7 research outputs found
Sensitivity in Binding Free Energies Due to Protein Reorganization
Tremendous
recent improvements in computer hardware, coupled with
advances in sampling techniques and force fields, are now allowing
proteināligand binding free energy calculations to be routinely
used to aid pharmaceutical drug discovery projects. However, despite
these recent innovations, there are still needs for further improvement
in sampling algorithms to more adequately sample protein motion relevant
to proteināligand binding. Here, we report our work identifying
and studying such clear and remaining needs in the apolar cavity of
T4 lysozyme L99A. In this study, we model recent experimental results
that show the progressive opening of the binding pocket in response
to a series of homologous ligands. Even
while using enhanced sampling techniques, we demonstrate that the
predicted relative binding free energies (RBFE) are sensitive to the
initial protein conformational state. Particularly, we highlight the
importance of sufficient sampling of protein conformational changes
and demonstrate how inclusion of three key protein residues in the
āhotā region of the FEP/REST simulation improves the
sampling and resolves this sensitivity, given enough simulation time
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Calculating Partition Coefficients of Small Molecules in Octanol/Water and Cyclohexane/Water
Partition coefficients describe how
a solute is distributed between
two immiscible solvents. They are used in drug design as a measure
of a soluteās hydrophobicity and a proxy for its membrane permeability.
We calculate partition coefficients from transfer free energies using
molecular dynamics simulations in explicit solvent. Setup is done
by our new Solvation Toolkit which automates the process of creating
input files for any combination of solutes and solvents for many popular
molecular dynamics software packages. We calculate partition coefficients
between octanol/water and cyclohexane/water with the Generalized AMBER
Force Field (GAFF) and the Dielectric Corrected GAFF (GAFF-DC). With
similar methods in the past we found a root-mean-squared error (RMSE)
of 6.3 kJ/mol in hydration free energies which would correspond to
an error of around 1.6 log units in partition coefficients if solvation
free energies in both solvents were estimated with comparable accuracy.
Here we find an overall RMSE of about 1.2 log units with both force
fields. Results from GAFF and GAFF-DC seem to exhibit systematic biases
in opposite directions for calculated cyclohexane/water partition
coefficients
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Using MD Simulations To Calculate How Solvents Modulate Solubility
Here, our interest is in predicting
solubility in general, and
we focus particularly on predicting how the solubility of particular
solutes is modulated by the solvent environment. Solubility in general
is extremely important, both for theoretical reasons ā it provides
an important probe of the balance between soluteāsolute and
soluteāsolvent interactions ā and for more practical
reasons, such as how to control the solubility of a given solute via
modulation of its environment, as in process chemistry and separations.
Here, we study how the change of solvent affects the solubility of
a given compound. That is, we calculate relative solubilities. We
use MD simulations to calculate relative solubility and compare our
calculated values with experiment as well as with results from several
other methods, SMD and UNIFAC, the latter of which is commonly used
in chemical engineering design. We find that straightforward solubility
calculations based on molecular simulations using a general small-molecule
force field outperform SMD and UNIFAC both in terms of accuracy and
coverage of the relevant chemical space
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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 proteināligand
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 proteināligand
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
Collaborative Assessment of Molecular Geometries and Energies from the Open Force Field
Force fields form the basis for classical molecular simulations,
and their accuracy is crucial for the quality of, for instance, proteināligand
binding simulations in drug discovery. The huge diversity of small-molecule
chemistry makes it a challenge to build and parameterize a suitable
force field. The Open Force Field Initiative is a combined industry
and academic consortium developing a state-of-the-art small-molecule
force field. In this report, industry members of the consortium worked
together to objectively evaluate the performance of the force fields
(referred to here as OpenFF) produced by the initiative on a combined
public and proprietary dataset of 19,653 relevant molecules selected
from their internal research and compound collections. This evaluation
was important because it was completely blind; at most partners, none
of the molecules or data were used in force field development or testing
prior to this work. We compare the Open Force Field āSageā
version 2.0.0 and āParsleyā version 1.3.0 with GAFF-2.11-AM1BCC,
OPLS4, and SMIRNOFF99Frosst. We analyzed force-field-optimized geometries
and conformer energies compared to reference quantum mechanical data.
We show that OPLS4 performs best, and the latest Open Force Field
release shows a clear improvement compared to its predecessors. The
performance of established force fields such as GAFF-2.11 was generally
worse. While OpenFF researchers were involved in building the benchmarking
infrastructure used in this work, benchmarking was done entirely in-house
within industrial organizations and the resulting assessment is reported
here. This work assesses the force field performance using separate
benchmarking steps, external datasets, and involving external research
groups. This effort may also be unique in terms of the number of different
industrial partners involved, with 10 different companies participating
in the benchmark efforts
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Accurate and Reliable Prediction of Relative Ligand Binding Potency in Prospective Drug Discovery by Way of a Modern Free-Energy Calculation Protocol and Force Field
Designing
tight-binding ligands is a primary objective of small-molecule
drug discovery. Over the past few decades, free-energy calculations
have benefited from improved force fields and sampling algorithms,
as well as the advent of low-cost parallel computing. However, it
has proven to be challenging to reliably achieve the level of accuracy
that would be needed to guide lead optimization (ā¼5Ć in
binding affinity) for a wide range of ligands and protein targets.
Not surprisingly, widespread commercial application of free-energy
simulations has been limited due to the lack of large-scale validation
coupled with the technical challenges traditionally associated with
running these types of calculations. Here, we report an approach that
achieves an unprecedented level of accuracy across a broad range of
target classes and ligands, with retrospective results encompassing
200 ligands and a wide variety of chemical perturbations, many of
which involve significant changes in ligand chemical structures. In
addition, we have applied the method in prospective drug discovery
projects and found a significant improvement in the quality of the
compounds synthesized that have been predicted to be potent. Compounds
predicted to be potent by this approach have a substantial reduction
in false positives relative to compounds synthesized on the basis
of other computational or medicinal chemistry approaches. Furthermore,
the results are consistent with those obtained from our retrospective
studies, demonstrating the robustness and broad range of applicability
of this approach, which can be used to drive decisions in lead optimization