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The SARS-CoV-2 nucleocapsid (N) protein is an abundant RNA-binding protein critical for viral genome packaging, yet the molecular details that underlie this process are poorly understood. Here we combine single-molecule spectroscopy with all-atom simulations to uncover the molecular details that contribute to N protein function. N protein contains three dynamic disordered regions that house putative transiently-helical binding motifs. The two folded domains interact minimally such that full-length N protein is a flexible and multivalent RNA-binding protein. N protein also undergoes liquid-liquid phase separation when mixed with RNA, and polymer theory predicts that the same multivalent interactions that drive phase separation also engender RNA compaction. We offer a simple symmetry-breaking model that provides a plausible route through which single-genome condensation preferentially occurs over phase separation, suggesting that phase separation offers a convenient macroscopic readout of a key nanoscopic interaction. SARS-CoV-2 nucleocapsid (N) protein is responsible for viral genome packaging. Here the authors employ single-molecule spectroscopy with all-atom simulations to provide the molecular details of N protein and show that it undergoes phase separation with RNA.

SARS-CoV-2 has intricate mechanisms for initiating infection, immune evasion/suppression and replication that depend on the structure and dynamics of its constituent proteins. Many protein structures have been solved, but far less is known about their relevant conformational changes. To address this challenge, over a million citizen scientists banded together through the Folding@home distributed computing project to create the first exascale computer and simulate 0.1 seconds of the viral proteome. Our adaptive sampling simulations predict dramatic opening of the apo spike complex, far beyond that seen experimentally, explaining and predicting the existence of ‘cryptic’ epitopes. Different spike variants modulate the probabilities of open versus closed structures, balancing receptor binding and immune evasion. We also discover dramatic conformational changes across the proteome, which reveal over 50 ‘cryptic’ pockets that expand targeting options for the design of antivirals. All data and models are freely available online, providing a quantitative structural atlas.Simulations of the SARS-CoV-2 proteome that include over 0.1 s of aggregate data are reported. Spike opening was observed, revealing cryptic epitopes that differ between variants, explaining differential interactions with antibodies and receptors that determine pathogenicity. The cryptic pockets described provide new targets for antivirals and a wealth of mechanistic insight.

Protein-protein and protein-nucleic acid interactions are often considered difficult drug targets because the surfaces involved lack obvious druggable pockets. Cryptic pockets could present opportunities for targeting these interactions, but identifying and exploiting these pockets remains challenging. Here, we apply a general pipeline for identifying cryptic pockets to the interferon inhibitory domain (IID) of Ebola virus viral protein 35 (VP35). VP35 plays multiple essential roles in Ebola’s replication cycle but lacks pockets that present obvious utility for drug design. Using adaptive sampling simulations and machine learning algorithms, we predict VP35 harbors a cryptic pocket that is allosterically coupled to a key dsRNA-binding interface. Thiol labeling experiments corroborate the predicted pocket and mutating the predicted allosteric network supports our model of allostery. Finally, covalent modifications that mimic drug binding allosterically disrupt dsRNA binding that is essential for immune evasion. Based on these results, we expect this pipeline will be applicable to other proteins. Many viral proteins are thought to be unlikely candidates for drug discovery as they lack obvious drug binding sites. Here, the authors use computational approaches followed by experimental validation to identify a cryptic pocket within the Ebola virus protein VP35.

Activation of heterotrimeric G proteins is a key step in many signaling cascades. However, a complete mechanism for this process, which requires allosteric communication between binding sites that are ~30 Å apart, remains elusive. We construct an atomically detailed model of G protein activation by combining three powerful computational methods: metadynamics, Markov state models (MSMs), and CARDS analysis of correlated motions. We uncover a mechanism that is consistent with a wide variety of structural and biochemical data. Surprisingly, the rate-limiting step for GDP release correlates with tilting rather than translation of the GPCR-binding helix 5. β-Strands 1 – 3 and helix 1 emerge as hubs in the allosteric network that links conformational changes in the GPCR-binding site to disordering of the distal nucleotide-binding site and consequent GDP release. Our approach and insights provide foundations for understanding disease-implicated G protein mutants, illuminating slow events in allosteric networks, and examining unbinding processes with slow off-rates. Cells communicate with each other by exchanging chemical signals, which allow them to coordinate their activities and relay important information about their environment. Often, cells secrete specific signals into their surroundings, which are then picked up by a receiving cell that has the right receptors to recognize the message. Once the signal attaches to the receptor, its shape or activity changes, which in turn triggers cascades inside the cell to convey the signal, much like a circuit would. A group of proteins called heterotrimeric G-proteins play an important role in these pathways. They act as molecular switches inside the cells to help transmit signals from the outside of the cell to the inside. The proteins are made up of three parts, one of which is G-alpha. When G-alpha receives a signal from its receptor, it becomes activated. To turn on, G-alpha needs to release a molecule called GDP – which is bound to G-alpha when turned off – and instead bind to another molecule called GTP. However, it remains unclear how exactly GDP is released when it receives a signal from its receptor. Faulty G-alphas have been linked to many diseases, including cancer and heart conditions. However, current treatments do not currently target this part of G-protein signaling. To develop new drugs in the future, we first need a better understanding about the critical steps driving G-alpha activation, such as the release of GDP. Now, Sun, Singh et al. used computer simulations and mathematical models to investigate how G-alpha is activated, and to identify the structural changes underlying the release of GDP. The simulations allow to observe how the atoms within G-alpha behave and were obtained from citizen-scientist volunteers, who ran simulations on their personal computers using the Folding@home app. Together, they generated an enormous amount of data that would normally take over 150 years to collect with one computer. Subsequent analyses identified the critical atomic motions driving the release of GDP and a network of amino acids located within G-alpha. These amino acids allow G-alpha to act like a switch and connect the part that receives the signal from the receptor to the GDP-binding site. In the future, this model could serve as a platform for developing drugs that target G-alpha and shed more light into how signals are transmitted within our cells.

Kinase inhibitors are successful therapeutics in the treatment of cancers and autoimmune diseases and are useful tools in biomedical research. However, the high sequence and structural conservation of the catalytic kinase domain complicate the development of selective kinase inhibitors. Inhibition of off-target kinases makes it difficult to study the mechanism of inhibitors in biological systems. Current efforts focus on the development of inhibitors with improved selectivity. Here, we present an alternative solution to this problem by combining inhibitors with divergent off-target effects. We develop a multicompound–multitarget scoring (MMS) method that combines inhibitors to maximize target inhibition and to minimize off-target inhibition. Additionally, this framework enables optimization of inhibitor combinations for multiple on-targets. Using MMS with published kinase inhibitor datasets we determine potent inhibitor combinations for target kinases with better selectivity than the most selective single inhibitor and validate the predicted effect and selectivity of inhibitor combinations using in vitro and in cellulo techniques. MMS greatly enhances selectivity in rational multitargeting applications. The MMS framework is generalizable to other non-kinase biological targets where compound selectivity is a challenge and diverse compound libraries are available.

Chromatin-binding proteins are critical regulators of cell state in haematopoiesis 1 , 2 . Acute leukaemias driven by rearrangement of the mixed lineage leukaemia 1 gene ( KMT2A r) or mutation of the nucleophosmin gene ( NPM1 ) require the chromatin adapter protein menin, encoded by the MEN1 gene, to sustain aberrant leukaemogenic gene expression programs 3 – 5 . In a phase 1 first-in-human clinical trial, the menin inhibitor revumenib, which is designed to disrupt the menin–MLL1 interaction, induced clinical responses in patients with leukaemia with KMT2A r or mutated NPM1 (ref. 6 ). Here we identified somatic mutations in MEN1 at the revumenib–menin interface in patients with acquired resistance to menin inhibition. Consistent with the genetic data in patients, inhibitor–menin interface mutations represent a conserved mechanism of therapeutic resistance in xenograft models and in an unbiased base-editor screen. These mutants attenuate drug–target binding by generating structural perturbations that impact small-molecule binding but not the interaction with the natural ligand MLL1, and prevent inhibitor-induced eviction of menin and MLL1 from chromatin. To our knowledge, this study is the first to demonstrate that a chromatin-targeting therapeutic drug exerts sufficient selection pressure in patients to drive the evolution of escape mutants that lead to sustained chromatin occupancy, suggesting a common mechanism of therapeutic resistance. Somatic mutations in MEN1 are identified in patients with leukaemia treated with a novel chromatin-targeting therapy, and the mechanism by which these mutations mediate therapeutic resistance is characterized.

Understanding the functional role of protein-excited states has important implications in protein design and drug discovery. However, because these states are difficult to find and study, it is still unclear if excited states simply result from thermal fluctuations and generally detract from function or if these states can actually enhance protein function. To investigate this question, we consider excited states in β-lactamases and particularly a subset of states containing a cryptic pocket which forms under the Ω-loop. Given the known importance of the Ω-loop and the presence of this pocket in at least two homologs, we hypothesized that these excited states enhance enzyme activity. Using thiol-labeling assays to probe Ω-loop pocket dynamics and kinetic assays to probe activity, we find that while this pocket is not completely conserved across β-lactamase homologs, those with the Ω-loop pocket have a higher activity against the substrate benzylpenicillin. We also find that this is true for TEM β-lactamase variants with greater open Ω-loop pocket populations. We further investigate the open population using a combination of NMR chemical exchange saturation transfer experiments and molecular dynamics simulations. To test our understanding of the Ω-loop pocket’s functional role, we designed mutations to enhance/suppress pocket opening and observed that benzylpenicillin activity is proportional to the probability of pocket opening in our designed variants. The work described here suggests that excited states containing cryptic pockets can be advantageous for function and may be favored by natural selection, increasing the potential utility of such cryptic pockets as drug targets.

Protein conformational landscapes contain much of the functionally relevant information that is useful for understanding biological processes at the chemical scale. Understanding and mapping out these conformational landscapes can provide valuable insight into protein behaviors and biological phenomena, and has relevance to the process of therapeutic design.While structural biology methods have been transformative in studying protein dynamics, they are limited by technical imitations and have inherent resolution limits. Molecular dynamics (MD) simulations are a powerful tool for exploring conformational landscapes, and provide atomic-scale information that is useful in understanding protein behaviors. With recent advances in generating datasets of large timescale simulations (using Folding @home) and powerful methods to interpret conformational landscapes such as Markov State Models (MSMs), it is now possible to study complex biological phenomena and long-timescale processes. However, inferring communication between residues across long distances, referred to as allosteric communication, remains a challenge.Allostery is a ubiquitous biological phenomena by which two distant regions of a protein are coupled to one another over large distances. Allosteric coupling is the mechanism through which events in one region (such as ligand binding) alter the conformation or dynamics of another region (ie. large conformational domain motions). For example, allostery plays a critical role in cellular signaling, such as in the transfer of a signal from outside the cell to cytosolic proteins for generating a cellular response.While many methods have made tremendous progress in inferring and measuring allosteric communication using structures or molecular simulations, they rely on a structural view of allostery and do not account for the role of conformational entropy. Furthermore, it remains a challenge to interpret allosteric coupling in large, complex biomolecules relevant to physiology and disease.In this thesis, I present a method to measure the Correlation of All Rotameric and Dynamical States (CARDS) which is used to construct and interpret allosteric networks in biological systems. CARDS allows us to infer allostery both via concerted changes in protein structure and in correlated changes in conformational entropy (dynamic allostery).CARDS does so by parsing trajectories into dynamical states which reflect whether a residue is locally ordered (ie. stable in a single rotameric basin) or disordered (ie. rapidly hopping between rotamers).Here I explain the CARDS methodology (chapter 2) and demonstrate applications to a variety of disease-relevant systems. In particular, I apply CARDS and other sophisticated computational methods to understand the process of G protein activation (chapter 3), a protein whose mutations are linked to cancers such as uveal melanoma. I further demonstrate the utility of CARDS in the study a potentially druggable pocket in the ebolavirus protein VP35 (chapter4). The analyses and models constructed in this work are supported by experimental testing. Lastly, I demonstrate how integrating MD with experiments, sometimes with the help of citizen-scientists around the world, can provide unique insight into biological systems and identify potentially useful targets. In particular, I highlight our recent effort converting Folding @home into an exascale computer platform to hunt for potentially druggable pockets in the proteome of SARS-CoV-2 (chapter 7) (the cause of the COVID19 pandemic).

Relative alchemical binding free energy calculations are routinely used in drug discovery projects to optimize the affinity of small molecules for their drug targets. Alchemical methods can also be used to estimate the impact of amino acid mutations on protein:protein binding affinities, but these calculations can involve sampling challenges due to the complex networks of protein and water interactions frequently present in protein:protein interfaces. We investigate these challenges by extending a GPU-accelerated open-source relative free energy calculation package (Perses) to predict the impact of amino acid mutations on protein:protein binding. Using the well-characterized model system barnase:barstar, we describe analyses for identifying and characterizing sampling problems in protein:protein relative free energy calculations. We find that mutations with sampling problems often involve charge-changes, and inadequate sampling can be attributed to slow degrees of freedom that are mutation-specific. We also explore the accuracy and efficiency of current state-of-the-art approaches-alchemical replica exchange and alchemical replica exchange with solute tempering-for overcoming relevant sampling problems. By employing sufficiently long simulations, we achieve accurate predictions (RMSE 1.61, 95% CI: [1.12, 2.11] kcal/mol), with 86% of estimates within 1 kcal/mol of the experimentally-determined relative binding free energies and 100% of predictions correctly classifying the sign of the changes in binding free energies. Ultimately, we provide a model workflow for applying protein mutation free energy calculations to protein:protein complexes, and importantly, catalog the sampling challenges associated with these types of alchemical transformations. Our free open-source package (Perses) is based on OpenMM and available at https://github.com/choderalab/perses .

Structure-based drug discovery technologies generally require the prediction of putative bound poses of protein:small molecule complexes to prioritize them for synthesis. The predicted structures are used for a variety of downstream tasks such as pose-scoring functions or as a starting point for binding free energy estimation. The accuracy of downstream models depends on how well predicted poses match experimentally-validated poses. Although the ideal input to these downstream tasks would be experimental structures, the time and cost required to collect new experimental structures for synthesized compounds makes obtaining this structure for every input intractable. Thus, leveraging available structural data is required to efficiently extrapolate new designs. Using data from the open science COVID Moonshot project-where nearly every compound synthesized was crystallographically screened-we assess several popular strategies for generating docked poses in a structure-enabled discovery program using both retrospective and prospective analyses. We explore the tradeoff between the cost of obtaining crystal structures and the utility for accurately predicting poses of newly designed molecules. We find that a simple strategy using molecular similarity to identify relevant structures for template-guided docking is successful in predicting poses for the SARS-CoV-2 main viral protease. Further efficiency analysis suggests template-based docking of a scaffold series is a robust strategy even when the quantity of available structural data is limited. The resulting open source pipeline and curated datasets should prove useful for automated modeling of bound poses for downstream scoring, machine learning, and free energy calculation tasks for structure-based drug discovery programs.