Characterizing the Effects of Protein and RNA Variability in Molecular Function and Interactions

University Of Texas Dallas, Richardson TX

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Abstract

Project Summary The scientific goals of the funded parent project of this proposal (R35 MIRA for ESI) include the development of global probabilistic and computational models of biomolecules that characterize and quantify the landscape of protein variability and their interactions. We aim to elucidate the landscape of functional mutations, which is hidden within the much larger non- functional space. We are using this functional landscape to engineer hybrid transcriptional regulators, bio-sensors, the effects of mutations in disease-related proteins as well as to predict specificity networks in two-component systems. Another important goal es to devise models to characterize the sequence dependance on protein-protein and protein-nucleic acid interactions. These models will allow us to encode and predict recognition from inferred landscapes and to integrate our results with experimental technologies. Devising the spectrum of functional biomolecular variability sculpted by evolutionary processes will be used to estimate the effects of mutations in disease, antibiotic resistance, biomolecular sensor design and the impact of sequence composition on interaction networks. As the research goals of the parent grant benefits from algorithmic development, it also aims to make such developments available to the scientific community. An undergraduate summer research experience will focus on the development of a software pipeline to characterize protein-nucleic acid recognition and make it publicly available online to facilitate analysis, prediction and discovery of nucleic acid sequences that interact with key proteins, including some related to disease.