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.

Motta, S., Callea, L., Bonati, L., 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), 1957-1968 [10.1021/acs.jctc.1c01163].

PathDetect-SOM: A Neural Network Approach for the Identification of Pathways in Ligand Binding Simulations

Motta, Stefano
Primo
;
Callea, Lara;Bonati, Laura;Pandini, Alessandro
2022

Abstract

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.
Articolo in rivista - Articolo scientifico
PathDetect-SOM, ligand pathways, molecular dynamics, self-organizing map
English
8-mar-2022
2022
18
3
1957
1968
open
Motta, S., Callea, L., Bonati, L., 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), 1957-1968 [10.1021/acs.jctc.1c01163].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/420663
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