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      Alchemical Free Energy Estimators and Molecular Dynamics Engines: Accuracy, Precision, and Reproducibility

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          Abstract

          The binding free energy between a ligand and its target protein is an essential quantity to know at all stages of the drug discovery pipeline. Assessing this value computationally can offer insight into where efforts should be focused in the pursuit of effective therapeutics to treat a myriad of diseases. In this work, we examine the computation of alchemical relative binding free energies with an eye for assessing reproducibility across popular molecular dynamics packages and free energy estimators. The focus of this work is on 54 ligand transformations from a diverse set of protein targets: MCL1, PTP1B, TYK2, CDK2, and thrombin. These targets are studied with three popular molecular dynamics packages: OpenMM, NAMD2, and NAMD3 alpha. Trajectories collected with these packages are used to compare relative binding free energies calculated with thermodynamic integration and free energy perturbation methods. The resulting binding free energies show good agreement between molecular dynamics packages with an average mean unsigned error between them of 0.50 kcal/mol. The correlation between packages is very good, with the lowest Spearman’s, Pearson’s and Kendall’s tau correlation coefficients being 0.92, 0.91, and 0.76, respectively. Agreement between thermodynamic integration and free energy perturbation is shown to be very good when using ensemble averaging.

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          Most cited references56

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          DrugBank 5.0: a major update to the DrugBank database for 2018

          Abstract DrugBank (www.drugbank.ca) is a web-enabled database containing comprehensive molecular information about drugs, their mechanisms, their interactions and their targets. First described in 2006, DrugBank has continued to evolve over the past 12 years in response to marked improvements to web standards and changing needs for drug research and development. This year’s update, DrugBank 5.0, represents the most significant upgrade to the database in more than 10 years. In many cases, existing data content has grown by 100% or more over the last update. For instance, the total number of investigational drugs in the database has grown by almost 300%, the number of drug-drug interactions has grown by nearly 600% and the number of SNP-associated drug effects has grown more than 3000%. Significant improvements have been made to the quantity, quality and consistency of drug indications, drug binding data as well as drug-drug and drug-food interactions. A great deal of brand new data have also been added to DrugBank 5.0. This includes information on the influence of hundreds of drugs on metabolite levels (pharmacometabolomics), gene expression levels (pharmacotranscriptomics) and protein expression levels (pharmacoprotoemics). New data have also been added on the status of hundreds of new drug clinical trials and existing drug repurposing trials. Many other important improvements in the content, interface and performance of the DrugBank website have been made and these should greatly enhance its ease of use, utility and potential applications in many areas of pharmacological research, pharmaceutical science and drug education.
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            Development and testing of a general amber force field.

            We describe here a general Amber force field (GAFF) for organic molecules. GAFF is designed to be compatible with existing Amber force fields for proteins and nucleic acids, and has parameters for most organic and pharmaceutical molecules that are composed of H, C, N, O, S, P, and halogens. It uses a simple functional form and a limited number of atom types, but incorporates both empirical and heuristic models to estimate force constants and partial atomic charges. The performance of GAFF in test cases is encouraging. In test I, 74 crystallographic structures were compared to GAFF minimized structures, with a root-mean-square displacement of 0.26 A, which is comparable to that of the Tripos 5.2 force field (0.25 A) and better than those of MMFF 94 and CHARMm (0.47 and 0.44 A, respectively). In test II, gas phase minimizations were performed on 22 nucleic acid base pairs, and the minimized structures and intermolecular energies were compared to MP2/6-31G* results. The RMS of displacements and relative energies were 0.25 A and 1.2 kcal/mol, respectively. These data are comparable to results from Parm99/RESP (0.16 A and 1.18 kcal/mol, respectively), which were parameterized to these base pairs. Test III looked at the relative energies of 71 conformational pairs that were used in development of the Parm99 force field. The RMS error in relative energies (compared to experiment) is about 0.5 kcal/mol. GAFF can be applied to wide range of molecules in an automatic fashion, making it suitable for rational drug design and database searching. Copyright 2004 Wiley Periodicals, Inc.
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              ff14SB: Improving the Accuracy of Protein Side Chain and Backbone Parameters from ff99SB.

              Molecular mechanics is powerful for its speed in atomistic simulations, but an accurate force field is required. The Amber ff99SB force field improved protein secondary structure balance and dynamics from earlier force fields like ff99, but weaknesses in side chain rotamer and backbone secondary structure preferences have been identified. Here, we performed a complete refit of all amino acid side chain dihedral parameters, which had been carried over from ff94. The training set of conformations included multidimensional dihedral scans designed to improve transferability of the parameters. Improvement in all amino acids was obtained as compared to ff99SB. Parameters were also generated for alternate protonation states of ionizable side chains. Average errors in relative energies of pairs of conformations were under 1.0 kcal/mol as compared to QM, reduced 35% from ff99SB. We also took the opportunity to make empirical adjustments to the protein backbone dihedral parameters as compared to ff99SB. Multiple small adjustments of φ and ψ parameters were tested against NMR scalar coupling data and secondary structure content for short peptides. The best results were obtained from a physically motivated adjustment to the φ rotational profile that compensates for lack of ff99SB QM training data in the β-ppII transition region. Together, these backbone and side chain modifications (hereafter called ff14SB) not only better reproduced their benchmarks, but also improved secondary structure content in small peptides and reproduction of NMR χ1 scalar coupling measurements for proteins in solution. We also discuss the Amber ff12SB parameter set, a preliminary version of ff14SB that includes most of its improvements.
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                Author and article information

                Journal
                J Chem Theory Comput
                J Chem Theory Comput
                ct
                jctcce
                Journal of Chemical Theory and Computation
                American Chemical Society
                1549-9618
                1549-9626
                24 May 2022
                14 June 2022
                : 18
                : 6
                : 3972-3987
                Affiliations
                []Centre for Computational Science, Department of Chemistry, University College London , London WC1H 0AJ, UK
                []Informatics Institute, University of Amsterdam , Amsterdam 1098XH, The Netherlands
                []Advanced Research Computing Centre, University College London , London WC1H 0AJ, UK
                Author notes
                [* ]Email: p.v.coveney@ 123456ucl.ac.uk . Phone: +44 (0)20 7679 4560.
                Author information
                https://orcid.org/0000-0001-7192-1999
                https://orcid.org/0000-0002-8787-7256
                Article
                10.1021/acs.jctc.2c00114
                9202356
                35609233
                c5f0d76b-81a1-4d66-a4cd-204536968075
                © 2022 The Authors. Published by American Chemical Society

                Permits the broadest form of re-use including for commercial purposes, provided that author attribution and integrity are maintained ( https://creativecommons.org/licenses/by/4.0/).

                History
                : 02 February 2022
                Funding
                Funded by: National Science Foundation, doi 10.13039/100000001;
                Award ID: 1713749
                Funded by: MRC Medical Bioinformatics project, doi NA;
                Award ID: MR/L016311/1
                Funded by: Software Environment for Actionable and VVUQ- evaluated Exascale Applications (SEAVEA), doi NA;
                Award ID: EP/W007711/1
                Funded by: University College London, doi 10.13039/501100000765;
                Award ID: NA
                Funded by: Engineering and Physical Sciences Research Council, doi 10.13039/501100000266;
                Award ID: EP/R029598/1
                Funded by: H2020 European Research Council, doi 10.13039/100010663;
                Award ID: 671564
                Funded by: Horizon 2020 Framework Programme, doi 10.13039/100010661;
                Award ID: 823712
                Categories
                Article
                Custom metadata
                ct2c00114
                ct2c00114

                Computational chemistry & Modeling
                Computational chemistry & Modeling

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