Thermodynamic and Functional Analysis of Consensus and Covariance in Proteins, and Bivalent Notch Complexes

Johns Hopkins University, Baltimore MD

Investigators

Abstract

SUMMARY It is an exciting time for research in molecular biosciences. The scientific community has amassed a huge database of protein sequences and structures, and techniques to analyze their structures and stabilities have undergone steady improvement. These advances have helped fuel a revolution in computational methods, based on artificial intelligence (AI), to interpret protein sequence information and to predict new structures and sequences. Together, these advances will help treat disease and provide a deep understanding of how proteins work. Research in our lab focuses on protein structure, stability, assembly, and function. We have leveraged large sequence databases to discover ways to stabilize proteins, and to understand the sequence features that contribute to protein function. We have found that “consensus proteins”— where sequence is determined simply selecting the most frequent residue at each position—are extraordinarily stable. These proteins provide a route to design active and resilient proteins that can be used in therapeutic and industrial applications. In the next five years, we will expand our design strategy to include sequence correlations between groups of residues. These correlations have been important for the success of AI in predicting protein structures, and have been suggested to enhance protein stability, fitness, and activity. Our preliminary findings provide a different interpretation, and suggest a new and unrecognized way to stabilize proteins beyond consensus while maintaining biological activity. We will use a set of proteins developed in our lab to test and extend these findings. We will also use this set of proteins to test how well AI-based methods generate high-stability proteins, which will help to evaluate and improve current AI tools. In parallel, our lab studies the molecular fundamentals of a specific signaling system, the Notch signaling pathway, in cell differentiation and disease. We are focusing on a multiprotein transcription complex central to Notch activation, and have developed new methods to measure the overall energy of assembly and dissect the contributions of different parts of the complex. Beyond providing new methods for analyzing molecular complexes, we will learn how disordered chains can couple together distinct binding motifs to enhance binding and switch the complex between different states of assembly. Our studies will also reveal how different but related Notch molecules lead to different biological outcomes, and how disease mutations disrupt different steps in complex assembly.