RT Journal Article
SR Electronic
T1 The SAMPL6 SAMPLing challenge: Assessing the reliability and efficiency of binding free energy calculations
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 795005
DO 10.1101/795005
A1 Rizzi, Andrea
A1 Jensen, Travis
A1 Slochower, David R.
A1 Aldeghi, Matteo
A1 Gapsys, Vytautas
A1 Ntekoumes, Dimitris
A1 Bosisio, Stefano
A1 Papadourakis, Michail
A1 Henriksen, Niel M.
A1 de Groot, Bert L.
A1 Cournia, Zoe
A1 Dickson, Alex
A1 Michel, Julien
A1 Gilson, Michael K.
A1 Shirts, Michael R.
A1 Mobley, David L.
A1 Chodera, John D.
YR 2019
UL http://biorxiv.org/content/early/2019/10/07/795005.abstract
AB 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 times 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.