155 results on '"Gregory R. Bowman"'
Search Results
2. The intrinsically disordered protein TgIST from Toxoplasma gondii inhibits STAT1 signaling by blocking cofactor recruitment
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Zhou Huang, Hejun Liu, Jay Nix, Rui Xu, Catherine R. Knoverek, Gregory R. Bowman, Gaya K. Amarasinghe, and L. David Sibley
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Science - Abstract
Intrinsically disordered proteins (IDP) are pleotropic proteins with diverse functions. Here the authors show that an IDP, TgIST, from T. gondii blocks interferon-induced gene expression by binding to the STAT1 dimer interface and preventing the recruitment of co-transcriptional activators, CBP/p300, to STAT1 to inhibit expression of immunity genes.
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- 2022
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3. A cryptic pocket in Ebola VP35 allosterically controls RNA binding
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Matthew A. Cruz, Thomas E. Frederick, Upasana L. Mallimadugula, Sukrit Singh, Neha Vithani, Maxwell I. Zimmerman, Justin R. Porter, Katelyn E. Moeder, Gaya K. Amarasinghe, and Gregory R. Bowman
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Science - Abstract
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.
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- 2022
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4. Discovery of a cryptic pocket in the AI-predicted structure of PPM1D phosphatase explains the binding site and potency of its allosteric inhibitors
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Artur Meller, Saulo De Oliveira, Aram Davtyan, Tigran Abramyan, Gregory R. Bowman, and Henry van den Bedem
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allosteric inhibition ,cryptic site ,molecular dynamics simulation ,markov state models ,deep learning ,virtual high throughput screening (vHTS) ,Biology (General) ,QH301-705.5 - Abstract
Virtual screening is a widely used tool for drug discovery, but its predictive power can vary dramatically depending on how much structural data is available. In the best case, crystal structures of a ligand-bound protein can help find more potent ligands. However, virtual screens tend to be less predictive when only ligand-free crystal structures are available, and even less predictive if a homology model or other predicted structure must be used. Here, we explore the possibility that this situation can be improved by better accounting for protein dynamics, as simulations started from a single structure have a reasonable chance of sampling nearby structures that are more compatible with ligand binding. As a specific example, we consider the cancer drug target PPM1D/Wip1 phosphatase, a protein that lacks crystal structures. High-throughput screens have led to the discovery of several allosteric inhibitors of PPM1D, but their binding mode remains unknown. To enable further drug discovery efforts, we assessed the predictive power of an AlphaFold-predicted structure of PPM1D and a Markov state model (MSM) built from molecular dynamics simulations initiated from that structure. Our simulations reveal a cryptic pocket at the interface between two important structural elements, the flap and hinge regions. Using deep learning to predict the pose quality of each docked compound for the active site and cryptic pocket suggests that the inhibitors strongly prefer binding to the cryptic pocket, consistent with their allosteric effect. The predicted affinities for the dynamically uncovered cryptic pocket also recapitulate the relative potencies of the compounds (τb = 0.70) better than the predicted affinities for the static AlphaFold-predicted structure (τb = 0.42). Taken together, these results suggest that targeting the cryptic pocket is a good strategy for drugging PPM1D and, more generally, that conformations selected from simulation can improve virtual screening when limited structural data is available.
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- 2023
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5. Multiple conserved states characterize the twist landscape of the bacterial actin homolog MreB
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Benjamin D. Knapp, Michael D. Ward, Gregory R. Bowman, Handuo Shi, and Kerwyn Casey Huang
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Chiral twist ,Bacterial cytoskeleton ,Actin homologs ,Molecular dynamics ,Deep learning ,DiffNets ,Biotechnology ,TP248.13-248.65 - Abstract
Filament formation by cytoskeletal proteins is critical to their involvement in myriad cellular processes. The bacterial actin homolog MreB, which is essential for cell-shape determination in many rod-shaped bacteria, has served as a model system for studying the mechanics of cytoskeletal filaments. Previous molecular dynamics (MD) simulations revealed that the twist of MreB double protofilaments is dependent on the bound nucleotide, as well as binding to the membrane or the accessory protein RodZ, and MreB mutations that modulate twist also affect MreB spatial organization and cell shape. Here, we show that MreB double protofilaments can adopt multiple twist states during microsecond-scale MD simulations. A deep learning algorithm trained only on high- and low-twist states robustly identified all twist conformations across most perturbations of ATP-bound MreB, suggesting the existence of a conserved set of states whose occupancy is affected by each perturbation to MreB. Simulations replacing ATP with ADP indicated that twist states were generally stable after hydrolysis. These findings suggest a rich twist landscape that could provide the capacity to tune MreB activity and therefore its effects on cell shape.
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- 2022
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6. Deep learning the structural determinants of protein biochemical properties by comparing structural ensembles with DiffNets
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Michael D. Ward, Maxwell I. Zimmerman, Artur Meller, Moses Chung, S. J. Swamidass, and Gregory R. Bowman
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Science - Abstract
Comparing and contrasting structural ensembles of different protein variants helps connect specific structural features to a protein’s biochemical properties. Here, the authors propose DiffNets, a self-supervised, deep learning method that streamlines this process.
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- 2021
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7. The SARS-CoV-2 nucleocapsid protein is dynamic, disordered, and phase separates with RNA
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Jasmine Cubuk, Jhullian J. Alston, J. Jeremías Incicco, Sukrit Singh, Melissa D. Stuchell-Brereton, Michael D. Ward, Maxwell I. Zimmerman, Neha Vithani, Daniel Griffith, Jason A. Wagoner, Gregory R. Bowman, Kathleen B. Hall, Andrea Soranno, and Alex S. Holehouse
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Science - Abstract
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.
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- 2021
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8. Editorial: Experiments and Simulations: A Pas de Deux to Unravel Biological Function
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Maya Topf, Edina Rosta, Gregory R. Bowman, and Massimiliano Bonomi
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modeling ,molecular dynamic (MD) ,integrative approaches ,functional dynamics ,experimental-computational method ,molecular simulation ,Biology (General) ,QH301-705.5 - Published
- 2021
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9. Prediction of New Stabilizing Mutations Based on Mechanistic Insights from Markov State Models
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Maxwell I. Zimmerman, Kathryn M. Hart, Carrie A. Sibbald, Thomas E. Frederick, John R. Jimah, Catherine R. Knoverek, Niraj H. Tolia, and Gregory R. Bowman
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Chemistry ,QD1-999 - Published
- 2017
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10. The Cap-Snatching SFTSV Endonuclease Domain Is an Antiviral Target
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Wenjie Wang, Woo-Jin Shin, Bojie Zhang, Younho Choi, Ji-Seung Yoo, Maxwell I. Zimmerman, Thomas E. Frederick, Gregory R. Bowman, Michael L. Gross, Daisy W. Leung, Jae U. Jung, and Gaya K. Amarasinghe
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Biology (General) ,QH301-705.5 - Abstract
Summary: Severe fever with thrombocytopenia syndrome virus (SFTSV) is a tick-borne virus with 12%–30% case mortality rates and is related to the Heartland virus (HRTV) identified in the United States. Together, SFTSV and HRTV are emerging segmented, negative-sense RNA viral (sNSV) pathogens with potential global health impact. Here, we characterize the amino-terminal cap-snatching endonuclease domain of SFTSV polymerase (L) and solve a 2.4-Å X-ray crystal structure. While the overall structure is similar to those of other cap-snatching sNSV endonucleases, differences near the C terminus of the SFTSV endonuclease suggest divergence in regulation. Influenza virus endonuclease inhibitors, including the US Food and Drug Administration (FDA) approved Baloxavir (BXA), inhibit the endonuclease activity in in vitro enzymatic assays and in cell-based studies. BXA displays potent activity with a half maximal inhibitory concentration (IC50) of ∼100 nM in enzyme inhibition and an EC50 value of ∼250 nM against SFTSV and HRTV in plaque assays. Together, our data support sNSV endonucleases as an antiviral target. : Wang et al. solve the X-ray crystal structure of SFTSV L endonuclease domain and investigate the characteristics of SFTSV and HRTV endonuclease function. Resulting data support a mechanism for regulation. Baloxavir effectively inhibits the endonuclease activity of SFTSV and HRTV. Keywords: severe fever with thrombocytopenia syndrome virus, Heartland virus, endonuclease, X-ray structure, antiviral target, mass spectrometry, Baloxavir
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- 2020
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11. Modelling proteins’ hidden conformations to predict antibiotic resistance
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Kathryn M. Hart, Chris M. W. Ho, Supratik Dutta, Michael L. Gross, and Gregory R. Bowman
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Science - Abstract
Expression of TEM β-lactamase is a predominant mechanism underlying antibiotic resistance in pathogenic Gram-negative bacteria. Here, the authors use Markov state models to reveal and experimentally confirm hidden conformations that determine TEM substrate specificity.
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- 2016
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12. Protein sequence models for prediction and comparative analysis of the SARS-CoV-2 - human interactome.
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Meghana Kshirsagar 0001, Nure Tasnina, Michael D. Ward, Jeffrey N. Law, T. M. Murali 0001, Juan M. Lavista Ferres, Gregory R. Bowman, and Judith Klein-Seetharaman
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- 2021
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13. Remembering the Work of Phillip L. Geissler: A Coda to His Scientific Trajectory
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Gregory R. Bowman, Stephen J. Cox, Christoph Dellago, Kateri H. DuBay, Joel D. Eaves, Daniel A. Fletcher, Layne B. Frechette, Michael Grünwald, Katherine Klymko, JiYeon Ku, Ahmad K. Omar, Eran Rabani, David R. Reichman, Julia R. Rogers, Andreana M. Rosnik, Grant M. Rotskoff, Anna R. Schneider, Nadine Schwierz, David A. Sivak, Suriyanarayanan Vaikuntanathan, Stephen Whitelam, Asaph Widmer-Cooper, Cox, Stephen [0000-0003-2708-8711], and Apollo - University of Cambridge Repository
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Male ,Chemical Physics ,Statistical Mechanics (cond-mat.stat-mech) ,Chemistry, Physical ,Physics ,biography ,Physics - History and Philosophy of Physics ,algorithm development ,biological systems ,FOS: Physical sciences ,Chemistry ,chemical dynamics ,Theoretical and Computational Chemistry ,model development ,Physical ,History and Philosophy of Physics (physics.hist-ph) ,Humans ,ddc:530 ,statistical mechanics ,Physical and Theoretical Chemistry ,Condensed Matter - Statistical Mechanics ,aqueous environments ,Physical Chemistry (incl. Structural) ,Retrospective Studies - Abstract
Phillip L. Geissler made important contributions to the statistical mechanics of biological polymers, heterogeneous materials, and chemical dynamics in aqueous environments. He devised analytical and computational methods that revealed the underlying organization of complex systems at the frontiers of biology, chemistry, and materials science. In this retrospective we celebrate his work at these frontiers.
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- 2023
14. Folding@home: Achievements from over 20 years of citizen science herald the exascale era
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Vincent A. Voelz, Vijay S. Pande, and Gregory R. Bowman
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Biophysics - Published
- 2023
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15. Apolipoprotein E4 has extensive conformational heterogeneity in lipid-free and lipid-bound forms
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Melissa D. Stuchell-Brereton, Maxwell I. Zimmerman, Justin J. Miller, Upasana L. Mallimadugula, J. Jeremías Incicco, Debjit Roy, Louis G. Smith, Jasmine Cubuk, Berevan Baban, Gregory T. DeKoster, Carl Frieden, Gregory R. Bowman, and Andrea Soranno
- Subjects
Multidisciplinary - Abstract
The ε4-allele variant of apolipoprotein E (ApoE4) is the strongest genetic risk factor for Alzheimer’s disease, although it only differs from its neutral counterpart ApoE3 by a single amino acid substitution. While ApoE4 influences the formation of plaques and neurofibrillary tangles, the structural determinants of pathogenicity remain undetermined due to limited structural information. Previous studies have led to conflicting models of the C-terminal region positioning with respect to the N-terminal domain across isoforms largely because the data are potentially confounded by the presence of heterogeneous oligomers. Here, we apply a combination of single-molecule spectroscopy and molecular dynamics simulations to construct an atomically detailed model of monomeric ApoE4 and probe the effect of lipid association. Importantly, our approach overcomes previous limitations by allowing us to work at picomolar concentrations where only the monomer is present. Our data reveal that ApoE4 is far more disordered and extended than previously thought and retains significant conformational heterogeneity after binding lipids. Comparing the proximity of the N- and C-terminal domains across the three major isoforms (ApoE4, ApoE3, and ApoE2) suggests that all maintain heterogeneous conformations in their monomeric form, with ApoE2 adopting a slightly more compact ensemble. Overall, these data provide a foundation for understanding how ApoE4 differs from nonpathogenic and protective variants of the protein.
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- 2023
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16. Accurately modeling nanosecond protein dynamics requires at least microseconds of simulation.
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Gregory R. Bowman
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- 2016
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17. Structure–function correlates of fibrinogen binding by Acinetobacter adhesins critical in catheter-associated urinary tract infections
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Kevin O. Tamadonfar, Gisela Di Venanzio, Jerome S. Pinkner, Karen W. Dodson, Vasilios Kalas, Maxwell I. Zimmerman, Jesus Bazan Villicana, Gregory R. Bowman, Mario F. Feldman, and Scott J. Hultgren
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Multidisciplinary - Abstract
Multidrug-resistant Acinetobacter baumannii infections are an urgent clinical problem and can cause difficult-to-treat nosocomial infections. During such infections, like catheter-associated urinary tract infections (CAUTI), A. baumannii rely on adhesive, extracellular fibers, called chaperone-usher pathway (CUP) pili for critical binding interactions. The A. baumannii uropathogenic strain, UPAB1, and the pan-European subclone II isolate, ACICU, use the CUP pili Abp1 and Abp2 (previously termed Cup and Prp, respectively) in tandem to establish CAUTIs, specifically to facilitate bacterial adherence and biofilm formation on the implanted catheter. Abp1 and Abp2 pili are tipped with two domain tip adhesins, Abp1D and Abp2D, respectively. We discovered that both adhesins bind fibrinogen, a critical host wound response protein that is released into the bladder upon catheterization and is subsequently deposited on the catheter. The crystal structures of the Abp1D and Abp2D receptor-binding domains were determined and revealed that they both contain a large, distally oriented pocket, which mediates binding to fibrinogen and other glycoproteins. Genetic, biochemical, and biophysical studies revealed that interactions with host proteins are governed by several critical residues in and along the edge of the binding pocket, one of which regulates the structural stability of an anterior loop motif. K34, located outside of the pocket but interacting with the anterior loop, also regulates the binding affinity of the protein. This study illuminates the mechanistic basis of the critical fibrinogen-coated catheter colonization step in A. baumannii CAUTI pathogenesis.
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- 2023
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18. Author response: Drug specificity and affinity are encoded in the probability of cryptic pocket opening in myosin motor domains
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Artur Meller, Jeffrey M Lotthammer, Louis G Smith, Borna Novak, Lindsey A Lee, Catherine C Kuhn, Lina Greenberg, Leslie A Leinwand, Michael J Greenberg, and Gregory R Bowman
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- 2023
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19. Copernicus: a new paradigm for parallel adaptive molecular dynamics.
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Sander Pronk, Per Larsson, Iman Pouya, Gregory R. Bowman, Imran S. Haque, Kyle Beauchamp, Berk Hess, Vijay S. Pande, Peter M. Kasson, and Erik Lindahl
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- 2011
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20. Deep Generative Design of Epitope-Specific Binding Proteins by Latent Conformation Optimization
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Raphael R. Eguchi, Christian A. Choe, Udit Parekh, Irene S. Khalek, Michael D. Ward, Neha Vithani, Gregory R. Bowman, Joseph G. Jardine, and Po-Ssu Huang
- Abstract
Designingde novobinding proteins against arbitrary epitopes using a single scaffold, as seen with natural antibodies, remains an unsolved challenge in protein design. Current design methods are unable to capture the structural dynamics of flexible loops nor search loop conformational space in a principled way. Here we present Sculptor, a deep generative design algorithm that creates epitope-specific protein binders. The Sculptor algorithm constitutes a joint search over the positions, interactions, and generated conformations of a fold, and crafts a backbone to complement a user-specified epitope. Sequences are designed onto generated backbones using a combination of a residue-wise interaction database, a convolutional sequence design module, and Rosetta. Instead of relying on static structures, we capture the local conformational landscape of a single fold using molecular dynamics, and demonstrate that a model trained on such dense conformational data can generate backbones tailor-fit to an epitope. We use Sculptor to design binders against a conserved epitope on venom toxins implicated in neuromuscular paralysis, and obtain a multi-toxin binder from a small naïve library – a promising step towards creating broadly neutralizing binders. This study constitutes a novel application of deep generative modeling for epitope-targeted design, leveraging conformational dynamics to achieve function.
- Published
- 2022
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21. Solution of the protein structure prediction problem at last: crucial innovations and next frontiers
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David A. Agard, Gregory R. Bowman, William DeGrado, Nikolay V. Dokholyan, and Huan-Xiang Zhou
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Review Article - Abstract
The protein structure prediction problem is solved, at last, thanks in large part to the use of artificial intelligence. The structures predicted by AlphaFold and RoseTTAFold are becoming the requisite starting point for many protein scientists. New frontiers, such as the conformational sampling of intrinsically disordered proteins, are emerging.
- Published
- 2022
22. Accelerating cryptic pocket discovery using AlphaFold
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Artur Meller, Soumendranath Bhakat, Shahlo Solieva, and Gregory R. Bowman
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Physical and Theoretical Chemistry ,Computer Science Applications - Abstract
Cryptic pockets, or pockets absent in ligand-free, experimentally determined structures, hold great potential as drug targets. However, cryptic pocket opening is often beyond the reach of conventional biomolecular simulations because certain cryptic pocket openings involve slow motions. Here, we investigate whether AlphaFold can be used to accelerate cryptic pocket discovery either by generating structures with open pockets directly or generating structures with partially open pockets that can be used as starting points for simulations. We use AlphaFold to generate ensembles for 10 known cryptic pocket examples, including 5 that were deposited after AlphaFold’s training data was extracted from the PDB. We find that in 6 out of 10 cases AlphaFold samples the open state. For plasmepsin II, an aspartic protease from the causative agent of malaria, AlphaFold only captures partial pocket opening. As a result, we ran simulations from an ensemble of AlphaFold-generated structures and show that this strategy samples cryptic pocket opening, even though an equivalent amount of simulations launched from a ligand-free experimental structure fails to do so. Markov state models (MSMs) constructed from the AlphaFold-seeded simulations quickly yield a free energy landscape of cryptic pocket opening that is in good agreement with the same landscape generated with well-tempered metadynamics. Taken together, our results demonstrate that AlphaFold has a useful role to play in cryptic pocket discovery but that many cryptic pockets may remain difficult to sample using AlphaFold alone.
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- 2022
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23. Constructing Multi-Resolution Markov State Models (MSMs) to Elucidate RNA Hairpin Folding Mechanisms.
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Xuhui Huang, Yuan Yao 0011, Gregory R. Bowman, Jian Sun 0002, Leonidas J. Guibas, Gunnar E. Carlsson, and Vijay S. Pande
- Published
- 2010
24. Naturally Occurring Genetic Variants in the Oxytocin Receptor Alter Receptor Signaling Profiles
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Yingye Fang, Thomas P. Burris, Antonina I. Frolova, P. I. Imoukhuede, Sarah K. England, Michael D. Ward, Thomas Koelblen, Gregory R. Bowman, Maxwell I. Zimmerman, Justin R. Porter, Manasi Malik, and Michelle Roh
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Pharmacology ,medicine.medical_specialty ,media_common.quotation_subject ,Cell ,Biology ,Oxytocin receptor ,Cell membrane ,medicine.anatomical_structure ,Endocrinology ,Oxytocin ,Desensitization (telecommunications) ,Internal medicine ,medicine ,Pharmacology (medical) ,Internalization ,Intracellular ,medicine.drug ,media_common ,Hormone - Abstract
The hormone oxytocin is commonly administered during childbirth to initiate and strengthen uterine contractions and prevent postpartum hemorrhage. However, patients have wide variation in the oxytocin dose required for a clinical response. To begin to uncover the mechanisms underlying this variability, we screened the 11 most prevalent missense genetic variants in the oxytocin receptor (OXTR) gene. We found that five variants, V45L, P108A, L206V, V281M, and E339K, significantly altered oxytocin-induced Ca2+ signaling or β-arrestin recruitment and proceeded to assess the effects of these variants on OXTR trafficking to the cell membrane, desensitization, and internalization. The variants P108A and L206V increased OXTR localization to the cell membrane, whereas V281M and E339K caused OXTR to be retained inside the cell. We examined how the variants altered the balance between OXTR activation and desensitization, which is critical for appropriate oxytocin dosing. The E339K variant impaired OXTR activation, internalization, and desensitization to roughly equal extents. In contrast, V281M decreased OXTR activation but had no effect on internalization and desensitization. V45L and P108A did not alter OXTR activation but did impair β-arrestin recruitment, internalization, and desensitization. Molecular dynamics simulations predicted that V45L and P108A prevent extension of the first intracellular loop of OXTR, thus inhibiting β-arrestin binding. Overall, our data suggest mechanisms by which OXTR genetic variants could alter clinical response to oxytocin.
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- 2021
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25. Drug specificity and affinity are encoded in the probability of cryptic pocket opening in myosin motor domains
- Author
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Artur Meller, Jeffrey M. Lotthammer, Louis G. Smith, Borna Novak, Lindsey A. Lee, Catherine C. Kuhn, Lina Greenberg, Leslie A. Leinwand, Michael J. Greenberg, and Gregory R. Bowman
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General Immunology and Microbiology ,General Neuroscience ,General Medicine ,General Biochemistry, Genetics and Molecular Biology - Abstract
The design of compounds that can discriminate between closely related target proteins remains a central challenge in drug discovery. Specific therapeutics targeting the highly conserved myosin motor family are urgently needed as mutations in at least 6 of its members cause numerous diseases. Allosteric modulators, like the myosin-II inhibitor blebbistatin, are a promising means to achieve specificity. However, it remains unclear why blebbistatin inhibits myosin-II motors with different potencies given that it binds at a highly conserved pocket that is always closed in blebbistatin-free experimental structures. We hypothesized that the probability of pocket opening is an important determinant of the potency of compounds like blebbistatin. To test this hypothesis, we used Markov state models (MSMs) built from over 2 milliseconds of aggregate molecular dynamics simulations with explicit solvent. We find that blebbistatin’s binding pocket readily opens in simulations of blebbistatin-sensitive myosin isoforms. Comparing these conformational ensembles reveals that the probability of pocket opening correctly identifies which isoforms are most sensitive to blebbistatin inhibition and that docking against MSMs quantitatively predicts blebbistatin binding affinities (R2=0.82). To test our ability to make blind predictions, we predicted blebbistatin’s binding affinity for an isoform (Myh7b) whose blebbistatin sensitivity was unknown. Encouragingly, we find good agreement between the predicted and measured IC50 (0.67 µM vs. 0.36 µM). Therefore, we expect this framework to be useful for the development of novel specific drugs across numerous protein targets.SignificanceDrug development requires the discovery of compounds which specifically target one member of a protein family without triggering side effects that arise from interactions with other related proteins. Myosins are a family of motor proteins that are drug targets for heart diseases, cancer, and parasitic infections. Here, we investigate why the compound blebbistatin specifically inhibits some myosins more potently than others, even though its binding site is closed in all known experimental structures. We find that the blebbistatin binding pocket opens in molecular dynamics simulations of certain myosin motors, and that the probability of opening predicts how potently blebbistatin inhibits a particular motor. Our work suggests that differences in cryptic pocket formation can be exploited to develop specific therapeutics.
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- 2022
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26. SARS-CoV-2 simulations go exascale to predict dramatic spike opening and cryptic pockets across the proteome
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Sukrit Singh, Catherine E. Kuhn, Aoife M. Harbison, Vincent A. Voelz, John D. Chodera, Justin R. Porter, Neha Vithani, Joseph E. Coffland, Rafal P. Wiewiora, Artur Meller, Carl A Fogarty, Maxwell I. Zimmerman, Jonathan H. Borowsky, Gregory R. Bowman, Michael D. Ward, Matthew F. D. Hurley, Elisa Fadda, and Upasana L. Mallimadugula
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2019-20 coronavirus outbreak ,Coronavirus disease 2019 (COVID-19) ,010405 organic chemistry ,Extramural ,Chemistry ,General Chemical Engineering ,Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) ,Evasion (network security) ,General Chemistry ,Computational biology ,010402 general chemistry ,01 natural sciences ,0104 chemical sciences ,Protein structure ,Proteome ,Spike (software development) - Abstract
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.
- Published
- 2021
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27. Predicting the locations of cryptic pockets from single protein structures using the PocketMiner graph neural network
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Artur Meller, Michael Ward, Jonathan Borowsky, Jeffrey M. Lotthammer, Meghana Kshirsagar, Felipe Oviedo, Juan Lavista Ferres, and Gregory R. Bowman
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Biophysics - Abstract
Cryptic pockets expand the scope of drug discovery by enabling targeting of proteins currently considered undruggable because they lack pockets in their ground state structures. However, identifying cryptic pockets is labor-intensive and slow. The ability to accurately and rapidly predict if and where cryptic pockets are likely to form from a protein structure would greatly accelerate the search for druggable pockets. Here, we present PocketMiner, a graph neural network trained to predict where pockets are likely to open in molecular dynamics simulations. Applying PocketMiner to single structures from a newly-curated dataset of 39 experimentally-confirmed cryptic pockets demonstrates that it accurately identifies cryptic pockets (ROC-AUC: 0.87) >1,000-fold faster than existing methods. We apply PocketMiner across the human proteome and show that predicted pockets open in simulations, suggesting that over half of proteins thought to lack pockets based on available structures are likely to contain cryptic pockets, vastly expanding the druggable proteome.
- Published
- 2022
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28. A nepenthesin insert allosterically controls catalysis in the malaria parasite protease plasmepsin V
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Alexander J. Polino, Justin J. Miller, Soumendranath Bhakat, Suhas Bobba, Gregory R. Bowman, and Daniel E. Goldberg
- Abstract
Plasmepsin V (PM V) is a pepsin-like aspartic protease essential for growth of the malaria parasite Plasmodium falciparum. Previous work has shown PM V to be an ER-resident protease that processes parasite proteins destined for export into the host cell. Depletion or inhibition of the enzyme is lethal during asexual replication within red blood cells, as well as during the formation of sexual stage gametocytes. The structure of the P. vivax PM V has been characterized by x-ray crystallography, revealing a canonical pepsin fold punctuated by structural features uncommon to secretory aspartic proteases. Here we use parasite genetics to probe these structural features by attempting to rescue lethal PM V depletion with various mutant enzymes. We find an unusual nepenthesin 1-type insert to be essential for parasite growth and PM V activity. Mutagenesis of the nepenthesin insert suggests that both its amino acid sequence and one of the two disulfide bonds that undergird its structure are required for the nepenthesin insert’s role in PM V function. Molecular dynamics simulations paired with Markov state modelling suggest that the nepenthesin insert allosterically controls PM V catalysis through multiple mechanisms.
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- 2022
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29. Apolipoprotein E4 has extensive conformational heterogeneity in lipid free and bound forms
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Melissa D. Stuchell-Brereton, Maxwell I. Zimmerman, Justin J. Miller, Upasana L. Mallimadugula, J. Jeremias Incicco, Debjit Roy, Louis G. Smith, Berevan Baban, Gregory T. DeKoster, Carl Frieden, Gregory R. Bowman, and Andrea Soranno
- Subjects
mental disorders ,lipids (amino acids, peptides, and proteins) ,human activities - Abstract
The ε4-allele variant of Apolipoprotein E (ApoE4) is the strongest genetic risk factor for Alzheimer’s disease, though it only differs from its neutral counterpart ApoE3 by a single amino acid substitution. While ApoE4 influences the formation of plaques and neurofibrillary tangles, the structural determinants of pathogenicity remain undetermined due to limited structural information. We apply a combination of single-molecule spectroscopy and molecular dynamics simulations to construct an atomically-detailed model of monomeric ApoE4 and probe the effect of lipid association. Our data reveal that ApoE4 is far more disordered than previously thought and retains significant conformational heterogeneity after binding lipids. In particular, the behavior of the hinge region and C-terminal domain of ApoE4 differs substantially from that proposed in previous models and provides a crucial foundation for understanding how ApoE4 differs from non-pathogenic and protective variants of the protein.
- Published
- 2022
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30. Abstract PL05-03: What if all your favorite proteins are druggable
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Gregory R. Bowman
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Cancer Research ,Oncology - Abstract
Many proteins that play key roles in cancer are considered difficult drug targets, if not entirely undruggable, due to an apparent lack of pockets where small molecules can bind with the affinity and specificity required for a drug. ‘Cryptic’ pockets that are absent in known structures of proteins but form due to protein dynamics could alleviate this problem. However, it has been hard to assess or exploit the opportunities that cryptic pockets present due to the inherent difficulty in identifying such pockets. Here, I will discuss progress from my lab on combining biophysical experiments, computer simulations, and machine learning to identify and target cryptic pockets. In one case study, we have used computer simulations deployed on the Folding@home distributed computing environment to predict a cryptic pocket that can allosterically control a protein-RNA interaction, and then experimentally confirmed our predictions. Based on data from this study and others in our lab, we have trained a machine learning algorithm to predict where cryptic pockets are likely to form from single protein structures, enabling us to assess how prevalent cryptic pockets are on a large scale. Citation Format: Gregory R. Bowman. What if all your favorite proteins are druggable. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr PL05-03.
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- 2023
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31. Benchmarking a novel ensemble docking method
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Louis G. Smith, Borna Novak, and Gregory R. Bowman
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Biophysics - Published
- 2023
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32. Single-molecule spectroscopy of Apolipoprotein E reveals a complex conformational ensemble
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Melissa D. Stuchell-Brereton, Maxwell I. Zimmerman, Justin J. Miller, Upasana L. Mallimadugula, Juan J. Incicco, Debjit Roy, Louis G. Smith, Jasmine Cubuk, Berevan Baban, Gregory T. DeKoster, Carl Frieden, Gregory R. Bowman, and Andrea Soranno
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Biophysics - Published
- 2023
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33. A cryptic pocket in Ebola VP35 allosterically controls RNA binding
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Sukrit Singh, Gregory R. Bowman, Neha Vithani, Thomas E. Frederick, Maxwell I. Zimmerman, Matthew A. Cruz, Justin R. Porter, Katelyn E. Moeder, Gaya K. Amarasinghe, and Upasana L. Mallimadugula
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Viral protein ,Allosteric regulation ,Druggability ,General Physics and Astronomy ,Computational biology ,Biology ,medicine.disease_cause ,01 natural sciences ,General Biochemistry, Genetics and Molecular Biology ,03 medical and health sciences ,Viral Proteins ,0103 physical sciences ,medicine ,Humans ,Viral Regulatory and Accessory Proteins ,030304 developmental biology ,RNA, Double-Stranded ,0303 health sciences ,Multidisciplinary ,010304 chemical physics ,DNA Viruses ,RNA ,General Chemistry ,Hemorrhagic Fever, Ebola ,Ebolavirus ,Replication cycle ,3. Good health ,DsRNA binding - Abstract
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.
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- 2021
34. Decision letter: An empirical energy landscape reveals mechanism of proteasome in polypeptide translocation
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Gregory R Bowman
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- 2021
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35. Intrinsically disordered pathogen effector alters the STAT1 dimer to prevent recruitment of co-transcriptional activators CBP/p300
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L. David Sibley, Gaya K. Amarasinghe, Jay C. Nix, Hejun Liu, Gregory R. Bowman, Catherine R. Knoverek, and Zhou Huang
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biology ,Transcription (biology) ,Effector ,Chemistry ,Gene expression ,STAT protein ,biology.protein ,Phosphorylation ,STAT1 ,SH2 domain ,Receptor ,Cell biology - Abstract
Signal transducer and activator of transcription (STATs) proteins signal from cell-surface receptors to drive transcription of immune response genes. The parasiteToxoplasma gondiiblocks STAT1-mediated gene expression by secreting the intrinsically disordered protein TgIST that traffics to the host nucleus, binds phosphorylated STAT1 dimers, and occupies nascent transcription sites that unexpectantly remain silenced. Here we define a core repeat region within internal repeats of TgIST that is necessary and sufficient to block STAT1-mediated gene expression. Cellular, biochemical, mutational, and structural studies demonstrate that the repeat region of TgIST adopts a helical conformation upon binding to STAT1 dimers. The binding interface is defined by a groove formed from two loops in the STAT1 SH2 domains that reorient during dimerization. TgIST binding to this newly exposed site at the STAT1 dimer interface altered its conformation and prevented recruitment of co-transcriptional activators, thus defining the mechanism of blocked transcription.
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- 2021
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36. Exploring the myosin active/auto-inhibited state equilibrium by Markov state modeling
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Jeffrey M. Lotthammer, Artur Meller, Michael J. Greenberg, and Gregory R. Bowman
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Biophysics - Published
- 2022
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37. Advanced Methods for Accessing Protein Shape-Shifting Present New Therapeutic Opportunities
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Catherine R. Knoverek, Gregory R. Bowman, and Gaya K. Amarasinghe
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0303 health sciences ,Protein function ,Protein Conformation ,Computer science ,Mechanism (biology) ,Protein dynamics ,media_common.quotation_subject ,Computational Biology ,Proteins ,A protein ,Biochemistry ,Data science ,Article ,03 medical and health sciences ,0302 clinical medicine ,Humans ,Experimental methods ,Function (engineering) ,Set (psychology) ,Molecular Biology ,030217 neurology & neurosurgery ,030304 developmental biology ,media_common - Abstract
A protein is a dynamic shape-shifter whose function is determined by the set of structures it adopts. Unfortunately, atomically-detailed structures are only available for a few conformations of any given protein, and these structures have limited explanatory and predictive power. Here, we provide a brief historical perspective on protein dynamics and introduce recent advances in computational and experimental methods that are providing unprecedented access to protein shape-shifting. Next, we focus on how these tools are revealing the mechanism of allosteric communication and features like cryptic pockets, both of which present new therapeutic opportunities. A major theme is the importance of considering the relative probabilities of different structures and the control one can exert over protein function by modulating this balance.
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- 2019
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38. Cooperative Changes in Solvent Exposure Identify Cryptic Pockets, Switches, and Allosteric Coupling
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Michael J. Greenberg, Kathryn M. Hart, Justin R. Porter, Carrie A. Sibbald, Katelyn E. Moeder, Maxwell I. Zimmerman, and Gregory R. Bowman
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Models, Molecular ,0303 health sciences ,Cyclic AMP Receptor Protein ,Protein Conformation ,New and Notable ,Computer science ,Escherichia coli Proteins ,Allosteric regulation ,Biophysics ,Proteins ,Computational biology ,beta-Lactamases ,03 medical and health sciences ,Broad spectrum ,chemistry.chemical_compound ,0302 clinical medicine ,Protein structure ,chemistry ,Coupling (computer programming) ,Solvents ,Solvent exposure ,Allosteric Site ,030217 neurology & neurosurgery ,030304 developmental biology - Abstract
Proteins are dynamic molecules that undergo conformational changes to a broad spectrum of different excited states. Unfortunately, the small populations of these states make it difficult to determine their structures or functional implications. Computer simulations are an increasingly powerful means to identify and characterize functionally relevant excited states. However, this advance has uncovered a further challenge: it can be extremely difficult to identify the most salient features of large simulation data sets. We reasoned that many functionally relevant conformational changes are likely to involve large, cooperative changes to the surfaces that are available to interact with potential binding partners. To examine this hypothesis, we introduce a method that returns a prioritized list of potentially functional conformational changes by segmenting protein structures into clusters of residues that undergo cooperative changes in their solvent exposure, along with the hierarchy of interactions between these groups. We term these groups exposons to distinguish them from other types of clusters that arise in this analysis and others. We demonstrate, using three different model systems, that this method identifies experimentally validated and functionally relevant conformational changes, including conformational switches, allosteric coupling, and cryptic pockets. Our results suggest that key functional sites are hubs in the network of exposons. As a further test of the predictive power of this approach, we apply it to discover cryptic allosteric sites in two different β-lactamase enzymes that are widespread sources of antibiotic resistance. Experimental tests confirm our predictions for both systems. Importantly, we provide the first evidence, to our knowledge, for a cryptic allosteric site in CTX-M-9 β-lactamase. Experimentally testing this prediction did not require any mutations and revealed that this site exerts the most potent allosteric control over activity of any pockets found in β-lactamases to date. Discovery of a similar pocket that was previously overlooked in the well-studied TEM-1 β-lactamase demonstrates the utility of exposons.
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- 2019
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39. The nepenthesin insert in the Plasmodium falciparum aspartic protease plasmepsin V is necessary for enzyme function
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Alexander J. Polino, Justin J. Miller, Soumendranath Bhakat, Sumit Mukherjee, Suhas Bobba, Gregory R. Bowman, and Daniel E. Goldberg
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Plasmodium falciparum ,Protozoan Proteins ,Aspartic Acid Endopeptidases ,Humans ,Disulfides ,Cell Biology ,Malaria, Falciparum ,Molecular Biology ,Biochemistry ,Pepsin A - Abstract
Plasmepsin V (PM V) is a pepsin-like aspartic protease essential for growth of the malarial parasite Plasmodium falciparum. Previous work has shown PM V to be an endoplasmic reticulum-resident protease that processes parasite proteins destined for export into the host cell. Depletion or inhibition of the enzyme is lethal during asexual replication within red blood cells as well as during the formation of sexual stage gametocytes. The structure of the Plasmodium vivax PM V has been characterized by X-ray crystallography, revealing a canonical pepsin fold punctuated by structural features uncommon to secretory aspartic proteases; however, the function of this unique structure is unclear. Here, we used parasite genetics to probe these structural features by attempting to rescue lethal PM V depletion with various mutant enzymes. We found an unusual nepenthesin 1-type insert in the PM V gene to be essential for parasite growth and PM V activity. Mutagenesis of the nepenthesin insert suggests that both its amino acid sequence and one of the two disulfide bonds that undergird its structure are required for the insert's role in PM V function. Furthermore, molecular dynamics simulations paired with Markov state modeling suggest that mutations to the nepenthesin insert may allosterically affect PM V catalysis through multiple mechanisms. Taken together, these data provide further insights into the structure of the P. falciparum PM V protease.
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- 2022
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40. Bexarotene derivatives modify responses in acute myeloid leukemia
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John S. Welch, di Martino O, Thomas E. Frederick, Anh Vu, Peter G. Ruminski, Gregory R. Bowman, Carl E. Wagner, Gayla Hadwiger, and Margaret A. Ferris
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Acute promyelocytic leukemia ,Bexarotene ,medicine.drug_class ,Chemistry ,Retinoic acid ,Myeloid leukemia ,medicine.disease ,chemistry.chemical_compound ,medicine ,Cancer research ,Potency ,Retinoid ,Bioisostere ,neoplasms ,Corepressor ,medicine.drug - Abstract
The retinoids all-trans retinoic acid (ATRA) and bexarotene are active in acute myeloid leukemia (AML), but responses beyond acute promyelocytic leukemia (APL) have been more modest than APL responses. To determine whether chemical modification of bexarotene might augment retinoid responses in AML, we screened a series of 38 bexarotene derivatives for activity in a mouse MLL-AF9 leukemia cell line, which exhibits strong synergistic sensitivity to the combination of ATRA and bexarotene. We found that RXRA potency correlated with anti-leukemic activity and that only one compound (103-4) with dual RARA/RXRA activity was capable of ATRA-independent anti-leukemic activity. We evaluated bioisostere and cyclohexane modifications for potential resistance to P450 metabolism and found that bioisosteres reduced potency and that bezopyran, cyclopentane, and cyclohexene modifications only modestly reduced susceptibility to metabolism. Collectively, these studies provide a map of the structure-activity relationships of bexarotene with outcomes related to RXRA and RARA activity, corepressor binding, compound stability, and anti-leukemic potential.
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- 2021
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41. Opening of a Cryptic Pocket in β-lactamase Increases Penicillinase Activity
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Shreya Raavicharla, Gregory R. Bowman, Upasana L. Mallimadugula, Catherine R. Knoverek, Lewis E. Kay, Thomas E. Frederick, Tairan Yuwen, Enrico Rennella, and Sukrit Singh
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Protein Conformation ,Open population ,Stereochemistry ,cryptic pockets ,Protein design ,Molecular Dynamics Simulation ,010402 general chemistry ,01 natural sciences ,Omega ,beta-Lactamases ,03 medical and health sciences ,Molecular dynamics ,Escherichia coli ,protein evolution ,Beta (finance) ,030304 developmental biology ,0303 health sciences ,Binding Sites ,Multidisciplinary ,Chemistry ,Drug discovery ,Escherichia coli Proteins ,Protein dynamics ,Proteins ,Penicillin G ,Biological Sciences ,Penicillinase ,0104 chemical sciences ,Biophysics and Computational Biology ,Saturation transfer ,Excited state ,protein dynamics ,Mutation ,Biophysics ,Penicillinase activity ,Function (biology) - Abstract
Significance A protein is a shape-shifter, but it is currently unclear which of the many structures a protein can adopt are relevant for its function. Here, we examine conformations that contain a “cryptic” pocket (i.e., a pocket absent in ligand-free structures). Cryptic pockets have potential utility in drug discovery efforts because they provide a means to target “undruggable” proteins (i.e., proteins lacking known pockets) or enhance rather than inhibit protein function. In this study, we use a combination of thiol-labeling and kinetic assays, NMR, and molecular dynamic simulations to identify the function of the Ω-loop cryptic pocket in β-lactamase enzymes. We find that an open pocket population is beneficial for hydrolysis of the substrate benzylpenicillin., 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.
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- 2021
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42. SARS-CoV2 Nsp16 activation mechanism and a cryptic pocket with pan-coronavirus antiviral potential
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Neha Vithani, Gregory R. Bowman, Michael D. Ward, Jonathan H. Borowsky, Sukrit Singh, Borna Novak, and Maxwell I. Zimmerman
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0303 health sciences ,Messenger RNA ,Mechanism (biology) ,Biophysics ,Evasion (network security) ,Translation (biology) ,Computational biology ,Biology ,medicine.disease_cause ,Small molecule ,Molecular machine ,Article ,03 medical and health sciences ,0302 clinical medicine ,Immune system ,Viral replication ,medicine ,030217 neurology & neurosurgery ,030304 developmental biology ,Coronavirus - Abstract
Coronaviruses have caused multiple epidemics in the past two decades, in addition to the current COVID-19 pandemic that is severely damaging global health and the economy. Coronaviruses employ between twenty and thirty proteins to carry out their viral replication cycle including infection, immune evasion, and replication. Among these, nonstructural protein 16 (Nsp16), a 2’-O-methyltransferase, plays an essential role in immune evasion. Nsp16 achieves this by mimicking its human homolog, CMTr1, which methylates mRNA to enhance translation efficiency and distinguish self from other. Unlike human CMTr1, Nsp16 requires a binding partner, Nsp10, to activate its enzymatic activity. The requirement of this binding partner presents two questions that we investigate in this manuscript. First, how does Nsp10 activate Nsp16? While experimentally-derived structures of the active Nsp16/Nsp10 complex exist, structures of inactive, monomeric Nsp16 have yet to be solved. Therefore, it is unclear how Nsp10 activates Nsp16. Using over one millisecond of molecular dynamics simulations of both Nsp16 and its complex with Nsp10, we investigate how the presence of Nsp10 shifts Nsp16’s conformational ensemble in order to activate it. Second, guided by this activation mechanism and Markov state models (MSMs), we investigate if Nsp16 adopts inactive structures with cryptic pockets that, if targeted with a small molecule, could inhibit Nsp16 by stabilizing its inactive state. After identifying such a pocket in SARS-CoV-2 Nsp16, we show that this cryptic pocket also opens in SARS-CoV-1 and MERS, but not in human CMTr1. Therefore, it may be possible to develop pan-coronavirus antivirals that target this cryptic pocket.Statement of SignificanceCoronaviruses are a major threat to human health. These viruses employ molecular machines, called proteins, to infect host cells and replicate. Characterizing the structure and dynamics of these proteins could provide a basis for designing small molecule antivirals. In this work, we use computer simulations to understand the moving parts of an essential SARS-CoV-2 protein, understand how a binding partner turns it on and off, and identify a novel pocket that antivirals could target to shut this protein off. The pocket is also present in other coronaviruses but not in the related human protein, so it could be a valuable target for pan-coronavirus antivirals.
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- 2021
43. A Role for Both Conformational Selection and Induced Fit in Ligand Binding by the LAO Protein.
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Daniel-Adriano Silva, Gregory R. Bowman, Alejandro Sosa-Peinado, and Xuhui Huang
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- 2011
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44. Protein sequence models for prediction and comparative analysis of the SARS-CoV-2 —human interactome
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Gregory R. Bowman, Nure Tasnina, Judith Klein-Seetharaman, T. M. Murali, Juan Lavista Ferres, Meghana Kshirsagar, Michael D. Ward, and Jeffrey N. Law
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biology ,viruses ,fungi ,virus diseases ,Computational biology ,biology.organism_classification ,medicine.disease_cause ,Virus ,Protein sequencing ,Human interactome ,medicine ,Coronaviridae ,skin and connective tissue diseases ,Peptide sequence ,Virus classification ,Coronavirus ,Sequence (medicine) - Abstract
Viruses such as the novel coronavirus, SARS-CoV-2, that is wreaking havoc on the world, depend on interactions of its own proteins with those of the human host cells. Relatively small changes in sequence such as between SARS-CoV and SARS-CoV-2 can dramatically change clinical phenotypes of the virus, including transmission rates and severity of the disease. On the other hand, highly dissimilar virus families such as Coronaviridae, Ebola, and HIV have overlap in functions. In this work we aim to analyze the role of protein sequence in the binding of SARS-CoV-2 virus proteins towards human proteins and compare it to that of the above other viruses. We build supervised machine learning models, using Generalized Additive Models to predict interactions based on sequence features and find that our models perform well with an AUC-PR of 0.65 in a class-skew of 1:10. Analysis of the novel predictions using an independent dataset showed statistically significant enrichment. We further map the importance of specific amino-acid sequence features in predicting binding and summarize what combinations of sequences from the virus and the host is correlated with an interaction. By analyzing the sequence-based embeddings of the interactomes from different viruses and clustering them together we find some functionally similar proteins from different viruses. For example, vif protein from HIV-1, vp24 from Ebola and orf3b from SARS-CoV all function as interferon antagonists. Furthermore, we can differentiate the functions of similar viruses, for example orf3a's interactions are more diverged than orf7b interactions when comparing SARS-CoV and SARS-CoV-2.
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- 2020
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45. Open Science Discovery of Potent Non-Covalent SARS-CoV-2 Main Protease Inhibitors
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von Delft F, Ronen Gabizon, Wild Cf, Anastassia L. Kantsadi, Peter W. Kenny, Koekemoer L, Matteo P. Ferla, Noam Erez, Sharon Melamed, Adam Smalley, Gijs J. Overheul, Jag Paul Heer, Shaikh A, Tika R. Malla, R.S. Fernandes, Christopher J. Schofield, Moustakas D, Pai R, MacLean B, T. Krojer, Finny S. Varghese, Elad Bar-David, Hagit Achdout, Gregory R. Bowman, Lefker Ba, Kovar B, Charlie Weatherall, Tennant R, Griffen Ej, Yfat Yahalom-Ronen, Louise Dunnett, Emma Cattermole, Bvnbs S, Chernyshenko E, Ripka Eg, Kim Donckers, Efrat Resnick, Nir Paran, J. L. Kiappes, Einat B. Vitner, Dotson Dl, Mark Daniel Calmiano, Juliane Brun, Victor L. Rangel, Matthew F. D. Hurley, Richard Foster, Garrett M. Morris, Vaschetto M, Austin Clyde, Shay Weiss, Pan J, Nir London, William McCorkindale, Dudgeon T, Martin A. Walsh, Borden B, Haim Barr, John Spencer, Zaidmann D, Alice Douangamath, Robinson Rp, Alexandre Dias, John D. Chodera, Morris A, Marian V. Gorichko, Oleg Fedorov, V.O. Gawriljuk, Petra Lukacik, Puni R, Pinjari J, Shafeev M, Dirk Jochmans, Assa Sittner, T.J. Gorrie-Stone, White Km, Amir Ben-Shmuel, Ioannis Vakonakis, Boaz Politi, Rambabu N. Reddi, Joseph E. Coffland, Itai Glinert, Matthew C. Robinson, Ferrins L, Tomasio S, Alpha A. Lee, Khriesto A. Shurrush, Holly Foster, A. Aimon, Boby Ml, Andrea Volkamer, Alessandro Contini, Voelz, Tobias John, Galit Cohen, A.M. Nakamura, Horrell S, G.D. Noske, Jim Bennett, Oleg M. Michurin, Nicholas A. Wright, Smilova, von Delft A, Ward W, Haim Levy, Tomer Israely, Fate G, McGovern Bl, Anna Carbery, David R. Owen, Zidane H, Cox L, Michael Fairhead, Psenak, Carina Gileadi, Wittmann M, Morwitzer Mj, Solmesky Lj, Anthony Tumber, Robert C. Glen, Eric Jnoff, Reid Sp, Sukrit Singh, Steven De Jonghe, Claire Strain-Damerell, Jason C. Cole, A.J. Powell, Rosales R, Nicole Zitzmann, D. Fearon, Nguyen L, Rodriguez-Guerra J, Shirly Duberstein, Andrew Thompson, Johan Neyts, Benjamin Ian Perry, van Rij Rp, Jose Brandao Neto, William G. Glass, Rufa D, Charline Giroud, Peter Eastman, Hannah E. Bruce Macdonald, Glaucius Oliva, Mark A. Hill, Laura Vangeel, Jiye Shi, Hadas Tamir, R. Skyner, Mikolajek H, Adolfo García-Sastre, Oleinikovas, Pingle M, Henry M, Cvitkovic M, Milne Bf, Hart Sh, Eyermann Cj, Thompson W, Matviiuk T, Andre S. Godoy, Swamy, P. Gehrtz, and Jajack A
- Subjects
Open science ,Open knowledge ,Protease ,Structural biology ,Drug discovery ,Computer science ,Non covalent ,Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) ,medicine.medical_treatment ,medicine ,Protease inhibitor (pharmacology) ,Computational biology - Abstract
The COVID-19 pandemic was a stark reminder that a barren global antiviral pipeline has grave humanitarian consequences. Pandemics could be prevented in principle by accessible, easily deployable broad-spectrum oral antivirals. Here we report the results of theCOVID Moonshot, a fully open-science, crowd sourced, structure-enabled drug discovery campaign targeting the SARS-CoV-2 main protease. We discovered a novel chemical series that is differentiated from current Mpro inhibitors in that it maintains a new non-covalent, non-peptidic scaffold with nanomolar potency. Our approach leveraged crowdsourcing, high-throughput structural biology, machine learning, and exascale molecular simulations and high-throughput chemistry. In the process, we generated a detailed map of the structural plasticity of the SARS-CoV-2 main protease, extensive structure-activity relationships for multiple chemotypes, and a wealth of biochemical activity data. In a first for a structure-based drug discovery campaign, all compound designs (>18,000 designs), crystallographic data (>840 ligand-bound X-ray structures), assay data (>10,000 measurements), and synthesized molecules (>2,400 compounds) for this campaign were shared rapidly and openly, creating a rich open and IP-free knowledgebase for future anti-coronavirus drug discovery.
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- 2020
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46. SARS-CoV-2 Simulations Go Exascale to Capture Spike Opening and Reveal Cryptic Pockets Across the Proteome
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Sukrit Singh, Rafal P. Wiewiora, John D. Chodera, Upasana L. Mallimadugula, Aoife M. Harbison, Maxwell I. Zimmerman, Jonathan H. Borowsky, Vincent A. Voelz, Matthew F. D. Hurley, Neha Vithani, Carl A Fogarty, Elisa Fadda, Gregory R. Bowman, Catherine E. Kuhn, Justin R. Porter, Joseph E. Coffland, Michael D. Ward, and Artur Meller
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Binding Sites ,Proteome ,Protein Conformation ,SARS-CoV-2 ,Computer science ,Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) ,COVID-19 ,Evasion (network security) ,Computational biology ,Molecular Dynamics Simulation ,Article ,Protein structure ,Spike Glycoprotein, Coronavirus ,Humans ,Computer Simulation ,Spike (software development) ,Protein Binding - Abstract
SARS-CoV-2 has intricate mechanisms for initiating infection, immune evasion/suppression, and replication, which 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 an unprecedented 0.1 seconds of the viral proteome. Our simulations capture dramatic opening of the apo Spike complex, far beyond that seen experimentally, which explains and successfully predicts the existence of ‘cryptic’ epitopes. Different Spike homologues modulate the probabilities of open versus closed structures, balancing receptor binding and immune evasion. We also observe 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.
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- 2020
47. DiffNets: deep learning the structural determinants of proteins biochemical properties by comparing different structural ensembles
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Artur Meller, Michael D. Ward, Moses Chung, Maxwell I. Zimmerman, Gregory R. Bowman, and S.J. Swamidass
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business.industry ,Dimensionality reduction ,Deep learning ,Stability (learning theory) ,Computational biology ,Artificial intelligence ,business - Abstract
Understanding the structural determinants of a protein’s biochemical properties, such as activity and stability, is a major challenge in biology and medicine. Comparing computer simulations of protein variants with different biochemical properties is an increasingly powerful means to drive progress. However, success often hinges on dimensionality reduction algorithms for simplifying the complex ensemble of structures each variant adopts. Unfortunately, common algorithms rely on potentially misleading assumptions about what structural features are important, such as emphasizing larger geometric changes over smaller ones. Here we present DiffNets, self-supervised autoencoders that avoid such assumptions, and automatically identify the relevant features, by requiring that the low-dimensional representations they learn are sufficient to predict the biochemical differences between protein variants. For example, DiffNets automatically identify subtle structural signatures that predict the relative stabilities of β-lactamase variants and duty ratios of myosin isoforms. DiffNets should also be applicable to understanding other perturbations, such as ligand binding.
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- 2020
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48. The SARS-CoV-2 nucleocapsid protein is dynamic, disordered, and phase separates with RNA
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Jason A. Wagoner, Andrea Soranno, Jhullian J. Alston, Daniel Griffith, Neha Vithani, Melissa D. Stuchell-Brereton, Alex S. Holehouse, Sukrit Singh, J. Jeremías Incicco, Kathleen B. Hall, Maxwell I. Zimmerman, Michael D. Ward, Jasmine Cubuk, and Gregory R. Bowman
- Subjects
0301 basic medicine ,Protein Conformation ,viruses ,Science ,Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) ,Protein domain ,General Physics and Astronomy ,RNA-binding protein ,Molecular Dynamics Simulation ,010402 general chemistry ,01 natural sciences ,General Biochemistry, Genetics and Molecular Biology ,Article ,Genome packaging ,03 medical and health sciences ,Viral genome packaging ,Molecular dynamics ,Protein structure ,Viral life cycle ,Single-molecule biophysics ,Protein Domains ,Transcription (biology) ,Phase (matter) ,Computational models ,Coronavirus Nucleocapsid Proteins ,Binding site ,Multidisciplinary ,Intrinsically disordered proteins ,Binding Sites ,Chemistry ,SARS-CoV-2 ,Condensation ,RNA ,COVID-19 ,General Chemistry ,Phosphoproteins ,0104 chemical sciences ,030104 developmental biology ,Biophysics ,RNA, Viral ,Dimerization - Abstract
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.
- Published
- 2020
49. Conformational distributions of isolated myosin motor domains encode their mechanochemical properties
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Michael J. Greenberg, Artur Meller, Gregory R. Bowman, Justin R. Porter, and Maxwell I. Zimmerman
- Subjects
Protein Conformation ,Structural Biology and Molecular Biophysics ,Molecular dynamics ,0302 clinical medicine ,Myosin ,Dictyostelium ,Biology (General) ,0303 health sciences ,biology ,Chemistry ,General Neuroscience ,030302 biochemistry & molecular biology ,General Medicine ,Chicken ,Biomechanical Phenomena ,Adenosine Diphosphate ,machine learning ,energy landscapes ,Thermodynamics ,Medicine ,Research Article ,Human ,QH301-705.5 ,Science ,Chemical biology ,conformational heterogeneity ,Computational biology ,Molecular Dynamics Simulation ,Myosins ,ENCODE ,General Biochemistry, Genetics and Molecular Biology ,Avian Proteins ,03 medical and health sciences ,Protein Domains ,Biochemistry and Chemical Biology ,markov sate models ,Animals ,Humans ,030304 developmental biology ,Sequence (medicine) ,General Immunology and Microbiology ,Markov chain ,Molecular biophysics ,biology.organism_classification ,molecular dynamics ,Kinetics ,Structural biology ,Biophysics ,Chickens ,030217 neurology & neurosurgery ,Function (biology) - Abstract
Myosin motor domains perform an extraordinary diversity of biological functions despite sharing a common mechanochemical cycle. Motors are adapted to their function, in part, by tuning the thermodynamics and kinetics of steps in this cycle. However, it remains unclear how sequence encodes these differences, since biochemically distinct motors often have nearly indistinguishable crystal structures. We hypothesized that sequences produce distinct biochemical phenotypes by modulating the relative probabilities of an ensemble of conformations primed for different functional roles. To test this hypothesis, we modeled the distribution of conformations for twelve myosin motor domains by building Markov state models (MSMs) from an unprecedented two milliseconds of all-atom, explicit-solvent molecular dynamics simulations. Comparing motors reveals shifts in the balance between nucleotide-favorable and nucleotide-unfavorable P-loop conformations that predict experimentally-measured duty ratios and ADP release rates better than sequence or individual structures. This result demonstrates the power of an ensemble perspective for interrogating sequence-function relationships.Subject AreasBiochemistry and Chemical Biology, Structural Biology and Molecular Physics
- Published
- 2020
50. Author response: Conformational distributions of isolated myosin motor domains encode their mechanochemical properties
- Author
-
Justin R. Porter, Artur Meller, Maxwell I. Zimmerman, Michael J. Greenberg, and Gregory R. Bowman
- Subjects
Chemistry ,Myosin ,Biophysics ,ENCODE - Published
- 2020
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