Abstract
Cosolvent Molecular Dynamics (MD) simulations are increasingly popular techniques developed for prediction and characterization of allosteric and cryptic binding sites, which can be rendered “druggable” by small molecule ligands. Despite their conceptual simplicity and effectiveness, the analysis of cosolvent MD trajectories relies on pocket volume data, which requires a high level of manual investigation and may introduce a bias. In this work, we present CAT (Cosolvent Analysis Toolkit): an open-source, freely accessible analytical tool, suitable for automated analysis of cosolvent MD trajectories. CAT is compatible with commonly used molecular graphics software packages such as UCSF Chimera and VMD. Using a novel hybrid empirical force field scoring function, CAT accurately ranks the dynamic interactions between the macromolecular target and cosolvent molecules. To benchmark, CAT was used for three validated protein targets with allosteric and orthosteric binding sites, using five chemically distinct cosolvent molecules. For all systems, CAT has accurately identified all known sites. CAT can thus assist in computational studies aiming at identification of protein “hotspots” in a wide range of systems. As an easy-to-use computational tool, we expect that CAT will contribute to an increase in the size of the potentially ‘druggable’ human proteome.
Highlights
Cosolvent Molecular Dynamics (MD) simulations are increasingly popular techniques developed for prediction and characterization of allosteric and cryptic binding sites, which can be rendered “druggable” by small molecule ligands
The comparison concludes that the explicit solvent/cosolvent interactions and MD sampling is crucial for the right assessment of cryptic binding sites, and Cosolvent Analysis Toolkit (CAT) scoring function can filter and reasonably rank binding regions
Through its ligand binding domain (LBD) it binds to steroid hormones such as testosterone, androsterone, or dihydrotestosterone; the binding event occurs at the orthosteric binding site
Summary
Cosolvent Molecular Dynamics (MD) simulations are increasingly popular techniques developed for prediction and characterization of allosteric and cryptic binding sites, which can be rendered “druggable” by small molecule ligands. I.e. pockets that form in a protein upon ligand binding, but are not apparent in the crystal structure of the apo (unliganded) protein, and transient pockets, i.e. pockets with transiently form in a subset of an ensemble of protein conformations, offer immense opportunities to target proteins deemed ‘undruggable’ by conventional structure-based drug design (SBDD) approaches and are of considerable interest in academia and the pharmaceutical industry These ‘hotspots’ are notoriously difficult to identify, but the molecular mechanisms by which they form are still debated[2,3]. The major shortcoming of MCSS is the fact that the probes do not interact with one another, which results in the loss of any possible cooperativity in their binding Another limitation lies in the static structure of the protein target analyzed: any ligand-induced conformational changes cannot be observed, which precludes its applicability to the identification of cryptic and transient pockets. To overcome the accessibility problem, easy to use tools for non-experts offering scans for potential cryptic, allosteric, and transient pockets have been established and they have gained popularity in recent years[10,11,12,13,14]
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