What do we do?
At the Molecular Modelling Group (MMG) we study mechanisms of molecular recognition in particular protein-protein or protein-small ligand interactions, using molecular modelling techniques such as homology modelling, molecular dynamics, docking and free energy simulations. Our main activity consists of the development and application of state-of-the-art methods in computer-aided protein engineering and drug design. Most efforts are concentrated on the development of new small molecule inhibitors of important targets for cancer therapy, as well as the design of optimized proteins like T cell receptor (TCR), for cancer immunotherapy. We develop and maintain several web tools for drug design, such as SwissDock, SwissBioisostere and SwissTargetPrediction.
We also act as the Protein Modeling Facility (PMF) of the University of Lausanne.
Molecular docking predicts the position of ligands in the binding sites of macromolecules, and constitutes the cornerstone of structure-based drug design. In 2015, we developed a new algorithm for docking – Attracting Cavities – which transiently replaces the rough potential energy of the protein by a smooth attracting potential that drives the ligands into protein cavities. The approach achieved a success rate of 80% in reproducing experimental binding modes. It will join the team’s SwissDock web interface for docking.
During the course of 2015, the group developed SwissSimilarity, a new web tool for rapid ligand-based virtual screening. Screenable compounds include drugs, bioactive and commercial compounds, as well as an unprecedented ultra-large library of 205 million virtual compounds that are readily synthesizable. Predictions can be carried out using five different approaches. SwissSimilarity is part of a large SIB initiative to provide online tools for drug design – such as SwissDock, SwissBioisostere and SwissTargetPrediction with which SwissSimilarity can interoperate.
Main publications 2015
- Gfeller D et al. Protein homology reveals new targets for bioactive small molecules. Bioinformatics 2015;31(16):2721-7.
- Röhrig UF et al. Challenges in the Discovery of Indoleamine 2,3-Dioxygenase 1 (IDO1) Inhibitors. J Med Chem 2015;58(24):9421-37.
- Zoete V et al. Attracting cavities for docking. Replacing the rough energy landscape of the protein by a smooth attracting landscape. J Comput Chem 2016;37(4):437-47.