Decrypting the functional significance of cryptic surfaces by combining simulations and experiments

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Proteins are molecular machines that are responsible for many of the active processes we associate with life, from muscle contraction to sensing light and sound. Like cars and other familiar machines, proteins have moving parts that are essential to their function. However, the field is typically only able to capture a snapshot of what a protein typically looks like using experimental methods. The goal of this project is to combine computer simulations and experiments to gain a better understanding of proteins’ moving parts, particularly motions that expose parts of the protein thought to be buried based on existing experimental structures. The team will explore the hypothesis that these ‘cryptic’ surfaces can play important roles in function (e.g. by binding to other proteins) and provide a new means to control a protein’s function (e.g. by designing small molecules that bind cryptic surfaces and inhibit or enhance function). As specific examples, the project will focus on proteins from Ebola and coronaviruses. They will also engage the public in this research through the Folding@home distributed computing project, which enables anyone with a computer and an internet connection to become a citizen scientist by contributing their personal computing power to run simulations of proteins. This project will also involve members of blind community.In this project, the research team will explore whether excited states that expose cryptic surfaces play an important role in protein-protein and protein-nucleic acid interactions, either by providing a means to allosterically control function or by directly engaging with natural binding partners Specific aims include: 1) assessing the functional significance of a cryptic pocket that the team recently discovered in the interferon inhibitory domain (IID) of Ebola’s viral protein 35 (VP35) in filoviruses, and 2) assessing the functional significance of dramatic opening of the team predicted in the SARS-CoV-2 spike in coronaviruses. This work will be enabled by the powerful combination of approaches the research team has developed for characterizing the ensemble of structures that a protein adopts that includes physics-based simulations, machine learning, the Folding@home distributed computing platform, and biophysical experiments. For example, the team has developed adaptive sampling algorithms for uncovering cryptic pockets, deep learning methods for inferring how mutations modulate the structural preferences of distant functional sites, and a variety of experimental assays for testing computational predictions. These include thiol labeling assays to test predicted cryptic surfaces and high-throughput screens for testing the impact of mutations and compounds on protein function. This project is funded by the Molecular Biophysics Cluster in the Division of Molecular and Cellular Biosciences.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.