Discovery of promiscuous HLA-II-restricted T cell epitopes with TEPITOPE

Bian, Hongjin; Hammer, Juergen

doi:10.1016/j.ymeth.2004.06.002

Cited by 72 publications

(54 citation statements)

References 9 publications

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“…These authors used peptide libraries to obtain a quantitative representation of the interaction of all natural amino acid residues with structurally identified binding pockets of HLA-DR molecules (pocket binding profile). Subsequently, they showed that the binding affinity of a peptide to any HLA-DR allele can be predicted by selecting the appropriated binding pocket profiles, using the TEPITOPE [74] system. TEPITOPE QMs are readily available for free public use at the PROPRED site ( Table I).…”

Section: Quantitative Additive Modelsmentioning

confidence: 99%

Prediction of MHC-Peptide Binding: A Systematic and Comprehensive Overview

Lafuente¹,

Reche

2009

CPD

133

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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.

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Section: Quantitative Additive Modelsmentioning

confidence: 99%

Prediction of MHC-Peptide Binding: A Systematic and Comprehensive Overview

Lafuente¹,

Reche

2009

CPD

133

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“…TEPITOPE is a tool, which implements an algorithm based on the prediction of HLA-II-peptide binding [ 23 ]. It consists of 11 position specifi c scoring matrices (PSSM) to represent MHC-peptide binding specifi cities.…”

Section: Epimer and Optimermentioning

confidence: 99%

T-Cell Epitope Prediction Methods: An Overview

Desai¹,

Kulkarni‐Kale²

2014

Methods in Molecular Biology

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The scientific community is overwhelmed by the voluminous increase in the quantum of data on biological systems, including but not limited to the immune system. Consequently, immunoinformatics databases are continually being developed to accommodate this ever increasing data and analytical tools are continually being developed to analyze the same. Therefore, researchers are now equipped with numerous databases, analytical and prediction tools, in anticipation of better means of prevention of and therapeutic intervention in diseases of humans and other animals. Epitope is a part of an antigen, recognized either by B- or T-cells and/or molecules of the host immune system. Since only a few amino acid residues that comprise an epitope (instead of the whole protein) are sufficient to elicit an immune response, attempts are being made to identify or predict this critical stretch or patch of amino acid residues, i.e., T-cell epitopes and B-cell epitopes to be included in multiple-subunit vaccines. T-cell epitope prediction is a challenge owing to the high degree of MHC polymorphism and disparity in the volume of data on various steps encountered in the generation and presentation of T-cell epitopes in the living systems. Many algorithms/methods developed to predict T-cell epitopes and Web servers incorporating the same are available. These are based on approaches like considering amphipathicity profiles of proteins, sequence motifs, quantitative matrices (QM), artificial neural networks (ANN), support vector machines (SVM), quantitative structure activity relationship (QSAR) and molecular docking simulations, etc. This chapter aims to introduce the reader to the principle(s) underlying some of these methods/algorithms as well as procedural and practical aspects of using the same.

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“…在预测 MHC II 类分子结合抗原肽的研究工作中, 主要目标是寻找抗原肽的核心结合序列 [9,10] . 由 MHC II 类分子的晶体结构可知, MHC II 类分子的抗原结合槽一般可以容纳长度为 13 个氨基酸的抗原肽 [11] .…”

Section: Cd4unclassified