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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 163 records · Page 9
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Assessing the potential of deep learning for protein–ligand docking

The effects of ligand binding on protein structures and their in vivo functions carry numerous implications for modern biomedical research and biotechnology development efforts such as drug discovery. Although several deep learning (DL) methods and benchmarks designed for protein–ligand docking have recently been introduced, so far no previous works have systematically studied the behaviour of the latest docking and structure prediction methods within the broadly applicable context of: (1) using predicted (apo) protein structures for docking (for example, for applicability to new proteins); (2) binding multiple (cofactor) ligands concurrently to a given target protein (for example, for enzyme design); and (3) having no previous knowledge of binding pockets (for example, for generalization to unknown pockets). To enable a deeper understanding of the real-world utility of docking methods, we introduce PoseBench, a comprehensive benchmark for broadly applicable protein–ligand docking. PoseBench enables researchers to rigorously and systematically evaluate DL methods for apo-to-holo protein–ligand docking and protein–ligand structure prediction using both primary ligand and multiligand benchmark datasets, the latter of which we introduce to the DL community. Empirically, using PoseBench, we find that: (1) DL cofolding methods generally outperform comparable conventional and DL docking baseline algorithms, but popular methods such as AlphaFold 3 are still challenged by prediction targets with new protein–ligand binding poses; (2) certain DL cofolding methods are highly sensitive to their input multiple sequence alignments, whereas others are not; and (3) DL methods struggle to strike a balance between structural accuracy and chemical specificity when predicting new or multiligand protein targets.

Morehead, Alex [Lawrence Berkeley National Laborat↗
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 OSTI2026

A curated benchmark for cofolding models on kinase conformational states

Abstract Protein kinases are critical drug targets, requiring therapeutics that can modulate their active and inactive conformational states. While cofolding models can generate global folds directly from kinase sequences and ligand SMILES strings, these models have not yet been tested on their ability to recover ligand-induced-fit conformational states of the kinase proteins. Here, we introduce KinConfBench, a curated benchmark of 2225 high-quality human kinase chains to evaluate the ability of four state-of-the-art cofolding models—Boltz-2, Chai-1, Protenix, and RoseTTAFold-All-Atom—to recover both canonical and rare conformational states. We show that geometric success metrics of a ligand pose in the active site do not correlate strongly with the correct kinase conformational state, motivating a new set of dynamical benchmarks for assessing cofolding models. While all four cofolding models achieve ~60–80% prediction accuracy for kinase conformational classification, they exhibit severe mode collapse when performing multiple inferences, show negligible structural diversity in sampling induced-fit motions, and display a prevalent “apo-drift” in which most cofolding models predominantly predict the kinase to be in its ligand-free state. Our results highlight that capturing ligand-induced protein conformational diversity, not just geometric fit, is critical for next-generation structure-based drug discovery.

Sun, Kunyang↗
DOE OSTI2023

Plant glycosyltransferases for expanding bioactive glycoside diversity

Glycosylation is a successful strategy to alter the pharmacological properties of small molecules, and it has emerged as a unique approach to expand the chemical space of natural products that can be explored in drug discovery. Traditionally, most glycosylation events have been carried out chemically, often requiring many protection and deprotection steps to achieve a target molecule. Further, enzymatic glycosylation by glycosyltransferases could provide an alternative strategy for producing new glycosides. In particular, the glycosyltransferase family has greatly expanded in plants, representing a rich enzymatic resource to mine and expand the diversity of glycosides with novel bioactive properties. This article highlights previous and prospective uses for plant glycosyltransferases in generating bioactive glycosides and altering their pharmacological properties.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Photo-Brook rearrangement of acyl silanes as a strategy for photoaffinity probe design

Photoaffinity labeling (PAL) is a powerful tool for the identification of non-covalent small molecule–protein interactions that are critical to drug discovery and medicinal chemistry, but this approach is limited to only a small subset of robust photocrosslinkers. The identification of new photoreactive motifs capable of covalent target capture is therefore highly desirable. Herein, we report the design, synthesis, and evaluation of a new class of PAL warheads based on the UV-triggered 1,2-photo-Brook rearrangement of acyl silanes, which hitherto have not been explored for PAL workflows. Irradiation of a series of probes in cell lysate revealed an iPr-substituted acyl silane with superior photolabeling and minimal thermal background labeling compared to other substituted acyl silanes. Further, small molecule (+)-JQ1- and rapamycin-derived iPr acyl silanes were shown to selectively label recombinant BRD4-BD1 and FKBP12, respectively, with minimal background. Together, these data highlight the untapped potential of acyl silanes as a novel, tunable scaffold for photoaffinity labeling.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Modular droplet injector for sample conservation providing new structural insight for the conformational heterogeneity in the disease-associated NQO1 enzyme

Droplet injection strategies are a promising tool to reduce the large amount of sample consumed in serial femtosecond crystallography (SFX) measurements at X-ray free electron lasers (XFELs) with continuous injection approaches. Here, we demonstrate a new modular microfluidic droplet injector (MDI) design that was successfully applied to deliver microcrystals of the human NAD(P)H:quinone oxidoreductase 1 (NQO1) and phycocyanin. We investigated droplet generation conditions through electrical stimulation for both protein samples and implemented hardware and software components for optimized crystal injection at the Macromolecular Femtosecond Crystallography (MFX) instrument at the Stanford Linac Coherent Light Source (LCLS). Under optimized droplet injection conditions, we demonstrate that up to 4-fold sample consumption savings can be achieved with the droplet injector. In addition, we collected a full data set with droplet injection for NQO1 protein crystals with a resolution up to 2.7 Å, leading to the first room-temperature structure of NQO1 at an XFEL. NQO1 is a flavoenzyme associated with cancer, Alzheimer's and Parkinson's disease, making it an attractive target for drug discovery. Further, our results reveal for the first time that residues Tyr128 and Phe232, which play key roles in the function of the protein, show an unexpected conformational heterogeneity at room temperature within the crystals. These results suggest that different substates exist in the conformational ensemble of NQO1 with functional and mechanistic implications for the enzyme's negative cooperativity through a conformational selection mechanism. Our study thus demonstrates that microfluidic droplet injection constitutes a robust sample-conserving injection method for SFX studies on protein crystals that are difficult to obtain in amounts necessary for continuous injection, including the large sample quantities required for time-resolved mix-and-inject studies.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

A covalent inhibitor targeting the papain-like protease from SARS-CoV-2 inhibits viral replication

Covalent inhibitors of the papain-like protease (PLpro) from SARS-CoV-2 have great potential as antivirals, but their non-specific reactivity with thiols has limited their development. In this report, we performed an 8000 molecule electrophile screen against PLpro and identified an α-chloro amide fragment, termed compound 1, which inhibited SARS-CoV-2 replication in cells, and also had low non-specific reactivity with thiols. Compound 1 covalently reacts with the active site cysteine of PLpro, and had an IC50 of 18 μM for PLpro inhibition. Compound 1 also had low non-specific reactivity with thiols and reacted with glutathione 1–2 orders of magnitude slower than other commonly used electrophilic warheads. Finally, compound 1 had low toxicity in cells and mice and has a molecular weight of only 247 daltons and consequently has great potential for further optimization. Collectively, these results demonstrate that compound 1 is a promising lead fragment for future PLpro drug discovery campaigns.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

Uncertainty quantification for molecular property predictions with graph neural architecture search

Graph Neural Networks (GNNs) have emerged as a prominent class of data-driven methods for molecular property prediction. However, a key limitation of typical GNN models is their inability to quantify uncertainties in the predictions. This capability is crucial for ensuring the trustworthy use and deployment of models in downstream tasks. To that end, we introduce AutoGNNUQ, an automated uncertainty quantification (UQ) approach for molecular property prediction. AutoGNNUQ leverages architecture search to generate an ensemble of high-performing GNNs, enabling the estimation of predictive uncertainties. Our approach employs variance decomposition to separate data (aleatoric) and model (epistemic) uncertainties, providing valuable insights for reducing them. In our computational experiments, we demonstrate that AutoGNNUQ outperforms existing UQ methods in terms of both prediction accuracy and UQ performance on multiple benchmark datasets, and generalizes well to out-of-distribution datasets. Additionally, we utilize t-SNE visualization to explore correlations between molecular features and uncertainty, offering insight for dataset improvement. AutoGNNUQ has broad applicability in domains such as drug discovery and materials science, where accurate uncertainty quantification is crucial for decision-making.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Advanced manufacturing provides tailor-made solutions for crystallography with x-ray free-electron lasers

Serial crystallography at large facilities, such as x-ray free-electron lasers and synchrotrons, evolved as a powerful method for the high-resolution structural investigation of proteins that are critical for human health, thus advancing drug discovery and novel therapies. However, a critical barrier to successful serial crystallography experiments lies in the efficient handling of the protein microcrystals and solutions at microscales. Microfluidics are the obvious approach for any high-throughput, nano-to-microliter sample handling, that also requires design flexibility and rapid prototyping to deal with the variable shapes, sizes, and density of crystals. Here, we discuss recent advances in polymer 3D printing for microfluidics-based serial crystallography research and present a demonstration of emerging, large-scale, nano-3D printing approaches leading into the future of 3D sample environment and delivery device fabrication from liquid jet gas-dynamic virtual nozzles devices to fixed-target sample environment technology.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Dual-action kinase inhibitors influence p38α MAP kinase dephosphorylation

Reversible protein phosphorylation directs essential cellular processes including cell division, cell growth, cell death, inflammation, and differentiation. Because protein phosphorylation drives diverse diseases, kinases and phosphatases have been targets for drug discovery, with some achieving remarkable clinical success. Most protein kinases are activated by phosphorylation of their activation loops, which shifts the conformational equilibrium of the kinase toward the active state. To turn off the kinase, protein phosphatases dephosphorylate these sites, but how the conformation of the dynamic activation loop contributes to dephosphorylation was not known. To answer this, we modulated the activation loop conformational equilibrium of human p38α ΜΑP kinase with existing kinase inhibitors that bind and stabilize specific inactive activation loop conformations. From this, we identified three inhibitors that increase the rate of dephosphorylation of the activation loop phospho-threonine by the PPM serine/threonine phosphatase WIP1. Hence, these compounds are “dual-action” inhibitors that simultaneously block the active site and promote p38α dephosphorylation. Our X-ray crystal structures of phosphorylated p38α bound to the dual-action inhibitors reveal a shared flipped conformation of the activation loop with a fully accessible phospho-threonine. In contrast, our X-ray crystal structure of phosphorylated apo human p38α reveals a different activation loop conformation with an inaccessible phospho-threonine, thereby explaining the increased rate of dephosphorylation upon inhibitor binding. These findings reveal a conformational preference of phosphatases for their targets and suggest a unique approach to achieving improved potency and specificity for therapeutic kinase inhibitors.

Science & Technology - Other Topics↗
DOE OSTI2021

Structural analysis of the catalytic domain of Artemis endonuclease/SNM1C reveals distinct structural features

The endonuclease Artemis is responsible for opening DNA hairpins during V(D)J recombination and for processing a subset of pathological DNA double-strand breaks. Artemis is an attractive target for the development of therapeutics to manage various B cell and T cell tumors, because failure to open DNA hairpins and accumulation of chromosomal breaks may reduce the proliferation and viability of pre-T and pre-B cell derivatives. However, structure-based drug discovery of specific Artemis inhibitors has been hampered by a lack of crystal structures. Here, we report the structure of the catalytic domain of recombinant human Artemis. The catalytic domain displayed a polypeptide fold similar overall to those of other members in the DNA cross-link repair gene SNM1 family and in mRNA 3'-end-processing endonuclease CPSF-73, containing metallo-β-lactamase and β-CASP domains and a cluster of conserved histidine and aspartate residues capable of binding two metal atoms in the catalytic site. As in SNM1A, only one zinc ion was located in the Artemis active site. However, Artemis displayed several unique features. Unlike in other members of this enzyme class, a second zinc ion was present in the β-CASP domain that leads to structural reorientation of the putative DNA-binding surface and extends the substrate-binding pocket to a new pocket, pocket III. Moreover, the substrate-binding surface exhibited a dominant and extensive positive charge distribution compared with that in the structures of SNM1A and SNM1B, presumably because of the structurally distinct DNA substrate of Artemis. The structural features identified here may provide opportunities for designing selective Artemis inhibitors.

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

In crystallo screening for proline analog inhibitors of the proline cycle enzyme PYCR1

Pyrroline-5-carboxylate reductase 1 (PYCR1) catalyzes the biosynthetic half-reaction of the proline cycle by reducing Δ 1 -pyrroline-5-carboxylate (P5C) to proline through the oxidation of NAD(P)H. Many cancers alter their proline metabolism by up-regulating the proline cycle and proline biosynthesis, and knockdowns of PYCR1 lead to decreased cell proliferation. Thus, evidence is growing for PYCR1 as a potential cancer therapy target. Inhibitors of cancer targets are useful as chemical probes for studying cancer mechanisms and starting compounds for drug discovery; however, there is a notable lack of validated inhibitors for PYCR1. To fill this gap, we performed a small-scale focused screen of proline analogs using X-ray crystallography. Five inhibitors of human PYCR1 were discovered: l-tetrahydro-2-furoic acid, cyclopentanecarboxylate, l-thiazolidine-4-carboxylate, l-thiazolidine-2-carboxylate, and N-formyl l-proline (NFLP). The most potent inhibitor was NFLP, which had a competitive (with P5C) inhibition constant of 100 μm. The structure of PYCR1 complexed with NFLP shows that inhibitor binding is accompanied by conformational changes in the active site, including the translation of an α-helix by 1 Å. These changes are unique to NFLP and enable additional hydrogen bonds with the enzyme. NFLP was also shown to phenocopy the PYCR1 knockdown in MCF10A H-RAS V12 breast cancer cells by inhibiting de novo proline biosynthesis and impairing spheroidal growth. In summary, we generated the first validated chemical probe of PYCR1 and demonstrated proof-of-concept for screening proline analogs to discover inhibitors of the proline cycle.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

Data imbalance in drug response prediction: multi-objective optimization approach in deep learning setting

Abstract Drug response prediction (DRP) methods tackle the complex task of associating the effectiveness of small molecules with the specific genetic makeup of the patient. Anti-cancer DRP is a particularly challenging task requiring costly experiments as underlying pathogenic mechanisms are broad and associated with multiple genomic pathways. The scientific community has exerted significant efforts to generate public drug screening datasets, giving a path to various machine learning models that attempt to reason over complex data space of small compounds and biological characteristics of tumors. However, the data depth is still lacking compared to application domains like computer vision or natural language processing domains, limiting current learning capabilities. To combat this issue and improves the generalizability of the DRP models, we are exploring strategies that explicitly address the imbalance in the DRP datasets. We reframe the problem as a multi-objective optimization across multiple drugs to maximize deep learning model performance. We implement this approach by constructing Multi-Objective Optimization Regularized by Loss Entropy loss function and plugging it into a Deep Learning model. We demonstrate the utility of proposed drug discovery methods and make suggestions for further potential application of the work to achieve desirable outcomes in the healthcare field.

Biochemistry & Molecular Biology↗
DOE OSTI2023

A gated graph transformer for protein complex structure quality assessment and its performance in CASP15

Abstract Motivation Proteins interact to form complexes to carry out essential biological functions. Computational methods such as AlphaFold-multimer have been developed to predict the quaternary structures of protein complexes. An important yet largely unsolved challenge in protein complex structure prediction is to accurately estimate the quality of predicted protein complex structures without any knowledge of the corresponding native structures. Such estimations can then be used to select high-quality predicted complex structures to facilitate biomedical research such as protein function analysis and drug discovery. Results In this work, we introduce a new gated neighborhood-modulating graph transformer to predict the quality of 3D protein complex structures. It incorporates node and edge gates within a graph transformer framework to control information flow during graph message passing. We trained, evaluated and tested the method (called DProQA) on newly-curated protein complex datasets before the 15th Critical Assessment of Techniques for Protein Structure Prediction (CASP15) and then blindly tested it in the 2022 CASP15 experiment. The method was ranked 3rd among the single-model quality assessment methods in CASP15 in terms of the ranking loss of TM-score on 36 complex targets. The rigorous internal and external experiments demonstrate that DProQA is effective in ranking protein complex structures. Availability and implementation The source code, data, and pre-trained models are available at https://github.com/jianlin-cheng/DProQA.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

AdenPredictor: accurate prediction of the adenylation domain specificity of nonribosomal peptide biosynthetic gene clusters in microbial genomes

Microbial natural products represent a major source of bioactive compounds for drug discovery. Among these molecules, nonribosomal peptides (NRPs) represent a diverse class that include antibiotics, immunosuppressants, anticancer agents, toxins, siderophores, pigments, and cytostatics. The discovery of novel NRPs remains a laborious process because many NRPs consist of nonstandard amino acids that are assembled by nonribosomal peptide synthetases (NRPSs). Adenylation domains (A-domains) in NRPSs are responsible for selection and activation of monomers appearing in NRPs. During the past decade, several support vector machine-based algorithms have been developed for predicting the specificity of the monomers present in NRPs. These algorithms utilize physiochemical features of the amino acids present in the A-domains of NRPSs. In this article, we benchmarked the performance of various machine learning algorithms and features for predicting specificities of NRPSs and we showed that the extra trees model paired with one-hot encoding features outperforms the existing approaches. Moreover, we show that unsupervised clustering of 453560 A-domains reveals many clusters that correspond to potentially novel amino acids. While it is challenging to predict the chemical structure of these amino acids, we developed novel techniques to predict their various properties, including polarity, hydrophobicity, charge, and presence of aromatic rings, carboxyl, and hydroxyl groups.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

A glimpse into the fungal metabolomic abyss: Novel network analysis reveals relationships between exogenous compounds and their outputs

Fungal specialized metabolites are a major source of beneficial compounds that are routinely isolated, characterized, and manufactured as pharmaceuticals, agrochemical agents, and industrial chemicals. The production of these metabolites is encoded by biosynthetic gene clusters that are often silent under standard growth conditions. There are limited resources for characterizing the direct link between abiotic stimuli and metabolite production. Herein, we introduce a network analysis-based, data-driven algorithm comprising two routes to characterize the production of specialized fungal metabolites triggered by different exogenous compounds: the direct route and the auxiliary route. Both routes elucidate the influence of treatments on the production of specialized metabolites from experimental data. The direct route determines known and putative metabolites induced by treatments and provides additional insight over traditional comparison methods. The auxiliary route is specific for discovering unknown analytes, and further identification can be curated through online bioinformatic resources. We validated our algorithm by applying chitooligosaccharides and lipids at two different temperatures to the fungal pathogen Aspergillus fumigatus. After liquid chromatography–mass spectrometry quantification of significantly produced analytes, we used network centrality measures to rank the treatments’ ability to elucidate these analytes and confirmed their identity through fragmentation patterns or in silico spiking with commercially available standards. Later, we examined the transcriptional regulation of these metabolites through real-time quantitative polymerase chain reaction. Our data-driven techniques can complement existing metabolomic network analysis by providing an approach to track the influence of any exogenous stimuli on metabolite production. Our experimental-based algorithm can overcome the bottlenecks in elucidating novel fungal compounds used in drug discovery.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Integrated sample-handling and mounting system for fixed-target serial synchrotron crystallography

Serial synchrotron crystallography (SSX) is enabling the efficient use of small crystals for structure–function studies of biomolecules and for drug discovery. An integrated SSX system has been developed comprising ultralow background-scatter sample holders suitable for room and cryogenic temperature crystallographic data collection, a sample-loading station and a humid `gloveless' glovebox. The sample holders incorporate thin-film supports with a variety of designs optimized for different crystal-loading challenges. These holders facilitate the dispersion of crystals and the removal of excess liquid, can be cooled at extremely high rates, generate little background scatter, allow data collection over >90° of oscillation without obstruction or the risk of generating saturating Bragg peaks, are compatible with existing infrastructure for high-throughput cryocrystallography and are reusable. The sample-loading station allows sample preparation and loading onto the support film, the application of time-varying suction for optimal removal of excess liquid, crystal repositioning and cryoprotection, and the application of sealing films for room-temperature data collection, all in a controlled-humidity environment. The humid glovebox allows microscope observation of the sample-loading station and crystallization trays while maintaining near-saturating humidities that further minimize the risks of sample dehydration and damage, and maximize working times. This integrated system addresses common problems in obtaining properly dispersed, properly hydrated and isomorphous microcrystals for fixed-orientation and oscillation data collection. Its ease of use, flexibility and optimized performance make it attractive not just for SSX but also for single-crystal and few-crystal data collection. Fundamental concepts that are important in achieving desired crystal distributions on a sample holder via time-varying suction-induced liquid flows are also discussed.

47 OTHER INSTRUMENTATION↗
DOE OSTI2022

Portability for GPU-accelerated molecular docking applications for cloud and HPC: can portable compiler directives provide performance across all platforms?

High-throughput structure-based screening of drug-like molecules has become a common tool in biomedical research. Recently, acceleration with graphics processing units (GPUs) has provided a large performance boost for molecular docking programs. Both cloud and high-performance computing (HPC) resources have been used for large screens with molecular docking programs; while NVIDIA GPUs have dominated cloud and HPC resources, new vendors such as AMD and Intel are now entering the field, creating the problem of software portability across different GPUs. Ideally, software productivity could be maximized with portable programming models that are able to maintain high performance across architectures. While in many cases compiler directives have been used as an easy way to offload parallel regions of a CPU-based program to a GPU accelerator, they may also be an attractive programming model for providing portability across different GPU vendors, in which case the porting process may proceed in the reverse direction: from low-level, architecture-specific code to higher-level directive-based abstractions. MiniMDock is a new mini-application (miniapp) designed to capture the essential computational kernels found in molecular docking calculations, such as are used in phar-maceutical drug discovery efforts, in order to test different solutions for porting across GPU architectures. Here we extend MiniMDock to GPU offloading with OpenMP directives, and compare to performance of kernels using CUDA and HIP on NVIDIA and AMD GPUs, respectively, as well as across different compilers, exploring performance bottlenecks. We document this reverse-porting process, from highly optimized device code to a higher-level version using directives, compare code structure, and describe barriers that were overcome in this effort.

Thavappiragasam, Mathialakan↗