Skip to main content
bioRxiv
  • Home
  • About
  • Submit
  • ALERTS / RSS
Advanced Search
New Results

OPUS-Mut: studying the effect of protein mutation through side-chain modeling

View ORCID ProfileGang Xu, Qinghua Wang, Jianpeng Ma
doi: https://doi.org/10.1101/2022.05.10.491420
Gang Xu
1Multiscale Research Institute of Complex Systems, Fudan University, Shanghai, 200433, China
2Zhangjiang Fudan International Innovation Center, Fudan University, Shanghai, 201210, China
3Shanghai AI Laboratory, Shanghai, 200030, China
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Gang Xu
Qinghua Wang
4Center for Biomolecular Innovation, Harcam Biomedicines, Shanghai, China
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Jianpeng Ma
1Multiscale Research Institute of Complex Systems, Fudan University, Shanghai, 200433, China
2Zhangjiang Fudan International Innovation Center, Fudan University, Shanghai, 201210, China
3Shanghai AI Laboratory, Shanghai, 200030, China
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • For correspondence: jpma@fudan.edu.cn
  • Abstract
  • Full Text
  • Info/History
  • Metrics
  • Supplementary material
  • Preview PDF
Loading

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.

Competing Interest Statement

The authors have declared no competing interest.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
Back to top
PreviousNext
Posted May 11, 2022.
Download PDF

Supplementary Material

Email

Thank you for your interest in spreading the word about bioRxiv.

NOTE: Your email address is requested solely to identify you as the sender of this article.

Enter multiple addresses on separate lines or separate them with commas.
OPUS-Mut: studying the effect of protein mutation through side-chain modeling
(Your Name) has forwarded a page to you from bioRxiv
(Your Name) thought you would like to see this page from the bioRxiv website.
CAPTCHA
This question is for testing whether or not you are a human visitor and to prevent automated spam submissions.
Share
OPUS-Mut: studying the effect of protein mutation through side-chain modeling
Gang Xu, Qinghua Wang, Jianpeng Ma
bioRxiv 2022.05.10.491420; doi: https://doi.org/10.1101/2022.05.10.491420
Digg logo Reddit logo Twitter logo Facebook logo Google logo LinkedIn logo Mendeley logo
Citation Tools
OPUS-Mut: studying the effect of protein mutation through side-chain modeling
Gang Xu, Qinghua Wang, Jianpeng Ma
bioRxiv 2022.05.10.491420; doi: https://doi.org/10.1101/2022.05.10.491420

Citation Manager Formats

  • BibTeX
  • Bookends
  • EasyBib
  • EndNote (tagged)
  • EndNote 8 (xml)
  • Medlars
  • Mendeley
  • Papers
  • RefWorks Tagged
  • Ref Manager
  • RIS
  • Zotero
  • Tweet Widget
  • Facebook Like
  • Google Plus One

Subject Area

  • Bioinformatics
Subject Areas
All Articles
  • Animal Behavior and Cognition (3514)
  • Biochemistry (7367)
  • Bioengineering (5346)
  • Bioinformatics (20324)
  • Biophysics (10045)
  • Cancer Biology (7776)
  • Cell Biology (11352)
  • Clinical Trials (138)
  • Developmental Biology (6453)
  • Ecology (9980)
  • Epidemiology (2065)
  • Evolutionary Biology (13356)
  • Genetics (9373)
  • Genomics (12612)
  • Immunology (7725)
  • Microbiology (19103)
  • Molecular Biology (7465)
  • Neuroscience (41153)
  • Paleontology (301)
  • Pathology (1235)
  • Pharmacology and Toxicology (2142)
  • Physiology (3178)
  • Plant Biology (6880)
  • Scientific Communication and Education (1276)
  • Synthetic Biology (1900)
  • Systems Biology (5328)
  • Zoology (1091)