Ligand-protein binding models have experienced an evolution during time: from the lock-key model to induced-fit and conformational selection, the role of protein flexibility has become more and more relevant. Understanding binding mechanism is of great importance in drug-discovery, because it could help to rationalize the activity of known binders and to optimize them. The application of computational techniques to drug-discovery has been reported since the 1980s, with the advent computer-aided drug design. During the years several techniques have been developed to address the protein flexibility issue.
The present work proposes a strategy to consider protein structure variability in molecular docking, through a ligand-based/structure-based integrated approach and through the development of a fully automatic cross-docking benchmark pipeline.
Moreover, a full exploration of protein flexibility during the binding process is proposed through the Supervised Molecular Dynamics. The application of a tabu-like algorithm to classical molecular dynamics accelerates the binding process from the micro-millisecond to the nanosecond timescales. In the present work, an implementation of this algorithm has been performed to study peptide-protein recognition processes
