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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 1,027 records · Page 57
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Identifying Critical Mineral Binding Mechanisms and Distribution in Acid Mine Drainage Treatment Solids to Inform Targeted Recovery Methods

In this study, micro-X-ray Fluorescence and micro/bulk X-ray Adsorption Near Edge Spectroscopy were collected at Stanford Synchrotron Radiation Lightsource and the Advanced Photon Source to: (1) gain a detailed characterization of critical minerals in Fe/Mn (hydr)oxide host phases; and (2) investigate the Co/Ni coordination and redox speciation associated with Fe/Mn (hydr)oxides phases in AMD solids with diverse composition (e.g., Al-rich, Mn-rich, Al/Mn/Fe-rich). Preliminary results show co-location of Co/Ni within Mn-rich hotspots, while Fe-rich hotspots were associated with heavy REEs. In Al-rich solids, Mn speciation was mostly comprised of Mn+3/+4 oxides, while the Mn-rich solids contain predominately Mn+4. These results suggest the AMD matrix plays an important role in how Co/Ni are coordinated, which aids in evaluating the efficacy to utilize AMD solids as a resource, and informs other novel sorbent and recovery technologies.

Hoffman, Colleen↗
DOE OSTI2022

Classifying Metal-Binding Sites with Neural Networks

A pre-trained convolutional neural network was fine-tuned for three separate classification tasks, distinguishing 2D images of: 1) single amino acids, 2) protein structural ball and stick images of metalloproteins, and 3) protein structural ball and stick images of metalloenzymes with the metal cofactors removed. Images used in the training, testing and validation are shared here.

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

TCR-H: explainable machine learning prediction of T-cell receptor epitope binding on unseen datasets

Artificial-intelligence and machine-learning (AI/ML) approaches to predicting T-cell receptor (TCR)-epitope specificity achieve high performance metrics on test datasets which include sequences that are also part of the training set but fail to generalize to test sets consisting of epitopes and TCRs that are absent from the training set, i.e., are ‘unseen’ during training of the ML model. We present TCR-H, a supervised classification Support Vector Machines model using physicochemical features trained on the largest dataset available to date using only experimentally validated non-binders as negative datapoints. TCR-H exhibits an area under the curve of the receiver-operator characteristic (AUC of ROC) of 0.87 for epitope ‘hard splitting’ (i.e., on test sets with all epitopes unseen during ML training), 0.92 for TCR hard splitting and 0.89 for ‘strict splitting’ in which neither the epitopes nor the TCRs in the test set are seen in the training data. Furthermore, we employ the SHAP (Shapley additive explanations) eXplainable AI (XAI) method for post hoc interrogation to interpret the models trained with different hard splits, shedding light on the key physiochemical features driving model predictions. TCR-H thus represents a significant step towards general applicability and explainability of epitope:TCR specificity prediction.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

Drugsniffer: An Open Source Workflow for Virtually Screening Billions of Molecules for Binding Affinity to Protein Targets

The SARS-CoV2 pandemic has highlighted the importance of efficient and effective methods for identification of therapeutic drugs, and in particular has laid bare the need for methods that allow exploration of the full diversity of synthesizable small molecules. While classical high-throughput screening methods may consider up to millions of molecules, virtual screening methods hold the promise of enabling appraisal of billions of candidate molecules, thus expanding the search space while concurrently reducing costs and speeding discovery. Here, we describe a new screening pipeline, called drugsniffer, that is capable of rapidly exploring drug candidates from a library of billions of molecules, and is designed to support distributed computation on cluster and cloud resources. As an example of performance, our pipeline required ~40,000 total compute hours to screen for potential drugs targeting three SARS-CoV2 proteins among a library of ~3.7 billion candidate molecules.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Discovery of a Natural Product That Binds to the Mycobacterium tuberculosis Protein Rv1466 Using Native Mass Spectrometry

Elucidation of the mechanism of action of compounds with cellular bioactivity is important for progressing compounds into future drug development. In recent years, phenotype-based drug discovery has become the dominant approach to drug discovery over target-based drug discovery, which relies on the knowledge of a specific drug target of a disease. Still, when targeting an infectious disease via a high throughput phenotypic assay it is highly advantageous to identifying the compound’s cellular activity. A fraction derived from the plant Polyalthia sp. showed activity against Mycobacterium tuberculosis at 62.5 μge/μL. A known compound, altholactone, was identified from this fraction that showed activity towards M. tuberculosis at an minimum inhibitory concentration (MIC) of 64 μM. Retrospective analysis of a target-based screen against a TB proteome panel using native mass spectrometry established that the active fraction was bound to the mycobacterial protein Rv1466 with an estimated pseudo-Kd of 42.0 ± 6.1 µM. Our findings established Rv1466 as the potential molecular target of altholactone, which is responsible for the observed in vivo toxicity towards M. tuberculosis .

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Enhanced Nuclear Binding near the Proton Drip Line Opens Possible Bypass of the 64 Ge Rapid Proton Capture Process Waiting Point

Abstract We performed astrophysics model calculations with updated nuclear data to identify a possible bypass of the 64 Ge waiting point, a defining feature of the rapid proton capture (rp) process that powers type I X-ray bursts on accreting neutron stars. We find that the rp-process flow through the 64 Ge bypass could be up to 36% for astrophysically relevant conditions. Our results call for new studies of 65 Se, including the nuclear mass, β -delayed proton emission branching, and nuclear structure as it pertains to the 64 As( p , γ ) reaction rate at X-ray burst temperatures.

Nuclear astrophysics↗