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OPUS-Mut: studying the effect of protein mutation through side-chain modeling
Open accessPreprint — published as 10.1021/acs.jctc.2c00847 · Paper page
Abstract
Predicting the effect of protein mutation is crucial in many applications such as protein design, protein evolution, and genetic disease analysis. Structurally, the mutation is basically the replacement of the side chain of a particular residue. Therefore, accurate side-chain modeling is useful in studying the effect of mutation. Here, we propose a computational method, namely OPUS-Mut, which significantly outperforms other backbone-dependent side-chain modeling methods including our previous method OPUS-Rota4. We evaluate OPUS-Mut by four case studies on Myoglobin, p53, HIV-1 protease, and T4 lysozyme. The results show that the predicted structures of side chains of different mutants are consistent well with their experimentally determined results. In addition, when the residues with significant structural shifts upon the mutation are considered, it is found that the extent of the predicted structural shift of these affected residues can be correlated reasonably well with the functional changes of the mutant measured by experiments. OPUS-Mut can also help one to identify the harmful and benign mutations, and thus may guide the construction of a protein with relatively low sequence homology but with similar structure.
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Metadata
- Authors
- Xu, G., Wang, Q., & Ma, J.
- Affiliations
- Fudan University (CN)
- Corresponding author
- Ma, Jianpeng
- Venue
- Journal of Chemical Theory and Computation
- Publisher
- openRxiv
- Published
- 2022-05-11
- Volume
- 19
- Issue
- 5
- Pages
- 1629-1640
- ISSN
- 1549-9618 (ISSN-L)
- PMC
- PMC10018731
- OpenAlex
- W4321479867
- Cited by
- 12
- References
- 1
- Citations by year
- 2026: 3 · 2025: 2 · 2024: 5 · 2023: 1 · 2022: 1
- FWCI
- 0.98
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by-nc-nd
- Locations
- Journal of Chemical Theory and Computation (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
- Ministry of Science and Technology of the People's Republic of China (2021YFF1200400)
- Science and Technology Commission of Shanghai Municipality (2018SHZDZX01)
- National Key Research and Development Program of China (2021YFF1200400, 2018SHZDZX01)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Structural Biology › Enzyme Structure and Function
- Life Sciences › Pharmacology, Toxicology and Pharmaceutics › Drug Discovery › Computational Drug Discovery Methods
- Keywords
- protein design, protein evolution, structural change, protein structure prediction, myoglobin, p53, HIV-1 protease, T4 lysozyme
- MeSH terms
- Models, Molecular, Mutation, Protein Conformation, Proteins
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