Tinker User's Guide
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Latest Release Rather Than Working on the Develop Branch
WarpX Documentation Release 21.09 WarpX collaboration Sep 28, 2021 CONTENTS 1 Contact us 3 1.1 Code of Conduct.............................................3 1.2 Acknowledge WarpX...........................................4 2 Installation 7 2.1 Users...................................................7 2.2 Developers................................................9 2.3 HPC................................................... 18 3 Usage 47 3.1 Run WarpX................................................ 47 3.2 Input Parameters............................................. 48 3.3 Python (PICMI)............................................. 84 3.4 Examples................................................. 85 3.5 In-Depth: PWFA............................................. 87 3.6 Workflows................................................ 91 4 Data Analysis 103 4.1 Output formats.............................................. 103 4.2 yt-project................................................. 120 4.3 openPMD-viewer............................................. 126 4.4 openPMD-api.............................................. 127 4.5 3D Visualization: ParaView....................................... 127 4.6 3D Visualization: VisIt.......................................... 127 4.7 VisualPIC................................................. 129 4.8 PICViewer................................................ 129 4.9 Back-Transformed Diagnostics...................................... 131 4.10 Reduced diagnostics........................................... 132 5 Theory -
20 Years of Polarizable Force Field Development
20 YEARS OF POLARIZABLE FORCEFIELDDEVELOPMENTfor biomolecular systems thor van heesch Supervised by Daan P. Geerke and Paola Gori-Giorg 1 contents 2 1 Introduction 3 2contentsForce fields: Basics, Caveats and Extensions 4 2.1 The Classical Approach . 4 2.2 The caveats of point-charge electrostatics . 8 2.3 Physical phenomenon of polarizability . 10 2.4 Common implementation methods of electronic polarization . 11 2.5 Accounting for anisotropic interactions . 14 3 Knitting the reviews and perspectives together 17 3.1 Polarizability: A smoking gun? . 17 3.2 New branches of electronic polarization . 18 3.3 Descriptions of electrostatics . 19 3.4 Solvation and polarization . 20 3.5 The rise of new challenges . 21 3.6 Parameterization or polarization? . 22 3.7 Enough response: how far away? . 23 3.8 The last perspectives . 23 3.9 A new hope: the next-generation force fields . 29 4 Learning with machines 29 4.1 Replace the functional form with machine learned force fields . 31 4.2 A different take on polarizable force fields . 33 4.3 Are transferable parameters an universal requirement? . 33 4.4 The difference between derivation and prediction . 34 4.5 From small molecules to long range interactions . 36 4.6 Enough knowledge to fold a protein? . 37 4.7 Boltzmann generators, a not so hypothetical machine anymore . 38 5 Summary: The Red Thread 41 introduction 3 In this literature study we aimed to answer the following question: What has changedabstract in the outlook on polarizable force field development during the last 20 years? The theory, history, methods, and applications of polarizable force fields have been discussed to address this question. -
Validated Products List, 1995 No. 3: Programming Languages, Database
NISTIR 5693 (Supersedes NISTIR 5629) VALIDATED PRODUCTS LIST Volume 1 1995 No. 3 Programming Languages Database Language SQL Graphics POSIX Computer Security Judy B. Kailey Product Data - IGES Editor U.S. DEPARTMENT OF COMMERCE Technology Administration National Institute of Standards and Technology Computer Systems Laboratory Software Standards Validation Group Gaithersburg, MD 20899 July 1995 QC 100 NIST .056 NO. 5693 1995 NISTIR 5693 (Supersedes NISTIR 5629) VALIDATED PRODUCTS LIST Volume 1 1995 No. 3 Programming Languages Database Language SQL Graphics POSIX Computer Security Judy B. Kailey Product Data - IGES Editor U.S. DEPARTMENT OF COMMERCE Technology Administration National Institute of Standards and Technology Computer Systems Laboratory Software Standards Validation Group Gaithersburg, MD 20899 July 1995 (Supersedes April 1995 issue) U.S. DEPARTMENT OF COMMERCE Ronald H. Brown, Secretary TECHNOLOGY ADMINISTRATION Mary L. Good, Under Secretary for Technology NATIONAL INSTITUTE OF STANDARDS AND TECHNOLOGY Arati Prabhakar, Director FOREWORD The Validated Products List (VPL) identifies information technology products that have been tested for conformance to Federal Information Processing Standards (FIPS) in accordance with Computer Systems Laboratory (CSL) conformance testing procedures, and have a current validation certificate or registered test report. The VPL also contains information about the organizations, test methods and procedures that support the validation programs for the FIPS identified in this document. The VPL includes computer language processors for programming languages COBOL, Fortran, Ada, Pascal, C, M[UMPS], and database language SQL; computer graphic implementations for GKS, COM, PHIGS, and Raster Graphics; operating system implementations for POSIX; Open Systems Interconnection implementations; and computer security implementations for DES, MAC and Key Management. -
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 ................................................. -
Real-Time Pymol Visualization for Rosetta and Pyrosetta
Real-Time PyMOL Visualization for Rosetta and PyRosetta Evan H. Baugh1, Sergey Lyskov1, Brian D. Weitzner1, Jeffrey J. Gray1,2* 1 Department of Chemical and Biomolecular Engineering, The Johns Hopkins University, Baltimore, Maryland, United States of America, 2 Program in Molecular Biophysics, The Johns Hopkins University, Baltimore, Maryland, United States of America Abstract Computational structure prediction and design of proteins and protein-protein complexes have long been inaccessible to those not directly involved in the field. A key missing component has been the ability to visualize the progress of calculations to better understand them. Rosetta is one simulation suite that would benefit from a robust real-time visualization solution. Several tools exist for the sole purpose of visualizing biomolecules; one of the most popular tools, PyMOL (Schro¨dinger), is a powerful, highly extensible, user friendly, and attractive package. Integrating Rosetta and PyMOL directly has many technical and logistical obstacles inhibiting usage. To circumvent these issues, we developed a novel solution based on transmitting biomolecular structure and energy information via UDP sockets. Rosetta and PyMOL run as separate processes, thereby avoiding many technical obstacles while visualizing information on-demand in real-time. When Rosetta detects changes in the structure of a protein, new coordinates are sent over a UDP network socket to a PyMOL instance running a UDP socket listener. PyMOL then interprets and displays the molecule. This implementation also allows remote execution of Rosetta. When combined with PyRosetta, this visualization solution provides an interactive environment for protein structure prediction and design. Citation: Baugh EH, Lyskov S, Weitzner BD, Gray JJ (2011) Real-Time PyMOL Visualization for Rosetta and PyRosetta. -
Structural Insight Into Pichia Pastoris Fatty Acid Synthase Joseph S
www.nature.com/scientificreports OPEN Structural insight into Pichia pastoris fatty acid synthase Joseph S. Snowden, Jehad Alzahrani, Lee Sherry, Martin Stacey, David J. Rowlands, Neil A. Ranson* & Nicola J. Stonehouse* Type I fatty acid synthases (FASs) are critical metabolic enzymes which are common targets for bioengineering in the production of biofuels and other products. Serendipitously, we identifed FAS as a contaminant in a cryoEM dataset of virus-like particles (VLPs) purifed from P. pastoris, an important model organism and common expression system used in protein production. From these data, we determined the structure of P. pastoris FAS to 3.1 Å resolution. While the overall organisation of the complex was typical of type I FASs, we identifed several diferences in both structural and enzymatic domains through comparison with the prototypical yeast FAS from S. cerevisiae. Using focussed classifcation, we were also able to resolve and model the mobile acyl-carrier protein (ACP) domain, which is key for function. Ultimately, the structure reported here will be a useful resource for further eforts to engineer yeast FAS for synthesis of alternate products. Fatty acid synthases (FASs) are critical metabolic enzymes for the endogenous biosynthesis of fatty acids in a diverse range of organisms. Trough iterative cycles of chain elongation, FASs catalyse the synthesis of long-chain fatty acids that can produce raw materials for membrane bilayer synthesis, lipid anchors of peripheral membrane proteins, metabolic energy stores, or precursors for various fatty acid-derived signalling compounds1. In addition to their key physiological importance, microbial FAS systems are also a common target of metabolic engineering approaches, usually with the aim of generating short chain fatty acids for an expanded repertoire of fatty acid- derived chemicals, including chemicals with key industrial signifcance such as α-olefns2–6. -
An Explicit-Solvent Conformation Search Method Using Open Software
An explicit-solvent conformation search method using open software Kari Gaalswyk and Christopher N. Rowley Department of Chemistry, Memorial University of Newfoundland, St. John’s, Newfoundland and Labrador, Canada ABSTRACT Computer modeling is a popular tool to identify the most-probable conformers of a molecule. Although the solvent can have a large effect on the stability of a conformation, many popular conformational search methods are only capable of describing molecules in the gas phase or with an implicit solvent model. We have developed a work-flow for performing a conformation search on explicitly-solvated molecules using open source software. This method uses replica exchange molecular dynamics (REMD) to sample the conformational states of the molecule efficiently. Cluster analysis is used to identify the most probable conformations from the simulated trajectory. This work-flow was tested on drug molecules a-amanitin and cabergoline to illustrate its capabilities and effectiveness. The preferred conformations of these molecules in gas phase, implicit solvent, and explicit solvent are significantly different. Subjects Biophysics, Pharmacology, Computational Science Keywords Conformation search, Explicit solvent, Cluster analysis, Replica exchange molecular dynamics INTRODUCTION Many molecules can exist in multiple conformational isomers. Conformational isomers have the same chemical bonds, but differ in their 3D geometry because they hold different torsional angles (Crippen & Havel, 1988). The conformation of a molecule can affect Submitted 5 April 2016 chemical reactivity, molecular binding, and biological activity (Struthers, Rivier & Accepted 6May2016 Published 31 May 2016 Hagler, 1985; Copeland, 2011). Conformations differ in stability because they experience different steric, electrostatic, and solute-solvent interactions. The probability, p,ofa Corresponding author Christopher N. -
Francisella Novicida Cas9 Interrogates Genomic DNA with Very High Specificity and Can Be Used for Mammalian Genome Editing
Francisella novicida Cas9 interrogates genomic DNA with very high specificity and can be used for mammalian genome editing Sundaram Acharyaa,b,1, Arpit Mishraa,1,2, Deepanjan Paula,1, Asgar Hussain Ansaria,b, Mohd. Azhara,b, Manoj Kumara,b, Riya Rauthana,b, Namrata Sharmaa, Meghali Aicha,b, Dipanjali Sinhaa,b, Saumya Sharmaa,b, Shivani Jaina, Arjun Raya,3, Suman Jainc, Sivaprakash Ramalingama,b, Souvik Maitia,b,d, and Debojyoti Chakrabortya,b,4 aGenomics and Molecular Medicine Unit, Council of Scientific and Industrial Research—Institute of Genomics & Integrative Biology, New Delhi, 110025, India; bAcademy of Scientific & Innovative Research, Ghaziabad, 201002, India; cKamala Hospital and Research Centre, Thalassemia and Sickle Cell Society, Rajendra Nagar, Hyderabad, 500052, India; and dInstitute of Genomics and Integrative Biology (IGIB)-National Chemical Laboratory (NCL) Joint Center, Council of Scientific and Industrial Research—National Chemical Laboratory, Pune, 411008, India Edited by K. VijayRaghavan, Tata Institute of Fundamental Research, Bangalore, India, and approved September 6, 2019 (received for review October 27, 2018) Genome editing using the CRISPR/Cas9 system has been used to has shown variable levels of off targeting due to tolerance of make precise heritable changes in the DNA of organisms. Although mismatches predominantly in the “nonseed” region in the sgRNA, the widely used Streptococcus pyogenes Cas9 (SpCas9) and its wherever these are encountered in the genome (20). To what engineered variants have been efficiently harnessed for numerous extent FnCas9 mediates this high specificity of target interrogation gene-editing applications across different platforms, concerns re- is not known and whether these properties can be harnessed for main regarding their putative off-targeting at multiple loci across highly specific genome editing at a given DNA loci has not been the genome. -
Python Tools in Computational Chemistry (And Biology)
Python Tools in Computational Chemistry (and Biology) Andrew Dalke Dalke Scientific, AB Göteborg, Sweden EuroSciPy, 26-27 July, 2008 “Why does ‘import numpy’ take 0.4 seconds? Does it need to import 228 libraries?” - My first Numpy-discussion post (paraphrased) Your use case isn't so typical and so suffers on the import time end of the balance. - Response from Robert Kern (Others did complain. Import time down to 0.28s.) 52,000 structures PDB doubles every 2½ years HEADER PHOTORECEPTOR 23-MAY-90 1BRD 1BRD 2 COMPND BACTERIORHODOPSIN 1BRD 3 SOURCE (HALOBACTERIUM $HALOBIUM) 1BRD 4 EXPDTA ELECTRON DIFFRACTION 1BRD 5 AUTHOR R.HENDERSON,J.M.BALDWIN,T.A.CESKA,F.ZEMLIN,E.BECKMANN, 1BRD 6 AUTHOR 2 K.H.DOWNING 1BRD 7 REVDAT 3 15-JAN-93 1BRDB 1 SEQRES 1BRDB 1 REVDAT 2 15-JUL-91 1BRDA 1 REMARK 1BRDA 1 .. ATOM 54 N PRO 8 20.397 -15.569 -13.739 1.00 20.00 1BRD 136 ATOM 55 CA PRO 8 21.592 -15.444 -12.900 1.00 20.00 1BRD 137 ATOM 56 C PRO 8 21.359 -15.206 -11.424 1.00 20.00 1BRD 138 ATOM 57 O PRO 8 21.904 -15.930 -10.563 1.00 20.00 1BRD 139 ATOM 58 CB PRO 8 22.367 -14.319 -13.591 1.00 20.00 1BRD 140 ATOM 59 CG PRO 8 22.089 -14.564 -15.053 1.00 20.00 1BRD 141 ATOM 60 CD PRO 8 20.647 -15.054 -15.103 1.00 20.00 1BRD 142 ATOM 61 N GLU 9 20.562 -14.211 -11.095 1.00 20.00 1BRD 143 ATOM 62 CA GLU 9 20.192 -13.808 -9.737 1.00 20.00 1BRD 144 ATOM 63 C GLU 9 19.567 -14.935 -8.932 1.00 20.00 1BRD 145 ATOM 64 O GLU 9 19.815 -15.104 -7.724 1.00 20.00 1BRD 146 ATOM 65 CB GLU 9 19.248 -12.591 -9.820 1.00 99.00 1 1BRD 147 ATOM 66 CG GLU 9 19.902 -11.351 -10.387 1.00 99.00 1 1BRD 148 ATOM 67 CD GLU 9 19.243 -10.169 -10.980 1.00 99.00 1 1BRD 149 ATOM 68 OE1 GLU 9 18.323 -10.191 -11.782 1.00 99.00 1 1BRD 150 ATOM 69 OE2 GLU 9 19.760 -9.089 -10.597 1.00 99.00 1 1BRD 151 ATOM 70 N TRP 10 18.764 -15.737 -9.597 1.00 20.00 1BRD 152 ATOM 71 CA TRP 10 18.034 -16.884 -9.090 1.00 20.00 1BRD 153 ATOM 72 C TRP 10 18.843 -17.908 -8.318 1.00 20.00 1BRD 154 ATOM 73 O TRP 10 18.376 -18.310 -7.230 1.00 20.00 1BRD 155 . -
Melissa Gajewski & Jonathan Mane
Melissa Gajewski & Jonathan Mane Molecular Mechanics (MM) Methods Force fields & potential energy calculations Example using noscapine Molecular Dynamics (MD) Methods Ensembles & trajectories Example using 18-crown-6 Quantum Mechanics (QM) Methods Schrödinger’s equation Semi-empirical (SE) Wave Functional Theory (WFT) Density Functional Theory (DFT) Hybrid QM/MM & MD Methods Comparison of hybrid methods 2 3 Molecular Mechanics (MM) Methods Force fields & potential energy calculations Example using noscapine Molecular Dynamics (MD) Methods Ensembles & trajectories Example using 18-crown-6 Quantum Mechanics (QM) Methods Schrödinger’s equation Semi-empirical (SE) Wave Functional Theory (WFT) Density Functional Theory (DFT) Hybrid QM/MM & MD Methods Comparison of hybrid methods 4 Useful for all system size ◦ Small molecules, proteins, material assemblies, surface science, etc … ◦ Based on Newtonian mechanics (classical mechanics) d F = (mv) dt ◦ The potential energy of the system is calculated using a force field € 5 An atom is considered as a single particle Example: H atom Particle variables: ◦ Radius (typically van der Waals radius) ◦ Polarizability ◦ Net charge Obtained from experiment or QM calculations ◦ Bond interactions Equilibrium bond lengths & angles from experiment or QM calculations 6 All atom approach ◦ Provides parameters for every atom in the system (including hydrogen) Ex: In –CH3 each atom is assigned a set of data MOLDEN MOLDEN (Radius, polarizability, netMOLDENMOLDENMOLDENMOLDENMOLDEN charge, -
Free Notification App for Android
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BOSS, Version 4.6 Biochemical and Organic Simulation System User’S Manual for UNIX, Linux, and Windows
BOSS, Version 4.6 Biochemical and Organic Simulation System User’s Manual for UNIX, Linux, and Windows William L. Jorgensen Department of Chemistry, Yale University P. O. Box 208107 New Haven, Connecticut 06520-8107 Phone: (203) 432-6278 Fax: (203) 432-6299 Internet: [email protected] January 2004 Contents Page Introduction 4 1 Can’t Wait to Start – Use the x Scripts 6 2 Statistical Mechanics Simulation – Theory 6 3 Energy and Free Energy Evaluation 7 4 New Features 9 5 Operating Systems 14 6 Installation 14 7 Files 14 8 Command or bat File Input 16 9 Parameter File Input 18 10 Z-matrix File Input 26 10.1 Atom Input 29 10.2 Geometry Variations 29 10.3 Bond Length Variations 29 10.4 Additional Bonds 30 10.5 Harmonic Restraints 30 10.6 Bond Angle Variations 30 10.7 Additional Bond Angles 31 10.8 Dihedral Angle Variations 31 10.9 Additional Dihedral Angles 33 10.10 Domain Definitions 34 10.11 Conformational Searching 34 10.12 Sample Z-matrix 34 10.13 Z-matrix Input for Custom Solvents 35 11 PDB Input 37 12 Coordinate Input in Mind Format and Reaction Path Following 38 13 Pure Liquid Simulations 39 14 Cluster Simulations 39 2 15 Solventless and Molecular Mechanics Calculations 40 15.1 Continuum Simulations 40 15.2 Energy Minimizations 41 15.3 Dihedral Angle Driving 42 15.4 Potential Surface Scanning 42 15.5 Normal Coordinate Analysis 43 15.6 Conformational Searching 45 16 Available Solvent Boxes 48 17 Ewald Summation for Long-Range Electrostatics 50 18 Test Jobs 51 19 Output 56 19.1 Some Variable Definitions 58 20 Contents of the Distribution Files 59 21 Appendix – No.