Molecular Dynamics Simulations in Drug Discovery and Pharmaceutical Development
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MODELLER 10.1 Manual
MODELLER A Program for Protein Structure Modeling Release 10.1, r12156 Andrej Saliˇ with help from Ben Webb, M.S. Madhusudhan, Min-Yi Shen, Guangqiang Dong, Marc A. Martı-Renom, Narayanan Eswar, Frank Alber, Maya Topf, Baldomero Oliva, Andr´as Fiser, Roberto S´anchez, Bozidar Yerkovich, Azat Badretdinov, Francisco Melo, John P. Overington, and Eric Feyfant email: modeller-care AT salilab.org URL https://salilab.org/modeller/ 2021/03/12 ii Contents Copyright notice xxi Acknowledgments xxv 1 Introduction 1 1.1 What is Modeller?............................................. 1 1.2 Modeller bibliography....................................... .... 2 1.3 Obtainingandinstallingtheprogram. .................... 3 1.4 Bugreports...................................... ............ 4 1.5 Method for comparative protein structure modeling by Modeller ................... 5 1.6 Using Modeller forcomparativemodeling. ... 8 1.6.1 Preparinginputfiles . ............. 8 1.6.2 Running Modeller ......................................... 9 2 Automated comparative modeling with AutoModel 11 2.1 Simpleusage ..................................... ............ 11 2.2 Moreadvancedusage............................... .............. 12 2.2.1 Including water molecules, HETATM residues, and hydrogenatoms .............. 12 2.2.2 Changing the default optimization and refinement protocol ................... 14 2.2.3 Getting a very fast and approximate model . ................. 14 2.2.4 Building a model from multiple templates . .................. 15 2.2.5 Buildinganallhydrogenmodel -
Binding Kinetics Calculations of the Inclusion of Some Benzoic Acid Derivatives and Paracetamol in Β CD Djamel Khatmi, Belgacem Bezzina, Stéphane Humbel
Binding kinetics calculations of the inclusion of some benzoic acid derivatives and paracetamol in β CD Djamel Khatmi, Belgacem Bezzina, Stéphane Humbel To cite this version: Djamel Khatmi, Belgacem Bezzina, Stéphane Humbel. Binding kinetics calculations of the inclusion of some benzoic acid derivatives and paracetamol in β CD. Journal of Inclusion Phenomena and Macrocyclic Chemistry, Springer Verlag, 2020, 96 (3-4), pp.373-379. 10.1007/s10847-020-00976-1. hal-02922032 HAL Id: hal-02922032 https://hal.archives-ouvertes.fr/hal-02922032 Submitted on 25 Aug 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. Binding kinetics calculations of the inclusion of some benzoic acid derivatives and paracetamol in β CD. Djamel Eddine Khatmi,a, b* Belgacem Bezzina, b, c Stéphane Humbel,a a- Aix Marseille University, CNRS, Centrale Marseille, iSm2, Marseille, France b- Laboratory of Computational Chemistry and Nanostructures, University of 08 May 45 Guelma, Algeria c- Research Center in Industrial Technology (CRTI), P.O.BOX 64, Chéraga 16014, Algiers, Algeria *corresponding author: [email protected] KEYWORDS: Cyclodextrin. Inclusion complexes. Molecular dynamic simulation. Binding Kinetics. Benzoic acid derivatives. Paracetamol. ABSTRACT This theoretical study is designed to identify the main driving forces of the inclusion of some benzoic acid (BA) derivatives as well as Paracetamol (ParCM) with β-cyclodextrin (β-CD) by computing the kinetics of binding. -
The Detection and Determination of Esters
Louisiana State University LSU Digital Commons LSU Historical Dissertations and Theses Graduate School 1958 The etD ection and Determination of Esters. Mohd. Mohsin Qureshi Louisiana State University and Agricultural & Mechanical College Follow this and additional works at: https://digitalcommons.lsu.edu/gradschool_disstheses Recommended Citation Qureshi, Mohd. Mohsin, "The eD tection and Determination of Esters." (1958). LSU Historical Dissertations and Theses. 501. https://digitalcommons.lsu.edu/gradschool_disstheses/501 This Dissertation is brought to you for free and open access by the Graduate School at LSU Digital Commons. It has been accepted for inclusion in LSU Historical Dissertations and Theses by an authorized administrator of LSU Digital Commons. For more information, please contact [email protected]. Copright by Mohcl Mohsin Qureshi 1959 THE DETECTION AND DETERMINATION OF ESTERS A Dissertation Submitted to the Graduate Faculty of the Louisiana State University and Agricultural and Mechanical College in partial fulfillment of the requirements for the degree of Doctor of Philosophy in The Department of Chemistry by Mohd. Mohsin Qureshi M.Sc., Aligarh University, 1944 August, 1958 ACKNOWLEDGMENT The author wishes to express his sincere apprecia tion and gratitude to Dr. Philip W. West under whose guidance this research was carried out. He is grateful to Dr. James G. Traynham for sup plying him with a number of esters and for his many helpful suggestions. The financial support given to him by the Continental Oil Company is gratefully acknowledged. He offers his sincere thanks to Miss Magdalena Usategul for her valuable suggestions and her ungrudging help during the course of this investigation. Dr. Anil K. -
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 ................................................. -
GROMACS: Fast, Flexible, and Free
GROMACS: Fast, Flexible, and Free DAVID VAN DER SPOEL,1 ERIK LINDAHL,2 BERK HESS,3 GERRIT GROENHOF,4 ALAN E. MARK,4 HERMAN J. C. BERENDSEN4 1Department of Cell and Molecular Biology, Uppsala University, Husargatan 3, Box 596, S-75124 Uppsala, Sweden 2Stockholm Bioinformatics Center, SCFAB, Stockholm University, SE-10691 Stockholm, Sweden 3Max-Planck Institut fu¨r Polymerforschung, Ackermannweg 10, D-55128 Mainz, Germany 4Groningen Biomolecular Sciences and Biotechnology Institute, University of Groningen, Nijenborgh 4, NL-9747 AG Groningen, The Netherlands Received 12 February 2005; Accepted 18 March 2005 DOI 10.1002/jcc.20291 Published online in Wiley InterScience (www.interscience.wiley.com). Abstract: This article describes the software suite GROMACS (Groningen MAchine for Chemical Simulation) that was developed at the University of Groningen, The Netherlands, in the early 1990s. The software, written in ANSI C, originates from a parallel hardware project, and is well suited for parallelization on processor clusters. By careful optimization of neighbor searching and of inner loop performance, GROMACS is a very fast program for molecular dynamics simulation. It does not have a force field of its own, but is compatible with GROMOS, OPLS, AMBER, and ENCAD force fields. In addition, it can handle polarizable shell models and flexible constraints. The program is versatile, as force routines can be added by the user, tabulated functions can be specified, and analyses can be easily customized. Nonequilibrium dynamics and free energy determinations are incorporated. Interfaces with popular quantum-chemical packages (MOPAC, GAMES-UK, GAUSSIAN) are provided to perform mixed MM/QM simula- tions. The package includes about 100 utility and analysis programs. -
Secondary Structure Propensities in Peptide Folding Simulations: a Systematic Comparison of Molecular Mechanics Interaction Schemes
Biophysical Journal Volume 97 July 2009 599–608 599 Secondary Structure Propensities in Peptide Folding Simulations: A Systematic Comparison of Molecular Mechanics Interaction Schemes Dirk Matthes and Bert L. de Groot* Department of Theoretical and Computational Biophysics, Computational Biomolecular Dynamics Group, Max-Planck-Institute for Biophysical Chemistry, Go¨ttingen, Germany ABSTRACT We present a systematic study directed toward the secondary structure propensity and sampling behavior in peptide folding simulations with eight different molecular dynamics force-field variants in explicit solvent. We report on the combi- national result of force field, water model, and electrostatic interaction schemes and compare to available experimental charac- terization of five studied model peptides in terms of reproduced structure and dynamics. The total simulation time exceeded 18 ms and included simulations that started from both folded and extended conformations. Despite remaining sampling issues, a number of distinct trends in the folding behavior of the peptides emerged. Pronounced differences in the propensity of finding prominent secondary structure motifs in the different applied force fields suggest that problems point in particular to the balance of the relative stabilities of helical and extended conformations. INTRODUCTION Molecular dynamics (MD) simulations are routinely utilized to that study, simulations of relatively short lengths were per- study the folding dynamics of peptides and small proteins as formed and the natively folded state was used as starting well as biomolecular aggregation. The critical constituents of point, possibly biasing the results (13). such molecular mechanics studies are the validity of the under- For folding simulations, such a systematic test has not lyingphysical models together with theassumptionsofclassical been carried out so far, although with growing computer dynamics and a sufficient sampling of the conformational power several approaches toward the in silico folding space. -
Using Constrained Density Functional Theory to Track Proton Transfers and to Sample Their Associated Free Energy Surface
Using Constrained Density Functional Theory to Track Proton Transfers and to Sample Their Associated Free Energy Surface Chenghan Li and Gregory A. Voth* Department of Chemistry, Chicago Center for Theoretical Chemistry, James Franck Institute, and Institute for Biophysical Dynamics, University of Chicago, Chicago, IL, 60637 Keywords: free energy sampling, proton transport, density functional theory, proton transfer ABSTRACT: The ab initio molecular dynamics (AIMD) and quantum mechanics/molecular mechanics (QM/MM) methods are powerful tools for studying proton solvation, transfer, and transport processes in various environments. However, due to the high computational cost of such methods, achieving sufficient sampling of rare events involving excess proton motion – especially when Grotthuss proton shuttling is involved – usually requires enhanced free energy sampling methods to obtain informative results. Moreover, an appropriate collective variable (CV) that describes the effective position of the net positive charge defect associated with an excess proton is essential for both tracking the trajectory of the defect and for the free energy sampling of the processes associated with the resulting proton transfer and transport. In this work, such a CV is derived from first principles using constrained density functional theory (CDFT). This CV is applicable to a broad array of proton transport and transfer processes as studied via AIMD and QM/MM simulation. 1 INTRODUCTION The accurate and efficient delineation of proton transport (PT) and -
Neural Networks Based Variationally Enhanced Sampling
NEURAL NETWORKS BASED VARIATIONALLY ENHANCED SAMPLING A PREPRINT Luigi Bonati Department of Physics, ETH Zurich, 8092 Zurich, Switzerland and Institute of Computational Sciences, Università della Svizzera italiana, via G. Buffi 13, 6900 Lugano, Switzerland Yue-Yu Zhang Department of Chemistry and Applied Biosciences, ETH Zurich, 8092 Zurich, Switzerland and Institute of Computational Sciences, Università della Svizzera italiana, via G. Buffi 13, 6900 Lugano, Switzerland Michele Parrinello Department of Chemistry and Applied Biosciences, ETH Zurich, 8092 Zurich, Switzerland, Institute of Computational Sciences, Università della Svizzera italiana, via G. Buffi 13, 6900 Lugano, Switzerland, and Italian Institute of Technology, Via Morego 30, 16163 Genova, Italy September 25, 2019 ABSTRACT Sampling complex free energy surfaces is one of the main challenges of modern atomistic simulation methods. The presence of kinetic bottlenecks in such surfaces often renders a direct approach useless. A popular strategy is to identify a small number of key collective variables and to introduce a bias potential that is able to favor their fluctuations in order to accelerate sampling. Here we propose to use machine learning techniques in conjunction with the recent variationally enhanced sampling method [Valsson and Parrinello, Phys. Rev. Lett. 2014] in order to determine such potential. This is achieved by expressing the bias as a neural network. The parameters are determined in a variational learning scheme aimed at minimizing an appropriate functional. This required the development of a new and more efficient minimization technique. The expressivity of neural networks allows representing rapidly varying free energy surfaces, removes boundary effects artifacts and allows several collective variables to be handled. -
Ee9e60701e814784783a672a9
International Journal of Technology (2017) 4: 611‐621 ISSN 2086‐9614 © IJTech 2017 A PRELIMINARY STUDY ON SHIFTING FROM VIRTUAL MACHINE TO DOCKER CONTAINER FOR INSILICO DRUG DISCOVERY IN THE CLOUD Agung Putra Pasaribu1, Muhammad Fajar Siddiq1, Muhammad Irfan Fadhila1, Muhammad H. Hilman1, Arry Yanuar2, Heru Suhartanto1* 1Faculty of Computer Science, Universitas Indonesia, Kampus UI Depok, Depok 16424, Indonesia 2Faculty of Pharmacy, Universitas Indonesia, Kampus UI Depok, Depok 16424, Indonesia (Received: January 2017 / Revised: April 2017 / Accepted: June 2017) ABSTRACT The rapid growth of information technology and internet access has moved many offline activities online. Cloud computing is an easy and inexpensive solution, as supported by virtualization servers that allow easier access to personal computing resources. Unfortunately, current virtualization technology has some major disadvantages that can lead to suboptimal server performance. As a result, some companies have begun to move from virtual machines to containers. While containers are not new technology, their use has increased recently due to the Docker container platform product. Docker’s features can provide easier solutions. In this work, insilico drug discovery applications from molecular modelling to virtual screening were tested to run in Docker. The results are very promising, as Docker beat the virtual machine in most tests and reduced the performance gap that exists when using a virtual machine (VirtualBox). The virtual machine placed third in test performance, after the host itself and Docker. Keywords: Cloud computing; Docker container; Molecular modeling; Virtual screening 1. INTRODUCTION In recent years, cloud computing has entered the realm of information technology (IT) and has been widely used by the enterprise in support of business activities (Foundation, 2016). -
NINDS Custom Collection II
ACACETIN ACEBUTOLOL HYDROCHLORIDE ACECLIDINE HYDROCHLORIDE ACEMETACIN ACETAMINOPHEN ACETAMINOSALOL ACETANILIDE ACETARSOL ACETAZOLAMIDE ACETOHYDROXAMIC ACID ACETRIAZOIC ACID ACETYL TYROSINE ETHYL ESTER ACETYLCARNITINE ACETYLCHOLINE ACETYLCYSTEINE ACETYLGLUCOSAMINE ACETYLGLUTAMIC ACID ACETYL-L-LEUCINE ACETYLPHENYLALANINE ACETYLSEROTONIN ACETYLTRYPTOPHAN ACEXAMIC ACID ACIVICIN ACLACINOMYCIN A1 ACONITINE ACRIFLAVINIUM HYDROCHLORIDE ACRISORCIN ACTINONIN ACYCLOVIR ADENOSINE PHOSPHATE ADENOSINE ADRENALINE BITARTRATE AESCULIN AJMALINE AKLAVINE HYDROCHLORIDE ALANYL-dl-LEUCINE ALANYL-dl-PHENYLALANINE ALAPROCLATE ALBENDAZOLE ALBUTEROL ALEXIDINE HYDROCHLORIDE ALLANTOIN ALLOPURINOL ALMOTRIPTAN ALOIN ALPRENOLOL ALTRETAMINE ALVERINE CITRATE AMANTADINE HYDROCHLORIDE AMBROXOL HYDROCHLORIDE AMCINONIDE AMIKACIN SULFATE AMILORIDE HYDROCHLORIDE 3-AMINOBENZAMIDE gamma-AMINOBUTYRIC ACID AMINOCAPROIC ACID N- (2-AMINOETHYL)-4-CHLOROBENZAMIDE (RO-16-6491) AMINOGLUTETHIMIDE AMINOHIPPURIC ACID AMINOHYDROXYBUTYRIC ACID AMINOLEVULINIC ACID HYDROCHLORIDE AMINOPHENAZONE 3-AMINOPROPANESULPHONIC ACID AMINOPYRIDINE 9-AMINO-1,2,3,4-TETRAHYDROACRIDINE HYDROCHLORIDE AMINOTHIAZOLE AMIODARONE HYDROCHLORIDE AMIPRILOSE AMITRIPTYLINE HYDROCHLORIDE AMLODIPINE BESYLATE AMODIAQUINE DIHYDROCHLORIDE AMOXEPINE AMOXICILLIN AMPICILLIN SODIUM AMPROLIUM AMRINONE AMYGDALIN ANABASAMINE HYDROCHLORIDE ANABASINE HYDROCHLORIDE ANCITABINE HYDROCHLORIDE ANDROSTERONE SODIUM SULFATE ANIRACETAM ANISINDIONE ANISODAMINE ANISOMYCIN ANTAZOLINE PHOSPHATE ANTHRALIN ANTIMYCIN A (A1 shown) ANTIPYRINE APHYLLIC -
Preorganization of Molecular Binding Sites in Designed Diiron Proteins
Preorganization of molecular binding sites in designed diiron proteins Ornella Maglio*, Flavia Nastri*, Vincenzo Pavone*, Angela Lombardi*†, and William F. DeGrado†‡ *Department of Chemistry, University of Napoli Federico II, Complesso Universitario Monte S. Angelo, I-80126 Napoli, Italy; and ‡Department of Biochemistry and Biophysics, School of Medicine, University of Pennsylvania, Philadelphia, PA 19104-6059 Contributed by William F. DeGrado, February 6, 2003 De novo protein design provides an attractive approach to criti- (Dueferri 1) (7–10). This protein is a small, 48-residue ho- cally test the features that are required for metalloprotein struc- modimeric model for the family of O2-using diiron proteins ture and function. Previously we designed and crystallographi- that includes methane monooxygenase and the radical-form- cally characterized an idealized dimeric model for the four-helix ing R2 subunit of the ribonucleotide reductase from Esche- bundle class of diiron and dimanganese proteins [Dueferri 1 richia coli (11–14). The DF1 dimer comprises two nonco- (DF1)]. Although the protein bound metal ions in the expected valently associated helix–loop–helix motifs that bind the manner, access to its active site was blocked by large bulky hy- diiron cofactor near the center of the protein (8). The crystal- drophobic residues. Subsequently, a substrate-access channel lographic structures of di-Zn(II) (8) and di-Mn(II) DF1 (un- was introduced proximal to the metal-binding center, resulting published results) are very similar to the di-Mn(II) and di- in a protein with properties more closely resembling those of Fe(II) forms of other natural diiron proteins (15–18). -
Structural Ensembles of Intrinsically Disordered Proteins Depend Strongly on Force Field: a Comparison to Experiment Sarah Rauscher,*,† Vytautas Gapsys,† Michal J
Article pubs.acs.org/JCTC Structural Ensembles of Intrinsically Disordered Proteins Depend Strongly on Force Field: A Comparison to Experiment Sarah Rauscher,*,† Vytautas Gapsys,† Michal J. Gajda,‡ Markus Zweckstetter,‡,§,∥ Bert L. de Groot,† and Helmut Grubmüller† † Department of Theoretical and Computational Biophysics, Max Planck Institute for Biophysical Chemistry, Göttingen 37077, Germany ‡ Department of NMR-based Structural Biology, Max Planck Institute for Biophysical Chemistry, Göttingen 37077, Germany § German Center for Neurodegenerative Diseases (DZNE), Göttingen 37077, Germany ∥ Center for Nanoscale Microscopy and Molecular Physiology of the Brain (CNMPB), University Medical Center, Göttingen 37073, Germany *S Supporting Information ABSTRACT: Intrinsically disordered proteins (IDPs) are notoriously challenging to study both experimentally and computationally. The structure of IDPs cannot be described by a single conformation but must instead be described as an ensemble of interconverting conformations. Atomistic simu- lations are increasingly used to obtain such IDP conformational ensembles. Here, we have compared the IDP ensembles generated by eight all-atom empirical force fields against primary small-angle X-ray scattering (SAXS) and NMR data. Ensembles obtained with different force fields exhibit marked differences in chain dimensions, hydrogen bonding, and secondary structure content. These differences are unexpect- edly large: changing the force field is found to have a stronger effect on secondary structure content than changing the entire peptide sequence. The CHARMM 22* ensemble performs best in this force field comparison: it has the lowest error in chemical shifts and J-couplings and agrees well with the SAXS data. A high population of left-handed α-helix is present in the CHARMM 36 ensemble, which is inconsistent with measured scalar couplings.