Coevolution-based prediction of key allosteric residues for protein function regulation

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Abstract

Allostery is fundamental to many biological processes. Due to the distant regulation nature, how allosteric mutations, modifications and effector binding impact protein function is difficult to forecast. In protein engineering, remote mutations cannot be rationally designed without large-scale experimental screening. Allosteric drugs have raised much attention due to their high specificity and possibility of overcoming existing drug-resistant mutations. However, optimization of allosteric compounds remains challenging. Here, we developed a novel computational method KeyAlloSite to predict allosteric site and to identify key allosteric residues (allo-residues) based on the evolutionary coupling model. We found that protein allosteric sites are strongly coupled to orthosteric site compared to non-functional sites. We further inferred key allo-residues by pairwise comparing the difference of evolutionary coupling scores of each residue in the allosteric pocket with the functional site. Our predicted key allo-residues are in accordance with previous experimental studies for typical allosteric proteins like BCR-ABL1, Tar and PDZ3, as well as key cancer mutations. We also showed that KeyAlloSite can be used to predict key allosteric residues distant from the catalytic site that are important for enzyme catalysis. Our study demonstrates that weak coevolutionary couplings contain important information of protein allosteric regulation function. KeyAlloSite can be applied in studying the evolution of protein allosteric regulation, designing and optimizing allosteric drugs, performing functional protein design and enzyme engineering.

Data availability

All data that support the results of this study are included in the manuscript, supplementary files, and GitHub repository(https://github.com/huilan1210/KeyAlloSite). Source Data files have been provided for all Figures(except Figure 6 and Figure 1-figure supplement 1).

The following previously published data sets were used

(2015) ASBench: benchmarking sets for allosteric discovery
ASBench, http://mdl.shsmu.edu.cn/asbench.

https://doi.org/10.1093/bioinformatics/btv169

Article and author information

Author details

Juan Xie

Center for Quantitative Biology, Peking University, Beijing, China

Competing interests
The authors declare that no competing interests exist.
Weilin Zhang

College of Chemistry and Molecular Engineering, Peking University, Beijing, China

Competing interests
The authors declare that no competing interests exist.
Xiaolei Zhu

School of Sciences, Anhui Agricultural University, Hefei, China

Competing interests
The authors declare that no competing interests exist.
Minghua Deng

Center for Quantitative Biology, Peking University, Beijing, China

Competing interests
The authors declare that no competing interests exist.
Luhua Lai

College of Chemistry and Molecular Engineering, Peking University, Beijing, China

For correspondence
lhlai@pku.edu.cn

Competing interests
The authors declare that no competing interests exist.

"This ORCID iD identifies the author of this article:" 0000-0002-8343-7587

Funding

National Key R&D Program of China (2022YFA1303700)

Luhua Lai

National Natural Science Foundation of China (21633001,22237002)

Luhua Lai

Chinese Academy of Medical Sciences (2021-I2M-5-014)

Luhua Lai

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Copyright

This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.