BACKGROUND Infection by Trypanosoma cruzi, the agent of Chagas disease, is endemic to the Americas and can irreparably damage the cardiac and gastrointestinal systems during decades of parasite persistence. Diagnosis of chronic infection requires confirmation by multiple serological assays due to the imperfect performance of existing tests. Current serology tests were developed using small specimen sets predominantly from South America, and lower performance has been observed in patients who acquired infection in Central America and Mexico.METHODS To improve Chagas disease serology, we evaluated antibody responses against the entire T. cruzi proteome with phage display immunoprecipitation sequencing and further evaluated high-prevalence antigens by immunoassay. We utilized specimen sets representing Mexico, Central America, and South America and varying cardiac disease presentations, from 185 cases and 143 controls.RESULTS We identified over 1,300 antigenic T. cruzi peptides. A trans-sialidase antigen demonstrated high seroprevalence across all regions and has not previously been described as a diagnostic target to our knowledge. Orthogonal validation of this peptide demonstrated increased antibody reactivity for infections originating from Central America.CONCLUSION This study provides proteome-wide identification of seroreactive T. cruzi peptides across a range of endemic populations not previously represented in antigen discovery and identifies a trans-sialidase peptide antigen (TS23) with potential for translation into diagnostic serological assays.FUNDING Chan Zuckerberg Biohub, the Chan Zuckerberg Biohub Physician-Scientist Fellowship Program, the NIH National Heart Lung and Blood Institute award K38HL154203, and the NIH Eunice Kennedy Shriver National Institute of Child Health and Human Development award F30HD117526.

By combining hydrogen-deuterium exchange monitored by mass spectrometry (HDX-MS) with the ability of enveloped virus-like particles (eVLPs) to display full-length native-like severe acute respiratory syndrome coronavirus 2 spike protein, we have determined the energetic and conformational effects of both the membrane environment and unique sequence features that are considered incompatible with soluble protein constructs. We find that eVLP-displayed spike can sample the open-interface trimer conformation observed in soluble constructs of spike, including sequences from engineered vaccine constructs and native viral sequences inaccessible to studies on soluble constructs. Moreover, the D614G mutation, which arose early in the pandemic, favors the canonical "closed-interface" prefusion conformation, potentially mitigating premature S1 shedding in the presence of a cleaved furin site and providing an evolutionary advantage to the virus. Furin cleavage at the S1/S2 boundary allosterically increases the flexibility of the S2 ' site, which may facilitate increased TMPRSS2 processing, enhancing viral infectivity. The use of eVLPs in HDX-MS studies provides a powerful platform for studying viral and membrane proteins in near-native environments.

Science frequently benefits from teams of interdisciplinary researchers1-3, but many scientists do not have easy access to experts from multiple fields4,5. Although large language models (LLMs) have shown an impressive ability to aid researchers across diverse domains, their uses have been largely limited to answering specific scientific questions rather than performing open-ended research6-11. Here we expand the capabilities of LLMs for science by introducing the Virtual Lab, an artificial intelligence (AI)-human research collaboration to perform sophisticated, interdisciplinary science research. The Virtual Lab consists of an LLM Principal Investigator agent guiding a team of LLM scientist agents through a series of research meetings, with a human researcher providing high-level feedback. We applied the Virtual Lab to design nanobody binders to recent variants of SARS-CoV-2. The Virtual Lab created a novel computational nanobody design pipeline that incorporates the protein language model ESM, the protein folding model AlphaFold-Multimer and the computational biology software Rosetta and designed 92 new nanobodies. Experimental validation reveals a range of functional nanobodies with promising binding profiles across SARS-CoV-2 variants. In particular, two new nanobodies exhibit improved binding to the recent JN.1 or KP.3 variants12,13 while maintaining strong binding to the ancestral viral spike protein, suggesting that these are suitable candidates for further investigation. This work demonstrates how the Virtual Lab can rapidly make an impactful, real-world scientific discovery.

We report a detailed analysis of the full-length SARS-CoV-2 spike dynamics within a native-like membrane environment and variants inaccessible to studies on soluble constructs by conducting hydrogen-deuterium exchange mass spectrometry (HDX-MS) on enveloped virus-like particles (eVLPs) displaying various spike constructs. We find that the previously identified open-interface trimer conformation is sampled in all eVLP-displayed spike variants studied including sequences from engineered vaccine constructs and native viral sequences. The D614G mutation, which arose early in the pandemic, favors the canonical 'closed-interface' prefusion conformation, potentially mitigating premature S1 shedding in the presence of a cleaved furin site and providing an evolutionary advantage to the virus. Remarkably, furin cleavage at the S1/S2 boundary allosterically increases the flexibility of the S2' site, which may facilitate increased TMPRSS2 processing, enhancing viral infectivity. The use of eVLPs in HDX-MS studies provides a powerful platform for studying viral and membrane proteins in near-native environments.

The N-heptad repeat (NHR) of the HIV-1 gp41 prehairpin intermediate (PHI) is an attractive potential vaccine target with high sequence conservation across diverse strains. However, despite the potency of NHR-targeting peptides and clinical efficacy of the NHR-targeting entry inhibitor enfuvirtide, no potently neutralizing NHR-directed monoclonal antibodies (mAbs) nor antisera have been identified or elicited to date. The lack of potent NHR-binding mAbs both dampens enthusiasm for vaccine development efforts at this target and presents a barrier to performing passive immunization experiments with NHR-targeting antibodies. To address this challenge, we previously developed an improved variant of the NHR-directed mAb D5, called D5_AR, which is capable of neutralizing diverse tier-2 viruses. Building on that work, here we present the 2.7Å-crystal structure of D5_AR bound to NHR mimetic peptide IQN17. We then utilize protein language models and supervised machine learning to generate small (n < 100) libraries of D5_AR variants that are subsequently screened for improved neutralization potency. We identify a variant with 5-fold improved neutralization potency, D5_FI, which is the most potent NHR-directed monoclonal antibody characterized to date and exhibits broad neutralization of tier-2 and -3 pseudoviruses as well as replicating R5 and X4 challenge strains. Additionally, our work highlights the ability of protein language models to efficiently identify improved mAb variants from relatively small libraries.

Natural evolution must explore a vast landscape of possible sequences for desirable yet rare mutations, suggesting that learning from natural evolutionary strategies could guide artificial evolution. Here we report that general protein language models can efficiently evolve human antibodies by suggesting mutations that are evolutionarily plausible, despite providing the model with no information about the target antigen, binding specificity or protein structure. We performed language-model-guided affinity maturation of seven antibodies, screening 20 or fewer variants of each antibody across only two rounds of laboratory evolution, and improved the binding affinities of four clinically relevant, highly mature antibodies up to sevenfold and three unmatured antibodies up to 160-fold, with many designs also demonstrating favorable thermostability and viral neutralization activity against Ebola and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pseudoviruses. The same models that improve antibody binding also guide efficient evolution across diverse protein families and selection pressures, including antibiotic resistance and enzyme activity, suggesting that these results generalize to many settings.

Experimental autoimmune encephalomyelitis (EAE) is a model for central nervous system (CNS) autoimmune demyelinating diseases such as multiple sclerosis (MS) and MOG antibody-associated disease (MOGAD). Immunization with the extracellular domain of recombinant human MOG (rhMOG), which contains pathogenic antibody and T cell epitopes, induces B cell-dependent EAE for studies in mice. However, these studies have been hampered by rhMOG availability due to its insolubility when overexpressed in bacterial cells, and the requirement for inefficient denaturation and refolding. Here, we describe a new protocol for the high-yield production of soluble rhMOG in SHuffle cells, a commercially available E. coli strain engineered to facilitate disulfide bond formation in the cytoplasm. SHuffle cells can produce a soluble fraction of rhMOG yielding >100 mg/L. Analytical size exclusion chromatography multi-angle light scattering (SEC-MALS) and differential scanning fluorimetry of purified rhMOG reveals a homogeneous monomer with a high melting temperature, indicative of a well-folded protein. An in vitro proliferation assay establishes that purified rhMOG can be processed and recognized by T cells expressing a T cell receptor (TCR) specific for the immunodominant MOG35-55 peptide epitope. Lastly, immunization of wild-type, but not B cell deficient, mice with rhMOG resulted in robust induction of EAE, indicating a B cell-dependent induction. Our SHuffle cell method greatly simplifies rhMOG production by combining the high yield and speed of bacterial cell expression with enhanced disulfide bond formation and folding, which will enable further investigation of B cell-dependent EAE and expand human research of MOG in CNS demyelinating diseases.

Science frequently benefits from teams of interdisciplinary researchers. However, most scientists don't have access to experts from multiple fields. Fortunately, large language models (LLMs) have recently shown an impressive ability to aid researchers across diverse domains by answering scientific questions. Here, we expand the capabilities of LLMs for science by introducing the Virtual Lab, an AI-human research collaboration to perform sophisticated, interdisciplinary science research. The Virtual Lab consists of an LLM principal investigator agent guiding a team of LLM agents with different scientific backgrounds (e.g., a chemist agent, a computer scientist agent, a critic agent), with a human researcher providing high-level feedback. We design the Virtual Lab to conduct scientific research through a series of team meetings, where all the agents discuss a scientific agenda, and individual meetings, where an agent accomplishes a specific task. We demonstrate the power of the Virtual Lab by applying it to design nanobody binders to recent variants of SARS-CoV-2, which is a challenging, open-ended research problem that requires reasoning across diverse fields from biology to computer science. The Virtual Lab creates a novel computational nanobody design pipeline that incorporates ESM, AlphaFold-Multimer, and Rosetta and designs 92 new nanobodies. Experimental validation of those designs reveals a range of functional nanobodies with promising binding profiles across SARS-CoV-2 variants. In particular, two new nanobodies exhibit improved binding to the recent JN.1 or KP.3 variants of SARS-CoV-2 while maintaining strong binding to the ancestral viral spike protein, suggesting exciting candidates for further investigation. This demonstrates the ability of the Virtual Lab to rapidly make impactful, real-world scientific discovery. ### Competing Interest Statement The authors have declared no competing interest.

Liquid chromatography purification of multiple recombinant proteins, in parallel, could catalyze research and discovery if the processes are fast and approach the robustness of traditional, “one-protein-at-a-time” purification. Here, we report an automated, four channel chromatography platform that we have designed and validated for parallelized protein purification at milligram scales. The device can purify up to four proteins (each with its own single column), has inputs for up to eight buffers or solvents that can be directed to any of the four columns via a network of software-driven valves, and includes an automated fraction collector with ten positions for 1.5 or 5.0 mL collection tubes and four positions for 50 mL collection tubes for each column output. The control software can be accessed either via Python scripting, giving users full access to all steps of the purification process, or via a simple-to-navigate touch screen graphical user interface that does not require knowledge of the command line or any programming language. Using our instrument, we report milligram-scale, parallelized, single-column purification of a panel of mammalian cell expressed coronavirus (SARS-CoV-2, HCoV-229E, HCoV-OC43, HCoV-229E) trimeric Spike and monomeric Receptor Binding Domain (RBD) antigens, and monoclonal antibodies targeting SARS-CoV-2 Spike (S) and Influenza Hemagglutinin (HA). We include a detailed hardware build guide, and have made the controlling software open source, to allow others to build and customize their own protein purifier systems.

A multiplexed enzyme-linked immunosorbent assay (ELISA) that simultaneously measures antibody binding to multiple antigens can extend the impact of serosurveillance studies, particularly if the assay approaches the simplicity, robustness, and accuracy of a conventional single-antigen ELISA. Here, we report on the development of multiSero, an open-source multiplex ELISA platform for measuring antibody responses to viral infection. Our assay consists of three parts: (1) an ELISA against an array of proteins in a 96-well format; (2) automated imaging of each well of the ELISA array using an open-source plate reader; and (3) automated measurement of optical densities for each protein within the array using an open-source analysis pipeline. We validated the platform by comparing antibody binding to Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) antigens in 217 human sera samples, showing high sensitivity (0.978), specificity (0.977), positive predictive value (0.978), and negative predictive value (0.977) for classifying seropositivity, a high correlation of multiSero determined antibody titers with commercially available SARS-CoV-2 antibody tests, and antigen-specific changes in antibody titer dynamics upon vaccination. The open-source format and accessibility of our multiSero platform can contribute to the adoption of multiplexed ELISA arrays for serosurveillance studies, for SARS-CoV-2 and other pathogens of significance.

Rabies virus is a member of the lyssavirus family and causes over 60,000 deaths a year. There are currently five strains of rabies virus circulating throughout the continental United States. In order to better control the spread of rabies virus, the Center of Disease Control monitors the prevalence and host range of different rabies virus variants (RVV) throughout the United States. This surveillance relies on a panel of monoclonal antibodies that are known to differentially bind to the nucleoprotein of RVV in a direct fluorescent antibody test (DFA).

Rabies virus, a member of the lyssavirus family, causes on the order of 60,000 deaths per year, disproportionately affecting historically exploited regions of the world. While prophylactic measures such as vaccines and immunoglobulin therapies are very effective at preventing rabies upon exposure, these treatments are very expensive and require cold-chain logistics, making them inaccessible to much of the world. Furthermore, there is no cure for rabies after the onset of symptoms, making it a death sentence for people who do not seek treatment because either they lack access or they do not suspect the animal they were bitten by was infectious. The rabies virus glycoprotein (RABV-G) is densely displayed on the surface of the virion, making it an attractive target for vaccine and drug development, yet this protein remains poorly understood. RABV-G is a highly dynamic protein that is known to reversibly sample its pre- and post-fusion conformations in equilibrium at physiological pH, making it difficult to study using many in vitro methods. Hydrogen deuterium exchange monitored by mass spectrometry (HDX-MS) is an ideal technique to probe the conformational landscape of proteins that occupy more than one conformation in solution. This work uses HDX-MS to measure the local stability of the protein when biased to both the prefusion and postfusion conformation as well as quantify the kinetics of the conformational change between the two conformations. This work will be useful in exploring how antibodies interact with the different conformations of rabies G in order to design better therapeutic strategies to combat this lethal virus.

The sequencing of antibody repertoires of B-cells at increasing coverage and depth has led to the identification of vast numbers of immunoglobulin heavy and light chains. However, the size and complexity of these Adaptive Immune Receptor Repertoire sequencing (AIRR-seq) datasets makes it difficult to perform exploratory analyses. To aid in data exploration, we have developed AIRRscape, an R Shiny-based interactive web browser application that enables B-cell receptor (BCR) and antibody feature discovery through comparisons among multiple repertoires. Using AIRR-seq data as input, AIRRscape starts by aggregating and sorting repertoires into interactive and explorable bins of germline V-gene, germline J-gene, and CDR3 length, providing a high-level view of the entire repertoire. Interesting subsets of repertoires can be quickly identified and selected, and then network topologies of CDR3 motifs can be generated for further exploration. Here we demonstrate AIRRscape using patient BCR repertoires and sequences of published monoclonal antibodies to investigate patterns of humoral immunity to three viral pathogens: SARS-CoV-2, HIV-1, and DENV (dengue virus). AIRRscape reveals convergent antibody sequences among datasets for all three pathogens, although HIV-1 antibody datasets display limited convergence and idiosyncratic responses. We have made AIRRscape available as a web-based Shiny application, along with code on GitHub to encourage its open development and use by immuno-informaticians, virologists, immunologists, vaccine developers, and other scientists that are interested in exploring and comparing multiple immune receptor repertoires.

Current COVID-19 vaccines and many clinical diagnostics are based on the structure and function of the SARS-CoV-2 spike ectodomain. Using hydrogen–deuterium exchange monitored by mass spectrometry, we have uncovered that, in addition to the prefusion structure determined by cryo-electron microscopy, this protein adopts an alternative conformation that interconverts slowly with the canonical prefusion structure. This new conformation—an open trimer—contains easily accessible receptor-binding domains. It exposes the conserved trimer interface buried in the prefusion conformation, thus exposing potential epitopes for pan-coronavirus antibody and ligand recognition. The population of this state and kinetics of interconversion are modulated by temperature, receptor binding, antibody binding, and sequence variants observed in the natural population. Knowledge of the structure and populations of this conformation will help improve existing diagnostics, therapeutics, and vaccines. HDX–MS analysis reveals the SARS-CoV-2 spike ectodomain reversibly samples an open-trimer conformation that reveals epitopes for a pan-coronavirus antibody; interconversion with the prefusion conformation is modulated by temperature, ACE2 receptor binding, and sequence variants.

Dengue virus (DENV) is a mosquito-borne disease that poses a public health threat to sub/tropical areas worldwide. Vaccination drives require differential diagnosis of serotype-specific DENV exposure to reduce severe dengue risks, yet state-of-the-art DENV serology relies upon short-lived serotype-specific IgM or labor intensive neutralization assays. The need for high-throughput differential diagnosis is met with our multiSero platform (Byrum et al.), a screening technique capable of detecting 48 antigen-antibody pairs simultaneously, demonstrating utility for population-wide screening. Through machine-vision techniques, we quantify and classify antibody-response signals with high sensitivity to develop automated analysis pipelines capable of diagnosing serotype-specific DENV exposure.

Severe COVID-19 is associated with epithelial and endothelial barrier dysfunction within the lung as well as in distal organs. While it is appreciated that an exaggerated inflammatory response is associated with barrier dysfunction, the triggers of vascular leak are unclear. Here, we report that cell-intrinsic interactions between the Spike (S) glycoprotein of SARS-CoV-2 and epithelial/endothelial cells are sufficient to induce barrier dysfunction in vitro and vascular leak in vivo, independently of viral replication and the ACE2 receptor. We identify an S-triggered transcriptional response associated with extracellular matrix reorganization and TGF-β signaling. Using genetic knockouts and specific inhibitors, we demonstrate that glycosaminoglycans, integrins, and the TGF-β signaling axis are required for S-mediated barrier dysfunction. Notably, we show that SARS-CoV-2 infection caused leak in vivo, which was reduced by inhibiting integrins. Our findings offer mechanistic insight into SARS-CoV-2-triggered vascular leak, providing a starting point for development of therapies targeting COVID-19.

Many existing protein detection strategies depend on highly functionalized antibody reagents. A simpler and easier to produce class of detection reagent is highly desirable. We designed a single-component, recombinant, luminescent biosensor that can be expressed in laboratory strains of E. coli and S. cerevisiae . This biosensor is deployed in multiple homogenous and immobilized assay formats to detect recombinant SARS-CoV-2 spike antigen and cultured virus. The chemiluminescent signal generated facilitates detection by an un-augmented cell phone camera. B inding A ctivated T andem split-enzyme (BAT) biosensors may serve as a useful template for diagnostics and reagents that detect SARS-CoV-2 antigens and other proteins of interest.

Omicron and its subvariants have rendered most authorized monoclonal antibody-based treatments for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) ineffective, highlighting the need for biologics capable of overcoming SARS-CoV-2 evolution. These mostly ineffective antibodies target variable epitopes. Here we describe broad-spectrum SARS-CoV-2 inhibitors developed by tethering the SARS-CoV-2 receptor, angiotensin-converting enzyme 2 (ACE2), to known non-neutralizing antibodies that target highly conserved epitopes in the viral spike protein. These inhibitors, called receptor-blocking conserved non-neutralizing antibodies (ReconnAbs), potently neutralize all SARS-CoV-2 variants of concern (VOCs), including Omicron. Neutralization potency is lost when the linker joining the binding and inhibitory ReconnAb components is severed. In addition, a bi-functional ReconnAb, made by linking ACE2 to a bi-specific antibody targeting two non-overlapping conserved epitopes, defined here, shows sub-nanomolar neutralizing activity against all VOCs, including Omicron and BA.2. Given their conserved targets and modular nature, ReconnAbs have the potential to act as broad-spectrum therapeutics against SARS-CoV-2 and other emerging pandemic diseases.

motions and intraring interactions that support them, the stability of the oligomeric states of the enzymes, and potential modes of interaction be-tween enzymes and a microtubule leading to first steps in severing. I will discuss the results in the context of existing models, experimental probes of microtubule severing, and data regarding the action of other AAA þ motors.

Coronaviruses (CoVs), including the severe acute respiratory syndrome (SARS) and middle east respiratory syndrome (MERS) viruses, have and continue to pose a major threat to human health. In October of 2020 the COVID-19 pandemic, caused by the coronavirus SARS-CoV-2, surpassed one million global deaths. CoVs are enveloped positive strand RNA viruses that display surface spike proteins which recognize host receptors. The spike proteins then undergo conformational changes that allow for attachment to the host membrane and eventually facilitate membrane fusion and viral entry. Due to their exposure on the virus surface and their essential role in coronavirus infection, most antibody development strategies and many therapeutic development strategies have focused on the spike protein. While much focus has been given to the structure of spike, successful design of therapeutics requires understanding the conformational dynamics and alternative conformations not accessible using traditional structural methods. We have successfully applied Hydrogen/Deuterium Exchange Mass Spectrometry (HDX/MS) on this large (>400 kDa) glycosylated trimeric complex to investigate these important conformational changes; allowing us to identify the binding interfaces as well as the induced allosteric changes upon binding to the human receptor ACE2, neutralizing patient antibodies, and synthetic binders. We also compare the conformational flexibility of coronavirus homologues and naturally occurring SARS-CoV-2 spike variants allowing us to connect changes in the conformational ensemble to functional, phenotypic differences in these variants. Understanding the native conformational ensemble, and interactions between receptors or antibodies with CoV spike proteins will not only improve our understanding of CoV biology and the host immune response to CoV but also aid in the design of therapeutics and vaccines for current and future CoVs.