Predicting permeation of compounds across the outer membrane of P. aeruginosa using molecular descriptors

The ability Gram-negative pathogens have at adapting and protecting themselves against antibiotics has increasingly become a public health threat. Data-driven models identifying molecular properties that correlate with outer membrane (OM) permeation and growth inhibition while avoiding efflux could guide the discovery of novel classes of antibiotics. Here we evaluate 174 molecular descriptors in 1260 antimicrobial compounds and study their correlations with antibacterial activity in Gram-negative Pseudomonas aeruginosa. The descriptors are derived from traditional approaches quantifying the compounds' intrinsic physicochemical properties, together with, bacterium-specific from ensemble docking of compounds targeting specific MexB binding pockets, and all-atom molecular dynamics simulations in different subregions of the OM model. Using these descriptors and the measured inhibitory concentrations, we design a statistical protocol to identify predictors of OM permeation/inhibition. We find consistent rules across most of our data highlighting the role of the interaction between the compounds and the OM. An implementation of the rules uncovered in our study is shown, and it demonstrates the accuracy of our approach in a set of previously unseen compounds. Our analysis sheds new light on the key properties drug candidates need to effectively permeate/inhibit P. aeruginosa, and opens the gate to similar data-driven studies in other Gram-negative pathogens.

Monatomic ions influence substrate permeation across bacterial microcompartment shells

Bacterial microcompartments (BMCs) are protein organelles consisting of an inner enzymatic core encased within a selectively permeable shell. BMC shells are modular, tractable architectures that can be repurposed with new interior enzymes for biomanufacturing purposes. The permeability of BMC shells is function-specific and regulated by biophysical properties of the shell subunits, especially its pores. We hypothesized that ions may interact with pore residues in a manner that influences the substrate permeation process. In vitro activity comparisons between native and broken BMCs demonstrated that increasing NaCl negatively affects permeation rates. Molecular dynamics simulations of the dominant shell protein (BMC-H) revealed that chloride ions preferentially occupy the positive pore, hindering substrate permeation, while sodium cations remain excluded. Overall, these results demonstrate that shell properties influence ion permeability and leverages the integration of experimental and computational techniques to improve our understanding of BMC shells towards their repurposing for biotechnological applications.

Machine Learning-Driven Multiscale Modeling: Bridging the Scales with a Next-Generation Simulation Infrastructure

Interdependence across time and length scales is common in biology, where atomic interactions can impact larger-scale phenomenon. Such dependence is especially true for a well-known cancer signaling pathway, where the membrane-bound RAS protein binds an effector protein called RAF. To capture the driving forces that bring RAS and RAF (represented as two domains, RBD and CRD) together on the plasma membrane, simulations with the ability to calculate atomic detail while having long time and large length- scales are needed. The Multiscale Machine-Learned Modeling Infrastructure (MuMMI) is able to resolve RAS/RAF protein-membrane interactions that identify specific lipid-protein fingerprints that enhance protein orientations viable for effector binding. MuMMI is a fully automated, ensemble-based multiscale approach connecting three resolution scales: (1) the coarsest scale is a continuum model able to simulate milliseconds of time for a 1 μm² membrane, (2) the middle scale is a coarse-grained (CG) Martini bead model to explore protein-lipid interactions, and (3) the finest scale is an all-atom (AA) model capturing specific interactions between lipids and proteins. MuMMI dynamically couples adjacent scales in a pairwise manner using machine learning (ML). The dynamic coupling allows for better sampling of the refined scale from the adjacent coarse scale (forward) and on-the-fly feedback to improve the fidelity of the coarser scale from the adjacent refined scale (backward). MuMMI operates efficiently at any scale, from a few compute nodes to the largest supercomputers in the world, and is generalizable to simulate different systems. As computing resources continue to increase and multiscale methods continue to advance, fully automated multiscale simulations (like MuMMI) will be commonly used to address complex science questions.

Inferring Pathways of Oxidative Folding from Prefolding Free Energy Landscapes of Disulfide-Rich Toxins

Short, cysteine-rich peptides can exist in stable or metastable structural ensembles due to the number of possible patterns of formation of their disulfide bonds. One interesting subset of this peptide group is the conotoxins, which are produced by aquatic snails in the family Conidae. The μ conotoxins, which are antagonists and blockers of the voltage-gated sodium channel, exist in a folding spectrum: on one end of the spectrum are more hirudin-like folders, which form disulfide bonds and then reshuffle them, leading to an ensemble of kinetically trapped isomers, and on the other end are more BPTI-like folders, which form the native disulfide bonds one by one in a particular order, leading to a preponderance of conformations existing in a single stable state. In this Article, we employ the composite diffusion map approach to study the unified free energy surface of prefolding μ-conotoxin equilibrium. We identify the two most important nonlinear collective modes of the unified folding landscape and demonstrate that in the absence of their disulfides, the conotoxins can be thought of as largely disordered polymers. A small increase in the number of hydrophobic residues in the protein shifts the free energy landscape toward hydrophobically collapsed coil conformations responsible for cysteine proximity in hirudin-like folders, compared to semiextended coil conformations with more distal cysteines in BPTI-like folders. Overall, this work sheds important light on the folding processes and free energy landscapes of cysteine-rich peptides and demonstrates the extent to which sequence and length contribute to these landscapes.

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.

New understanding of multidrug efflux and permeation in antibiotic resistance, persistence, and heteroresistance

Antibiotics effective against Gram-negative ESKAPE pathogens are a critical area of unmet need. Infections caused by these pathogens are not only difficult to treat but finding new therapies to overcome Gram-negative resistance is also a challenge. There are not enough antibiotics in development that target the most dangerous pathogens and there are not enough novel drugs in the pipeline. The major obstacle in the antibiotic discovery pipeline is the lack of understanding of how to breach antibiotic permeability barriers of Gram-negative pathogens. These barriers are created by active efflux pumps acting across both the inner and the outer membranes. Overproduction of efflux pumps alone or together with either modification of the outer membrane or antibiotic-inactivating enzymes and target mutations contribute to clinical levels of antibiotics resistance. Recent efforts have generated significant advances in the rationalization of compound efflux and permeation across the cell envelopes of Gram-negative pathogens. Combined with earlier studies and novel mathematical models, these efforts have led to a multilevel understanding of how antibiotics permeate these barriers and how multidrug efflux and permeation contribute to the development of antibiotic resistance and heteroresistance. Here, we discuss the new developments in this area.

Development of Martini 2.2 parameters for N-glycans: a case study of the HIV-1 Env glycoprotein dynamics

N-linked glycans are ubiquitous in nature and play key roles in biology. For example, glycosylation of pathogenic proteins is a common immune evasive mechanism, hampering the development of successful vaccines. Due to their chemical variability and complex dynamics, an accurate molecular understanding of glycans is still limited by the lack of effective resolution of current experimental approaches. Here, we have developed and implemented a reductive model based on the popular Martini 2.2 coarse-grained force field for the computational study of N-glycosylation. We used the HIV-1 Env as a direct applied example of a highly glycosylated protein. Our results indicate that the model not only reproduces many observables in very good agreement with a fully atomistic force field but also can be extended to study large amount of glycosylation variants, a fundamental property that can aid in the development of drugs and vaccines.

Unveiling the Dynamics of KRAS4b on Lipid Model Membranes

Small GTPase proteins are ubiquitous and responsible for regulating several processes related to cell growth and differentiation. Mutations that stabilize their active state can lead to uncontrolled cell proliferation and cancer. Although these proteins are well characterized at the cellular scale, the molecular mechanisms governing their functions are still poorly understood. In addition, there is limited information about the regulatory function of the cell membrane which supports their activity. Thus, we have studied the dynamics and conformations of the farnesylated KRAS4b in various membrane model systems, ranging from binary fluid mixtures to heterogeneous raft mimics. Our approach combines long time-scale coarse-grained (CG) simulations and Markov state models to dissect the membrane-supported dynamics of KRAS4b. Our simulations reveal that protein dynamics is mainly modulated by the presence of anionic lipids and to some extent by the nucleotide state (activation) of the protein. In addition, our results suggest that both the farnesyl and the polybasic hypervariable region (HVR) are responsible for its preferential partitioning within the liquid-disordered (Ld) domains in membranes, potentially enhancing the formation of membrane-driven signaling platforms.

Microscopic Approach to Intrinsic Antibiotic Resistance

The emergence of multidrug resistance in Gram-negative pathogens is critically determined by the interplay between efflux pumps activity and low permeation outer membrane. Although phenotypic heterogeneity in isogenic cells is recognized as a key factor of treatment failure, a mathematical framework able to integrate growth dynamics and single-cell heterogeneity in antimicrobial resistance, remains absent. Here we provide such framework that bridges single-cell and colony scales in the context of bacterial survival and efficacy against drugs. Using experimental inputs, our approach produces testable outputs and reveals nontrivial collective effects with key implications for fitness and survival of the colony. This framework provides a mathematical tool to test stress response strategies in organisms that can potentially guide experiments in natural and synthetic cellular systems.

Effects of pH on an IDP conformational ensemble explored by molecular dynamics simulation

The conformational ensemble of intrinsically disordered proteins, such as α-synuclein, are responsible for their function and malfunction. Misfolding of α-synuclein can lead to neurodegenerative diseases, and the ability to study their conformations and those of other intrinsically disordered proteins under varying physiological conditions can be crucial to understanding and preventing pathologies. In contrast to well-folded peptides, a consensus feature of IDPs is their low hydropathy and high charge, which makes their conformations sensitive to pH perturbation. We examine a prominent member of this subset of IDPs, α-synuclein, using a divide-and-conquer scheme that provides enhanced sampling of IDP structural ensembles. We constructed conformational ensembles of α-synuclein under neutral (pH ~ 7) and low (pH ~ 3) pH conditions and compared our results with available information obtained from smFRET, SAXS, and NMR studies. Specifically, α-synuclein has been found to in a more compact state at low pH conditions and the structural changes observed are consistent with those from experiments. We also characterize the conformational and dynamic differences between these ensembles and discussed the implication on promoting pathogenic fibril formation. We find that under low pH conditions, neutralization of negatively charged residues leads to compaction of the C-terminal portion of α-synuclein while internal reorganization allows α-synuclein to maintain its overall end-to-end distance. We also observe different levels of intra-protein interaction between three regions of α-synuclein at varying pH and a shift towards more hydrophilic interactions with decreasing pH.

The SARS-CoV-2 Spike variant D614G favors an open conformational state

The COVID-19 (coronavirus disease 2019) pandemic underwent a rapid transition with the emergence of a dominant viral variant (from the "D-form" to the "G-form") that carried an amino acid substitution D614G in its "Spike" protein. The G-form is more infectious in vitro and is associated with increased viral loads in the upper airways. To gain insight into the molecular-level underpinnings of these characteristics, we used microsecond all-atom simulations. We show that changes in the protein energetics favor a higher population of infection-capable states in the G-form through release of asymmetry present in the D-form inter-protomer interactions. Thus, the increased infectivity of the G-form is likely due to a higher rate of profitable binding encounters with the host receptor. It is also predicted to be more neutralization sensitive owing to enhanced exposure of the receptor binding domain, a key target region for neutralizing antibodies. These results are critical for vaccine design.

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.

Anionic Lipids Impact RAS-Binding Site Accessibility and Membrane Binding Affinity of CRAF RBD-CRD

CRAF activation requires binding to membrane-anchored and active GTP-bound RAS. Whereas its RAS-binding domain (RBD) contains the main binding interface to the RAS G domain, its cysteine-rich domain (CRD) is responsible for association to anionic lipid-rich membranes. Both RAF domains are connected by a short linker, and it remains unclear if the two domains act independently or if one domain can impact the function of the other. Here, we used a combination of coarse-grained and all-atom molecular dynamics simulations of a CRAF RBD-CRD construct to investigate the dynamics of the RBD when it is tethered to CRD that is anchored to a POPC:POPS model membrane. First, we show that the RBD positioning is very dynamic with a preferential localization near the membrane surface. Next, we show that membrane-localized RBD has its RAS-binding interface mostly inaccessible because of its proximity to the membrane. Several positively charged residues in this interface were identified from simulations as important for driving RBD association to the membrane. Surface plasmon resonance (SPR) measurements confirmed that mutations of these RBD residues reduced the liposome partitioning of RBD-CRD. Last, simulations indicated that the presence of RBD near the membrane led to a local enrichment of anionic lipids that could potentially enhance the membrane affinity of the entire RBD-CRD construct. This was supported by SPR measurements that showed stronger liposome partitioning of RBD-CRD relative to CRD alone. These findings thus suggest that the RBD and CRD have synergistic effects on their membrane dynamics, with CRD bringing RBD closer to the membrane that impacts its accessibility to RAS and with RBD causing local anionic lipid enrichment that enhances the overall affinity between the membrane and RBD-CRD. These mechanisms have potential implications on the order of events of the interactions between RAS and CRAF at the membrane.

The SARS-CoV-2 Spike Variant D614G Favors an Open Conformational State

The COVID-19 pandemic underwent a rapid transition with the emergence of a SARS-CoV-2 variant that carried the amino acid substitution D614G in the Spike protein that became globally prevalent. The G-form is both more infectious in vitro and associated with increased viral loads in infected people. To gain insight into the mechanism underlying these distinctive characteristics, we employed multiple replicas of microsecond all-atom simulations to probe the molecular-level impact of this substitution on Spike closed and open states. The open state enables Spike interactions with its human cellular receptor, ACE2. Here we show that changes in the inter-protomer energetics due to the D614G substitution favor a higher population of infection-capable (open) states. The inter-protomer interactions between S1 and S2 subunits in the open state of the D-form are asymmetric. This asymmetry is resolved in the G-form due to the release of tensile hydrogen bonds resulting in an increased population of open conformations. Thus, the increased infectivity of the G-form is likely due to a higher rate of profitable binding encounters with the host receptor. It is also predicted to be more neutralization sensitive due to enhanced exposure of the receptor binding domain, a key target region for neutralizing antibodies.

Emergence of SARS-CoV-2 through recombination and strong purifying selection

COVID-19 has become a global pandemic caused by the novel coronavirus SARS-CoV-2. Understanding the origins of SARS-CoV-2 is critical for deterring future zoonosis, discovering new drugs, and developing a vaccine. We show evidence of strong purifying selection around the receptor binding motif (RBM) in the spike and other genes among bat, pangolin, and human coronaviruses, suggesting similar evolutionary constraints in different host species. We also demonstrate that SARS-CoV-2's entire RBM was introduced through recombination with coronaviruses from pangolins, possibly a critical step in the evolution of SARS-CoV-2's ability to infect humans. Similar purifying selection in different host species, together with frequent recombination among coronaviruses, suggests a common evolutionary mechanism that could lead to new emerging human coronaviruses.

Machine Learning Algorithm Identifies an Antibiotic Vocabulary for Permeating Gram-Negative Bacteria

Drug discovery faces a crisis. The industry has used up the "obvious" space in which to find novel drugs for biomedical applications, and productivity is declining. One strategy to combat this is rational approaches to expand the search space without relying on chemical intuition, to avoid rediscovery of similar spaces. In this work, we present proof of concept of an approach to rationally identify a "chemical vocabulary" related to a specific drug activity of interest without employing known rules. We focus on the pressing concern of multidrug resistance in Pseudomonas aeruginosa by searching for submolecules that promote compound entry into this bacterium. By synergizing theory, computation, and experiment, we validate our approach, explain the molecular mechanism behind identified fragments promoting compound entry, and select candidate compounds from an external library that display good permeation ability.

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.

Computing long time scale biomolecular dynamics using quasi-stationary distribution kinetic Monte Carlo (QSD-KMC)

It is a challenge to obtain an accurate model of the state-to-state dynamics of a complex biological system from molecular dynamics (MD) simulations. In recent years, Markov state models have gained immense popularity for computing state-to-state dynamics from a pool of short MD simulations. However, the assumption that the underlying dynamics on the reduced space is Markovian induces a systematic bias in the model, especially in biomolecular systems with complicated energy landscapes. To address this problem, we have devised a new approach we call quasistationary distribution kinetic Monte Carlo (QSD-KMC) that gives accurate long time state-to-state evolution while retaining the entire time resolution even when the dynamics is highly non-Markovian. The proposed method is a kinetic Monte Carlo approach that takes advantage of two concepts: (i) the quasistationary distribution, the distribution that results when a trajectory remains in one state for a long time (the dephasing time), such that the next escape is Markovian, and (ii) dynamical corrections theory, which properly accounts for the correlated events that occur as a trajectory passes from state to state before it settles again. In practice, this is achieved by specifying, for each escape, the intermediate states and the final state that has resulted from the escape. Implementation of QSD-KMC imposes stricter requirements on the lengths of the trajectories than in a Markov state model approach as the trajectories must be long enough to dephase. However, the QSD-KMC model produces state-to-state trajectories that are statistically indistinguishable from an MD trajectory mapped onto the discrete set of states for an arbitrary choice of state decomposition. Furthermore, the aforementioned concepts can be used to construct a Monte Carlo approach to optimize the state boundaries regardless of the initial choice of states. We demonstrate the QSD-KMC method on two one-dimensional model systems, one of which is a driven nonequilibrium system, and on two well-characterized biomolecular systems.

Combinatorial diversity of Syk recruitment driven by its multivalent engagement with FcεRIγ

Syk/Zap70 family kinases are essential for signaling via multichain immune-recognition receptors such as tetrameric (αβγ2) FcεRI. Syk activation is generally attributed to cis binding of its tandem SH2 domains to dual phosphotyrosines within FcεRIγ-ITAMs (immunoreceptor tyrosine-based activation motifs). However, the mechanistic details of Syk docking on γ homodimers are unresolved. Here, we estimate that multivalent interactions for WT Syk improve cis-oriented binding by three orders of magnitude. We applied molecular dynamics (MD), hybrid MD/worm-like chain polymer modeling, and live cell imaging to evaluate relative binding and signaling output for all possible cis and trans Syk-FcεRIγ configurations. Syk binding is likely modulated during signaling by autophosphorylation on Y130 in interdomain A, since a Y130E phosphomimetic form of Syk is predicted to lead to reduced helicity of interdomain A and alter Syk's bias for cis binding. Experiments in reconstituted γ-KO cells, whose γ subunits are linked by disulfide bonds, as well as in cells expressing monomeric ITAM or hemITAM γ-chimeras, support model predictions that short distances between γ ITAM pairs are required for trans docking. We propose that the full range of docking configurations improves signaling efficiency by expanding the combinatorial possibilities for Syk recruitment, particularly under conditions of incomplete ITAM phosphorylation.

HIV-1 Neutralizing Antibody Signatures and Application to Epitope-Targeted Vaccine Design

Eliciting HIV-1-specific broadly neutralizing antibodies (bNAbs) remains a challenge for vaccine development, and the potential of passively delivered bNAbs for prophylaxis and therapeutics is being explored. We used neutralization data from four large virus panels to comprehensively map viral signatures associated with bNAb sensitivity, including amino acids, hypervariable region characteristics, and clade effects across four different classes of bNAbs. The bNAb signatures defined for the variable loop 2 (V2) epitope region of HIV-1 Env were then employed to inform immunogen design in a proof-of-concept exploration of signature-based epitope targeted (SET) vaccines. V2 bNAb signature-guided mutations were introduced into Env 459C to create a trivalent vaccine, and immunization of guinea pigs with V2-SET vaccines resulted in increased breadth of NAb responses compared with Env 459C alone. These data demonstrate that bNAb signatures can be utilized to engineer HIV-1 Env vaccine immunogens capable of eliciting antibody responses with greater neutralization breadth.

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HIV-1 bNAb sensitivity signatures from 4 large virus panels mapped across 4 Ab classes

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Non-contact hypervariable region characteristics are critical for bNAb sensitivity

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HIV-1 Env 459C used alone as a vaccine can elicit modest tier 2 NAbs in guinea pigs

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V2 bNAb signature-guided modifications in 459C enhanced neutralization breadth

Snails In Silico: A Review of Computational Studies on the Conopeptides

Marine cone snails are carnivorous gastropods that use peptide toxins called conopeptides both as a defense mechanism and as a means to immobilize and kill their prey. These peptide toxins exhibit a large chemical diversity that enables exquisite specificity and potency for target receptor proteins. This diversity arises in terms of variations both in amino acid sequence and length, and in posttranslational modifications, particularly the formation of multiple disulfide linkages. Most of the functionally characterized conopeptides target ion channels of animal nervous systems, which has led to research on their therapeutic applications. Many facets of the underlying molecular mechanisms responsible for the specificity and virulence of conopeptides, however, remain poorly understood. In this review, we will explore the chemical diversity of conopeptides from a computational perspective. First, we discuss current approaches used for classifying conopeptides. Next, we review different computational strategies that have been applied to understanding and predicting their structure and function, from machine learning techniques for predictive classification to docking studies and molecular dynamics simulations for molecular-level understanding. We then review recent novel computational approaches for rapid high-throughput screening and chemical design of conopeptides for particular applications. We close with an assessment of the state of the field, emphasizing important questions for future lines of inquiry.

Molecular recognition of RAS/RAF complex at the membrane: Role of RAF cysteine-rich domain

Activation of RAF kinase involves the association of its RAS-binding domain (RBD) and cysteine-rich domain (CRD) with membrane-anchored RAS. However, the overall architecture of the RAS/RBD/CRD ternary complex and the orientations of its constituent domains at the membrane remain unclear. Here, we have combined all-atom and coarse-grained molecular dynamics (MD) simulations with experimental data to construct and validate a model of membrane-anchored CRD, and used this as a basis to explore models of membrane-anchored RAS/RBD/CRD complex. First, simulations of the CRD revealed that it anchors to the membrane via insertion of its two hydrophobic loops, which is consistent with our NMR measurements of CRD bound to nanodiscs. Simulations of the CRD in the context of membrane-anchored RAS/RBD then show how CRD association with either RAS or RBD could play an unexpected role in guiding the membrane orientations of RAS/RBD. This finding has implications for the formation of RAS-RAS dimers, as different membrane orientations of RAS expose distinct putative dimerization interfaces.

Dynamics of Intact MexAB-OprM Efflux Pump: Focusing on the MexA-OprM Interface

Antibiotic efflux is one of the most critical mechanisms leading to bacterial multidrug resistance. Antibiotics are effluxed out of the bacterial cell by a tripartite efflux pump, a complex machinery comprised of outer membrane, periplasmic adaptor, and inner membrane protein components. Understanding the mechanism of efflux pump assembly and its dynamics could facilitate discovery of novel approaches to counteract antibiotic resistance in bacteria. We built here an intact atomistic model of the Pseudomonas aeruginosa MexAB-OprM pump in a Gram-negative membrane model that contained both inner and outer membranes separated by a periplasmic space. All-atom molecular dynamics (MD) simulations confirm that the fully assembled pump is stable in the microsecond timescale. Using a combination of all-atom and coarse-grained MD simulations and sequence covariation analysis, we characterized the interface between MexA and OprM in the context of the entire efflux pump. These analyses suggest a plausible mechanism by which OprM is activated via opening of its periplasmic aperture through a concerted interaction with MexA.

Switch Loop Flexibility Affects Substrate Transport of the AcrB Efflux Pump

The functionally important switch loop of the trimeric multidrug transporter AcrB separates the access and deep drug binding pockets in every protomer. This loop, comprising 11-amino-acid residues, has been shown to be crucial for substrate transport, as drugs have to travel past the loop to reach the deep binding pocket and from there are transported outside the cell via the connected AcrA and TolC channels. It contains four symmetrically arranged glycine residues suggesting that flexibility is a key feature for pump activity. Upon combinatorial substitution of these glycine residues to proline, functional and structural asymmetry was observed. Proline substitutions on the PC1-proximal side completely abolished transport and reduced backbone flexibility of the switch loop, which adopted a conformation restricting the pathway toward the deep binding pocket. Two phenylalanine residues located adjacent to the substitution sensitive glycine residues play a role in blocking the pathway upon rigidification of the loop, since the removal of the phenyl rings from the rigid loop restores drug transport activity.

Effect of Glycosylation on an Immunodominant Region in the V1V2 Variable Domain of the HIV-1 Envelope gp120 Protein

Heavy glycosylation of the envelope (Env) surface subunit, gp120, is a key adaptation of HIV-1; however, the precise effects of glycosylation on the folding, conformation and dynamics of this protein are poorly understood. Here we explore the patterns of HIV-1 Env gp120 glycosylation, and particularly the enrichment in glycosylation sites proximal to the disulfide linkages at the base of the surface-exposed variable domains. To dissect the influence of glycans on the conformation these regions, we focused on an antigenic peptide fragment from a disulfide bridge-bounded region spanning the V1 and V2 hyper-variable domains of HIV-1 gp120. We used replica exchange molecular dynamics (MD) simulations to investigate how glycosylation influences its conformation and stability. Simulations were performed with and without N-linked glycosylation at two sites that are highly conserved across HIV-1 isolates (N156 and N160); both are contacts for recognition by V1V2-targeted broadly neutralizing antibodies against HIV-1. Glycosylation stabilized the pre-existing conformations of this peptide construct, reduced its propensity to adopt other secondary structures, and provided resistance against thermal unfolding. Simulations performed in the context of the Env trimer also indicated that glycosylation reduces flexibility of the V1V2 region, and provided insight into glycan-glycan interactions in this region. These stabilizing effects were influenced by a combination of factors, including the presence of a disulfide bond between the Cysteines at 131 and 157, which increased the formation of beta-strands. Together, these results provide a mechanism for conservation of disulfide linkage proximal glycosylation adjacent to the variable domains of gp120 and begin to explain how this could be exploited to enhance the immunogenicity of those regions. These studies suggest that glycopeptide immunogens can be designed to stabilize the most relevant Env conformations to focus the immune response on key neutralizing epitopes.

Heavy glycosylation of the envelope surface subunit, gp120, is a key adaptation of HIV-1, however, the precise effects of glycosylation on the folding, conformation and dynamics of this protein are poorly understood. The network of glycans on gp120 is of particular interest with regards to vaccine design, because the glycans both serve as targets for many classes of broadly neutralizing antibodies, and contribute to patterns of immune evasion and escape during HIV-1 infection. In this manuscript, we report on how glycosylation influences an immunogenic but disordered region of gp120. Glycosylation stabilizes the pre-existing conformation, and reduces its propensity to form other secondary structures. It also stabilizes preformed conformation against thermal unfolding. These complementary effects originate from a combination of multiple factors, including the observation that having a glycosylation site adjacent to the disulfide bond further promotes the formation of beta-strand structure in this peptide.

Broadly targeted CD8⁺ T cell responses restricted by major histocompatibility complex E

Major histocompatibility complex E (MHC-E) is a highly conserved, ubiquitously expressed, nonclassical MHC class Ib molecule with limited polymorphism that is primarily involved in the regulation of natural killer (NK) cells. We found that vaccinating rhesus macaques with rhesus cytomegalovirus vectors in which genes Rh157.5 and Rh157.4 are deleted results in MHC-E-restricted presentation of highly varied peptide epitopes to CD8αβ(+) T cells, at ~4 distinct epitopes per 100 amino acids in all tested antigens. Computational structural analysis revealed that MHC-E provides heterogeneous chemical environments for diverse side-chain interactions within a stable, open binding groove. Because MHC-E is up-regulated to evade NK cell activity in cells infected with HIV, simian immunodeficiency virus, and other persistent viruses, MHC-E-restricted CD8(+) T cell responses have the potential to exploit pathogen immune-evasion adaptations, a capability that might endow these unconventional responses with superior efficacy.

Permeability Barrier of Gram-Negative Cell Envelopes and Approaches To Bypass It

Gram-negative bacteria are intrinsically resistant to many antibiotics. Species that have acquired multidrug resistance and cause infections that are effectively untreatable present a serious threat to public health. The problem is broadly recognized and tackled at both the fundamental and applied levels. This paper summarizes current advances in understanding the molecular bases of the low permeability barrier of Gram-negative pathogens, which is the major obstacle in discovery and development of antibiotics effective against such pathogens. Gaps in knowledge and specific strategies to break this barrier and to achieve potent activities against difficult Gram-negative bacteria are also discussed.

Membrane-mediated regulation of the intrinsically disordered CD3ϵ cytoplasmic tail of the TCR

The regulation of T-cell-mediated immune responses depends on the phosphorylation of immunoreceptor tyrosine-based activation motifs (ITAMs) on T-cell receptors. Although many details of the signaling cascades are well understood, the initial mechanism and regulation of ITAM phosphorylation remains unknown. We used molecular dynamics simulations to study the influence of different compositions of lipid bilayers on the membrane association of the CD3ϵ cytoplasmic tails of the T-cell receptors. Our results show that binding of CD3ϵ to membranes is modulated by both the presence of negatively charged lipids and the lipid order of the membrane. Free-energy calculations reveal that the protein-membrane interaction is favored by the presence of nearby basic residues and the ITAM tyrosines. Phosphorylation minimizes membrane association, rendering the ITAM motif more accessible to binding partners. In systems mimicking biological membranes, the CD3ϵ chain localization is modulated by different facilitator lipids (e.g., gangliosides or phosphoinositols), revealing a plausible regulatory effect on activation through the regulation of lipid composition in cell membranes.

MARTINI coarse-grained model for crystalline cellulose microfibers

Commercial-scale biofuel production requires a deep understanding of the structure and dynamics of its principal target: cellulose. However, an accurate description and modeling of this carbohydrate structure at the mesoscale remains elusive, particularly because of its overwhelming length scale and configurational complexity. We have derived a set of MARTINI coarse-grained force field parameters for the simulation of crystalline cellulose fibers. The model is adapted to reproduce different physicochemical and mechanical properties of native cellulose Iβ. The model is able not only to handle a transition from cellulose Iβ to another cellulose allomorph, cellulose IIII, but also to capture the physical response to temperature and mechanical bending of longer cellulose nanofibers. By developing the MARTINI model of a solid cellulose crystalline fiber from the building blocks of a soluble cellobiose coarse-grained model, we have provided a systematic way to build MARTINI models for other crystalline biopolymers.

A data-driven approach to modeling the tripartite structure of multidrug resistance efflux pumps

Many bacterial pathogens are becoming increasingly resistant to antibiotic treatments, and a detailed understanding of the molecular basis of antibiotic resistance is critical for the development of next-generation approaches for combating bacterial infections. Studies focusing on pathogens have revealed the profile of resistance in these organisms to be due primarily to the presence of multidrug resistance efflux pumps: tripartite protein complexes which span the periplasm bridging the inner and outer membranes of Gram-negative bacteria. An atomic-level resolution tripartite structure remains imperative to advancing our understanding of the molecular mechanisms of pump function using both theoretical and experimental approaches. We develop a fast and consistent method for constructing tripartite structures which leverages existing data-driven models and provide molecular modeling approaches for constructing tripartite structures of multidrug resistance efflux pumps. Our modeling studies reveal that conformational changes in the inner membrane component responsible for drug translocation have limited impact on the conformations of the other pump components, and that two distinct models derived from conflicting experimental data are both consistent with all currently available measurements. Additionally, we investigate putative drug translocation pathways via geometric simulations based on the available crystal structures of the inner membrane pump component, AcrB, bound to two drugs which occupy distinct binding sites: doxorubicin and linezolid. These simulations suggest that smaller drugs may enter the pump through a channel from the cytoplasmic leaflet of the inner membrane, while both smaller and larger drug molecules may enter through a vestibule accessible from the periplasm.

Sodium ion interactions with aqueous glucose: insights from quantum mechanics, molecular dynamics, and experiment

In the last several decades, significant efforts have been conducted to understand the fundamental reactivity of glucose derived from plant biomass in various chemical environments for conversion to renewable fuels and chemicals. For reactions of glucose in water, it is known that inorganic salts naturally present in biomass alter the product distribution in various deconstruction processes. However, the molecular-level interactions of alkali metal ions and glucose are unknown. These interactions are of physiological interest as well, for example, as they relate to cation-glucose cotransport. Here, we employ quantum mechanics (QM) to understand the interaction of a prevalent alkali metal, sodium, with glucose from a structural and thermodynamic perspective. The effect on β-glucose is subtle: a sodium ion perturbs bond lengths and atomic partial charges less than rotating a hydroxymethyl group. In contrast, the presence of a sodium ion significantly perturbs the partial charges of α-glucose anomeric and ring oxygens. Molecular dynamics (MD) simulations provide dynamic sampling in explicit water, and both the QM and the MD results show that sodium ions associate at many positions with respect to glucose with reasonably equivalent propensity. This promiscuous binding nature of Na(+) suggests that computational studies of glucose reactions in the presence of inorganic salts need to ensure thorough sampling of the cation positions, in addition to sampling glucose rotamers. The effect of NaCl on the relative populations of the anomers is experimentally quantified with light polarimetry. These results support the computational findings that Na(+) interacts similarly with α- and β-glucose.

Deducing conformational variability of intrinsically disordered proteins from infrared spectroscopy with Bayesian statistics

As it remains practically impossible to generate ergodic ensembles for large intrinsically disordered proteins (IDP) with molecular dynamics (MD) simulations, it becomes critical to compare spectroscopic characteristics of the theoretically generated ensembles to corresponding measurements. We develop a Bayesian framework to infer the ensemble properties of an IDP using a combination of conformations generated by MD simulations and its measured infrared spectrum. We performed 100 different MD simulations totaling more than 10 µs to characterize the conformational ensemble of αsynuclein, a prototypical IDP, in water. These conformations are clustered based on solvent accessibility and helical content. We compute the amide-I band for these clusters and predict the thermodynamic weights of each cluster given the measured amide-I band. Bayesian analysis produces a reproducible and non-redundant set of thermodynamic weights for each cluster, which can then be used to calculate the ensemble properties. In a rigorous validation, these weights reproduce measured chemical shifts.

Taste of sugar at the membrane: thermodynamics and kinetics of the interaction of a disaccharide with lipid bilayers

Sugar recognition at the membrane is critical in various physiological processes. Many aspects of sugar-membrane interaction are still unknown. We take an integrated approach by combining conventional molecular-dynamics simulations with enhanced sampling methods and analytical models to understand the thermodynamics and kinetics of a di-mannose molecule in a phospholipid bilayer system. We observe that di-mannose has a slight preference to localize at the water-phospholipid interface. Using umbrella sampling, we show the free energy bias for this preferred location to be just -0.42 kcal/mol, which explains the coexistence of attraction and exclusion mechanisms of sugar-membrane interaction. Accurate estimation of absolute entropy change of water molecules with a two-phase model indicates that the small energy bias is the result of a favorable entropy change of water molecules. Then, we incorporate results from molecular-dynamics simulation in two different ways to an analytical diffusion-reaction model to obtain association and dissociation constants for di-mannose interaction with membrane. Finally, we verify our approach by predicting concentration dependence of di-mannose recognition at the membrane that is consistent with experiment. In conclusion, we provide a combined approach for the thermodynamics and kinetics of a weak ligand-binding system, which has broad implications across many different fields.

Immune exposure to a specific sequence of antibody escape variants could program long-term potential for neutralization breadth in subtype A HIV-1 infection (P6161)

Approximately one quarter of HIV-1 infected individuals will generate broadly neutralizing antibodies, but the exact mechanisms for triggering and maturation of these responses are presently undefined. We consequently investigated the roots of neutralization breadth. In a subtype A HIV-1 infected Rwandan seroconverter, we pinpointed the primary neutralizing antibody target and the successive routes of viral escape using envelope glycoproteins from the transmitted/founder virus and longitudinal escape variants along with autologous plasma and monoclonal antibodies. Initially, a single mutation at one of three clustered residues proximal to gp120’s third hypervariable loop conferred viral escape. This putative epitope subsequently elicited at least two somatically related monoclonal antibodies, which bound and neutralized the established escape mutations. Resistance to this secondary wave of immune pressure then arose in later viral envelopes through introduction of two glycans that obscured this consistently targeted space. At 16-months post-infection, what had been a narrow, regional response evolved to force recognition and neutralization of distinct envelope portions, which resulted in moderate cross-clade humoral breadth. Our data suggest that unveiling a certain chain of envelope mutations could drive B cells toward the production of broadly neutralizing antibodies. Appreciating this knowledge during immunogen construction could positively impact HIV-1 vaccine design.

Viral escape from neutralizing antibodies in early subtype A HIV-1 infection drives an increase in autologous neutralization breadth

Antibodies that neutralize (nAbs) genetically diverse HIV-1 strains have been recovered from a subset of HIV-1 infected subjects during chronic infection. Exact mechanisms that expand the otherwise narrow neutralization capacity observed during early infection are, however, currently undefined. Here we characterized the earliest nAb responses in a subtype A HIV-1 infected Rwandan seroconverter who later developed moderate cross-clade nAb breadth, using (i) envelope (Env) glycoproteins from the transmitted/founder virus and twenty longitudinal nAb escape variants, (ii) longitudinal autologous plasma, and (iii) autologous monoclonal antibodies (mAbs). Initially, nAbs targeted a single region of gp120, which flanked the V3 domain and involved the alpha2 helix. A single amino acid change at one of three positions in this region conferred early escape. One immunoglobulin heavy chain and two light chains recovered from autologous B cells comprised two mAbs, 19.3H-L1 and 19.3H-L3, which neutralized the founder Env along with one or three of the early escape variants carrying these mutations, respectively. Neither mAb neutralized later nAb escape or heterologous Envs. Crystal structures of the antigen-binding fragments (Fabs) revealed flat epitope contact surfaces, where minimal light chain mutation in 19.3H-L3 allowed for additional antigenic interactions. Resistance to mAb neutralization arose in later Envs through alteration of two glycans spatially adjacent to the initial escape signatures. The cross-neutralizing nAbs that ultimately developed failed to target any of the defined V3-proximal changes generated during the first year of infection in this subject. Our data demonstrate that this subject's first recognized nAb epitope elicited strain-specific mAbs, which incrementally acquired autologous breadth, and directed later B cell responses to target distinct portions of Env. This immune re-focusing could have triggered the evolution of cross-clade antibodies and suggests that exposure to a specific sequence of immune escape variants might promote broad humoral responses during HIV-1 infection.

Identification of minimally interacting modules in an intrinsically disordered protein

The conformational characterization of intrinsically disordered proteins (IDPs) is complicated by their conformational heterogeneity and flexibility. If an IDP could somehow be divided into smaller fragments and reconstructed later, theoretical and spectroscopic studies could probe its conformational variability in detail. Here, we used replica molecular-dynamics simulations and network theory to explore whether such a divide-and-conquer strategy is feasible for α-synuclein, a prototypical IDP. We characterized the conformational variability of α-synuclein by conducting >100 unbiased all-atom molecular-dynamics simulations, for a total of >10 μs of trajectories. In these simulations, α-synuclein formed a heterogeneous ensemble of collapsed coil states in an aqueous environment. These states were stabilized by heterogeneous contacts between sequentially distant regions. We find that α-synuclein contains residual secondary structures in the collapsed states, and the heterogeneity in the collapsed state makes it feasible to split α-synuclein into sequentially contiguous minimally interacting fragments. This study reveals previously unknown characteristics of α-synuclein and provides a new (to our knowledge) approach for studying other IDPs.