Quantitative prediction of mouse class I MHC peptide binding affinity using support vector machine regression (SVR) models

BACKGROUND: The binding between peptide epitopes and major histocompatibility complex proteins (MHCs) is an important event in the cellular immune response. Accurate prediction of the binding between short peptides and the MHC molecules has long been a principal challenge for immunoinformatics. Recently, the modeling of MHC-peptide binding has come to emphasize quantitative predictions: instead of categorizing peptides as "binders" or "non-binders" or as "strong binders" and "weak binders", recent methods seek to make predictions about precise binding affinities. RESULTS: We developed a quantitative support vector machine regression (SVR) approach, called SVRMHC, to model peptide-MHC binding affinities. As a non-linear method, SVRMHC was able to generate models that out-performed existing linear models, such as the "additive method". By adopting a new "11-factor encoding" scheme, SVRMHC takes into account similarities in the physicochemical properties of the amino acids constituting the input peptides. When applied to MHC-peptide binding data for three mouse class I MHC alleles, the SVRMHC models produced more accurate predictions than those produced previously. Furthermore, comparisons based on Receiver Operating Characteristic (ROC) analysis indicated that SVRMHC was able to out-perform several prominent methods in identifying strongly binding peptides. CONCLUSION: As a method with demonstrated performance in the quantitative modeling of MHC-peptide binding and in identifying strong binders, SVRMHC is a promising immunoinformatics tool with not inconsiderable future potential

SVRMHC prediction server for MHC-binding peptides

BACKGROUND: The binding between antigenic peptides (epitopes) and the MHC molecule is a key step in the cellular immune response. Accurate in silico prediction of epitope-MHC binding affinity can greatly expedite epitope screening by reducing costs and experimental effort. RESULTS: Recently, we demonstrated the appealing performance of SVRMHC, an SVR-based quantitative modeling method for peptide-MHC interactions, when applied to three mouse class I MHC molecules. Subsequently, we have greatly extended the construction of SVRMHC models and have established such models for more than 40 class I and class II MHC molecules. Here we present the SVRMHC web server for predicting peptide-MHC binding affinities using these models. Benchmarked percentile scores are provided for all predictions. The larger number of SVRMHC models available allowed for an updated evaluation of the performance of the SVRMHC method compared to other well- known linear modeling methods. CONCLUSION: SVRMHC is an accurate and easy-to-use prediction server for epitope-MHC binding with significant coverage of MHC molecules. We believe it will prove to be a valuable resource for T cell epitope researchers

Predicting Class II MHC-Peptide binding: a kernel based approach using similarity scores

BACKGROUND: Modelling the interaction between potentially antigenic peptides and Major Histocompatibility Complex (MHC) molecules is a key step in identifying potential T-cell epitopes. For Class II MHC alleles, the binding groove is open at both ends, causing ambiguity in the positional alignment between the groove and peptide, as well as creating uncertainty as to what parts of the peptide interact with the MHC. Moreover, the antigenic peptides have variable lengths, making naive modelling methods difficult to apply. This paper introduces a kernel method that can handle variable length peptides effectively by quantifying similarities between peptide sequences and integrating these into the kernel. RESULTS: The kernel approach presented here shows increased prediction accuracy with a significantly higher number of true positives and negatives on multiple MHC class II alleles, when testing data sets from MHCPEP [1], MCHBN [2], and MHCBench [3]. Evaluation by cross validation, when segregating binders and non-binders, produced an average of 0.824 A(ROC )for the MHCBench data sets (up from 0.756), and an average of 0.96 A(ROC )for multiple alleles of the MHCPEP database. CONCLUSION: The method improves performance over existing state-of-the-art methods of MHC class II peptide binding predictions by using a custom, knowledge-based representation of peptides. Similarity scores, in contrast to a fixed-length, pocket-specific representation of amino acids, provide a flexible and powerful way of modelling MHC binding, and can easily be applied to other dynamic sequence problems

POPISK: T-cell reactivity prediction using support vector machines and string kernels

BACKGROUND: Accurate prediction of peptide immunogenicity and characterization of relation between peptide sequences and peptide immunogenicity will be greatly helpful for vaccine designs and understanding of the immune system. In contrast to the prediction of antigen processing and presentation pathway, the prediction of subsequent T-cell reactivity is a much harder topic. Previous studies of identifying T-cell receptor (TCR) recognition positions were based on small-scale analyses using only a few peptides and concluded different recognition positions such as positions 4, 6 and 8 of peptides with length 9. Large-scale analyses are necessary to better characterize the effect of peptide sequence variations on T-cell reactivity and design predictors of a peptide's T-cell reactivity (and thus immunogenicity). The identification and characterization of important positions influencing T-cell reactivity will provide insights into the underlying mechanism of immunogenicity. RESULTS: This work establishes a large dataset by collecting immunogenicity data from three major immunology databases. In order to consider the effect of MHC restriction, peptides are classified by their associated MHC alleles. Subsequently, a computational method (named POPISK) using support vector machine with a weighted degree string kernel is proposed to predict T-cell reactivity and identify important recognition positions. POPISK yields a mean 10-fold cross-validation accuracy of 68% in predicting T-cell reactivity of HLA-A2-binding peptides. POPISK is capable of predicting immunogenicity with scores that can also correctly predict the change in T-cell reactivity related to point mutations in epitopes reported in previous studies using crystal structures. Thorough analyses of the prediction results identify the important positions 4, 6, 8 and 9, and yield insights into the molecular basis for TCR recognition. Finally, we relate this finding to physicochemical properties and structural features of the MHC-peptide-TCR interaction. CONCLUSIONS: A computational method POPISK is proposed to predict immunogenicity with scores which are useful for predicting immunogenicity changes made by single-residue modifications. The web server of POPISK is freely available at http://iclab.life.nctu.edu.tw/POPISK

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

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

Overlapping Clusters and Support Vector Machines Based Interval Type-2 Fuzzy System for the Prediction of Peptide Binding Affinity

In the post-genome era, it is becoming more complex to process high dimensional, low-instance available, and nonlinear biological datasets. This paper aims to address these characteristics as they have adverse effects on the performance of predictive models in bioinformatics. In this paper, an interval type-2 Takagi Sugeno fuzzy predictive model is proposed in order to manage high-dimensionality and nonlinearity of such datasets which is the common feature in bioinformatics. A new clustering framework is proposed for this purpose to simplify antecedent operations for an interval type-2 fuzzy system. This new clustering framework is based on overlapping regions between the clusters. The cluster analysis of partitions and statistical information derived from them has identified the upper and lower membership functions forming the premise part. This is further enhanced by adapting the regression version of support vector machines in the consequent part. The proposed method is used in experiments to quantitatively predict affinities of peptide bindings to biomolecules. This case study imposes a challenge in post-genome studies and remains an open problem due to the complexity of the biological system, diversity of peptides, and curse of dimensionality of amino acid index representation characterizing the peptides. Utilizing four different peptide binding affinity datasets, the proposed method resulted in better generalization ability for all of them yielding an improved prediction accuracy of up to 58.2% on unseen peptides in comparison with the predictive methods presented in the literature. Source code of the algorithm is available at https://github.com/sekerbigdatalab

Using epitope predictions to evaluate efficacy and population coverage of the Mtb72f vaccine for tuberculosis

<p>Abstract</p> <p>Background</p> <p>The Mtb72f subunit vaccine for tuberculosis, currently in clinical trials, is hoped to provide improved protection compared to the current BCG vaccine. It is not clear, however, whether Mtb72f would be equally protective in the different human populations suffering from a high burden of tuberculosis. Previous work by Hebert and colleagues demonstrated that the PPE18 protein of Mtb72f had significant variability in a sample of clinical <it>M. tuberculosis </it>isolates. However, whether this variation might impact the efficacy of Mtb72f in the context of the microbial and host immune system interactions remained to be determined. The present study assesses Mtb72f's predicted efficacy in people with different DRB1 genotypes to predict whether the vaccine will protect against diverse clinical strains of <it>M. tuberculosis </it>in a diverse host population.</p> <p>Results</p> <p>We evaluated the binding of epitopes in the vaccine to different alleles of the human DRB1 Class II MHC protein using freely available epitope prediction programs and compared protein sequences from clinical isolates to the sequences included in the Mtb72f vaccine. This analysis predicted that the Mtb72f vaccine would be less effective for several DRB1 genotypes, due either to limited vaccine epitope binding to the DRB1 proteins or to binding primarily by unconserved PPE18 epitopes. Furthermore, we found that these less-protective DRB1 alleles are found at a very high frequency in several populations with a high burden of tuberculosis.</p> <p>Conclusion</p> <p>Although the Mtb72f vaccine candidate has shown promise in animal and clinical trials thus far, it may not be optimally effective in some genotypic backgrounds. Due to variation in both <it>M. tuberculosis </it>protein sequences and epitope-binding capabilities of different HLA alleles, certain human populations with a high burden of tuberculosis may not be optimally protected by the Mtb72f vaccine. The efficacy of the Mtb72f vaccine should be further examined in these particular populations to determine whether additional protective measures might be necessary for these regions.</p

Prodepth: Predict Residue Depth by Support Vector Regression Approach from Protein Sequences Only

Residue depth (RD) is a solvent exposure measure that complements the information provided by conventional accessible surface area (ASA) and describes to what extent a residue is buried in the protein structure space. Previous studies have established that RD is correlated with several protein properties, such as protein stability, residue conservation and amino acid types. Accurate prediction of RD has many potentially important applications in the field of structural bioinformatics, for example, facilitating the identification of functionally important residues, or residues in the folding nucleus, or enzyme active sites from sequence information. In this work, we introduce an efficient approach that uses support vector regression to quantify the relationship between RD and protein sequence. We systematically investigated eight different sequence encoding schemes including both local and global sequence characteristics and examined their respective prediction performances. For the objective evaluation of our approach, we used 5-fold cross-validation to assess the prediction accuracies and showed that the overall best performance could be achieved with a correlation coefficient (CC) of 0.71 between the observed and predicted RD values and a root mean square error (RMSE) of 1.74, after incorporating the relevant multiple sequence features. The results suggest that residue depth could be reliably predicted solely from protein primary sequences: local sequence environments are the major determinants, while global sequence features could influence the prediction performance marginally. We highlight two examples as a comparison in order to illustrate the applicability of this approach. We also discuss the potential implications of this new structural parameter in the field of protein structure prediction and homology modeling. This method might prove to be a powerful tool for sequence analysis

Prediction of MHC-peptide binding: a systematic and comprehensive overview

T cell immune responses are driven by the recognition of peptide antigens (T cell epitopes) that are bound to major histocompatibility complex (MHC) molecules. T cell epitope immunogenicity is thus contingent on several events, including appropriate and effective processing of the peptide from its protein source, stable peptide binding to the MHC molecule, and recognition of the MHC-bound peptide by the T cell receptor. Of these three hallmarks, MHC-peptide binding is the most selective event that determines T cell epitopes. Therefore, prediction of MHC-peptide binding constitutes the principal basis for anticipating potential T cell epitopes. The tremendous relevance of epitope identification in vaccine design and in the monitoring of T cell responses has spurred the development of many computational methods for predicting MHC-peptide binding that improve the efficiency and economics of T cell epitope identification. In this report, we will systematically examine the available methods for predicting MHC-peptide binding and discuss their most relevant advantages and drawbacks