Determining the Effects of Sequence Variation on Membrane Fusion Proteins through High-Resolution Characterization of Protein Energy Landscapes

Every protein is defined by a unique sequence of amino acids that, together with the environment, dictates its function. Today, thanks to advances in machine learning, genomic data bases, and the large number of known structures, we have the ability to accurately predict a structural model for the folded state of a protein given only this amino-acid sequence. But the sequence encodes more than just the structure; the protein is an ensemble, constantly sampling other conformations. Small changes in sequence, through mutation or evolution, rarely change the folded structure, yet they can have dramatic effects on behavior and function. The conformational diversity and the dynamics associated with this diversity play an important role; this ensemble view of proteins explains how small genetic changes can alter phenotype and drive evolution. Therefore, in spite of the recent advances in predicting structure, we still cannot predict the behavior of a protein from sequence. In order to harvest all of the information encoded in the genome, it is imperative that we understand this relationship between protein sequence and the entire ensemble (referred to as the energy landscape). A critical example of this is the variants of the SARS-CoV-2 spike protein that are continuing to arise in the population - the lack of a molecular understanding of the effects of these changes has impeded our understanding of the effects on the viral lifecycle and the evolutionary pressures on the virus. The proposal has broader impacts beyond the proposed research outcomes. The PI will continue to grow her career development program, establishing a dedicated post-doctoral affairs program for the >300 postdoctoral fellows at Berkeley (Department of Molecular and Cell Biology). The PI and her lab will provide access to HDX-MS instrumentation and expertise to the local scientific community and the methodological developments will enhance the resolution of HDX-MS studies beyond the SARS-CoV-2 spike protein. The objective of this award is to develop and use new hydrogen exchange approaches (HDX-MS) that will allow facile and rapid characterization of a protein's energy landscape and determine the effects of sequence variation. Amide Hydrogen-Deuterium Exchange is uniquely suited to characterizing a protein's energy landscape; it is 'blind' to the folded-state and sensitive to high-energy conformations and fluctuations. By deconvoluting the information in the time-dependent changes of the isotope envelope of a protein, the Principal Investigator (PI) will define a rate profile (fingerprint) - a set of individual amide exchange rates accounting for the entire sequence of a protein. This fingerprint is a unique reflection of the energy landscape for a protein under the given conditions. Then, using a combination of intact protein and peptide-level HDX-MS, the investigators will carry out experiments to characterize and quantify the distribution of conformations accessible from the native conformation. The PI and her team will use these new developments together with conventional HDX-MS to look at the effect of sequence variants in the SARS-CoV-2 spike protein and the isolated receptor binding domain to directly compare the changes in its ensemble of conformations to observed changes in fitness and function. The results will provide a deeper understanding of the relationship between SARS-CoV-2 variants and their effects on the virus and its lifecycle. The methodological developments will enhance the resolution of HDX-MS studies and the PI and her lab will provide access to HDX-MS instrumentation and expertise to the local scientific community. 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.