publication . Preprint . 2018

OpenPathSampling: A Python framework for path sampling simulations. I. Basics

Frank Noé; Jan-Hendrik Prinz; John D. Chodera; Peter G. Bolhuis; David W. H. Swenson;
Open Access English
  • Published: 20 Jun 2018
  • Publisher: Cold Spring Harbor Laboratory
Abstract
<jats:p>Transition path sampling techniques allow molecular dynamics simulations of complex systems to focuson rare <jats:italic>dynamical events</jats:italic>, providing insight into mechanisms and the ability to calculate rates inaccessibleby ordinary dynamics simulations. While path sampling algorithms are conceptually as simple as importancesampling Monte Carlo, the technical complexity of their implementation has kept these techniquesout of reach of the broad community. Here, we introduce an easy-to-use Python framework called Open-PathSampling (OPS) that facilitates path sampling for (bio)molecular systems with minimal effort and yetis still extensible. In...
Subjects
free text keywords: Python (programming language), computer.programming_language, computer, Computational science, Gibbs sampling, symbols.namesake, symbols, Importance sampling, Complex system, Sampling (statistics), Replica, Monte Carlo method, Transition path sampling, Computer science
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NIH| The role of reorganization energy in achieving selective kinase inhibition
Project
  • Funder: National Institutes of Health (NIH)
  • Project Code: 5R01GM121505-02
  • Funding stream: NATIONAL INSTITUTE OF GENERAL MEDICAL SCIENCES
,
NIH| MOUSE GENETICS
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  • Funder: National Institutes of Health (NIH)
  • Project Code: 2P30CA008748-43
  • Funding stream: NATIONAL CANCER INSTITUTE
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EC| E-CAM
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E-CAM
An e-infrastructure for software, training and consultancy in simulation and modelling
  • Funder: European Commission (EC)
  • Project Code: 676531
  • Funding stream: H2020 | RIA
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EC| ScaleCell
Project
ScaleCell
Scalable Kinetic Models: From Molecular Dynamics to Cellular Signaling
  • Funder: European Commission (EC)
  • Project Code: 772230
  • Funding stream: H2020 | ERC | ERC-COG
98 references, page 1 of 7

TPSNetwork: A, B [A, B], [B, C] [A, B, ..., N], [A, B, ..., N] MISTISNetwork: [(A, mAB, B)] [(A, mAB, B) (A, mAC, C), ..., (A, mAN, N)] [(A, mAB, B), ..., (A, mAN, N), ... ..., (N, mNA, A)] cv_A = paths.FunctionCV( name='opA', f=circle_degree, center=state_centers_A, cv_1=psi, cv_2=phi) state_A = paths.CVDefinedVolume(cv_A, lambda_min=0, lambda_max=10).named("A") iface_A = paths.VolumeInterfaceSet(cv_A, minvals=0.0, maxvals=interface_lambda_levels_A) 0.020 30 40 50 60 70 80 90 100 λ A B trajectories = [t.ensembles[-1].split(long_trajectory) for t in network.sampling_transitions]

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