Rational design of modified proteins

Proteins carry out a large variety of biological functions in living organisms. Some proteins, for example, act as particularly specific and efficient catalysts. The possibility to exploit the functional properties of proteins in industrial applications (food industry, chemical processes, pharmaceutical developments, etc.) is extremely interesting; however, one of its major limitations is that proteins generally lose their stability and activity under non-physiological conditions. The ability to design modified proteins that remain structured and active, at a higher temperature for example, is therefore an important objective of research. Moreover, the understanding of how changes in the coding sequence of proteins affect their biophysical properties is essential in fundamental research such as molecular evolution and phylogeny. This project consists in developing efficient and fast computational tools to predict changes in stability, solubility or interactions of proteins upon mutations, which are applicable on a genome-wide scale. This software can then be applied to rationally modify proteins of industrial interest and increasing their efficiency within a given application, or to study fundamental questions of interest in biophysics and molecular evolution. This project is part of a spin-off company creation and often involves collaborations with experimental laboratories in academia or the industry.

Prediction of disease-causing variants in the human genome

Next Generation Sequencing produces massive amounts of genome data that are revolutionizing biological and medical research, and paves the way towards personalized medicine. Among the exome variants that lead to amino acid mutations, most are neutral in the sense that they only modify the individual’s phenotype, but some are the cause of diseases. The identification of deleterious mutations and their characterization are of prime importance for setting up personalized therapies. This project consists in developing and applying bioinformatics tools to predict disease-causing protein variants and trying to understand why they are so, in terms of protein characteristics such as stability, solubility, flexibility and function, or in terms of DNA characteristics such as ionisation potential. Moreover, this approach can be applied at a genome scale to specific case studies such as autism or personal genomes.

Prediction of B-cell epitopes for the rational design of vaccines

Maintaining global health requires the development of generic and versatile technologies that allow fast and effective responses to the large variety of disorders, in particular cancer and emerging infectious diseases. Among these, peptide and protein vaccines play an important role. The in silico identification of immunogenic B-cell epitopes on potential antigens, which could be included in vaccines, is thus a challenging goal which requires the development of reliable B-cell epitope prediction tools. To design such predictors, we rely on experimentally characterized antibody-antigen complexes, detect informative sequence- and structure-based features, and combine them into a predictor using state-of-the-art machine learning techniques. This project can be applied to specific case studies such as chronic lymphocytic leukemia and involves collaborations with cancer immunologists.

Synthetic gene circuits and noise control

Synthetic biology is a relatively new field whose focus is on improving already existing and engineering new gene circuits. In order to implement a new gene circuit one must understand its behavior in terms of the system parameters. This requires that we understand not only the system's averaged deterministic behavior, but also how random variations, i.e. internal and external noise, affect its performance. This project consists in modelling systems of increasing complexity, using stochastic differential equations, and analyzing their noise levels defined as the variance of the number of molecules. We will investigate models of various biological systems, such as prokaryotic and eukaryotic gene regulation and protein oligomerization, and attempt to understand the general relationship between noise, complexity, and some key characteristics of the systems. 

Study of the dynamics of house dust mite allergens from family 5

House dust mites allergy represents an important public health problem. House dust mite allergens are grouped in several families. Allergens from family 5 are proteins whose biological function is still unknown, as well as how they provoke allergy. They show a three-helix bundle, and some of them are monomers, whereas others are dimers. In the dimers, one of the helices is kinked, and a hydrophobic cavity that could accommodate a ligand is observed. The relationships between these structural features and the allergenic properties of these allergens are also unknown. In this project, we are studying the influence of mutations and solvent conditions on the dynamics of these allergens, primarily using molecular dynamics methods.
Updated on November 26, 2021