Most previously authorized clinical antibodies against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) have lost neutralizing activity to recent variants due to rapid viral evolution. To mitigate such escape, we preemptively enhance AZD3152, an antibody authorized for prophylaxis in immunocompromised individuals. Using deep mutational scanning (DMS) on the SARS-CoV-2 antigen, we identify AZD3152 vulnerabilities at antigen positions F456 and D420. Through two iterations of computational antibody design that integrates structure-based modeling, machine-learning, and experimental validation, we co-optimize AZD3152 against 24 contemporary and previous SARS-CoV-2 variants, as well as 20 potential future escape variants. Our top candidate, 3152-1142, restores full potency (100-fold improvement) against the more recently emerged XBB.1.5+F456L variant that escaped AZD3152, maintains potency against previous variants of concern, and shows no additional vulnerability as assessed by DMS. This preemptive mitigation demonstrates a generalizable approach for optimizing existing antibodies against potential future viral escape.

Selecting an optimal antigen is a crucial step in vaccine development, significantly influencing both the vaccine’s effectiveness and the breadth of protection it provides. High antigen sequence variability, as seen in pathogens like rhinovirus, HIV, influenza virus, complicates the design of a single cross-protective antigen. Consequently, vaccination with a single antigen molecule often confers protection against only a single variant. In this study, machine learning methods were applied to the design of factor H binding protein (fHbp), an antigen from the bacterial pathogen Neisseria meningitidis. The vast number of potential antigen mutants presents a significant challenge for improving fHbp antigenicity. Moreover, limited data on antigen-antibody binding in public databases constrains the training of machine learning models. To address these challenges, we used computational models to predict fHbp properties and machine learning was applied to select both the most promising and informative mutants using a Gaussian process (GP) model. These mutants were experimentally evaluated to both confirm promising leads and refine the machine learning model for future iterations. In our current model, mutants were designed that enabled the transfer of fHbp v1.1 specific conformational epitopes onto fHbp v3.28, while maintaining binding to overlapping cross-reactive epitopes. The top mutant identified underwent biophysical and x-ray crystallographic characterization to confirm that the overall structure of fHbp was maintained throughout this epitope engineering experiment. The integrated strategy presented here could form the basis of a next-generation, iterative antigen design platform, potentially accelerating the development of new broadly protective vaccines.

Oncogenic KRAS mutations impair GTP hydrolysis and increase the active GTP-bound KRAS population, which leads to growth-factor-independent cell proliferation and survival of cancer cells. Despite notable successes of small-molecule inhibitors in the treatment of KRASG12C cancer, many of these small-molecule inhibitors preferentially bind to inactive (GDP-bound) mutant KRAS, whose availability is limited by the slow rate of intrinsic GTP hydrolysis. A better understanding of how KRAS mutations impair intrinsic hydrolysis is important for designing more effective small-molecule therapeutics. In this work, experimental and computational approaches were utilized to investigate how the most important oncogenic mutations affect the intrinsic hydrolysis of GTP. We found that Q61H, G12V, and G12R mutations impair intrinsic hydrolysis by around 7-fold, 9-fold, and more than 20-fold, respectively, whereas G12A, G12C, G12D, and G13D have less effect. Based on mechanistic investigations, we propose that KRAS mutations impair intrinsic hydrolysis by disrupting the interactions needed to align the nucleophilic water molecule with GTP for nucleophilic attack. These results can assist small-molecule inhibitor design and also benefit the development of other therapeutic strategies, such as rescuing hydrolysis.

The polymorphic membrane proteins (Pmps) are a family of autotransporters that play an important role in infection, adhesion and immunity in Chlamydia trachomatis. Here we show that the characteristic GGA(I,L,V) and FxxN tetrapeptide repeats fit into a larger repeat sequence, which correspond to the coils of a large beta-helical domain in high quality structure predictions. Analysis of the protein using structure prediction algorithms provided novel insight to the chlamydial Pmp family of proteins. While the tetrapeptide motifs themselves are predicted to play a structural role in folding and close stacking of the beta-helical backbone of the passenger domain, we found many of the interesting features of Pmps are localized to the side loops jutting out from the beta helix including protease cleavage, host cell adhesion, and B-cell epitopes; while T-cell epitopes are predominantly found in the beta-helix itself. This analysis more accurately defines the Pmp family of Chlamydia and may better inform rational vaccine design and functional studies.

ABSTRACTThe COVID-19 pandemic underscored the promise of monoclonal antibody-based prophylactic and therapeutic drugs1–3, but also revealed how quickly viral escape can curtail effective options4, 5. With the emergence of the SARS-CoV-2 Omicron variant in late 2021, many clinically used antibody drug products lost potency, including EvusheldTMand its constituent, cilgavimab4, 6. Cilgavimab, like its progenitor COV2-2130, is a class 3 antibody that is compatible with other antibodies in combination4and is challenging to replace with existing approaches. Rapidly modifying such high-value antibodies with a known clinical profile to restore efficacy against emerging variants is a compelling mitigation strategy. We sought to redesign COV2-2130 to rescue in vivo efficacy against Omicron BA.1 and BA.1.1 strains while maintaining efficacy against the contemporaneously dominant Delta variant. Here we show that our computationally redesigned antibody, 2130-1-0114-112, achieves this objective, simultaneously increases neutralization potency against Delta and many variants of concern that subsequently emerged, and provides protectionin vivoagainst the strains tested, WA1/2020, BA.1.1, and BA.5. Deep mutational scanning of tens of thousands pseudovirus variants reveals 2130-1-0114-112 improves broad potency without incurring additional escape liabilities. Our results suggest that computational approaches can optimize an antibody to target multiple escape variants, while simultaneously enriching potency. Because our approach is computationally driven, not requiring experimental iterations or pre-existing binding data, it could enable rapid response strategies to address escape variants or pre-emptively mitigate escape vulnerabilities.

Subetadex-alpha-methyl (SBX-Me), a modified, polyanionic cyclodextrin scaffold, has been evaluated for its utilization as a medical countermeasure (MCM) to neutralize the effects of fentanyl and related opioids. Initial in vitro toxicity assays demonstrate that SBX-Me has a nontoxic profile, comparable to the FDA-approved cyclodextrin-based drug Sugammadex. Pharmacokinetic analysis showed rapid clearance of SBX-Me with an elimination half-life of similar to 7.4 h and little accumulation in major organs. SBX-Me was also evaluated for its ability to counteract the effects of fentanyl, carfentanil, and remifentanil in rats. Recovery times in rats exposed to sublethal fentanyl doses were found to be shorter when treated with SBX-Me after opioid exposure. The recovery times were reduced from similar to 35 to similar to 17 min for fentanyl, similar to 172 to similar to 59 min for carfentanil, and similar to 18 to similar to 12 min for remifentanil. SBX-Me increased the elimination half-life for fentanyl and remifentanil from 5.37 to 6.42 h and 8.24 to 9.74 h, respectively. These data support SBX-Me as a solid platform from which further research can be launched for the development of a MCM against the effects of fentanyl and its analogs. Furthermore, the data suggests that SBX-Me and other analogs are attractive candidates as broad spectrum opioids targeting MCMs.

Synthetic materials designed to enable rapid and finely tuned diffusion through semi-permeable nano and microscale pores have generally fallen short when compared to their biological counterparts. As a result, applications such as hemodialysis, drug delivery, and biomolecule purifications often require extensive run times and produce imperfect permeate solutions. Towards bridging this gap, we have recently demonstrated that, under a concentration gradient, a wide range of ions diffuse through atomically smooth carbon nanotube pores at rates more than an order of magnitude above those seen in bulk. This observation comes in a pore/molecule size regime where hindered diffusion was previously thought to apply. Here, we extend these results to include more complex and biologically relevant molecules and study how transport rates driven by diffusion depend on molecular properties such as size and flexibility. Further, we work to understand how the unique nanofluidic phenomena occurring in our pores may enable improved performance in dialysis applications by impacting selectivity, permeability, and molecular cutoff profiles. These results help to shed light on mechanisms governing diffusive transport in certain nanoscale pores that remain only partially understood and bring us closer to harnessing them in future separation technologies.

Supplementary Figures 1-4, Methods from Halogenated Benzimidazole Carboxamides Target Integrin α₄β₁ on T-Cell and B-Cell Lymphomas

Cyclodextrins (CDs) have been previously shown to display modest equilibrium binding affinities ( K a ~ 100–200 M -1 ) for the synthetic opioid analgesic fentanyl. In this work, we describe the synthesis of new CDs possessing extended thioalkylcarboxyl or thioalkylhydroxyl moieties and assess their binding affinity towards fentanyl hydrochloride. The optimal CD studied displays a remarkable affinity for the opioid of K a = 66,500 M −1 , the largest value reported for such an inclusion complex to date. One dimensional 1 H Nuclear Magnetic Resonance (NMR) as well as Rotational Frame Overhauser Spectroscopy (2D-ROESY) experiments supported by molecular dynamics (MD) simulations suggest an unexpected binding behavior, with fentanyl able to bind the CD interior in one of two distinct orientations. Binding energies derived from the MD simulations work correlate strongly with NMR-derived affinities highlighting its utility as a predictive tool for CD candidate optimization. The performance of these host molecules portends their utility as platforms for medical countermeasures for opioid exposure, as biosensors, and in other forensic science applications.

The ability of the cyclodextrin-oxime construct 6-OxP-CD to bind and degrade the nerve agents Cyclosarin (GF), Soman (GD) and S -[2-[Di(propan-2-yl)amino]ethyl] O -ethyl methylphosphonothioate (VX) has been studied using 31 P-nuclear magnetic resonance (NMR) under physiological conditions. While 6-OxP-CD was found to degrade GF instantaneously under these conditions, it was found to form an inclusion complex with GD and significantly improve its degradation (t 1/2 ~ 2 hrs) relative over background (t 1/2 ~ 22 hrs). Consequently, effective formation of the 6-OxP-CD:GD inclusion complex results in the immediate neutralization of GD and thus preventing it from inhibiting its biological target. In contrast, NMR experiments did not find evidence for an inclusion complex between 6-OxP-CD and VX, and the agent’s degradation profile was identical to that of background degradation (t 1/2 ~ 24 hrs). As a complement to this experimental work, molecular dynamics (MD) simulations coupled with Molecular Mechanics-Generalized Born Surface Area (MM-GBSA) calculations have been applied to the study of inclusion complexes between 6-OxP-CD and the three nerve agents. These studies provide data that informs the understanding of the different degradative interactions exhibited by 6-OxP-CD with each nerve agent as it is introduced in the CD cavity in two different orientations (up and down). For its complex with GF, it was found that the oxime in 6-OxP-CD lies in very close proximity (P GF ⋯O Oxime ~ 4–5 Å) to the phosphorus center of GF in the ‘down GF ’ orientation for most of the simulation accurately describing the ability of 6-OxP-CD to degrade this nerve agent rapidly and efficiently. Further computational studies involving the center of masses (COMs) for both components (GF and 6-OxP-CD) also provided some insight on the nature of this inclusion complex. Distances between the COMs (ΔCOM) lie closer in space in the ‘down GF ’ orientation than in the ‘up GF ’ orientation; a correlation that seems to hold true not only for GF but also for its congener, GD. In the case of GD, calculations for the ‘down GD ’ orientation showed that the oxime functional group in 6-OxP-CD although lying in close proximity (P GD ⋯O Oxime ~ 4–5 Å) to the phosphorus center of the nerve agent for most of the simulation, adopts another stable conformation that increase this distance to ~ 12–14 Å, thus explaining the ability of 6-OxP-CD to bind and degrade GD but with less efficiency as observed experimentally (t 1/2 ~ 4 hr. vs. immediate). Lastly, studies on the VX:6-OxP-CD system demonstrated that VX does not form a stable inclusion complex with the oxime-bearing cyclodextrin and as such does not interact in a way that is conducive to an accelerated degradation scenario. Collectively, these studies serve as a basic platform from which the development of new cyclodextrin scaffolds based on 6-OxP-CD can be designed in the development of medical countermeasures against these highly toxic chemical warfare agents.

Here, we report on the direct sequential imaging of laser-induced cavitation of micron and nanoscale bubbles using Movie-Mode Dynamic Transmission Electron Microscopy (MM-DTEM). A 532 nm laser pulse (∼12 ns) was used to excite gold nanoparticles inside a ∼1.2 μm layer of water, and the resulting bubbles were observed with a series of nine electron pulses (∼10 ns) separated by as little as 40 ns peak to peak. Isolated nanobubbles were observed to collapse in less than 50 ns, while larger (∼2-3 μm) bubbles were observed to grow and collapse in less than 200 ns. Temporal profiles were generally asymmetric, possibly indicating faster growth than collapse dynamics, and the collapse time scale was found to be consistent with modeling and literature data from other techniques. More complex behavior was also observed for bubbles within proximity to each other, with interaction leading to longer lifetimes and more likely rebounding after collapse.

Alchemical free energy perturbation (FEP) is a rigorous and powerful technique to calculate the free energy difference between distinct chemical systems. Here we report our implementation of automated large-scale FEP calculations, using the Amber software package, to facilitate antibody design and evaluation. In combination with Hamiltonian replica exchange, our FEP simulations aim to predict the effect of mutations on both the binding affinity and the structural stability. Importantly, we incorporate multiple strategies to faithfully estimate the statistical uncertainties in the FEP results. As a case study, we apply our protocols to systematically evaluate variants of the m396 antibody for their conformational stability and their binding affinity to the spike proteins of SARS-CoV-1 and SARS-CoV-2. By properly adjusting relevant parameters, the particle collapse problems in the FEP simulations are avoided. Furthermore, large statistical errors in a small fraction of the FEP calculations are effectively reduced by extending the sampling, such that acceptable statistical uncertainties are achieved for the vast majority of the cases with a modest total computational cost. Finally, our predicted conformational stability for the m396 variants is qualitatively consistent with the experimentally measured melting temperatures. Our work thus demonstrates the applicability of FEP in computational antibody design.

The accurate and efficient calculation of protein-protein binding affinities is an essential component in antibody and antigen design and optimization, and in computer modeling of antibody affinity maturation. Such calculations remain challenging despite advances in computer hardware and algorithms, primarily because proteins are flexible molecules, and thus, require explicit or implicit incorporation of multiple conformational states into the computational procedure. The astronomical size of the amino acid sequence space further compounds the challenge by requiring predictions to be computed within a short time so that many sequence variants can be tested. In this study, we compare three classes of methods for antibody/antigen (Ab/Ag) binding affinity calculations: (i) a method that relies on the physical separation of the Ab/Ag complex in equilibrium molecular dynamics (MD) simulations, (ii) a collection of 18 scoring functions that act on an ensemble of structures created using homology modeling software, and (iii) methods based on the molecular mechanics-generalized Born surface area (MM-GBSA) energy decomposition, in which the individual contributions of the energy terms are scaled to optimize agreement with the experiment. When applied to a set of 49 antibody mutations in two Ab/HIV gp120 complexes, all of the methods are found to have modest accuracy, with the highest Pearson correlations reaching about 0.6. In particular, the most computationally intensive method, i.e., MD simulation, did not outperform several scoring functions. The optimized energy decomposition methods provided marginally higher accuracy, but at the expense of requiring experimental data for parametrization. Within each method class, we examined the effect of the number of independent computational replicates, i.e., modeled structures or reinitialized MD simulations, on the prediction accuracy. We suggest using about ten modeled structures for scoring methods, and about five simulation replicates for MD simulations as a rule of thumb for obtaining reasonable convergence. We anticipate that our study will be a useful resource for practitioners working to incorporate binding affinity calculations within their protein design and optimization process.

Molecular diffusion across semi-permeable biological membranes capitalizes on complex nanoscale transport mechanisms that enable high permeation rates and finely tuned control over the profile of permeating molecules. Synthetic materials have thus far been unable to match their biological counterparts. Overcoming this obstacle has the potential to significantly impact applications such as hemodialysis, drug delivery, and biomolecular purification. Fluid flow through carbon nanotubes (CNTs) has been demonstrated to have enormous transport rates under a pressure driving force, yet comparatively little attention has been given to concentration driven transport. While bulk/hindered diffusion for small molecules has been assumed in some studies on nm-wide CNTs, other simulations and NMR experiments support the idea that self-diffusion coefficients may be several times larger than in bulk. These large uncertainties in the magnitude of the diffusion rates through CNTs have hampered their full exploitation in nanofluidic devices and membrane platforms. Here, we fabricate membranes with a large, well-characterized number of single-walled CNTs which enable an accurate extrapolation of the per-pore diffusive flow rate. A series of novel and stringent control experiments rule out the possibility of defects at all scales, including those smaller than the pore openings. Once corrected for boundary layer resistance at the membrane/fluid interface, our measurements indicate that the transport diffusivity of small ions in 2-nm single-walled carbon nanotubes is approximately 10-30 times larger than in bulk. By employing a variety of permeating molecules, we are compiling a large dataset to better understand the flow enhancement dependence on the chemico-physical properties of permeating molecules and to demonstrate the benefit of this transport phenomenon for dialysis separations. These results shed further light into the unique transport properties of graphitic channels and enable a more accurate design of CNT-based fluidic systems for a broad range of applications.

A rapid response is necessary to contain emergent biological outbreaks before they can become pandemics. The novel coronavirus (SARS-CoV-2) that causes COVID-19 was first reported in December of 2019 in Wuhan, China and reached most corners of the globe in less than two months. In just over a year since the initial infections, COVID-19 infected almost 100 million people worldwide. Although similar to SARS-CoV and MERS-CoV, SARS-CoV-2 has resisted treatments that are effective against other coronaviruses. Crystal structures of two SARS-CoV-2 proteins, spike protein and main protease, have been reported and can serve as targets for studies in neutralizing this threat. We have employed molecular docking, molecular dynamics simulations, and machine learning to identify from a library of 26 million molecules possible candidate compounds that may attenuate or neutralize the effects of this virus. The viability of selected candidate compounds against SARS-CoV-2 was determined experimentally by biolayer interferometry and FRET-based activity protein assays along with virus-based assays. In the pseudovirus assay, imatinib and lapatinib had IC50 values below 10 μM, while candesartan cilexetil had an IC50 value of approximately 67 µM against Mpro in a FRET-based activity assay. Comparatively, candesartan cilexetil had the highest selectivity index of all compounds tested as its half-maximal cytotoxicity concentration 50 (CC50) value was the only one greater than the limit of the assay (>100 μM).

Multimodal chromatography is a powerful approach for purifying proteins that uses ligands containing multiple modes of interaction. Recent studies have shown that selectivity in multimodal chromatographic separations is a function of the ligand structure and geometry. Here, we performed molecular dynamics simulations to explore how the ligand structure and geometry affect ligand-water interactions and how these differences in solution affect the nature of protein-ligand interactions. Our investigation focused on three chromatography ligands: Capto MMC, Nuvia cPrime, and Prototype 4, a structural variant of Nuvia cPrime. First, the solvation characteristics of each ligand were quantified via three metrics: average water density, fluctuations, and residence time. We then explored how solvation was perturbed when the ligand was bound to the protein surface and found that the probability of the phenyl ring dewetting followed the order: Capto MMC > Prototype 4 > Nuvia cPrime. To explore how these differences in dewetting affect protein-ligand interactions, we calculated the probability of each ligand binding to different types of residues on the protein surface and found that the probability of binding to a hydrophobic residue followed the same order as the dewetting behavior. This study illustrates the role that wetting and dewetting play in modulating protein-ligand interactions.

Understanding ion solvation and transport under confinement is critical for a wide range of emerging technologies, including water desalination and energy storage. While molecular dynamics (MD) simulations have been widely used to study the behavior of confined ions, considerable deviations between simulation results depending on the specific treatment of intermolecular interactions remain. In the following, we present a systematic investigation of the structure and dynamics of two representative solutions, that is, KCl and LiCl, confined in narrow carbon nanotubes (CNTs) with a diameter of 1.1 and 1.5 nm, using a combination of first-principles and classical MD simulations. Our simulations show that the inclusion of both polarization and cation-π interactions is essential for the description of ion solvation under confinement, particularly for large ions with weak hydration energies. Beyond the variation in ion solvation, we find that cation-π interactions can significantly influence the transport properties of ions in CNTs, particularly for KCl, where our simulations point to a strong correlation between ion dehydration and diffusion. Our study highlights the complex interplay between nanoconfinement and specific intermolecular interactions that strongly control the solvation and transport properties of ions.

SummaryRapid assessment of whether a pandemic pathogen may have increased transmissibility or be capable of evading existing vaccines and therapeutics is critical to mounting an effective public health response. Over the period of seven days, we utilized rapid computational prediction methods to evaluate potential public health implications of the emerging SARS-CoV-2 Omicron variant. Specifically, we modeled the structure of the Omicron variant, examined its interface with human angiotensin converting enzyme 2 (ACE-2) and evaluated the change in binding affinity between Omicron, ACE-2 and publicly known neutralizing antibodies. We also compared the Omicron variant to known Variants of Concern (VoC). Seven of the 15 Omicron mutations occurring in the spike protein receptor binding domain (RBD) occur at the ACE-2 cell receptor interface, and therefore may play a critical role in enhancing binding to ACE-2. Our estimates of Omicron RBD-ACE-2 binding affinities indicate that at least two of RBD mutations, Q493R and N501Y, contribute to enhanced ACE-2 binding, nearly doubling delta-delta-G (ddG) free energies calculated for other VoC’s. Binding affinity estimates also were calculated for 55 known neutralizing SARS-CoV-2 antibodies. Analysis of the results showed that Omicron substantially degrades binding for more than half of these neutralizing SARS-CoV-2 antibodies, and for roughly 10 times as many of the antibodies than the currently dominant Delta variant. This early study lends support to use of rapid computational risk assessments to inform public health decision-making while awaiting detailed experimental characterization and confirmation.

Structure-based Deep Fusion models were recently shown to outperform several physics- and machine learning-based protein-ligand binding affinity prediction methods. As part of a multi-institutional COVID-19 pandemic response, over 500 million small molecules were computationally screened against four protein structures from the novel coronavirus (SARS-CoV-2), which causes COVID-19. Three enhancements to Deep Fusion were made in order to evaluate more than 5 billion docked poses on SARS-CoV-2 protein targets. First, the Deep Fusion concept was refined by formulating the architecture as one, coherently backpropagated model (Coherent Fusion) to improve binding-affinity prediction accuracy. Secondly, the model was trained using a distributed, genetic hyper-parameter optimization. Finally, a scalable, high-throughput screening capability was developed to maximize the number of ligands evaluated and expedite the path to experimental evaluation. In this work, we present both the methods developed for machine learning-based high-throughput screening and results from using our computational pipeline to find SARS-CoV-2 inhibitors.

The only medication available currently to prevent and treat opioid overdose (naloxone) was approved by the US Food and Drug Administration (FDA) nearly 50 years ago. Because of its pharmacokinetic and pharmacodynamic properties, naloxone has limited utility under some conditions and would not be effective to counteract mass casualties involving large‐scale deployment of weaponized synthetic opioids. To address shortcomings of current medical countermeasures for opioid toxicity, a trans‐agency scientific meeting was convened by the US National Institute of Allergy and Infectious Diseases/National Institutes of Health (NIAID/NIH) on August 6 and 7, 2019, to explore emerging alternative approaches for treating opioid overdose in the event of weaponization of synthetic opioids. The meeting was initiated by the Chemical Countermeasures Research Program (CCRP), was organized by NIAID, and was a collaboration with the National Institute on Drug Abuse/NIH (NIDA/NIH), the FDA, the Defense Threat Reduction Agency (DTRA), and the Biomedical Advanced Research and Development Authority (BARDA). This paper provides an overview of several presentations at that meeting that discussed emerging new approaches for treating opioid overdose, including the following: (1) intranasal nalmefene, a competitive, reversible opioid receptor antagonist with a longer duration of action than naloxone; (2) methocinnamox, a novel opioid receptor antagonist; (3) covalent naloxone nanoparticles; (4) serotonin (5‐HT) 1A receptor agonists; (5) fentanyl‐binding cyclodextrin scaffolds; (6) detoxifying biomimetic “nanosponge” decoy receptors; and (7) antibody‐based strategies. These approaches could also be applied to treat opioid use disorder.