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Updated: Jan 13, 2026
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Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues
Published on: July 14, 2015
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Computational design of conformation-biasing mutations to alter protein functions
Peter E Cavanagh1, Andrew G Xue2, Shizhong A Dai3
1Department of Biochemistry, Stanford University, Stanford, CA, USA.
Science (New York, N.Y.)
|January 8, 2026
Summary
Conformational biasing (CB) is a computational method that predicts protein variants with specific conformational states. This approach enhances protein function and reveals new mechanisms, with broad applications in biotechnology and medicine.
Area of Science:
- Biochemistry
- Computational Biology
- Protein Engineering
Background:
- Protein conformational states are crucial for function.
- Predicting and engineering these states computationally is challenging.
Purpose of the Study:
- To introduce and validate Conformational Biasing (CB), a novel computational method.
- To engineer protein variants with desired conformational biases and functions.
Main Methods:
- Utilized inverse folding models for contrastive scoring.
- Applied CB to diverse protein datasets including K-Ras, SARS-CoV-2 spike, and kinases.
- Investigated lipoic acid ligase (LplA) conformational dynamics.
Main Results:
- Validated CB across seven datasets, identifying functional protein variants.
- Discovered a mechanism linking LplA conformation to enzymatic promiscuity.
- Demonstrated that open-biased LplA variants are more promiscuous, while closed-biased variants are more selective.
Conclusions:
- CB is a rapid and effective method for engineering protein dynamics.
- CB enhances protein function, such as LplA's utility for site-specific labeling.
- CB has broad applications in basic research, biotechnology, and medicine.
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