Projektbeschreibung
Neuartige Berechnungsmethoden zur Untersuchung molekularer Bindungen
Die Vorhersagekraft von Berechnungen bei Untersuchungen molekularer Bindungsinteraktionen ist immer noch gering, was hauptsächlich darauf zurückzuführen ist, dass In-silico-Modelle schlecht der realen Welt zuzuordnen sind. Das ist bei der Wirkstoffforschung dann der Fall, wenn die Korrelation zwischen der Wirksamkeit von Wirkstoffen in vivo und der Verweildauer des Liganden mit den aktuell üblichen Berechnungsverfahren nicht vorhergesagt werden kann. Ziel des EU-finanzierten Projekts CoMMBi ist die Neugestaltung des Stands der Technik bei der Berechnung molekularer Bindungen. Das Projekt wird nun modernste Berechnungsverfahren auf der Grundlage von Berechnungen der freien Energie, maschinellem Lernen und Mehrskalen-Molekulardynamiksimulationen entwickeln und anwenden. Die Vorhersagekraft der entwickelten Ansätze wird anhand der Aufklärung der Funktionsmechanismen von G-Protein-gekoppelten Rezeptoren erprobt. Auf diese pharmakologisch wichtigen Membranproteine zielen viele der vermarkteten Arzneimittel ab.
Ziel
Molecular binding is a major research topic that has undoubtedly benefited of recent technological innovation, especially in the area of the so-called computational sciences. However, the predictive power of computations remains low mainly due to the poor correlation of the in-silico models with the real world. A clear example is drug discovery where the drug in vivo efficacy is seen correlated to the ligand residence time, which is hardly predictable by current computational methods. The present proposal tackles the challenge aiming at reshaping the border of the state-of-the-art simulations in molecular binding. I outline a research program that realises a vision where drug design is entrusted to ligand binding affinity and kinetics prediction, and molecular binding interactions are simulated in a realistic plasma membrane model. To achieve the ambitious goals of the research, my team will develop and apply cutting-edge computational techniques based on free-energy calculations, machine learning and multiscale molecular dynamics simulations. Evidence of the innovative nature of the developed approaches will be given by elucidating fundamental aspects of the functional mechanism of the G-protein coupled receptors (GPCRs), a pharmacologically prominent membrane protein family targeted by ~ 40% of marketed drugs. We will achieve a thorough characterization of the binding thermodynamics and kinetics of signal molecules (antagonists and agonists) that will be used by an original machine learning model to identify novel receptor antagonists with prescribed binding affinity and residence time. We will then investigate the receptor conformational transition from the inactive to the active state and develop an ad hoc multiscale approach to characterize the formation of GPCRs dimers, oligomers and clusters in cell membrane and their interaction with the G-protein that activates the signal transduction. Experiments will be performed to validate all the in-silico results.
Wissenschaftliches Gebiet
- natural sciencesphysical sciencesthermodynamics
- natural sciencesbiological sciencesbiochemistrybiomoleculesproteins
- natural sciencesphysical sciencesopticsmicroscopy
- natural sciencescomputer and information sciencescomputational science
- natural sciencescomputer and information sciencesartificial intelligencemachine learning
Programm/Programme
Thema/Themen
Finanzierungsplan
ERC-COG - Consolidator GrantGastgebende Einrichtung
6900 Lugano
Schweiz