Literature DB >> 12112675

Physicochemical explanation of peptide binding to HLA-A*0201 major histocompatibility complex: a three-dimensional quantitative structure-activity relationship study.

Irini A Doytchinova1, Darren R Flower.   

Abstract

A three-dimensional quantitative structure-activity relationship method for the prediction of peptide binding affinities to the MHC class I molecule HLA-A*0201 was developed by applying the CoMSIA technique on a set of 266 peptides. To increase the self consistency of the initial CoMSIA model, the poorly predicted peptides were excluded from the training set in a stepwise manner and then included in the study as a test set. The final model, based on 236 peptides and considering the steric, electrostatic, hydrophobic, hydrogen bond donor, and hydrogen bond acceptor fields, had q2 = 0.683 and r2 = 0.891. The stability of this model was proven by cross-validations in two and five groups and by a bootstrap analysis of the non-cross-validated model. The residuals between the experimental pIC50 (-logIC50) values and those calculated by "leave-one-out" cross-validation were analyzed. According to the best model, 63.2% of the peptides were predicted with /residuals/ < or = 0.5 log unit; 29.3% with 1.0 < or = /residuals/ < 0.5; and 7.5% with /residuals/ > 1.0 log unit. The mean /residual/ value was 0.489. The coefficient contour maps identify the physicochemical property requirements at each position in the peptide molecule and suggest amino acid sequences for high-affinity binding to the HLA-A*0201 molecule. Copyright 2002 Wiley-Liss, Inc.

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Year:  2002        PMID: 12112675     DOI: 10.1002/prot.10154

Source DB:  PubMed          Journal:  Proteins        ISSN: 0887-3585


  12 in total

1.  A comparative molecular similarity index analysis (CoMSIA) study identifies an HLA-A2 binding supermotif.

Authors:  Irini A Doytchinova; Darren R Flower
Journal:  J Comput Aided Mol Des       Date:  2002 Aug-Sep       Impact factor: 3.686

2.  Robust quantitative modeling of peptide binding affinities for MHC molecules using physical-chemical descriptors.

Authors:  Ovidiu Ivanciuc; Werner Braun
Journal:  Protein Pept Lett       Date:  2007       Impact factor: 1.890

3.  A comprehensive analysis of the thermodynamic events involved in ligand-receptor binding using CoRIA and its variants.

Authors:  Jitender Verma; Vijay M Khedkar; Arati S Prabhu; Santosh A Khedkar; Alpeshkumar K Malde; Evans C Coutinho
Journal:  J Comput Aided Mol Des       Date:  2008-01-25       Impact factor: 3.686

4.  Exploring classification strategies with the CoEPrA 2006 contest.

Authors:  Ozgur Demir-Kavuk; Henning Riedesel; Ernst-Walter Knapp
Journal:  Bioinformatics       Date:  2010-01-22       Impact factor: 6.937

5.  Side-chain conformational space analysis (SCSA): a multi conformation-based QSAR approach for modeling and prediction of protein-peptide binding affinities.

Authors:  Peng Zhou; Xiang Chen; Zhicai Shang
Journal:  J Comput Aided Mol Des       Date:  2008-10-08       Impact factor: 3.686

6.  A comprehensive review and performance evaluation of bioinformatics tools for HLA class I peptide-binding prediction.

Authors:  Shutao Mei; Fuyi Li; André Leier; Tatiana T Marquez-Lago; Kailin Giam; Nathan P Croft; Tatsuya Akutsu; A Ian Smith; Jian Li; Jamie Rossjohn; Anthony W Purcell; Jiangning Song
Journal:  Brief Bioinform       Date:  2020-07-15       Impact factor: 11.622

7.  Proteomics in Vaccinology and Immunobiology: An Informatics Perspective of the Immunone.

Authors:  Irini A. Doytchinova; Paul Taylor; Darren R. Flower
Journal:  J Biomed Biotechnol       Date:  2003

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

Authors:  Pingping Guan; Irini A Doytchinova; Christianna Zygouri; Darren R Flower
Journal:  Nucleic Acids Res       Date:  2003-07-01       Impact factor: 16.971

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

Authors:  Wen Liu; Xiangshan Meng; Qiqi Xu; Darren R Flower; Tongbin Li
Journal:  BMC Bioinformatics       Date:  2006-03-31       Impact factor: 3.169

10.  SVRMHC prediction server for MHC-binding peptides.

Authors:  Ji Wan; Wen Liu; Qiqi Xu; Yongliang Ren; Darren R Flower; Tongbin Li
Journal:  BMC Bioinformatics       Date:  2006-10-23       Impact factor: 3.169

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