Title: Coevolutionary Landscapes: from structures to design and back to evolution
Abstract: Over the last several years, the use of coevolutionary information, i.e., the knowledge of amino acid or nucleotide interactions maintained through evolution in each protein/RNA family, has provided a framework to study structural biology in a predictive manner. Amino acid couplings, obtained from statistical inference algorithms like Direct Coupling Analysis, have been fundamental to predict non-local interactions in protein three-dimensional structure, being now a core concept of state-of-the-art protein structure prediction algorithms. Other applications include accurate inference of structural complexes as well as models for specificity in molecular recognition for signaling proteins and Protein-RNA interactions. Recently, we have used these global approaches to create unifying models of molecular evolution that account for epistatic interactions. Wealsodemonstratedhow these landscapes can be beneficial to understand amino acid variability and can serve as proxies for phenotypical fitness in proteins. We show how non-functional chimeric proteins can reestablish function by modulating their domain-domain communications via mutations. We validate, experimentally, how a strategy based on optimizing evolutionary couplings can reach functional repressors with a set of rationally selected mutants. This idea can be used to guide the design of fluorescent chloride sensors by reducing the search space of in vitro evolution experiments aimed to fine tune turn-on and fluorescent properties upon chloride binding. Finally, we provide strategies to uncover mutational changes that restore proper function in molecules known to be connected to disease due to polymorphisms. We provide evidence of this restauration using all-atomic molecular dynamics simulations and analysis in the Poly(ADP-ribose) polymerase 1. Overall, our results provide theoretical and experimental support to the idea of using inferred sequence landscapes to improve our insight on molecular evolution and protein design.
Web: https://profiles.utdallas.edu/faruckm