Enhancing Ligand and Protein Sampling Using Sequential Monte Carlo

The sampling problem
is one of the most widely studied topics in
computational chemistry. While various methods exist for sampling
along a set of reaction coordinates, many require system-dependent
hyperparameters to achieve maximum efficiency. In this work, we present
an alchemical variation of adaptive sequential Monte Carlo (SMC),
an irreversible importance resampling method that is part of a well-studied
class of methods that have been used in various applications but have
been underexplored in computational biophysics. Afterward, we apply
alchemical SMC on a variety of test cases, including torsional rotations
of solvated ligands (butene and a terphenyl derivative), translational
and rotational movements of protein-bound ligands, and protein side
chain rotation coupled to the ligand degrees of freedom (T4-lysozyme,
protein tyrosine phosphatase 1B, and transforming growth factor β).
We find that alchemical SMC is an efficient way to explore targeted
degrees of freedom and can be applied to a variety of systems using
the same hyperparameters to achieve a similar performance. Alchemical
SMC is a promising tool for preparatory exploration of systems where
long-timescale sampling of the entire system can be traded off against
short-timescale sampling of a particular set of degrees of freedom
over a population of conformers.