Journal
INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES
Volume 22, Issue 11, Pages -Publisher
MDPI
DOI: 10.3390/ijms22116032
Keywords
structural bioinformatics; deep learning; protein sequence homology; 3D structure of proteins; drug discovery
Funding
- Mid-career Researcher Program [NRF-2020R1A2C2101636]
- Medical Research Center (MRC) [2018R1A5A2025286]
- Bio & Medical Technology Development Program - Ministry of Science and ICT (MSIT) [NRF-2019M3E5D4065251]
- Ministry of Health and Welfare (MOHW) through the National Research Foundation of Korea (NRF)
- NRF [2020R1F1A1072119]
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The new advances in deep learning methods have greatly impacted protein structure prediction, especially demonstrated in CASP competitions. These techniques are expected to play crucial roles in protein structural bioinformatics and drug discovery in the near future.
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.
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