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      High-throughput binding affinity calculations at extreme scales

      research-article
      1 , 1 , 2 , 2 , 1 , 2 , 2 , 1 , 3 , 4 ,
      BMC Bioinformatics
      BioMed Central
      Computational Approaches for Cancer at SC17
      17 November 2017

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          Abstract

          Background

          Resistance to chemotherapy and molecularly targeted therapies is a major factor in limiting the effectiveness of cancer treatment. In many cases, resistance can be linked to genetic changes in target proteins, either pre-existing or evolutionarily selected during treatment. Key to overcoming this challenge is an understanding of the molecular determinants of drug binding. Using multi-stage pipelines of molecular simulations we can gain insights into the binding free energy and the residence time of a ligand, which can inform both stratified and personal treatment regimes and drug development. To support the scalable, adaptive and automated calculation of the binding free energy on high-performance computing resources, we introduce the High-throughput Binding Affinity Calculator (HTBAC). HTBAC uses a building block approach in order to attain both workflow flexibility and performance.

          Results

          We demonstrate close to perfect weak scaling to hundreds of concurrent multi-stage binding affinity calculation pipelines. This permits a rapid time-to-solution that is essentially invariant of the calculation protocol, size of candidate ligands and number of ensemble simulations.

          Conclusions

          As such, HTBAC advances the state of the art of binding affinity calculations and protocols. HTBAC provides the platform to enable scientists to study a wide range of cancer drugs and candidate ligands in order to support personalized clinical decision making based on genome sequencing and drug discovery.

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          Most cited references42

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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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              Scalable molecular dynamics with NAMD.

              NAMD is a parallel molecular dynamics code designed for high-performance simulation of large biomolecular systems. NAMD scales to hundreds of processors on high-end parallel platforms, as well as tens of processors on low-cost commodity clusters, and also runs on individual desktop and laptop computers. NAMD works with AMBER and CHARMM potential functions, parameters, and file formats. This article, directed to novices as well as experts, first introduces concepts and methods used in the NAMD program, describing the classical molecular dynamics force field, equations of motion, and integration methods along with the efficient electrostatics evaluation algorithms employed and temperature and pressure controls used. Features for steering the simulation across barriers and for calculating both alchemical and conformational free energy differences are presented. The motivations for and a roadmap to the internal design of NAMD, implemented in C++ and based on Charm++ parallel objects, are outlined. The factors affecting the serial and parallel performance of a simulation are discussed. Finally, typical NAMD use is illustrated with representative applications to a small, a medium, and a large biomolecular system, highlighting particular features of NAMD, for example, the Tcl scripting language. The article also provides a list of the key features of NAMD and discusses the benefits of combining NAMD with the molecular graphics/sequence analysis software VMD and the grid computing/collaboratory software BioCoRE. NAMD is distributed free of charge with source code at www.ks.uiuc.edu. (c) 2005 Wiley Periodicals, Inc.
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                Author and article information

                Contributors
                jumana.dakka@rutgers.edu
                matteo.turilli@rutgers.edu
                dave.wright@ucl.ac.uk
                stefan.zasada@ucl.ac.uk
                vivek.balasubramanian@rutgers.edu
                shunzhou.wan@ucl.ac.uk
                p.v.coveney@ucl.ac.uk
                shantenu.jha@rutgers.edu
                Conference
                BMC Bioinformatics
                BMC Bioinformatics
                BMC Bioinformatics
                BioMed Central (London )
                1471-2105
                21 December 2018
                21 December 2018
                2018
                : 19
                Issue : Suppl 18 Issue sponsor : Publication of this supplement has not been supported by sponsorship. Information about the source of funding for publication charges can be found in the individual articles. The articles have undergone the journal's standard peer review process for supplements. ES is the Frederick National Laboratory for Cancer Research co-program lead for the Joint Design of Advanced Computing Solutions for Cancer (JDACS4C) program. Several of the papers presented in the workshop and included in the supplement are the result of work supported by the JDACS4C program and, as such, ES is listed as a co-author on one of the articles in the supplement but was not involved in its review. SC declares no competing interests.
                : 482
                Affiliations
                [1 ]ISNI 0000 0004 1936 8796, GRID grid.430387.b, Department Electrical and Computer Engineering, Rutgers University, ; 94 Brett Road, Piscataway, NJ USA
                [2 ]ISNI 0000000121901201, GRID grid.83440.3b, Centre for Computational Sciences, UCL, ; 20 Gordon Street, London, UK
                [3 ]ISNI 0000 0001 2216 9681, GRID grid.36425.36, Institute for Advanced Computational Sciences, Stony Brook University, NY, USA, ; Lake Dr, Laufer Center, Stony Brook, NY USA
                [4 ]ISNI 0000 0001 2188 4229, GRID grid.202665.5, Computational Science Initiative, Brookhaven National Laboratory, ; 98 Rochester St, Shirley, NY USA
                Article
                2506
                10.1186/s12859-018-2506-6
                6302294
                30577753
                b7954067-9f93-4068-8c98-ea0eecaa8cc8
                © The Author(s) 2018

                Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

                Computational Approaches for Cancer at SC17
                Denver, CO, USA
                17 November 2017
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                © The Author(s) 2018

                Bioinformatics & Computational biology
                Bioinformatics & Computational biology

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