DOCSLIB.ORG

Interactive Visualization of Molecular Dynamics Simulation Data

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Interactive Visualization of Molecular Dynamics Simulation Data Copyright: The author, Naif Ghazi Alharbi, 2020. Interactive Visualization of Molecular Dynamics Simulation Data Naif Ghazi Alharbi Submitted to Swansea University in fulfilment of the requirements for the Degree of Doctor of Philosophy Department of Computer Science Swansea University April 26, 2020 Declaration This work has not been previously accepted in substance for any degree and is not being con- currently submitted in candidature for any degree. Signed ................. ..................... (candidate) Date ............................................................ Statement 1 This thesis is the result of my own investigations, except where otherwise stated. Other sources are acknowledged by footnotes giving explicit references. A bibliography is appended. Signed ........................ ................ (candidate) Date ............................................................ Statement 2 I hereby give my consent for my thesis, if accepted, to be available for photocopying and for inter-library loan, and for the title and summary to be made available to outside organisations. Signed .................. .................... (candidate) Date ............................................................ Abstract Molecular Dynamics Simulations (MD) plays an essential role in the field of computational biology. The simulations produce extensive high-dimensional, spatio-temporal data describ- ing the motion of atoms and molecules. A central challenge in the field is the extraction and visualization of useful behavioral patterns from these simulations. Throughout this thesis, I collaborated with a computational biologist who works on Molecular Dynamics (MD) Simu- lation data. For the sake of exploration, I was provided with a large and complex membrane simulation. I contributed solutions to his data challenges by developing a set of novel visual- ization tools to help him get a better understanding of his simulation data. I employed both scientific and information visualization, and applied concepts of abstraction and dimensions projection in the proposed solutions. The first solution enables the user to interactively fil- ter and highlight dynamic and complex trajectory constituted by motions of molecules. The molecular dynamic trajectories are identified based on path length, edge length, curvature, and normalized curvature, and their combinations. The tool exploits new interactive visual- ization techniques and provides a combination of 2D-3D path rendering in a dual dimension representation to highlight differences arising from the 2D projection on a plane. The sec- ond solution introduces a novel abstract interaction space for Protein-Lipid interaction. The proposed solution addresses the challenge of visualizing complex, time-dependent interactions between protein and lipid molecules. It also proposes a fast GPU-based implementation that maps lipid-constituents involved in the interaction onto the abstract protein interaction space. I also introduced two abstract level-of-detail (LoD) representations with six levels of detail for lipid molecules and protein interaction. Finally, I proposed a novel framework consisting of four linked views: A time-dependent 3D view, a novel hybrid view, a clustering timeline, and a details-on-demand window. The framework exploits abstraction and projection to enable the user to study the molecular interaction and the behavior of the protein-protein interaction iii and clusters. I introduced a selection of visual designs to convey the behavior of protein-lipid interaction and protein-protein interaction through a unified coordinate system. Abstraction is used to present proteins in hybrid 2D space, and a projected tiled space is used to present both Protein-Lipid Interaction (PLI) and Protein-Protein Interaction (PPI) at the particle level in a heat-map style visual design. Glyphs are used to represent PPI at the molecular level. I coupled visually separable visual designs in a unified coordinate space. The result lets the user study both PLI and PPI separately, or together in a unified visual analysis framework. Acknowledgements Undertaking this Ph.D. has been a truly life-changing experience for me, and it would not have been possible without the support and guidance that I received from many people. Firstly, I would like to thank The Almighty God for His help and guidance, and for choosing me to be supervised by Dr. Robert S. Laramee. I believe that being supervised by Dr. Laramee is one of the bounties of Almighty God. I would like to give a very big thank-you to my supervisor Dr. Laramee for all the support and encouragement he gave me during my Ph.D. He has been so generous with his time and efforts—supporting above and beyond what is typical of a Ph.D. supervisor. Without his guidance and constant feedback, this Ph.D. would not have been achievable. I would also like to thank my second supervisor Dr. Adeline Paiement, who was always ready to answer my questions and provide me with advice regarding my research topic. Many thanks also to Dr. Matthieu Chavent for his support and guidance throughout our projects. I would also like to thank Mohammed Alharbi, Xavier Martinez, Alexander Rose, and Marc Baaden for their help and valuable feedback. A special thanks to Michael Krone for his continuous feedback. I would like to thank the Ministry of Education of Saudi Arabia and the Saudi Cultural Bureau in London for their financial support. The community of Ph.D. students in our research group has been such a valuable support network. In particular, I would also like to thank Dr. Sean Walton, Dr. Richard Roberts, Liam McNabb, Dylan Rees, Dave Greten, and Rhodri Fabbro who have proofread my work throughout the project. An additional thanks to Dr. Joss Whittle for his advice and guidance on GPU techniques. Lastly, I would like to express my deepest gratitude to my family for their endless support throughout my life. I am incredibly thankful for my mother, wife, and children for supporting me spiritually throughout writing this thesis and being supportive in all that I do. v List of Publications This thesis is based on the following publications: 1. Naif Alharbi, Robert S Laramee, and Matthieu Chavant, MolePathFinder: Interactive Multi-Dimensional Path Filtering of Molecular Dynamics Simulation Data, The Com- puter Graphics and Visual Computing (CGVC) Conference 2016, pages 9-16, 15-16 September 2016, Bournemouth University, UK 2. Naif Alharbi, Mohammed Alharbi, Xavier Martinez, Michael Krone, Alexander Rose, Marc Baaden, Robert S. Laramee, Matthieu Chavent, Molecular Visualization of Com- putational Biology Data: A Survey of Surveys in EuroVis Short Papers 2017 , pages 133-137 3. Naif Alharbi, Micheal Krone, Matthieu Chavant, Robert S Laramee, VAPLI: Novel Vi- sual Abstraction for Protein-Lipid Interactions, IEEE SciVis Short Papers, IEEE VIS 2018, 21-26 October 2018, Berlin, Germany 4. Naif Alharbi, Micheal Krone, Matthieu Chavant, Robert S Laramee, LoD LPI: Level of Detail for Visualizing Time-Dependent, Protein-Lipid Interaction, International Con- ference on Information Visualization and Applications (IVAPP) 2019, 25-27 February 2019, Prague, Czech Republic 5. Naif Alharbi, Micheal Krone, Matthieu Chavant, Robert S Laramee, Hybrid Visualiza- tion of Protein-Lipid and Protein-Protein Interaction, the Eurographics Workshop on Visual Computing in Biology and Medicine (VCBM) 2019, 4-6 September 2019, Brno, Czech Republic Supplementary Material Each chapter includes a supplementary material. I highly recommend that the reader views these videos before reading their corresponding chapter—they are designed to provide an overview of the software features and demonstrate their utility. These videos and the source code are available online here: Chapter Material type URL Chapter 2 Video https://github.com/NaifAlhar6i/Chapter2 Chapter 3 Video https://github.com/NaifAlhar6i/Chapter3 Chapter 4 Video https://github.com/NaifAlhar6i/Chapter4 Chapter 5 Video https://github.com/NaifAlhar6i/Chapter5 Chapter 6 Video https://github.com/NaifAlhar6i/Chapter6 Chapter 7 Source Code https://github.com/NaifAlhar6i/Chapter7 Table of Contents Table of Contents 1 List of Tables 4 List of Figures 5 1 Introduction and Motivation 15 1.1 Molecular Dynamics Simulation ......................... 18 1.2 Interactive Visualization ............................. 19 1.3 Thesis Pipeline .................................. 20 1.4 Thesis Overview and Contributions ........................ 21 2 Literature Review 28 2.1 Introduction and Motivation ........................... 29 2.2 Survey of Molecular Dynamics Visualization Surveys . 30 2.2.1 From Text to Information: Meta-analysis of the Reviews . 33 2.3 Survey of Molecular Dynamics Visualization Articles . 36 2.3.1 Challenges ................................ 37 2.3.2 Survey Scope ............................... 38 2.3.3 Literature Search Methodology ..................... 38 2.3.4 Molecular Dynamic Visualization .................... 39 2.3.5 Visualization of Protein Interaction and Clusters . 58 2.4 Conclusion .................................... 63 3 Interactive Multi-Dimensional Path Filtering of Molecular Dynamics Simula- tion Data 64 1 Table of Contents 3.1 Introduction and Motivation ........................... 65 3.2 Background .................................... 66 3.3 Visualization of MD Data with MolPathFinder . 67 3.3.1 Visualization Techniques and Data Representation . 69 3.4 Experimental Results ............................... 77 3.5 Conclusion ...................................
Load more

Recommended publications
  • Open Babel Documentation Release 2.3.1
    Open Babel Documentation Release 2.3.1 Geoffrey R Hutchison Chris Morley Craig James Chris Swain Hans De Winter Tim Vandermeersch Noel M O’Boyle (Ed.) December 05, 2011 Contents 1 Introduction 3 1.1 Goals of the Open Babel project ..................................... 3 1.2 Frequently Asked Questions ....................................... 4 1.3 Thanks .................................................. 7 2 Install Open Babel 9 2.1 Install a binary package ......................................... 9 2.2 Compiling Open Babel .......................................... 9 3 obabel and babel - Convert, Filter and Manipulate Chemical Data 17 3.1 Synopsis ................................................. 17 3.2 Options .................................................. 17 3.3 Examples ................................................. 19 3.4 Differences between babel and obabel .................................. 21 3.5 Format Options .............................................. 22 3.6 Append property values to the title .................................... 22 3.7 Filtering molecules from a multimolecule file .............................. 22 3.8 Substructure and similarity searching .................................. 25 3.9 Sorting molecules ............................................ 25 3.10 Remove duplicate molecules ....................................... 25 3.11 Aliases for chemical groups ....................................... 26 4 The Open Babel GUI 29 4.1 Basic operation .............................................. 29 4.2 Options .................................................
    [Show full text]
  • BIOINFORMATICS APPLICATIONS NOTE Doi:10.1093/Bioinformatics/Btu426
    Vol. 30 no. 20 2014, pages 2981–2982 BIOINFORMATICS APPLICATIONS NOTE doi:10.1093/bioinformatics/btu426 Structural bioinformatics Advance Access publication July 4, 2014 YASARA View—molecular graphics for all devices—from smartphones to workstations Elmar Krieger* and Gert Vriend Centre for Molecular and Biomolecular Informatics, NCMLS, Radboud University Nijmegen Medical Centre, 6500 HB Nijmegen, the Netherlands Downloaded from https://academic.oup.com/bioinformatics/article-abstract/30/20/2981/2422272 by guest on 17 March 2019 Associate Editor: Janet Kelso ABSTRACT 2 METHODS Summary: Today’s graphics processing units (GPUs) compose the The general idea is very simple and has been used ever since texture scene from individual triangles. As about 320 triangles are needed to mapping became part of 3D graphics: if an object is too complex (like approximate a single sphere—an atom—in a convincing way, visualiz- the 960 triangles required to draw a single water molecule in Fig. 1A) it is ing larger proteins with atomic details requires tens of millions of tri- replaced with ‘impostors’, i.e. fewer triangles that have precalculated tex- angles, far too many for smooth interactive frame rates. We describe a tures attached, which make them look like the original object. Texture mapping means that a triangle is not rendered with a single color, but an new approach to solve this ‘molecular graphics problem’, which image (the texture) is attached to it instead. For each of the three triangle shares the work between GPU and multiple CPU cores, generates vertices, the programmer can specify the corresponding 2D coordinates in high-quality results with perfectly round spheres, shadows and ambi- the texture, and the hardware interpolates in between.
    [Show full text]
  • “One Ring to Rule Them All”
    OpenMM library “One ring to rule them all” https://simtk.org/home/openmm CHARMM * Abalone Presto * ACEMD NAMD * ADUN * Ascalaph Gromacs AMBER * COSMOS ??? HOOMD * Desmond * Culgi * ESPResSo Tinker * GROMOS * GULP * Hippo LAMMPS * Kalypso MD * LPMD * MacroModel * MDynaMix * MOLDY * Materials Studio * MOSCITO * ProtoMol The OpenM(olecular)M(echanics) API * RedMD * YASARA * ORAC https://simtk.org/home/openmm * XMD Goals of OpenMM l Complete library l provide what is most needed l easy to learn and use l Fast, general, extensible l optimize for speed l hide hardware specifics l support new hardware l add new force fields, integration methods etc. l Available APIs in C++, Fortran95, Python l Advantages l Object oriented l Modular l Extensible l Disadvantages l Programs written in other languages need to invoke it through a layer of C++ code Features • Electrostatics: cut-off, Ewald, PME • Implicit solvent • Integrators: Verlet, Langevin, Brownian, custom • Thermostat: Andersen • Barostat: MonteCarlo • Others: energy minimization, virtual sites 5 The OpenMM Architecture Public Interface OpenMM Public API Platform Independent Code OpenMM Implementation Layer Platform Abstraction Layer OpenMM Low Level API Computational Kernels CUDA/OpenCL/Brook/MPI etc. Public API Classes (1) l System l A collection of interaction particles l Defines the mass of each particle (needed for integration) l Specifies distance restraints l Contains a list of Force objects that define the interactions l OpenMMContext l Contains all state information for a particular simulation − Positions, velocities, other parmeters Public API Classes (2) l Force l Anything that affects the system's behavior: forces, barostats, thermostats, etc. l A Force may: − Apply forces to particles − Contribute to the potential energy − Define adjustable parameters − Modify positions, velocities, and parameters at the start of each time step l Existing Force subclasses − HarmonicBond, HarmonicAngle, PeriodicTorsion, RBTorsion, Nonbonded, GBSAOBC, AndersenThermostat, CMMotionRemover..
    [Show full text]
  • Foldx 5.0: Working with RNA, Small Molecules and a New Graphical Interface Javier Delgado1,†, Leandro G
    Bioinformatics, 35(20), 2019, 4168–4169 doi: 10.1093/bioinformatics/btz184 Advance Access Publication Date: 15 March 2019 Applications Note Structural bioinformatics FoldX 5.0: working with RNA, small molecules and a new graphical interface Javier Delgado1,†, Leandro G. Radusky1,†, Damiano Cianferoni1 and Luis Serrano1,2,3,* 1Centre for Genomic Regulation (CRG), The Barcelona Institute for Science and Technology, 08003 Barcelona, Spain, 2Universitat Pompeu Fabra (UPF), Barcelona, Spain and 3Institucio´ Catalana de Recerca i Estudis Avanc¸ats (ICREA), 08010 Barcelona, Spain *To whom correspondence should be addressed. †The authors wish it to be known that, in their opinion, the first two authors should be regarded as Joint First Authors. Associate Editor: Alfonso Valencia Received on November 20, 2018; revised on January 29, 2019; editorial decision on March 9, 2019; accepted on March 14, 2019 Abstract Summary: A new version of FoldX, whose main new features allows running classic FoldX com- mands on structures containing RNA molecules and includes a module that allows parametrization of ligands or small molecules (ParamX) that were not previously recognized in old versions, has been released. An extended FoldX graphical user interface has also being developed (available as a python plugin for the YASARA molecular viewer) allowing user-friendly parametrization of new custom user molecules encoded using JSON format. Availability and implementation: http://foldxsuite.crg.eu/ Contact: [email protected] 1 Introduction 2 Implementation and requirements The FoldX toolsuite (Guerois et al., 2002; Schymkowitz et al., FoldX 5.0 was written in Cþþ, and has been compiled as a univer- 2005) was developed for the rapid evaluation of the effect of muta- sal and portable binary file for each of the three main platforms tions on the stability, folding and dynamics of proteins.
    [Show full text]
  • ACPYPE - Antechamber Python Parser Interface Alan W Sousa Da Silva1,2,3* and Wim F Vranken2,4
    da Silva and Vranken BMC Research Notes 2012, 5:367 http://www.biomedcentral.com/1756-0500/5/367 TECHNICAL NOTE Open Access ACPYPE - AnteChamber PYthon Parser interfacE Alan W Sousa da Silva1,2,3* and Wim F Vranken2,4 Abstract Background: ACPYPE (or AnteChamber PYthon Parser interfacE) is a wrapper script around the ANTECHAMBER software that simplifies the generation of small molecule topologies and parameters for a variety of molecular dynamics programmes like GROMACS, CHARMM and CNS. It is written in the Python programming language and was developed as a tool for interfacing with other Python based applications such as the CCPN software suite (for NMR data analysis) and ARIA (for structure calculations from NMR data). ACPYPE is open source code, under GNU GPL v3, and is available as a stand-alone application at http://www.ccpn.ac.uk/acpype and as a web portal application at http://webapps.ccpn.ac.uk/acpype. Findings: We verified the topologies generated by ACPYPE in three ways: by comparing with default AMBER topologies for standard amino acids; by generating and verifying topologies for a large set of ligands from the PDB; and by recalculating the structures for 5 protein–ligand complexes from the PDB. Conclusions: ACPYPE is a tool that simplifies the automatic generation of topology and parameters in different formats for different molecular mechanics programmes, including calculation of partial charges, while being object oriented for integration with other applications. Keywords: MD, GROMACS, AMBER, CNS, ANTECHAMBER, NMR, Ligand, Topology Findings Background Here we introduce ACPYPE, a tool based on Molecular Mechanics (MM) has evolved substantially ANTECHAMBER [1] for generating automatic topolo- over the last decades, not only because of major advances gies and parameters in different formats for different in computational power, but also due to more accurate molecular mechanics programmes, including calcula- and diverse force field descriptions.
    [Show full text]
  • Ballview a Molecular Viewer and Modeling Tool
    BALLView A molecular viewer and modeling tool Dissertation zur Erlangung des Grades des Doktors der Ingenieurwissenschaften der Naturwissenschaftlich– Technischen Fakultäten der Universität des Saarlandes vorgelegt von Diplom-Biologe Andreas Moll Saarbrücken im Mai 2007 Tag des Kolloquiums: 18. Juli 2007 Dekan: Prof. Dr. Thorsten Herfet Mitglieder des Prüfungsausschusses: Prof. Dr. Philipp Slusallek Prof. Dr. Hans-Peter Lenhof Prof. Dr. Oliver Kohlbacher Dr. Dirk Neumann Acknowledgments The work on this thesis was carried out during the years 2002-2007 at the Center for Bioinformatics in the group of Prof. Dr. Hans-Peter Lenhof who also was the supervisor of the thesis. With his deeply interesting lecture on bioinformatics, Prof. Dr. Hans-Peter Lenhof kindled my interest in this field and gave me the freedom to do research in those areas that fascinated me most. The implementation of BALLView would have been unthinkable without the help of all the people who contributed code and ideas. In particular, I want to thank Prof. Dr. Oliver Kohlbacher for his splendid work on the BALL library, on which this thesis is based on. Furthermore, Prof. Kohlbacher had at any time an open ear for my questions. Next I want to thank Dr. Andreas Hildebrandt, who had good advices for the majority of problems that I was confronted with. In addition he contributed code for database access, field line calculations, spline points calculations, 2D depiction of molecules, and for the docking in- terface. Heiko Klein wrote the precursor of the VIEW library. Anne Dehof implemented the peptide builder, the secondary structure assignment, and a first version of the editing mo- de.
    [Show full text]
  • Refinement of the Docking Component of Virtual Screening for Pparγ Therapeutics Using Pharmacophore Analysis and Molecular Dynamics
    Refinement of the Docking Component of Virtual Screening for PPARγ Therapeutics Using Pharmacophore Analysis and Molecular Dynamics Stephanie Nicole Lewis Dissertation submitted to the faculty of the Virginia Polytechnic Institute and State University in partial fulfillment of the requirements for the degree of Doctor of Philosophy In Genetics, Bioinformatics, and Computational Biology David R. Bevan, Chair Josep Bassaganya-Riera Jill Sible Liqing Zhang June 11, 2013 Blacksburg, VA Keywords: Virtual screening, PPARγ, Drug discovery, Molecular docking, Pharmacophore screening, Molecular dynamics Copyright 2013, Stephanie Nicole Lewis Refinement of the Docking Component of Virtual Screening for PPARγ Therapeutics Using Pharmacophore Analysis and Molecular Dynamics Stephanie Nicole Lewis ABSTRACT Exploration of peroxisome proliferator-activated receptor-gamma (PPARγ) as a drug target holds applications for treating a wide variety of chronic inflammation-related diseases. Type 2 diabetes (T2D), which is a metabolic disease influenced by chronic inflammation, is quickly reaching epidemic proportions. Although some treatments are available to control T2D, more efficacious compounds with fewer side effects are in great demand. Drugs targeting PPARγ typically are compounds that function as agonists toward this receptor, which means they bind to and activate the protein. Identifying compounds that bind to PPARγ (i.e. binders) using computational docking methods has proven difficult given the large binding cavity of the protein, which yields a large target area and variations in ligand positions within the binding site. We applied a combined computational and experimental concept for characterizing PPARγ and identifying binders. The goal was to establish a time- and cost-effective way to screen a large, diverse compound database potentially containing natural and synthetic compounds for PPARγ agonists that are more efficacious and safer than currently available T2D treatments.
    [Show full text]
  • Pemodelan Dan Simulasi Dinamika
    PLAGIAT MERUPAKAN TINDAKAN TIDAK TERPUJI PEMODELAN DAN SIMULASI DINAMIKA MOLEKUL HUMAN MATRIX METALLOPROTEINASE 9 (hMMP9) UTUH SEBAGAI TARGET VIRTUAL PENEMUAN LIGAN PADA CATALYTIC SITE MAUPUN HEMOPEXIN-LIKE DOMAIN TESIS Diajukan untuk Memenuhi Salah Satu Syarat Memperoleh Gelar Magister Farmasi (M.Farm.) Program Studi Magister Farmasi Diajukan oleh: Roy Gunawan Wicaksono, S.Farm. Nomor Induk Mahasiswa: 188122101 FAKULTAS FARMASI UNIVERSITAS SANATA DHARMA YOGYAKARTA 2020 PLAGIAT MERUPAKAN TINDAKAN TIDAK TERPUJI PEMODELAN DAN SIMULASI DINAMIKA MOLEKUL HUMAN MATRIX METALLOPROTEINASE 9 (hMMP9) UTUH SEBAGAI TARGET VIRTUAL PENEMUAN LIGAN PADA CATALYTIC SITE MAUPUN HEMOPEXIN-LIKE DOMAIN TESIS Diajukan untuk Memenuhi Salah Satu Syarat Memperoleh Gelar Magister Farmasi (M.Farm.) Program Studi Magister Farmasi Diajukan oleh: Roy Gunawan Wicaksono, S.Farm. Nomor Induk Mahasiswa: 188122101 FAKULTAS FARMASI UNIVERSITAS SANATA DHARMA YOGYAKARTA 2020 PLAGIAT MERUPAKAN TINDAKAN TIDAK TERPUJI }ERSETUJUAN PEMBIMBING PEMODELAN DAN SIMULASI DINAMIKA MIOLF]KTJL HUMAN MATRD( METALLOPROTEINASE 9 (hMNTPg) IITUH SEBAGAT TARGET VTRTUAL PENEMUAN LIGAN PADA CATALYTIC SITEiMAI|PIITN HEMOPEXIN DOfuLAIN Tesis yang diajukan oleh: Roy Crunawan Wicaksono, S.Farm. (f 88122101) T€lah disetujui tar.ggdZr 19 ot"t, Dosen ing Utarna Enade Ph.D., Apt. o,lPP/NrD 1969/050608790 I ) PLAGIAT MERUPAKAN TINDAKAN TIDAK TERPUJI PENGESAIIAN TESIS BERJUDT]L PEMODELAIT DAN SIMULASI DINAMIKA MOLEKIJL HUMAN MATRD( METALLOPROTEINASE 9 (htvfrvfp9) UTUH SEBAGAI TARGET VIRTUAL PENEMUAN LIGA}I PADA CITALYTIC SITE MAUI{JN HEMOPHfiN-LIEE DOMAIN oleh: Roy Gunawan Wicaksono, S.Farm. (188122101) Dipertahankan di hadapan panitia penguji tesis Fakultas Farmasi Universitas Sanata Dharma Pada tanggal 23 lanuai 2020 Mengetahui Fakultas Farmasi Universitas Sanata Dharma tr!? ,: utl >-_ | {'2-_ Hartini, Apt. Panitia Penguji: l.
    [Show full text]
  • On the Feasibility of FPGA Acceleration of Molecular Dynamics Simulations
    On the Feasibility of FPGA Acceleration of Molecular Dynamics Simulations Technical Report (v0.1) Michael Schaffnery, Luca Beniniy yETH Zurich, Integrated Systems Lab IIS, Zurich, Switzerland Abstract—Classical molecular dynamics (MD) simulations are Hence, the core algorithms used in classical MD simulations important tools in life and material sciences since they allow have largely remained in recent years, and most simulation studying chemical and biological processes in detail. However, speed improvements are due to the use of MPI parallelization the inherent scalability problem of particle-particle interactions and the sequential dependency of subsequent time steps render in combination with GPGPUs that handle the computationally MD computationally intensive and difficult to scale. To this dominant parts. Specialized supercomputers such as the Anton end, specialized FPGA-based accelerators have been repeatedly systems are very inaccessible and expensive, and are hence not proposed to ameliorate this problem. However, to date none of the widely used today. leading MD simulation packages fully support FPGA acceleration and a direct comparison of GPU versus FPGA accelerated codes Besides these MPI and GPU-based solutions, FPGA accel- has remained elusive so far. erators have repeatedly been proposed as a viable alternative With this report, we aim at clarifying this issue by comparing to accelerate the compute intensive parts [10–22] . However, measured application performance on GPU-dense compute nodes these studies only show estimated or measured speedup with with performance and cost estimates of a FPGA-based single- respect to older CPU implementations. To date none of the node system. Our results show that an FPGA-based system can leading MD simulation packages fully support FPGA accel- indeed outperform a similarly configured GPU-based system, but the overall application-level speedup remains in the order of 2× eration [23] and a direct comparison of GPU versus FPGA due to software overheads on the host.
    [Show full text]
  • Visualization of Macromolecular Structures
    REVIEW Visualization of macromolecular structures Seán I O’Donoghue1, David S Goodsell2, Achilleas S Frangakis3, Fabrice Jossinet4, Roman A Laskowski5, Michael Nilges6, Helen R Saibil7, Andrea Schafferhans1, Rebecca C Wade8, Eric Westhof4 & Arthur J Olson2 Structural biology is rapidly accumulating a wealth of detailed information about protein function, binding sites, RNA, large assemblies and molecular motions. These data are increasingly of interest to a broader community of life scientists, not just structural experts. Visualization is a primary means for accessing and using these data, yet visualization is also a stumbling block that prevents many life scientists from benefiting from three-dimensional structural data. In this review, we focus on key biological questions where visualizing three-dimensional structures can provide insight and describe available methods and tools. Decades ago, when structural biology was still in its most of them are not prepared to spend months learn- infancy, structures were rare and structural biologists ing complex user interfaces or scripting languages. often dedicated years of their life to studying just one Even today, complex user interfaces in visualization structure at atomic detail. The first tools used for visualiz- tools are often a stumbling block, preventing many ing macromolecular structures were tools for specialists. scientists from benefiting from structural data. Even Today’s situation is very different: the rate at which structural experts have come to expect ease of use from structures are solved has greatly increased, with over molecular graphics tools, in addition to improved 60,000 high-resolution protein structures now avail- speed, features and capabilities. able in the consolidated Worldwide Protein Data Bank In the past, molecular graphics tools were invariably (wwPDB)1.
    [Show full text]
  • Targeting Tmprss2, S-Protein:Ace2, and 3Clpro for Synergetic Inhibitory Engagement Mathew Coban1, Juliet Morrison Phd2, William
    Targeting Tmprss2, S-protein:Ace2, and 3CLpro for Synergetic Inhibitory Engagement Mathew Coban1, Juliet Morrison PhD2, William D. Freeman MD3, Evette Radisky PhD1, Karine G. Le Roch PhD4, Thomas R. Caulfield, PhD1,5-8 Affiliations 1 Department of Cancer Biology, Mayo Clinic, 4500 San Pablo Road South, Jacksonville, FL, 32224 USA 2 Department of Microbiology and Plant Pathology, University of California, Riverside, 900 University, Riverside, CA, 92521 USA 3 Department of Neurology, Mayo Clinic, 4500 San Pablo South, Jacksonville, FL, 32224 USA 4Department of Molecular, Cell and Systems Biology, University of California, Riverside, 900 University, Riverside, CA, 92521 USA 5 Department of Neuroscience, Mayo Clinic, 4500 San Pablo South, Jacksonville, FL, 32224 USA 6 Department of Neurosurgery, Mayo Clinic, 4500 San Pablo South, Jacksonville, FL, 32224 USA 7 Department of Health Science Research (BSI), Mayo Clinic, 4500 San Pablo South, Jacksonville, FL, 32224 USA 8Department of Clinical Genomics (Enterprise), Mayo Clinic, Rochester, MN 55905, USA Correspondence to: Thomas R. Caulfield, PhD, Dept of Neuroscience, Cancer Biology, Neurosurgery, Health Science Research, & Clinical Genomics Mayo Clinic, 4500 San Pablo Road South Jacksonville, FL 32224 Telephone: +1 904-953-6072, E-mail: [email protected] SUMMARY Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a devastating respiratory and inflammatory illness caused by a new coronavirus that is rapidly spreading throughout the human population. Over the past 6 months, SARS-CoV-2, the virus responsible for COVID-19, has already infected over 11.6 million (25% located in United States) and killed more than 540K people around the world. As we face one of the most challenging times in our recent history, there is an urgent need to identify drug candidates that can attack SARS-CoV-2 on multiple fronts.
    [Show full text]
  • Open Babel Documentation
    Open Babel Documentation Geoffrey R Hutchison Chris Morley Craig James Chris Swain Hans De Winter Tim Vandermeersch Noel M O’Boyle (Ed.) Mar 26, 2021 Contents 1 Introduction 3 1.1 Goals of the Open Babel project.....................................3 1.2 Frequently Asked Questions.......................................4 1.3 Thanks..................................................7 2 Install Open Babel 11 2.1 Install a binary package......................................... 11 2.2 Compiling Open Babel.......................................... 11 3 obabel - Convert, Filter and Manipulate Chemical Data 19 3.1 Synopsis................................................. 19 3.2 Options.................................................. 19 3.3 Examples................................................. 22 3.4 Format Options.............................................. 24 3.5 Append property values to the title.................................... 24 3.6 Generating conformers for structures.................................. 24 3.7 Filtering molecules from a multimolecule file.............................. 25 3.8 Substructure and similarity searching.................................. 28 3.9 Sorting molecules............................................ 28 3.10 Remove duplicate molecules....................................... 28 3.11 Aliases for chemical groups....................................... 29 3.12 Forcefield energy and minimization................................... 30 3.13 Aligning molecules or substructures..................................
    [Show full text]