Proposal Summary This MIRA for ESI project proposes to investigate and characterize functional mutational variability in biomolecules, its connections to molecular evolution and the ability to use these landscapes in scientific and biomedical applications. It is composed of two scientific objectives that delineate the vision of our laboratory. Our first goal is to develop global probabilistic and computational models that will help us answer the hypothesis that the landscape of protein variability and their interactions can be characterized and quantified. We will build our framework by creating probabilistic models based on large quantities of data obtained from sequencing and we will make detailed predictions and experimental confirmation on the effect of specific mutations predicted by our methodology. We are interested in the landscape of functional mutations, which is much harder to characterize than the disruptive mutational space. For our second goal, we will expand our hypothesis to molecular interactions that include nucleic acids, particularly protein-RNA interactions. We will integrate sequencing technology and computational approaches to infer mutational landscapes of protein- RNA recognition. We will test the hypothesis that not only native nucleic acid motifs can be selectively recognized but also variants derived from our quantitative models. We will develop a framework to encode and predict recognition from inferred landscapes and plan to integrate our results with experimental technologies on RNA binding proteins that could help confirm our hypothesis. In the past few years our lab has been able to infer global models of families of protein sequences and quantify coevolutionary signals from these models successfully. These global models have had an impact in the study of protein folding, protein dynamics and the prediction of protein complexes as well as their applications in druggable interface discovery and drug-gene interactions. Functional variants of biomolecules are hard to elucidate, as the disruptive mutational space is dominant. The PI was able to show that signals of amino acid or nucleic acid coevolution can also be used as predictive tools to explore and encode the functional mutational space of biomolecules. This idea represents a paradigm shift where quantification of evolutionary signals can be used as a discovery mechanism. The overall vision of this project aims to quantify and uncover the spectrum of functional biomolecular variability sculpted by evolutionary processes. This will help us work on developments related to biomedicine, such as inferring the effects of mutations in disease, antibiotic resistance, biomolecular sensor design and how sequence composition has an impact on interaction networks.