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 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>

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 Computation of protein–ligand binding free energies using quantum mechanical bespoke force fields

MedChemComm ◽  
2019 ◽  
Vol 10 (7) ◽  
pp. 1116-1120 ◽  
Author(s):  
Daniel J. Cole ◽  
Israel Cabeza de Vaca ◽  
William L. Jorgensen
Keyword(s):  
Ligand Binding ◽  
Force Field ◽  
Molecular Mechanics ◽  
Force Fields ◽  
Quantum Mechanical ◽  
T4 Lysozyme ◽  
Free Energies ◽  
Binding Free Energies

A quantum mechanical bespoke molecular mechanics force field is derived for the L99A mutant of T4 lysozyme and used to compute absolute binding free energies of six benzene analogs to the protein.

scholarly journals Alchemical Absolute Protein-Ligand Binding Free Energies for Drug Design

2021 ◽  
Author(s):  
Yuriy Khalak ◽  
Gary Tresdern ◽  
Matteo Aldeghi ◽  
Hannah Magdalena Baumann ◽  
David L. Mobley ◽  
...  
Keyword(s):  
Free Energy ◽  
Drug Discovery ◽  
Drug Design ◽  
Ligand Binding ◽  
Binding Free Energy ◽  
Free Energy Calculations ◽  
Free Energies ◽  
Energy Calculations ◽  
Binding Free Energy Calculations ◽  
Binding Free Energies

The recent advances in relative protein-ligand binding free energy calculations have shown the value of alchemical methods in drug discovery. Accurately assessing absolute binding free energies, although highly desired, remains...

scholarly journals Ligand Gaussian accelerated molecular dynamics (LiGaMD): Characterization of ligand binding thermodynamics and kinetics

2020 ◽  
Author(s):  
Yinglong Miao ◽  
Apurba Bhattarai ◽  
Jinan Wang
Keyword(s):  
Experimental Data ◽  
Molecular Dynamics ◽  
Ligand Binding ◽  
Free Energies ◽  
Kinetic Rate ◽  
Binding Thermodynamics ◽  
Accelerated Molecular Dynamics ◽  
Thermodynamics And Kinetics ◽  
Kinetic Rate Constants ◽  
Binding Free Energies

AbstractCalculations of ligand binding free energies and kinetic rates are important for drug design. However, such tasks have proven challenging in computational chemistry and biophysics. To address this challenge, we have developed a new computational method “LiGaMD”, which selectively boosts the ligand non-bonded interaction potential energy based on the Gaussian accelerated molecular dynamics (GaMD) enhanced sampling technique. Another boost potential could be applied to the remaining potential energy of the entire system in a dual-boost algorithm (LiGaMD_Dual) to facilitate ligand binding. LiGaMD has been demonstrated on host-guest and protein-ligand binding model systems. Repetitive guest binding and unbinding in the β-cyclodextrin host were observed in hundreds-of-nanosecond LiGaMD simulations. The calculated binding free energies of guest molecules with sufficient sampling agreed excellently with experimental data (< 1.0 kcal/mol error). In comparison with previous microsecond-timescale conventional molecular dynamics simulations, accelerations of ligand kinetic rate constants in LiGaMD simulations were properly estimated using Kramers’ rate theory. Furthermore, LiGaMD allowed us to capture repetitive dissociation and binding of the benzamidine inhibitor in trypsin within 1 μs simulations. The calculated ligand binding free energy and kinetic rate constants compared well with the experimental data. In summary, LiGaMD provides a promising approach for characterizing ligand binding thermodynamics and kinetics simultaneously, which is expected to facilitate computer-aided drug design.

scholarly journals Sensitivity in binding free energies due to protein reorganization

2016 ◽  
Author(s):  
Nathan M. Lim ◽  
Lingle Wang ◽  
Robert Abel ◽  
David L. Mobley
Keyword(s):  
Ligand Binding ◽  
Conformational Changes ◽  
Binding Free Energy ◽  
Binding Pocket ◽  
Free Energy Calculations ◽  
Computer Hardware ◽  
Sampling Techniques ◽  
Free Energies ◽  
Sampling Algorithms ◽  
Binding Free Energies

AbstractTremendous recent improvements in computer hardware, coupled with advances in sampling techniques and force fields, are now allowing protein-ligand 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 protein-ligand 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.1 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 ‘hot’ region of the FEP/REST simulation improves the sampling and resolves this sensitivity.

scholarly journals Comparing Dimerization Free Energies and Binding Modes of Small Aromatic Molecules with Different Force Fields

Molecules ◽  
2021 ◽  
Vol 26 (19) ◽  
pp. 6069
Author(s):  
Ilias Patmanidis ◽  
Riccardo Alessandri ◽  
Alex H. de de Vries ◽  
Siewert J. Marrink
Keyword(s):  
Free Energy ◽  
Force Field ◽  
Force Fields ◽  
Supramolecular Assembly ◽  
Model Systems ◽  
Binding Modes ◽  
Free Energies ◽  
Care Needs ◽  
Strength Of Interaction ◽  
Dynamics Simulations

Dimerization free energies are fundamental quantities that describe the strength of interaction of different molecules. Obtaining accurate experimental values for small molecules and disentangling the conformations that contribute most to the binding can be extremely difficult, due to the size of the systems and the small energy differences. In many cases, one has to resort to computational methods to calculate such properties. In this work, we used molecular dynamics simulations in conjunction with metadynamics to calculate the free energy of dimerization of small aromatic rings, and compared three models from popular online servers for atomistic force fields, namely G54a7, CHARMM36 and OPLS. We show that, regardless of the force field, the profiles for the dimerization free energy of these compounds are very similar. However, significant care needs to be taken when studying larger molecules, since the deviations from the trends increase with the size of the molecules, resulting in force field dependent preferred stacking modes; for example, in the cases of pyrene and tetracene. Our results provide a useful background study for using topology builders to model systems which rely on stacking of aromatic moieties, and are relevant in areas ranging from drug design to supramolecular assembly.

scholarly journals Alchemical Absolute Protein-Ligand Binding Free Energies for Drug Design

2021 ◽  
Author(s):  
Yuriy Khalak ◽  
Gary Tresadern ◽  
Matteo Aldeghi ◽  
Hannah Magdalena Baumann ◽  
David L. Mobley ◽  
...  
Keyword(s):  
Free Energy ◽  
Drug Discovery ◽  
Ligand Binding ◽  
Binding Free Energy ◽  
Free Energy Calculations ◽  
Free Energies ◽  
Energy Calculations ◽  
Binding Free Energy Calculations ◽  
Binding Free Energies ◽  
Apo State

The recent advances in relative protein-ligand binding free energy calculations have shown the value of alchemical methods in drug discovery. Accurately assessing absolute binding free energies, although highly desired, remains a challenging endeavour, mostly limited to small model cases. Here, we demonstrate accurate first principles based absolute binding free energy estimates for 128 pharmaceutically relevant targets. We use a novel rigorous method to generate protein-ligand ensembles for the ligand in its decoupled state. Not only do the calculations deliver accurate protein-ligand binding affinity estimates, but they also provide detailed physical insight into the structural determinants of binding. We identify subtle rotamer rearrangements between apo and holo states of a protein that are crucial for binding. When compared to relative binding free energy calculations, obtaining absolute binding free energies is considerably more challenging in large part due to the need to explicitly account for the protein in its apo state. In this work we present several approaches to obtain apo state ensembles for accurate absolute ΔG calculations, thus outlining protocols for prospective application of the methods for drug discovery.

Validation of AMBER/GAFF for Relative Free Energy Calculations

2019 ◽  
Author(s):  
Lin Song ◽  
Tai-Sung Lee ◽  
Chun Zhu ◽  
Darrin M. York ◽  
Kenneth M. Merz Jr.
Keyword(s):  
Free Energy ◽  
Force Field ◽  
Free Energy Calculations ◽  
Enhanced Sampling ◽  
Free Energies ◽  
Data Set ◽  
Relative Free Energy ◽  
Relative Binding ◽  
Sampling Approach ◽  
Binding Free Energies

We computed relative binding free energies using GPU accelerated Thermodynamic Integration (GPU-TI) on a dataset originally assembled by Schrödinger, Inc.. Using their GPU enabled free energy code (FEP+) and the OPLS2.1 force field combined with REST2 enhanced sampling approach, these authors obtained an overall MUE of 0.9 kcal/mol and an overall RMSD of 1.14 kcal/mol.<b> </b>In our study using GPU-TI of AMBER with the AMBER14SB/GAFF1.8 force field but without enhanced sampling, we obtained an overall MUE of 1.17 kcal/mol and an overall RMSD of 1.50 kcal/mol for the 330 mutations contained in this data set.

Validation of AMBER/GAFF for Relative Free Energy Calculations

2019 ◽  
Author(s):  
Lin Song ◽  
Tai-Sung Lee ◽  
Chun Zhu ◽  
Darrin M. York ◽  
Kenneth M. Merz Jr.
Keyword(s):  
Free Energy ◽  
Force Field ◽  
Free Energy Calculations ◽  
Enhanced Sampling ◽  
Free Energies ◽  
Data Set ◽  
Relative Free Energy ◽  
Relative Binding ◽  
Sampling Approach ◽  
Binding Free Energies

We computed relative binding free energies using GPU accelerated Thermodynamic Integration (GPU-TI) on a dataset originally assembled by Schrödinger, Inc.. Using their GPU enabled free energy code (FEP+) and the OPLS2.1 force field combined with REST2 enhanced sampling approach, these authors obtained an overall MUE of 0.9 kcal/mol and an overall RMSD of 1.14 kcal/mol.<b> </b>In our study using GPU-TI of AMBER with the AMBER14SB/GAFF1.8 force field but without enhanced sampling, we obtained an overall MUE of 1.17 kcal/mol and an overall RMSD of 1.50 kcal/mol for the 330 mutations contained in this data set.

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