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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

American Heart Association Protein Atlas Portal for Accelerated Drug Discovery

The American Heart Association's Protein Atlas Data Portal is a cutting-edge, open-source atlas of the structural determinants of the binding of drugs to (all of) their target proteins as an unbiased means to accelerate drug discovery. The portal will provide researchers with access to comprehensive data that can help increase the selection of effective drugs and cut the time-to-market of new drugs by half.

drug targets↗
DOE OSTI2023

Developing a SARS-CoV-2 main protease binding prediction random forest model for drug repurposing for COVID-19 treatment

The coronavirus disease 2019 (COVID-19) global pandemic resulted in millions of people becoming infected with the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus and close to seven million deaths worldwide. It is essential to further explore and design effective COVID-19 treatment drugs that target the main protease of SARS-CoV-2, a major target for COVID-19 drugs. In this study, machine learning was applied for predicting the SARS-CoV-2 main protease binding of Food and Drug Administration (FDA)-approved drugs to assist in the identification of potential repurposing candidates for COVID-19 treatment. Ligands bound to the SARS-CoV-2 main protease in the Protein Data Bank and compounds experimentally tested in SARS-CoV-2 main protease binding assays in the literature were curated. These chemicals were divided into training (516 chemicals) and testing (360 chemicals) data sets. To identify SARS-CoV-2 main protease binders as potential candidates for repurposing to treat COVID-19, 1188 FDA-approved drugs from the Liver Toxicity Knowledge Base were obtained. A random forest algorithm was used for constructing predictive models based on molecular descriptors calculated using Mold2 software. Model performance was evaluated using 100 iterations of fivefold cross-validations which resulted in 78.8% balanced accuracy. The random forest model that was constructed from the whole training dataset was used to predict SARS-CoV-2 main protease binding on the testing set and the FDA-approved drugs. Model applicability domain and prediction confidence on drugs predicted as the main protease binders discovered 10 FDA-approved drugs as potential candidates for repurposing to treat COVID-19. Our results demonstrate that machine learning is an efficient method for drug repurposing and, thus, may accelerate drug development targeting SARS-CoV-2.

Research & Experimental Medicine↗
DOE OSTI2020

Predicting Small Molecule Transfer Free Energies by Combining Molecular Dynamics Simulations and Deep Learning

Accurately predicting small molecule partitioning and hydrophobicity is critical in the drug discovery process. There are many heterogeneous chemical environments within a cell and entire human body. For example, drugs must be able to cross the hydrophobic cellular membrane to reach their intracellular targets, and hydrophobicity is an important driving force for drug–protein binding. Atomistic molecular dynamics (MD) simulations are routinely used to calculate free energies of small molecules binding to proteins, crossing lipid membranes, and solvation but are computationally expensive. Machine learning (ML) and empirical methods are also used throughout drug discovery but rely on experimental data, limiting the domain of applicability. We present atomistic MD simulations calculating 15,000 small molecule free energies of transfer from water to cyclohexane. This large data set is used to train ML models that predict the free energies of transfer. We show that a spatial graph neural network model achieves the highest accuracy, followed closely by a 3D-convolutional neural network, and shallow learning based on the chemical fingerprint is significantly less accurate. A mean absolute error of ~4 kJ/mol compared to the MD calculations was achieved for our best ML model. We also show that including data from the MD simulation improves the predictions, tests the transferability of each model to a diverse set of molecules, and show multitask learning improves the predictions. This work provides insight into the hydrophobicity of small molecules and ML cheminformatics modeling, and our data set will be useful for designing and testing future ML cheminformatics methods.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS1992

Commercial researcher perspective

Protein crystallography--a research tool used to study the structure of the complex building blocks of living systems--has a lot to gain from space-based research. In order to know how a protein works in the human body, researchers must understand its molecular structure. Researchers have identified 150,000 different proteins in the body, but they now know the structure of less than a third of them. The only viable technique for analyzing the structure of these proteins is x-ray diffraction of the proteins in their crystal form. The better the quality of a protein crystal, the more useful it is to researchers who are trying to delineate its structure. The microgravity environment of space allows protein crystals to grow nearly undisturbed by convection and other gravity-driven forces that cause flaws to form in them on the ground. In space, lack of convection enables protein crystals to grow more slowly than they do on Earth, and the slower a protein crystal grows, the fewer flaws it will have. Protein crystal growth experiments have already flown on 14 Space Shuttle missions. This year's USML-1 Spacelab mission included protein crystal growth experiments conducted for commercial researchers. The results of protein crystal experiments flown thus far have been larger crystals with more uniform morphologies. The Center for Macromolecular Crystallography (A NASA-cosponsored CCDS) currently builds flight hardware to meet researchers' needs and handles sample loading and retrieval for flight experiments. Protein crystallography enables 'rational drug design': the development of drugs that bind only with the target protein and, hence, do not cause side effects. For example, pharmaceutical companies presently are interested in developing drugs that can inhibit purine nucleoside phosphorylase (PNP), a protein that plays a role in auto-immune diseases. To continue these kinds of investigations, researchers need a constant supply of protein crystals that are as free of flaws as possible. Space Station Freedom will provide the kind of research environment that will enable the production of such supplies. In addition, Freedom will provide the kind of long-duration facility required by protein crystal researchers: 40 percent of proteins require more than two weeks to crystallize.

Delucas, Larry↗
NASA NTRS2002

A site-directed mutagenesis analysis of tNOX functional domains

Constitutive NADH oxidase proteins of the mammalian cell surface exhibit two different activities, oxidation of hydroquinones (or NADH) and protein disulfide-thiol interchange which alternate to yield oscillatory patterns with period lengths of 24 min. A drug-responsive tNOX (tumor-associated NADH oxidase) has a period length of about 22 min. The tNOX cDNA has been cloned and expressed. These two proteins are representative of cycling oxidase proteins of the plant and animal cell surface. In this report, we describe a series of eight amino acid replacements in tNOX which, when expressed in Escherichia coli, were analyzed for enzymatic activity, drug response and period length. Replacement sites selected include six cysteines that lie within the processed plasma membrane (34 kDa) form of the protein, and amino acids located in putative drug and adenine nucleotide (NADH) binding domains. The latter, plus two of the cysteine replacements, resulted in a loss of enzymatic activity. The recombinant tNOX with the modified drug binding site retained activity but the activity was no longer drug-responsive. The four remaining cysteine replacements were of interest in that both activity and drug response were retained but the period length for both NADH oxidation and protein disulfide-thiol interchange was increased from 22 min to 36 or 42 min. The findings confirm the correctness of the drug and adenine nucleotide binding motifs within the tNOX protein and imply a potential critical role of cysteine residues in determining the period length.

Non-NASA Center↗
DOE OSTI2023

Targeting tissues via dynamic human systems modeling in generative design

Drug discovery is a complex, costly process with high failure rates. A successful drug should bind to a target, be deliverable to an intended site of activity, and promote a desired pharmacological effect without causing toxicity. Typically, these factors are evaluated in series over the course of a pipeline where the number of candidates is sequentially whittled down from a very large initial pool. One promise of AI-driven discovery is the opportunity to evaluate multiple facets of drug performance in parallel. However, despite ML-driven advancements, current models for pharmacological property prediction are exclusively trained to predict molecular properties, ignoring important, dynamic biodistribution and bioactivity effects. Here, we present our progress towards incorporating quantitative systems physiology models into an AI-enabled drug design and molecular generation pipeline. Within a genetic algorithm, we include human-relevant physiologically based pharmacokinetic (PBPK) models. These PBPK models leverage properties that are predicted by a fine-tuned molecular language model. Together, these models will aid in capturing the mapping between molecules and therapeutic outcomes that is necessary to accelerate the drug discovery process.

Fox, Zach↗
DOE OSTI2023

Human cytochrome P450 17A1 structures with metabolites of prostate cancer drug abiraterone reveal substrate-binding plasticity and a second binding site

Abiraterone acetate is a first-line therapy for castration-resistant prostate cancer. This prodrug is deacetylated in vivo to abiraterone, which is a potent and specific inhibitor of cytochrome P450 17A1 (CYP17A1). CYP17A1 performs two sequential steps that are required for the biosynthesis of androgens that drive prostate cancer proliferation, analogous to estrogens in breast cancer. Abiraterone can be further metabolized in vivo on the steroid A ring to multiple metabolites that also inhibit CYP17A1. Despite its design as an active-site-directed substrate analog, abiraterone and its metabolites demonstrate mixed competitive/noncompetitive inhibition. To understand their binding, we solved the X-ray structures of CYP17A1 with three primary abiraterone metabolites. Despite different conformations of the steroid A ring and substituents, all three bound in the CYP17A1 active site with the steroid core packed against the I helix and the A ring C3 keto or hydroxyl oxygen forming a hydrogen bond with N202 similar to abiraterone itself. The structure of CYP17A1 with 3-keto, 5α-abiraterone was solved to 2.0 Å, the highest resolution to date for a CYP17A1 complex. This structure had additional electron density near the F/G loop, which is likely a second molecule of the inhibitor and which may explain the noncompetitive inhibition. Mutation of the adjacent Asn52 to Tyr positions its side chain in this space, maintains enzyme activity, and prevents binding of the peripheral ligand. Collectively, our findings provide further insight into abiraterone metabolite binding and CYP17A1 function.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Sensitive Detection of Structural Differences using a Statistical Framework for Comparative Crystallography

Chemical and conformational changes underlie the functional cycles of proteins. Comparative crystallography can reveal these changes over time, over ligands, and over chemical and physical perturbations in atomic detail. A key difficulty, however, is that the resulting observations must be placed on the same scale by correcting for experimental factors. We recently introduced a Bayesian framework for correcting (scaling) X-ray diffraction data by combining deep learning with statistical priors informed by crystallographic theory. To scale comparative crystallography data, we here combine this framework with a multivariate statistical theory of comparative crystallography. By doing so, we find strong improvements in the detection of protein dynamics, element-specific anomalous signal, and the binding of drug fragments.

Hekstra, Doeke R. [Harvard Univ., Cambridge, MA (U↗
DOE OSTI2025

Sensitive detection of structural dynamics using a statistical framework for comparative crystallography

Chemical and conformational changes are crucial to protein function and its pharmacological control. X-ray crystallography can reveal these changes in atomic detail, but standard analysis methods, which refine separate datasets, often overlook differences that are subtle or arise in only a subset of molecules. Direct comparison of crystallographic datasets is, in principle, more powerful, but systematic errors (“scales”) often mask changes in the crystallographic observables (“structure factors”). Machine learning algorithms that jointly estimate scales and structure factors can address this limitation. Here, we augment this approach with multivariate, structured priors derived from crystallographic theory, implemented in the variational deep learning framework Careless. Doing so strongly improves the detection of protein dynamics, element-specific anomalous signals, and the binding of drug candidates, offering a robust approach to comparative crystallography and, potentially, to detection of protein dynamics by other structure determination methods.

Hekstra, Doeke R. [Harvard Univ., Cambridge, MA (U↗
NASA NTRS1986

Interactions of the interferon system with cellular metabolism

The results of studies concerning the interaction of the interferon (Inf) system with the activities of carcinogens, tumor promoters, and cytochrome P-450 are presented. The results show that the addition of a tumor promoter (TPA or 4-O-methyl-TPA) to a tissue culture enhances virus-induced Inf-gamma production, suggesting a potential value of tumor promoters in the biosynthesis of commercial Inf. On the other hand, the carcinogens were reported to inhibit the induction of Inf-alpha/beta in cultured cells and in intact animals (with no effect on the administered or preformed Inf). The demonstration of a correlation between the carcinogenic potential of a compound and its inhibitive effect on Inf production suggests a possible use of the Inf production assay in the evaluation of the carcinogenicity of chemicals. In addition, it was shown that the induction of Inf-alpha/beta as well as the administration of this Inf depresses the levels of rat liver cytochrome P-450 which is responsible for binding lipophilic drugs, steroids, and carcinogens, thus increasing the toxicity of the respective chemical.

Sonnenfeld, Gerald↗
DOE OSTI2020

Biased localization of actin binding proteins by actin filament conformation

The assembly of actin filaments into distinct cytoskeletal structures plays a critical role in cell physiology, but how proteins localize differentially to these structures within a shared cytoplasm remains unclear. Here, we show that the actin-binding domains of accessory proteins can be sensitive to filament conformational changes. Using a combination of live cell imaging and in vitro single molecule binding measurements, we show that tandem calponin homology domains (CH1–CH2) can be mutated to preferentially bind actin networks at the front or rear of motile cells. We demonstrate that the binding kinetics of CH1–CH2 domain mutants varies as actin filament conformation is altered by perturbations that include stabilizing drugs and other binding proteins. These findings suggest that conformational changes of actin filaments in cells could help to direct accessory binding proteins to different actin cytoskeletal structures through a biophysical feedback loop.

42 ENGINEERING↗
NASA NTRS1994

Crystals of Human Serum Albumin for Use in Genetic Engineering and Rational Drug Design

This invention pertains to crystals of serum albumin and processes for growing them. The purpose of the invention is to provide crystals of serum albumin which can be studied to determine binding sites for drugs. Form 2 crystals grow in the monoclinic space P2(sub 1), and possesses the following unit cell constraints: a = 58.9 +/- 7, b = 88.3 +/- 7, c = 60.7 +/- 7, Beta = 101.0 +/- 2 degrees. One advantage of the invention is that it will allow rational drug design

Carter, Daniel C.↗
DOE OSTI2026

SmileyLlama: modifying large language models for directed chemical space exploration

Here we show that large language models (LLMs) can be transformed via supervised fine-tuning of engineered prompts into SmileyLlama for exploring the chemical space of drug molecules. We benchmark SmileyLlama against pretrained LLMs and chemical language models trained from scratch for generating valid and novel drug-like molecules, and use direct preference optimization to both improve SmileyLlama’s adherence to a prompt and as part of the iMiner reinforcement learning framework to predict molecules with optimized three-dimensional conformations and high binding affinity to drug targets. By training an LLM to speak directly as a chemical language model, while retaining most of its natural language capabilities, we show that SmileyLlama can reliably generate molecules with user-specified properties rather than acting only as a chatbot with knowledge of chemistry or as a virtual assistant. While SmileyLlama is geared toward drug discovery, the supervised fine-tuning/direct preference optimization/LLM framework can be extended to other chemical, biological and materials applications.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

D3R grand challenge 4: blind prediction of protein–ligand poses, affinity rankings, and relative binding free energies

The Drug Design Data Resource (D3R) aims to identify best practice methods for computer aided drug design through blinded ligand pose prediction and affinity challenges. Herein, we report on the results of Grand Challenge 4 (GC4). GC4 focused on proteins beta secretase 1 and Cathepsin S, and was run in an analogous manner to prior challenges. In Stage 1, participant ability to predict the pose and affinity of BACE1 ligands were assessed. Following the completion of Stage 1, all BACE1 co-crystal structures were released, and Stage 2 tested affinity rankings with co-crystal structures. Finally, we provide an analysis of the results and discuss insights into determined best practice methods.

59 BASIC BIOLOGICAL 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↗
DOE OSTI2025

Structural analysis of extracellular ATP-independent chaperones of streptococcal species and protein substrate interactions

ABSTRACT During infection, bacterial pathogens rely on secreted virulence factors to manipulate the host cell. However, in gram-positive bacteria, the molecular mechanisms underlying the folding and activity of these virulence factors after membrane translocation are not clear. Here, we solved the protein structures of two secreted parvulin and two secreted cyclophilin-like peptidyl-prolyl isomerase (PPIase) ATP-independent chaperones found in gram-positive streptococcal species. The extracellular parvulin-type PPIase, PrsA inStreptococcus pneumoniaeandStreptococcus mutansmaintain dimeric crystal structures reminiscent of folding catalysts that consist of two domains, a PPIase and foldase domain. Structural comparison of the two cyclophilin-like extracellular chaperones fromS. pneumoniaeandStreptococcus pyogeneswith other cyclophilins demonstrates that this group of cyclophilin-like chaperones has novel structural appendages formed by 9- and 24-residue insertions. Furthermore, we demonstrate that deletion ofprsAandslrAgenes impairs the secretion of the cholesterol-dependent pore-forming toxin, pneumolysin inS. pneumoniae. Using protein pull-down and biophysical assays, we demonstrate a direct interaction between PrsA and SlrA with Ply. Then, we developed chaperone-assisted folding assays that show that theS. pneumoniaePrsA and SlrA extracellular chaperones accelerate pneumolysin folding. In addition, we demonstrate that SlrA and, for the first time,S. pyogenes PpiA exhibit PPIase activity and can bind the immunosuppressive drug, cyclosporine A. Altogether, these findings suggest a mechanistic role for streptococcal PPIase chaperones in the activity and folding of secreted virulence factors such as pneumolysin. IMPORTANCE Streptococcal species are a leading cause of lower respiratory infections that annually affect millions of people worldwide. During infection, streptococcal species secrete a medley of virulence factors that allow the bacteria to colonize and translocate to deeper tissues. In many gram-positive bacteria, virulence factors are secreted from the cytosol across the bacterial membrane in an unfolded state. The bacterial membrane-cell wall interface is exposed to the potentially harsh extracellular environment, making it difficult for native virulence factors to fold before being released into the host. ATP-independent PPIase-type chaperones, PrsA and SlrA, are thought to facilitate folding and stabilization of several unfolded proteins to promote the colonization and spread of streptococci. Here, we present crystal structures of the molecular chaperones of PrsA and SlrA homologs from streptococcal species. We provide evidence that theStreptococcus pyogenesSlrA homolog, PpiA, has PPIase activity and binds to cyclosporine A. In addition, we show thatStreptococcus pneumoniaePrsA and SlrA directly interact and fold the cholesterol-dependent pore-forming toxin and critical virulence determinant, pneumolysin.

Microbiology↗