A Companion Guide to the String Method with Swarms of Trajectories: Characterization, Performance, and Pitfalls
The string method with swarms of trajectories (SMwST) is an algorithm that identifies a physically meaningful transition pathwaya one-dimensional curve, embedded within a high-dimensional space of selected collective variables. The SMwST algorithm leans on a series of short, unbiased molecular dyna...
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| Published in | Journal of chemical theory and computation Vol. 18; no. 3; pp. 1406 - 1422 |
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| Main Authors | , , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
United States
American Chemical Society
08.03.2022
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| Subjects | |
| Online Access | Get full text |
| ISSN | 1549-9618 1549-9626 1549-9626 |
| DOI | 10.1021/acs.jctc.1c01049 |
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| Abstract | The string method with swarms of trajectories (SMwST) is an algorithm that identifies a physically meaningful transition pathwaya one-dimensional curve, embedded within a high-dimensional space of selected collective variables. The SMwST algorithm leans on a series of short, unbiased molecular dynamics simulations spawned at different locations of the discretized path, from whence an average dynamic drift is determined to evolve the string toward an optimal pathway. However conceptually simple in both its theoretical formulation and practical implementation, the SMwST algorithm is computationally intensive and requires a careful choice of parameters for optimal cost-effectiveness in applications to challenging problems in chemistry and biology. In this contribution, the SMwST algorithm is presented in a self-contained manner, discussing with a critical eye its theoretical underpinnings, applicability, inherent limitations, and use in the context of path-following free-energy calculations and their possible extension to kinetics modeling. Through multiple simulations of a prototypical polypeptide, combining the search of the transition pathway and the computation of the potential of mean force along it, several practical aspects of the methodology are examined with the objective of optimizing the computational effort, yet without sacrificing accuracy. In light of the results reported here, we propose some general guidelines aimed at improving the efficiency and reliability of the computed pathways and free-energy profiles underlying the conformational transitions at hand. |
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| AbstractList | The string method with swarms of trajectories (SMwST) is an algorithm that identifies a physically meaningful transition pathway─a one-dimensional curve, embedded within a high-dimensional space of selected collective variables. The SMwST algorithm leans on a series of short, unbiased molecular dynamics simulations spawned at different locations of the discretized path, from whence an average dynamic drift is determined to evolve the string toward an optimal pathway. However conceptually simple in both its theoretical formulation and practical implementation, the SMwST algorithm is computationally intensive and requires a careful choice of parameters for optimal cost-effectiveness in applications to challenging problems in chemistry and biology. In this contribution, the SMwST algorithm is presented in a self-contained manner, discussing with a critical eye its theoretical underpinnings, applicability, inherent limitations, and use in the context of path-following free-energy calculations and their possible extension to kinetics modeling. Through multiple simulations of a prototypical polypeptide, combining the search of the transition pathway and the computation of the potential of mean force along it, several practical aspects of the methodology are examined with the objective of optimizing the computational effort, yet without sacrificing accuracy. In light of the results reported here, we propose some general guidelines aimed at improving the efficiency and reliability of the computed pathways and free-energy profiles underlying the conformational transitions at hand. The string method with swarms of trajectories (SMwST) is an algorithm that identifies a physically meaningful transition pathwaya one-dimensional curve, embedded within a high-dimensional space of selected collective variables. The SMwST algorithm leans on a series of short, unbiased molecular dynamics simulations spawned at different locations of the discretized path, from whence an average dynamic drift is determined to evolve the string toward an optimal pathway. However conceptually simple in both its theoretical formulation and practical implementation, the SMwST algorithm is computationally intensive and requires a careful choice of parameters for optimal cost-effectiveness in applications to challenging problems in chemistry and biology. In this contribution, the SMwST algorithm is presented in a self-contained manner, discussing with a critical eye its theoretical underpinnings, applicability, inherent limitations, and use in the context of path-following free-energy calculations and their possible extension to kinetics modeling. Through multiple simulations of a prototypical polypeptide, combining the search of the transition pathway and the computation of the potential of mean force along it, several practical aspects of the methodology are examined with the objective of optimizing the computational effort, yet without sacrificing accuracy. In light of the results reported here, we propose some general guidelines aimed at improving the efficiency and reliability of the computed pathways and free-energy profiles underlying the conformational transitions at hand. The string method with swarms of trajectories (SMwST) is an algorithm that identifies a physically meaningful transition pathway—a one-dimensional curve, embedded within a high-dimensional space of selected collective variables. The SMwST algorithm leans on a series of short, unbiased molecular dynamics simulations spawned at different locations of the discretized path, from whence an average dynamic drift is determined to evolve the string towards an optimal pathway. However conceptually simple in both its theoretical formulation and practical implementation, the SMwST algorithm is computationally intensive and requires careful choice of its parameters for optimal cost-effectiveness in applications to challenging problems in chemistry and biology. In this contribution, the SMwST algorithm is presented in a self-contained manner, discussing with a critical eye its theoretical underpinnings, applicability, inherent limitations, and use in the context of path-following free-energy calculations and their possible extension to kinetics modeling. Through multiple simulations of a prototypical polypeptide, combining the search of the transition pathway and the computation of the potential of mean force along it, several practical aspects of the methodology are examined with the objective to optimize the computational effort, yet without sacrificing accuracy. In light of the results reported here, we propose some general guidelines aimed at improving the efficiency and reliability of the computed pathways and free-energy profiles underlying the conformational transitions at hand. The string method with swarms of trajectories (SMwST) is an algorithm that identifies a physically meaningful transition pathway─a one-dimensional curve, embedded within a high-dimensional space of selected collective variables. The SMwST algorithm leans on a series of short, unbiased molecular dynamics simulations spawned at different locations of the discretized path, from whence an average dynamic drift is determined to evolve the string toward an optimal pathway. However conceptually simple in both its theoretical formulation and practical implementation, the SMwST algorithm is computationally intensive and requires a careful choice of parameters for optimal cost-effectiveness in applications to challenging problems in chemistry and biology. In this contribution, the SMwST algorithm is presented in a self-contained manner, discussing with a critical eye its theoretical underpinnings, applicability, inherent limitations, and use in the context of path-following free-energy calculations and their possible extension to kinetics modeling. Through multiple simulations of a prototypical polypeptide, combining the search of the transition pathway and the computation of the potential of mean force along it, several practical aspects of the methodology are examined with the objective of optimizing the computational effort, yet without sacrificing accuracy. In light of the results reported here, we propose some general guidelines aimed at improving the efficiency and reliability of the computed pathways and free-energy profiles underlying the conformational transitions at hand.The string method with swarms of trajectories (SMwST) is an algorithm that identifies a physically meaningful transition pathway─a one-dimensional curve, embedded within a high-dimensional space of selected collective variables. The SMwST algorithm leans on a series of short, unbiased molecular dynamics simulations spawned at different locations of the discretized path, from whence an average dynamic drift is determined to evolve the string toward an optimal pathway. However conceptually simple in both its theoretical formulation and practical implementation, the SMwST algorithm is computationally intensive and requires a careful choice of parameters for optimal cost-effectiveness in applications to challenging problems in chemistry and biology. In this contribution, the SMwST algorithm is presented in a self-contained manner, discussing with a critical eye its theoretical underpinnings, applicability, inherent limitations, and use in the context of path-following free-energy calculations and their possible extension to kinetics modeling. Through multiple simulations of a prototypical polypeptide, combining the search of the transition pathway and the computation of the potential of mean force along it, several practical aspects of the methodology are examined with the objective of optimizing the computational effort, yet without sacrificing accuracy. In light of the results reported here, we propose some general guidelines aimed at improving the efficiency and reliability of the computed pathways and free-energy profiles underlying the conformational transitions at hand. |
| Author | Ogden, Dylan Roux, Benoît Cai, Wensheng Chen, Haochuan Pant, Shashank Moradi, Mahmoud Chipot, Christophe Tajkhorshid, Emad |
| AuthorAffiliation | Department of Biochemistry and Center for Biophysics and Quantitative Biology Laboratoire International Associé Centre National de la Recherche Scientifique et University of Illinois at Urbana−Champaign, Unité Mixte de Recherche no 7019 Department of Biochemistry and Molecular Biology Theoretical and Computational Biophysics Group, NIH Center for Macromolecular Modeling and Bioinformatics, Beckman Institute for Advanced Science and Technology Department of Chemistry and Biochemistry University of Illinois at Urbana−Champaign Université de Lorraine Research Center for Analytical Sciences, College of Chemistry, Tianjin Key Laboratory of Biosensing and Molecular Recognition Department of Physics |
| AuthorAffiliation_xml | – name: Theoretical and Computational Biophysics Group, NIH Center for Macromolecular Modeling and Bioinformatics, Beckman Institute for Advanced Science and Technology – name: Research Center for Analytical Sciences, College of Chemistry, Tianjin Key Laboratory of Biosensing and Molecular Recognition – name: Université de Lorraine – name: Department of Biochemistry and Center for Biophysics and Quantitative Biology – name: Department of Physics – name: University of Illinois at Urbana−Champaign – name: Laboratoire International Associé Centre National de la Recherche Scientifique et University of Illinois at Urbana−Champaign, Unité Mixte de Recherche no 7019 – name: Department of Chemistry and Biochemistry – name: Department of Biochemistry and Molecular Biology – name: Department of Biochemistry and Molecular Biology, University of Chicago, Chicago, Illinois 60637, USA – name: Department of Biochemistry and Center for Biophysics and Quantitative Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, USA – name: Research Center for Analytical Sciences, College of Chemistry, Tianjin Key Laboratory of Biosensing and Molecular Recognition, Nankai University, Tianjin 300071, China – name: Laboratoire International Associé Centre National de la Recherche Scientifique et University of Illinois at Urbana-Champaign, Unité Mixte de Recherche n°7019, Universite de Lorraine, B.P. 70239, 54506 Vandœuvre-lès-Nancy cedex, France – name: Theoretical and Computational Biophysics Group, NIH Center for Macromolecular Modeling and Bioinformatics, Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, USA – name: Present Address: Loxo Oncology @ Lilly., Louisville, CO 80027, USA – name: @ Department of Physics, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, USA – name: Department of Chemistry and Biochemistry, University of Arkansas, Fayetteville, Arkansas 72701, USA |
| Author_xml | – sequence: 1 givenname: Haochuan orcidid: 0000-0001-6447-1096 surname: Chen fullname: Chen, Haochuan organization: Université de Lorraine – sequence: 2 givenname: Dylan surname: Ogden fullname: Ogden, Dylan organization: Department of Chemistry and Biochemistry – sequence: 3 givenname: Shashank orcidid: 0000-0002-8222-3616 surname: Pant fullname: Pant, Shashank organization: Theoretical and Computational Biophysics Group, NIH Center for Macromolecular Modeling and Bioinformatics, Beckman Institute for Advanced Science and Technology – sequence: 4 givenname: Wensheng orcidid: 0000-0002-6457-7058 surname: Cai fullname: Cai, Wensheng organization: Research Center for Analytical Sciences, College of Chemistry, Tianjin Key Laboratory of Biosensing and Molecular Recognition – sequence: 5 givenname: Emad orcidid: 0000-0001-8434-1010 surname: Tajkhorshid fullname: Tajkhorshid, Emad organization: University of Illinois at Urbana−Champaign – sequence: 6 givenname: Mahmoud orcidid: 0000-0002-0601-402X surname: Moradi fullname: Moradi, Mahmoud organization: Department of Chemistry and Biochemistry – sequence: 7 givenname: Benoît orcidid: 0000-0002-5254-2712 surname: Roux fullname: Roux, Benoît organization: Department of Biochemistry and Molecular Biology – sequence: 8 givenname: Christophe orcidid: 0000-0002-9122-1698 surname: Chipot fullname: Chipot, Christophe email: chipot@illinois.edu organization: University of Illinois at Urbana−Champaign |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/35138832$$D View this record in MEDLINE/PubMed |
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| Snippet | The string method with swarms of trajectories (SMwST) is an algorithm that identifies a physically meaningful transition pathwaya one-dimensional curve,... The string method with swarms of trajectories (SMwST) is an algorithm that identifies a physically meaningful transition pathway─a one-dimensional curve,... The string method with swarms of trajectories (SMwST) is an algorithm that identifies a physically meaningful transition pathway—a one-dimensional curve,... |
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| SubjectTerms | Algorithms Free energy Molecular dynamics Optimization Polypeptides Statistical Mechanics Strings Trajectories |
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| Title | A Companion Guide to the String Method with Swarms of Trajectories: Characterization, Performance, and Pitfalls |
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