Related Experiment Video
Updated: Jul 13, 2025
05:08
Application of I TASSER, trRosetta, UCSF Chimera, HADDOCK server, and HEX loria for De Novo and In Silico Design of Proteins
Published on: July 8, 2025
21
Mutexa: A Computational Ecosystem for Intelligent Protein Engineering
Zhongyue J Yang1,2,3,4,5, Qianzhen Shao1, Yaoyukun Jiang1
1Department of Chemistry, Vanderbilt University, Nashville, Tennessee 37235, United States.
Journal of Chemical Theory and Computation
|October 13, 2023
Summary
Mutexa is a computational ecosystem for intelligent protein engineering, enabling researchers to design protein variants with desired functions. This system integrates databases, modeling software, and scoring functions for advanced protein design.
Area of Science:
- Biotechnology
- Biomedicine
- Computational Biology
Background:
- Protein engineering is crucial for advancing biomedicine and biotechnology.
- Current methods for designing protein variants with specific functions are often complex and time-consuming.
Purpose of the Study:
- To introduce Mutexa, a computational ecosystem for intelligent protein engineering.
- To provide researchers with a seamless way to acquire protein variant sequences with desired functions.
Main Methods:
- Development of IntEnzyDB: a database relating enzyme structures and functions.
- Creation of EnzyHTP and LassoHTP: workflow software for high-throughput protein modeling.
- Implementation of EnzyKR and DeepLasso: scoring functions mapping sequence-structure-function relationships.
Main Results:
- Demonstrated applications in benchmarking enzyme functional descriptors.
- Investigated protein electrostatics and cavity distributions in SAM-dependent methyltransferases.
- Highlighted the role of dynamic effects in enzyme catalysis.
Conclusions:
- Mutexa provides a foundation for intelligent protein engineering.
- Future development aims to enhance Mutexa's capabilities in identifying beneficial protein mutants.
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