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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 91 records · Page 5
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Uncovering heterogeneous intercommunity disease transmission from neutral allele frequency time series

The COVID-19 pandemic has underscored the need for accurate epidemic forecasting to predict pathogen spread, evolution, and evaluate intervention strategies. Forecast reliability hinges on detailed knowledge of disease transmission across population segments, which may be inferred from contact surveys or mobility data. However, these indirect approaches make it difficult to estimate rare transmissions between socially or geographically distant communities. We show that the steep ramp-up of genome sequencing surveillance during the pandemic can be leveraged to directly identify transmission patterns between geographically defined communities. Our approach uses a hidden Markov model to infer the fraction of infections a community imports from others based on how rapidly allele frequencies in the focal community converge to those in the donor communities. Applying this method to SARS-CoV-2 sequencing data from England and the United States, we uncover networks of intercommunity transmission that reflect geographical relationships while exposing significant long-range interactions. The scaling of importation rate with distance is consistent across both countries, yet weaker than expected based on mobility data, highlighting limitations of indirect inference. We show that transmission patterns can change between waves of variants of concern and analyze how the inferred heterogeneity in intercommunity transmission impacts evolutionary forecasts. While applied here to geographically defined communities, our approach could be applied to those defined by other traits (e.g., age, socioeconomic status), provided time-series data can be stratified accordingly. Overall, our study highlights population genomic time series data as a crucial record of epidemiological interactions, which can be deciphered using tree-free inference methods.

Okada, Takashi [Department of Physics; University ↗
DOE OSTI2025

Cross-reactive sarbecovirus antibodies induced by mosaic RBD nanoparticles

Broad immune responses are needed to mitigate viral evolution and escape. To induce antibodies against conserved receptor-binding domain (RBD) regions of SARS-like betacoronavirus (sarbecovirus) spike proteins that recognize SARS-CoV-2 variants of concern and zoonotic sarbecoviruses, we developed mosaic-8b RBD nanoparticles presenting eight sarbecovirus RBDs arranged randomly on a 60-mer nanoparticle. Mosaic-8b immunizations protected animals from challenges from viruses whose RBDs were matched or mismatched to those on nanoparticles. Here, we describe neutralizing mAbs isolated from mosaic-8b-immunized rabbits, some on par with Pemgarda, the only currently FDA-approved therapeutic mAb. Deep mutational scanning, in vitro selection of spike resistance mutations, and single-particle cryo-electron microscopy structures of spike–antibody complexes demonstrated targeting of conserved RBD epitopes. Rabbit mAbs included critical D-gene segment RBD-recognizing features in common with human anti-RBD mAbs, despite rabbit genomes lacking an equivalent human D-gene segment, thus demonstrating that the immune systems of humans and other mammals can utilize different antibody gene segments to arrive at similar modes of antigen recognition. These results suggest that animal models can be used to elicit anti-RBD mAbs with similar properties to those raised in humans, which can then be humanized for therapeutic use, and that mosaic RBD nanoparticle immunization coupled with multiplexed screening represents an efficient way to generate and select broadly cross-reactive therapeutic pan-sarbecovirus and pan-SARS-CoV-2 variant mAbs.

Science & Technology - Other Topics↗
DOE OSTI2025

Including frameworks of public health ethics in computational modelling of infectious disease interventions

Decisions on public health interventions to control infectious diseases are often informed by computational models. Interpreting the predicted outcomes of a public health decision requires not only high-quality modelling but also an ethical framework for assessing the benefits and harms associated with different options. The design and specification of ethical frameworks matured independently of computational modelling, so many values recognized as important for ethical decision-making are missing from computational models. We demonstrate a proof-of-concept approach to incorporate multiple public health values into the evaluation of a simple computational model for vaccination against a pathogen such as SARS-CoV-2. By examining a bounded space of alternative prioritizations of three values relevant to public health ethics (aggregate clinical burden, equity in clinical burden, equity in adverse effects from vaccination), we identify value trade-offs, where the outcomes of optimal strategies differ depending on the ethical framework. This work demonstrates an approach to incorporating diverse values into decision criteria used to evaluate outcomes of models of infectious disease interventions.

"Mathematical Biology"↗
DOE OSTI2024

CoVTransformer

The code is a transformer model to forecast SARS-CoV-2 lineage frequencies in the future.

Feng, Yinan↗
DOE OSTI2025

PRIME: Protein Representation Inference for Mutation Evaluation

Protein language machine learning models built upon existing ESM-2 model developed by Evolutionary Scale (evolutionaryscale.ai) and an in-house protein language model based on the BERT model developed by Google. The code also includes model training scripts and saved checkpoints from our own training using publicly available SARS-CoV-2 protein sequences.

Gibson, Kaetlyn [Los Alamos National Lab]↗
DOE OSTI2024

Isolation and characterization of IgG3 glycan-targeting antibodies with exceptional cross-reactivity for diverse viral families

Broadly reactive antibodies that target sequence-diverse antigens are of interest for vaccine design and monoclonal antibody therapeutic development because they can protect against multiple strains of a virus and provide a barrier to evolution of escape mutants. Using LIBRA-seq (linking B cell receptor to antigen specificity through sequencing) data for the B cell repertoire of an individual chronically infected with human immunodeficiency virus type 1 (HIV-1), we identified a lineage of IgG3 antibodies predicted to bind to HIV-1 Envelope (Env) and influenza A Hemagglutinin (HA). Two lineage members, antibodies 2526 and 546, were confirmed to bind to a large panel of diverse antigens, including several strains of HIV-1 Env, influenza HA, coronavirus (CoV) spike, hepatitis C virus (HCV) E protein, Nipah virus (NiV) F protein, and Langya virus (LayV) F protein. We found that both antibodies bind to complex glycans on the antigenic surfaces. Antibody 2526 targets the stem region of influenza HA and the N-terminal domain (NTD) region of SARS-CoV-2 spike. A crystal structure of 2526 Fab bound to mannose revealed the presence of a glycan-binding pocket on the light chain. Antibody 2526 cross-reacted with antigens from multiple pathogens and displayed no signs of autoreactivity. These features distinguish antibody 2526 from previously described glycan-reactive antibodies. Further study of this antibody class may aid in the selection and engineering of broadly reactive antibody therapeutics and can inform the development of effective vaccines with exceptional breadth of pathogen coverage.

Microbiology↗
DOE OSTI2024

Annual Report for Structure-Aware Unsupervised, Transformational Machine Learning for Drug Discovery

The major goal of this project is to develop machine learning (ML) methods to enable improved predictive power on real drug discovery for novel targets. More specifically, we plan to demonstrate the capability and effectiveness of ML tools utilizing unlabeled large-volume protein-ligand datasets. We also plan to demonstrate the capability and effectiveness of the developed methods by testing on a realistic drug discovery task to identify pan-coronavirus protease inhibitors such as SARS-CoV-2. While the overall goals and milestones remain consistent with the original proposal, certain technical details have been modified, which we will describe in this report.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

In Vitro Evolution and Computational Approaches to Predict, Prevent and Control Future Pandemics

The natural tendency of virus to mutate and the under-sampling of the environment makes it difficult to become aware of the emergence of new viral strains with pandemic potential. Being able to predict what mutations make a virus more infective might allow to spot such strains with minimal sampling and potentially allow to predict/prevent the next pandemic. The team attempted to mimic natural viral mutations and recombination through computational and experimental methods producing a variety of mutants of a SARS-COV-2 protein (receptor binding domain, RBD, of spike protein) responsible for viral entry in mammalian cells. The library of mutants was then interrogated for ability and lack-there-of to interact with the host cell receptor ushering viral entry, Angiotensin-converting enzyme 2 (ACE2). The negative and positive data set is intended to “teach the rules” of virus-host receptor interaction. Additionally, the positive clones were used to screen a set of antibody mutants designed to widen the breadth of viral mutants recognition, to demonstrate that this kind of libraries could also be a tool to produce antibody therapeutics impervious to viral mutation, even before a pandemic strain is discovered.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Machine learning guided selection of broad-spectrum epitope-specific functional antibodies for "Disease X"

Our project established and demonstrated a transfer learning framework that enables prediction of antibody–antigen interactions across related viruses. The approach focused on three major activities: 1. Conserved region and epitope identification – We compared viral protein structures and sequences to identify shared receptor-binding domains and neutralizing epitope regions across variants and related viruses. These conserved features formed the foundation for discovering broadly functional antibodies. 2. Machine learning model development – We built neural network–based models that integrate epitope features with antibody sequence information. Instead of relying solely on structural or physical properties, the models learned transferable patterns that describe antibody binding potential across different viral families. 3. Transfer learning and validation – Using SARS-CoV-2 and Ebola as source systems, we successfully transferred learned epitope features to predict antibody interactions for SARS CoV-1 and Marburg virus. Iterative cycles of dataset generation, retraining, and evaluation improved generalization and predictive power, ensuring the framework can adapt to new threats.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Structure-Aware Unsupervised, Transformational Machine Learning for Drug Discovery (DTRA Basic Research Final Report)

The major goal of this project is to develop machine learning (ML) methods to enable improved predictive power on real drug discovery for novel targets. More specifically, we planned to demonstrate the capability and effectiveness of ML tools utilizing unlabeled large-volume protein-ligand datasets. We investigated multiple pre-training approaches for 3D protein-ligand structure-based foundation models, without relying on experimental binding data. We also addressed scenarios in which crystal structures are unavailable or binding data are limited. We also planned to develop a complete pipeline to screen novel compounds as well as to demonstrate the capability and effectiveness of the developed methods by testing on a realistic drug discovery task such as SARS-CoV-2. While the major goals and milestones remain consistent with the original proposal, certain technical details have been adjusted, based on the experimental results and related outcomes.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Quantification of heterogeneity in human CD8 + T cell responses to vaccine antigens: an HLA-guided perspective

Vaccines have historically played a pivotal role in controlling epidemics. Effective vaccines for viruses causing significant human disease, e.g., Ebola, Lassa fever, or Crimean Congo hemorrhagic fever virus, would be invaluable to public health strategies and counter-measure development missions. Here, we propose coverage metrics to quantify vaccine-induced CD8 + T cell-mediated immune protection, as well as metrics to characterize immuno-dominant epitopes, in light of human genetic heterogeneity and viral evolution. Proof-of-principle of our approach and methods are demonstrated for Ebola virus, SARS-CoV-2, and Burkholderia pseudomallei (vaccine) proteins.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

Protein-ligand binding affinity prediction using multi-instance learning with docking structures

Recent advances in 3D structure-based deep learning approaches demonstrate improved accuracy in predicting protein-ligand binding affinity in drug discovery. These methods complement physics-based computational modeling such as molecular docking for virtual high-throughput screening. Despite recent advances and improved predictive performance, most methods in this category primarily rely on utilizing co-crystal complex structures and experimentally measured binding affinities as both input and output data for model training. Nevertheless, co-crystal complex structures are not readily available and the inaccurate predicted structures from molecular docking can degrade the accuracy of the machine learning methods. We introduce a novel structure-based inference method utilizing multiple molecular docking poses for each complex entity. Our proposed method employs multi-instance learning with an attention network to predict binding affinity from a collection of docking poses. We validate our method using multiple datasets, including PDBbind and compounds targeting the main protease of SARS-CoV-2. The results demonstrate that our method leveraging docking poses is competitive with other state-of-the-art inference models that depend on co-crystal structures. This method offers binding affinity prediction without requiring co-crystal structures, thereby increasing its applicability to protein targets lacking such data.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2021

Towards Disentangling Lockdown-Driven Air Quality Changes in the Northeastern U.S.

In the absence of preventive therapies or effective treatment for most cases of coronavirus disease 2019 (COVID-19), governments worldwide have sought to minimize person-to-person severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) transmission through a variety of lock-down measures and social distancing policies. Extreme events like the COVID-19 pandemic present a tremendous opportunity to make quantitative connections between changes in anthropogenic forcing, social and economic activity, and the related Earth system response. In this comment, we examine air quality impacts associated with pandemic response measures in the Northeastern United States.

Air Quality↗
NASA NTRS2024

Herpesviruses in Saliva and Their Clinical Significance

Saliva has been used as a source of biological markers for a wide spectrum of normal and disease states for a long time. It is a non-invasive, easily accessible, and self-collected body fluid that contains a variety of measurable biological substances. While mostly water, saliva also contains ions, carbohydrates, proteins and peptides, exfoliated cells, nucleic acids, and microorganisms. Saliva can reflect tissue levels of some natural substances and a large variety of molecules introduced for therapeutic use, emotional status; hormonal status, immunological status, neurological effects, and nutritional and metabolic status. It can also be used to monitor a variety of drugs including marijuana, cocaine, and alcohol. It is the most cost-effective approach for screening large population in community mass screening programs and for longitudinal sampling of hospitalized individuals aimed at monitoring viral load dynamics and treatment response. During the COVID-19 pandemic, scientific evidence emerged indicating that molecular tests performed on saliva have diagnostic sensitivity and specificity comparable to those observed with nasopharyngeal swabs for SARS-CoV-2 RNA detection. The presence of IgA and IgG antibodies at the mucosal level has been demonstrated to influence the progression of viral infection and the severity of clinical manifestation. As saliva contains both respiratory secretions and immunological components, it has wide applications, ranging from clinical diagnostics to post-vaccine disease burden and immunity surveillance.

Douglass Diak↗
DOE OSTI2024

Coronavirus neutralizing humanized antibodies and uses thereof

The present disclosure relates to an isolated or purified antibody, or a fragment thereof, having a binding domain that binds to a coronavirus (e.g., SARS-COV-2) or a portion thereof. In other embodiments, the antibody includes a binding domain that competes with binding to angiotensin converting enzyme 2 (ACE2) or a portion thereof. Methods of using such antibodies are also described herein, such as methods of treating or delaying the progression of a disease associated with a coronavirus.

Harmon, Brooke Nicole↗
DOE OSTI2025

Automated Label‐Free Assay for Viral Detection and Inhibitor Screening via Biomembrane‐Functionalized Microelectrode Arrays

Most virus infection assays have indirect readout such as virus number following entry (e.g., PCR, cell lysis). While effective, these technologies are labor‐intensive, require specialized environments (e.g., sterile or RNA‐free), and detect later‐stage viral events like lysis or cell death, lacking sensitivity to early fusion events. To address these limitations, we present biologically relevant 2D membrane materials, host‐cell‐derived supported lipid bilayers (hcd‐SLBs), integrated with organic microelectrode arrays (OMEAs) for detection of severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) fusion. By overexpressing angiotensin‐converting enzyme 2 (ACE2) receptors on the native membranes, the platform functions as a viral sensor capable of detecting virus pseudo particles (VPPs) through the late pathway. Additionally, hcd‐SLBs extracted from human lung epithelium expressing native ACE2 detect fusion events through the early pathway. The platform's utility as a drug‐screening tool is demonstrated by testing antibodies targeting either the ACE2 on the host membrane or the viral spike (S) proteins. To enhance the throughput, microfluidics are integrated for automation and OMEAs are incorporated within each channel, miniaturizing the testing units. This system supports high‐throughput data generation, automation, and scalability, providing an efficient platform for viral fusion detection that advances the study of pathogen‐host interactions and accelerates antiviral drug discovery.

Biology↗