In silico design of MHC class I high binding affinity peptides through motifs activation map

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journal

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Author(s)

  • Zhoujian Xiao
  • Runsheng Yu
  • Yin Chen
  • Xiaosen Jiang
  • Ziwei Wang

Related Research Unit(s)

Detail(s)

Original languageEnglish
Article number516
Journal / PublicationBMC Bioinformatics
Volume19
Issue numberSuppl 19
Publication statusOnline published - 31 Dec 2018

Link(s)

Abstract

Background: Finding peptides with high binding affinity to Class I major histocompatibility complex (MHC-I) attracts intensive research, and it serves a crucial part of developing a better vaccine for precision medicine. Traditional methods cost highly for designing such peptides. The advancement of computational approaches reduces the cost of new drug discovery dramatically. Compared with flourishing computational drug discovery area, the immunology area lacks tools focused on in silico design for the peptides with high binding affinity. Attributed to the ever-expanding amount of MHC-peptides binding data, it enables the tremendous influx of deep learning techniques for modeling MHC-peptides binding. To leverage the availability of these data, it is of great significance to find MHC-peptides binding specificities. The binding motifs are one of the key components to decide the MHC-peptides combination, which generally refer to a combination of some certain amino acids at certain sites which highly contribute to the binding affinity. 

Result: In this work, we propose the Motif Activation Mapping (MAM) network for MHC-I and peptides binding to extract motifs from peptides. Then, we substitute amino acid randomly according to the motifs for generating peptides with high affinity. We demonstrated the MAM network could extract motifs which are the features of peptides of highly binding affinities, as well as generate peptides with high-affinities; that is, 0.859 for HLA-A*0201, 0.75 for HLA-A*0206, 0.92 for HLA-B*2702, 0.9 for HLA-A*6802 and 0.839 for Mamu-A1*001:01. Besides, its binding prediction result reaches the state of the art. The experiment also reveals the network is appropriate for most MHC-I with transfer learning. 

Conclusions: We design the MAM network to extract the motifs from MHC-peptides binding through prediction, which are proved to generate the peptides with high binding affinity successfully. The new peptides preserve the motifs but vary in sequences.

Research Area(s)

  • Convolutional neural network, Design new peptides with high binding affinity to MHC-I molecule, Motifs activation map

Bibliographic Note

Full text of this publication does not contain sufficient affiliation information. With consent from the author(s) concerned, the Research Unit(s) information for this record is based on the existing academic department affiliation of the author(s).

Citation Format(s)

In silico design of MHC class I high binding affinity peptides through motifs activation map. / Xiao, Zhoujian; Zhang, Yuwei; Yu, Runsheng; Chen, Yin; Jiang, Xiaosen; Wang, Ziwei; Li, Shuaicheng.

In: BMC Bioinformatics, Vol. 19, No. Suppl 19, 516, 31.12.2018.

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journal

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