A mechanistic understanding of allosteric immune escape pathways in the HIV-1 envelope glycoprotein

Mouse and human immune responses share neutralization epitopes of HAstV-VA1

Astroviruses are highly divergent and infect a wide variety of animal hosts. In 2009, a genetically divergent human astrovirus (HAstV) strain VA1 was first identified in an outbreak of acute gastroenteritis. This strain has also been associated with fatal central nervous system disease. In this work, we report the isolation of three high-affinity neutralizing monoclonal antibodies (Nt-MAbs) targeting the capsid spike domain of HAstV-VA1. These antibodies (7C8, 2A2, 3D8) were used to select individual HAstV-VA1 mutants resistant to their neutralizing activity and a HAstV-VA1 triple mutant that escapes neutralization from all three Nt-MAbs. Sequencing of the virus genome capsid region revealed escape mutations that map to the surface of the capsid spike domain, define three potentially independent neutralization epitopes, and help delineate four antigenic sites in human astroviruses. Notably, two of the escape mutations were found to be present in the spike sequence of the HAstV-VA1-PS strain isolated from an immunodeficient patient with encephalitis, suggesting that those mutations arose as a result of the immune pressure generated by the patient's immunotherapy. In agreement with this observation, human serum samples exhibiting strong neutralization activity against wild-type HAstV-VA1 had a 2.6-fold reduction in neutralization titer when evaluated against the triple-escape HAstV-VA1 mutant, suggesting that both mouse and human antibody responses target shared neutralization epitopes. The isolated Nt-MAbs reported in this work will help to characterize the functional domains of the virus during cell entry and have the potential for developing a specific antibody therapy for the neurological disease associated with HAstV-VA1.

IMPORTANCE

Human astroviruses (HAstVs) have been historically associated with acute gastroenteritis. However, the genetically divergent HAstV-VA1 strain has been associated with central nervous system disease. In this work high-affinity neutralizing monoclonal antibodies directed to HAstV-VA1 were isolated and characterized. The proposed binding sites for these antibodies and for neutralizing antibodies against classical HAstVs suggest that there are at least four neutralization sites on the capsid spike of astroviruses. Our data show that natural infection with human astrovirus VA1 elicits a robust humoral immune response that targets the same antigenic sites recognized by the mouse monoclonal antibodies and strongly suggests the emergence of a variant HAstV-VA1 virus in an immunodeficient patient with prolonged astrovirus infection. The isolated Nt-MAb reported in this work will help to define the functional sites of the virus involved in cell entry and hold promise for developing a specific antibody therapy for the neurological disease associated with HAstV-VA1.

Network analysis uncovers the communication structure of SARS-CoV-2 spike protein identifying sites for immunogen design

The COVID-19 pandemic, caused by the SARS-CoV-2 virus, has triggered myriad efforts to understand the structure and dynamics of this complex pathogen. The spike glycoprotein of SARS-CoV-2 is a significant target for immunogens as it is the means by which the virus enters human cells, while simultaneously sporting mutations responsible for immune escape. These functional and escape processes are regulated by complex molecular-level interactions. Our study presents quantitative insights on domain and residue contributions to allosteric communication, immune evasion, and local- and global-level control of functions through the derivation of a weighted graph representation from all-atom MD simulations. Focusing on the ancestral form and the D614G-variant, we provide evidence of the utility of our approach by guiding the selection of a mutation that alters the spike's stability. Taken together, the network approach serves as a valuable tool to evaluate communication "hot-spots" in proteins to guide design of stable immunogens.

psnGPCRdb: The Structure-network Database of G Protein Coupled Receptors

G protein coupled receptors (GPCRs) are critical eukaryotic signal transduction gatekeepers and represent the largest protein superfamily in the human proteome, with more than 800 members. They share seven transmembrane helices organized in an up-down bundle architecture. GPCR-mediated signaling pathways have been linked to numerous human diseases, and GPCRs are the targets of approximately 35% of all drugs currently on the market. Structure network analysis, a graph theory-based approach, represents a cutting-edge tool to deeply understand GPCR function, which strongly relies on communication between the extracellular and intracellular poles of their structure. psnGPCRdb stores the structure networks (i.e., linked nodes, hubs, communities and communication pathways) computed on all updated GPCR structures in the Protein Data Bank, in their isolated states or in complex with extracellular and/or intracellular molecules. The structure communication signatures of a sub-family or family of GPCRs as well as of their small-molecule activators or inhibitors are stored as consensus networks. The database stores also all meaningful structure network-based comparisons (i.e., difference networks) of functionally different states (i.e., inactive or active) of a given receptor sub-type, or of consensus networks representative of a receptor sub-type, type, sub-family or family. Single or consensus GPCR networks hold also information on amino acid conservation. The database allows to graphically analyze 3D structure networks together with interactive data-tables. Ligand-centric networks can be analyzed as well. psnGPCRdb is unique and represents a powerful resource to unravel GPCR function with important implications in cell signaling and drug design. psnGPCRdb is freely available at: http://webpsn.hpc.unimo.it/psngpcr.php.

Complexity of Viral Epitope Surfaces as Evasive Targets for Vaccines and Therapeutic Antibodies

The dynamic interplay between virus and host plays out across many interacting surfaces as virus and host evolve continually in response to one another. In particular, epitope-paratope interactions (EPIs) between viral antigen and host antibodies drive much of this evolutionary race. In this review, we describe a series of recent studies examining aspects of epitope complexity that go beyond two interacting protein surfaces as EPIs are typically understood. To structure our discussion, we present a framework for understanding epitope complexity as a spectrum along a series of axes, focusing primarily on 1) epitope biochemical complexity (e.g., epitopes involving N-glycans) and 2) antigen conformational/dynamic complexity (e.g., epitopes with differential properties depending on antigen state or fold-axis). We highlight additional epitope complexity factors including epitope tertiary/quaternary structure, which contribute to epistatic relationships between epitope residues within- or adjacent-to a given epitope, as well as epitope overlap resulting from polyclonal antibody responses, which is relevant when assessing antigenic pressure against a given epitope. Finally, we discuss how these different forms of epitope complexity can limit EPI analyses and therapeutic antibody development, as well as recent efforts to overcome these limitations.

Insights on the mutational landscape of the SARS-CoV-2 Omicron variant receptor-binding domain

The Omicron variant features enhanced transmissibility and antibody escape. Here, we describe the Omicron receptor-binding domain (RBD) mutational landscape using amino acid interaction (AAI) networks, which are well suited for interrogating constellations of mutations that function in an epistatic manner. Using AAI, we map Omicron mutations directly and indirectly driving increased escape breadth and depth in class 1-4 antibody epitopes. Further, we present epitope networks for authorized therapeutic antibodies and assess perturbations to each antibody's epitope. Since our initial modeling following the identification of Omicron, these predictions have been realized by experimental findings of Omicron neutralization escape from therapeutic antibodies ADG20, AZD8895, and AZD1061. Importantly, the AAI predicted escape resulting from indirect epitope perturbations was not captured by previous sequence or point mutation analyses. Finally, for several Omicron RBD mutations, we find evidence for a plausible role in enhanced transmissibility via disruption of RBD-down conformational stability at the RBDdown-RBDdown interface.

Distinct allosteric pathways in imidazole glycerol phosphate synthase from yeast and bacteria

Understanding the relationship between protein structures and their function is still an open question that becomes very challenging when allostery plays an important functional role. Allosteric proteins, in fact, exploit different ranges of motions (from sidechain local fluctuations to long-range collective motions) to effectively couple distant binding sites, and of particular interest is whether allosteric proteins of the same families with similar functions and structures also necessarily share the same allosteric mechanisms. Here, we compared the early dynamics initiating the allosteric communication of a prototypical allosteric enzyme from two different organisms, i.e., the imidazole glycerol phosphate synthase (IGPS) enzymes from the thermophilic bacteria and the yeast, working at high and room temperatures, respectively. By combining molecular dynamics simulations and network models derived from graph theory, we found rather distinct early allosteric dynamics in the IGPS from the two organisms, involving significatively different allosteric pathways in terms of both local and collective motions. Given the successful prediction of key allosteric residues in the bacterial IGPS, whose mutation disrupts its allosteric communication, the outcome of this study paves the way for future experimental studies on the yeast IGPS that could foster therapeutic applications by exploiting the control of IGPS enzyme allostery.

PSNtools for standalone and web-based structure network analyses of conformational ensembles

Structure graphs, in which interacting amino acids/nucleotides correspond to linked nodes, represent cutting-edge tools to investigate macromolecular function.

The graph-based approach defined as Protein Structure Network (PSN) was initially implemented in the Wordom software and subsequently in the webPSN server. PSNs are computed either on a molecular dynamics (MD) trajectory (PSN-MD) or on a single structure. In the latter case, information on atomic fluctuations is inferred from the Elastic Network Model-Normal Mode Analysis (ENM-NMA) (PSN-ENM). While Wordom performs both PSN-ENM and PSN-MD analyses but without output post-processing, the webPSN server performs only single-structure PSN-EMN but assisting the user in input setup and output analysis.

Here we release for the first time the standalone software PSNtools, which allows calculation and post-processing of PSN analyses carried out either on single structures or on conformational ensembles. Relevant unique and novel features of PSNtools are either comparisons of two networks or computations of consensus networks on sets of homologous/analogous macromolecular structures or conformational ensembles. Network comparisons and consensus serve to infer differences in functionally different states of the same system or network-based signatures in groups of bio-macromolecules sharing either the same functionality or the same fold.

In addition to the new software, here we release also an updated version of the webPSN server, which allows performing an interactive graphical analysis of PSN-MD, following the upload of the PSNtools output.

PSNtools, the auxiliary binary version of Wordom software, and the WebPSN server are freely available at http://webpsn.hpc.unimo.it/wpsn3.php.

Viral surface geometry shapes influenza and coronavirus spike evolution through antibody pressure

The evolution of circulating viruses is shaped by their need to evade antibody response, which mainly targets the viral spike. Because of the high density of spikes on the viral surface, not all antigenic sites are targeted equally by antibodies. We offer here a geometry-based approach to predict and rank the probability of surface residues of SARS spike (S protein) and influenza H1N1 spike (hemagglutinin) to acquire antibody-escaping mutations utilizing in-silico models of viral structure. We used coarse-grained MD simulations to estimate the on-rate (targeting) of an antibody model to surface residues of the spike protein. Analyzing publicly available sequences, we found that spike surface sequence diversity of the pre-pandemic seasonal influenza H1N1 and the sarbecovirus subgenus highly correlates with our model prediction of antibody targeting. In particular, we identified an antibody-targeting gradient, which matches a mutability gradient along the main axis of the spike. This identifies the role of viral surface geometry in shaping the evolution of circulating viruses. For the 2009 H1N1 and SARS-CoV-2 pandemics, a mutability gradient along the main axis of the spike was not observed. Our model further allowed us to identify key residues of the SARS-CoV-2 spike at which antibody escape mutations have now occurred. Therefore, it can inform of the likely functional role of observed mutations and predict at which residues antibody-escaping mutation might arise.

Changes in the V1 Loop of HIV-1 Envelope Glycoproteins Can Allosterically Modulate the Trimer Association Domain and Reduce PGT145 Sensitivity

Human immunodeficiency virus (HIV-1) envelope glycoproteins (Envs) are a main focus of immunogen design and vaccine development. Broadly neutralizing antibodies (bnAbs) against HIV-1 Envs target conserved epitopes and neutralize multiple HIV-1 viral strains. Nevertheless, application of bnAbs to therapy and prevention is limited by resistant strains that are developed or preexist within the viral population. Here we studied the HIV-1_NAB9 Envs that were isolated from a person who injects drugs and exhibits high and broad resistance to multiple bnAbs. We identified an insertion of 11 amino acids in the V1 loop that allosterically modulates HIV-1_NAB9 sensitivity to the PGT145 bnAb, which targets the Env trimer association domain and supports high level viral infectivity. Our data provide new insights into the mechanisms of HIV-1 resistance to bnAbs and into allosteric connectivity between different HIV-1 Env domains.

HIV-1 Envelope Conformation, Allostery, and Dynamics

The HIV-1 envelope glycoprotein (Env) mediates host cell fusion and is the primary target for HIV-1 vaccine design. The Env undergoes a series of functionally important conformational rearrangements upon engagement of its host cell receptor, CD4. As the sole target for broadly neutralizing antibodies, our understanding of these transitions plays a critical role in vaccine immunogen design. Here, we review available experimental data interrogating the HIV-1 Env conformation and detail computational efforts aimed at delineating the series of conformational changes connecting these rearrangements. These studies have provided a structural mapping of prefusion closed, open, and transition intermediate structures, the allosteric elements controlling rearrangements, and state-to-state transition dynamics. The combination of these investigations and innovations in molecular modeling set the stage for advanced studies examining rearrangements at greater spatial and temporal resolution.

Viral surface geometry shapes influenza and coronavirus spike evolution through antibody pressure

The evolution of circulating viruses is shaped by their need to evade antibody response, which mainly targets the glycoprotein (spike). However, not all antigenic sites are targeted equally by antibodies, leading to complex immunodominance patterns. We used 3D computational models to estimate antibody pressure on the seasonal influenza H1N1 and SARS spikes. Analyzing publically available sequences, we show that antibody pressure, through the geometrical organization of spikes on the viral surface, shaped their mutability. Studying the mutability patterns of SARS-CoV-2 and the 2009 H1N1 pandemic spikes, we find that they are not predominantly shaped by antibody pressure. However, for SARS-CoV-2, we find that over time, it acquired mutations at antibody-accessible positions, which could indicate possible escape as define by our model. We offer a geometry-based approach to predict and rank the probability of surface resides of SARS-CoV-2 spike to acquire antibody escaping mutations.

Quantification of the Resilience and Vulnerability of HIV-1 Native Glycan Shield at Atomistic Detail

Dense surface glycosylation on the HIV-1 envelope (Env) protein acts as a shield from the adaptive immune system. However, the molecular complexity and flexibility of glycans make experimental studies a challenge. Here we have integrated high-throughput atomistic modeling of fully glycosylated HIV-1 Env with graph theory to capture immunologically important features of the shield topology. This is the first complete all-atom model of HIV-1 Env SOSIP glycan shield that includes both oligomannose and complex glycans, providing physiologically relevant insights of the glycan shield. This integrated approach including quantitative comparison with cryo-electron microscopy data provides hitherto unexplored details of the native shield architecture and its difference from the high-mannose glycoform. We have also derived a measure to quantify the shielding effect over the antigenic protein surface that defines regions of relative vulnerability and resilience of the shield and can be harnessed for rational immunogen design.

Shaping Polyclonal Responses via Antigen-Mediated Antibody Interference

Broadly neutralizing antibodies (bnAbs) recognize conserved features of rapidly mutating pathogens and confer universal protection, but they emerge rarely in natural infection. Increasing evidence indicates that seemingly passive antibodies may interfere with natural selection of B cells. Yet, how such interference modulates polyclonal responses is unknown. Here we provide a framework for understanding the role of antibody interference—mediated by multi-epitope antigens—in shaping B cell clonal makeup and the fate of bnAb lineages. We find that, under heterogeneous interference, clones with different intrinsic fitness can collectively persist. Furthermore, antagonism among fit clones (specific for variable epitopes) promotes expansion of unfit clones (targeting conserved epitopes), at the cost of repertoire potency. This trade-off, however, can be alleviated by synergy toward the unfit. Our results provide a physical basis for antigen-mediated clonal interactions, stress system-level impacts of molecular synergy and antagonism, and offer principles to amplify naturally rare clones.

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Multi-epitope antigens mediate antibody interference that couples B cell lineages

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Trade-off exists between repertoire potency and persistence of broad lineages

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Antigen-mediated synergy toward intrinsically unfit clones alleviates the trade-off

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Amplifying rare clones by leveraging molecular interference structure

webPSN v2.0: a webserver to infer fingerprints of structural communication in biomacromolecules

A mixed Protein Structure Network (PSN) and Elastic Network Model-Normal Mode Analysis (ENM-NMA)-based strategy (i.e. PSN-ENM) was developed to investigate structural communication in bio-macromolecules. Protein Structure Graphs (PSGs) are computed on a single structure, whereas information on system dynamics is supplied by ENM-NMA. The approach was implemented in a webserver (webPSN), which was significantly updated herein. The webserver now handles both proteins and nucleic acids and relies on an internal upgradable database of network parameters for ions and small molecules in all PDB structures. Apart from the radical restyle of the server and some changes in the calculation setup, other major novelties concern the possibility to: a) compute the differences in nodes, links, and communication pathways between two structures (i.e. network difference) and b) infer links, hubs, communities, and metapaths from consensus networks computed on a number of structures. These new features are useful to identify commonalties and differences between two different functional states of the same system or structural-communication signatures in homologous or analogous systems. The output analysis relies on 3D-representations, interactive tables and graphs, also available for download. Speed and accuracy make this server suitable to comparatively investigate structural communication in large sets of bio-macromolecular systems. URL: http://webpsn.hpc.unimore.it.

Identification of potential allosteric binding sites in cathepsin K based on intramolecular communication

Network theory methods and molecular dynamics (MD) simulations are accepted tools to study allosteric regulation. Indeed, dynamic networks built upon correlation analysis of MD trajectories provide detailed information about communication paths between distant sites. In this context, we aimed to understand whether the efficiency of intramolecular communication could be used to predict the allosteric potential of a given site. To this end, we performed MD simulations and network theory analyses in cathepsin K (catK), whose allosteric sites are well defined. To obtain a quantitative measure of the efficiency of communication, we designed a new protocol that enables the comparison between properties related to ensembles of communication paths obtained from different sites. Further, we applied our strategy to evaluate the allosteric potential of different catK cavities not yet considered for drug design. Our predictions of the allosteric potential based on intramolecular communication correlate well with previous catK experimental and theoretical data. We also discuss the possibility of applying our approach to other proteins from the same family.

Allosteric Regulation at the Crossroads of New Technologies: Multiscale Modeling, Networks, and Machine Learning

Allosteric regulation is a common mechanism employed by complex biomolecular systems for regulation of activity and adaptability in the cellular environment, serving as an effective molecular tool for cellular communication. As an intrinsic but elusive property, allostery is a ubiquitous phenomenon where binding or disturbing of a distal site in a protein can functionally control its activity and is considered as the "second secret of life." The fundamental biological importance and complexity of these processes require a multi-faceted platform of synergistically integrated approaches for prediction and characterization of allosteric functional states, atomistic reconstruction of allosteric regulatory mechanisms and discovery of allosteric modulators. The unifying theme and overarching goal of allosteric regulation studies in recent years have been integration between emerging experiment and computational approaches and technologies to advance quantitative characterization of allosteric mechanisms in proteins. Despite significant advances, the quantitative characterization and reliable prediction of functional allosteric states, interactions, and mechanisms continue to present highly challenging problems in the field. In this review, we discuss simulation-based multiscale approaches, experiment-informed Markovian models, and network modeling of allostery and information-theoretical approaches that can describe the thermodynamics and hierarchy allosteric states and the molecular basis of allosteric mechanisms. The wealth of structural and functional information along with diversity and complexity of allosteric mechanisms in therapeutically important protein families have provided a well-suited platform for development of data-driven research strategies. Data-centric integration of chemistry, biology and computer science using artificial intelligence technologies has gained a significant momentum and at the forefront of many cross-disciplinary efforts. We discuss new developments in the machine learning field and the emergence of deep learning and deep reinforcement learning applications in modeling of molecular mechanisms and allosteric proteins. The experiment-guided integrated approaches empowered by recent advances in multiscale modeling, network science, and machine learning can lead to more reliable prediction of allosteric regulatory mechanisms and discovery of allosteric modulators for therapeutically important protein targets.

Revisiting allostery in CREB-binding protein (CBP) using residue-based interaction energy

CREB-binding protein (CBP) is a multi-subunit scaffold protein complex in transcription regulation process, binding and interacting with ligands such as mixed-lineage leukemia (MLL) and c-Myb allosterically. Here in this study, we have revisited the concept of allostery in CBP via residue-based interaction energy calculation based on molecular dynamics (MD) simulations. To this end, we conducted MD simulations of KIX:MLL:c-Myb ternary complex, its binary components and kinase-inducible domain (KID) interacting domain (KIX) backbone. Interaction energy profiles and cross correlation analysis were performed and the results indicated that KIX:MLL and KIX:c-Myb:MLL complexes demonstrate significant similarities according to both analysis methods. Two regions in the KIX backbone were apparent from the interaction energy and cross correlation maps that hold a key to allostery phenomena observed in CBP. While one of these regions are related to the ligand binding residues, the other comprises of L₁₂-G₂ loop and α₃ helix regions that have been found to have a significant role in allosteric signal propagation. All in all, residue-based interaction energy calculation method is demonstrated to be a valuable calculation technique for the detection of allosteric signal propagation and ligand interaction regions.

Graph-Directed Approach for Downselecting Toxins for Experimental Structure Determination

Conotoxins are short, cysteine-rich peptides of great interest as novel therapeutic leads and of great concern as lethal biological agents due to their high affinity and specificity for various receptors involved in neuromuscular transmission. Currently, of the approximately 6000 known conotoxin sequences, only about 3% have associated structural characterization, which leads to a bottleneck in rapid high-throughput screening (HTS) for identification of potential leads or threats. In this work, we combine a graph-based approach with homology modeling to expand the library of conotoxin structures and to identify those conotoxin sequences that are of the greatest value for experimental structural characterization. The latter would allow for the rapid expansion of the known structural space for generating high quality template-based models. Our approach generalizes to other evolutionarily-related, short, cysteine-rich venoms of interest. Overall, we present and validate an approach for venom structure modeling and experimental guidance and employ it to produce a 290%-larger library of approximate conotoxin structures for HTS. We also provide a set of ranked conotoxin sequences for experimental structure determination to further expand this library.

Data-driven computational analysis of allosteric proteins by exploring protein dynamics, residue coevolution and residue interaction networks

BACKGROUND

Computational studies of allosteric interactions have witnessed a recent renaissance fueled by the growing interest in modeling of the complex molecular assemblies and biological networks. Allosteric interactions in protein structures allow for molecular communication in signal transduction networks.

METHODS

In this work, we performed a large scale comprehensive and multi-faceted analysis of >300 diverse allosteric proteins and complexes with allosteric modulators. By modeling and exploring coarse-grained dynamics, residue coevolution, and residue interaction networks for allosteric proteins, we have determined unifying molecular signatures shared by allosteric systems.

RESULTS

The results of this study have suggested that allosteric inhibitors and allosteric activators may differentially affect global dynamics and network organization of protein systems, leading to diverse allosteric mechanisms. By using structural and functional data on protein kinases, we present a detailed case study that that included atomic-level analysis of coevolutionary networks in kinases bound with allosteric inhibitors and activators.

CONCLUSIONS

We have found that coevolutionary networks can form direct communication pathways connecting functional regions and can recapitulate key regulatory sites and interactions responsible for allosteric signaling in the studied protein systems. The results of this computational investigation are compared with the experimental studies and reveal molecular signatures of known regulatory hotspots in protein kinases.

GENERAL SIGNIFICANCE

This study has shown that allosteric inhibitors and allosteric activators can have a different effect on residue interaction networks and can exploit distinct regulatory mechanisms, which could open up opportunities for probing allostery and new drug combinations with broad range of activities.

Network analysis of dynamically important residues in protein structures mediating ligand-binding conformational changes

According to the generalized conformational selection model, ligand binding involves the co-existence of at least two conformers with different ligand-affinities in a dynamical equilibrium. Conformational transitions between them should be guaranteed by intramolecular vibrational dynamics associated to each conformation. These motions are, therefore, related to the biological function of a protein. Positions whose mutations are found to alter these vibrations the most can be defined as key positions, that is, dynamically important residues that mediate the ligand-binding conformational change. In a previous study, we have shown that these positions are evolutionarily conserved. They correspond to buried aliphatic residues mostly localized in regular structured regions of the protein like β-sheets and α-helices. In the present paper, we perform a network analysis of these key positions for a large dataset of paired protein structures in the ligand-free and ligand-bound form. We observe that networks of interactions between these key positions present larger and more integrated networks with faster transmission of the information. Besides, networks of residues result that are robust to conformational changes. Our results reveal that the conformational diversity of proteins seems to be guaranteed by a network of strongly interconnected key positions rather than individual residues.

ProSNEx: a web-based application for exploration and analysis of protein structures using network formalism

ProSNEx (Protein Structure Network Explorer) is a web service for construction and analysis of Protein Structure Networks (PSNs) alongside amino acid flexibility, sequence conservation and annotation features. ProSNEx constructs a PSN by adding nodes to represent residues and edges between these nodes using user-specified interaction distance cutoffs for either carbon-alpha, carbon-beta or atom-pair contact networks. Different types of weighted networks can also be constructed by using either (i) the residue-residue interaction energies in the format returned by gRINN, resulting in a Protein Energy Network (PEN); (ii) the dynamical cross correlations from a coarse-grained Normal Mode Analysis (NMA) of the protein structure; (iii) interaction strength. Upon construction of the network, common network metrics (such as node centralities) as well as shortest paths between nodes and k-cliques are calculated. Moreover, additional features of each residue in the form of conservation scores and mutation/natural variant information are included in the analysis. By this way, tool offers an enhanced and direct comparison of network-based residue metrics with other types of biological information. ProSNEx is free and open to all users without login requirement at http://prosnex-tool.com.

Atomistic Modeling of the ABL Kinase Regulation by Allosteric Modulators Using Structural Perturbation Analysis and Community-Based Network Reconstruction of Allosteric Communications

In this work, we have examined the molecular mechanisms of allosteric regulation of the ABL tyrosine kinase at the atomic level. Atomistic modeling of the ABL complexes with a panel of allosteric modulators has been performed using a combination of molecular dynamics simulations, structural residue perturbation scanning, and a novel community analysis of the residue interaction networks. Our results have indicated that allosteric inhibitors and activators may exert a differential control on allosteric signaling between the kinase binding sites and functional regions. While the inhibitor binding can strengthen the closed ABL state and induce allosteric communications directed from the allosteric pocket to the ATP binding site, the DPH activator may induce a more dynamic open form and activate allosteric couplings between the ATP and substrate binding sites. By leveraging a network-centric theoretical framework, we have introduced a novel community analysis method and global topological parameters that have unveiled the hierarchical modularity and the intercommunity bridging sites in the residue interaction network. We have found that allosteric functional hotspots responsible for the kinase regulation may serve the intermodular bridges in the global interaction network. The central conclusion from this analysis is that the regulatory switch centers play a fundamental role in the modular network organization of ABL as the unique intercommunity bridges that connect the SH2 and SH3 domains with the catalytic core into a functional kinase assembly. The hierarchy of network organization in the ABL regulatory complexes may allow for the synergistic action of dense intercommunity links required for the robust signal transfer in the catalytic core and sparse network bridges acting as the regulatory control points that orchestrate allosteric transitions between the inhibited and active kinase forms.

An Antigenic Atlas of HIV-1 Escape from Broadly Neutralizing Antibodies Distinguishes Functional and Structural Epitopes

Anti-HIV broadly neutralizing antibodies (bnAbs) have revealed vaccine targets on the virus's envelope (Env) protein and are themselves promising immunotherapies. The efficacy of bnAb-based therapies and vaccines depends in part on how readily the virus can escape neutralization. Although structural studies can define contacts between bnAbs and Env, only functional studies can define mutations that confer escape. Here, we mapped how all possible single amino acid mutations in Env affect neutralization of HIV by nine bnAbs targeting five epitopes. For most bnAbs, mutations at only a small fraction of structurally defined contact sites mediated escape, and most escape occurred at sites near, but not in direct contact with, the antibody. The Env mutations selected by two pooled bnAbs were similar to those expected from the combination of the bnAbs's independent action. Overall, our mutation-level antigenic atlas provides a comprehensive dataset for understanding viral immune escape and refining therapies and vaccines.

All-atom virus simulations

The constant threat of viral disease can be combated by the development of novel vaccines and therapeutics designed to disrupt key features of virus structure or infection cycle processes. Such development relies on high-resolution characterization of viruses and their dynamical behaviors, which are often challenging to obtain solely by experiment. In response, all-atom molecular dynamics simulations are widely leveraged to study the structural components of viruses, leading to some of the largest simulation endeavors undertaken to date. The present work reviews exemplary all-atom simulation work on viruses, as well as progress toward simulating entire virions.

CAMERRA: An analysis tool for the computation of conformational dynamics by evaluating residue-residue associations

A computational method which extracts the dominant motions from an ensemble of biomolecular conformations via a correlation analysis of residue-residue contacts is presented. The algorithm first renders the structural information into contact matrices, then constructs the collective modes based on the correlated dynamics of a selected set of dynamic contacts. Associated programs can bridge the results for further visualization using graphics software. The aim of this method is to provide an analysis of conformations of biopolymers from the contact viewpoint. It may assist a systematical uncovering of conformational switching mechanisms existing in proteins and biopolymer systems in general by statistical analysis of simulation snapshots. In contrast to conventional correlation analyses of Cartesian coordinates (such as distance covariance analysis and Cartesian principal component analysis), this program also provides an alternative way to locate essential collective motions in general. Herein, we detail the algorithm in a stepwise manner and comment on the importance of the method as applied to decoding allosteric mechanisms. © 2018 Wiley Periodicals, Inc.

Identification of Novel Structural Determinants in MW965 Env That Regulate the Neutralization Phenotype and Conformational Masking Potential of Primary HIV-1 Isolates

The subtype C HIV-1 isolate MW965.26 is a highly neutralization-sensitive tier 1a primary isolate that is widely used in vaccine studies, but the basis for the sensitive neutralization phenotype of this isolate is not known. Substituting the MW965.26 V1/V2 domain into a neutralization-sensitive SF162 Env clone resulted in high resistance to standard anti-V3 monoclonal antibodies, demonstrating that this region possesses strong masking activity in a standard Env backbone and indicating that determinants elsewhere in MW965.26 Env are responsible for its unusual neutralization sensitivity. Key determinants for this phenotype were mapped by generating chimeric Envs between MW965.26 Env and a typical resistant Env clone, the consensus C (ConC) clone, and localized to two residues, Cys384 in the C3 domain and Asn502 in the C5 domain. Substituting the sensitizing mutations Y384C and K502N at these positions into several resistant primary Envs resulted in conversion to neutralization-sensitive phenotypes, demonstrating the generalizability of this effect. In contrast to the sensitizing effects of these substitutions on normally masked epitopes, these mutations reduced the sensitivity of VRC01-like epitopes overlapping the CD4-binding domain, while they had no effect on several other classes of broadly neutralizing epitopes, including members of several lineages of V2-dependent quaternary epitopes and representatives of N332 glycan-dependent epitopes (PGT121) and quaternary, cleavage-dependent epitopes centered at the gp41-gp120 interface on intact HIV-1 Env trimers (PGT151). These results identify novel substitutions in gp120 that regulate the expression of alternative conformations of Env and differentially affect the exposure of different classes of epitopes, thereby influencing the neutralization phenotype of primary HIV-1 isolates.IMPORTANCE A better understanding of the mechanisms that determine the wide range of neutralization sensitivity of circulating primary HIV-1 isolates would provide important information about the natural structural and conformational diversity of HIV-1 Env and how this affects the neutralization phenotype. A useful way of studying this is to determine the molecular basis for the unusually high neutralization sensitivities of the limited number of available tier 1a viruses. This study localized the neutralization sensitivity of MW965.26, an extremely sensitive subtype C-derived primary isolate, to two rare substitutions in the C3 and C5 domains and demonstrated that the sequences at these positions differentially affect the presentation of epitopes recognized by different classes of standard and conformation-dependent broadly neutralizing antibodies. These results provide novel insight into how these regions regulate the neutralization phenotype and provide tools for controlling the Env conformation that could have applications both for structural studies and in vaccine design.

Flow Virometry: a Powerful Tool To Functionally Characterize Viruses

For several decades, flow cytometry has been a common approach to analyze cells and sort them to near-purity. It enables one to probe inner cellular molecules, surface receptors, or infected cells. However, the analysis of smaller entities such as viruses and exocytic vesicles has been more difficult but is becoming mainstream. This has in part been due to the development of new instrumentation with resolutions below that of conventional cytometers. It is also attributed to the several means employed to fluorescently label viruses, hence enabling them to stand out from similarly sized particles representing background noise. Thus far, more than a dozen different viruses ranging in size from 40 nm to giant viruses have been probed by this approach, which was recently dubbed "flow virometry." These studies have collectively highlighted the breadth of the applications of this method, which, for example, has elucidated the maturation of dengue virus, served as quality control for vaccinia vaccines, and enabled the sorting of herpes simplex virus discrete viral particles. The present review focuses on the means employed to characterize and sort viruses by this powerful technology and on the emerging uses of flow virometry. It similarly addresses some of its current challenges and limitations.

Ensemble-based modeling and rigidity decomposition of allosteric interaction networks and communication pathways in cyclin-dependent kinases: Differentiating kinase clients of the Hsp90-Cdc37 chaperone

The overarching goal of delineating molecular principles underlying differentiation of protein kinase clients and chaperone-based modulation of kinase activity is fundamental to understanding activity of many oncogenic kinases that require chaperoning of Hsp70 and Hsp90 systems to attain a functionally competent active form. Despite structural similarities and common activation mechanisms shared by cyclin-dependent kinase (CDK) proteins, members of this family can exhibit vastly different chaperone preferences. The molecular determinants underlying chaperone dependencies of protein kinases are not fully understood as structurally similar kinases may often elicit distinct regulatory responses to the chaperone. The regulatory divergences observed for members of CDK family are of particular interest as functional diversification among these kinases may be related to variations in chaperone dependencies and can be exploited in drug discovery of personalized therapeutic agents. In this work, we report the results of a computational investigation of several members of CDK family (CDK5, CDK6, CDK9) that represented a broad repertoire of chaperone dependencies—from nonclient CDK5, to weak client CDK6, and strong client CDK9. By using molecular simulations of multiple crystal structures we characterized conformational ensembles and collective dynamics of CDK proteins. We found that the elevated dynamics of CDK9 can trigger imbalances in cooperative collective motions and reduce stability of the active fold, thus creating a cascade of favorable conditions for chaperone intervention. The ensemble-based modeling of residue interaction networks and community analysis determined how differences in modularity of allosteric networks and topography of communication pathways can be linked with the client status of CDK proteins. This analysis unveiled depleted modularity of the allosteric network in CDK9 that alters distribution of communication pathways and leads to impaired signaling in the client kinase. According to our results, these network features may uniquely define chaperone dependencies of CDK clients. The perturbation response scanning and rigidity decomposition approaches identified regulatory hotspots that mediate differences in stability and cooperativity of allosteric interaction networks in the CDK structures. By combining these synergistic approaches, our study revealed dynamic and network signatures that can differentiate kinase clients and rationalize subtle divergences in the activation mechanisms of CDK family members. The therapeutic implications of these results are illustrated by identifying structural hotspots of pathogenic mutations that preferentially target regions of the increased flexibility to enable modulation of activation changes. Our study offers a network-based perspective on dynamic kinase mechanisms and drug design by unravelling relationships between protein kinase dynamics, allosteric communications and chaperone dependencies.

Norovirus Escape from Broadly Neutralizing Antibodies Is Limited to Allostery-Like Mechanisms

The simplest and most common way for viruses to escape antibody neutralization is by mutating residues that are essential for antibody binding. Escape mutations are strongly selected for by their effect on viral fitness, which is most often related to issues of protein folding, particle assembly, and capsid function. The studies presented here demonstrated that a broadly neutralizing antibody to mouse norovirus binds to an exposed surface but that the only escape mutants that arose were distal to the antibody binding surface. To understand this finding, we performed an in silico analysis that suggested that those escape mutations blocked antibody binding by affecting structural plasticity. This kind of antigenic region—one that gives rise to broadly neutralizing antibodies but that the virus finds difficult to escape from—is therefore ideal for vaccine development.

Ideal antiviral vaccines elicit antibodies (Abs) with broad strain recognition that bind to regions that are difficult to mutate for escape. Using 10 murine norovirus (MNV) strains and 5 human norovirus (HuNoV) virus-like particles (VLPs), we identified monoclonal antibody (MAb) 2D3, which broadly neutralized all MNV strains tested. Importantly, escape mutants corresponding to this antibody were very slow to develop and were distal to those raised against our previously studied antibody, A6.2. To understand the atomic details of 2D3 neutralization, we determined the cryo-electron microscopy (cryo-EM) structure of the 2D3/MNV1 complex. Interestingly, 2D3 binds to the top of the P domain, very close to where A6.2 binds, but the only escape mutations identified to date fall well outside the contact regions of both 2D3 and A6.2. To determine how mutations in distal residues could block antibody binding, we used molecular dynamics flexible fitting simulations of the atomic structures placed into the density map to examine the 2D3/MNV1 complex and these mutations. Our findings suggest that the escape mutant, V339I, may stabilize a salt bridge network at the P-domain dimer interface that, in an allostery-like manner, affects the conformational relaxation of the P domain and the efficiency of binding. They further highlight the unusual antigenic surface bound by MAb 2D3, one which elicits cross-reactive antibodies but which the virus is unable to alter to escape neutralization. These results may be leveraged to generate norovirus (NoV) vaccines containing broadly neutralizing antibodies.

IMPORTANCE The simplest and most common way for viruses to escape antibody neutralization is by mutating residues that are essential for antibody binding. Escape mutations are strongly selected for by their effect on viral fitness, which is most often related to issues of protein folding, particle assembly, and capsid function. The studies presented here demonstrated that a broadly neutralizing antibody to mouse norovirus binds to an exposed surface but that the only escape mutants that arose were distal to the antibody binding surface. To understand this finding, we performed an in silico analysis that suggested that those escape mutations blocked antibody binding by affecting structural plasticity. This kind of antigenic region—one that gives rise to broadly neutralizing antibodies but that the virus finds difficult to escape from—is therefore ideal for vaccine development.

Homology modeling in a dynamical world

A key concept in template-based modeling (TBM) is the high correlation between sequence and structural divergence, with the practical consequence that homologous proteins that are similar at the sequence level will also be similar at the structural level. However, conformational diversity of the native state will reduce the correlation between structural and sequence divergence, because structural variation can appear without sequence diversity. In this work, we explore the impact that conformational diversity has on the relationship between structural and sequence divergence. We find that the extent of conformational diversity can be as high as the maximum structural divergence among families. Also, as expected, conformational diversity impairs the well-established correlation between sequence and structural divergence, which is nosier than previously suggested. However, we found that this noise can be resolved using a priori information coming from the structure-function relationship. We show that protein families with low conformational diversity show a well-correlated relationship between sequence and structural divergence, which is severely reduced in proteins with larger conformational diversity. This lack of correlation could impair TBM results in highly dynamical proteins. Finally, we also find that the presence of order/disorder can provide useful beforehand information for better TBM performance.

Flow virometry analysis of envelope glycoprotein conformations on individual HIV virions

HIV-1 envelope proteins (Envs) play a critical role in HIV infection. In a correct trimeric conformation, Env mediates virus–cell binding and fusion. Malfunctioning of this machinery renders virions incapable of infecting cells. Each HIV-1 virion carries 10–14 Envs, and therefore a defective Env may not necessarily render a HIV virion non-infectious, since other Env on the same virion may still be functional. Alternatively, it is possible that on a given virion either all the spikes are defective or all are functional. Here, we investigate Env conformations on individual virions using our new nanotechnology, “flow virometry”, and a panel of antibodies that discriminate between various Env conformations. We found that the majority of HIV-1 virions carry either only trimeric (“functional”) or only defective spikes. The relatively small subfraction of virions that carry both functional and nonfunctional Envs contributes little to HIV infection of human lymphoid tissue ex vivo. The observation that the majority of virions exclusively express either functional or nonfunctional forms of Env has important implications for understanding the role of neutralizing and non-neutralizing antibodies in the immune control of HIV infection as well as for the development of effective prophylactic strategies.

Diversification in the HIV-1 Envelope Hyper-variable Domains V2, V4, and V5 and Higher Probability of Transmitted/Founder Envelope Glycosylation Favor the Development of Heterologous Neutralization Breadth

A recent study of plasma neutralization breadth in HIV-1 infected individuals at nine International AIDS Vaccine Initiative (IAVI) sites reported that viral load, HLA-A*03 genotype, and subtype C infection were strongly associated with the development of neutralization breadth. Here, we refine the findings of that study by analyzing the impact of the transmitted/founder (T/F) envelope (Env), early Env diversification, and autologous neutralization on the development of plasma neutralization breadth in 21 participants identified during recent infection at two of those sites: Kigali, Rwanda (n = 9) and Lusaka, Zambia (n = 12). Single-genome analysis of full-length T/F Env sequences revealed that all 21 individuals were infected with a highly homogeneous population of viral variants, which were categorized as subtype C (n = 12), A1 (n = 7), or recombinant AC (n = 2). An extensive amino acid sequence-based analysis of variable loop lengths and glycosylation patterns in the T/F Envs revealed that a lower ratio of NXS to NXT-encoded glycan motifs correlated with neutralization breadth. Further analysis comparing amino acid sequence changes, insertions/deletions, and glycan motif alterations between the T/F Env and autologous early Env variants revealed that extensive diversification focused in the V2, V4, and V5 regions of gp120, accompanied by contemporaneous viral escape, significantly favored the development of breadth. These results suggest that more efficient glycosylation of subtype A and C T/F Envs through fewer NXS-encoded glycan sites is more likely to elicit antibodies that can transition from autologous to heterologous neutralizing activity following exposure to gp120 diversification. This initiates an Env-antibody co-evolution cycle that increases neutralization breadth, and is further augmented over time by additional viral and host factors. These findings suggest that understanding how variation in the efficiency of site-specific glycosylation influences neutralizing antibody elicitation and targeting could advance the design of immunogens aimed at inducing antibodies that can transition from autologous to heterologous neutralizing activity.

Addressing the Role of Conformational Diversity in Protein Structure Prediction

Computational modeling of tertiary structures has become of standard use to study proteins that lack experimental characterization. Unfortunately, 3D structure prediction methods and model quality assessment programs often overlook that an ensemble of conformers in equilibrium populates the native state of proteins. In this work we collected sets of publicly available protein models and the corresponding target structures experimentally solved and studied how they describe the conformational diversity of the protein. For each protein, we assessed the quality of the models against known conformers by several standard measures and identified those models ranked best. We found that model rankings are defined by both the selected target conformer and the similarity measure used. 70% of the proteins in our datasets show that different models are structurally closest to different conformers of the same protein target. We observed that model building protocols such as template-based or ab initio approaches describe in similar ways the conformational diversity of the protein, although for template-based methods this description may depend on the sequence similarity between target and template sequences. Taken together, our results support the idea that protein structure modeling could help to identify members of the native ensemble, highlight the importance of considering conformational diversity in protein 3D quality evaluations and endorse the study of the variability of the native structure for a meaningful biological analysis.

Dynamical network of residue-residue contacts reveals coupled allosteric effects in recognition, catalysis, and mutation

Detailed understanding of how conformational dynamics orchestrates function in allosteric regulation of recognition and catalysis remains ambiguous. Here, we simulate CypA using multiple-microsecond-long atomistic molecular dynamics in explicit solvent and carry out NMR experiments. We analyze a large amount of time-dependent multidimensional data with a coarse-grained approach and map key dynamical features within individual macrostates by defining dynamics in terms of residue-residue contacts. The effects of substrate binding are observed to be largely sensed at a location over 15 Å from the active site, implying its importance in allostery. Using NMR experiments, we confirm that a dynamic cluster of residues in this distal region is directly coupled to the active site. Furthermore, the dynamical network of interresidue contacts is found to be coupled and temporally dispersed, ranging over 4 to 5 orders of magnitude. Finally, using network centrality measures we demonstrate the changes in the communication network, connectivity, and influence of CypA residues upon substrate binding, mutation, and during catalysis. We identify key residues that potentially act as a bottleneck in the communication flow through the distinct regions in CypA and, therefore, as targets for future mutational studies. Mapping these dynamical features and the coupling of dynamics to function has crucial ramifications in understanding allosteric regulation in enzymes and proteins, in general.

A broadly neutralizing human monoclonal antibody is effective against H7N9

Emerging strains of influenza represent a significant public health threat with potential pandemic consequences. Of particular concern are the recently emerged H7N9 strains which cause pneumonia with acute respiratory distress syndrome. Estimates are that nearly 80% of hospitalized patients with H7N9 have received intensive care unit support. VIS410, a human antibody, targets a unique conserved epitope on influenza A. We evaluated the efficacy of VIS410 for neutralization of group 2 influenza strains, including H3N2 and H7N9 strains in vitro and in vivo. VIS410, administered at 50 mg/kg, protected DBA mice infected with A/Anhui/2013 (H7N9), resulting in significant survival benefit upon single-dose (-24 h) or double-dose (-12 h, +48 h) administration (P < 0.001). A single dose of VIS410 at 50 mg/kg (-12 h) combined with oseltamivir at 50 mg/kg (-12 h, twice daily for 7 d) in C57BL/6 mice infected with A/Shanghai 2/2013 (H7N9) resulted in significant decreased lung viral load (P = 0.002) and decreased lung cytokine responses for nine of the 11 cytokines measured. Based on these results, we find that VIS410 may be effective either as monotherapy or combined with antivirals in treating H7N9 disease, as well as disease from other influenza strains.

Kinesin-5 allosteric inhibitors uncouple the dynamics of nucleotide, microtubule, and neck-linker binding sites

Kinesin motor domains couple cycles of ATP hydrolysis to cycles of microtubule binding and conformational changes that result in directional force and movement on microtubules. The general principles of this mechanochemical coupling have been established; however, fundamental atomistic details of the underlying allosteric mechanisms remain unknown. This lack of knowledge hampers the development of new inhibitors and limits our understanding of how disease-associated mutations in distal sites can interfere with the fidelity of motor domain function. Here, we combine unbiased molecular-dynamics simulations, bioinformatics analysis, and mutational studies to elucidate the structural dynamic effects of nucleotide turnover and allosteric inhibition of the kinesin-5 motor. Multiple replica simulations of ATP-, ADP-, and inhibitor-bound states together with network analysis of correlated motions were used to create a dynamic protein structure network depicting the internal dynamic coordination of functional regions in each state. This analysis revealed the intervening residues involved in the dynamic coupling of nucleotide, microtubule, neck-linker, and inhibitor binding sites. The regions identified include the nucleotide binding switch regions, loop 5, loop 7, α4-α5-loop 13, α1, and β4-β6-β7. Also evident were nucleotide- and inhibitor-dependent shifts in the dynamic coupling paths linking functional sites. In particular, inhibitor binding to the loop 5 region affected β-sheet residues and α1, leading to a dynamic decoupling of nucleotide, microtubule, and neck-linker binding sites. Additional analyses of point mutations, including P131 (loop 5), Q78/I79 (α1), E166 (loop 7), and K272/I273 (β7) G325/G326 (loop 13), support their predicted role in mediating the dynamic coupling of distal functional surfaces. Collectively, our results and approach, which we make freely available to the community, provide a framework for explaining how binding events and point mutations can alter dynamic couplings that are critical for kinesin motor domain function.

The Patterns of Coevolution in Clade B HIV Envelope's N-Glycosylation Sites

The co-evolution of the potential N-glycosylation sites of HIV Clade B gp120 was mapped onto the coevolution network of the protein structure using mean field direct coupling analysis (mfDCA). This was possible for 327 positions with suitable entropy and gap content. Indications of pressure to preserve the evolving glycan shield are seen as well as strong dependencies between the majority of the potential N-glycosylation sites and the rest of the structure. These findings indicate that although mainly an adaptation against antibody neutralization, the evolving glycan shield is structurally related to the core polypeptide, which, thus, is also under pressure to reflect the changes in the N-glycosylation. The map we propose fills the gap in previous attempts to tease out sequon evolution by providing a more general molecular context. Thus, it will help design strategies guiding HIV gp120 evolution in a rational way.

Mapping allostery through computational glycine scanning and correlation analysis of residue-residue contacts

Understanding allosteric mechanisms is essential for the physical control of molecular switches and downstream cellular responses. However, it is difficult to decode essential allosteric motions in a high-throughput scheme. A general two-pronged approach to performing automatic data reduction of simulation trajectories is presented here. The first step involves coarse-graining and identifying the most dynamic residue-residue contacts. The second step is performing principal component analysis of these contacts and extracting the large-scale collective motions expressed via these residue-residue contacts. We demonstrated the method using a protein complex of nuclear receptors. Using atomistic modeling and simulation, we examined the protein complex and a set of 18 glycine point mutations of residues that constitute the binding pocket of the ligand effector. The important motions that are responsible for the allostery are reported. In contrast to conventional induced-fit and lock-and-key binding mechanisms, a novel "frustrated-fit" binding mechanism of RXR for allosteric control was revealed.

Using THz time-scale infrared spectroscopy to examine the role of collective, thermal fluctuations in the formation of myoglobin allosteric communication pathways and ligand specificity

In this investigation we use THz time-scale spectroscopy to conduct an initial set of studies on myoglobin with the aim of providing further insight into the global, collective thermal fluctuations in the protein that have been hypothesized to play a prominent role in the dynamic formation of transient ligand channels as well as in shaping the molecular level basis for ligand discrimination. Using the two ligands O2 and CO, we have determined that the perturbation from the heme-ligand complex has a strong influence on the characteristics of the myoglobin collective dynamics that are excited upon binding. Further, the differences detected in the collective protein motions in Mb-O2 compared with those in Mb-CO appear to be intimately tied with the pathways of long-range allosteric communication in the protein, which ultimately determine the trajectories selected by the respective ligands on the path to and from the heme-binding cavity.

Isolate-specific differences in the conformational dynamics and antigenicity of HIV-1 gp120

The HIV-1 envelope glycoprotein (Env) mediates viral entry into host cells and is the sole target of neutralizing antibodies. Much of the sequence diversity in the HIV-1 genome is concentrated within Env, particularly within its gp120 surface subunit. While dramatic functional diversity exists among HIV-1 Env isolates-observable even in the context of monomeric gp120 proteins as differences in antigenicity and immunogenicity-we have little understanding of the structural features that distinguish Env isolates and lead to isolate-specific functional differences, as crystal structures of truncated gp120 "core" proteins from diverse isolates reveal a high level of structural conservation. Because gp120 proteins are used as prospective vaccine immunogens, it is critical to understand the structural factors that influence their reactivity with antibodies. Here, we studied four full-length, glycosylated gp120 monomers from diverse HIV-1 isolates by using small-angle X-ray scattering (SAXS) to probe the overall subunit morphology and hydrogen/deuterium-exchange with mass spectrometry (HDX-MS) to characterize the local structural order of each gp120. We observed that while the overall subunit architecture was similar among isolates by SAXS, dramatic isolate-specific differences in the conformational stability of gp120 were evident by HDX-MS. These differences persisted even with the CD4 receptor bound. Furthermore, surface plasmon resonance (SPR) and enzyme-linked immunosorbance assays (ELISAs) showed that disorder was associated with poorer recognition by antibodies targeting conserved conformational epitopes. These data provide additional insight into the structural determinants of gp120 antigenicity and suggest that conformational dynamics should be considered in the selection and design of optimized Env immunogens.