About Professor F. L. Gervasio

Prof. Francesco Luigi Gervasio (FLG) is Full Professor and leader of the Biomolecular & Pharmaceutical Modelling Group at the University of Geneva, Professor of Chemistry, Professor of Structural and Molecular Biology and Chair of Biomolecular Modelling at University College London.

His multidisciplinary research spans computational biophysics, computational chemistry, structural biology, bioinformatics and drug discovery and focusses on the development of computational methods to solve challenges in life and chemical sciences. While he was at ETH Zurich (2002-2009), he crucially contributed to the development of widely-used methods for overcoming the timescale problem of molecular dynamics simulations and to compute free energy surfaces, including metadynamics, parallel-tempering metadynamics and the path-like collective variables method. These “enhanced-sampling” algorithms can efficiently sample complex events in biomolecules computing the associated free energy surfaces and have been implemented in open source codes such as PLUMED http://www.plumed.org/.

As the leader of the Computational Biophysics group at the Spanish National Cancer Research Centre (2009-2013) and more recently as a professor at UCL and at University of Geneva, he continued the development of computational methods, including a combined path sampling/metadynamics method (TS-PPTIS) to compute binding and folding kinetics and COMet Path a machine-learning approach based on spectral gap optimization to devise optimal collective variables for Metadynamics. An highlight was the development of the first computational approach combining evolutionary principles with a physics-based coarse-grained model to predict protein structure and dynamics (Sutto PNAS 2015).

FLG has been studying cryptic binding pockets for a number of years. Cryptic or hidden pockets are protein pockets that open up in response to the binding of specific ligands and are thus intrinsically difficult to find, also due to the many open questions about their nature. Already in 2013, in two seminal back-to-back papers in Cancer Cell (Herbert et al. Cancer Cell 2013), our group has shown how enhanced-sampling simulations can be used to reveal hidden pockets.

We addressed some of the knowledge gaps in the dynamics of cryptic pocket opening and used the new insight to develop an efficient algorithm (SWISH) to systematically detect druggable cryptic pockets in targets of biopharmaceutical interest (Oleinikovas et al. JACS 2016). SWISH is based on Hamiltonian replica exchange and enhances the conformational sampling of proteins by scaling the interaction of apolar atoms with water. In so doing, it induces the opening of hydrophobic cavities that can successively be stabilized by mixed-solvents or organic fragments. SWISH succeeded in exploring the nontrivial cryptic binding sites harboured by a diversified and interesting set of targets and was successfully used to discover a druggable cryptic binding pocket in Nsp1, an important SARS-CoV-2 target (Borsato et al. eLife 2022).