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

  1. Juan Xie
  2. Weilin Zhang
  3. Xiaolei Zhu
  4. Minghua Deng
  5. Luhua Lai  Is a corresponding author
  1. Peking University, China
  2. Anhui Agricultural University, China

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

Article and author information

Author details

  1. Juan Xie

    Center for Quantitative Biology, Peking University, Beijing, China
    Competing interests
    The authors declare that no competing interests exist.
  2. Weilin Zhang

    College of Chemistry and Molecular Engineering, Peking University, Beijing, China
    Competing interests
    The authors declare that no competing interests exist.
  3. Xiaolei Zhu

    School of Sciences, Anhui Agricultural University, Hefei, China
    Competing interests
    The authors declare that no competing interests exist.
  4. Minghua Deng

    Center for Quantitative Biology, Peking University, Beijing, China
    Competing interests
    The authors declare that no competing interests exist.
  5. 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.
    ORCID icon "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

© 2023, Xie et al.

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.

Metrics

  • 2,822
    views
  • 478
    downloads
  • 18
    citations

Views, downloads and citations are aggregated across all versions of this paper published by eLife.

Download links

A two-part list of links to download the article, or parts of the article, in various formats.

Downloads (link to download the article as PDF)

Open citations (links to open the citations from this article in various online reference manager services)

Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)

  1. Juan Xie
  2. Weilin Zhang
  3. Xiaolei Zhu
  4. Minghua Deng
  5. Luhua Lai
(2023)
Coevolution-based prediction of key allosteric residues for protein function regulation
eLife 12:e81850.
https://doi.org/10.7554/eLife.81850

Share this article

https://doi.org/10.7554/eLife.81850

Further reading

    1. Biochemistry and Chemical Biology
    2. Microbiology and Infectious Disease
    Stephanie M Stuteley, Ghader Bashiri
    Insight

    In the bacterium M. smegmatis, an enzyme called MftG allows the the cofactor mycofactocin to transfers electrons released during ethanol metabolism to the electron transport chain.

    1. Biochemistry and Chemical Biology
    2. Structural Biology and Molecular Biophysics
    Cristina Paissoni, Sarita Puri ... Carlo Camilloni
    Research Article

    Both immunoglobulin light-chain (LC) amyloidosis (AL) and multiple myeloma (MM) share the overproduction of a clonal LC. However, while LCs in MM remain soluble in circulation, AL LCs misfold into toxic-soluble species and amyloid fibrils that accumulate in organs, leading to distinct clinical manifestations. The significant sequence variability of LCs has hindered the understanding of the mechanisms driving LC aggregation. Nevertheless, emerging biochemical properties, including dimer stability, conformational dynamics, and proteolysis susceptibility, distinguish AL LCs from those in MM under native conditions. This study aimed to identify a2 conformational fingerprint distinguishing AL from MM LCs. Using small-angle X-ray scattering (SAXS) under native conditions, we analyzed four AL and two MM LCs. We observed that AL LCs exhibited a slightly larger radius of gyration and greater deviations from X-ray crystallography-determined or predicted structures, reflecting enhanced conformational dynamics. SAXS data, integrated with molecular dynamics simulations, revealed a conformational ensemble where LCs adopt multiple states, with variable and constant domains either bent or straight. AL LCs displayed a distinct, low-populated, straight conformation (termed H state), which maximized solvent accessibility at the interface between constant and variable domains. Hydrogen-deuterium exchange mass spectrometry experimentally validated this H state. These findings reconcile diverse experimental observations and provide a precise structural target for future drug design efforts.