Structural bioinformatics methods are fundamental for the analysis and understanding of sequence/structure/function relationships of biological macromolecules. The research topics of our group focus on methodological developments in structural bioinformatics and more specifically on methods for protein structure exploration, analysis and prediction.
The dramatic breakthroughs of the last five years in the field of machine learning and the involvement of GAFAM*, capable of mobilizing unprecedented intellectual, human and material resources, have led to major successes in structural bioinformatics, particularly in the field of protein structure prediction, the most remarkable examples being AlphaFold 2 and ESMFold.
This seminar will focus on the different methodological developments that our group has proposed for protein structure analysis and prediction at both local (conformation, flexibility) and global levels (protein domain identification, membrane protein, fold recognition). We will also discuss the integration of recent advances in deep learning in our methodological approaches and the impact that these methods have had in these developments.