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An attempt to incorporate effect of direct interaction between a ligand and explicit water molecules into MM/3D‐RISM

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AbstractEndpoint methods using continuum‐solvent models are widely used to estimate protein–ligand affinity. A recently developed method, MM/3D‐RISM, estimates the solvation energy using statistical mechanics by 3D‐RISM. This method is theoretically expected to accurately describe solvation effects and to also be less dependent on protein–ligand systems. In this study, we examined the performance of MM/3D‐RISM for a set of α‐thrombin inhibitors with a non‐congeneric series of ligands, containing three diverse chemical scaffolds. The standard MM/3D‐RISM showed a weak correlation (R2 = 0.191) but correctly estimated affinity for two of the three scaffolds. However, the simplest inhibitor, benzamidine, was not ranked appropriately. From visual inspection of inhibitor‐binding modes, an attempt was made to incorporate the direct interaction between a ligand and water molecules into MM/3D‐RISM. A model (Model‐1) dealing with directly interacting water molecules (Wat) as an independent component of a protein (R)–ligand (L) complex‐formation, that is, R + L + Wat → R–L–Wat, showed a better linearity (R2 = 0.422) than that of the standard MM/3D‐RISM model and achieved a good ranking of all three scaffolds of α‐thrombin inhibitors. Additionally, an attempt was made to model avidin–biotin system with a congeneric series of inhibitors, and results showed that both the standard MM/3D‐RISM model (R2 = 0.839) and Model‐1 (R2 = 0.695) satisfactorily estimated the affinity.
Title: An attempt to incorporate effect of direct interaction between a ligand and explicit water molecules into MM/3D‐RISM
Description:
AbstractEndpoint methods using continuum‐solvent models are widely used to estimate protein–ligand affinity.
A recently developed method, MM/3D‐RISM, estimates the solvation energy using statistical mechanics by 3D‐RISM.
This method is theoretically expected to accurately describe solvation effects and to also be less dependent on protein–ligand systems.
In this study, we examined the performance of MM/3D‐RISM for a set of α‐thrombin inhibitors with a non‐congeneric series of ligands, containing three diverse chemical scaffolds.
The standard MM/3D‐RISM showed a weak correlation (R2 = 0.
191) but correctly estimated affinity for two of the three scaffolds.
However, the simplest inhibitor, benzamidine, was not ranked appropriately.
From visual inspection of inhibitor‐binding modes, an attempt was made to incorporate the direct interaction between a ligand and water molecules into MM/3D‐RISM.
A model (Model‐1) dealing with directly interacting water molecules (Wat) as an independent component of a protein (R)–ligand (L) complex‐formation, that is, R + L + Wat → R–L–Wat, showed a better linearity (R2 = 0.
422) than that of the standard MM/3D‐RISM model and achieved a good ranking of all three scaffolds of α‐thrombin inhibitors.
Additionally, an attempt was made to model avidin–biotin system with a congeneric series of inhibitors, and results showed that both the standard MM/3D‐RISM model (R2 = 0.
839) and Model‐1 (R2 = 0.
695) satisfactorily estimated the affinity.

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