From Sequence to Function: exploring dimensionality reduction approaches in protein dynamics and evolution

The extraordinary successes achieved in the last years by bioinformatics, structural biology, and functional studies of proteins have produced an unprecedented amount of data that holds promise to shed light on the workings of these fundamental cell’s molecular machines. In particular theoretical and computational approaches produced powerful methods to explore, piecewise, the renowned paradigm sequence-structure-function, rationalizing the microscopic determinants of protein folding and providing interpretations and testable hypothesis for experiments concerning protein dynamics and function. But is it possible to recapitulate the entire flow of information contained in the paradigm relying only on homologous sequences of the protein of interest? To address this question, I present here a new method based on a network analysis of co-evolutionary coupling aimed at identify groups of residues, named Evolutionary Domains (EDs), which evolved concertedly in a protein family. When applied to a curated sequence database of hundreds of protein families, these clusters result spatially separated but individually compact and show a strong correlation with the dynamical domains of the protein. Finally I will discuss possible future application of the method.