Computer-aided molecular engineering

The laboratory specializes in the development of computer-aided algorithms, programs and databases for protein engineering and drug design, with applications in oncology, notably in immunotherapy of cancer. We also provide support in Molecular Modeling for the Molecular Tumor Board of the Lausanne University Hospital.

Computer-aided protein engineering

The laboratory is developing computer-aided approaches for protein engineering, notably to optimize the recognition between two macromolecules of interest. These methods are based on the Molecular Mechanics – Generalized Born Surface Area (MM-GBSA) estimation of binding free energies, which allows decomposing this energy at the atomic scale to calculate the role played by each residue on the association process between. We are currently optimizing the approach to include conformational penalties upon mutation in the estimation of the binding free energy. In collaboration with groups from the Department of Oncology, we are also applying these approaches to the design of new immunotherapies against cancer, by engineering protein sequences that could lead to a better recognition of tumor cells by T-cells, and to a stronger T-cell activity.

Computer-aided drug design

We are also developing programs for computer-aided drug design. For instance, we are currently developing a novel algorithm that designs in silico virtual small-molecule ligands of targeted proteins, based on their 3D structure, under the control of a docking software.
The approach adds molecular fragments to a starting compound, and systematically estimates its possible binding mode and affinity for the target, along with its drug-likeness, ADME properties and possible secondary targets. We are also developing novel ligand-based docking algorithms, which capitalize on the wealth of experimental information contained in the protein databank to accelerate and enhance the prediction of the binding mode of small molecules on proteins, notably kinases. Such methods could be useful in the context of clinical Molecular Tumor Boards, where a =rapid identification of the kinase residues in contact with a given drug could be used to estimate the impact of possible mutations on drug resistance.

Molecular Tumor Board

In addition, we are developing new approaches to contribute facilitating the choice of the most appropriate cancer treatment as a function of the particular genetic alterations found in the cancer cells of the patient. Our work consists in developing web tools that could be used in Molecular Tumor Boards to simplify and accelerate the prediction of the possible effects of newly encountered mutations. This include the development of a rapid estimation of the potential impact of a given mutation based not only on the nature of the mutation, the sequence conservation across species of this particular residue, but also on 3D structural information, when available.

• Krebs, F; Zoete, V*; Trottet, M; Pouchon, T; Bovigny, C; Michielin, O. Swiss-PO: a new tool to analyze the impact of mutations on protein three-dimensional structures for precision oncology. Npj Precision Oncology. 2021, 5(1):19.

• Krebs, FA; Gérard, C; Wicky, A; Aedo-Lopez, V; Missiaglia, E; Bisig, B;Trimech, M; Michielin, O; Homicsko, K; Zoete, V. Trametinib Induces the  Stabilization of a Dual GNAQ p.Gly48Leu- and FGFR4 p.Cys172Gly-Mutated Uveal  Melanoma. The Role of Molecular Modelling in Personalized Oncology. International Journal Of Molecular Sciences. 2020, 21(21):8021.

• Röhrig UF, Reynaud A, Majjigapu SR, Vogel P, Pojer F, Zoete V. Inhibition Mechanisms of Indoleamine 2,3-Dioxygenase 1 (IDO1). J Med Chem. 2019, 62(19):8784-8795.

• Daina A, Zoete V. Application of the SwissDrugDesign Online Resources in Virtual Screening. Int J Mol Sci. 2019 Sep 18;20(18).

• Daina A, Michielin O, Zoete V. SwissTargetPrediction: updated data and new features for efficient prediction of protein targets of small molecules. Nucleic Acids Res. 2019, 47(W1):W357-W364.

• SNSF Agora CRSK-3_190400

• SNSF Research grant 205321_192019

• SNSF Research grant 315230_197616

• SNSF Sinergia CRSII5_193749

• InnoSuisse 43016.1 IP-LS