Human PLCG2 haploinsufficiency results in a novel natural killer cell immunodeficiency.

BACKGROUND: Although most individuals effectively control herpesvirus infections, some suffer from severe and/or recurrent infections. A subset of these patients possess defects in natural killer (NK) cells, lymphocytes that recognize and lyse herpesvirus-infected cells; however, the genetic etiology is rarely diagnosed. PLCG2 encodes a signaling protein in NK-cell and B-cell signaling. Dominant-negative or gain-of-function variants in PLCG2 cause cold urticaria, antibody deficiency, and autoinflammation. However, loss-of-function variants and haploinsufficiency have not been reported to date. OBJECTIVES: The investigators aimed to identify the genetic cause of NK-cell immunodeficiency in 2 families and herein describe the functional consequences of 2 novel loss-of-function variants in PLCG2. METHODS: The investigators employed whole-exome sequencing in conjunction with mass cytometry, microscopy, functional assays, and a mouse model of PLCG2 haploinsufficiency to investigate 2 families with NK-cell immunodeficiency. RESULTS: The investigators identified novel heterozygous variants in PLCG2 in 2 families with severe and/or recurrent herpesvirus infections. In vitro studies demonstrated that these variants were loss of function due to haploinsufficiency with impaired NK-cell calcium flux and cytotoxicity. In contrast to previous PLCG2 variants, B-cell function remained intact. Plcg2+/- mice also displayed impaired NK-cell function with preserved B-cell function, phenocopying human disease. CONCLUSIONS: PLCG2 haploinsufficiency represents a distinct syndrome from previous variants characterized by NK-cell immunodeficiency with herpesvirus susceptibility, expanding the spectrum of PLCG2-related disease.

A cryptic pocket in Ebola VP35 allosterically controls RNA binding.

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.

Naturally Occurring Genetic Variants in the Oxytocin Receptor Alter Receptor Signaling Profiles.

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 ß-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 ß-arrestin recruitment, internalization, and desensitization. Molecular dynamics simulations predicted that V45L and P108A prevent extension of the first intracellular loop of OXTR, thus inhibiting ß-arrestin binding. Overall, our data suggest mechanisms by which OXTR genetic variants could alter clinical response to oxytocin.

SARS-CoV-2 simulations go exascale to predict dramatic spike opening and cryptic pockets across the proteome.

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.

SARS-CoV-2 Simulations Go Exascale to Capture Spike Opening and Reveal Cryptic Pockets Across the Proteome.

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.

Conformational distributions of isolated myosin motor domains encode their mechanochemical properties.

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 12 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.

Antagonism between substitutions in ß-lactamase explains a path not taken in the evolution of bacterial drug resistance.

CTX-M ß-lactamases are widespread in Gram-negative bacterial pathogens and provide resistance to the cephalosporin cefotaxime but not to the related antibiotic ceftazidime. Nevertheless, variants have emerged that confer resistance to ceftazidime. Two natural mutations, causing P167S and D240G substitutions in the CTX-M enzyme, result in 10-fold increased hydrolysis of ceftazidime. Although the combination of these mutations would be predicted to increase ceftazidime hydrolysis further, the P167S/D240G combination has not been observed in a naturally occurring CTX-M variant. Here, using recombinantly expressed enzymes, minimum inhibitory concentration measurements, steady-state enzyme kinetics, and X-ray crystallography, we show that the P167S/D240G double mutant enzyme exhibits decreased ceftazidime hydrolysis, lower thermostability, and decreased protein expression levels compared with each of the single mutants, indicating negative epistasis. X-ray structures of mutant enzymes with covalently trapped ceftazidime suggested that a change of an active-site Ω-loop to an open conformation accommodates ceftazidime leading to enhanced catalysis. 10-µs molecular dynamics simulations further correlated Ω-loop opening with catalytic activity. We observed that the WT and P167S/D240G variant with acylated ceftazidime both favor a closed conformation not conducive for catalysis. In contrast, the single substitutions dramatically increased the probability of open conformations. We conclude that the antagonism is due to restricting the conformation of the Ω-loop. These results reveal the importance of conformational heterogeneity of active-site loops in controlling catalytic activity and directing evolutionary trajectories.

Cooperative Changes in Solvent Exposure Identify Cryptic Pockets, Switches, and Allosteric Coupling.

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 ß-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 ß-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 ß-lactamases to date. Discovery of a similar pocket that was previously overlooked in the well-studied TEM-1 ß-lactamase demonstrates the utility of exposons.

Choice of Adaptive Sampling Strategy Impacts State Discovery, Transition Probabilities, and the Apparent Mechanism of Conformational Changes.

Interest in atomically detailed simulations has grown significantly with recent advances in computational hardware and Markov state modeling (MSM) methods, yet outstanding questions remain that hinder their widespread adoption. Namely, how do alternative sampling strategies explore conformational space and how might this influence predictions generated from the data? Here, we seek to answer these questions for four commonly used sampling methods: (1) a single long simulation, (2) many short simulations run in parallel, (3) adaptive sampling, and (4) our recently developed goal-oriented sampling algorithm, FAST. We first develop a theoretical framework for analytically calculating the probability of discovering select states on simple landscapes, where we uncover the drastic effects of varying the number and length of simulations. We then use kinetic Monte Carlo simulations on a variety of physically inspired landscapes to characterize the probability of discovering particular states and transition pathways for each of the four methods. Consistently, we find that FAST simulations discover each target state with the highest probability, while traversing realistic pathways. Furthermore, we uncover the potential pathology that short parallel simulations sometimes predict an incorrect transition pathway by crossing large energy barriers that long simulations would typically circumnavigate. We refer to this pathology as "pathway tunneling". To protect against this phenomenon when using adaptive-sampling and FAST simulations, we introduce the FAST-string method. This method enhances sampling along the highest-flux transition paths to refine an MSMs transition probabilities and discriminate between competing pathways. Additionally, we compare the performance of a variety of MSM estimators in describing accurate thermodynamics and kinetics. For adaptive sampling, we recommend simply normalizing the transition counts out of each state after adding small pseudocounts to avoid creating sources or sinks. Lastly, we evaluate whether our insights from simple landscapes hold for all-atom molecular dynamics simulations of the folding of the λ-repressor protein. Remarkably, we find that FAST-contacts predicts the same folding pathway as a set of long simulations but with orders of magnitude less simulation time.

Electron Cryo-microscopy Structure of Ebola Virus Nucleoprotein Reveals a Mechanism for Nucleocapsid-like Assembly.

Ebola virus nucleoprotein (eNP) assembles into higher-ordered structures that form the viral nucleocapsid (NC) and serve as the scaffold for viral RNA synthesis. However, molecular insights into the NC assembly process are lacking. Using a hybrid approach, we characterized the NC-like assembly of eNP, identified novel regulatory elements, and described how these elements impact function. We generated a three-dimensional structure of the eNP NC-like assembly at 5.8 Å using electron cryo-microscopy and identified a new regulatory role for eNP helices α22-α23. Biochemical, biophysical, and mutational analyses revealed that inter-eNP contacts within α22-α23 are critical for viral NC assembly and regulate viral RNA synthesis. These observations suggest that the N terminus and α22-α23 of eNP function as context-dependent regulatory modules (CDRMs). Our current study provides a framework for a structural mechanism for NC-like assembly and a new therapeutic target.

A Framework to Simplify Combined Sampling Strategies in Rosetta.

A core task in computational structural biology is the search of conformational space for low energy configurations of a biological macromolecule. Because conformational space has a very high dimensionality, the most successful search methods integrate some form of prior knowledge into a general sampling algorithm to reduce the effective dimensionality. However, integrating multiple types of constraints can be challenging. To streamline the incorporation of diverse constraints, we developed the Broker: an extension of the Rosetta macromolecular modeling suite that can express a wide range of protocols using constraints by combining small, independent modules, each of which implements a different set of constraints. We demonstrate expressiveness of the Broker through several code vignettes. The framework enables rapid protocol development in both biomolecular design and structural modeling tasks and thus is an important step towards exposing the rich functionality of Rosetta's core libraries to a growing community of users addressing a diverse set of tasks in computational biology.

Access to Cα backbone dynamics of biological solids by 13C T1 relaxation and molecular dynamics simulation.

We introduce a labeling scheme for magic angle spinning (MAS) solid-state NMR that is based on deuteration in combination with dilution of the carbon spin system. The labeling strategy achieves spectral editing by simplification of the HαCα and aliphatic side chain spectral region. A reduction in both proton and carbon spin density in combination with fast spinning (≥50 kHz) is essential to retrieve artifact-free (13)C-R1 relaxation data for aliphatic carbons. We obtain good agreement between the NMR experimental data and order parameters extracted from a molecular dynamics (MD) trajectory, which indicates that carbon based relaxation parameters can yield complementary information on protein backbone as well as side chain dynamics.

Extending RosettaDock with water, sugar, and pH for prediction of complex structures and affinities for CAPRI rounds 20-27.

Rounds 20-27 of the Critical Assessment of PRotein Interactions (CAPRI) provided a testing platform for computational methods designed to address a wide range of challenges. The diverse targets drove the creation of and new combinations of computational tools. In this study, RosettaDock and other novel Rosetta protocols were used to successfully predict four of the 10 blind targets. For example, for DNase domain of Colicin E2-Im2 immunity protein, RosettaDock and RosettaLigand were used to predict the positions of water molecules at the interface, recovering 46% of the native water-mediated contacts. For α-repeat Rep4-Rep2 and g-type lysozyme-PliG inhibitor complexes, homology models were built and standard and pH-sensitive docking algorithms were used to generate structures with interface RMSD values of 3.3 Å and 2.0 Å, respectively. A novel flexible sugar-protein docking protocol was also developed and used for structure prediction of the BT4661-heparin-like saccharide complex, recovering 71% of the native contacts. Challenges remain in the generation of accurate homology models for protein mutants and sampling during global docking. On proteins designed to bind influenza hemagglutinin, only about half of the mutations were identified that affect binding (T55: 54%; T56: 48%). The prediction of the structure of the xylanase complex involving homology modeling and multidomain docking pushed the limits of global conformational sampling and did not result in any successful prediction. The diversity of problems at hand requires computational algorithms to be versatile; the recent additions to the Rosetta suite expand the capabilities to encompass more biologically realistic docking problems.

Computational design of high-affinity epitope scaffolds by backbone grafting of a linear epitope.

Computational grafting of functional motifs onto scaffold proteins is a promising way to engineer novel proteins with pre-specified functionalities. Typically, protein grafting involves the transplantation of protein side chains from a functional motif onto structurally homologous regions of scaffold proteins. Using this approach, we previously transplanted the human immunodeficiency virus 2F5 and 4E10 epitopes onto heterologous proteins to design novel "epitope-scaffold" antigens. However, side-chain grafting is limited by the availability of scaffolds with compatible backbone for a given epitope structure and offers no route to modify backbone structure to improve mimicry or binding affinity. To address this, we report here a new and more aggressive computational method-backbone grafting of linear motifs-that transplants the backbone and side chains of linear functional motifs onto scaffold proteins. To test this method, we first used side-chain grafting to design new 2F5 epitope scaffolds with improved biophysical characteristics. We then independently transplanted the 2F5 epitope onto three of the same parent scaffolds using the newly developed backbone grafting procedure. Crystal structures of side-chain and backbone grafting designs showed close agreement with both the computational models and the desired epitope structure. In two cases, backbone grafting scaffolds bound antibody 2F5 with 30- and 9-fold higher affinity than corresponding side-chain grafting designs. These results demonstrate that flexible backbone methods for epitope grafting can significantly improve binding affinities over those achieved by fixed backbone methods alone. Backbone grafting of linear motifs is a general method to transplant functional motifs when backbone remodeling of the target scaffold is necessary.

Open preperitoneal retrofascial mesh repair for multiply recurrent ventral incisional hernias.

BACKGROUND: Because herniorrhaphy failure and complication rates appear proportional to the number of previous repairs, multiply recurrent hernias (MRH) represent a formidable challenge. We sought to determine the safety and efficacy of open preperitoneal retrofascial mesh repair of MRH. STUDY DESIGN: We conducted a retrospective review of consecutive patients undergoing an open preperitoneal retrofascial mesh repair of multiply (two or more) recurrent hernias at a tertiary care referral center. RESULTS: From January 2001 to May 2005, 128 patients underwent surgical repair of an MRH; 32 of these underwent an open preperitoneal repair. The average body mass index was 39.1 +/- 8.4 kg/m2 (range 28.9 to 61.0 kg/m2). All patients had significant comorbidities; 18.8% were smokers. The number of previous herniorrhaphies was 3.6 (range 2 to 24). Polypropylene mesh was used in all patients, including lightweight polypropylene in 10 (31.2%) patients. The average mesh size was 937 +/- 531 cm2 (range 225 to 1,800 cm2). There were no major intraoperative complications. Wound infection occurred in 4 patients (12.5%, all smokers), requiring partial mesh excision in 1 patient. Univariate analysis revealed smoking as the only predictor of wound or mesh-related morbidity (p = 0.0004). At a mean followup of 28.1 months (range 8 to 60 months), there has been 1 recurrence (3.1%) in the patient requiring partial mesh removal. CONCLUSIONS: Open preperitoneal retrofascial mesh repair resulted in an effective herniorrhaphy with low perioperative morbidity in patients with MRH. Smoking cessation appears to be important in minimizing infectious complications. Given the technical challenge, surgical care of patients with MRH may be best provided in referral centers with interest and expertise in complex hernia repairs.