Prediction of allosteric sites and signaling: Insights from benchmarking datasets.
| Autor: | Wu N; Department of Chemistry, Imperial College London, London W12 0BZ, UK., Strömich L; Department of Chemistry, Imperial College London, London W12 0BZ, UK., Yaliraki SN; Department of Chemistry, Imperial College London, London W12 0BZ, UK. |
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| Jazyk: | angličtina |
| Zdroj: | Patterns (New York, N.Y.) [Patterns (N Y)] 2021 Dec 09; Vol. 3 (1), pp. 100408. Date of Electronic Publication: 2021 Dec 09 (Print Publication: 2022). |
| DOI: | 10.1016/j.patter.2021.100408 |
| Abstrakt: | Allostery is a pervasive mechanism that regulates protein activity through ligand binding at a site different from the orthosteric site. The universality of allosteric regulation complemented by the benefits of highly specific and potentially non-toxic allosteric drugs makes uncovering allosteric sites invaluable. However, there are few computational methods to effectively predict them. Bond-to-bond propensity analysis has successfully predicted allosteric sites in 19 of 20 cases using an energy-weighted atomistic graph. We here extended the analysis onto 432 structures of 146 proteins from two benchmarking datasets for allosteric proteins: ASBench and CASBench. We further introduced two statistical measures to account for the cumulative effect of high-propensity residues and the crucial residues in a given site. The allosteric site is recovered for 127 of 146 proteins (407 of 432 structures) knowing only the orthosteric sites or ligands. The quantitative analysis using a range of statistical measures enables better characterization of potential allosteric sites and mechanisms involved. Competing Interests: The authors declare no competing interests. (© 2021 The Authors.) |
| Databáze: | MEDLINE |
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