Sequence-based peptide identification, generation, and property prediction with deep learning: A review

Xumin Chen, Chen Li, Matthew T. Bernards, Yao Shi, Qing Shao, Yi He

Research output: Contribution to journalReview articlepeer-review

7 Scopus citations

Abstract

Over the past few years, deep learning has demonstrated itself to be a powerful tool in many areas, especially bioinformatics. With its previous success in DNA and protein related studies, deep learning has now been brought to the field of peptide science as well. It has been widely used in sequence-based peptide identification, generation, and property prediction. The publications on this subject over the past two years are summarized in this review. The deep learning models reported are mainly convolutional neural networks, recurrent neural networks, hybrid models, transformers, and other generative models like variational autoencoders and generative adversarial networks, as well as algorithms like input optimization. Application areas include antimicrobial peptides, signal peptides, and major histocompatibility complex binding peptides, among others. This review develops content according to the general workflow of deep learning, while illustrating adaptations and techniques specific to certain example problems. Some issues and future directions are also discussed, such as approaches for model interpretation, benchmark datasets, automation in deep learning, and rational peptide design techniques.

Original languageEnglish
Pages (from-to)406-428
Number of pages23
JournalMolecular Systems Design and Engineering
Volume6
Issue number6
DOIs
StatePublished - Jun 1 2021

Bibliographical note

Funding Information:
This work is supported by the National Key Research and Development Program of China (grant number 2017YFB0702502) and the National Natural Science Foundation of China (grant number 51933009).

Publisher Copyright:
© The Royal Society of Chemistry.

ASJC Scopus subject areas

  • Chemistry (miscellaneous)
  • Chemical Engineering (miscellaneous)
  • Biomedical Engineering
  • Energy Engineering and Power Technology
  • Process Chemistry and Technology
  • Industrial and Manufacturing Engineering
  • Materials Chemistry

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