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Articles linked to this work by shared authors, journal, and citation graph.

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OPUS-X: an open-source toolkit for protein torsion angles, secondary structure, solvent accessibility, contact map predictions and 3D folding.

Bioinformatics (Oxford, England)·2021
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OPUS-Rota3: Improving Protein Side-Chain Modeling by Deep Neural Networks and Ensemble Methods.

Journal of chemical information and modeling·2020
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OPUS-Fold: An Open-Source Protein Folding Framework Based on Torsion-Angle Sampling.

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OPUS-Mut: Studying the Effect of Protein Mutation through Side-Chain Modeling.

Gang Xu1,2,3, Qinghua Wang4, Jianpeng Ma1,2,3

  • 1Multiscale Research Institute of Complex Systems, Fudan University, Shanghai 200433, China.

Journal of Chemical Theory and Computation
|February 22, 2023
PubMed
Summary
This summary is machine-generated.

We developed OPUS-Mut, a computational method for predicting protein mutation effects by accurately modeling side-chain changes. This tool outperforms existing methods and aids in identifying harmful mutations for protein design.

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Area of Science:

  • Biochemistry and Structural Biology
  • Computational Biology
  • Protein Engineering

Background:

  • Predicting protein mutation effects is vital for protein design, evolution, and understanding genetic diseases.
  • Accurate side-chain modeling is essential for studying mutation impacts, as mutations involve side-chain replacements.

Purpose of the Study:

  • To introduce OPUS-Mut, a novel computational method for predicting protein mutation effects.
  • To demonstrate OPUS-Mut's superior performance compared to existing backbone-dependent side-chain modeling techniques.

Main Methods:

  • Development of the OPUS-Mut computational method for side-chain modeling.
  • Evaluation of OPUS-Mut using four case studies: Myoglobin, p53, HIV-1 protease, and T4 lysozyme.

Main Results:

  • OPUS-Mut accurately predicts side-chain structures for various mutants, showing good agreement with experimental data.
  • The method demonstrates a reasonable correlation between predicted structural shifts and experimentally measured functional changes in mutants.
  • OPUS-Mut effectively distinguishes between harmful and benign mutations.

Conclusions:

  • OPUS-Mut offers a significant advancement in predicting protein mutation effects through accurate side-chain modeling.
  • The tool can guide protein design by identifying beneficial mutations and constructing proteins with desired structural properties.
  • OPUS-Mut aids in understanding the functional consequences of mutations and can assist in genetic disease analysis.