PMID- 28444127
OWN - NLM
STAT- MEDLINE
DCOM- 20180601
LR  - 20220316
IS  - 1367-4811 (Electronic)
IS  - 1367-4803 (Linking)
VI  - 33
IP  - 17
DP  - 2017 Sep 1
TI  - HLA class I binding prediction via convolutional neural networks.
PG  - 2658-2665
LID - 10.1093/bioinformatics/btx264 [doi]
AB  - MOTIVATION: Many biological processes are governed by protein-ligand 
      interactions. One such example is the recognition of self and non-self cells by 
      the immune system. This immune response process is regulated by the major 
      histocompatibility complex (MHC) protein which is encoded by the human leukocyte 
      antigen (HLA) complex. Understanding the binding potential between MHC and 
      peptides can lead to the design of more potent, peptide-based vaccines and 
      immunotherapies for infectious autoimmune diseases. RESULTS: We apply machine 
      learning techniques from the natural language processing (NLP) domain to address 
      the task of MHC-peptide binding prediction. More specifically, we introduce a new 
      distributed representation of amino acids, name HLA-Vec, that can be used for a 
      variety of downstream proteomic machine learning tasks. We then propose a deep 
      convolutional neural network architecture, name HLA-CNN, for the task of HLA 
      class I-peptide binding prediction. Experimental results show combining the new 
      distributed representation with our HLA-CNN architecture achieves 
      state-of-the-art results in the majority of the latest two Immune Epitope 
      Database (IEDB) weekly automated benchmark datasets. We further apply our model 
      to predict binding on the human genome and identify 15 genes with potential for 
      self binding. AVAILABILITY AND IMPLEMENTATION: Codes to generate the HLA-Vec and 
      HLA-CNN are publicly available at: https://github.com/uci-cbcl/HLA-bind . 
      CONTACT: xhx@ics.uci.edu. SUPPLEMENTARY INFORMATION: Supplementary data are 
      available at Bioinformatics online.
CI  - (c) The Author (2017). Published by Oxford University Press. All rights reserved. 
      For Permissions, please email: journals.permissions@oup.com
FAU - Vang, Yeeleng S
AU  - Vang YS
AD  - Department of Computer Science, University of California, Irvine, CA 92697, USA.
FAU - Xie, Xiaohui
AU  - Xie X
AD  - Department of Computer Science, University of California, Irvine, CA 92697, USA.
AD  - Institute for Genomics and Bioinformatics, University of California, Irvine, CA 
      92697, USA.
LA  - eng
PT  - Journal Article
PL  - England
TA  - Bioinformatics
JT  - Bioinformatics (Oxford, England)
JID - 9808944
RN  - 0 (Epitopes)
RN  - 0 (HLA Antigens)
RN  - 0 (Histocompatibility Antigens Class I)
RN  - 0 (Peptides)
SB  - IM
MH  - Epitopes
MH  - HLA Antigens/metabolism
MH  - Histocompatibility Antigens Class I/*metabolism
MH  - Humans
MH  - *Machine Learning
MH  - *Neural Networks, Computer
MH  - Peptides/*metabolism
MH  - Protein Binding
MH  - Proteomics/*methods
EDAT- 2017/04/27 06:00
MHDA- 2018/06/02 06:00
CRDT- 2017/04/27 06:00
PHST- 2016/12/21 00:00 [received]
PHST- 2017/04/18 00:00 [accepted]
PHST- 2017/04/27 06:00 [pubmed]
PHST- 2018/06/02 06:00 [medline]
PHST- 2017/04/27 06:00 [entrez]
AID - 3746909 [pii]
AID - 10.1093/bioinformatics/btx264 [doi]
PST - ppublish
SO  - Bioinformatics. 2017 Sep 1;33(17):2658-2665. doi: 10.1093/bioinformatics/btx264.