane-1-carboxylic acid (ACBA) derivatives with the same or different absolute and relative configuration. The cis-form of the monomeric ACBA gives rise to two conformers, namely, Z6 and Z8, while the trans-form manifests uniquely as an H8 structure. By combining these subunits in oligo- and polypeptides, their local structural preference remains, thus allowing the rational design of new short foldamers. A lego-type molecular architecture evolves; the overall look depends only on the conformational properties of the structural building units. A versatile and efficient method to predict the backbone folds of designed cyclobutane β-peptides is based on QM calculations. Predictions are corroborated by high-resolution NMR studies on selected stereoisomers, most of them being new foldamers that have been synthesized and characterized for the first time. Thus, the chiral expression of monomeric building units results in the defined secondary structures of small oligomers. As a result of this study, a new set of chirality controlled foldamers is provided to probe as biocompatible biopolymers. PMID:23030251

  13. A numerical method for predicting hypersonic flowfields

    NASA Technical Reports Server (NTRS)

    Maccormack, Robert W.; Candler, Graham V.

    1989-01-01

    The flow about a body traveling at hypersonic speed is energetic enough to cause the atmospheric gases to chemically react and reach states in thermal nonequilibrium. The prediction of hypersonic flowfields requires a numerical method capable of solving the conservation equations of fluid flow, the chemical rate equations for specie formation and dissociation, and the transfer of energy relations between translational and vibrational temperature states. Because the number of equations to be solved is large, the numerical method should also be as efficient as possible. The proposed paper presents a fully implicit method that fully couples the solution of the fluid flow equations with the gas physics and chemistry relations. The method flux splits the inviscid flow terms, central differences of the viscous terms, preserves element conservation in the strong chemistry source terms, and solves the resulting block matrix equation by Gauss Seidel line relaxation.

  14. NetMHCcons: a consensus method for the major histocompatibility complex class I predictions.

    PubMed

    Karosiene, Edita; Lundegaard, Claus; Lund, Ole; Nielsen, Morten

    2012-03-01

    A key role in cell-mediated immunity is dedicated to the major histocompatibility complex (MHC) molecules that bind peptides for presentation on the cell surface. Several in silico methods capable of predicting peptide binding to MHC class I have been developed. The accuracy of these methods depends on the data available characterizing the binding specificity of the MHC molecules. It has, moreover, been demonstrated that consensus methods defined as combinations of two or more different methods led to improved prediction accuracy. This plethora of methods makes it very difficult for the non-expert user to choose the most suitable method for predicting binding to a given MHC molecule. In this study, we have therefore made an in-depth analysis of combinations of three state-of-the-art MHC-peptide binding prediction methods (NetMHC, NetMHCpan and PickPocket). We demonstrate that a simple combination of NetMHC and NetMHCpan gives the highest performance when the allele in question is included in the training and is characterized by at least 50 data points with at least ten binders. Otherwise, NetMHCpan is the best predictor. When an allele has not been characterized, the performance depends on the distance to the training data. NetMHCpan has the highest performance when close neighbours are present in the training set, while the combination of NetMHCpan and PickPocket outperforms either of the two methods for alleles with more remote neighbours. The final method, NetMHCcons, is publicly available at www.cbs.dtu.dk/services/NetMHCcons , and allows the user in an automatic manner to obtain the most accurate predictions for any given MHC molecule. PMID:22009319

  15. Airframe Noise Prediction Using the Sngr Method

    NASA Astrophysics Data System (ADS)

    Chen, Rongqian; Wu, Yizhao; Xia, Jian

    In this paper, the Stochastic Noise Generation and Radiation method (SNGR) is used to predict airframe noise. The SNGR method combines a stochastic model with Computational Fluid Dynamics (CFD), and it can give acceptable noise results while the computation cost is relatively low. In the method, the time-averaged mean flow field is firstly obtained by solving Reynolds Averaged Navier-Stokes equations (RANS), and a stochastic velocity is generated based on the obtained information. Then the turbulent field is used to generate the source for the Acoustic Perturbation Equations (APEs) that simulate the noise propagation. For numerical methods, timeaveraged RANS equations are solved by finite volume method, and the turbulent model is K - ɛ model; APEs are solved by finite difference method, and the numerical scheme is the Dispersion-Relation-Preserving (DRP) scheme, with explicit optimized 5-stage Rung-Kutta scheme time step. In order to test the APE solver, propagation of a Gaussian pulse in a uniform mean flow is firstly simulated and compared with the analytical solution. Then, using the method, the trailing edge noise of NACA0012 airfoil is calculated. The results are compared with reference data, and good agreements are demonstrated.

  16. PEP-FOLD: an updated de novo structure prediction server for both linear and disulfide bonded cyclic peptides

    PubMed Central

    Thévenet, Pierre; Shen, Yimin; Maupetit, Julien; Guyon, Frédéric; Derreumaux, Philippe; Tufféry, Pierre

    2012-01-01

    In the context of the renewed interest of peptides as therapeutics, it is important to have an on-line resource for 3D structure prediction of peptides with well-defined structures in aqueous solution. We present an updated version of PEP-FOLD allowing the treatment of both linear and disulphide bonded cyclic peptides with 9–36 amino acids. The server makes possible to define disulphide bonds and any residue–residue proximity under the guidance of the biologists. Using a benchmark of 34 cyclic peptides with one, two and three disulphide bonds, the best PEP-FOLD models deviate by an average RMS of 2.75 Å from the full NMR structures. Using a benchmark of 37 linear peptides, PEP-FOLD locates lowest-energy conformations deviating by 3 Å RMS from the NMR rigid cores. The evolution of PEP-FOLD comes as a new on-line service to supersede the previous server. The server is available at: http://bioserv.rpbs.univ-paris-diderot.fr/PEP-FOLD. PMID:22581768

  17. Computational predictive methods for fracture and fatigue

    NASA Astrophysics Data System (ADS)

    Cordes, J.; Chang, A. T.; Nelson, N.; Kim, Y.

    1994-09-01

    The damage-tolerant design philosophy as used by aircraft industries enables aircraft components and aircraft structures to operate safely with minor damage, small cracks, and flaws. Maintenance and inspection procedures insure that damages developed during service remain below design values. When damage is found, repairs or design modifications are implemented and flight is resumed. Design and redesign guidelines, such as military specifications MIL-A-83444, have successfully reduced the incidence of damage and cracks. However, fatigue cracks continue to appear in aircraft well before the design life has expired. The F16 airplane, for instance, developed small cracks in the engine mount, wing support, bulk heads, the fuselage upper skin, the fuel shelf joints, and along the upper wings. Some cracks were found after 600 hours of the 8000 hour design service life and design modifications were required. Tests on the F16 plane showed that the design loading conditions were close to the predicted loading conditions. Improvements to analytic methods for predicting fatigue crack growth adjacent to holes, when multiple damage sites are present, and in corrosive environments would result in more cost-effective designs, fewer repairs, and fewer redesigns. The overall objective of the research described in this paper is to develop, verify, and extend the computational efficiency of analysis procedures necessary for damage tolerant design. This paper describes an elastic/plastic fracture method and an associated fatigue analysis method for damage tolerant design. Both methods are unique in that material parameters such as fracture toughness, R-curve data, and fatigue constants are not required. The methods are implemented with a general-purpose finite element package. Several proof-of-concept examples are given. With further development, the methods could be extended for analysis of multi-site damage, creep-fatigue, and corrosion fatigue problems.

  18. Computational predictive methods for fracture and fatigue

    NASA Technical Reports Server (NTRS)

    Cordes, J.; Chang, A. T.; Nelson, N.; Kim, Y.

    1994-01-01

    The damage-tolerant design philosophy as used by aircraft industries enables aircraft components and aircraft structures to operate safely with minor damage, small cracks, and flaws. Maintenance and inspection procedures insure that damages developed during service remain below design values. When damage is found, repairs or design modifications are implemented and flight is resumed. Design and redesign guidelines, such as military specifications MIL-A-83444, have successfully reduced the incidence of damage and cracks. However, fatigue cracks continue to appear in aircraft well before the design life has expired. The F16 airplane, for instance, developed small cracks in the engine mount, wing support, bulk heads, the fuselage upper skin, the fuel shelf joints, and along the upper wings. Some cracks were found after 600 hours of the 8000 hour design service life and design modifications were required. Tests on the F16 plane showed that the design loading conditions were close to the predicted loading conditions. Improvements to analytic methods for predicting fatigue crack growth adjacent to holes, when multiple damage sites are present, and in corrosive environments would result in more cost-effective designs, fewer repairs, and fewer redesigns. The overall objective of the research described in this paper is to develop, verify, and extend the computational efficiency of analysis procedures necessary for damage tolerant design. This paper describes an elastic/plastic fracture method and an associated fatigue analysis method for damage tolerant design. Both methods are unique in that material parameters such as fracture toughness, R-curve data, and fatigue constants are not required. The methods are implemented with a general-purpose finite element package. Several proof-of-concept examples are given. With further development, the methods could be extended for analysis of multi-site damage, creep-fatigue, and corrosion fatigue problems.

  19. Prediction and Analysis of Quorum Sensing Peptides Based on Sequence Features

    PubMed Central

    Rajput, Akanksha; Gupta, Amit Kumar; Kumar, Manoj

    2015-01-01

    Quorum sensing peptides (QSPs) are the signaling molecules used by the Gram-positive bacteria in orchestrating cell-to-cell communication. In spite of their enormous importance in signaling process, their detailed bioinformatics analysis is lacking. In this study, QSPs and non-QSPs were examined according to their amino acid composition, residues position, motifs and physicochemical properties. Compositional analysis concludes that QSPs are enriched with aromatic residues like Trp, Tyr and Phe. At the N-terminal, Ser was a dominant residue at maximum positions, namely, first, second, third and fifth while Phe was a preferred residue at first, third and fifth positions from the C-terminal. A few motifs from QSPs were also extracted. Physicochemical properties like aromaticity, molecular weight and secondary structure were found to be distinguishing features of QSPs. Exploiting above properties, we have developed a Support Vector Machine (SVM) based predictive model. During 10-fold cross-validation, SVM achieves maximum accuracy of 93.00%, Mathew’s correlation coefficient (MCC) of 0.86 and Receiver operating characteristic (ROC) of 0.98 on the training/testing dataset (T200p+200n). Developed models performed equally well on the validation dataset (V20p+20n). The server also integrates several useful analysis tools like “QSMotifScan”, “ProtFrag”, “MutGen” and “PhysicoProp”. Our analysis reveals important characteristics of QSPs and on the basis of these unique features, we have developed a prediction algorithm “QSPpred” (freely available at: http://crdd.osdd.net/servers/qsppred). PMID:25781990

  20. Trailing edge noise prediction using Amiet's method

    NASA Technical Reports Server (NTRS)

    Brooks, T. F.

    1981-01-01

    Amiet's (1976, 1978) solution to the problem of airfoil trailing edge noise prediction is discussed in light of the results of evanescent wave theory's application to the measured surface pressure behavior near the trailing edge of an airfoil with a turbulent boundary layer. The method employed by Amiet has the advantage of incorporating the effect of finite chord in its solution. The assumed form of the pressure distribution is examined as well as the constant turbulent boundary layer convection assumption, which is found to be unnecessarily restrictive.

  1. Predicting Three-Dimensional Conformations of Peptides Constructed of Only Glycine, Alanine, Aspartic Acid, and Valine

    NASA Astrophysics Data System (ADS)

    Oda, Akifumi; Fukuyoshi, Shuichi

    2015-06-01

    The GADV hypothesis is a form of the protein world hypothesis, which suggests that life originated from proteins (Lacey et al. 1999; Ikehara 2002; Andras 2006). In the GADV hypothesis, life is thought to have originated from primitive proteins constructed of only glycine, alanine, aspartic acid, and valine ([GADV]-proteins). In this study, the three-dimensional (3D) conformations of randomly generated short [GADV]-peptides were computationally investigated using replica-exchange molecular dynamics (REMD) simulations (Sugita and Okamoto 1999). Because the peptides used in this study consisted of only 20 residues each, they could not form certain 3D structures. However, the conformational tendencies of the peptides were elucidated by analyzing the conformational ensembles generated by REMD simulations. The results indicate that secondary structures can be formed in several randomly generated [GADV]-peptides. A long helical structure was found in one of the hydrophobic peptides, supporting the conjecture of the GADV hypothesis that many peptides aggregated to form peptide multimers with enzymatic activity in the primordial soup. In addition, these results indicate that REMD simulations can be used for the structural investigation of short peptides.

  2. A novel conformational B-cell epitope prediction method based on mimotope and patch analysis.

    PubMed

    Sun, Pingping; Qi, Jialiang; Zhao, Yizhu; Huang, Yanxin; Yang, Guifu; Ma, Zhiqiang; Li, Yuxin

    2016-04-01

    A B-cell epitope is a group of residues on the surface of an antigen that stimulates humoral immune responses. Identifying B-cell epitopes is important for effective vaccine design. Predicting epitopes by experimental methods is expensive in terms of time, cost and effort; therefore, computational methods that have a low cost and high speed are widely used to predict B-cell epitopes. Recently, epitope prediction based on random peptide library screening has been viewed as a promising method. Some novel software and web-based servers have been proposed that have succeeded in some test cases. Herein, we propose a novel epitope prediction method based on amino acid pairs and patch analysis. The method first divides antigen surfaces into overlapping patches based on both radius (R) and number (N), then predict epitopes based on Amino Acid Pairs (AAPs) from mimotopes and the surface patch. The proposed method yields a mean sensitivity of 0.53, specificity of 0.77, ACC of 0.75 and F-measure of 0.45 for 39 test cases. Compared with mimotope-based methods, patch-based methods and two other prediction methods, the sensitivity of the new method offers a certain improvement. Our findings demonstrate that this proposed method was successful for patch and AAPs analysis and allowed for conformational B-cell epitope prediction. PMID:26804644

  3. Antibody Production with Synthetic Peptides.

    PubMed

    Lee, Bao-Shiang; Huang, Jin-Sheng; Jayathilaka, Lasanthi P; Lee, Jenny; Gupta, Shalini

    2016-01-01

    Peptides (usually 10-20 amino acid residues in length) can be used as effectively as proteins in raising antibodies producing both polyclonal and monoclonal antibodies routinely with titers higher than 20,000. Peptide antigens do not function as immunogens unless they are conjugated to proteins. Production of high quality antipeptide antibodies is dependent upon peptide sequence selection, the success of peptide synthesis, peptide-carrier protein conjugation, the humoral immune response in the host animal, the adjuvant used, the peptide dose administered, the injection method, and the purification of the antibody. Peptide sequence selection is probably the most critical step in the production of antipeptide antibodies. Although the process for designing peptide antigens is not exact, several guidelines and computational B-cell epitope prediction methods can help maximize the likelihood of producing antipeptide antibodies that recognize the protein. Antibodies raised by peptides have become essential tools in life science research. Virtually all phospho-specific antibodies are now produced using phosphopeptides as antigens. Typically, 5-20 mg of peptide is enough for antipeptide antibody production. It takes 3 months to produce a polyclonal antipeptide antibody in rabbits that yields ~100 mL of serum which corresponds to ~8-10 mg of the specific antibody after affinity purification using a peptide column. PMID:27515072

  4. The depsipeptide method for solid-phase synthesis of difficult peptides.

    PubMed

    Coin, Irene

    2010-05-01

    After about one century of peptide chemistry, the main limitation to the accessibility of peptides and proteins via chemosynthesis is the arising of folding and aggregation phenomena. This is true not only for sequences above a critical length but also for several biologically relevant substrates that are relatively short yet form either highly folded structures (e.g. WW domains) or fibrils and aggregates after final deprotection (beta-amyloid peptide). Such so-called difficult sequences may be more easily obtained via their corresponding depsipeptides (O-acyl isopeptides), ester isomers that are often easier to assemble and purify, and are smoothly converted to the parent amides under mild conditions. The depsipeptide method is the most recent technique to improve the outcome of difficult syntheses, applicable to sequences containing residues of serine or threonine. A brief overview is presented about chemical aspects of the method, the steps that have been undertaken for its optimization, and the evaluation of its efficiency. Further applications of analogous principles to other critical topics in peptide synthesis such as condensation of peptide segments and solid-phase synthesis of naturally occurring cyclodepsipeptides are addressed as well. PMID:20401924

  5. MALDI immunoscreening (MiSCREEN): a method for selection of anti-peptide monoclonal antibodies for use in immunoproteomics.

    PubMed

    Razavi, Morteza; Pope, Matthew E; Soste, Martin V; Eyford, Brett A; Jackson, Angela M; Anderson, N Leigh; Pearson, Terry W

    2011-02-01

    A scalable method for screening and selection of peptide-specific monoclonal antibodies (mAbs) is described. To identify high affinity anti-peptide mAbs in hybridoma supernatants, antibodies were captured by magnetic affinity beads followed by binding of specific peptides from solution. After timed washing steps, the remaining bound peptides were eluted from the beads and detected by matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) mass spectrometry (MS). This allowed measurement of monovalent interactions of peptides with single antigen binding sites on the antibodies, thus reflecting antibody affinity rather than avidity. Antibodies that were able to bind target peptides from solution phase and retain them during washing for a minimum of 10 min were identified by the strength of the appropriate m/z peptide MS signals obtained. This wash time reflects the minimum peptide dissociation time required for use of these antibodies in several current immuno-mass spectrometry assays. Kinetic analysis of antibody-peptide binding by surface plasmon resonance (SPR) showed that the selected antibodies were of high affinity and, most importantly, had low dissociation constants. This method, called MALDI immunoscreening (MiSCREEN), thus enables rapid screening and selection of high affinity anti-peptide antibodies that are useful for a variety of immunoproteomics applications. To demonstrate their functional utility in immuno-mass spectrometry assays, we used the selected, purified RabMAbs to enrich natural (tryptic) peptides from digested human plasma. PMID:21078325

  6. Minimizing Postsampling Degradation of Peptides by a Thermal Benchtop Tissue Stabilization Method.

    PubMed

    Segerström, Lova; Gustavsson, Jenny; Nylander, Ingrid

    2016-04-01

    Enzymatic degradation is a major concern in peptide analysis. Postmortem metabolism in biological samples entails considerable risk for measurements misrepresentative of true in vivo concentrations. It is therefore vital to find reliable, reproducible, and easy-to-use procedures to inhibit enzymatic activity in fresh tissues before subjecting them to qualitative and quantitative analyses. The aim of this study was to test a benchtop thermal stabilization method to optimize measurement of endogenous opioids in brain tissue. Endogenous opioid peptides are generated from precursor proteins through multiple enzymatic steps that include conversion of one bioactive peptide to another, often with a different function. Ex vivo metabolism may, therefore, lead to erroneous functional interpretations. The efficacy of heat stabilization was systematically evaluated in a number of postmortem handling procedures. Dynorphin B (DYNB), Leu-enkephalin-Arg(6) (LARG), and Met-enkephalin-Arg(6)-Phe(7) (MEAP) were measured by radioimmunoassay in rat hypothalamus, striatum (STR), and cingulate cortex (CCX). Also, simplified extraction protocols for stabilized tissue were tested. Stabilization affected all peptide levels to varying degrees compared to those prepared by standard dissection and tissue handling procedures. Stabilization increased DYNB in hypothalamus, but not STR or CCX, whereas LARG generally decreased. MEAP increased in hypothalamus after all stabilization procedures, whereas for STR and CCX, the effect was dependent on the time point for stabilization. The efficacy of stabilization allowed samples to be left for 2 hours in room temperature (20°C) without changes in peptide levels. This study shows that conductive heat transfer is an easy-to-use and efficient procedure for the preservation of the molecular composition in biological samples. Region- and peptide-specific critical steps were identified and stabilization enabled the optimization of tissue handling and opioid

  7. Minimizing Postsampling Degradation of Peptides by a Thermal Benchtop Tissue Stabilization Method

    PubMed Central

    Segerström, Lova; Gustavsson, Jenny

    2016-01-01

    Enzymatic degradation is a major concern in peptide analysis. Postmortem metabolism in biological samples entails considerable risk for measurements misrepresentative of true in vivo concentrations. It is therefore vital to find reliable, reproducible, and easy-to-use procedures to inhibit enzymatic activity in fresh tissues before subjecting them to qualitative and quantitative analyses. The aim of this study was to test a benchtop thermal stabilization method to optimize measurement of endogenous opioids in brain tissue. Endogenous opioid peptides are generated from precursor proteins through multiple enzymatic steps that include conversion of one bioactive peptide to another, often with a different function. Ex vivo metabolism may, therefore, lead to erroneous functional interpretations. The efficacy of heat stabilization was systematically evaluated in a number of postmortem handling procedures. Dynorphin B (DYNB), Leu-enkephalin-Arg6 (LARG), and Met-enkephalin-Arg6-Phe7 (MEAP) were measured by radioimmunoassay in rat hypothalamus, striatum (STR), and cingulate cortex (CCX). Also, simplified extraction protocols for stabilized tissue were tested. Stabilization affected all peptide levels to varying degrees compared to those prepared by standard dissection and tissue handling procedures. Stabilization increased DYNB in hypothalamus, but not STR or CCX, whereas LARG generally decreased. MEAP increased in hypothalamus after all stabilization procedures, whereas for STR and CCX, the effect was dependent on the time point for stabilization. The efficacy of stabilization allowed samples to be left for 2 hours in room temperature (20°C) without changes in peptide levels. This study shows that conductive heat transfer is an easy-to-use and efficient procedure for the preservation of the molecular composition in biological samples. Region- and peptide-specific critical steps were identified and stabilization enabled the optimization of tissue handling and opioid

  8. Improved Sequence Tag Generation Method for Peptide Identification in Tandem Mass Spectrometry

    PubMed Central

    Cao, Xia; Nesvizhskii, Alexey I.

    2013-01-01

    The sequence tag-based peptide identification methods are a promising alternative to the traditional database search approach. However, a more comprehensive analysis, optimization, and comparison with established methods are necessary before these methods can gain widespread use in the proteomics community. Using the InsPecT open source code base (Tanner et al., Anal Chem. 2005, 77:4626–39), we present an improved sequence tag generation method that directly incorporates multi-charged fragment ion peaks present in many tandem mass spectra of higher charge states. We also investigate the performance of sequence tagging under different settings using control datasets generated on five different types of mass spectrometers, as well as using a complex phosphopeptide-enriched sample. We also demonstrate that additional modeling of InsPecT search scores using a semi-parametric approach incorporating the accuracy of the precursor ion mass measurement provides additional improvement in the ability to discriminate between correct and incorrect peptide identifications. The overall superior performance of the sequence tag-based peptide identification method is demonstrated by comparison with a commonly used SEQUEST/PeptideProphet approach. PMID:18785767

  9. Structure of Trichamide, a Cyclic Peptide from the Bloom-Forming Cyanobacterium Trichodesmium erythraeum, Predicted from the Genome Sequence†

    PubMed Central

    Sudek, Sebastian; Haygood, Margo G.; Youssef, Diaa T. A.; Schmidt, Eric W.

    2006-01-01

    A gene cluster for the biosynthesis of a new small cyclic peptide, dubbed trichamide, was discovered in the genome of the global, bloom-forming marine cyanobacterium Trichodesmium erythraeum ISM101 because of striking similarities to the previously characterized patellamide biosynthesis cluster. The tri cluster consists of a precursor peptide gene containing the amino acid sequence for mature trichamide, a putative heterocyclization gene, an oxidase, two proteases, and hypothetical genes. Based upon detailed sequence analysis, a structure was predicted for trichamide and confirmed by Fourier transform mass spectrometry. Trichamide consists of 11 amino acids, including two cysteine-derived thiazole groups, and is cyclized by an N—C terminal amide bond. As the first natural product reported from T. erythraeum, trichamide shows the power of genome mining in the prediction and discovery of new natural products. PMID:16751554

  10. Cyanine-based probe\\tag-peptide pair for fluorescence protein imaging and fluorescence protein imaging methods

    DOEpatents

    Mayer-Cumblidge, M. Uljana; Cao, Haishi

    2010-08-17

    A molecular probe comprises two arsenic atoms and at least one cyanine based moiety. A method of producing a molecular probe includes providing a molecule having a first formula, treating the molecule with HgOAc, and subsequently transmetallizing with AsCl.sub.3. The As is liganded to ethanedithiol to produce a probe having a second formula. A method of labeling a peptide includes providing a peptide comprising a tag sequence and contacting the peptide with a biarsenical molecular probe. A complex is formed comprising the tag sequence and the molecular probe. A method of studying a peptide includes providing a mixture containing a peptide comprising a peptide tag sequence, adding a biarsenical probe to the mixture, and monitoring the fluorescence of the mixture.

  11. An Efficient Method for the In Vitro Production of Azol(in)e-Based Cyclic Peptides**

    PubMed Central

    Houssen, Wael E; Bent, Andrew F; McEwan, Andrew R; Pieiller, Nathalie; Tabudravu, Jioji; Koehnke, Jesko; Mann, Greg; Adaba, Rosemary I; Thomas, Louise; Hawas, Usama W; Liu, Huanting; Schwarz-Linek, Ulrich; Smith, Margaret C M; Naismith, James H; Jaspars, Marcel

    2014-01-01

    Heterocycle-containing cyclic peptides are promising scaffolds for the pharmaceutical industry but their chemical synthesis is very challenging. A new universal method has been devised to prepare these compounds by using a set of engineered marine-derived enzymes and substrates obtained from a family of ribosomally produced and post-translationally modified peptides called the cyanobactins. The substrate precursor peptide is engineered to have a non-native protease cleavage site that can be rapidly cleaved. The other enzymes used are heterocyclases that convert Cys or Cys/Ser/Thr into their corresponding azolines. A macrocycle is formed using a macrocyclase enzyme, followed by oxidation of the azolines to azoles with a specific oxidase. The work is exemplified by the production of 17 macrocycles containing 6–9 residues representing 11 out of the 20 canonical amino acids. PMID:25331823

  12. Anti-citrullinated peptide antibodies and their value for predicting responses to biologic agents: a review.

    PubMed

    Martin-Mola, Emilio; Balsa, Alejandro; García-Vicuna, Rosario; Gómez-Reino, Juan; González-Gay, Miguel Angel; Sanmartí, Raimon; Loza, Estíbaliz

    2016-08-01

    Anti-citrullinated peptide antibodies (ACPAs) play an important pathogenic role both at the onset and during the disease course. These antibodies precede the clinical appearance of rheumatoid arthritis (RA) and are associated with a less favorable prognosis, both clinically and radiologically. The objective of this work was to conduct a comprehensive review of studies published through September 2015 of ACPAs' role as a predictor of the therapeutic response to the biological agents in RA patients. The review also includes summary of the biology and detection of ACPAs as well as ACPAs in relation to joint disease and CV disease and the possible role of seroconversion. The reviews of studies examining TNF inhibitors and tocilizumab yielded negative results. In the case of rituximab, the data indicated a greater probability of clinical benefit in ACPA(+) patients versus ACPA(-) patients, as has been previously described for rheumatoid factor. Nonetheless, the effect is discreet and heterogeneous. Another drug that may have greater effectiveness in ACPA(+) patients is abatacept. Some studies have suggested that the drug is more efficient in ACPA(+) patients and that those patients show greater drug retention. In a subanalysis of the AMPLE trial, patients with very high ACPA titers who were treated with abatacept had a statistically significant response compared to patients with lower titers. In summary, the available studies suggest that the presence of or high titers of ACPA may predict a better response to rituximab and/or abatacept. Evidence regarding TNFi and tocilizumab is lacking. However, there is a lack of studies with appropriate designs to demonstrate that some drugs are superior to others for ACPA(+) patients. PMID:27271502

  13. Systems and methods for predicting materials properties

    DOEpatents

    Ceder, Gerbrand; Fischer, Chris; Tibbetts, Kevin; Morgan, Dane; Curtarolo, Stefano

    2007-11-06

    Systems and methods for predicting features of materials of interest. Reference data are analyzed to deduce relationships between the input data sets and output data sets. Reference data includes measured values and/or computed values. The deduced relationships can be specified as equations, correspondences, and/or algorithmic processes that produce appropriate output data when suitable input data is used. In some instances, the output data set is a subset of the input data set, and computational results may be refined by optionally iterating the computational procedure. To deduce features of a new material of interest, a computed or measured input property of the material is provided to an equation, correspondence, or algorithmic procedure previously deduced, and an output is obtained. In some instances, the output is iteratively refined. In some instances, new features deduced for the material of interest are added to a database of input and output data for known materials.

  14. A nozzle internal performance prediction method

    NASA Technical Reports Server (NTRS)

    Carlson, John R.

    1992-01-01

    A prediction method was written and incorporated into a three-dimensional Navier-Stokes code (PAB3D) for the calculation of nozzle internal performance. The following quantities are calculated: (1) discharge coefficient; (2) normal, side, and axial thrust ratios; (3) rolling, pitching, and yawing moments; and (4) effective pitch and yaw vector angles. Four different case studies are presented to confirm the applicability of the methodology. Internal and, in most situations, external flow-field regions are required to be modeled. The computed nozzle discharge coefficient matches both the level and the trend of the experimental data within quoted experimental data accuracy (0.5 percent). Moment and force ratios are generally within 1 to 2 percent of the absolute level of experimental data, with the trends of data matched accurately.

  15. Mode Decomposition Methods for Soil Moisture Prediction

    NASA Astrophysics Data System (ADS)

    Jana, R. B.; Efendiev, Y. R.; Mohanty, B.

    2014-12-01

    Lack of reliable, well-distributed, long-term datasets for model validation is a bottle-neck for most exercises in soil moisture analysis and prediction. Understanding what factors drive soil hydrological processes at different scales and their variability is very critical to further our ability to model the various components of the hydrologic cycle more accurately. For this, a comprehensive dataset with measurements across scales is very necessary. Intensive fine-resolution sampling of soil moisture over extended periods of time is financially and logistically prohibitive. Installation of a few long term monitoring stations is also expensive, and needs to be situated at critical locations. The concept of Time Stable Locations has been in use for some time now to find locations that reflect the mean values for the soil moisture across the watershed under all wetness conditions. However, the soil moisture variability across the watershed is lost when measuring at only time stable locations. We present here a study using techniques such as Dynamic Mode Decomposition (DMD) and Discrete Empirical Interpolation Method (DEIM) that extends the concept of time stable locations to arrive at locations that provide not simply the average soil moisture values for the watershed, but also those that can help re-capture the dynamics across all locations in the watershed. As with the time stability, the initial analysis is dependent on an intensive sampling history. The DMD/DEIM method is an application of model reduction techniques for non-linearly related measurements. Using this technique, we are able to determine the number of sampling points that would be required for a given accuracy of prediction across the watershed, and the location of those points. Locations with higher energetics in the basis domain are chosen first. We present case studies across watersheds in the US and India. The technique can be applied to other hydro-climates easily.

  16. Prediction of optimal peptide mixtures to induce broadly neutralizing antibodies to human immunodeficiency virus type 1.

    PubMed Central

    Holley, L H; Goudsmit, J; Karplus, M

    1991-01-01

    Sequences of the principal neutralizing determinant (PND) of the external envelope protein, gp120, from 245 isolates of human immunodeficiency virus type 1 are analyzed. The minimal set of peptides that would elicit antibodies to neutralize a majority of U.S. and European isolates of human immunodeficiency virus type 1 is determined with the assumption that peptides of a given length including the central Gly-Pro-Gly triad are required. In spite of the hypervariability of the PND, 90% of these 245 sequences include peptides from a set of 7 pentapeptides, 13 hexapeptides, or 17 heptapeptides. Tests of these peptide sets on 78 additional PND sequences show that 95% are covered by the 7 pentapeptides, 94% by the 13 hexapeptides, and 86% by the 17 heptapeptides. To anticipate variants not yet observed, single amino acid mutation frequencies from the 245 isolates are used to calculate an expanded set of the 10,000 most probable PND sequences. These sequences cover 86% of the total distribution expected for the central portion of the PND. Peptide lists derived from this expanded set when tested on the 78 additional sequences show that 7 pentapeptides cover 95%, 13 hexapeptides cover 94%, and 17 heptapeptides cover 94%. These results suggest that peptide cocktails of limited size with the potential to cover a large fraction of PND sequence variation may be feasible vaccine candidates. PMID:1862103

  17. A Support Vector Machine model for the prediction of proteotypic peptides for accurate mass and time proteomics

    SciTech Connect

    Webb-Robertson, Bobbie-Jo M.; Cannon, William R.; Oehmen, Christopher S.; Shah, Anuj R.; Gurumoorthi, Vidhya; Lipton, Mary S.; Waters, Katrina M.

    2008-07-01

    Motivation: The standard approach to identifying peptides based on accurate mass and elution time (AMT) compares these profiles obtained from a high resolution mass spectrometer to a database of peptides previously identified from tandem mass spectrometry (MS/MS) studies. It would be advantageous, with respect to both accuracy and cost, to only search for those peptides that are detectable by MS (proteotypic). Results: We present a Support Vector Machine (SVM) model that uses a simple descriptor space based on 35 properties of amino acid content, charge, hydrophilicity, and polarity for the quantitative prediction of proteotypic peptides. Using three independently derived AMT databases (Shewanella oneidensis, Salmonella typhimurium, Yersinia pestis) for training and validation within and across species, the SVM resulted in an average accuracy measure of ~0.8 with a standard deviation of less than 0.025. Furthermore, we demonstrate that these results are achievable with a small set of 12 variables and can achieve high proteome coverage. Availability: http://omics.pnl.gov/software/STEPP.php

  18. The Predikin webserver: improved prediction of protein kinase peptide specificity using structural information

    PubMed Central

    Saunders, Neil F. W.

    2008-01-01

    The Predikin webserver allows users to predict substrates of protein kinases. The Predikin system is built from three components: a database of protein kinase substrates that links phosphorylation sites with specific protein kinase sequences; a perl module to analyse query protein kinases and a web interface through which users can submit protein kinases for analysis. The Predikin perl module provides methods to (i) locate protein kinase catalytic domains in a sequence, (ii) classify them by type or family, (iii) identify substrate-determining residues, (iv) generate weighted scoring matrices using three different methods, (v) extract putative phosphorylation sites in query substrate sequences and (vi) score phosphorylation sites for a given kinase, using optional filters. The web interface provides user-friendly access to each of these functions and allows users to obtain rapidly a set of predictions that they can export for further analysis. The server is available at http://predikin.biosci.uq.edu.au. PMID:18477637

  19. Method For Determining And Modifying Protein/Peptide Solubilty

    DOEpatents

    Waldo, Geoffrey S.

    2005-03-15

    A solubility reporter for measuring a protein's solubility in vivo or in vitro is described. The reporter, which can be used in a single living cell, gives a specific signal suitable for determining whether the cell bears a soluble version of the protein of interest. A pool of random mutants of an arbitrary protein, generated using error-prone in vitro recombination, may also be screened for more soluble versions using the reporter, and these versions may be recombined to yield variants having further-enhanced solubility. The method of the present invention includes "irrational" (random mutagenesis) methods, which do not require a priori knowledge of the three-dimensional structure of the protein of interest. Multiple sequences of mutation/genetic recombination and selection for improved solubility are demonstrated to yield versions of the protein which display enhanced solubility.

  20. An equation to estimate the difference between theoretically predicted and SDS PAGE-displayed molecular weights for an acidic peptide.

    PubMed

    Guan, Yihong; Zhu, Qinfang; Huang, Delai; Zhao, Shuyi; Jan Lo, Li; Peng, Jinrong

    2015-01-01

    The molecular weight (MW) of a protein can be predicted based on its amino acids (AA) composition. However, in many cases a non-chemically modified protein shows an SDS PAGE-displayed MW larger than its predicted size. Some reports linked this fact to high content of acidic AA in the protein. However, the exact relationship between the acidic AA composition and the SDS PAGE-displayed MW is not established. Zebrafish nucleolar protein Def is composed of 753 AA and shows an SDS PAGE-displayed MW approximately 13 kDa larger than its predicted MW. The first 188 AA in Def is defined by a glutamate-rich region containing ~35.6% of acidic AA. In this report, we analyzed the relationship between the SDS PAGE-displayed MW of thirteen peptides derived from Def and the AA composition in each peptide. We found that the difference between the predicted and SDS PAGE-displayed MW showed a linear correlation with the percentage of acidic AA that fits the equation y = 276.5x - 31.33 (x represents the percentage of acidic AA, 11.4% ≤ x ≤ 51.1%; y represents the average ΔMW per AA). We demonstrated that this equation could be applied to predict the SDS PAGE-displayed MW for thirteen different natural acidic proteins. PMID:26311515

  1. Methods of Predicting Solid Waste Characteristics.

    ERIC Educational Resources Information Center

    Boyd, Gail B.; Hawkins, Myron B.

    The project summarized by this report involved a preliminary design of a model for estimating and predicting the quantity and composition of solid waste and a determination of its feasibility. The novelty of the prediction model is that it estimates and predicts on the basis of knowledge of materials and quantities before they become a part of the…

  2. A Statistical Method for Assessing Peptide Identification Confidence in Accurate Mass and Time Tag Proteomics

    SciTech Connect

    Stanley, Jeffrey R.; Adkins, Joshua N.; Slysz, Gordon W.; Monroe, Matthew E.; Purvine, Samuel O.; Karpievitch, Yuliya V.; Anderson, Gordon A.; Smith, Richard D.; Dabney, Alan R.

    2011-07-15

    High-throughput proteomics is rapidly evolving to require high mass measurement accuracy for a variety of different applications. Increased mass measurement accuracy in bottom-up proteomics specifically allows for an improved ability to distinguish and characterize detected MS features, which may in turn be identified by, e.g., matching to entries in a database for both precursor and fragmentation mass identification methods. Many tools exist with which to score the identification of peptides from LC-MS/MS measurements or to assess matches to an accurate mass and time (AMT) tag database, but these two calculations remain distinctly unrelated. Here we present a statistical method, Statistical Tools for AMT tag Confidence (STAC), which extends our previous work incorporating prior probabilities of correct sequence identification from LC-MS/MS, as well as the quality with which LC-MS features match AMT tags, to evaluate peptide identification confidence. Compared to existing tools, we are able to obtain significantly more high-confidence peptide identifications at a given false discovery rate and additionally assign confidence estimates to individual peptide identifications. Freely available software implementations of STAC are available in both command line and as a Windows graphical application.

  3. Size-exclusion HPLC as a sensitive and calibrationless method for complex peptide mixtures quantification.

    PubMed

    Bodin, Alice; Framboisier, Xavier; Alonso, Dominique; Marc, Ivan; Kapel, Romain

    2015-12-01

    This work describes an original methodology to quantify complex peptide mixtures by size-exclusion high-performance liquid chromatography (SE-HPLC). The methodology was first tested on simulated elutions of peptide mixtures. For this set of experiments, a good estimation of the total peptide concentration was observed (error less than 10 %). Then 30 fractions obtained by ultrafiltration of hydrolysates from two different sources were titrated by Kjeldahl or BCA analysis and analysed by SE-HPLC for an experimental validation of the methodology. Very good matchs between methods were obtained. The linear working range depends on the hydrolysate but is generally between 0.2 and 4gL(-1) (i.e. between 10 and 200μg). Moreover, the presence of organic solvents or salts in samples does not impact the accuracy of the methodology contrary to common quantification methods. Hence, the findings of this study show that total concentration of complex peptide mixture can be efficiently determinate by the proposed methodology using simple SE-HPLC analysis. PMID:26523666

  4. A simple three-step method for design and affinity testing of new antisense peptides: an example of erythropoietin.

    PubMed

    Štambuk, Nikola; Manojlović, Zoran; Turčić, Petra; Martinić, Roko; Konjevoda, Paško; Weitner, Tin; Wardega, Piotr; Gabričević, Mario

    2014-01-01

    Antisense peptide technology is a valuable tool for deriving new biologically active molecules and performing peptide-receptor modulation. It is based on the fact that peptides specified by the complementary (antisense) nucleotide sequences often bind to each other with a higher specificity and efficacy. We tested the validity of this concept on the example of human erythropoietin, a well-characterized and pharmacologically relevant hematopoietic growth factor. The purpose of the work was to present and test simple and efficient three-step procedure for the design of an antisense peptide targeting receptor-binding site of human erythropoietin. Firstly, we selected the carboxyl-terminal receptor binding region of the molecule (epitope) as a template for the antisense peptide modeling; Secondly, we designed an antisense peptide using mRNA transcription of the epitope sequence in the 3'→5' direction and computational screening of potential paratope structures with BLAST; Thirdly, we evaluated sense-antisense (epitope-paratope) peptide binding and affinity by means of fluorescence spectroscopy and microscale thermophoresis. Both methods showed similar Kd values of 850 and 816 µM, respectively. The advantages of the methods were: fast screening with a small quantity of the sample needed, and measurements done within the range of physicochemical parameters resembling physiological conditions. Antisense peptides targeting specific erythropoietin region(s) could be used for the development of new immunochemical methods. Selected antisense peptides with optimal affinity are potential lead compounds for the development of novel diagnostic substances, biopharmaceuticals and vaccines. PMID:24865486

  5. Computational Prediction and Experimental Validation of Signal Peptide Cleavage in the Extracellular Proteome of a Natural Microbial Community

    SciTech Connect

    Erickson, Brian K; Mueller, Ryan; Verberkmoes, Nathan C; Shah, Manesh B; Singer, Steven; Thelen, Michael P.; Banfield, Jillian F.; Hettich, Robert {Bob} L

    2010-01-01

    An integrated computational/experimental approach was used to predict and identify signal peptide cleavages among microbial proteins of environmental biofilm communities growing in acid mine drainage (AMD). SignalP-3.0 was employed to computationally query the AMD protein database of >16,000 proteins, which resulted in 1,480 predicted signal peptide cleaved proteins. LC-MS/MS analyses of extracellular (secretome) microbial preparations from different locations and developmental states empirically confirmed 531 of these signal peptide cleaved proteins. The majority of signal-cleavage proteins (58.4%) are annotated to have unknown functions; however, Pfam domain analysis revealed that many may be involved in extracellular functions expected within the AMD system. Examination of the abundances of signal-cleaved proteins across 28 proteomes from biofilms collected over a 4-year period demonstrated a strong correlation with the developmental state of the biofilm. For example, class I cytochromes are abundant in early growth states, whereas cytochrome oxidases from the same organism increase in abundance later in development. These results likely reflect shifts in metabolism that occur as biofilms thicken and communities diversify. In total, these results provide experimental confirmation of proteins that are designed to function in the extreme acidic extracellular environment and will serve as targets for future biochemical analysis.

  6. A Potent and Selective Peptide Blocker of the Kv1.3 Channel: Prediction from Free-Energy Simulations and Experimental Confirmation

    PubMed Central

    Rashid, M. Harunur; Heinzelmann, Germano; Huq, Redwan; Tajhya, Rajeev B.; Chang, Shih Chieh; Chhabra, Sandeep; Pennington, Michael W.; Beeton, Christine; Norton, Raymond S.; Kuyucak, Serdar

    2013-01-01

    The voltage-gated potassium channel Kv1.3 is a well-established target for treatment of autoimmune diseases. ShK peptide from a sea anemone is one of the most potent blockers of Kv1.3 but its application as a therapeutic agent for autoimmune diseases is limited by its lack of selectivity against other Kv channels, in particular Kv1.1. Accurate models of Kv1.x-ShK complexes suggest that specific charge mutations on ShK could considerably enhance its specificity for Kv1.3. Here we evaluate the K18A mutation on ShK, and calculate the change in binding free energy associated with this mutation using the path-independent free energy perturbation and thermodynamic integration methods, with a novel implementation that avoids convergence problems. To check the accuracy of the results, the binding free energy differences were also determined from path-dependent potential of mean force calculations. The two methods yield consistent results for the K18A mutation in ShK and predict a 2 kcal/mol gain in Kv1.3/Kv1.1 selectivity free energy relative to wild-type peptide. Functional assays confirm the predicted selectivity gain for ShK[K18A] and suggest that it will be a valuable lead in the development of therapeutics for autoimmune diseases. PMID:24244345

  7. Prediction Methods in Solar Sunspots Cycles.

    PubMed

    Ng, Kim Kwee

    2016-01-01

    An understanding of the Ohl's Precursor Method, which is used to predict the upcoming sunspots activity, is presented by employing a simplified movable divided-blocks diagram. Using a new approach, the total number of sunspots in a solar cycle and the maximum averaged monthly sunspots number Rz(max) are both shown to be statistically related to the geomagnetic activity index in the prior solar cycle. The correlation factors are significant and they are respectively found to be 0.91 ± 0.13 and 0.85 ± 0.17. The projected result is consistent with the current observation of solar cycle 24 which appears to have attained at least Rz(max) at 78.7 ± 11.7 in March 2014. Moreover, in a statistical study of the time-delayed solar events, the average time between the peak in the monthly geomagnetic index and the peak in the monthly sunspots numbers in the succeeding ascending phase of the sunspot activity is found to be 57.6 ± 3.1 months. The statistically determined time-delayed interval confirms earlier observational results by others that the Sun's electromagnetic dipole is moving toward the Sun's Equator during a solar cycle. PMID:26868269

  8. Prediction Methods in Solar Sunspots Cycles

    NASA Astrophysics Data System (ADS)

    Ng, Kim Kwee

    2016-02-01

    An understanding of the Ohl’s Precursor Method, which is used to predict the upcoming sunspots activity, is presented by employing a simplified movable divided-blocks diagram. Using a new approach, the total number of sunspots in a solar cycle and the maximum averaged monthly sunspots number Rz(max) are both shown to be statistically related to the geomagnetic activity index in the prior solar cycle. The correlation factors are significant and they are respectively found to be 0.91 ± 0.13 and 0.85 ± 0.17. The projected result is consistent with the current observation of solar cycle 24 which appears to have attained at least Rz(max) at 78.7 ± 11.7 in March 2014. Moreover, in a statistical study of the time-delayed solar events, the average time between the peak in the monthly geomagnetic index and the peak in the monthly sunspots numbers in the succeeding ascending phase of the sunspot activity is found to be 57.6 ± 3.1 months. The statistically determined time-delayed interval confirms earlier observational results by others that the Sun’s electromagnetic dipole is moving toward the Sun’s Equator during a solar cycle.

  9. Prediction Methods in Solar Sunspots Cycles

    PubMed Central

    Ng, Kim Kwee

    2016-01-01

    An understanding of the Ohl’s Precursor Method, which is used to predict the upcoming sunspots activity, is presented by employing a simplified movable divided-blocks diagram. Using a new approach, the total number of sunspots in a solar cycle and the maximum averaged monthly sunspots number Rz(max) are both shown to be statistically related to the geomagnetic activity index in the prior solar cycle. The correlation factors are significant and they are respectively found to be 0.91 ± 0.13 and 0.85 ± 0.17. The projected result is consistent with the current observation of solar cycle 24 which appears to have attained at least Rz(max) at 78.7 ± 11.7 in March 2014. Moreover, in a statistical study of the time-delayed solar events, the average time between the peak in the monthly geomagnetic index and the peak in the monthly sunspots numbers in the succeeding ascending phase of the sunspot activity is found to be 57.6 ± 3.1 months. The statistically determined time-delayed interval confirms earlier observational results by others that the Sun’s electromagnetic dipole is moving toward the Sun’s Equator during a solar cycle. PMID:26868269

  10. Prediction of clinical outcomes using B-type natriuretic peptides in the general population: a systematic review.

    PubMed

    Don-Wauchope, Andrew C; Santaguida, Pasqualina L; McKelvie, Robert; Brown, Judy A; Oremus, Mark; Ali, Usman; Bustamam, Amy; Sohel, Nazmul; Hill, Stephen A; Booth, Ronald A; Balion, Cynthia; Raina, Parminder

    2014-08-01

    The use of B-type natriuretic peptides to predict outcomes in general populations has been investigated in a number of primary studies. A previous systematic review considering natriuretic peptides in cardiovascular disease included a subgroup of general population studies, which suggested an association with a number of clinical outcomes. We electronically searched Medline, Embase, AMED, Cochrane Central Register of Controlled Trials, Cochrane Database of Systematic Reviews, and CINAHL for English-language articles published between 1989 and mid-2012. We utilized trained reviewers and standardized forms to screen articles for inclusion and extract data from included articles. All included studies (n = 7) were summarized in narrative and tabular form. A general population was defined as one that was randomly selected from a community setting where no specific inclusion or exclusion criteria were specified. The seven included studies all used FDA approved assays for NT-proBNP. The range of clinical outcomes and heterogeneity did not allow for meta-analysis. The hazard ratios for predicting outcomes in the included studies ranged from 1.0 to 4.1 (all p values <0.05). The discrimination statistics reported in four studies all demonstrated statistically significant improvements in predicting outcomes. NT-proBNP is associated with heart failure, all-cause and cardiovascular mortality, and other combined cardiovascular events in a general unselected population. The discrimination statistics suggest modest improvements in risk stratification. No prospective studies exist to demonstrate the clinical utility of using B-type natriuretic peptides to predict clinical outcomes in a general population. PMID:25052419

  11. Fibpredictor: a computational method for rapid prediction of amyloid fibril structures.

    PubMed

    Tabatabaei Ghomi, Hamed; Topp, Elizabeth M; Lill, Markus A

    2016-09-01

    Amyloid fibrils are important in diseases such as Alzheimer's disease and Parkinson's disease, and are also a common instability in peptide and protein drug products. Despite their importance, experimental structures of amyloid fibrils in atomistic detail are rare. To address this limitation, we have developed a novel, rapid computational method to predict amyloid fibril structures (Fibpredictor). The method combines β-sheet model building, β-sheet replication, and symmetry operations with side-chain prediction and statistical scoring functions. When applied to nine amyloid fibrils with experimentally determined structures, the method predicted the correct structures of amyloid fibrils and enriched those among the top-ranked structures. These models can be used as the initial heuristic structures for more complicated computational studies. Fibpredictor is available at http://nanohub.org/resources/fibpredictor . PMID:27502172

  12. High throughput peptide mapping method for analysis of site specific monoclonal antibody oxidation.

    PubMed

    Li, Xiaojuan; Xu, Wei; Wang, Yi; Zhao, Jia; Liu, Yan-Hui; Richardson, Daisy; Li, Huijuan; Shameem, Mohammed; Yang, Xiaoyu

    2016-08-19

    Oxidation of therapeutic monoclonal antibodies (mAbs) often occurs on surface exposed methionine and tryptophan residues during their production in cell culture, purification, and storage, and can potentially impact the binding to their targets. Characterization of site specific oxidation is critical for antibody quality control. Antibody oxidation is commonly determined by peptide mapping/LC-MS methods, which normally require a long (up to 24h) digestion step. The prolonged sample preparation procedure could result in oxidation artifacts of susceptible methionine and tryptophan residues. In this paper, we developed a rapid and simple UV based peptide mapping method that incorporates an 8-min trypsin in-solution digestion protocol for analysis of oxidation. This method is able to determine oxidation levels at specific residues of a mAb based on the peptide UV traces within <1h, from either TBHP treated or UV light stressed samples. This is the simplest and fastest method reported thus far for site specific oxidation analysis, and can be applied for routine or high throughput analysis of mAb oxidation during various stability and degradation studies. By using the UV trace, the method allows more accurate measurement than mass spectrometry and can be potentially implemented as a release assay. It has been successfully used to monitor antibody oxidation in real time stability studies. PMID:27432793

  13. Peptide synthesis triggered by comet impacts: A possible method for peptide delivery to the early Earth and icy satellites

    NASA Astrophysics Data System (ADS)

    Sugahara, Haruna; Mimura, Koichi

    2015-09-01

    We performed shock experiments simulating natural comet impacts in an attempt to examine the role that comet impacts play in peptide synthesis. In the present study, we selected a mixture of alanine (DL-alanine), water ice, and silicate (forsterite) to make a starting material for the experiments. The shock experiments were conducted under cryogenic conditions (77 K), and the shock pressure range achieved in the experiments was 4.8-25.8 GPa. The results show that alanine is oligomerized into peptides up to tripeptides due to the impact shock. The synthesized peptides were racemic, indicating that there was no enantioselective synthesis of peptides from racemic amino acids due to the impact shock. We also found that the yield of linear peptides was a magnitude higher than those of cyclic diketopiperazine. Furthermore, we estimated the amount of cometary-derived peptides to the early Earth based on two models (the Lunar Crating model and the Nice model) during the Late Heavy Bombardment (LHB) using our experimental data. The estimation based on the Lunar Crating model gave 3 × 109 mol of dialanine, 4 × 107 mol of trialanine, and 3 × 108 mol of alanine-diketopiperazine. Those based on the Nice model, in which the main impactor of LHB is comets, gave 6 × 1010 mol of dialanine, 1 × 109 mol of trialanine, and 8 × 109 mol of alanine-diketopiperazine. The estimated amounts were comparable to those originating from terrestrial sources (Cleaves, H.J., Aubrey, A.D., Bada, J.L. [2009]. Orig. Life Evol. Biosph. 39, 109-126). Our results indicate that comet impacts played an important role in chemical evolution as a supplier of linear peptides, which are important for further chemical evolution on the early Earth. Our study also highlights the importance of icy satellites, which were formed by comet accumulation, as prime targets for missions searching for extraterrestrial life.

  14. Predictive Role of Intraoperative Serum Brain Natriuretic Peptide for Early Allograft Dysfunction in Living Donor Liver Transplantation.

    PubMed

    Chae, Min Suk; Koo, Jung Min; Park, Chul Soo

    2016-01-01

    BACKGROUND Early allograft dysfunction (EAD) is considered an important complication in liver transplantation. Serum brain natriuretic peptide (BNP) is a marker of cardiac dysfunction related to end-stage liver disease. We investigated the intraoperative change in the serum BNP level and its contribution to EAD after living donor liver transplantation (LDLT). MATERIAL AND METHODS The perioperative data of 104 patients who underwent LDLT were retrospectively reviewed and compared between patients with and without EAD. Serum BNPs were obtained at each phase, and potentially significant factors (P<0.1) were measured by univariate analysis. The intraoperative mean serum BNP level was compared with other predictors using the AUC, and was analyzed for its relationship with EAD by multivariate logistic regression. RESULTS A total of 31 patients (29.8%) developed EAD after LDLT. In all phases, the EAD group showed higher serum BNP levels than the non-EAD group. The serum BNP level at each phase was less accurate than the mean serum BNP level for EAD. The intraoperative mean serum BNP level showed higher predictive accuracy than the Child-Pugh-Turcotte, model for end-stage liver disease (MELD), and D-MELD (donor age × recipient MELD) scores (p<0.05 for all). After multivariate adjustment, intraoperative mean serum BNP level ≥100 pg/mL was identified as an independent risk factor for EAD, along with kidney disease and graft ischemic time. CONCLUSIONS During LDLT, the EAD group showed higher serum BNP levels than the non-EAD group. An intraoperative mean serum BNP level ≥100 pg/mL is independently associated with EAD after LDLT. PMID:27572618

  15. A Simple Three-Step Method for Design and Affinity Testing of New Antisense Peptides: An Example of Erythropoietin

    PubMed Central

    Štambuk, Nikola; Manojlović, Zoran; Turčić, Petra; Martinić, Roko; Konjevoda, Paško; Weitner, Tin; Wardega, Piotr; Gabričević, Mario

    2014-01-01

    Antisense peptide technology is a valuable tool for deriving new biologically active molecules and performing peptide–receptor modulation. It is based on the fact that peptides specified by the complementary (antisense) nucleotide sequences often bind to each other with a higher specificity and efficacy. We tested the validity of this concept on the example of human erythropoietin, a well-characterized and pharmacologically relevant hematopoietic growth factor. The purpose of the work was to present and test simple and efficient three-step procedure for the design of an antisense peptide targeting receptor-binding site of human erythropoietin. Firstly, we selected the carboxyl-terminal receptor binding region of the molecule (epitope) as a template for the antisense peptide modeling; Secondly, we designed an antisense peptide using mRNA transcription of the epitope sequence in the 3'→5' direction and computational screening of potential paratope structures with BLAST; Thirdly, we evaluated sense–antisense (epitope–paratope) peptide binding and affinity by means of fluorescence spectroscopy and microscale thermophoresis. Both methods showed similar Kd values of 850 and 816 µM, respectively. The advantages of the methods were: fast screening with a small quantity of the sample needed, and measurements done within the range of physicochemical parameters resembling physiological conditions. Antisense peptides targeting specific erythropoietin region(s) could be used for the development of new immunochemical methods. Selected antisense peptides with optimal affinity are potential lead compounds for the development of novel diagnostic substances, biopharmaceuticals and vaccines. PMID:24865486

  16. Many overlapping peptides for protein hydrogen exchange experiments by the fragment separation-mass spectrometry method.

    PubMed

    Mayne, Leland; Kan, Zhong-Yuan; Chetty, Palaniappan Sevugan; Ricciuti, Alec; Walters, Benjamin T; Englander, S Walter

    2011-11-01

    Measurement of the naturally occurring hydrogen exchange (HX) behavior of proteins can in principle provide highly resolved thermodynamic and kinetic information on protein structure, dynamics, and interactions. The HX fragment separation-mass spectrometry method (HX-MS) is able to measure hydrogen exchange in biologically important protein systems that are not accessible to NMR methods. In order to achieve high structural resolution in HX-MS experiments, it will be necessary to obtain many sequentially overlapping peptide fragments and be able to identify and analyze them efficiently and accurately by mass spectrometry. This paper describes operations which, when applied to four different proteins ranging in size from 140 to 908 residues, routinely provides hundreds of useful unique peptides, covering the entire protein length many times over. Coverage in terms of the average number of peptide fragments that span each amino acid exceeds 10. The ability to achieve these results required the integrated application of experimental methods that are described here and a computer analysis program, called ExMS, described in a following paper. PMID:21952777

  17. Assay of Protein and Peptide Adducts of Cholesterol Ozonolysis Products by Hydrophobic and Click Enrichment Methods

    PubMed Central

    2015-01-01

    Cholesterol undergoes ozonolysis to afford a variety of oxysterol products, including cholesterol-5,6-epoxide (CholEp) and the isomeric aldehydes secosterol A (seco A) and secosterol B (seco B). These oxysterols display numerous important biological activities, including protein adduction; however, much remains to be learned about the identity of the reactive species and the range of proteins modified by these oxysterols. Here, we synthesized alkynyl derivatives of cholesterol-derived oxysterols and employed a straightforward detection method to establish secosterols A and B as the most protein-reactive of the oxysterols tested. Model adduction studies with an amino acid, peptides, and proteins provide evidence for the potential role of secosterol dehydration products in protein adduction. Hydrophobic separation methods—Folch extraction and solid phase extraction (SPE)—were successfully applied to enrich oxysterol-adducted peptide species, and LC-MS/MS analysis of a model peptide–seco adduct revealed a unique fragmentation pattern (neutral loss of 390 Da) for that species. Coupling a hydrophobic enrichment method with proteomic analysis utilizing characteristic fragmentation patterns facilitates the identification of secosterol-modified peptides and proteins in an adducted protein. More broadly, these improved enrichment methods may give insight into the role of oxysterols and ozone exposure in the pathogenesis of a variety of diseases, including atherosclerosis, Alzheimer’s disease, Parkinson’s disease, and asthma. PMID:25185119

  18. Earthquake prediction: Simple methods for complex phenomena

    NASA Astrophysics Data System (ADS)

    Luen, Bradley

    2010-09-01

    Earthquake predictions are often either based on stochastic models, or tested using stochastic models. Tests of predictions often tacitly assume predictions do not depend on past seismicity, which is false. We construct a naive predictor that, following each large earthquake, predicts another large earthquake will occur nearby soon. Because this "automatic alarm" strategy exploits clustering, it succeeds beyond "chance" according to a test that holds the predictions _xed. Some researchers try to remove clustering from earthquake catalogs and model the remaining events. There have been claims that the declustered catalogs are Poisson on the basis of statistical tests we show to be weak. Better tests show that declustered catalogs are not Poisson. In fact, there is evidence that events in declustered catalogs do not have exchangeable times given the locations, a necessary condition for the Poisson. If seismicity followed a stochastic process, an optimal predictor would turn on an alarm when the conditional intensity is high. The Epidemic-Type Aftershock (ETAS) model is a popular point process model that includes clustering. It has many parameters, but is still a simpli_cation of seismicity. Estimating the model is di_cult, and estimated parameters often give a non-stationary model. Even if the model is ETAS, temporal predictions based on the ETAS conditional intensity are not much better than those of magnitude-dependent automatic (MDA) alarms, a much simpler strategy with only one parameter instead of _ve. For a catalog of Southern Californian seismicity, ETAS predictions again o_er only slight improvement over MDA alarms

  19. Usefulness of ELISA Methods for Assessing LPS Interactions with Proteins and Peptides

    PubMed Central

    Martínez-Sernández, Victoria; Orbegozo-Medina, Ricardo A.; Romarís, Fernanda; Paniagua, Esperanza; Ubeira, Florencio M.

    2016-01-01

    Lipopolysaccharide (LPS), the major constituent of the outer membrane of Gram-negative bacteria, can trigger severe inflammatory responses during bacterial infections, possibly leading to septic shock. One approach to combatting endotoxic shock is to neutralize the most conserved part and major mediator of LPS activity (lipid A) with LPS-binding proteins or peptides. Although several available assays evaluate the biological activity of these molecules on LPS (e.g. inhibition of LPS-induced TNF-α production in macrophages), the development of simple and cost-effective methods that would enable preliminary screening of large numbers of potential candidate molecules is of great interest. Moreover, it would be also desirable that such methods could provide information about the possible biological relevance of the interactions between proteins and LPS, which may enhance or neutralize LPS-induced inflammatory responses. In this study, we designed and evaluated different types of ELISA that could be used to study possible interactions between LPS and any protein or peptide. We also analysed the usefulness and limitations of the different ELISAs. Specifically, we tested the capacity of several proteins and peptides to bind FITC-labeled LPSs from Escherichia coli serotypes O111:B4 and O55:B5 in an indirect ELISA and in two competitive ELISAs including casein hydrolysate (hCAS) and biotinylated polymyxin B (captured by deglycosylated avidin; PMX) as LPS-binding agents in the solid phase. We also examined the influence of pH, detergents and different blocking agents on LPS binding. Our results showed that the competitive hCAS-ELISA performed under mildly acidic conditions can be used as a general method for studying LPS interactions, while the more restrictive PMX-ELISA may help to identify proteins/peptides that are likely to have neutralizing properties in vitro or in vivo. PMID:27249227

  20. Potential Method of Predicting Coronal Mass Ejection

    NASA Astrophysics Data System (ADS)

    Imholt, Timothy

    2001-10-01

    Coronal Mass Ejections (CME) may be described as a blast of gas and highly charged solar mass fragments ejected into space. These ejections, when directed toward Earth, have many different effects on terrestrial systems ranging from the Aurora Borealis to changes in wireless communication. The early prediction of these solar events cannot be overlooked. There are several models currently accepted and utilized to predict these events, however, with earlier prediction of both the event and the location on the sun where the event occurs allows us to have earlier warnings as to when they will affect man-made systems. A better prediction could perhaps be achieved by utilizing low angular resolution radio telescope arrays to catalog data from the sun at different radio frequencies on a regular basis. Once this data is cataloged a better predictor for these CME’s could be found. We propose a model that allows a prediction to be made that appears to be longer than 24 hours.

  1. Potential Method of Predicting Coronal Mass Ejection

    NASA Astrophysics Data System (ADS)

    Imholt, Timothy; Roberts, J. A.; Scott, J. B.; University Of North Texas Team

    2000-10-01

    Coronal Mass Ejections (CME) may be described as a blast of gas and highly charged solar mass fragments ejected into space. These ejections, when directed toward Earth, have many different effects on terrestrial systems ranging from the Aurora Borealis to changes in wireless communications. The importance of an early prediction of these solar events cannot be overlooked. There are several models currently accepted and utilized to predict these events, however, with earlier prediction of both the event and the location on the sun where the event occur allows us to have earlier warnings as to when they will effect man-made systems. A better prediction could perhaps be achieved by utilizing low angular resolution radio telescope arrays to catalog data from the sun at different radio frequencies on a regular basis. Once this data is cataloged a better predictor for these CME's could be found. We propose a model that allows a prediction to be made that appears to be longer than 24 hours.

  2. Analysis of peptide-protein binding using amino acid descriptors: prediction and experimental verification for human histocompatibility complex HLA-A0201.

    PubMed

    Guan, Pingping; Doytchinova, Irini A; Walshe, Valerie A; Borrow, Persephone; Flower, Darren R

    2005-11-17

    Amino acid descriptors are often used in quantitative structure-activity relationship (QSAR) analysis of proteins and peptides. In the present study, descriptors were used to characterize peptides binding to the human MHC allele HLA-A0201. Two sets of amino acid descriptors were chosen: 93 descriptors taken from the amino acid descriptor database AAindex and the z descriptors defined by Wold and Sandberg. Variable selection techniques (SIMCA, genetic algorithm, and GOLPE) were applied to remove redundant descriptors. Our results indicate that QSAR models generated using five z descriptors had the highest predictivity and explained variance (q2 between 0.6 and 0.7 and r2 between 0.6 and 0.9). Further to the QSAR analysis, 15 peptides were synthesized and tested using a T2 stabilization assay. All peptides bound to HLA-A0201 well, and four peptides were identified as high-affinity binders. PMID:16279801

  3. Peptide motif analysis predicts lymphocytic choriomeningitis virus as trigger for multiple sclerosis.

    PubMed

    Hogeboom, Charissa

    2015-10-01

    The etiology of multiple sclerosis (MS) involves both genetic and environmental factors. Genetically, the strongest link is with HLA DRB1*1501, but the environmental trigger, probably a virus, remains uncertain. This investigation scans a panel of proteins from encephalitogenic viruses for peptides homologous to the primary autoantigen from myelin basic protein (MBP), then evaluates candidate peptides against a motif required for T cell cross-reactivity and compares viral prevalence patterns to epidemiological characteristics of MS. The only peptide meeting criteria for cross-reactivity with MBP was one from lymphocytic choriomeningitis virus (LCMV), a zoonotic agent. In contrast to current candidates such as Epstein-Barr virus, the distribution of LCMV is consistent with epidemiological features of MS, including concentration in the temperate zone, higher prevalence farther from the equator, and increased prevalence in proximity to regions of peak MS incidence, while lack of person-to-person transmission is consistent with low MS concordance across monozygotic twins. Further, LCMV blocks induction of type I interferon (IFN). Hypothetically this would dysregulate immune processes in favor of proinflammatory pathways as well as upregulating HLA class II and providing more binding sites for autoantigen. The combination of molecular mimicry with virally-induced immune dysregulation has the potential to explain aspects of autoimmunity not addressed by either mechanism alone. PMID:26319106

  4. Development of a method for environmentally friendly chemical peptide synthesis in water using water-dispersible amino acid nanoparticles

    PubMed Central

    2011-01-01

    Due to the vast importance of peptides in biological processes, there is an escalating need for synthetic peptides to be used in a wide variety of applications. However, the consumption of organic solvent is extremely large in chemical peptide syntheses because of the multiple condensation steps in organic solvents. That is, the current synthesis method is not environmentally friendly. From the viewpoint of green sustainable chemistry, we focused on developing an organic solvent-free synthetic method using water, an environmentally friendly solvent. Here we described in-water synthesis technology using water-dispersible protected amino acids. PMID:21867548

  5. A survey of aftbody flow prediction methods

    NASA Technical Reports Server (NTRS)

    Putnam, L. E.; Mace, J.

    1981-01-01

    A survey of computational methods used in the calculation of nozzle aftbody flows is presented. One class of methods reviewed are those which patch together solutions for the inviscid, boundary layer, and plume flow regions. The second class of methods reviewed are those which computationally solve the Navier Stokes equations over nozzle aftbodies with jet exhaust flow. Computed results from the methods are compared with experiment. Advantages and disadvantages of the various methods are discussed along with opportunities for further development of these methods.

  6. IUS solid rocket motor contamination prediction methods

    NASA Technical Reports Server (NTRS)

    Mullen, C. R.; Kearnes, J. H.

    1980-01-01

    A series of computer codes were developed to predict solid rocket motor produced contamination to spacecraft sensitive surfaces. Subscale and flight test data have confirmed some of the analytical results. Application of the analysis tools to a typical spacecraft has provided early identification of potential spacecraft contamination problems and provided insight into their solution; e.g., flight plan modifications, plume or outgassing shields and/or contamination covers.

  7. Echo state network prediction method and its application in flue gas turbine condition prediction

    NASA Astrophysics Data System (ADS)

    Wang, Shaohong; Chen, Tao; Xu, Xiaoli

    2010-12-01

    On the background of the complex production process of fluid catalytic cracking energy recovery system in large-scale petrochemical refineries, this paper introduced an improved echo state network (ESN) model prediction method which is used to address the condition trend prediction problem of the key power equipment--flue gas turbine. Singular value decomposition method was used to obtain the ESN output weight. Through selecting the appropriate parameters and discarding small singular value, this method overcame the defective solution problem in the prediction by using the linear regression algorithm, improved the prediction performance of echo state network, and gave the network prediction process. In order to solve the problem of noise contained in production data, the translation-invariant wavelet transform analysis method is combined to denoise the noisy time series before prediction. Condition trend prediction results show the effectiveness of the proposed method.

  8. Evaluation of a generalized use of the log Sum(k+1)AA descriptor in a QSRR model to predict peptide retention on RPLC systems.

    PubMed

    Bodzioch, Karolina; Dejaegher, Bieke; Baczek, Tomasz; Kaliszan, Roman; Vander Heyden, Yvan

    2009-06-01

    At the current state of knowledge, the rational optimization of the chromatographic separation of peptides, as well as the identification of proteins in proteomics are challenges for analytical chemists. In this paper the generalized applicability of a recently derived descriptor log Sum(k+1)AA in a QSRR equation to model peptide retention in RP-LC systems was evaluated. For that purpose, two sets of peptides analyzed on dissimilar RP-LC systems were considered. A first set of 28 peptides was measured on 17 columns/systems, while a second of 70 peptides was eluted on four. The aim of this work was to confirm the usefulness of the partly experimental log Sum(k+1)AA descriptor for the prediction of peptides retention compared to the initially applied, fully experimental log SumAA descriptor. The verification of the predictive abilities of both QSRR models, applying either the initial or the alternative descriptor, was done by using the leave-one-out and leave-three-out cross-validation procedures. The results seem to demonstrate that the QSRR model with log Sum(k+1)AA, for which the retention measurement of only seven out of 20 existing amino acids is necessary, possesses similar or in some cases even better predictive abilities than that containing log SumAA. PMID:19479750

  9. Peptide-based methods for assembling and controlling the morphologies, metrics, and properties of gold nanoparticle superstructures

    NASA Astrophysics Data System (ADS)

    Zhang, Chen

    This dissertation describes new peptide-based methods for assembling and controlling the morphologies, metrics, and properties of gold nanoparticle superstructures. The aim of this research is to develop the peptide-based method by modifying the peptide sequences and controlling the reaction conditions for the synthesis and assembly of gold nanoparticle superstructures to achieve reliable control over their morphology and metrics, and furthermore study their properties and applications. With this goal in mind, the C-terminus of a gold-binding peptide was modified with different numbers of hydrophobic phenylalanines to affect peptide assembly and ultimately nanoparticle assembly. The addition of hydrophobic phenylalanines to the C-terminus of peptide conjugates promoted fiber bundling which in turn lead to the formation of thick or intertwined 1-D nanoparticle superstructures. Furthermore, I prepared spherical gold nanoparticle superstructures with varied diameters (˜40nm, ˜75nm, and ˜150nm) and visible to near-infrared optical properties by using a single peptide conjugate molecule yet varied reaction conditions. Theoretical simulation and experiment were coupled to further understand their optical properties. Finally, I studied and demonstrated the drug storage and release properties of hollow spherical gold nanoparticle superstructures; this was the first demonstrated application of this class of nanoparticle superstructure.

  10. Gaussian mixture models as flux prediction method for central receivers

    NASA Astrophysics Data System (ADS)

    Grobler, Annemarie; Gauché, Paul; Smit, Willie

    2016-05-01

    Flux prediction methods are crucial to the design and operation of central receiver systems. Current methods such as the circular and elliptical (bivariate) Gaussian prediction methods are often used in field layout design and aiming strategies. For experimental or small central receiver systems, the flux profile of a single heliostat often deviates significantly from the circular and elliptical Gaussian models. Therefore a novel method of flux prediction was developed by incorporating the fitting of Gaussian mixture models onto flux profiles produced by flux measurement or ray tracing. A method was also developed to predict the Gaussian mixture model parameters of a single heliostat for a given time using image processing. Recording the predicted parameters in a database ensures that more accurate predictions are made in a shorter time frame.

  11. Application of Hybrid Method for Aerodynamic Noise Prediction

    NASA Astrophysics Data System (ADS)

    Yu, L.; Song, W. P.

    2011-09-01

    A hybrid prediction method for aerodynamic noise is performed using high order accuracy method in this paper. The method combines a two-dimensional Unsteady Reynolds-Averaged Navier-Stokes(URANS) solver with the acoustic analogy method using Ffowcs Williams-Hawkings equation with penetrable data surface (FW-Hpds). Tandem cylinders are chosen to validate the prediction method. The computations are conducted at a Reynolds number of 1.66 × 105 based on the cylinder diameter. Both the aerodynamic and acoustic results show good agreement with the experimental data, showing a successful application of the hybrid prediction method using two-dimensional URANS simulation.

  12. A survey of the broadband shock associated noise prediction methods

    NASA Technical Reports Server (NTRS)

    Kim, Chan M.; Krejsa, Eugene A.; Khavaran, Abbas

    1992-01-01

    Several different prediction methods to estimate the broadband shock associated noise of a supersonic jet are introduced and compared with experimental data at various test conditions. The nozzle geometries considered for comparison include a convergent and a convergent-divergent nozzle, both axisymmetric. Capabilities and limitations of prediction methods in incorporating the two nozzle geometries, flight effect, and temperature effect are discussed. Predicted noise field shows the best agreement for a convergent nozzle geometry under static conditions. Predicted results for nozzles in flight show larger discrepancies from data and more dependable flight data are required for further comparison. Qualitative effects of jet temperature, as observed in experiment, are reproduced in predicted results.

  13. Adaptive method for electron bunch profile prediction

    NASA Astrophysics Data System (ADS)

    Scheinker, Alexander; Gessner, Spencer

    2015-10-01

    We report on an experiment performed at the Facility for Advanced Accelerator Experimental Tests (FACET) at SLAC National Accelerator Laboratory, in which a new adaptive control algorithm, one with known, bounded update rates, despite operating on analytically unknown cost functions, was utilized in order to provide quasi-real-time bunch property estimates of the electron beam. Multiple parameters, such as arbitrary rf phase settings and other time-varying accelerator properties, were simultaneously tuned in order to match a simulated bunch energy spectrum with a measured energy spectrum. The simple adaptive scheme was digitally implemented using matlab and the experimental physics and industrial control system. The main result is a nonintrusive, nondestructive, real-time diagnostic scheme for prediction of bunch profiles, as well as other beam parameters, the precise control of which are important for the plasma wakefield acceleration experiments being explored at FACET.

  14. Adaptive method for electron bunch profile prediction

    SciTech Connect

    Scheinker, Alexander; Gessner, Spencer

    2015-10-01

    We report on an experiment performed at the Facility for Advanced Accelerator Experimental Tests (FACET) at SLAC National Accelerator Laboratory, in which a new adaptive control algorithm, one with known, bounded update rates, despite operating on analytically unknown cost functions, was utilized in order to provide quasi-real-time bunch property estimates of the electron beam. Multiple parameters, such as arbitrary rf phase settings and other time-varying accelerator properties, were simultaneously tuned in order to match a simulated bunch energy spectrum with a measured energy spectrum. The simple adaptive scheme was digitally implemented using matlab and the experimental physics and industrial control system. The main result is a nonintrusive, nondestructive, real-time diagnostic scheme for prediction of bunch profiles, as well as other beam parameters, the precise control of which are important for the plasma wakefield acceleration experiments being explored at FACET. © 2015 authors. Published by the American Physical Society.

  15. Screening and selection of synthetic peptides for a novel and optimized endotoxin detection method.

    PubMed

    Mujika, M; Zuzuarregui, A; Sánchez-Gómez, S; Martínez de Tejada, G; Arana, S; Pérez-Lorenzo, E

    2014-09-30

    The current validated endotoxin detection methods, in spite of being highly sensitive, present several drawbacks in terms of reproducibility, handling and cost. Therefore novel approaches are being carried out in the scientific community to overcome these difficulties. Remarkable efforts are focused on the development of endotoxin-specific biosensors. The key feature of these solutions relies on the proper definition of the capture protocol, especially of the bio-receptor or ligand. The aim of the presented work is the screening and selection of a synthetic peptide specifically designed for LPS detection, as well as the optimization of a procedure for its immobilization onto gold substrates for further application to biosensors. PMID:25034430

  16. Simulation of peptide folding with explicit water--a mean solvation method.

    PubMed

    Wu, X W; Sung, S S

    1999-02-15

    A new approach to efficiently calculate solvent effect in computer simulation of macromolecular systems has been developed. Explicit solvent molecules are included in the simulation to provide a mean solvation force for the solute conformational search. Simulations of an alanine dipeptide in aqueous solution showed that the new approach is significantly more efficient than conventional molecular dynamics method in conformational search, mainly because the mean solvation force reduced the solvent damping effect. This approach allows the solute and solvent to be simulated separately with different methods. For the macromolecule, the rigid fragment constraint dynamics method we developed previously allows large time-steps. For the solvent, a combination of a modified force-bias Monte Carlo method and a preferential sampling can efficiently sample the conformational space. A folding simulation of a 16-residue peptide in water showed high efficiency of the new approach. PMID:10024017

  17. Method for Predicting Which Customers' Time Deposit Balances Will Increase

    NASA Astrophysics Data System (ADS)

    Ono, Toshiyuki; Yoshikawa, Hiroshi; Morita, Masahiro; Komoda, Norihisa

    This paper proposes a method of predicting which customers' account balances will increase by using data mining to effectively and efficiently promote sales. Prediction by mining all the data in a business is difficult because of much time required to collect, process, and calculate it. The selection of which features are used for prediction is a critical issue. We propose a method of selecting features to improve the accuracy of prediction within practical time limits. It consists of three parts: (1) converting collected features into financial behavior features that reflect customer actions, (2) extracting features affecting increases in account balances from these collected and financial behavior features, and (3) predicting customers whose account balances will increase based on the extracted features. We found the accuracy of prediction in an experiment with our method to be higher than with other conventional methods.

  18. Efficient Methods to Compute Genomic Predictions

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Efficient methods for processing genomic data were developed to increase reliability of estimated breeding values and simultaneously estimate thousands of marker effects. Algorithms were derived and computer programs tested on simulated data for 50,000 markers and 2,967 bulls. Accurate estimates of ...

  19. Peptide arrays for screening cancer specific peptides.

    PubMed

    Ahmed, Sahar; Mathews, Anu Stella; Byeon, Nara; Lavasanifar, Afsaneh; Kaur, Kamaljit

    2010-09-15

    In this paper, we describe a novel method to screen peptides for specific recognition by cancer cells. Seventy peptides were synthesized on a cellulose membrane in an array format, and a direct method to study the peptide-whole cell interaction was developed. The relative binding affinity of the cells for different peptides with respect to a lead 12-mer p160 peptide, identified by phage display, was evaluated using the CyQUANT fluorescence of the bound cells. Screening allowed identification of at least five new peptides that displayed higher affinity (up to 3-fold) for MDA-MB-435 and MCF-7 human cancer cells compared to the p160 peptide. These peptides showed very little binding to the control (noncancerous) human umbilical vein endothelial cells (HUVECs). Three of these peptides were synthesized separately and labeled with fluorescein isothiocyanate (FITC) to study their uptake and interaction with the cancer and control cells using confocal laser scanning microscopy and flow cytometry. The results confirmed the high and specific affinity of an 11-mer peptide 11 (RGDPAYQGRFL) and a 10-mer peptide 18 (WXEAAYQRFL) for the cancer cells versus HUVECs. Peptide 11 binds different receptors on target cancer cells as its sequence contains multiple recognition motifs, whereas peptide 18 binds mainly to the putative p160 receptor. The peptide array-whole cell binding assay reported here is a complementary method to phage display for further screening and optimization of cancer targeting peptides for cancer therapy and diagnosis. PMID:20799711

  20. Statistical energy analysis response prediction methods for structural systems

    NASA Technical Reports Server (NTRS)

    Davis, R. F.

    1979-01-01

    The results of an effort to document methods for accomplishing response predictions for commonly encountered aerospace structural configurations is presented. Application of these methods to specified aerospace structure to provide sample analyses is included. An applications manual, with the structural analyses appended as example problems is given. Comparisons of the response predictions with measured data are provided for three of the example problems.

  1. A Versatile Nonlinear Method for Predictive Modeling

    NASA Technical Reports Server (NTRS)

    Liou, Meng-Sing; Yao, Weigang

    2015-01-01

    As computational fluid dynamics techniques and tools become widely accepted for realworld practice today, it is intriguing to ask: what areas can it be utilized to its potential in the future. Some promising areas include design optimization and exploration of fluid dynamics phenomena (the concept of numerical wind tunnel), in which both have the common feature where some parameters are varied repeatedly and the computation can be costly. We are especially interested in the need for an accurate and efficient approach for handling these applications: (1) capturing complex nonlinear dynamics inherent in a system under consideration and (2) versatility (robustness) to encompass a range of parametric variations. In our previous paper, we proposed to use first-order Taylor expansion collected at numerous sampling points along a trajectory and assembled together via nonlinear weighting functions. The validity and performance of this approach was demonstrated for a number of problems with a vastly different input functions. In this study, we are especially interested in enhancing the method's accuracy; we extend it to include the second-orer Taylor expansion, which however requires a complicated evaluation of Hessian matrices for a system of equations, like in fluid dynamics. We propose a method to avoid these Hessian matrices, while maintaining the accuracy. Results based on the method are presented to confirm its validity.

  2. In vitro susceptibility tests for cationic peptides: comparison of broth microdilution methods for bacteria that grow aerobically.

    PubMed

    Giacometti, A; Cirioni, O; Barchiesi, F; Del Prete, M S; Fortuna, M; Caselli, F; Scalise, G

    2000-06-01

    The in vitro susceptibilities of 90 clinical isolates of gram-positive and gram-negative aerobic bacteria to six cationic peptides, buforin II, cecropin P1, indolicidin, magainin II, nisin, and ranalexin, were evaluated by two broth microdilution methods. The first method was performed according to the procedures outlined by the National Committee for Clinical Laboratory Standards for bacteria that grow aerobically, while the second was performed according to the procedures recently proposed by the R. E. W. Hancock laboratory for testing antimicrobial peptides. Overall, the first method produced MICs two- and fourfold higher than the second method. PMID:10817731

  3. The predicted N-terminal signal sequence of the human α₂C-adrenoceptor does not act as a functional cleavable signal peptide.

    PubMed

    Jahnsen, Jan Anker; Uhlén, Staffan

    2012-06-01

    The N-terminal region of the human α(2C)-adrenoceptor has a 22 amino acid sequence MASPALAAALAVAAAAGPNASG. This stretch is predicted to be a cleavable signal peptide. Signal peptides facilitate the translocation of membrane proteins from ribosomes into the endoplasmatic reticulum (ER) for further transport to the plasma membrane. However, recently it has been suggested that the hydrophobic stretch ALAAALAAAAA in the N-tail of the rat α(2C)-adrenoceptor, rather than being part of a signal peptide, is an ER retention signal (Angelotti, 2010). Here, we have investigated the functionality of the N-terminal region of the human α(2C)-adrenoceptor further. The predicted signal peptide was found to be non-cleavable, as shown for a modified α(2C)-adrenoceptor construct equipped with a FLAG epitope. The influence of the N-terminal region on receptor translocation to the plasma membrane was investigated by rebuilding the N-tail and then by analyzing the expression level of binding-competent receptors in transfected COS-7 cell membranes. Truncated α(2C)-adrenoceptor constructs showed decreased expression levels as compared to the wild type α(2C)-adrenoceptor. Addition of, or exchange for, the influenza virus hemagglutinin signal peptide to the α(2C)-adrenoceptor had no effect, respectively decreased, the expression level of binding-competent receptor in the membranes. Our analysis supports the conclusions that the predicted signal peptide in the N-terminal tail of the α(2C)-adrenoceptor does not act as a cleavable signal peptide. In addition, the results indicate that the presence of an intact N-tail is augmenting the amount of binding-competent α(2C)-adrenoceptors at the cell surface. PMID:22503931

  4. Computational methods in sequence and structure prediction

    NASA Astrophysics Data System (ADS)

    Lang, Caiyi

    This dissertation is organized into two parts. In the first part, we will discuss three computational methods for cis-regulatory element recognition in three different gene regulatory networks as the following: (a) Using a comprehensive "Phylogenetic Footprinting Comparison" method, we will investigate the promoter sequence structures of three enzymes (PAL, CHS and DFR) that catalyze sequential steps in the pathway from phenylalanine to anthocyanins in plants. Our result shows there exists a putative cis-regulatory element "AC(C/G)TAC(C)" in the upstream of these enzyme genes. We propose this cis-regulatory element to be responsible for the genetic regulation of these three enzymes and this element, might also be the binding site for MYB class transcription factor PAP1. (b) We will investigate the role of the Arabidopsis gene glutamate receptor 1.1 (AtGLR1.1) in C and N metabolism by utilizing the microarray data we obtained from AtGLR1.1 deficient lines (antiAtGLR1.1). We focus our investigation on the putatively co-regulated transcript profile of 876 genes we have collected in antiAtGLR1.1 lines. By (a) scanning the occurrence of several groups of known abscisic acid (ABA) related cisregulatory elements in the upstream regions of 876 Arabidopsis genes; and (b) exhaustive scanning of all possible 6-10 bps motif occurrence in the upstream regions of the same set of genes, we are able to make a quantative estimation on the enrichment level of each of the cis-regulatory element candidates. We finally conclude that one specific cis-regulatory element group, called "ABRE" elements, are statistically highly enriched within the 876-gene group as compared to their occurrence within the genome. (c) We will introduce a new general purpose algorithm, called "fuzzy REDUCE1", which we have developed recently for automated cis-regulatory element identification. In the second part, we will discuss our newly devised protein design framework. With this framework we have developed

  5. A general method for making peptide therapeutics resistant to serine protease degradation: application to dipeptidyl peptidase IV substrates.

    PubMed

    Heard, Kathryn R; Wu, Wengen; Li, Youhua; Zhao, Peng; Woznica, Iwona; Lai, Jack H; Beinborn, Martin; Sanford, David G; Dimare, Matthew T; Chiluwal, Amrita K; Peters, Diane E; Whicher, Danielle; Sudmeier, James L; Bachovchin, William W

    2013-11-14

    Bioactive peptides have evolved to optimally fulfill specific biological functions, a fact which has long attracted attention for their use as therapeutic agents. While there have been some recent commercial successes fostered in part by advances in large-scale peptide synthesis, development of peptides as therapeutic agents has been significantly impeded by their inherent susceptibility to protease degradation in the bloodstream. Here we report that incorporation of specially designed amino acid analogues at the P1' position, directly C-terminal of the enzyme cleavage site, renders peptides, including glucagon-like peptide-1 (7-36) amide (GLP-1) and six other examples, highly resistant to serine protease degradation without significant alteration of their biological activity. We demonstrate the applicability of the method to a variety of proteases, including dipeptidyl peptidase IV (DPP IV), dipeptidyl peptidase 8 (DPP8), fibroblast activation protein α (FAPα), α-lytic protease (αLP), trypsin, and chymotrypsin. In summary, the "P1' modification" represents a simple, general, and highly adaptable method of generating enzymatically stable peptide-based therapeutics. PMID:24044354

  6. A survey of computational methods and error rate estimation procedures for peptide and protein identification in shotgun proteomics

    PubMed Central

    Nesvizhskii, Alexey I.

    2010-01-01

    This manuscript provides a comprehensive review of the peptide and protein identification process using tandem mass spectrometry (MS/MS) data generated in shotgun proteomic experiments. The commonly used methods for assigning peptide sequences to MS/MS spectra are critically discussed and compared, from basic strategies to advanced multi-stage approaches. A particular attention is paid to the problem of false-positive identifications. Existing statistical approaches for assessing the significance of peptide to spectrum matches are surveyed, ranging from single-spectrum approaches such as expectation values to global error rate estimation procedures such as false discovery rates and posterior probabilities. The importance of using auxiliary discriminant information (mass accuracy, peptide separation coordinates, digestion properties, and etc.) is discussed, and advanced computational approaches for joint modeling of multiple sources of information are presented. This review also includes a detailed analysis of the issues affecting the interpretation of data at the protein level, including the amplification of error rates when going from peptide to protein level, and the ambiguities in inferring the identifies of sample proteins in the presence of shared peptides. Commonly used methods for computing protein-level confidence scores are discussed in detail. The review concludes with a discussion of several outstanding computational issues. PMID:20816881

  7. Identification of measles virus epitopes using an ultra-fast method of panning phage-displayed random peptide libraries

    PubMed Central

    Yu, Xiaoli; Barmina, Olga; Burgoon, Mark; Gilden, Don

    2010-01-01

    Phage-displayed random peptide libraries, in which high affinity phage peptides are enriched by repetitive selection (panning) on target antibody, provide a unique tool for identifying antigen specificity. This paper describes a new panning method that enables selection of peptides in 1 day as compared to about 6 days required in traditional panning to identify virus-specific epitopes. The method, termed ultra-fast selection of peptide (UFSP), utilizes phage produced by bacterial infection (phage amplification) directly for subsequent panning. Phage amplified in less than 1 h of infection in Escherichia coli are used for binding to target antibody pre-coated in the same wells of an ELISA plate, obviating the need for traditional large-scale amplification and purification. Importantly, phage elution at 37 °C was superior to that at room temperature, and phage amplification in a 150-μl volume of E. coli cells was superior to that in 250-μl volume. Application of UFSP to two monoclonal antibodies generated from clonally expanded plasma cells in subacute sclerosing panencephalitis (SSPE) brain identified high-affinity measles virus-specific-peptide epitopes. The UFSP panning methodology will expedite identification of peptides reacting with antibodies generated in other diseases of unknown antigenic specificity such as multiple sclerosis (MS), sarcoidosis and Behcet’s disease. PMID:19095007

  8. Identification of measles virus epitopes using an ultra-fast method of panning phage-displayed random peptide libraries.

    PubMed

    Yu, Xiaoli; Barmina, Olga; Burgoon, Mark; Gilden, Don

    2009-03-01

    Phage-displayed random peptide libraries, in which high affinity phage peptides are enriched by repetitive selection (panning) on target antibody, provide a unique tool for identifying antigen specificity. This paper describes a new panning method that enables selection of peptides in 1 day as compared to about 6 days required in traditional panning to identify virus-specific epitopes. The method, termed ultra-fast selection of peptide (UFSP), utilizes phage produced by bacterial infection (phage amplification) directly for subsequent panning. Phage amplified in less than 1h of infection in Escherichia coli are used for binding to target antibody pre-coated in the same wells of an ELISA plate, obviating the need for traditional large-scale amplification and purification. Importantly, phage elution at 37 degrees C was superior to that at room temperature, and phage amplification in a 150-microl volume of E. coli cells was superior to that in 250-microl volume. Application of UFSP to two monoclonal antibodies generated from clonally expanded plasma cells in subacute sclerosing panencephalitis (SSPE) brain identified high-affinity measles virus-specific-peptide epitopes. The UFSP panning methodology will expedite identification of peptides reacting with antibodies generated in other diseases of unknown antigenic specificity such as multiple sclerosis (MS), sarcoidosis and Behcet's disease. PMID:19095007

  9. Preliminary thoughts on helicopter cabin noise prediction methods

    NASA Astrophysics Data System (ADS)

    Pollard, J. S.

    The problems of predicting helicopter cabin noise are discussed with particular reference to the Lynx helicopter. Available methods such as modal analysis adopted for propeller noise prediction do not cope with the higher frequency discrete tone content of helicopter gear noise, with the airborne and structureborne noise contributions. Statistical energy analysis methods may be the answer but until these are developed, one has to rely on classical noise transmission analysis and transfer function methods.

  10. Critical evaluation of in silico methods for prediction of coiled-coil domains in proteins.

    PubMed

    Li, Chen; Ching Han Chang, Catherine; Nagel, Jeremy; Porebski, Benjamin T; Hayashida, Morihiro; Akutsu, Tatsuya; Song, Jiangning; Buckle, Ashley M

    2016-03-01

    Coiled-coils refer to a bundle of helices coiled together like strands of a rope. It has been estimated that nearly 3% of protein-encoding regions of genes harbour coiled-coil domains (CCDs). Experimental studies have confirmed that CCDs play a fundamental role in subcellular infrastructure and controlling trafficking of eukaryotic cells. Given the importance of coiled-coils, multiple bioinformatics tools have been developed to facilitate the systematic and high-throughput prediction of CCDs in proteins. In this article, we review and compare 12 sequence-based bioinformatics approaches and tools for coiled-coil prediction. These approaches can be categorized into two classes: coiled-coil detection and coiled-coil oligomeric state prediction. We evaluated and compared these methods in terms of their input/output, algorithm, prediction performance, validation methods and software utility. All the independent testing data sets are available at http://lightning.med.monash.edu/coiledcoil/. In addition, we conducted a case study of nine human polyglutamine (PolyQ) disease-related proteins and predicted CCDs and oligomeric states using various predictors. Prediction results for CCDs were highly variable among different predictors. Only two peptides from two proteins were confirmed to be CCDs by majority voting. Both domains were predicted to form dimeric coiled-coils using oligomeric state prediction. We anticipate that this comprehensive analysis will be an insightful resource for structural biologists with limited prior experience in bioinformatics tools, and for bioinformaticians who are interested in designing novel approaches for coiled-coil and its oligomeric state prediction. PMID:26177815