Recent applications of deep learning methods on evolutionand contact-based protein structure prediction

Donghyuk Suh, Jai Woo Lee, Sun Choi, Yoonji Lee

Research output: Contribution to journalReview articlepeer-review

15 Scopus citations

Abstract

The new advances in deep learning methods have influenced many aspects of scientific research, including the study of the protein system. The prediction of proteins’ 3D structural components is now heavily dependent on machine learning techniques that interpret how protein sequences and their homology govern the inter-residue contacts and structural organization. Especially, methods employing deep neural networks have had a significant impact on recent CASP13 and CASP14 competition. Here, we explore the recent applications of deep learning methods in the protein structure prediction area. We also look at the potential opportunities for deep learning methods to identify unknown protein structures and functions to be discovered and help guide drug– target interactions. Although significant problems still need to be addressed, we expect these techniques in the near future to play crucial roles in protein structural bioinformatics as well as in drug discovery.

Original languageEnglish
Article number6032
JournalInternational Journal of Molecular Sciences
Volume22
Issue number11
DOIs
StatePublished - 1 Jun 2021

Bibliographical note

Funding Information:
Funding: This work was supported by the Mid-career Researcher Program (NRF-2020R1A2C2101636), Medical Research Center (MRC) grant (2018R1A5A2025286), and Bio & Medical Technology Development Program (NRF-2019M3E5D4065251) funded by the Ministry of Science and ICT (MSIT) and the Ministry of Health and Welfare (MOHW) through the National Research Foundation of Korea (NRF) (S.C); and General Research Program (2020R1F1A1072119) (Y.L.) through the NRF.

Publisher Copyright:
© 2021 by the authors. Licensee MDPI, Basel, Switzerland.

Keywords

  • 3D structure of proteins
  • Deep learning
  • Drug discovery
  • Protein sequence homology
  • Structural bioinformatics

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