11
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: not found
      • Article: not found

      Tinker-OpenMM: Absolute and relative alchemical free energies using AMOEBA on GPUs

      , , , , , , ,
      Journal of Computational Chemistry
      Wiley-Blackwell

      Read this article at

      ScienceOpenPublisherPMC
      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          <p class="first" id="P1">The capabilities of the polarizable force fields for alchemical free energy calculations have been limited by the high computational cost and complexity of the underlying potential energy functions. In this work, we present a GPU based general alchemical free energy simulation platform for polarizable potential AMOEBA. Tinker-OpenMM, the OpenMM implementation of the AMOEBA simulation engine has been modified to enable both absolute and relative alchemical simulations on GPUs, which leads to a ~200-fold improvement in simulation speed over a single CPU core. We show that free energy values calculated using this platform agree with the results of Tinker simulations for the hydration of organic compounds and binding of host-guest systems within the statistical errors. In addition to absolute binding, we designed a relative alchemical approach for computing relative binding affinities of ligands to the same host, where a special path was applied to avoid numerical instability due to polarization between the different ligands that bind to the same site. This scheme is general and does not require ligands to have similar scaffolds. We show that relative hydration and binding free energy calculated using this approach match those computed from the absolute free energy approach. </p><p id="P2"> <div class="figure-container so-text-align-c"> <img alt="" class="figure" src="/document_file/1ad4a6a4-bf6e-40bc-8c84-f5e9148c4664/PubMedCentral/image/nihms879192u1.jpg"/> </div> </p><p id="P3">We have developed Tinker-OpenMM, a computational platform for the calculation of free energies on Graphics Processing Units. This allows for free energy simulations to be run 200 times faster than is possible on a single CPU core. In addition, we have developed a platform for the calculation of relative free energies. </p>

          Related collections

          Most cited references67

          • Record: found
          • Abstract: found
          • Article: not found

          Improved protein-ligand docking using GOLD.

          The Chemscore function was implemented as a scoring function for the protein-ligand docking program GOLD, and its performance compared to the original Goldscore function and two consensus docking protocols, "Goldscore-CS" and "Chemscore-GS," in terms of docking accuracy, prediction of binding affinities, and speed. In the "Goldscore-CS" protocol, dockings produced with the Goldscore function are scored and ranked with the Chemscore function; in the "Chemscore-GS" protocol, dockings produced with the Chemscore function are scored and ranked with the Goldscore function. Comparisons were made for a "clean" set of 224 protein-ligand complexes, and for two subsets of this set, one for which the ligands are "drug-like," the other for which they are "fragment-like." For "drug-like" and "fragment-like" ligands, the docking accuracies obtained with Chemscore and Goldscore functions are similar. For larger ligands, Goldscore gives superior results. Docking with the Chemscore function is up to three times faster than docking with the Goldscore function. Both combined docking protocols give significant improvements in docking accuracy over the use of the Goldscore or Chemscore function alone. "Goldscore-CS" gives success rates of up to 81% (top-ranked GOLD solution within 2.0 A of the experimental binding mode) for the "clean list," but at the cost of long search times. For most virtual screening applications, "Chemscore-GS" seems optimal; search settings that give docking speeds of around 0.25-1.3 min/compound have success rates of about 78% for "drug-like" compounds and 85% for "fragment-like" compounds. In terms of producing binding energy estimates, the Goldscore function appears to perform better than the Chemscore function and the two consensus protocols, particularly for faster search settings. Even at docking speeds of around 1-2 min/compound, the Goldscore function predicts binding energies with a standard deviation of approximately 10.5 kJ/mol. Copyright 2003 Wiley-Liss, Inc.
            Bookmark
            • Record: found
            • Abstract: not found
            • Article: not found

            AMBER, a package of computer programs for applying molecular mechanics, normal mode analysis, molecular dynamics and free energy calculations to simulate the structural and energetic properties of molecules

              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              A critical assessment of docking programs and scoring functions.

              Docking is a computational technique that samples conformations of small molecules in protein binding sites; scoring functions are used to assess which of these conformations best complements the protein binding site. An evaluation of 10 docking programs and 37 scoring functions was conducted against eight proteins of seven protein types for three tasks: binding mode prediction, virtual screening for lead identification, and rank-ordering by affinity for lead optimization. All of the docking programs were able to generate ligand conformations similar to crystallographically determined protein/ligand complex structures for at least one of the targets. However, scoring functions were less successful at distinguishing the crystallographic conformation from the set of docked poses. Docking programs identified active compounds from a pharmaceutically relevant pool of decoy compounds; however, no single program performed well for all of the targets. For prediction of compound affinity, none of the docking programs or scoring functions made a useful prediction of ligand binding affinity.
                Bookmark

                Author and article information

                Journal
                Journal of Computational Chemistry
                J. Comput. Chem.
                Wiley-Blackwell
                01928651
                September 05 2017
                September 05 2017
                : 38
                : 23
                : 2047-2055
                Article
                10.1002/jcc.24853
                5539969
                28600826
                144092a9-a8a8-4eb2-bdbb-d3c118065da1
                © 2017

                http://doi.wiley.com/10.1002/tdm_license_1.1

                History

                Comments

                Comment on this article