Drug-target binding represents the first event at the basis of the therapeutic action of drugs. This complex phenomenon needs to be properly described at an atomistic level to identify the major determinants of drug potency and in vivo drug efficacy. Molecular dynamics (MD) is emerging as a powerful tool for investigating protein-ligand binding, and is getting increasing consensus from the drug discovery community. While extensive MD simulations in the microsecond to the millisecond timescale are nowadays able to simulate protein-ligand binding “spontaneously”, enhanced sampling methods, including metadynamics, steered-MD, umbrella sampling, etc., can improve the sampling of that part of free energy landscape that can be relevant for the biological process under investigation.

In this talk, I will be presenting the use of extensive MD simulations to investigate spontaneous protein-ligand binding. Then, I will show how free energy calculations allow the identification of the minimum free energy path from the bulk of the solvent into the protein-binding pocket, as well as the determination of thermodynamic and kinetic parameters associated to drug-target recognition and binding. The presentation will finally be focused on applications of enhanced sampling methods to accelerate ligand binding and unbinding and to estimate kinetics (kon and koff) and thermodynamics, in simulation timescale more compatible with the requirements of speed and accuracy of the pharmaceutical research. All these simulations will be discussed in the framework of drug design and discovery, highlighting the role of these approaches in real-life drug discovery endeavors.

Andrea Cavalli is Professor of Medicinal Chemistry at the University of Bologna and Director of CompuNet at the Italian Istitute of Tecnology, Genova, Italy. Prof. Cavalli received his PhD in Pharmaceutical Sciences from the University of Bologna in 1999 and did postdoctoral work at SISSA (Trieste, Italy) and ETH (Zurich, Switzerland). Prof. Cavalli’s research has combined computational chemistry with drug discovery, focusing on neurodegenerative diseases, cancer, and neglected tropical diseases. He has developed and applied algorithms and protocols to accelerate and enhance the discovery of novel lead and drug candidates. In particular, he has been a pioneer in the use of molecular dynamics simulations and related approaches to drug discovery. In an interdisciplinary effort, these approaches led to the identification and characterization of lead candidates within the framework of multitarget drug discovery and polypharmacology. He is an author of more than 190 scientific papers and inventor in several international patents. He has delivered more than 100 invited lectures and seminars at international congresses and prestigious institutions. He is member of the Editorial Board of several international journals, and in 2014 he founded a high-tech startup company (BiKi Technologies s.r.l.) focused on molecular dynamics and enhanced sampling in drug discovery. In 2003, he was awarded the Farmindustria Prize for Pharmaceutical Research.

De Vivo, M.; Masetti, M.; Bottegoni, G.; Cavalli, A. Role of Molecular Dynamics and Related Methods in Drug Discovery. J. Med. Chem. 2016, 59, 4035-4061.

Mollica, L.; Theret, I.; Antoine, M.; Perron-Sierra, F.; Charton, Y.; Fourquez, J.M.; Wierzbicki, M.; Boutin, J.A.; Ferry, G.; Decherchi, S.; Bottegoni, G.; Ducrot, P.; Cavalli, A. Molecular Dynamics Simulations and Kinetic Measurements to Estimate and Predict Protein-Ligand Residence Times. J. Med. Chem. 2016, 59, 7167-7176.

Decherchi, S.; Berteotti, A.; Bottegoni, G.; Rocchia, W.; Cavalli, A. The ligand binding mechanism to purine nucleoside phosphorylase elucidated via molecular dynamics and machine learning. Nature Commun. 2015, 6, 6155.

Cavalli, A.; Spitaleri, A.; Saladino, G.; Gervasio, F.L. Investigating Drug-Target Association and Dissociation Mechanisms Using Metadynamics-Based Algorithms. Acc. Chem. Res. 2015, 48, 277-85.

Patel, J.S.; Berteotti, A.; Ronsisvalle, S.; Rocchia, W.; Cavalli, A. Steered Molecular Dynamics Simulations for Studying Protein-Ligand Interaction in Cyclin-Dependent Kinase 5. J. Chem. Inf. Model. 2014, 54, 470-80.

Grazioso, G.; Limongelli, V.; Branduardi, D.; Novellino, E.; De Micheli, C.; Cavalli, A.; Parrinello, M. Investigating the Mechanism of Substrate Uptake and Release in the Glutamate Transporter Homologue GltPh through Metadynamics Simulations. J. Am. Chem. Soc. 2012,134, 453-463.

Colizzi, F.; Perozzo, R.; Scapozza, L.; Recanatini, M.; Cavalli, A. Single-Molecule Pulling Simulations Can Discern Active from Inactive Enzyme Inhibitors. J. Am. Chem. Soc. 2010, 132, 7361-71.​