Cited by 129Protein Structure and DynamicsMachine Learning in BioinformaticsEnzyme Structure and Function

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Deep learning techniques have significantly impacted protein structure prediction and protein design

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Pearce, Robin, Zhang, Yang·2021·Current Opinion in Structural Biology

DOI 10.1016/j.sbi.2021.01.007PMC PMC8222070

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Abstract

Protein structure prediction and design can be regarded as two inverse processes governed by the same folding principle. Although progress remained stagnant over the past two decades, the recent application of deep neural networks to spatial constraint prediction and end-to-end model training has significantly improved the accuracy of protein structure prediction, largely solving the problem at the fold level for single-domain proteins. The field of protein design has also witnessed dramatic improvement, where noticeable examples have shown that information stored in neural-network models can be used to advance functional protein design. Thus, incorporation of deep learning techniques into different steps of protein folding and design approaches represents an exciting future direction and should continue to have a transformative impact on both fields.

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Authors: Pearce, R., & Zhang, Y.
Affiliations: University of Michigan (US)
Corresponding author: Zhang, Yang
ORCID: Pearce, Robin
Zhang, Yang
Venue: Current Opinion in Structural Biology
Publisher: Elsevier BV
Published: 2021-06
Volume: 68
Pages: 194-207
ISSN: 0959-440X (ISSN-L)
DOI: 10.1016/j.sbi.2021.01.007
PMC: PMC8222070
OpenAlex: W3132448805
Cited by: 127
References: 66
Citations by year: 2026: 8 · 2025: 29 · 2024: 21 · 2023: 29 · 2022: 29 · 2021: 11
FWCI: 2.36
Type: journal article
Language: en
Open access: bronze
doi.org
Licence: www.elsevier.com/tdm/userlicense/1.0/ (tdm)
www.elsevier.com/legal/tdmrep-license (tdm)
www.elsevier.com/open-access/userlicense/1.0/ (am, 268 d)
doi.org/10.15223/policy-017 (stm-asf)
doi.org/10.15223/policy-037 (stm-asf)
doi.org/10.15223/policy-012 (stm-asf)
doi.org/10.15223/policy-029 (stm-asf)
doi.org/10.15223/policy-004 (stm-asf)
Locations: Current Opinion in Structural Biology (acceptedVersion)
PubMed (publishedVersion)
PubMed Central (acceptedVersion)
Funders: National Science Foundation (DBI2030790, IIS1901191, MTM2025426)
National Institute of General Medical Sciences (GM136422, S10OD026825)
National Institute of Allergy and Infectious Diseases (AI134678)
Topics: Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Machine Learning in Bioinformatics
Life Sciences › Biochemistry, Genetics and Molecular Biology › Structural Biology › Enzyme Structure and Function
Keywords: protein structure prediction, protein design, deep learning, deep neural network, protein folding, single-domain proteins
MeSH terms: Deep Learning, Proteins, Neural Networks, Computer, Protein Folding

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Deep learning techniques have significantly impacted protein structure prediction and protein design

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