Markov-chain modelling of dynamic interaction patterns in supramolecular complexes
Creators: name: Kleber, Julian Manuel
Publisher: name: Freie Universität Berlin
Year: 2022
Subjects: subject: Drug design
subject: Lean management
subject: Entrepreneurship
subject: Deep learning
subject: Slow clollective variables
subject: Variational principle
subject: Molecular dynamics
subject: Quantum mechanics
subject: Supramolecular complex
subject: Chemistry
subject: Protein
subject: Ligand
subject: Interaction
subject: Antivirals
subject: Resistance
subject: Mutations
subject: QSAR
subject: Markov
subject: Analysis
subject: Software
subject: Equation of motion
subject: 000 Informatik, Informationswissenschaft, allgemeine Werke::000 Informatik, Wissen, Systeme::004 Datenverarbeitung; Informatik
subject: 000 Informatik, Informationswissenschaft, allgemeine Werke::000 Informatik, Wissen, Systeme::005 Computerprogrammierung, Programme, Daten
subject: 000 Informatik, Informationswissenschaft, allgemeine Werke::000 Informatik, Wissen, Systeme::006 Spezielle Computerverfahren
subject: 500 Naturwissenschaften und Mathematik::500 Naturwissenschaften::500 Naturwissenschaften und Mathematik
subject: 600 Technik, Medizin, angewandte Wissenschaften::610 Medizin und Gesundheit::615 Pharmakologie, Therapeutik
subject: 600 Technik, Medizin, angewandte Wissenschaften::610 Medizin und Gesundheit::616 Krankheiten
subject: 500 Naturwissenschaften und Mathematik::530 Physik::539 Moderne Physik
subject: 500 Naturwissenschaften und Mathematik::540 Chemie::541 Physikalische Chemie

description: For decades, the drug design community was concerned with deriving insights for their targets from a static picture using X-ray crystallography and pharmacophores. Likewise, computational methods for in silico studies of ligand binding rely heavily on docking, a static method. Still, computational methods such as molecular dynamics (MD) simulations were gaining prominence in the field of computational drug design. In consequence, as the computational power increased and the simulation technology matured, MD simulations became more and more critical for the drug design process. Thus, innately, there have to be deep conflicts between the current pool of models derived from a static mindset and insights derived from dynamic modelling. The recent introduction of structure-based ensemble- QSAR methods[1] and dynamic pharmacophores, termed Dynophores[2] served as a primer to bridge the gap between the two ways of modelling supramolecular complexes common in computational drug design. Recently, Dynophores replaced classical pharmacophores in a virtual screening study on the DNA topoisomerase IIa[3]. The subsequent work focuses on the development and the application of a new method- ology deriving models from MD simulations using lean project management techniques, Dynophores and Markov modelling. The newly developed methodology abandons the static picture entirely, and investigates three prominent drug targets. The first target is the ZIKA virus protease and serves as a benchmark against domain expert knowledge for designing de novo inhibitors[4]. The second target is the human cyclin dependent kinase 2 (CDK2) complexed with three different ligands[5,6]. The system serves as a benchmark to derive new insights from experimental data, as the mechanisms of action and inhibition of CDK2 are up to now not genuinely understood. The third system is the hepatitis C virus (HCV) NS3/4A protease shall serve as a test if the method can explain resistance mechanisms in prominent pathogens against data derived from standard experimental protocols. Four different supramolecular complexes of the NS3/4A protease containing the wild type and three different mutations, each complexed with vaniprevir[7,8] were investigated. For all three test systems, new insights were generated