
My research centers around applying a combination of computational and experimental tools to solve problems in protein physical science. My lab focuses on two broad areas: biomolecular recognition and integrative structural biology. Biomolecular recognition is a fundamental process that underlies much of biochemistry, where biomolecules (often proteins) recognize and bind target molecules, modulating their function. Despite the importance of biomolecular recognition, we currently lack quantitative models that can accurately predict the strength of biomolecular interactions. I will present early results from our efforts to de novo design protein-protein interactions using physics-based modeling. The long-term goal of this research is enhance our knowledge of the physical chemistry of biomolecular recognition and to use this knowledge to develop new biological therapeutics, sensors, and biomaterials. Integrative structural biology is a field that combines new computational and experimental approaches to determine the structures of biomolecules and their complexes based on sparse, noisy, and ambiguous data. We have developed a computational approach called Modeling Employing Limited Data (MELD) that synergistically combines experimental data and physics-based computational modeling of biomolecules to infer their structures. We have successfully applied MELD to a variety of problems, and I will present results using sparse NMR data (both solution and solid-state), using contacts inferred from sequence coevolution, and using generic heuristics that apply to globular proteins.

My research centers around applying a combination of computational and experimental tools to solve problems in protein physical science. My lab focuses on two broad areas: biomolecular recognition and integrative structural biology.

Biomolecular recognition is a fundamental process that underlies much of biochemistry, where biomolecules (often proteins) recognize and bind target molecules, modulating their function. Despite the importance of biomolecular recognition, we currently lack quantitative models that can accurately predict the strength of biomolecular interactions. I will present early results from our efforts to de novo design protein-protein interactions using physics-based modeling. The long-term goal of this research is enhance our knowledge of the physical chemistry of biomolecular recognition and to use this knowledge to develop new biological therapeutics, sensors, and biomaterials.

Integrative structural biology is a field that combines new computational and experimental approaches to determine the structures of biomolecules and their complexes based on sparse, noisy, and ambiguous data. We have developed a computational approach called Modeling Employing Limited Data (MELD) that synergistically combines experimental data and physics-based computational modeling of biomolecules to infer their structures. We have successfully applied MELD to a variety of problems, and I will present results using sparse NMR data (both solution and solid-state), using contacts inferred from sequence coevolution, and using generic heuristics that apply to globular proteins.