High-order neural networks and kernel methods for peptide-MHC binding prediction

Motivation: Effective computational methods for peptide-protein binding prediction can greatly help clinical peptide vaccine search and design. However, previous computational methods fail to capture key nonlinear high-order dependencies between different amino acid positions. As a result, they ofte...

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Bibliographic Details
Published in:Bioinformatics (Oxford, England) Vol. 31; no. 22; pp. 3600 - 3607
Main Authors: Kuksa, Pavel P., Min, Martin Renqiang, Dugar, Rishabh, Gerstein, Mark
Format: Journal Article
Language:English
Published: England 15.11.2015
Subjects:
Algorithms
Amino Acid Sequence
Area Under Curve
Databases, Protein
Epitopes - chemistry
Humans
Major Histocompatibility Complex
Molecular Sequence Data
Neural Networks (Computer)
Peptides - chemistry
Peptides - metabolism
Protein Binding
ROC Curve
Support Vector Machine
ISSN:1367-4803, 1367-4811, 1367-4811
Online Access:Get full text
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Summary:Motivation: Effective computational methods for peptide-protein binding prediction can greatly help clinical peptide vaccine search and design. However, previous computational methods fail to capture key nonlinear high-order dependencies between different amino acid positions. As a result, they often produce low-quality rankings of strong binding peptides. To solve this problem, we propose nonlinear high-order machine learning methods including high-order neural networks (HONNs) with possible deep extensions and high-order kernel support vector machines to predict major histocompatibility complex-peptide binding. Results: The proposed high-order methods improve quality of binding predictions over other prediction methods. With the proposed methods, a significant gain of up to 25–40% is observed on the benchmark and reference peptide datasets and tasks. In addition, for the first time, our experiments show that pre-training with high-order semi-restricted Boltzmann machines significantly improves the performance of feed-forward HONNs. Moreover, our experiments show that the proposed shallow HONN outperform the popular pre-trained deep neural network on most tasks, which demonstrates the effectiveness of modelling high-order feature interactions for predicting major histocompatibility complex-peptide binding. Availability and implementation: There is no associated distributable software. Contact:  renqiang@nec-labs.com or mark.gerstein@yale.edu Supplementary information: Supplementary data are available at Bioinformatics online.
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ISSN:1367-4803
1367-4811
1367-4811
DOI:10.1093/bioinformatics/btv371