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DC Field | Value | Language |
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dc.contributor.author | Motta, S | - |
dc.contributor.author | Callea, L | - |
dc.contributor.author | Bonati, L | - |
dc.contributor.author | Pandini, A | - |
dc.date.accessioned | 2022-02-19T16:51:53Z | - |
dc.date.available | 2022-02-19T16:51:53Z | - |
dc.date.issued | 2022-02-25 | - |
dc.identifier.citation | Motta, S., Callea, L., Bonati, L. and Pandini, A. (2022) 'PathDetect-SOM: A Neural Network Approach for the Identification of Pathways in Ligand Binding Simulations', Journal of Chemical Theory and Computation, 18, 3, pp. 1957 - 1968. doi: 10.1021/acs.jctc.1c01163. | en_US |
dc.identifier.issn | 1549-9618 | - |
dc.identifier.uri | https://bura.brunel.ac.uk/handle/2438/24152 | - |
dc.description.abstract | Copyright © 2022 The Author(s). Understanding the process of ligand–protein recognition is important to unveil biological mechanisms and to guide drug discovery and design. Enhanced-sampling molecular dynamics is now routinely used to simulate the ligand binding process, resulting in the need for suitable tools for the analysis of large data sets of binding events. Here, we designed, implemented, and tested PathDetect-SOM, a tool based on self-organizing maps to build concise visual models of the ligand binding pathways sampled along single simulations or replicas. The tool performs a geometric clustering of the trajectories and traces the pathways over an easily interpretable 2D map and, using an approximate transition matrix, it can build a graph model of concurrent pathways. The tool was tested on three study cases representing different types of problems and simulation techniques. A clear reconstruction of the sampled pathways was derived in all cases, and useful information on the energetic features of the processes was recovered. The tool is available at https://github.com/MottaStefano/PathDetect-SOM. | - |
dc.description.sponsorship | Leverhulme Trust (An integrated computational-experimental method to redesign protein dynamics, RPG-2017-222). | en_US |
dc.format.extent | 1957 - 1968 (12) | - |
dc.format.medium | Print-Electronic | - |
dc.language.iso | en_US | en_US |
dc.publisher | American Chemical Society | en_US |
dc.rights | Copyright © 2022 The Author(s). Published by American Chemical Society after peer review under a Creative Commons (CC. BY) Attribution License. Credit must be given to the creator(s). | - |
dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | - |
dc.subject | ligand binding | en_US |
dc.subject | artificial neural networks | en_US |
dc.subject | self-organizing maps | en_US |
dc.subject | molecular dynamics | en_US |
dc.title | PathDetect-SOM: A Neural Network Approach for the Identification of Pathways in Ligand Binding Simulations | en_US |
dc.type | Article | en_US |
dc.identifier.doi | https://doi.org/10.1021/acs.jctc.1c01163 | - |
dc.relation.isPartOf | Journal of Chemical Theory and Computation | - |
pubs.issue | 3 | - |
pubs.publication-status | Published | - |
pubs.volume | 18 | - |
dc.identifier.eissn | 1549-9626 | - |
Appears in Collections: | Dept of Computer Science Research Papers |
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