scholarly journals Improving Small-Molecule Force Field Parameters in Ligand Binding Studies

2021 ◽  
Vol 8 ◽  
Author(s):  
Stefano Raniolo ◽  
Vittorio Limongelli
Keyword(s):  
Ligand Binding ◽  
Force Field ◽  
Small Molecules ◽  
Binding Mode ◽  
Free Energy Calculations ◽  
Binding Studies ◽  
Torsion Angles ◽  
Molecular Binding ◽  
Future Drug ◽  
Force Field Parameters

Small molecules are major players of many chemical processes in diverse fields, from material science to biology. They are made by a combination of carbon and heteroatoms typically organized in system-specific structures of different complexity. This peculiarity hampers the application of standard force field parameters and their in silico study by means of atomistic simulations. Here, we combine quantum-mechanics and atomistic free-energy calculations to achieve an improved parametrization of the ligand torsion angles with respect to the state-of-the-art force fields in the paradigmatic molecular binding system benzamidine/trypsin. Funnel-Metadynamics calculations with the new parameters greatly reproduced the high-resolution crystallographic ligand binding mode and allowed a more accurate description of the binding mechanism, when the ligand might assume specific conformations to cross energy barriers. Our study impacts on future drug design investigations considering that the vast majority of marketed drugs are small-molecules.

Binding Modes of Ligands Using Enhanced Sampling (BLUES): Rapid Decorrelation of Ligand Binding Modes Using Nonequilibrium Candidate Monte Carlo

2017 ◽  
Author(s):  
Samuel Gill ◽  
Nathan M. Lim ◽  
Patrick Grinaway ◽  
Ariën S. Rustenburg ◽  
Josh Fass ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Ligand Binding ◽  
Binding Mode ◽  
Free Energy Calculations ◽  
Dynamics Simulation ◽  
Binding Modes ◽  
Enhanced Sampling ◽  
Recent Success ◽  
Multiple Binding ◽  
Proper Sampling

<div>Accurately predicting protein-ligand binding 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 timescales, 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.</div><div><br></div><div>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 two orders of magnitude improvement in binding mode sampling efficiency compared to a brute force molecular dynamics simulation. This is a first step towards 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.</div>

Binding Modes of Ligands Using Enhanced Sampling (BLUES): Rapid Decorrelation of Ligand Binding Modes Using Nonequilibrium Candidate Monte Carlo

2018 ◽  
Author(s):  
Samuel Gill ◽  
Nathan M. Lim ◽  
Patrick Grinaway ◽  
Ariën S. Rustenburg ◽  
Josh Fass ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Ligand Binding ◽  
Binding Mode ◽  
Free Energy Calculations ◽  
Dynamics Simulation ◽  
Binding Modes ◽  
Enhanced Sampling ◽  
Recent Success ◽  
Multiple Binding ◽  
Proper Sampling

<div>Accurately predicting protein-ligand binding 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 timescales, 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.</div><div><br></div><div>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 two orders of magnitude improvement in binding mode sampling efficiency compared to a brute force molecular dynamics simulation. This is a first step towards 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.</div>

scholarly journals Binding thermodynamics of host-guest systems with SMIRNOFF99Frosst 1.0.5 from the Open Force Field Initiative

2019 ◽  
Author(s):  
David Slochower ◽  
Niel Henriksen ◽  
Lee-Ping Wang ◽  
John Chodera ◽  
David Mobley ◽  
...  
Keyword(s):  
Ligand Binding ◽  
Force Field ◽  
Small Molecule ◽  
Force Fields ◽  
Free Energy Calculations ◽  
Model Systems ◽  
Conformational Space ◽  
Free Energies ◽  
Binding Thermodynamics ◽  
Binding Free Energies

<div><div><div><p>Designing ligands that bind their target biomolecules with high affinity and specificity is a key step in small- molecule drug discovery, but accurately predicting protein-ligand binding free energies remains challenging. Key sources of errors in the calculations include inadequate sampling of conformational space, ambiguous protonation states, and errors in force fields. Noncovalent complexes between a host molecule with a binding cavity and a drug-like guest molecules have emerged as powerful model systems. As model systems, host-guest complexes reduce many of the errors in more complex protein-ligand binding systems, as their small size greatly facilitates conformational sampling, and one can choose systems that avoid ambiguities in protonation states. These features, combined with their ease of experimental characterization, make host-guest systems ideal model systems to test and ultimately optimize force fields in the context of binding thermodynamics calculations.</p><p><br></p><p>The Open Force Field Initiative aims to create a modern, open software infrastructure for automatically generating and assessing force fields using data sets. The first force field to arise out of this effort, named SMIRNOFF99Frosst, has approximately one tenth the number of parameters, in version 1.0.5, compared to typical general small molecule force fields, such as GAFF. Here, we evaluate the accuracy of this initial force field, using free energy calculations of 43 α and β-cyclodextrin host-guest pairs for which experimental thermodynamic data are available, and compare with matched calculations using two versions of GAFF. For all three force fields, we used TIP3P water and AM1-BCC charges. The calculations are performed using the attach-pull-release (APR) method as implemented in the open source package, pAPRika. For binding free energies, the root mean square error of the SMIRNOFF99Frosst calculations relative to experiment is 0.9 [0.7, 1.1] kcal/mol, while the corresponding results for GAFF 1.7 and GAFF 2.1 are 0.9 [0.7, 1.1] kcal/mol and 1.7 [1.5, 1.9] kcal/mol, respectively, with 95% confidence ranges in brackets. These results suggest that SMIRNOFF99Frosst performs competitively with existing small molecule force fields and is a parsimonious starting point for optimization.</p></div></div></div>

scholarly journals Pharmacological characterisation of novel adenosine A3 receptor antagonists

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Kerry Barkan ◽  
Panagiotis Lagarias ◽  
Margarita Stampelou ◽  
Dimitrios Stamatis ◽  
Sam Hoare ◽  
...  
Keyword(s):  
Ligand Binding ◽  
Binding Mode ◽  
The Body ◽  
Nitrogen Heterocycle ◽  
Thiazole Ring ◽  
Binding Studies ◽  
Dynamic Simulations ◽  
Subtype Selectivity ◽  
Poisson Boltzmann ◽  
Fluorescent Ligand

AbstractThe adenosine A3 receptor (A3R) belongs to a family of four adenosine receptor (AR) subtypes which all play distinct roles throughout the body. A3R antagonists have been described as potential treatments for numerous diseases including asthma. Given the similarity between (adenosine receptors) orthosteric binding sites, obtaining highly selective antagonists is a challenging but critical task. Here we screen 39 potential A3R, antagonists using agonist-induced inhibition of cAMP. Positive hits were assessed for AR subtype selectivity through cAMP accumulation assays. The antagonist affinity was determined using Schild analysis (pA2 values) and fluorescent ligand binding. Structure–activity relationship investigations revealed that loss of the 3-(dichlorophenyl)-isoxazolyl moiety or the aromatic nitrogen heterocycle with nitrogen at α-position to the carbon of carboximidamide group significantly attenuated K18 antagonistic potency. Mutagenic studies supported by molecular dynamic simulations combined with Molecular Mechanics—Poisson Boltzmann Surface Area calculations identified the residues important for binding in the A3R orthosteric site. We demonstrate that K18, which contains a 3-(dichlorophenyl)-isoxazole group connected through carbonyloxycarboximidamide fragment with a 1,3-thiazole ring, is a specific A3R (< 1 µM) competitive antagonist. Finally, we introduce a model that enables estimates of the equilibrium binding affinity for rapidly disassociating compounds from real-time fluorescent ligand-binding studies. These results demonstrate the pharmacological characterisation of a selective competitive A3R antagonist and the description of its orthosteric binding mode. Our findings may provide new insights for drug discovery.

Modelling flexible protein–ligand binding in p38α MAP kinase using the QUBE force field

2020 ◽  
Vol 56 (6) ◽  
pp. 932-935 ◽  
Author(s):  
Joshua T. Horton ◽  
Alice E. A. Allen ◽  
Daniel J. Cole
Keyword(s):  
Free Energy ◽  
Ligand Binding ◽  
Force Field ◽  
Map Kinase ◽  
Binding Free Energy ◽  
Free Energy Calculations ◽  
Quantum Mechanical ◽  
Flexible Protein ◽  
Binding Free Energy Calculations ◽  
P38α Map Kinase

The accuracy of quantum mechanical bespoke (QUBE) force fields for protein–ligand binding free energy calculations are benchmarked against experiment.

Binding Modes of Ligands Using Enhanced Sampling (BLUES): Rapid Decorrelation of Ligand Binding Modes Using Nonequilibrium Candidate Monte Carlo

2018 ◽  
Author(s):  
Samuel Gill ◽  
Nathan M. Lim ◽  
Patrick Grinaway ◽  
Ariën S. Rustenburg ◽  
Josh Fass ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Ligand Binding ◽  
Binding Mode ◽  
Free Energy Calculations ◽  
Dynamics Simulation ◽  
Binding Modes ◽  
Enhanced Sampling ◽  
Recent Success ◽  
Multiple Binding ◽  
Proper Sampling

<div>Accurately predicting protein-ligand binding 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 timescales, 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.</div><div><br></div><div>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 two orders of magnitude improvement in binding mode sampling efficiency compared to a brute force molecular dynamics simulation. This is a first step towards 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.</div>

Binding Modes of Ligands Using Enhanced Sampling (BLUES): Rapid Decorrelation of Ligand Binding Modes Using Nonequilibrium Candidate Monte Carlo

2017 ◽  
Author(s):  
Samuel Gill ◽  
Nathan M. Lim ◽  
Patrick Grinaway ◽  
Ariën S. Rustenburg ◽  
Josh Fass ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Ligand Binding ◽  
Binding Mode ◽  
Free Energy Calculations ◽  
Dynamics Simulation ◽  
Binding Modes ◽  
Enhanced Sampling ◽  
Recent Success ◽  
Multiple Binding ◽  
Proper Sampling

<div>Accurately predicting protein-ligand binding 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 timescales, 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.</div><div><br></div><div>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 two orders of magnitude improvement in binding mode sampling efficiency compared to a brute force molecular dynamics simulation. This is a first step towards 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.</div>

Relative stability of homochiral and heterochiral dialanine peptides. Effects of perturbation pathways and force-field parameters on free energy calculations

2005 ◽  
Vol 103 (14) ◽  
pp. 1961-1969 ◽  
Author(s):  
Yu Zhou ◽  
Chris Oostenbrink ◽  
Wilfred F. Van Gunsteren ◽  
Wilfred R. Hagen ◽  
Simon W. De Leeuw ◽  
...  
Keyword(s):  
Free Energy ◽  
Force Field ◽  
Relative Stability ◽  
Free Energy Calculations ◽  
Energy Calculations ◽  
Force Field Parameters

scholarly journals Automatic Assignment of Bonded Force Field Parameters for Small Molecules Using Machine Learning

2019 ◽  
Vol 116 (3) ◽  
pp. 289a-290a ◽  
Author(s):  
Praveer Narwelkar ◽  
Hui Sun Lee ◽  
Sihong Xie ◽  
Wonpil Im
Keyword(s):  
Machine Learning ◽  
Force Field ◽  
Small Molecules ◽  
Automatic Assignment ◽  
Force Field Parameters

scholarly journals Binding thermodynamics of host-guest systems with SMIRNOFF99Frosst 1.0.5 from the Open Force Field Initiative

2019 ◽  
Author(s):  
David Slochower ◽  
Niel Henriksen ◽  
Lee-Ping Wang ◽  
John Chodera ◽  
David Mobley ◽  
...  
Keyword(s):  
Ligand Binding ◽  
Force Field ◽  
Small Molecule ◽  
Force Fields ◽  
Free Energy Calculations ◽  
Model Systems ◽  
Conformational Space ◽  
Free Energies ◽  
Binding Thermodynamics ◽  
Binding Free Energies

<div><div><div><p>Designing ligands that bind their target biomolecules with high affinity and specificity is a key step in small- molecule drug discovery, but accurately predicting protein-ligand binding free energies remains challenging. Key sources of errors in the calculations include inadequate sampling of conformational space, ambiguous protonation states, and errors in force fields. Noncovalent complexes between a host molecule with a binding cavity and a drug-like guest molecules have emerged as powerful model systems. As model systems, host-guest complexes reduce many of the errors in more complex protein-ligand binding systems, as their small size greatly facilitates conformational sampling, and one can choose systems that avoid ambiguities in protonation states. These features, combined with their ease of experimental characterization, make host-guest systems ideal model systems to test and ultimately optimize force fields in the context of binding thermodynamics calculations.</p><p><br></p><p>The Open Force Field Initiative aims to create a modern, open software infrastructure for automatically generating and assessing force fields using data sets. The first force field to arise out of this effort, named SMIRNOFF99Frosst, has approximately one tenth the number of parameters, in version 1.0.5, compared to typical general small molecule force fields, such as GAFF. Here, we evaluate the accuracy of this initial force field, using free energy calculations of 43 α and β-cyclodextrin host-guest pairs for which experimental thermodynamic data are available, and compare with matched calculations using two versions of GAFF. For all three force fields, we used TIP3P water and AM1-BCC charges. The calculations are performed using the attach-pull-release (APR) method as implemented in the open source package, pAPRika. For binding free energies, the root mean square error of the SMIRNOFF99Frosst calculations relative to experiment is 0.9 [0.7, 1.1] kcal/mol, while the corresponding results for GAFF 1.7 and GAFF 2.1 are 0.9 [0.7, 1.1] kcal/mol and 1.7 [1.5, 1.9] kcal/mol, respectively, with 95% confidence ranges in brackets. These results suggest that SMIRNOFF99Frosst performs competitively with existing small molecule force fields and is a parsimonious starting point for optimization.</p></div></div></div>

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