Molecular Illustration in Research and Education: Past, Present, and Future
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Journal of Molecular Graphics and Modelling 73 (2017) 179–190
Journal of Molecular Graphics and Modelling 73 (2017) 179–190 Contents lists available at ScienceDirect Journal of Molecular Graphics and Modelling journa l homepage: www.elsevier.com/locate/JMGM Combined Monte Carlo/torsion-angle molecular dynamics for ensemble modeling of proteins, nucleic acids and carbohydrates a b c d,1 Weihong Zhang , Steven C. Howell , David W. Wright , Andrew Heindel , e a, b, Xiangyun Qiu , Jianhan Chen ∗, Joseph E. Curtis ∗∗ a Kansas State University, Manhattan, KS, USA b NIST Center for Neutron Research, 100 Bureau Drive, Gaithersburg, MD, USA c Centre for Computational Science, Department of Chemistry, University College London, 10 Gordon St., London, UK d James Madison University, Department of Chemistry & Biochemistry, Harrisonburg, VA, USA e Department of Physics, The George Washington University, Washington, DC, USA a r t i c l e i n f o a b s t r a c t Article history: We describe a general method to use Monte Carlo simulation followed by torsion-angle molecular dynam- Received 13 October 2016 ics simulations to create ensembles of structures to model a wide variety of soft-matter biological systems. Received in revised form 19 January 2017 Our particular emphasis is focused on modeling low-resolution small-angle scattering and reflectivity Accepted 17 February 2017 structural data. We provide examples of this method applied to HIV-1 Gag protein and derived fragment Available online 23 February 2017 proteins, TraI protein, linear B-DNA, a nucleosome core particle, and a glycosylated monoclonal antibody. This procedure will enable a large community of researchers to model low-resolution experimental data MSC: with greater accuracy by using robust physics based simulation and sampling methods which are a 00-01 99-00 significant improvement over traditional methods used to interpret such data. -
Biological Sciences 1
Biological Sciences 1 BIOL UN3208 Introduction to Evolutionary Biology or EEEB UN2001 BIOLOGICAL SCIENCES Environmental Biology I: Elements to Organisms. Departmental Office: 600 Fairchild, 212-854-4581; Interested students should consult listings in other departments for [email protected]; [email protected] courses related to biology. For courses in environmental studies, see listings for Earth and environmental sciences or for ecology, evolution, Director of Undergraduate Studies, Undergraduate Programs and and environmental biology. For courses in human evolution, see listings Laboratories: for anthropology or for ecology, evolution, and environmental biology. Prof. Deborah Mowshowitz, 744D Mudd; 212-854-4497; For courses in the history of evolution, see listings for history and for [email protected] philosophy of science. For a list of courses in computational biology and genomics, visit http://systemsbiology.columbia.edu/courses. Biology Major and Concentration Advisers: For a list of current biology, biochemistry, biophysics, and neuroscience and behavior advisers, please visit http://biology.columbia.edu/ Advanced Placement programs/advisors Transfer Credit A-F: Prof. Alice Heicklen, 744B Mudd; [email protected] Transfer credits granted toward the degree are not automatically counted G-O: Prof. Mary Ann Price, 744A Mudd; [email protected] toward the major. The department determines which transfer credits P-Z: Prof. Tulle Hazelrigg, 753A Mudd, [email protected] can be counted toward the major. For most majors, at least four biology Backup Advisor: Prof. Deborah Mowshowitz, 744D Mudd; 212-854-4497; or biochemistry courses and at least 18 credits of the total (biology, [email protected] biochemistry, math, physics, and chemistry) must be taken at Columbia. -
Visualization of Biomedical Data
Visualization of Biomedical Data Corresponding author: Seán I. O’Donoghue; email: [email protected] • Data61, Commonwealth Scientific and Industrial Research Organisation (CSIRO), Eveleigh NSW 2015, Australia • Genomics and Epigenetics Division, Garvan Institute of Medical Research, Sydney NSW 2010, Australia • School of Biotechnology and Biomolecular Sciences, UNSW, Kensington NSW 2033, Australia Benedetta Frida Baldi; email: [email protected] • Genomics and Epigenetics Division, Garvan Institute of Medical Research, Sydney NSW 2010, Australia Susan J Clark; email: [email protected] • Genomics and Epigenetics Division, Garvan Institute of Medical Research, Sydney NSW 2010, Australia Aaron E. Darling; email: [email protected] • The ithree institute, University of Technology Sydney, Ultimo NSW 2007, Australia James M. Hogan; email: [email protected] • School of Electrical Engineering and Computer Science, Queensland University of Technology, Brisbane QLD, 4000, Australia Sandeep Kaur; email: [email protected] • School of Computer Science and Engineering, UNSW, Kensington NSW 2033, Australia Lena Maier-Hein; email: [email protected] • Div. Computer Assisted Medical Interventions (CAMI), German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany Davis J. McCarthy; email: [email protected] • European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, CB10 1SD, Hinxton, Cambridge, UK • St. Vincent’s Institute of Medical Research, Fitzroy VIC 3065, Australia William -
CCP4 Molecular Graphics Documentation Contents Documentation On-Line Documentation Tutorials Ccp4mg Home Contents Quick Reference Print Version Contact References
CCP4 Molecular Graphics file:///E:/ccp4mg-win/help/index.html CCP4 Molecular Graphics Documentation Contents Documentation On-line Documentation Tutorials CCP4mg Home Contents Quick Reference Print version Contact References Documentation Contents General Graphics Window - mouse bindings and short cuts The Tools menu The View menu Projects - data organisation Installation of support programs Molecular models Reading coordinate files and atom typing The Coordinate Model Interface Atom selection Structure Analysis Surfaces and Electrostatic Potentials Symmetry Related Models Animations Other types of data Electron density maps Vectors Electron density in GAUSSIAN cube format Text and imported images Applications Superpose automatic protein superposition and interactive superposition of any structures Presentation Graphics Picture Wizard set up complex pictures quickly Movies Presentations Geometry Run Coot Other utilities Web tools History and Scripting Editing and Saving Files PDF Creator - PDF4Free v2.0 http://www.pdf4free.com 1 of 3 01/11/2007 18:42 CCP4 Molecular Graphics file:///E:/ccp4mg-win/help/index.html Lighting Saving the program status Picture Definition Files (also Object attributes and Introduction to Python) Making presentations Quick Reference See Display Table for general overview of the display table. For information on specific type of data click the appropriate data type in the File list below. File Tools View Applications Coordinate Files General tools General tools Movies Downloading Coordinate .. .. Superpose Files Read electron density Picture Save/restore view Lighting map/MTZ Wizard Read QM maps Save/restore status Transparency Geometry Add vectors/labels Picture definition files View from Rotate,translate,align Add image History and Scripting view Add legend Presentation/Notebook RocknRoll Add crystal List monomer definition Model definition file Preferences Open presentation Tutorials Output screen image Windows Bring a 'lost' window to the front. -
Coot: Model-Building Tools for Molecular Graphics Crystallography ISSN 0907-4449
research papers Acta Crystallographica Section D Biological Coot: model-building tools for molecular graphics Crystallography ISSN 0907-4449 Paul Emsley* and Kevin Cowtan CCP4mg is a project that aims to provide a general-purpose Received 26 February 2004 tool for structural biologists, providing tools for X-ray Accepted 4 August 2004 structure solution, structure comparison and analysis, and York Structural Biology Laboratory, University of York, Heslington, York YO10 5YW, England publication-quality graphics. The map-®tting tools are avail- able as a stand-alone package, distributed as `Coot'. Correspondence e-mail: [email protected] 1. Introduction Molecular graphics still plays an important role in the deter- mination of protein structures using X-ray crystallographic data, despite on-going efforts to automate model building. Functions such as side-chain placement, loop, ligand and fragment ®tting, structure comparison, analysis and validation are routinely performed using molecular graphics. Lower Ê resolution (dmin worse than 2.5 A) data in particular need interactive ®tting. The introduction of FRODO (Jones, 1978) and then O (Jones et al., 1991) to the ®eld of protein crystallography was in each case revolutionary, each in their time breaking new ground in demonstrating what was possible with the current hardware. These tools allowed protein crystallographers to enjoy what is widely held to be the most thrilling part of their work: giving birth (as it were) to a new protein structure. The CCP4 program suite (Collaborative Computational Project, Number 4, 1994) is an integrated collection of software for macromolecular crystallography, with a scope ranging from data processing to structure re®nement and validation. -
Data Visualization by Nils Gehlenborg
Data Visualization Nils Gehlenborg ([email protected]) Center for Biomedical Informatics / Harvard Medical School Cancer Program / Broad Institute of MIT and Harvard ISMB/ECCB 2011 http://www.biovis.net Flyers at ISCB booth! Data Visualization / ISMB/ECCB 2011 / Nils Gehlenborg A good sketch is better than a long speech. Napoleon Bonaparte Data Visualization / ISMB/ECCB 2011 / Nils Gehlenborg Minard 1869 Napoleon’s March on Moscow Data Visualization / ISMB/ECCB 2011 / Nils Gehlenborg 4 I believe when I see it. Unknown Data Visualization / ISMB/ECCB 2011 / Nils Gehlenborg Anscombe 1973, The American Statistician Anscombe’s Quartet mean(X) = 9, var(X) = 11, mean(Y) = 7.5, var(Y) = 4.12, cor(X,Y) = 0.816, linear regression line Y = 3 + 0.5*X Data Visualization / ISMB/ECCB 2011 / Nils Gehlenborg 6 Anscombe 1973, The American Statistician Anscombe’s Quartet Data Visualization / ISMB/ECCB 2011 / Nils Gehlenborg 7 Exploration: Hypothesis Generation trends gaps outliers clusters - A large data set is given and the goal is to learn something about it. - Visualization is employed to perform pattern detection using the human visual system. - The goal is to generate hypotheses that can be tested with statistical methods or follow-up experiments. Data Visualization / ISMB/ECCB 2011 / Nils Gehlenborg 8 Visualization Use Cases Presentation Confirmation Exploration Data Visualization / ISMB/ECCB 2011 / Nils Gehlenborg 9 Definition The use of computer-supported, interactive, visual representations of data to amplify cognition. Stu Card, Jock Mackinlay & Ben Shneiderman Computer-based visualization systems provide visual representations of datasets intended to help people carry out some task more effectively.effectively. -
Architecture De Dataflow Pour Des Systèmes Modulaires Et Génériques De Simulation De Plante Christophe Pradal
Architecture de dataflow pour des systèmes modulaires et génériques de simulation de plante Christophe Pradal To cite this version: Christophe Pradal. Architecture de dataflow pour des systèmes modulaires et génériques desim- ulation de plante. Modélisation et simulation. Université Montpellier, 2019. Français. NNT : 2019MONTS034. tel-02879776 HAL Id: tel-02879776 https://tel.archives-ouvertes.fr/tel-02879776 Submitted on 24 Jun 2020 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. THESE POUR OBTENIR LE GRADE DE DOCTEUR DE L’UNIVERSITE DE MONTPELLIER PAR LA VALIDATION DES ACQUIS DE L’EXPERIENCE Année universitaire 2018-2019 En Informatique École doctorale I2S – Information, Structures, Systèmes Unité de recherche UMR AGAP – Université de Montpellier SYNTHÈSE DES TRAVAUX DE RECHERCHE Architecture de Dataflow pour des systèmes modulaires et génériques de simulation de plante Présentée par Christophe PRADAL Le 17 juillet 2019 Référent : Christophe GODIN RAPPORT DE GESTION Devant le jury composé de Christophe GODIN, Directeur de recherche, Inria, Lyon -
Visualization and Exploration of Transcriptomics Data Nils Gehlenborg
Visualization and Exploration of Transcriptomics Data 05 The identifier 800 year identifier Nils Gehlenborg Sidney Sussex College To celebrate our 800 year history an adaptation of the core identifier has been commissioned. This should be used on communications in the time period up to and including 2009. The 800 year identifier consists of three elements: the shield, the University of Cambridge logotype and the 800 years wording. It should not be redrawn, digitally manipulated or altered. The elements should not be A dissertation submitted to the University of Cambridge used independently and their relationship should for the degree of Doctor of Philosophy remain consistent. The 800 year identifier must always be reproduced from a digital master reference. This is available in eps, jpeg and gif format. Please ensure the appropriate artwork format is used. File formats European Molecular Biology Laboratory, eps: all professionally printed applications European Bioinformatics Institute, jpeg: Microsoft programmes Wellcome Trust Genome Campus, gif: online usage Hinxton, Cambridge, CB10 1SD, Colour United Kingdom. The 800 year identifier only appears in the five colour variants shown on this page. Email: [email protected] Black, Red Pantone 032, Yellow Pantone 109 and white October 12, 2010 shield with black (or white name). Single colour black or white. Please try to avoid any other colour combinations. Pantone 032 R237 G41 B57 Pantone 109 R254 G209 B0 To Maureen. This dissertation is my own work and contains nothing which is the outcome of work done in collaboration with others, except as specified in the text and acknowledgements. This dissertation is not substantially the same as any I have submit- ted for a degree, diploma or other qualification at any other university, and no part has already been, or is currently being submitted for any degree, diploma or other qualification. -
Understanding Performance of Protein Structural Classifiers
Understanding Performance of Protein Structural Classifiers Alper Sarikaya∗ Danielle Albers† Michael Gleicher‡ University of Wisconsin – Madison University of Wisconsin – Madison University of Wisconsin – Madison ABSTRACT of proteins. The individual molecular detail view shows a surface- Many bioinformatics applications utilize machine learning tech- abstracted three-dimensional view of the protein, providing a scaf- niques to create models for predicting which parts of proteins will fold to display data mapped to the surface in a manner similar to bind to targets. Understanding the results of these protein surface canonical molecular graphics applications such as PyMol [3]. How- binding classifiers is challenging, as the individual answers are em- ever, we group spatially-congruent results on the molecular surface bedded spatially on the surface of the molecules, yet the perfor- to help collapse the data into a small set of regions that supports mance needs to be understood over an entire corpus of molecules. interactive and guided exploration. Our analytical approach is real- In this project, we introduce a multi-scale approach for assessing ized by a prototype that we have applied to various structural bind- the performance of these structural classifiers, providing coordi- ing classifiers. nated views for both corpus level overviews as well as spatially- 2 TASK ANALYSIS embedded results on the three-dimensional structures of proteins. Our problem domain focuses on proteins: large macro-molecules Keywords: J.3.1 [Computer Applications]: Life and Medical Sci- that are composed of 20 naturally-occurring amino acids (residues) ences — Biology and Genetics; chained together in sequence. The residues, in turn, fold over one another to form the conformation that governs the protein’s biolog- 1 INTRODUCTION ical and chemical function. -
Python Molecular Viewer
Python Molecular Viewer Written by Ruth Huey and Michel Sanner The Scripps Research Institute Molecular Graphics Laboratory 10550 N. Torrey Pines Rd. La Jolla, California 92037-1000 USA 11 October 2005 1 Contents Contents .......................................................................... 2 Introduction ..................................................................... 4 Before We Start….............................................................................4 FAQ – Frequently Asked Questions ..................................... 5 Exercise One: Getting Started: PMV Basics .......................... 7 Procedure: .......................................................................................8 Summary: what have we learned?....................................................12 Bonus Section: MSMS surfaces .......................................................13 Bonus Section: Binding Commands to Keys ......................................14 Hemolysin: Secondary Structure colored by Chain. .............. 16 Procedure: .....................................................................................16 Summary: what have we learned?....................................................20 Bonus Section: Color by Secondary Structure...................................21 Bonus Section: Measure hemolysin beta barrel.................................21 HIV Protease: Active Site Residues and Inhibitor ................. 23 Procedure: .....................................................................................24 Summary: -
Digital Scholarship Commons Presentation 09.24.14
A short introduction: Information visualizations in teaching & research Isabel Meirelles | [email protected] Associate Professor, Graphic Design, CAMD Digital Scholarship Commons NU Sept.24 Information design Infographics! Information visualization Data visualization Information design Infographics Graphical representations that aim at communicating information with the purpose to reveal patterns and relationships not known or not easily deduced without the aid of the visual presentation of information. ! Information visualization Data visualization “the use of computer-supported, interactive, visual representations of abstract data to amplify cognition” Card et al. : Readings in Information Visualization: Using Vision to Think Information From Latin informare to give form or shape to, from in into + formare to form, from forma a form or shape + -ation indicating a process or condition The Oxford American Thesaurus of Current English Information Definition* Definition information = well-formed and meaningful data *Weak definition. The strong definition includes the further condition of truthfulness. Luciano Floridi (2010): Information, A Very Short Introduction Information taxonomy by Floridi M. Chen & L. Floridi (2012): An Analysis of Information in Visualization in Synthese, Springer (historical) intermezzo Visual/diagrammatic representations over history 9th-ct., France: M. Capella, De nuptiis,. c. 1310, England: Table of the Ten Commandments Planetary diagrams from the De Lisle Psalter To increase working memory 1582: Giordano -
Steps to an Ecology of Networked Knowledge and Innovation Enabling New Forms of Collaboration Among Sciences, Engineering, Arts, and Design
Steps to an Ecology of Networked Knowledge and Innovation Enabling New Forms of Collaboration among Sciences, Engineering, Arts, and Design Roger F. Malina, Carol Strohecker, and Carol LaFayette on behalf of SEAD network contributors The MIT Press, Cambridge, Massachusetts and London, England Steps to an Ecology of Networked Knowledge and Innovation Enabling New Forms of Collaboration among Sciences, Engineering, Arts, and Design Roger F. Malina, Carol Strohecker, and Carol LaFayette on behalf of SEAD network contributors Cover image: “Map of Science Derived from Clickstream Data” (2009). Maps of science resulting from large-scale clickstream data provide a detailed, contemporary view of scientific activity and correct the under-representation of the social sciences and humanities that is commonly found in citation data. © Johan Bollen. Used with permission. Originally published in Bollen, J., H. Van de Sompel, A. Hagberg, L. Bettencourt, R. Chute, et al. (2009), “Clickstream Data Yields High-Resolution Maps of Science.” PLoS ONE 4 (3): e4803. doi: 10.1371/journal.pone.0004803. 3 This material is based on work supported by the National Science Foundation under Grant No. 1142510, IIS, Human-Centered Computing, “Collaborative Research: EAGER: Network for Science, Engineering, Arts and Design (NSEAD).” Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation. © 2015 ISAST Published under a Creative Commons Attribution-NonCommercial 4.0 International license (CC BY-NC 4.0) eISBN: 978-0-262-75863-5 CONTENTS Acknowledgments ......................................................................................i SEAD White Papers Committees ...............................................................ii Introduction ...............................................................................................1 1. SEAD White Papers Methodology ......................................................13 2.