Effect of set up protocols on the accuracy of alchemical free energy calculation over a set of ACK1 inhibitors

Abstract

Hit-to-lead virtual screening frequently relies on a cascade of computational methods that starts with rapid calculations applied to a large number of compounds and ends with more expensive computations restricted to a subset of compounds that passed initial filters. This work focuses on set up protocols for alchemical free energy (AFE) scoring in the context of a Docking – MM/PBSA – AFE cascade. A dataset of 15 congeneric inhibitors of the ACK1 protein was used to evaluate the performance of AFE set up protocols that varied in the steps taken to prepare input files (using previously docked and best scored poses, manual selection of poses, manual placement of binding site water molecules). The main finding is that use of knowledge derived from X-ray structures to model binding modes, together with the manual placement of a bridging water molecule, improves the R2 from 0.45 ± 0.06 to 0.76 ± 0.02 and decreases the mean unsigned error from 2.11 ± 0.08 to 1.24 ± 0.04 kcal mol-1. By contrast a brute force automated protocol that increased the sampling time ten-fold lead to little improvements in accuracy. Besides, it is shown that for the present dataset hysteresis can be used to flag poses that need further attention even without prior knowledge of experimental binding affinities.

Assessment of Binding Affinity via Alchemical Free-Energy Calculations

“…12,33,34 The SOMD package available within the Sire framework has been successfully applied to relative and absolute binding free energy studies of a range of fragments, drug-like small molecules, carbohydrates, host-guest systems. [5][6][7]30,[35][36][37][38] However, a systematic comparison on standard datasets has not been published to date. Here we report a large-scale validation of Flare's FEP implementation on the Wang et al 1 dataset, as well as on a smaller dataset of more challenging scaffold hopping modifications .…”

Hybrid Alchemical Free Energy/Machine-Learning Methodology for the Computation of Hydration Free Energies

“…[5][6][7][8][9][10] There is continued interest in the development of more accurate potential energy functions to benchmark FEP workflows on diverse well-curated protein-ligand datasets, [11][12][13][14] and for applications to blinded challenges or methodological studies. [15][16][17][18][19][20] The calculation of hydration free energies has historically been an important stepping stone towards more accurate forcefields for protein-ligand binding free energy calculations. [21][22][23] Blinded competitions such as SAMPL have also focused on hydration free energy calculations.…”

Implementation of the QUBE Force Field in SOMD for High-Throughput Alchemical Free-Energy Calculations

“…29 SOMD has been used within the Sire molecular simulation framework and successfully applied to alchemical free energy studies on a range of drug-like fragments, carbohydrates and host-guest systems. [30][31][32][33][34][35][36][37][38][39][40] The automated setup and processing of alchemical free energy calculations using Sire, BioSimSpace, SOMD and OpenMM has recently been implemented in Cresset's Flare package, 41 and benchmarked against 220 ligands bound to 14 protein targets, with accuracy comparable to previous reports. 3,9,26 Thus, the SOMD framework provides a promising basis for implementation and future benchmarking of alchemical transformations using the QUBE force field.…”