Literature DB >> 28076801

MSMBuilder: Statistical Models for Biomolecular Dynamics.

Matthew P Harrigan1, Mohammad M Sultan1, Carlos X Hernández2, Brooke E Husic1, Peter Eastman1, Christian R Schwantes1, Kyle A Beauchamp3, Robert T McGibbon4, Vijay S Pande5.   

Abstract

MSMBuilder is a software package for building statistical models of high-dimensional time-series data. It is designed with a particular focus on the analysis of atomistic simulations of biomolecular dynamics such as protein folding and conformational change. MSMBuilder is named for its ability to construct Markov state models (MSMs), a class of models that has gained favor among computational biophysicists. In addition to both well-established and newer MSM methods, the package includes complementary algorithms for understanding time-series data such as hidden Markov models and time-structure based independent component analysis. MSMBuilder boasts an easy to use command-line interface, as well as clear and consistent abstractions through its Python application programming interface. MSMBuilder was developed with careful consideration for compatibility with the broader machine learning community by following the design of scikit-learn. The package is used primarily by practitioners of molecular dynamics, but is just as applicable to other computational or experimental time-series measurements.
Copyright © 2017 Biophysical Society. Published by Elsevier Inc. All rights reserved.

Mesh:

Substances:

Year:  2017        PMID: 28076801      PMCID: PMC5232355          DOI: 10.1016/j.bpj.2016.10.042

Source DB:  PubMed          Journal:  Biophys J        ISSN: 0006-3495            Impact factor:   4.033


  25 in total

1.  EMMA: A Software Package for Markov Model Building and Analysis.

Authors:  Martin Senne; Benjamin Trendelkamp-Schroer; Antonia S J S Mey; Christof Schütte; Frank Noé
Journal:  J Chem Theory Comput       Date:  2012-06-18       Impact factor: 6.006

2.  Distribution of Reciprocal of Interatomic Distances: A Fast Structural Metric.

Authors:  Ting Zhou; Amedeo Caflisch
Journal:  J Chem Theory Comput       Date:  2012-07-20       Impact factor: 6.006

3.  Reactive flux and folding pathways in network models of coarse-grained protein dynamics.

Authors:  Alexander Berezhkovskii; Gerhard Hummer; Attila Szabo
Journal:  J Chem Phys       Date:  2009-05-28       Impact factor: 3.488

4.  Using generalized ensemble simulations and Markov state models to identify conformational states.

Authors:  Gregory R Bowman; Xuhui Huang; Vijay S Pande
Journal:  Methods       Date:  2009-05-04       Impact factor: 3.608

5.  Identification of slow molecular order parameters for Markov model construction.

Authors:  Guillermo Pérez-Hernández; Fabian Paul; Toni Giorgino; Gianni De Fabritiis; Frank Noé
Journal:  J Chem Phys       Date:  2013-07-07       Impact factor: 3.488

6.  Efficient maximum likelihood parameterization of continuous-time Markov processes.

Authors:  Robert T McGibbon; Vijay S Pande
Journal:  J Chem Phys       Date:  2015-07-21       Impact factor: 3.488

7.  Perspective: Markov models for long-timescale biomolecular dynamics.

Authors:  C R Schwantes; R T McGibbon; V S Pande
Journal:  J Chem Phys       Date:  2014-09-07       Impact factor: 3.488

8.  HTMD: High-Throughput Molecular Dynamics for Molecular Discovery.

Authors:  S Doerr; M J Harvey; Frank Noé; G De Fabritiis
Journal:  J Chem Theory Comput       Date:  2016-03-16       Impact factor: 6.006

Review 9.  Everything you wanted to know about Markov State Models but were afraid to ask.

Authors:  Vijay S Pande; Kyle Beauchamp; Gregory R Bowman
Journal:  Methods       Date:  2010-06-04       Impact factor: 3.608

10.  Improvements in Markov State Model Construction Reveal Many Non-Native Interactions in the Folding of NTL9.

Authors:  Christian R Schwantes; Vijay S Pande
Journal:  J Chem Theory Comput       Date:  2013-04-09       Impact factor: 6.006

View more
  69 in total

Review 1.  Machine Learning Force Fields and Coarse-Grained Variables in Molecular Dynamics: Application to Materials and Biological Systems.

Authors:  Paraskevi Gkeka; Gabriel Stoltz; Amir Barati Farimani; Zineb Belkacemi; Michele Ceriotti; John D Chodera; Aaron R Dinner; Andrew L Ferguson; Jean-Bernard Maillet; Hervé Minoux; Christine Peter; Fabio Pietrucci; Ana Silveira; Alexandre Tkatchenko; Zofia Trstanova; Rafal Wiewiora; Tony Lelièvre
Journal:  J Chem Theory Comput       Date:  2020-07-16       Impact factor: 6.006

2.  The dynamic conformational landscape of the protein methyltransferase SETD8.

Authors:  Shi Chen; Rafal P Wiewiora; Fanwang Meng; Nicolas Babault; Anqi Ma; Wenyu Yu; Kun Qian; Hao Hu; Hua Zou; Junyi Wang; Shijie Fan; Gil Blum; Fabio Pittella-Silva; Kyle A Beauchamp; Wolfram Tempel; Hualiang Jiang; Kaixian Chen; Robert J Skene; Yujun George Zheng; Peter J Brown; Jian Jin; Cheng Luo; John D Chodera; Minkui Luo
Journal:  Elife       Date:  2019-05-13       Impact factor: 8.140

3.  RNA folding kinetics using Monte Carlo and Gillespie algorithms.

Authors:  Peter Clote; Amir H Bayegan
Journal:  J Math Biol       Date:  2017-08-05       Impact factor: 2.259

4.  A Kepler Workflow Tool for Reproducible AMBER GPU Molecular Dynamics.

Authors:  Shweta Purawat; Pek U Ieong; Robert D Malmstrom; Garrett J Chan; Alan K Yeung; Ross C Walker; Ilkay Altintas; Rommie E Amaro
Journal:  Biophys J       Date:  2017-06-20       Impact factor: 4.033

5.  Identification of simple reaction coordinates from complex dynamics.

Authors:  Robert T McGibbon; Brooke E Husic; Vijay S Pande
Journal:  J Chem Phys       Date:  2017-01-28       Impact factor: 3.488

6.  Enspara: Modeling molecular ensembles with scalable data structures and parallel computing.

Authors:  J R Porter; M I Zimmerman; G R Bowman
Journal:  J Chem Phys       Date:  2019-01-28       Impact factor: 3.488

7.  Predicting ligand binding affinity using on- and off-rates for the SAMPL6 SAMPLing challenge.

Authors:  Tom Dixon; Samuel D Lotz; Alex Dickson
Journal:  J Comput Aided Mol Des       Date:  2018-08-23       Impact factor: 3.686

Review 8.  Whole-Cell Models and Simulations in Molecular Detail.

Authors:  Michael Feig; Yuji Sugita
Journal:  Annu Rev Cell Dev Biol       Date:  2019-07-12       Impact factor: 13.827

9.  Linking time-series of single-molecule experiments with molecular dynamics simulations by machine learning.

Authors:  Yasuhiro Matsunaga; Yuji Sugita
Journal:  Elife       Date:  2018-05-03       Impact factor: 8.140

10.  Simulations of the regulatory ACT domain of human phenylalanine hydroxylase (PAH) unveil its mechanism of phenylalanine binding.

Authors:  Yunhui Ge; Elias Borne; Shannon Stewart; Michael R Hansen; Emilia C Arturo; Eileen K Jaffe; Vincent A Voelz
Journal:  J Biol Chem       Date:  2018-10-04       Impact factor: 5.157

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.