Evaluation of Several Two-Step Scoring Functions Based on Linear Interaction Energy, Effective Ligand Size, and Empirical Pair Potentials for Prediction of Protein–Ligand Binding Geometry and Free Energy

Abstract

The performance of several two-step scoring approaches for molecular docking were assessed for their ability to predict binding geometries and free energies. Two new scoring functions designed for “step 2 discrimination” were proposed and compared to our CHARMM implementation of the linear interaction energy (LIE) approach using the Generalized-Born with Molecular Volume (GBMV) implicit solvation model. A scoring function S1 was proposed by considering only “interacting” ligand atoms as the “effective size” of the ligand, and extended to an empirical regression-based pair potential S2. The S1 and S2 scoring schemes were trained and five-fold cross validated on a diverse set of 259 protein-ligand complexes from the Ligand Protein Database (LPDB). The regression-based parameters for S1 and S2 also demonstrated reasonable transferability in the CSARdock 2010 benchmark using a new dataset (NRC HiQ) of diverse protein-ligand complexes. The ability of the scoring functions to accurately predict ligand geometry was evaluated by calculating the discriminative power (DP) of the scoring functions to identify native poses. The parameters for the LIE scoring function with the optimal discriminative power (DP) for geometry (step 1 discrimination) were found to be very similar to the best-fit parameters for binding free energy over a large number of protein-ligand complexes (step 2 discrimination). Reasonable performance of the scoring functions in enrichment of active compounds in four different protein target classes established that the parameters for S1 and S2 provided reasonable accuracy and transferability. Additional analysis was performed to definitively separate scoring function performance from molecular weight effects. This analysis included the prediction of ligand binding efficiencies for a subset of the CSARdock NRC HiQ dataset where the number of ligand heavy atoms ranged from 17 to 35. This range of ligand heavy atoms is where improved accuracy of predicted ligand efficiencies is most relevant to real-world drug design efforts.

Structural basis for selective inhibition of Cyclooxygenase-1 (COX-1) by diarylisoxazoles mofezolac and 3-(5-chlorofuran-2-yl)-5-methyl-4-phenylisoxazole (P6)

“…FLAP was employed in structure-based mode using the crystallographic structures of COX-1:mofezolac and COX-1: 1 (P6) as templates. Binding free energies were computed from atomic coordinates as described in reference [40]. …”

Akt kinase C-terminal modifications control activation loop dephosphorylation and enhance insulin response

“…A CHARMM (Chemistry at HARvard Macromolecular Mechanics)-based method [30] was utilized to approximate the free energy contribution of each PDK1-interacting fragment (PIFtide) residue side chain interaction with the kinase domain-binding groove interface. The free energy contributions of the side chains are approximated by first removing peptide backbone atoms, transforming the α -carbon into a methyl group and calculating the linear interaction energy (LIE) difference between the scaled potential energies of the bound and free states [30].…”

“…The free energy contributions of the side chains are approximated by first removing peptide backbone atoms, transforming the α -carbon into a methyl group and calculating the linear interaction energy (LIE) difference between the scaled potential energies of the bound and free states [30]. Calculations are performed utilizing the Generalized-Born with Molecular Volume (GBMV) implicit solvent model providing a rigorous treatment of desolvation penalties.…”

Identification of the functional binding pocket for compounds targeting small-conductance Ca2+-activated potassium channels

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