The SAMPL6 SAMPLing challenge: Assessing the reliability and efficiency of binding free energy calculations

Abstract

Approaches for computing small molecule binding free energies based on molecular simulations are now regularly being employed by academic and industry practitioners to study receptor-ligand systems and prioritize the synthesis of small molecules for ligand design. Given the variety of methods and implementations available, it is natural to ask how the convergence rates and final predictions of these methods compare. In this study we describe the concept and results for the SAMPL6 SAMPLing challenge, the first challenge from the SAMPL series focusing on the assessment of convergence properties and reproducibility of binding free energy methodologies. We provided parameter files, partial charges, and multiple initial geometries for two octa-acid (OA) and one cucurbit[8]uril (CB8) host-guest systems. Participants submitted binding free energy predictions as a function of the number of force and energy evaluations for seven different alchemical and physical-pathway (i.e., potential of mean force and weighted ensemble of trajectories) methodologies implemented with the GROMACS, AMBER, NAMD, or OpenMM simulation engines. To rank the methods, we developed an efficiency statistic based on bias and variance of the free energy estimates. For the two small OA binders, the free energy estimates computed with alchemical and potential of mean force approaches show relatively similar variance and bias as a function of the number of energy/force evaluations, with the attach-pull-release (APR), GROMACS expanded ensemble, and NAMD double decoupling submissions obtaining the greatest efficiency. The differences between the methods increase when analyzing the CB8-quinine system, where both the guest size and correlation 1 of 43 Preprint ahead of submission -October 5, 2019times for system dynamics are greater. For this system, nonequilibrium switching (GROMACS/NS-DS/SB) obtained the overall highest efficiency. Surprisingly, the results suggest that specifying force field parameters and partial charges is insufficient to generally ensure reproducibility, and we observe differences between seemingly converged predictions ranging approximately from 0.3 to 1.0 kcal/mol, even with almost identical simulations parameters and system setup (e.g., Lennard-Jones cutoff, ionic composition). Further work will be required to completely identify the exact source of these discrepancies. Among the conclusions emerging from the data, we found that Hamiltonian replica exchange-while displaying very small variance-can be affected by a slowly-decaying bias that depends on the initial population of the replicas, that bidirectional estimators are significantly more efficient than unidirectional estimators for nonequilibrium free energy calculations for systems considered, and that the Berendsen barostat introduces non-negligible artifacts in expanded ensemble simulations.

A Benchmark of Electrostatic Method Performance in Relative Binding Free Energy Calculations

“…that is outside of scope for this work, and (b) details will depend on the exact implementation in those software packages. Previous work has shown that different simulation engines are able to take in the same force field and same systems and give identical energies (with PME) within a reasonable tolerance, which would strongly suggest the implementations are equivalent or nearly so, 111,112 at least for PME.…”

Gaussian processes regression for cyclodextrin host-guest binding prediction

“…To enable using CD nano-carrier system for a novel class of drugs, one should perform a screening and/or biochemical characterization of potential complexes, which can be a quite laborious and expensive process [28]. Computational methods aim to automatize and reduce the time and cost to discover new drugs [18,15], however, traditional scoring functions (SF), empirical or physics-based, still struggle to rank potential binders, unless rigorous free energy calculations could be performed, this limitation has been addressed in several community challenges [24], highlighting the potential of Machine Learning Scoring Functions (MLSF) to improve predictions. Therefore, it is crucial to develop models for pre-screening to direct rigorous tests.…”

Expanded Ensemble Methods Can Be Used to Accurately Predict Protein-Ligand Relative Binding Free Energies

“…[23][24][25][26] Of the handful of studies that have previously used EE for ligand binding, all have focused on the problem of computing absolute binding free energies (ABFE). 22,27,28 In the SAMPL4 host-guest challenge, Monroe et al showed that EE yields estimates comparable to other methods, in a system where molecular flexibility and multiple binding modalities are important. 27 Rizzi et al report similarly results in the recent SAMPL6 host-guest challenge.…”

“…27 Rizzi et al report similarly results in the recent SAMPL6 host-guest challenge. 28 To our knowledge, no group has published an example of relative binding free energy (RBFE) estimates predicted using EE. In theory, expanded ensemble simulations are particularly well-suited for absolute binding free energies, as the ligand is able to freely bind and unbind, sampling di↵erent binding modes.…”