Pub Date : 2019-01-01DOI: 10.33011/LIVECOMS.1.1.5965
C. Wehmeyer, Martin K. Scherer, Tim Hempel, B. Husic, S. Olsson, F. Noé
{"title":"Introduction to Markov state modeling with the PyEMMA software [Article v1.0]","authors":"C. Wehmeyer, Martin K. Scherer, Tim Hempel, B. Husic, S. Olsson, F. Noé","doi":"10.33011/LIVECOMS.1.1.5965","DOIUrl":"https://doi.org/10.33011/LIVECOMS.1.1.5965","url":null,"abstract":"","PeriodicalId":74084,"journal":{"name":"Living journal of computational molecular science","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"69480742","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-01-01Epub Date: 2019-10-04DOI: 10.33011/livecoms.1.2.10607
Anthony T Bogetti, Barmak Mostofian, Alex Dickson, A J Pratt, Ali S Saglam, Page O Harrison, Joshua L Adelman, Max Dudek, Paul A Torrillo, Alex J DeGrave, Upendra Adhikari, Matthew C Zwier, Daniel M Zuckerman, Lillian T Chong
The weighted ensemble (WE) strategy has been demonstrated to be highly efficient in generating pathways and rate constants for rare events such as protein folding and protein binding using atomistic molecular dynamics simulations. Here we present five tutorials instructing users in the best practices for preparing, carrying out, and analyzing WE simulations for various applications using the WESTPA software. Users are expected to already have significant experience with running standard molecular dynamics simulations using the underlying dynamics engine of interest (e.g. Amber, Gromacs, OpenMM). The tutorials range from a molecular association process in explicit solvent to more complex processes such as host-guest association, peptide conformational sampling, and protein folding.
{"title":"A Suite of Tutorials for the WESTPA Rare-Events Sampling Software [Article v1.0].","authors":"Anthony T Bogetti, Barmak Mostofian, Alex Dickson, A J Pratt, Ali S Saglam, Page O Harrison, Joshua L Adelman, Max Dudek, Paul A Torrillo, Alex J DeGrave, Upendra Adhikari, Matthew C Zwier, Daniel M Zuckerman, Lillian T Chong","doi":"10.33011/livecoms.1.2.10607","DOIUrl":"https://doi.org/10.33011/livecoms.1.2.10607","url":null,"abstract":"<p><p>The weighted ensemble (WE) strategy has been demonstrated to be highly efficient in generating pathways and rate constants for rare events such as protein folding and protein binding using atomistic molecular dynamics simulations. Here we present five tutorials instructing users in the best practices for preparing, carrying out, and analyzing WE simulations for various applications using the WESTPA software. Users are expected to already have significant experience with running standard molecular dynamics simulations using the underlying dynamics engine of interest (e.g. Amber, Gromacs, OpenMM). The tutorials range from a molecular association process in explicit solvent to more complex processes such as host-guest association, peptide conformational sampling, and protein folding.</p>","PeriodicalId":74084,"journal":{"name":"Living journal of computational molecular science","volume":"1 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7213600/pdf/nihms-1584279.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"37924090","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-01-01DOI: 10.33011/livecoms.1.2.10841
Shailesh Kumar Panday, Mihiri H. B. Shashikala, Mahesh Koirala, S. Pahari, Arghya Chakrvorty, Yunhui Peng, Lin Li, Zhe Jia, Chuan Li, E. Alexov
This LiveCoMS document is maintained online on GitHub at https: //github.com/delphi001/ delphi_tutorial_livecoms; to provide feedback, suggestions, or help improve it, please visit the GitHub repository and participate via the issue tracker. This version dated November 25, 2019 Abstract Electrostatics play an indispensable role in practically any process in molecular biology. Indeed, at distances larger than several Angstroms, all other forces are negligibly small and electrostatic force dominates. However, modeling electrostatics in molecular biology is a complicated task due to presence of water phase, mobile ions and irregularly shaped inhomogeneous biological macromolecules. A particular approach to calculating electrostatics in such systems is to apply the Poisson-Boltzmann equation (PBE). Here, we provide a tutorial for the popular DelPhi package that solves PBE using a finite-difference method and delivers the electrostatic potential distribution throughout the modeling box. The tutorial comes with a detailed description of different tasks that DelPhi can handle, an assessment of the accuracy against cases with analytical solutions and recommendations about DelPhi usage. Furthermore, since electrostatics is a key component of virtually any modeling in molecular biology, we have created many additional resources utilizing DelPhi to model various biology relevant quantities. Tutorials for these resources are also provided along with examples of their usage.
{"title":"Modeling electrostatics in molecular biology: A tutorial of DelPhi and associated resources [Article v1.0]","authors":"Shailesh Kumar Panday, Mihiri H. B. Shashikala, Mahesh Koirala, S. Pahari, Arghya Chakrvorty, Yunhui Peng, Lin Li, Zhe Jia, Chuan Li, E. Alexov","doi":"10.33011/livecoms.1.2.10841","DOIUrl":"https://doi.org/10.33011/livecoms.1.2.10841","url":null,"abstract":"This LiveCoMS document is maintained online on GitHub at https: //github.com/delphi001/ delphi_tutorial_livecoms; to provide feedback, suggestions, or help improve it, please visit the GitHub repository and participate via the issue tracker. This version dated November 25, 2019 Abstract Electrostatics play an indispensable role in practically any process in molecular biology. Indeed, at distances larger than several Angstroms, all other forces are negligibly small and electrostatic force dominates. However, modeling electrostatics in molecular biology is a complicated task due to presence of water phase, mobile ions and irregularly shaped inhomogeneous biological macromolecules. A particular approach to calculating electrostatics in such systems is to apply the Poisson-Boltzmann equation (PBE). Here, we provide a tutorial for the popular DelPhi package that solves PBE using a finite-difference method and delivers the electrostatic potential distribution throughout the modeling box. The tutorial comes with a detailed description of different tasks that DelPhi can handle, an assessment of the accuracy against cases with analytical solutions and recommendations about DelPhi usage. Furthermore, since electrostatics is a key component of virtually any modeling in molecular biology, we have created many additional resources utilizing DelPhi to model various biology relevant quantities. Tutorials for these resources are also provided along with examples of their usage.","PeriodicalId":74084,"journal":{"name":"Living journal of computational molecular science","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"69480794","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-01-01Epub Date: 2019-08-09DOI: 10.33011/livecoms.1.2.9974
Rodrigo Galindo-Murillo, Thomas E Cheatham
Nucleic acids are highly charged macromolecules sensitive to their surroundings of water, salt, and other biomolecules. Molecular dynamics simulations with accurate biomolecular force fields provide a detailed atomistic view into DNA and RNA that has been useful to study the structure and dynamics of these molecules and their biological relevance. In this work we study the Drew-Dickerson dodecamer duplex with the sequence d(GCGCAATTGCGC)2 in three different salt concentrations and using different monvalent salt types to detect possible structural influence. Overall, the DNA shows no major structural changes regardless of amount or type of monovalent ions used. Our results show that only at very high salt conditions (5M) is a small structural effect observed in the DNA duplex, which mainly consist of narrowing of the grooves due to increased residence of ions. We also present the importance of sampling time to achieve a converged ensemble, which is of major relevance in any simulation to avoid biased or non-meaningful results.
{"title":"Lessons learned in atomistic simulation of double-stranded DNA: Solvation and salt concerns [Article v1.0].","authors":"Rodrigo Galindo-Murillo, Thomas E Cheatham","doi":"10.33011/livecoms.1.2.9974","DOIUrl":"10.33011/livecoms.1.2.9974","url":null,"abstract":"<p><p>Nucleic acids are highly charged macromolecules sensitive to their surroundings of water, salt, and other biomolecules. Molecular dynamics simulations with accurate biomolecular force fields provide a detailed atomistic view into DNA and RNA that has been useful to study the structure and dynamics of these molecules and their biological relevance. In this work we study the Drew-Dickerson dodecamer duplex with the sequence d(GCGCAATTGCGC)<sub>2</sub> in three different salt concentrations and using different monvalent salt types to detect possible structural influence. Overall, the DNA shows no major structural changes regardless of amount or type of monovalent ions used. Our results show that only at very high salt conditions (5M) is a small structural effect observed in the DNA duplex, which mainly consist of narrowing of the grooves due to increased residence of ions. We also present the importance of sampling time to achieve a converged ensemble, which is of major relevance in any simulation to avoid biased or non-meaningful results.</p>","PeriodicalId":74084,"journal":{"name":"Living journal of computational molecular science","volume":"1 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7561045/pdf/nihms-1610687.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"38505758","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-01-01DOI: 10.33011/LIVECOMS.1.1.6324
E. Maginn, Richard A. Messerly, Daniel J. Carlson, Daniel R. Roe, J. Richard Elliott
1Department of Chemical and Biomolecular Engineering, The University of Notre Dame; 2Thermodynamics Research Center, National Institute of Standards and Technology; 3Chemical Engineering Department, Brigham Young University; 4Laboratory of Computational Biology, National Heart Lung and Blood Institute, National Institutes of Health; 5Department of Chemical and Biomolecular Engineering, The University of Akron
{"title":"Best Practices for Computing Transport Properties 1. Self-Diffusivity and Viscosity from Equilibrium Molecular Dynamics [Article v1.0]","authors":"E. Maginn, Richard A. Messerly, Daniel J. Carlson, Daniel R. Roe, J. Richard Elliott","doi":"10.33011/LIVECOMS.1.1.6324","DOIUrl":"https://doi.org/10.33011/LIVECOMS.1.1.6324","url":null,"abstract":"1Department of Chemical and Biomolecular Engineering, The University of Notre Dame; 2Thermodynamics Research Center, National Institute of Standards and Technology; 3Chemical Engineering Department, Brigham Young University; 4Laboratory of Computational Biology, National Heart Lung and Blood Institute, National Institutes of Health; 5Department of Chemical and Biomolecular Engineering, The University of Akron","PeriodicalId":74084,"journal":{"name":"Living journal of computational molecular science","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"69480745","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-01-01Epub Date: 2018-10-27DOI: 10.33011/livecoms.1.1.5067
Alan Grossfield, Paul N Patrone, Daniel R Roe, Andrew J Schultz, Daniel W Siderius, Daniel M Zuckerman
The quantitative assessment of uncertainty and sampling quality is essential in molecular simulation. Many systems of interest are highly complex, often at the edge of current computational capabilities. Modelers must therefore analyze and communicate statistical uncertainties so that "consumers" of simulated data understand its significance and limitations. This article covers key analyses appropriate for trajectory data generated by conventional simulation methods such as molecular dynamics and (single Markov chain) Monte Carlo. It also provides guidance for analyzing some 'enhanced' sampling approaches. We do not discuss systematic errors arising, e.g., from inaccuracy in the chosen model or force field.
{"title":"Best Practices for Quantification of Uncertainty and Sampling Quality in Molecular Simulations [Article v1.0].","authors":"Alan Grossfield, Paul N Patrone, Daniel R Roe, Andrew J Schultz, Daniel W Siderius, Daniel M Zuckerman","doi":"10.33011/livecoms.1.1.5067","DOIUrl":"10.33011/livecoms.1.1.5067","url":null,"abstract":"<p><p>The quantitative assessment of uncertainty and sampling quality is essential in molecular simulation. Many systems of interest are highly complex, often at the edge of current computational capabilities. Modelers must therefore analyze and communicate statistical uncertainties so that \"consumers\" of simulated data understand its significance and limitations. This article covers key analyses appropriate for trajectory data generated by conventional simulation methods such as molecular dynamics and (single Markov chain) Monte Carlo. It also provides guidance for analyzing some 'enhanced' sampling approaches. We do not discuss <i>systematic</i> errors arising, e.g., from inaccuracy in the chosen model or force field.</p>","PeriodicalId":74084,"journal":{"name":"Living journal of computational molecular science","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2018-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6286151/pdf/nihms-994026.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"36768479","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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