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Title
Abstract
Introduction Introduction
Binding Motif Based Methods
Quantitative Matrix Based Methods
Virtual Matrix Based Methods
Neural Network Based Methods
Other Methods
Knowledge Based Free Energy Scoring Methods
Conclusion
References
All Topics
Biology
Immunology

MHC-Peptide binding prediction for epitope based vaccine design

Profile image of Meena SakharkarMeena Sakharkar

2007, … Journal of Integrative …

Abstract

The identification of MHC binding peptides for consideration as potential T-cell epitopes has application in peptide vaccine design and immunotherapy. Determination of MHCp binding using experimental measures is time-consuming and expensive. Therefore, efficient prediction models are required that facilitate systematic computational scanning of microbial genome for candidate T cell epitopes. These prediction models are either sequence or 3D structure based. This review provides a comparative analysis on the available prediction models with specific emphasis on the salient features of each model, its prediction efficiency, and merits and demerits.

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Motivation: Both modeling of antigen-processing pathway including major histocompatibility complex (MHC) binding and immunogenicity prediction of those MHC-binding peptides are essential to develop a computer-aided system of peptide-based vaccine design that is one goal of immunoinformatics. Numerous studies have dealt with modeling the immunogenic pathway but not the intractable problem of immunogenicity prediction due to complex effects of many intrinsic and extrinsic factors. Moderate affinity of the MHC–peptide complex is essential to induce immune responses, but the relationship between the affinity and peptide immunogenicity is too weak to use for predicting immunogenicity. This study focuses on mining informative physicochemical properties from known experimental immunogenicity data to understand immune responses and predict immunogenicity of MHC-binding peptides

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Prediction of MHC class I binding peptides using profile motifs
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Peptides that bind to a given major histocompatibility complex (MHC) molecule share sequence similarity. Therefore, a position specific scoring matrix (PSSM) or profile derived from a set of peptides known to bind to a specific MHC molecule would be a suitable predictor of whether other peptides might bind, thus anticipating possible T-cell epitopes within a protein. In this approach, the binding potential of any peptide sequence (query) to a given MHC molecule is linked to its similarity to a group of aligned peptides known to bind to that MHC, and can be obtained by comparing the query to the PSSM. This article describes the derivation of alignments and profiles from a collection of peptides known to bind a specific MHC, compatible with the structural and molecular basis of the peptide-MHC class I (MHCI) interaction. Moreover, in order to apply these profiles to the prediction of peptide-MHCI binding, we have developed a new search algorithm (RANKPEP) that ranks all possible peptides from an input protein using the PSSM coefficients. The predictive power of the method was evaluated by running RANKPEP on proteins known to bear MHCI K b -and D b -restricted T-cell epitopes. Analysis of the results indicates that Ͼ 80% of these epitopes are among the top 2% of scoring peptides. Prediction of peptide-MHC binding using a variety of MHCI-specific PSSMs is available on line at our RANK-PEP web server (www.mifoundation.org/Tools/rankpep.html). In addition, the RANKPEP server also allows the user to enter additional profiles, making the server a powerful and versatile computational biology benchmark for the prediction of peptide-MHC binding. Human Immunology 63, 701-709 (2002).

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Background: Initiation and regulation of immune responses in humans involves recognition of peptides presented by human leukocyte antigen class II (HLA-II) molecules. These peptides (HLA-II T-cell epitopes) are increasingly important as research targets for the development of vaccines and immunotherapies. HLA-II peptide binding studies involve multiple overlapping peptides spanning individual antigens, as well as complete viral proteomes. Antigen variation in pathogens and tumor antigens, and extensive polymorphism of HLA molecules increase the number of targets for screening studies. Experimental screening methods are expensive and time consuming and reagents are not readily available for many of the HLA class II molecules. Computational prediction methods complement experimental studies, minimize the number of validation experiments, and significantly speed up the epitope mapping process. We collected test data from four independent studies that involved 721 peptide binding assays. Full overlapping studies of four antigens identified binding affinity of 103 peptides to seven common HLA-DR molecules (DRB1*0101, 0301, 0401, 0701, 1101, 1301, and 1501). We used these data to analyze performance of 21 HLA-II binding prediction servers accessible through the WWW.

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iScience, 2022

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MHC Class II Binding Prediction—A Little Help from a Friend
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Journal of Biomedicine and Biotechnology, 2010

Vaccines are the greatest single instrument of prophylaxis against infectious diseases, with immeasurable benefits to human wellbeing. The accurate and reliable prediction of peptide-MHC binding is fundamental to the robust identification of T-cell epitopes and thus the successful design of peptide- and protein-based vaccines. The prediction of MHC class II peptide binding has hitherto proved recalcitrant and refractory. Here we illustrate the utility of existing computational tools for in silico prediction of peptides binding to class II MHCs. Most of the methods, tested in the present study, detect more than the half of the true binders in the top 5% of all possible nonamers generated from one protein. This number increases in the top 10% and 15% and then does not change significantly. For the top 15% the identified binders approach 86%. In terms of lab work this means 85% less expenditure on materials, labour and time. We show that while existing caveats are well founded, nonethe...

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Molecular Biology, 2010

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Yoram Louzoun

Journal of Immunological Methods, 2011

We propose a novel learning method that combines multiple experimental modalities to improve the MHC Class-I binding prediction. Multiple experimental modalities are often accessible in the context of a binding problem. Such modalities can provide different labels of data, such as binary classifications, affinity measurements, or direct estimations of the binding profile. Current machine learning algorithms usually focus on a given label type. We here present a novel Multi-Label Vector Optimization (MLVO) formalism to produce classifiers based on the simultaneous optimization of multiple labels. Within this methodology, all label types are combined into a single constrained quadratic dual optimization problem. We apply the MLVO to MHC class-I epitope prediction. We combine affinity measurements (IC50/EC50), binary classifications of epitopes as T cell activators and existing algorithms. The multi-label vector optimization algorithms produce classifiers significantly better than the ones resulting from any of its components. These matrix based classifiers are better or equivalent to the existing state of the art MHC-I epitope prediction tools in the studied alleles.

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Amr Badr

American Journal of Bioinformatics Research, 2012

Identification of major histocompatibility complex binding peptides is an important step in the selection of T-Cell epitope candidates suitable for usage in new vaccines.The binding groove of the MHC Class-II molecule is opened at both sides, which allows for high variability in length of the peptides that bind to this molecule and consequently complicates the prediction of the binding core motif. An accurate and efficient computational approach for the prediction of such peptides can greatly reduce the time and cost required for the design of new vaccines for infectious diseases and cancers. We have developed EpiGASVM, a new approach for the in silico prediction of MHC Class-II epitopes, by combining two artificial intelligence techniques namely: evolutionary algorithms and support vector machines. We have applied nine variations of EpiGASVM to a dataset of similarity-reduced benchmark data and we have calculated the prediction accuracy and the area under the receiver operating characteristic curve as measures of performance.The results indicate that Epi-GASVM is a promising new technique that could provide researchers with a new tool for the in silico selection of candidate peptides that can be used in rational vaccine design.

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DynaPred: A structure and sequence based method for the prediction of MHC class I binding peptide sequences and conformations
Weng Siu

Intelligent Systems in Molecular Biology, 2006

Motivation: The binding of endogenous,antigenic peptides to MHC classImoleculesisanimportantstepduringtheimmunologicresponse of a host against a pathogen. Thus, various sequence- and structure- based,prediction methods,have,been,proposed,for this purpose. The sequence-based methods are computationally efficient, but are hamperedbytheneedofsufficientexperimentaldataanddonotprovide a structural interpretation of their results. The structural methods,are data-independent, but are quite time-consuming and thus not suited forscreeningofwholegenomes.Here,wepresentanewmethod,which performs,sequence-based,prediction by incorporating,information obtained,from molecular,modeling. This allows

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Evaluation of MHC class I peptide binding prediction servers: applications for vaccine research
Songsak Tongchusak

BMC immunology, 2008

Protein antigens and their specific epitopes are formulation targets for epitope-based vaccines. A number of prediction servers are available for identification of peptides that bind major histocompatibility complex class I (MHC-I) molecules. The lack of standardized methodology and large number of human MHC-I molecules make the selection of appropriate prediction servers difficult. This study reports a comparative evaluation of thirty prediction servers for seven human MHC-I molecules. Of 147 individual predictors 39 have shown excellent, 47 good, 33 marginal, and 28 poor ability to classify binders from non-binders. The classifiers for HLA-A*0201, A*0301, A*1101, B*0702, B*0801, and B*1501 have excellent, and for A*2402 moderate classification accuracy. Sixteen prediction servers predict peptide binding affinity to MHC-I molecules with high accuracy; correlation coefficients ranging from r = 0.55 (B*0801) to r = 0.87 (A*0201). Non-linear predictors outperform matrix-based predicto...

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Related topics

  • Computer Science
  • Immunology
  • Peptide Chemistry
  • Biology
  • Drug Discovery
  • Experimental Measurement
  • D structure
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