MOTIVATION: Molecular simulation has historically been a low-throughput technique, but faster computers and increasing amounts of genomic and structural data are changing this by enabling large-scale automated simulation of, for instance, many conformers or mutants of biomolecules with or without a range of ligands. At the same time, advances in performance and scaling now make it possible to model complex biomolecular interaction and function in a manner directly testable by experiment. These applications share a need for fast and efficient software that can be deployed on massive scale in clusters, web servers, distributed computing or cloud resources. RESULTS: Here, we present a range of new simulation algorithms and features developed during the past 4 years, leading up to the GROMACS 4.5 software package. The software now automatically handles wide classes of biomolecules, such as proteins, nucleic acids and lipids, and comes with all commonly used force fields for these molecules built-in. GROMACS supports several implicit solvent models, as well as new free-energy algorithms, and the software now uses multithreading for efficient parallelization even on low-end systems, including windows-based workstations. Together with hand-tuned assembly kernels and state-of-the-art parallelization, this provides extremely high performance and cost efficiency for high-throughput as well as massively parallel simulations. AVAILABILITY: GROMACS is an open source and free software available from http://www.gromacs.org. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
MOTIVATION: Molecular simulation has historically been a low-throughput technique, but faster computers and increasing amounts of genomic and structural data are changing this by enabling large-scale automated simulation of, for instance, many conformers or mutants of biomolecules with or without a range of ligands. At the same time, advances in performance and scaling now make it possible to model complex biomolecular interaction and function in a manner directly testable by experiment. These applications share a need for fast and efficient software that can be deployed on massive scale in clusters, web servers, distributed computing or cloud resources. RESULTS: Here, we present a range of new simulation algorithms and features developed during the past 4 years, leading up to the GROMACS 4.5 software package. The software now automatically handles wide classes of biomolecules, such as proteins, nucleic acids and lipids, and comes with all commonly used force fields for these molecules built-in. GROMACS supports several implicit solvent models, as well as new free-energy algorithms, and the software now uses multithreading for efficient parallelization even on low-end systems, including windows-based workstations. Together with hand-tuned assembly kernels and state-of-the-art parallelization, this provides extremely high performance and cost efficiency for high-throughput as well as massively parallel simulations. AVAILABILITY: GROMACS is an open source and free software available from http://www.gromacs.org. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Authors: Hugues Nury; Frédéric Poitevin; Catherine Van Renterghem; Jean-Pierre Changeux; Pierre-Jean Corringer; Marc Delarue; Marc Baaden Journal: Proc Natl Acad Sci U S A Date: 2010-03-22 Impact factor: 11.205
Authors: Markus Christen; Philippe H Hünenberger; Dirk Bakowies; Riccardo Baron; Roland Bürgi; Daan P Geerke; Tim N Heinz; Mika A Kastenholz; Vincent Kräutler; Chris Oostenbrink; Christine Peter; Daniel Trzesniak; Wilfred F van Gunsteren Journal: J Comput Chem Date: 2005-12 Impact factor: 3.376
Authors: B R Brooks; C L Brooks; A D Mackerell; L Nilsson; R J Petrella; B Roux; Y Won; G Archontis; C Bartels; S Boresch; A Caflisch; L Caves; Q Cui; A R Dinner; M Feig; S Fischer; J Gao; M Hodoscek; W Im; K Kuczera; T Lazaridis; J Ma; V Ovchinnikov; E Paci; R W Pastor; C B Post; J Z Pu; M Schaefer; B Tidor; R M Venable; H L Woodcock; X Wu; W Yang; D M York; M Karplus Journal: J Comput Chem Date: 2009-07-30 Impact factor: 3.376
Authors: A A Mian; A Rafiei; I Haberbosch; A Zeifman; I Titov; V Stroylov; A Metodieva; O Stroganov; F Novikov; B Brill; G Chilov; D Hoelzer; O G Ottmann; M Ruthardt Journal: Leukemia Date: 2014-11-14 Impact factor: 11.528
Authors: Exequiel Medina; Pablo Villalobos; George L Hamilton; Elizabeth A Komives; Hugo Sanabria; César A Ramírez-Sarmiento; Jorge Babul Journal: J Mol Biol Date: 2020-07-28 Impact factor: 5.469
Authors: Pek U Ieong; Jesper Sørensen; Prasantha L Vemu; Celia W Wong; Özlem Demir; Nadya P Williams; Jianwu Wang; Daniel Crawl; Robert V Swift; Robert D Malmstrom; Ilkay Altintas; Rommie E Amaro Journal: Procedia Comput Sci Date: 2014
Authors: Elisabetta Flex; Marcello Niceta; Serena Cecchetti; Isabelle Thiffault; Margaret G Au; Alessandro Capuano; Emanuela Piermarini; Anna A Ivanova; Joshua W Francis; Giovanni Chillemi; Balasubramanian Chandramouli; Giovanna Carpentieri; Charlotte A Haaxma; Andrea Ciolfi; Simone Pizzi; Ganka V Douglas; Kara Levine; Antonella Sferra; Maria Lisa Dentici; Rolph R Pfundt; Jean-Baptiste Le Pichon; Emily Farrow; Frank Baas; Fiorella Piemonte; Bruno Dallapiccola; John M Graham; Carol J Saunders; Enrico Bertini; Richard A Kahn; David A Koolen; Marco Tartaglia Journal: Am J Hum Genet Date: 2016-09-22 Impact factor: 11.025
Authors: Carlyle Ribeiro Lima; Nicolas Carels; Ana Carolina Ramos Guimaraes; Pierre Tufféry; Philippe Derreumaux Journal: J Mol Model Date: 2016-09-24 Impact factor: 1.810