Efficiently sampling conformations and pathways using the concurrent adaptive sampling (CAS) algorithm

Ahn, Surl-Hee; Grate, Jay W.; Darve, Eric F.

doi:10.1063/1.4999097

Title: Efficiently sampling conformations and pathways using the concurrent adaptive sampling (CAS) algorithm

Journal Article · Mon Aug 21 00:00:00 EDT 2017 · Journal of Chemical Physics

DOI:https://doi.org/10.1063/1.4999097· OSTI ID:1398164

^[1];

^[2]; Darve, Eric F. ^[1]

Stanford Univ., CA (United States)
Pacific Northwest National Lab. (PNNL), Richland, WA (United States)

Molecular dynamics (MD) simulations are useful in obtaining thermodynamic and kinetic properties of bio-molecules but are limited by the timescale barrier, i.e., we may be unable to efficiently obtain properties because we need to run microseconds or longer simulations using femtoseconds time steps. While there are several existing methods to overcome this timescale barrier and efficiently sample thermodynamic and/or kinetic properties, problems remain in regard to being able to sample un- known systems, deal with high-dimensional space of collective variables, and focus the computational effort on slow timescales. Hence, in this work, a new sampling method, called the “Concurrent Adaptive Sampling (CAS) algorithm,” has been developed to tackle these three issues and efficiently obtain conformations and pathways. The method is not constrained to use only one or two collective variables, unlike most reaction coordinate-dependent methods. Instead, it can use a large number of collective vari- ables and uses macrostates (a partition of the collective variable space) to enhance the sampling. The exploration is done by running a large number of short simula- tions, and a clustering technique is used to accelerate the sampling. In this paper, we introduce the new methodology and show results from two-dimensional models and bio-molecules, such as penta-alanine and triazine polymer

View Accepted Manuscript (DOE)

View Accepted Manuscript (Publisher)

Cite

Export

Save

Research Organization:: Pacific Northwest National Lab. (PNNL), Richland, WA (United States)

Sponsoring Organization:: USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR)

Grant/Contract Number:: AC05-76RL01830

OSTI ID:: 1398164

Alternate ID(s):: OSTI ID: 1375660

Report Number(s):: PNNL-SA-125912

Journal Information:: Journal of Chemical Physics, Vol. 147, Issue 7; ISSN 0021-9606

Publisher:: American Institute of Physics (AIP)Copyright Statement

Country of Publication:: United States

Language:: English

Citation Metrics:

Cited by: 6 works

Citation information provided by
Web of Science

References (46)

The “weighted ensemble” path sampling method is statistically exact for a broad class of stochastic processes and binning procedures Zhang, Bin W.; Jasnow, David; Zuckerman, Daniel M. The Journal of Chemical Physics, Vol. 132, Issue 5 https://doi.org/10.1063/1.3306345	journal	February 2010
Nonphysical sampling distributions in Monte Carlo free-energy estimation: Umbrella sampling Torrie, G. M.; Valleau, J. P. Journal of Computational Physics, Vol. 23, Issue 2 https://doi.org/10.1016/0021-9991(77)90121-8	journal	February 1977
WExplore: Hierarchical Exploration of High-Dimensional Spaces Using the Weighted Ensemble Algorithm Dickson, Alex; Brooks, Charles L. The Journal of Physical Chemistry B, Vol. 118, Issue 13 https://doi.org/10.1021/jp411479c	journal	February 2014
Optimal use of data in parallel tempering simulations for the construction of discrete-state Markov models of biomolecular dynamics Prinz, Jan-Hendrik; Chodera, John D.; Pande, Vijay S. The Journal of Chemical Physics, Vol. 134, Issue 24 https://doi.org/10.1063/1.3592153	journal	June 2011
Diffusion maps Coifman, Ronald R.; Lafon, Stéphane Applied and Computational Harmonic Analysis, Vol. 21, Issue 1 https://doi.org/10.1016/j.acha.2006.04.006	journal	July 2006
Efficient and verified simulation of a path ensemble for conformational change in a united-residue model of calmodulin Zhang, B. W.; Jasnow, D.; Zuckerman, D. M. Proceedings of the National Academy of Sciences, Vol. 104, Issue 46 https://doi.org/10.1073/pnas.0706349104	journal	November 2007
Estimating the sampling error: Distribution of transition matrices and functions of transition matrices for given trajectory data Metzner, Philipp; Noé, Frank; Schütte, Christof Physical Review E, Vol. 80, Issue 2 https://doi.org/10.1103/physreve.80.021106	journal	August 2009
Simulating rare events using a weighted ensemble-based string method Adelman, Joshua L.; Grabe, Michael The Journal of Chemical Physics, Vol. 138, Issue 4 https://doi.org/10.1063/1.4773892	journal	January 2013
WESTPA: An Interoperable, Highly Scalable Software Package for Weighted Ensemble Simulation and Analysis Zwier, Matthew C.; Adelman, Joshua L.; Kaus, Joseph W. Journal of Chemical Theory and Computation, Vol. 11, Issue 2 https://doi.org/10.1021/ct5010615	journal	January 2015
Peptoid conformational free energy landscapes from implicit-solvent molecular simulations in AMBER Voelz, Vincent A.; Dill, Ken A.; Chorny, Ilya Biopolymers, Vol. 96, Issue 5 https://doi.org/10.1002/bip.21575	journal	December 2010
GROMACS 4.5: a high-throughput and highly parallel open source molecular simulation toolkit Pronk, Sander; Páll, Szilárd; Schulz, Roland Bioinformatics, Vol. 29, Issue 7 https://doi.org/10.1093/bioinformatics/btt055	journal	February 2013
Metadynamics: a method to simulate rare events and reconstruct the free energy in biophysics, chemistry and material science Laio, Alessandro; Gervasio, Francesco L. Reports on Progress in Physics, Vol. 71, Issue 12 https://doi.org/10.1088/0034-4885/71/12/126601	journal	November 2008
Calculating Free Energies Using a Scaled-Force Molecular Dynamics Algorithm Darve, Eric; Wilson, Michael A.; Pohorille, Andrew Molecular Simulation, Vol. 28, Issue 1-2 https://doi.org/10.1080/08927020211975	journal	January 2002
An adaptive weighted ensemble procedure for efficient computation of free energies and first passage rates Bhatt, Divesh; Bahar, Ivet The Journal of Chemical Physics, Vol. 137, Issue 10 https://doi.org/10.1063/1.4748278	journal	September 2012
Development and testing of a general amber force field Wang, Junmei; Wolf, Romain M.; Caldwell, James W. Journal of Computational Chemistry, Vol. 25, Issue 9 https://doi.org/10.1002/jcc.20035	journal	January 2004
Progress and challenges in the automated construction of Markov state models for full protein systems Bowman, Gregory R.; Beauchamp, Kyle A.; Boxer, George The Journal of Chemical Physics, Vol. 131, Issue 12 https://doi.org/10.1063/1.3216567	journal	September 2009
Markov state models based on milestoning Schütte, Christof; Noé, Frank; Lu, Jianfeng The Journal of Chemical Physics, Vol. 134, Issue 20 https://doi.org/10.1063/1.3590108	journal	May 2011
Triazine-Based Sequence-Defined Polymers with Side-Chain Diversity and Backbone-Backbone Interaction Motifs Grate, Jay W.; Mo, Kai-For; Daily, Michael D. Angewandte Chemie International Edition, Vol. 55, Issue 12 https://doi.org/10.1002/anie.201509864	journal	February 2016
Accurate Estimation of Protein Folding and Unfolding Times: Beyond Markov State Models Suárez, Ernesto; Adelman, Joshua L.; Zuckerman, Daniel M. Journal of Chemical Theory and Computation, Vol. 12, Issue 8 https://doi.org/10.1021/acs.jctc.6b00339	journal	July 2016
The Adaptive Biasing Force Method: Everything You Always Wanted To Know but Were Afraid To Ask Comer, Jeffrey; Gumbart, James C.; Hénin, Jérôme The Journal of Physical Chemistry B, Vol. 119, Issue 3 https://doi.org/10.1021/jp506633n	journal	August 2014
Temperature-accelerated dynamics for simulation of infrequent events So/rensen, Mads R.; Voter, Arthur F. The Journal of Chemical Physics, Vol. 112, Issue 21 https://doi.org/10.1063/1.481576	journal	June 2000
Efficient Computation, Sensitivity, and Error Analysis of Committor Probabilities for Complex Dynamical Processes Prinz, Jan-Hendrik; Held, Martin; Smith, Jeremy C. Multiscale Modeling & Simulation, Vol. 9, Issue 2 https://doi.org/10.1137/100789191	journal	April 2011
Using generalized ensemble simulations and Markov state models to identify conformational states Bowman, Gregory R.; Huang, Xuhui; Pande, Vijay S. Methods, Vol. 49, Issue 2 https://doi.org/10.1016/j.ymeth.2009.04.013	journal	October 2009
Accelerated molecular dynamics: A promising and efficient simulation method for biomolecules Hamelberg, Donald; Mongan, John; McCammon, J. Andrew The Journal of Chemical Physics, Vol. 120, Issue 24 https://doi.org/10.1063/1.1755656	journal	June 2004
Simultaneous Computation of Dynamical and Equilibrium Information Using a Weighted Ensemble of Trajectories Suárez, Ernesto; Lettieri, Steven; Zwier, Matthew C. Journal of Chemical Theory and Computation, Vol. 10, Issue 7 https://doi.org/10.1021/ct401065r	journal	March 2014
Identification of simple reaction coordinates from complex dynamics McGibbon, Robert T.; Husic, Brooke E.; Pande, Vijay S. The Journal of Chemical Physics, Vol. 146, Issue 4 https://doi.org/10.1063/1.4974306	journal	January 2017
A Bayesian Approach to Calculating Free Energies in Chemical and Biological Systems Pohorille, Andrew; Darve, Eric Bayesian Inference and Maximum Entropy Methods In Science and Engineering, AIP Conference Proceedings https://doi.org/10.1063/1.2423257	conference	January 2006
Efficient in silico exploration of RNA interhelical conformations using Euler angles and WExplore Dickson, Alex; Mustoe, Anthony M.; Salmon, Loïc Nucleic Acids Research, Vol. 42, Issue 19 https://doi.org/10.1093/nar/gku799	journal	October 2014
On the assumptions underlying milestoning Vanden-Eijnden, Eric; Venturoli, Maddalena; Ciccotti, Giovanni The Journal of Chemical Physics, Vol. 129, Issue 17 https://doi.org/10.1063/1.2996509	journal	November 2008
AWE-WQ: Fast-Forwarding Molecular Dynamics Using the Accelerated Weighted Ensemble Abdul-Wahid, Badi’; Feng, Haoyun; Rajan, Dinesh Journal of Chemical Information and Modeling, Vol. 54, Issue 10 https://doi.org/10.1021/ci500321g	journal	September 2014
Steady-state simulations using weighted ensemble path sampling Bhatt, Divesh; Zhang, Bin W.; Zuckerman, Daniel M. The Journal of Chemical Physics, Vol. 133, Issue 1 https://doi.org/10.1063/1.3456985	journal	July 2010
Building Markov state models along pathways to determine free energies and rates of transitions Pan, Albert C.; Roux, Benoît The Journal of Chemical Physics, Vol. 129, Issue 6 https://doi.org/10.1063/1.2959573	journal	August 2008
String method in collective variables: Minimum free energy paths and isocommittor surfaces Maragliano, Luca; Fischer, Alexander; Vanden-Eijnden, Eric The Journal of Chemical Physics, Vol. 125, Issue 2 https://doi.org/10.1063/1.2212942	journal	July 2006
Assessing the efficiency of free energy calculation methods Rodriguez-Gomez, David; Darve, Eric; Pohorille, Andrew The Journal of Chemical Physics, Vol. 120, Issue 8 https://doi.org/10.1063/1.1642607	journal	February 2004
Accelerated molecular dynamics simulations of protein folding Miao, Yinglong; Feixas, Ferran; Eun, Changsun Journal of Computational Chemistry, Vol. 36, Issue 20 https://doi.org/10.1002/jcc.23964	journal	June 2015
Duplex Oligomers Defined via Covalent Casting of a One-Dimensional Hydrogen-Bonding Motif Archer, Eric A.; Krische, Michael J. Journal of the American Chemical Society, Vol. 124, Issue 18, p. 5074-5083 https://doi.org/10.1021/ja012696h	journal	May 2002
Replica-exchange molecular dynamics method for protein folding Sugita, Yuji; Okamoto, Yuko Chemical Physics Letters, Vol. 314, Issue 1-2 https://doi.org/10.1016/s0009-2614(99)01123-9	journal	November 1999
Equilibrium Free Energies from Nonequilibrium Metadynamics Bussi, Giovanni; Laio, Alessandro; Parrinello, Michele Physical Review Letters, Vol. 96, Issue 9 https://doi.org/10.1103/physrevlett.96.090601	journal	March 2006
A tutorial on spectral clustering von Luxburg, Ulrike Statistics and Computing, Vol. 17, Issue 4 https://doi.org/10.1007/s11222-007-9033-z	journal	August 2007
Escaping free-energy minima Laio, A.; Parrinello, M. Proceedings of the National Academy of Sciences, Vol. 99, Issue 20 https://doi.org/10.1073/pnas.202427399	journal	September 2002
Thermodynamics and mechanism of .alpha. helix initiation in alanine and valine peptides Tobias, Douglas J.; Brooks, Charles L. Biochemistry, Vol. 30, Issue 24 https://doi.org/10.1021/bi00238a033	journal	June 1991
Adaptive biasing force method for scalar and vector free energy calculations Darve, Eric; Rodríguez-Gómez, David; Pohorille, Andrew The Journal of Chemical Physics, Vol. 128, Issue 14 https://doi.org/10.1063/1.2829861	journal	April 2008
Calculating free energies using average force Darve, Eric; Pohorille, Andrew The Journal of Chemical Physics, Vol. 115, Issue 20 https://doi.org/10.1063/1.1410978	journal	November 2001
A comparison of weighted ensemble and Markov state model methodologies Feng, Haoyun; Costaouec, Ronan; Darve, Eric The Journal of Chemical Physics, Vol. 142, Issue 21 https://doi.org/10.1063/1.4921890	journal	June 2015
Structural and conformational study of substituted triazines by15N NMR and x-ray analysis Amm, Michel; Platzer, Nicole; Guilhem, Jean Magnetic Resonance in Chemistry, Vol. 36, Issue 8 https://doi.org/10.1002/(sici)1097-458x(199808)36:8<587::aid-omr347>3.0.co;2-b	journal	August 1998
NMR studies of exchange between triazine rotamers Birkett, Helen E.; Harris, Robin K.; Hodgkinson, Paul Magnetic Resonance in Chemistry, Vol. 38, Issue 7 https://doi.org/10.1002/1097-458x(200007)38:7<504::aid-mrc710>3.0.co;2-7	journal	January 2000

Cited By (1)

Investigating the role of non-covalent interactions in conformation and assembly of triazine-based sequence-defined polymers Ahn, Surl-Hee; Grate, Jay W.; Darve, Eric F. The Journal of Chemical Physics, Vol. 149, Issue 7 https://doi.org/10.1063/1.5024552	journal	August 2018

Similar Records

Efficient sampling over rough energy landscapes with high barriers: A combination of metadynamics with integrated tempering sampling

Journal Article · Mon Mar 07 00:00:00 EST 2016 · Journal of Chemical Physics · OSTI ID:1398164

Yang, Y. Isaac; Zhang, Jun; Che, Xing; +1 more

Workflows in a secure environment

Conference · Tue Jan 01 00:00:00 EST 2008 · OSTI ID:1398164

Klasky, Scott A; Podhorszki, Norbert

An adaptive multi-level simulation algorithm for stochastic biological systems

Journal Article · Wed Jan 14 00:00:00 EST 2015 · Journal of Chemical Physics · OSTI ID:1398164

Giles, M. B.; Baker, R. E.; Yates, C. A.

Related Subjects

97 MATHEMATICS AND COMPUTING
59 BASIC BIOLOGICAL SCIENCES
macromolecular
molecular dynamics simulation
enhanced ssa

Title: Efficiently sampling conformations and pathways using the concurrent adaptive sampling (CAS) algorithm

Citation Formats

References (46)

Cited By (1)

Similar Records

Related Subjects