3 research outputs found
Biomolecular Simulations under Realistic Macroscopic Salt Conditions
Biomolecular
simulations are typically performed in an aqueous environment where
the number of ions remains fixed for the duration of the simulation,
generally with either a minimally neutralizing ion environment or
a number of salt pairs intended to match the macroscopic salt concentration.
In contrast, real biomolecules experience local ion environments where
the salt concentration is dynamic and may differ from bulk. The degree
of salt concentration variability and average deviation from the macroscopic
concentration remains, as yet, unknown. Here, we describe the theory
and implementation of a Monte Carlo <i>osmostat</i> that
can be added to explicit solvent molecular dynamics or Monte Carlo
simulations to sample from a semigrand canonical ensemble in which
the number of salt pairs fluctuates dynamically during the simulation.
The osmostat reproduces the correct equilibrium statistics for a simulation
volume that can exchange ions with a large reservoir at a defined
macroscopic salt concentration. To achieve useful Monte Carlo acceptance
rates, the method makes use of nonequilibrium candidate Monte Carlo
(NCMC) moves in which monovalent ions and water molecules are alchemically
transmuted using short nonequilibrium trajectories, with a modified
Metropolis-Hastings criterion ensuring correct equilibrium statistics
for an (<i>Δμ</i>, <i>N</i>, <i>p</i>, <i>T</i>) ensemble to achieve a ∼10<sup>46</sup>Ă— boost in acceptance rates. We demonstrate how typical
protein (DHFR and the tyrosine kinase Src) and nucleic acid (Drew–Dickerson
B-DNA dodecamer) systems exhibit salt concentration distributions
that significantly differ from fixed-salt bulk simulations and display
fluctuations that are on the same order of magnitude as the average
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Binding Modes of Ligands Using Enhanced Sampling (BLUES): Rapid Decorrelation of Ligand Binding Modes via Nonequilibrium Candidate Monte Carlo
Accurately predicting protein–ligand
binding affinities and binding modes is a major goal in computational
chemistry, but even the prediction of ligand binding modes in proteins
poses major challenges. Here, we focus on solving the binding mode
prediction problem for rigid fragments. That is, we focus on computing
the dominant placement, conformation, and orientations of a relatively
rigid, fragment-like ligand in a receptor, and the populations of
the multiple binding modes which may be relevant. This problem is
important in its own right, but is even more timely given the recent
success of alchemical free energy calculations. Alchemical calculations
are increasingly used to predict binding free energies of ligands
to receptors. However, the accuracy of these calculations is dependent
on proper sampling of the relevant ligand binding modes. Unfortunately,
ligand binding modes may often be uncertain, hard to predict, and/or
slow to interconvert on simulation time scales, so proper sampling
with current techniques can require prohibitively long simulations.
We need new methods which dramatically improve sampling of ligand
binding modes. Here, we develop and apply a nonequilibrium candidate
Monte Carlo (NCMC) method to improve sampling of ligand binding modes.
In this technique, the ligand is rotated and subsequently allowed
to relax in its new position through alchemical perturbation before
accepting or rejecting the rotation and relaxation as a nonequilibrium
Monte Carlo move. When applied to a T4 lysozyme model binding system,
this NCMC method shows over 2 orders of magnitude improvement in binding
mode sampling efficiency compared to a brute force molecular dynamics
simulation. This is a first step toward applying this methodology
to pharmaceutically relevant binding of fragments and, eventually,
drug-like molecules. We are making this approach available via our
new Binding modes of ligands using enhanced sampling (BLUES) package
which is freely available on GitHub
Binding Modes of Ligands Using Enhanced Sampling (BLUES): Rapid Decorrelation of Ligand Binding Modes via Nonequilibrium Candidate Monte Carlo
Accurately predicting protein–ligand
binding affinities and binding modes is a major goal in computational
chemistry, but even the prediction of ligand binding modes in proteins
poses major challenges. Here, we focus on solving the binding mode
prediction problem for rigid fragments. That is, we focus on computing
the dominant placement, conformation, and orientations of a relatively
rigid, fragment-like ligand in a receptor, and the populations of
the multiple binding modes which may be relevant. This problem is
important in its own right, but is even more timely given the recent
success of alchemical free energy calculations. Alchemical calculations
are increasingly used to predict binding free energies of ligands
to receptors. However, the accuracy of these calculations is dependent
on proper sampling of the relevant ligand binding modes. Unfortunately,
ligand binding modes may often be uncertain, hard to predict, and/or
slow to interconvert on simulation time scales, so proper sampling
with current techniques can require prohibitively long simulations.
We need new methods which dramatically improve sampling of ligand
binding modes. Here, we develop and apply a nonequilibrium candidate
Monte Carlo (NCMC) method to improve sampling of ligand binding modes.
In this technique, the ligand is rotated and subsequently allowed
to relax in its new position through alchemical perturbation before
accepting or rejecting the rotation and relaxation as a nonequilibrium
Monte Carlo move. When applied to a T4 lysozyme model binding system,
this NCMC method shows over 2 orders of magnitude improvement in binding
mode sampling efficiency compared to a brute force molecular dynamics
simulation. This is a first step toward applying this methodology
to pharmaceutically relevant binding of fragments and, eventually,
drug-like molecules. We are making this approach available via our
new Binding modes of ligands using enhanced sampling (BLUES) package
which is freely available on GitHub