On the allosteric puzzle and pocket crosstalk through computational means

Allostery is a constitutive, albeit often elusive, feature of biomolecular systems, which heavily determines their functioning. Its mechanical, entropic, long-range, ligand, and environment-dependent nature creates far from trivial interplays between residues and, in general, the secondary structure of proteins. This intricate scenario is mirrored in computational terms as different notions of correlation among residues and pockets can lead to different conclusions and outcomes. In this article, we put on a common ground and challenge three computational approaches for the correlation estimation task and apply them to three diverse targets of pharmaceutical interest: the androgen A(2A) receptor, the androgen receptor, and the EGFR kinase domain. Results show that partial results consensus can be attained, yet different notions lead to pointing the attention to different pockets and communications.

Automated summary

Allostery is a crucial feature of biomolecular systems, impacting their functioning through intricate interplays between residues and protein structures. Computational approaches for correlation estimation provide insights into these complexes but can lead to different conclusions and outcomes. This article compares three computational methods on pharmaceutical targets, revealing consensus in some aspects but also highlighting the importance of different notions in identifying specific pockets and communications.