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Deep learning techniques have significantly impacted protein structure prediction and protein design
Open accessAbstract
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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In the citing paper
“Despite recent progress10–14, existing methods fall far short of atomic accuracy, especially when no homologous structure is available.”
From Highly accurate protein structure prediction with AlphaFold (2021) · cites this paper as [14]
Experimental — accuracy not yet measuredIn the citing paper
“Deep learning has also been already exploited in structural modeling and design60,194−196 and analysis191 and linking these to function.197”
From AlphaFold, Artificial Intelligence (AI), and Allostery (2022) · cites this paper as [195]
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Metadata
- Authors
- Pearce, R., & Zhang, Y.
- Affiliations
- University of Michigan (US)
- Corresponding author
- Zhang, Yang
- Venue
- Current Opinion in Structural Biology
- Publisher
- Elsevier BV
- Published
- 2021-06
- Volume
- 68
- Pages
- 194-207
- ISSN
- 0959-440X (ISSN-L)
- 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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