title: Deciphering the Complexity of Ligand-Protein Recognition Pathways Using Supervised Molecular Dynamics (SuMD) Simulations.
abstract: Molecular recognition is a crucial issue when aiming to interpret the mechanism of known active substances as well as to develop novel active candidates. Unfortunately, simulating the binding process is still a challenging task because it requires classical MD experiments in a long microsecond time scale that are affordable only with a high-level computational capacity. In order to overcome this limiting factor, we have recently implemented an alternative MD approach, named supervised molecular dynamics (SuMD), and successfully applied it to G protein-coupled receptors (GPCRs). SuMD enables the investigation of ligand-receptor binding events independently from the starting position, chemical structure of the ligand, and also from its receptor binding affinity. In this article, we present an extension of the SuMD application domain including different types of proteins in comparison with GPCRs. In particular, we have deeply analyzed the ligand-protein recognition pathways of six different case studies that we grouped into two different classes: globular and membrane proteins. Moreover, we introduce the SuMD-Analyzer tool that we have specifically implemented to help the user in the analysis of the SuMD trajectories. Finally, we emphasize the limit of the SuMD applicability domain as well as its strengths in analyzing the complexity of ligand-protein recognition pathways.
authors.affiliation: Molecular Modeling Section (MMS), Department of Pharmaceutical and Pharmacological Sciences, University of Padova , via Marzolo 5, Padova, Italy.
authors.forename: Alberto
authors.initials: A
authors.lastname: Cuzzolin
authors.affiliation: Molecular Modeling Section (MMS), Department of Pharmaceutical and Pharmacological Sciences, University of Padova , via Marzolo 5, Padova, Italy.
authors.forename: Mattia
authors.initials: M
authors.lastname: Sturlese
authors.affiliation: Molecular Modeling Section (MMS), Department of Pharmaceutical and Pharmacological Sciences, University of Padova , via Marzolo 5, Padova, Italy.
authors.forename: Giuseppe
authors.initials: G
authors.lastname: Deganutti
authors.affiliation: Molecular Modeling Section (MMS), Department of Pharmaceutical and Pharmacological Sciences, University of Padova , via Marzolo 5, Padova, Italy.
authors.forename: Veronica
authors.initials: V
authors.lastname: Salmaso
authors.affiliation: Molecular Modeling Section (MMS), Department of Pharmaceutical and Pharmacological Sciences, University of Padova , via Marzolo 5, Padova, Italy.
authors.forename: Davide
authors.initials: D
authors.lastname: Sabbadin
authors.affiliation: Molecular Modeling Section (MMS), Department of Pharmaceutical and Pharmacological Sciences, University of Padova , via Marzolo 5, Padova, Italy.
authors.forename: Antonella
authors.initials: A
authors.lastname: Ciancetta
authors.affiliation: Molecular Modeling Section (MMS), Department of Pharmaceutical and Pharmacological Sciences, University of Padova , via Marzolo 5, Padova, Italy.
authors.forename: Stefano
authors.initials: S
authors.lastname: Moro
mesh_terms: D002462:Cell Membrane / Q000378:metabolism; D008024:Ligands; D056004:Molecular Dynamics Simulation; D011485:Protein Binding; D011487:Protein Conformation; D043562:Receptors, G-Protein-Coupled / Q000737:chemistry* / Q000378:metabolism*; D000069553:Supervised Machine Learning
chemical_list: D008024:Ligands; D043562:Receptors, G-Protein-Coupled