Unsupervised deep learning for molecular dynamics simulations: a novel analysis of protein–ligand interactions in SARS-CoV-2 M<sup>pro</sup>
Jessica Mustali, Ikki Yasuda, Yoshinori Hirano, Kenji Yasuoka, Alfonso Gautieri, Noriyoshi Arai
Abstract
. Dimensionality reduction yielded an embedding map that correlated ligand-induced dynamics and binding affinity. Notably, the high-affinity compounds showed pronounced effects on the protein's conformations. We also identified the key residues that contributed to these differences. Our findings emphasize the potential of combining unsupervised deep learning with MD simulations to extract valuable information and accelerate drug discovery.
Topics & Concepts
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)Ligand (biochemistry)Coronavirus disease 2019 (COVID-19)Molecular dynamicsEmbeddingChemistryDimension (graph theory)2019-20 coronavirus outbreakSars virusDynamics (music)BiophysicsPhysicsComputational chemistryComputer scienceArtificial intelligenceBiologyBiochemistryMathematicsVirologyReceptorCombinatoricsMedicineDiseasePathologyInfectious disease (medical specialty)OutbreakAcousticsComputational Drug Discovery MethodsProtein Structure and DynamicsEnzyme Structure and Function