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 Implementation of the QUBE Force Field in SOMD for High-Throughput Alchemical Free Energy Calculations

2021 ◽  
Author(s):  
Lauren Nelson ◽  
Sofia Bariami ◽  
Chris Ringrose ◽  
Joshua Horton ◽  
Vadiraj Kurdekar ◽  
...  
Keyword(s):  
Free Energy ◽  
Force Field ◽  
High Throughput ◽  
Free Energy Calculations ◽  
Dynamics Simulation ◽  
Energy Calculation ◽  
Energy Calculations ◽  
Alchemical Free Energy ◽  
Simulation Package ◽  
Alchemical Free Energy Calculations

<div><div><div><p>The quantum mechanical bespoke (QUBE) force field approach has been developed to facilitate the automated derivation of potential energy function parameters for modelling protein-ligand binding. To date the approach has been validated in the context of Monte Carlo simulations of protein-ligand complexes. We describe here the implementation of the QUBE force field in the alchemical free energy calculation molecular dynamics simulation package SOMD. The implementation is validated by demonstrating the reproducibility of absolute hydration free energies computed with the QUBE force field across the SOMD and GROMACS software packages. We further demonstrate, by way of a case study involving two series of non-nucleoside inhibitors of HIV-1 reverse transcriptase, that the availability of QUBE in a modern simulation package that makes efficient use of GPU acceleration will facilitate high-throughput alchemical free energy calculations.</p></div></div></div>

scholarly journals Molecular Environment Specific Atomic Charges Improve Binding Affinity Predictions of SAMPL5 Host-Guest Systems

2021 ◽  
Author(s):  
Duvan Gonzalez ◽  
Luis Macaya ◽  
Esteban Vöhringer-Martinez
Keyword(s):  
Free Energy ◽  
Force Field ◽  
Binding Affinity ◽  
Free Energy Calculations ◽  
Atomic Charges ◽  
Binding Affinity Prediction ◽  
Energy Calculations ◽  
Affinity Prediction ◽  
Alchemical Free Energy ◽  
Alchemical Free Energy Calculations

<div> <div> <div> <p>Host-guest systems are widely used in benchmarks as model systems to improve computational methods for absolute binding free energy predictions. Recent advances in sampling algorithms for alchemical free energy calculations and the increase in computational power have made their binding affinity prediction primarily dependent on the quality of the force field. Here, we propose a new methodology to derive the atomic charges of host-guest systems based on QM/MM calculations and the MBIS partitioning of the polarized electron density. A newly developed interface between the OpenMM and ORCA software package provides D-MBIS charges that best represent the guest’s average electrostatic interactions in the hosts or the solvent. The simulation workflow also calculates the average energy required to polarize the guest in the bound and unbound state. Alchemical free energy calculations using the GAFF force field parameters with D-MBIS charges improve the binding affinity prediction of six guests bound to two octa-acid hosts compared to the AM1-BCC charge set after correction with the average energetic polarization cost. This correction results from the difference in the energetic polarization cost between the bound and unbound state and contributes significantly to the binding affinity of anionic guests. </p></div></div></div><div><div><div> </div> </div> </div>

scholarly journals Molecular Environment Specific Atomic Charges Improve Binding Affinity Predictions of SAMPL5 Host-Guest Systems

2021 ◽  
Author(s):  
Duvan Gonzalez ◽  
Luis Macaya ◽  
Esteban Vöhringer-Martinez
Keyword(s):  
Free Energy ◽  
Force Field ◽  
Binding Affinity ◽  
Free Energy Calculations ◽  
Atomic Charges ◽  
Binding Affinity Prediction ◽  
Energy Calculations ◽  
Affinity Prediction ◽  
Alchemical Free Energy ◽  
Alchemical Free Energy Calculations

<div> <div> <div> <p>Host-guest systems are widely used in benchmarks as model systems to improve computational methods for absolute binding free energy predictions. Recent advances in sampling algorithms for alchemical free energy calculations and the increase in computational power have made their binding affinity prediction primarily dependent on the quality of the force field. Here, we propose a new methodology to derive the atomic charges of host-guest systems based on QM/MM calculations and the MBIS partitioning of the polarized electron density. A newly developed interface between the OpenMM and ORCA software package provides D-MBIS charges that best represent the guest’s average electrostatic interactions in the hosts or the solvent. The simulation workflow also calculates the average energy required to polarize the guest in the bound and unbound state. Alchemical free energy calculations using the GAFF force field parameters with D-MBIS charges improve the binding affinity prediction of six guests bound to two octa-acid hosts compared to the AM1-BCC charge set after correction with the average energetic polarization cost. This correction results from the difference in the energetic polarization cost between the bound and unbound state and contributes significantly to the binding affinity of anionic guests. </p></div></div></div><div><div><div> </div> </div> </div>

Automated Assessment of Binding Affinity via Alchemical Free Energy Calculations

2020 ◽  
Author(s):  
Maximilian Kuhn ◽  
Stuart Firth-Clark ◽  
Paolo Tosco ◽  
Antonia S. J. S. Mey ◽  
Mark Mackey ◽  
...  
Keyword(s):  
Free Energy ◽  
Protein Structures ◽  
Free Energy Calculations ◽  
Distinct Advantage ◽  
Binding Affinities ◽  
Structure Based Drug Design ◽  
Energy Calculations ◽  
Kendall’S Τ ◽  
Experimental Values ◽  
Better Than

Free energy calculations have seen increased usage in structure-based drug design. Despite the rising interest, automation of the complex calculations and subsequent analysis of their results are still hampered by the restricted choice of available tools. In this work, an application for automated setup and processing of free energy calculations is presented. Several sanity checks for assessing the reliability of the calculations were implemented, constituting a distinct advantage over existing open-source tools. The underlying workflow is built on top of the software Sire, SOMD, BioSimSpace and OpenMM and uses the AMBER14SB and GAFF2.1 force fields. It was validated on two datasets originally composed by Schrödinger, consisting of 14 protein structures and 220 ligands. Predicted binding affinities were in good agreement with experimental values. For the larger dataset the average correlation coefficient Rp was 0.70 ± 0.05 and average Kendall’s τ was 0.53 ± 0.05 which is broadly comparable to or better than previously reported results using other methods. <br>

Reaction-based Enumeration, Active Learning, and Free Energy Calculations to Rapidly Explore Synthetically Tractable Chemical Space and Optimize Potency of Cyclin Dependent Kinase 2 Inhibitors

2019 ◽  
Author(s):  
Kyle Konze ◽  
Pieter Bos ◽  
Markus Dahlgren ◽  
Karl Leswing ◽  
Ivan Tubert-Brohman ◽  
...  
Keyword(s):  
Free Energy ◽  
Drug Discovery ◽  
Active Learning ◽  
Large Scale ◽  
Chemical Space ◽  
Population Based ◽  
Free Energy Calculations ◽  
Computational Technique ◽  
Cyclin Dependent Kinase ◽  
Energy Calculations

We report a new computational technique, PathFinder, that uses retrosynthetic analysis followed by combinatorial synthesis to generate novel compounds in synthetically accessible chemical space. Coupling PathFinder with active learning and cloud-based free energy calculations allows for large-scale potency predictions of compounds on a timescale that impacts drug discovery. The process is further accelerated by using a combination of population-based statistics and active learning techniques. Using this approach, we rapidly optimized R-groups and core hops for inhibitors of cyclin-dependent kinase 2. We explored greater than 300 thousand ideas and identified 35 ligands with diverse commercially available R-groups and a predicted IC<sub>50</sub> < 100 nM, and four unique cores with a predicted IC<sub>50</sub> < 100 nM. The rapid turnaround time, and scale of chemical exploration, suggests that this is a useful approach to accelerate the discovery of novel chemical matter in drug discovery campaigns.

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