Quantitative modeling of peptide binding to TAP using support vector machine

被引:35
作者
Diez-Rivero, Carmen M. [1 ]
Chenlo, Bernardo [1 ]
Zuluaga, Pilar [2 ]
Reche, Pedro A. [1 ]
机构
[1] Univ Complutense, Lab InmunoMed, Dept Microbiol Immunol 1, Fac Med, E-28040 Madrid, Spain
[2] Univ Complutense, Dept Estadist & Invest Operat, Fac Med, E-28040 Madrid, Spain
关键词
antigen processing; peptide; TAP; prediction; WEKA; SVM; MHC CLASS-I; GENOME-WIDE; TRANSPORTER; PREDICTION; TRANSLOCATION; RECOGNITION; REPERTOIRE; MECHANISMS; MOLECULES; SELECTION;
D O I
10.1002/prot.22535
中图分类号
Q5 [生物化学]; Q7 [分子生物学];
学科分类号
071010 ; 081704 ;
摘要
The transport of peptides to the endoplasmic reticulum by the transporter associated with antigen processing (TAP) is a necessary step towards determining CD8 T cell epitopes. in this work, we have studied the predictive performance of support vector machine models trained on single residue positions and residue combinations drawn from a large dataset consisting of 613 nonamer pepticles of known affinity to TAP. Predictive performance of these TAP affinity models was evaluated under 10-fold cross-validation experiments and measured using Pearson's correlation coefficients (R-p). Our results show that every peptide position (P1-P9) contributes to TAP binding (minimum R-p of 0.26 +/- 0.11 was achieved by a model trained on the P6 residue), although the largest contributions to binding correspond to the C-terminal end (R-P = 0.68 +/- 0.06) and the P1 (R-p = 0.51 +/- 0.09) and P2 (0.57 +/- 0.08) residues of the peptide. Training the models on additional peptide residues generally improved their predictive performance and a maximum correlation (R-p = 0.89 +/- 0.03) was achieved by a model trained on the full-length sequences or a residue selection consisting of the first 5 N- and last 3 C-terminal residues of the peptides included in the training set. A system for predicting the binding affinity of peptides to TAP using the methods described here is readily available for free public use at http://imed.med.ucm.es/Tools/tapreg/.
引用
收藏
页码:63 / 72
页数:10
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