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Path Similarity Analysis: A Method for Quantifying Macromolecular Pathways

Diverse classes of proteins function through large-scale conformational changes and various sophisticated computational algorithms have been proposed to enhance sampling of these macromolecular transition paths. Because such paths are curves in a high-dimensional space, it has been difficult to quan...

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Autores principales: Seyler, Sean L., Kumar, Avishek, Thorpe, M. F., Beckstein, Oliver
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2015
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4619321/
https://www.ncbi.nlm.nih.gov/pubmed/26488417
http://dx.doi.org/10.1371/journal.pcbi.1004568
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author Seyler, Sean L.
Kumar, Avishek
Thorpe, M. F.
Beckstein, Oliver
author_facet Seyler, Sean L.
Kumar, Avishek
Thorpe, M. F.
Beckstein, Oliver
author_sort Seyler, Sean L.
collection PubMed
description Diverse classes of proteins function through large-scale conformational changes and various sophisticated computational algorithms have been proposed to enhance sampling of these macromolecular transition paths. Because such paths are curves in a high-dimensional space, it has been difficult to quantitatively compare multiple paths, a necessary prerequisite to, for instance, assess the quality of different algorithms. We introduce a method named Path Similarity Analysis (PSA) that enables us to quantify the similarity between two arbitrary paths and extract the atomic-scale determinants responsible for their differences. PSA utilizes the full information available in 3N-dimensional configuration space trajectories by employing the Hausdorff or Fréchet metrics (adopted from computational geometry) to quantify the degree of similarity between piecewise-linear curves. It thus completely avoids relying on projections into low dimensional spaces, as used in traditional approaches. To elucidate the principles of PSA, we quantified the effect of path roughness induced by thermal fluctuations using a toy model system. Using, as an example, the closed-to-open transitions of the enzyme adenylate kinase (AdK) in its substrate-free form, we compared a range of protein transition path-generating algorithms. Molecular dynamics-based dynamic importance sampling (DIMS) MD and targeted MD (TMD) and the purely geometric FRODA (Framework Rigidity Optimized Dynamics Algorithm) were tested along with seven other methods publicly available on servers, including several based on the popular elastic network model (ENM). PSA with clustering revealed that paths produced by a given method are more similar to each other than to those from another method and, for instance, that the ENM-based methods produced relatively similar paths. PSA was applied to ensembles of DIMS MD and FRODA trajectories of the conformational transition of diphtheria toxin, a particularly challenging example. For the AdK transition, the new concept of a Hausdorff-pair map enabled us to extract the molecular structural determinants responsible for differences in pathways, namely a set of conserved salt bridges whose charge-charge interactions are fully modelled in DIMS MD but not in FRODA. PSA has the potential to enhance our understanding of transition path sampling methods, validate them, and to provide a new approach to analyzing conformational transitions.
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spelling pubmed-46193212015-10-29 Path Similarity Analysis: A Method for Quantifying Macromolecular Pathways Seyler, Sean L. Kumar, Avishek Thorpe, M. F. Beckstein, Oliver PLoS Comput Biol Research Article Diverse classes of proteins function through large-scale conformational changes and various sophisticated computational algorithms have been proposed to enhance sampling of these macromolecular transition paths. Because such paths are curves in a high-dimensional space, it has been difficult to quantitatively compare multiple paths, a necessary prerequisite to, for instance, assess the quality of different algorithms. We introduce a method named Path Similarity Analysis (PSA) that enables us to quantify the similarity between two arbitrary paths and extract the atomic-scale determinants responsible for their differences. PSA utilizes the full information available in 3N-dimensional configuration space trajectories by employing the Hausdorff or Fréchet metrics (adopted from computational geometry) to quantify the degree of similarity between piecewise-linear curves. It thus completely avoids relying on projections into low dimensional spaces, as used in traditional approaches. To elucidate the principles of PSA, we quantified the effect of path roughness induced by thermal fluctuations using a toy model system. Using, as an example, the closed-to-open transitions of the enzyme adenylate kinase (AdK) in its substrate-free form, we compared a range of protein transition path-generating algorithms. Molecular dynamics-based dynamic importance sampling (DIMS) MD and targeted MD (TMD) and the purely geometric FRODA (Framework Rigidity Optimized Dynamics Algorithm) were tested along with seven other methods publicly available on servers, including several based on the popular elastic network model (ENM). PSA with clustering revealed that paths produced by a given method are more similar to each other than to those from another method and, for instance, that the ENM-based methods produced relatively similar paths. PSA was applied to ensembles of DIMS MD and FRODA trajectories of the conformational transition of diphtheria toxin, a particularly challenging example. For the AdK transition, the new concept of a Hausdorff-pair map enabled us to extract the molecular structural determinants responsible for differences in pathways, namely a set of conserved salt bridges whose charge-charge interactions are fully modelled in DIMS MD but not in FRODA. PSA has the potential to enhance our understanding of transition path sampling methods, validate them, and to provide a new approach to analyzing conformational transitions. Public Library of Science 2015-10-21 /pmc/articles/PMC4619321/ /pubmed/26488417 http://dx.doi.org/10.1371/journal.pcbi.1004568 Text en © 2015 Seyler et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited
spellingShingle Research Article
Seyler, Sean L.
Kumar, Avishek
Thorpe, M. F.
Beckstein, Oliver
Path Similarity Analysis: A Method for Quantifying Macromolecular Pathways
title Path Similarity Analysis: A Method for Quantifying Macromolecular Pathways
title_full Path Similarity Analysis: A Method for Quantifying Macromolecular Pathways
title_fullStr Path Similarity Analysis: A Method for Quantifying Macromolecular Pathways
title_full_unstemmed Path Similarity Analysis: A Method for Quantifying Macromolecular Pathways
title_short Path Similarity Analysis: A Method for Quantifying Macromolecular Pathways
title_sort path similarity analysis: a method for quantifying macromolecular pathways
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4619321/
https://www.ncbi.nlm.nih.gov/pubmed/26488417
http://dx.doi.org/10.1371/journal.pcbi.1004568
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