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Exploring the alternative conformation of a known protein structure based on contact map prediction
Open accessPreprint — published as 10.1021/acs.jcim.3c01381
Abstract
The rapid development of deep learning-based methods has considerably advanced the field of protein structure prediction. The accuracy of predicting the 3D structures of simple proteins is comparable to that of experimentally determined structures, providing broad possibilities for structure-based biological studies. Another critical question is whether and how multistate structures can be predicted from a given protein sequence. In this study, analysis of multiple two-state proteins demonstrated that deep learning-based contact map predictions contain structural information on both states, which suggests that it is probably appropriate to change the target of deep learningbased protein structure prediction from one specific structure to multiple likely structures. Furthermore, by combining deep learning- and physics-based computational methods, we developed a protocol for exploring alternative conformations from a known structure of a given protein, by which we successfully approached the holo-state conformation of a leucine-binding protein from its apo-state structure.
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Highly accurate protein structure prediction with AlphaFold
Jumper, J., Evans, R., Pritzel, A., Green, T., Figurnov, M., Ronneberger, O., Tunyasuvunakool, K., Bates, R., Žídek, A., Potapenko, A., et al. (2021) · Nature · doi:10.1038/s41586-021-03819-2 · Cited by 43,764
Sampling alternative conformational states of transporters and receptors with AlphaFold2
del Alamo, D., Sala, D., Mchaourab, H. S., & Meiler, J. (2022) · eLife · doi:10.7554/elife.75751 · Cited by 511
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Metadata
- Authors
- Li, J., Wang, L., Zhu, Z., & Song, C.
- Affiliations
- Peking University (CN)
- Corresponding author
- Song, Chen
- Venue
- Journal of Chemical Information and Modeling
- Publisher
- openRxiv
- Published
- 2022-06-09
- Volume
- 64
- Issue
- 1
- Pages
- 301-315
- ISSN
- 1549-9596 (ISSN-L)
- PMC
- PMC10777399
- OpenAlex
- W4389993920
- Cited by
- 21
- References
- 2
- Citations by year
- 2026: 5 · 2025: 10 · 2024: 4 · 2023: 1 · 2022: 1
- FWCI
- 1.78
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by-nc-nd
- Locations
- Journal of Chemical Information and Modeling (publishedVersion, cc-by-nc-nd)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- Funders
- National Natural Science Foundation of China (21873006, 32071251)
- Peking University
- National Key Research and Development Program of China (2021YFE0108100)
- 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
- contact map prediction, two-state proteins, deep learning protein structure prediction, conformational change
- MeSH terms
- Amino Acid Sequence, Protein Conformation, Proteins, Computational Biology
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