posted on 2023-01-09, 16:34authored byHsu-Chun Tsai, Tai-Sung Lee, Abir Ganguly, Timothy J. Giese, Maximilian CCJC Ebert, Paul Labute, Kenneth M. Merz, Darrin M. York
We develop a framework for the design
of optimized alchemical
transformation
pathways in free energy simulations using nonlinear mixing and a new
functional form for so-called “softcore” potentials.
We describe the implementation and testing of this framework in the
GPU-accelerated AMBER software suite. The new optimized alchemical
transformation pathways integrate a number of important features,
including (1) the use of smoothstep functions to stabilize behavior
near the transformation end points, (2) consistent power scaling of
Coulomb and Lennard-Jones (LJ) interactions with unitless control
parameters to maintain balance of electrostatic attractions and exchange
repulsions, (3) pairwise form based on the LJ contact radius for the
effective interaction distance with separation-shifted scaling, and
(4) rigorous smoothing of the potential at the nonbonded cutoff boundary.
The new softcore potential form is combined with smoothly transforming
nonlinear λ weights for mixing specific potential energy terms,
along with flexible λ-scheduling features, to enable robust
and stable alchemical transformation pathways. The resulting pathways
are demonstrated and tested, and shown to be superior to the traditional
methods in terms of numerical stability and minimal variance of the
free energy estimates for all cases considered. The framework presented
here can be used to design new alchemical enhanced sampling methods,
and leveraged in robust free energy workflows for large ligand data
sets.