ABSTRACT

In this Molecular Foundations for Biotechnology (MFB) project, Professors Corey J. Wilson, Matthew J. Realff, and Yao Xie at the Georgia Institute of Technology are leveraging both novel experimental and machine learning strategies to understand, predict, and design allosteric communication in a family of proteins called transcription factors that regulate gene expression in living systems. Protein allostery is an important protein function which enables communication between different parts of a functional protein that are widely separated. Our lack of understanding of the mechanism of allostery prevents scientist and engineers from designing this critically important function. By combining the tools of molecular biology and artificial intelligence, this project aims to decipher structure/activity patterns for naturally occurring and engineered transcription factors at the molecular level, specifically at the level of individual amino acids. Understanding the rules that govern allosteric communication would, in principle, enable investigators to design new transcription factors for a variety of high-impact applications such as manipulating the composition of bacteria in the gut. This project involves a blend of biochemistry, biophysics, engineering, and machine learning approaches that will facilitate student engagement across traditional disciplinary boundaries. In addition to diverse student involvement, the broader impacts of this project will include the development of innovative pedagogical modules in the areas of machine learning and biological engineering. This project will contribute to the development of a diverse and engaged STEM (science, technology, engineering and mathematics) workforce, building a firm foundation for a lifetime of contributions to research, education, and their integration.

Protein allostery is a vitally important protein function that has proven to be a vexing problem to understand at the molecular level. The goal of this project is to decipher the underlying molecular mechanisms by which the allosteric signal traverses the scaffold across several naturally occurring and engineer transcription factors with alternate allosteric controls from the broader LacI/GalR family of protein homologues. In general, allosteric communication involves networks of non-neighboring amino acid positions; therefore, traditional pairwise computational approaches (e.g., molecular mechanics simulations, and related computer-aided protein design strategies) are of limited use in understanding and designing allosteric networks a priori. Accordingly, this project seeks to develop novel machine learning approaches and complementary experimental strategies to accelerate scientific progress and transform the nature of studying and designing allosteric communication. This project has the potential to lead to a paradigm shift with regard to the origins and construction (design) of allosteric networks in a single fold. Moreover, the machine learning approaches developed in this project can in principle be applied to other complex network problems beyond the designated protein systems ? e.g., distillation column sequences, communication systems, and power grid systems.

This project is jointly supported by the Division of Chemistry, the Division of Chemical, Bioengineering, Environmental and Transport Systems, and the Division of Information and Intelligent Systems.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

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