Motivation: Understanding the process of ligand-protein recognition is important to unveil biological mechanisms and to inform drug design and discovery. Enhanced sampling molecular dynamics (MD) methods can be used to simulate the ligand-binding process, obtaining information regarding its thermodynamics and kinetics (Limongelli V, 2020). The extensive sampling of the binding/unbinding events, through several simulation replicas or with a single simulation that describes several re-crossing events, creates the need for tools able to analyze a huge amount of data and to provide a clear picture of the sampled pathways. Methods: We propose that binding events can be analysed using Self-Organizing Maps (SOMs), a type of artificial neural network useful for effective identification of patterns in the data (Kohonen T 2013). SOMs have already been used successfully for the analysis of MD simulations (Motta S et al. 2021). We designed, implemented and tested a SOM-based tool to build and analyze models of ligand binding pathways sampled along a simulation or in different replicas. The tool is written in R and can be used to perform a geometric clustering of the trajectories under study to obtain an overview of the conformations sampled during the simulation; to trace the sampled pathways to recover the temporal evolution of the system on the SOM; to build a network model that represents the pathways in a clear way using a transition matrix built from the SOM neurons. Results: We tested the tool on different systems to demonstrate its general applicability and good performance. For this reason, the study-cases differ in the choice of MD methods used to investigate the ligand binding, including steered and metadynamic simulations of the THS-020 ligand binding to the HIF-2α protein (Callea L et al. 2021) and the infrequent metadynamic simulations of the GC7 ligand unbinding from human deoxyhypusine synthase (D’Agostino M et al. 2020). For both systems the tool was able not only to identify the preferred unbinding pathway but also to detect the position of the energetic barrier for the transition.

Motta, S., Callea, L., Bonati, L., Pandini, A. (2021). A Neural Network Approach for the Identification of Pathways in Molecular Dynamics Simulations of Ligand Binding. Intervento presentato a: Bioinformatics and Computational Biology Conference (BBCC), Online.

A Neural Network Approach for the Identification of Pathways in Molecular Dynamics Simulations of Ligand Binding

Motta, S;Callea, L
Membro del Collaboration Group
;
Bonati, L
Membro del Collaboration Group
;
Pandini, A
2021

Abstract

Motivation: Understanding the process of ligand-protein recognition is important to unveil biological mechanisms and to inform drug design and discovery. Enhanced sampling molecular dynamics (MD) methods can be used to simulate the ligand-binding process, obtaining information regarding its thermodynamics and kinetics (Limongelli V, 2020). The extensive sampling of the binding/unbinding events, through several simulation replicas or with a single simulation that describes several re-crossing events, creates the need for tools able to analyze a huge amount of data and to provide a clear picture of the sampled pathways. Methods: We propose that binding events can be analysed using Self-Organizing Maps (SOMs), a type of artificial neural network useful for effective identification of patterns in the data (Kohonen T 2013). SOMs have already been used successfully for the analysis of MD simulations (Motta S et al. 2021). We designed, implemented and tested a SOM-based tool to build and analyze models of ligand binding pathways sampled along a simulation or in different replicas. The tool is written in R and can be used to perform a geometric clustering of the trajectories under study to obtain an overview of the conformations sampled during the simulation; to trace the sampled pathways to recover the temporal evolution of the system on the SOM; to build a network model that represents the pathways in a clear way using a transition matrix built from the SOM neurons. Results: We tested the tool on different systems to demonstrate its general applicability and good performance. For this reason, the study-cases differ in the choice of MD methods used to investigate the ligand binding, including steered and metadynamic simulations of the THS-020 ligand binding to the HIF-2α protein (Callea L et al. 2021) and the infrequent metadynamic simulations of the GC7 ligand unbinding from human deoxyhypusine synthase (D’Agostino M et al. 2020). For both systems the tool was able not only to identify the preferred unbinding pathway but also to detect the position of the energetic barrier for the transition.
Si
abstract + slide
PathDetect-SOM; HIF-2α; molecular dynamics; ligand binding; ligand unbinding; SOM
English
Bioinformatics and Computational Biology Conference (BBCC)
Motta, S., Callea, L., Bonati, L., Pandini, A. (2021). A Neural Network Approach for the Identification of Pathways in Molecular Dynamics Simulations of Ligand Binding. Intervento presentato a: Bioinformatics and Computational Biology Conference (BBCC), Online.
Motta, S; Callea, L; Bonati, L; Pandini, A
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/348764
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