Fisica statistica

Statistical-physics inspired modeling of protein sequences: Inferring structure, function, and mutational landscapes

by Martin Weigt (Computational and Quantitative Biology, Université Pierre et Marie Curie, Paris, FRANCE)

Europe/Rome
Aula 4 (Dip. di Fisica - Edificio E. Fermi)

Aula 4

Dip. di Fisica - Edificio E. Fermi

Description
Over the last years, biological research has been revolutionized by experimental high-throughput techniques. Unprecedented amounts of data are accumulating, causing an urgent need to develop data-driven modeling approaches to unveil information hidden in raw data, thereby helping to increase our understanding of complex biological systems. Inference approaches based on statistical physics have played an important role across diverse systems ranging from proteins over neural networks to the collective behaviour of animal groups. To give a specific example, proteins show a remarkable degree of structural and functional conservation in the course of evolution, despite a large variability in amino-acid sequences. Thanks to modern sequencing techniques, this amino-acid variability is easily observable, contrary to time- and labour-intensive experiments determining, e.g., the three-dimensional fold of a protein or its biological functionality. I will present recent developments around the so-called Direct-Coupling Analysis, a statistical-mechanics inspired inference approach, which links sequence variability to protein structure and function.