Development of a pipeline for parallel elucidation of protein structures

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Advances in biophysical technologies have accelerated our ability to probe the mechanisms of even the most complex cellular systems, and such studies have enabled researchers to design modifications to known protein structures and design completely new proteins. This “protein design” technology has given rise to an ability to manipulate protein structures as a means of improving on or introducing new medical diagnostics and therapeutics. The bases of these studies rely on computational modeling of protein candidates, although the accuracy of protein structure prediction, protein de novo design, and single-mutation effects prediction remain below the threshold for many use cases, such as structure-guided drug design and rational enzyme engineering. Thus, success of a protein engineering effort relies on high-resolution structure determination, which involves laborious screening and optimization in order to obtain stable proteins or active enzyme variants. However, our ability to observe protein structure using common structure determination strategies (X-ray crystallography, NMR, and cryo-electron microscopy (cryo-EM)) lags far behind our ability to design and produce new sequences, creating a knowledge gap that prevents biochemists from accessing the range of protein functions seen in nature. While current technologies enable rapid synthesis of hundreds of proteins with varied sequences, there do not exist technologies for rapid structural characterization of these generated proteins. The ability to obtain high- resolution structural information for hundreds of sequences in parallel would provide invaluable insights in protein engineering methods. Importantly, rapid structure determination would enable structural characterization of genetic variation in the human genome underlying disease by enabling the structural and mechanistic interpretation of rare and de novo disease-related variants. Cryo-EM enables numerous high-resolution structures to be determined from a small amount of sample without requiring homogeneity, an aspect of this method that we plan to exploit for parallel elucidation of protein structures. We will establish the feasibility of this technique for rapidly investigate the structures of engineered protein libraries, where the molecular weight range is near or below the lower detection limit of cryo-EM. We will also probe the limits of our ability to identify the location and structural impact of tested mutations at limited structural locations, such as active sites. We will explore the feasibility of our parallel structure determination approach in two aims: Aim 1 will identify the limit of current single-particle analysis methods to discriminate between structurally similar protein complexes. Aim 2 will implement machine learning algorithms to push the current limits of classification using a combination of synthetic and real data. These exploratory studies will pave the way to rapid structure determination of multiple protein complexes from a single cryo-EM experiment, providing the ability to rapidly obtain high-resolution structures for many engineered proteins, thereby enabling unprecedented design and testing feedback cycles to help treat human disease.