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Quantitative modeling of peptide binding to TAP using support vector machine by Carmen M. Diez-Rivero; Bernardo Chenlo; Pilar Zuluaga; Pedro A. Reche is a Biochemistry, Genetics and Molecular Biology article available to read on EtoBox.

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## Abstract 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 peptides 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

Who reads Quantitative modeling of peptide binding to TAP using support vector machine?

Author

Carmen M. Diez-Rivero; Bernardo Chenlo; Pilar Zuluaga; Pedro A. Reche

Publisher

John Wiley and Sons; Wiley (John Wiley & Sons); Wiley-Liss Inc; Wiley (ISSN 0887-3585)

Published

2009

Language

EN

Field

Biochemistry, Genetics and Molecular Biology (Life Sciences)