Abstract
(1) Background: Virtual screening studies on the therapeutically relevant proteins of the severe acute respiratory syndrome Coronavirus 2 (SARS-CoV-2) require a detailed characterization of their druggable binding sites, and, more generally, a convenient pocket mapping represents a key step for structure-based in silico studies; (2) Methods: Along with a careful literature search on SARS-CoV-2 protein targets, the study presents a novel strategy for pocket mapping based on the combination of pocket (as performed by the well-known FPocket tool) and docking searches (as performed by PLANTS or AutoDock/Vina engines); such an approach is implemented by the Pockets 2.0 plug-in for the VEGA ZZ suite of programs; (3) Results: The literature analysis allowed the identification of 16 promising binding cavities within the SARS-CoV-2 proteins and the here proposed approach was able to recognize them showing performances clearly better than those reached by the sole pocket detection; and (4) Conclusions: Even though the presented strategy should require more extended validations, this proved successful in precisely characterizing a set of SARS-CoV-2 druggable binding pockets including both orthosteric and allosteric sites, which are clearly amenable for virtual screening campaigns and drug repurposing studies. All results generated by the study and the Pockets 2.0 plug-in are available for download.
Highlights
The exploration of the potential binding cavities within a given target protein represents a key step among the computational tasks, which follow the target identification [1]
This study had two primary objectives: 1) finding putative orthosteric and allosteric pockets by which to assess the performances of the proposed method and 2) finding potential ligands to be used as probes during docking simulations
Not to mention that the here collected information is relevant per se, since it affords a better understanding of the catalytic mechanism and of the allosteric modulation for the analyzed enzymes and can guide the corresponding docking simulations
Summary
The exploration of the potential binding cavities within a given target protein represents a key step among the computational tasks, which follow the target identification [1]. The first crucial step in the cavity mapping is the recognition of those cavities that are accessible and can have a role in modulating the protein activity. While playing a role in protein activity, some cavities are unable to generate stable complexes with (at least) small drug-like molecules due to their structural, physicochemical, or electronic properties. This fact led to the concept of pocket druggability, the evaluation of which should allow the identification of those cavities for which the hit identification should be reasonably productive [3]
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