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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 361 records · Page 20
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DOE OSTI2022

Decoding the protein–ligand interactions using parallel graph neural networks

Abstract Protein–ligand interactions (PLIs) are essential for biochemical functionality and their identification is crucial for estimating biophysical properties for rational therapeutic design. Currently, experimental characterization of these properties is the most accurate method, however, this is very time-consuming and labor-intensive. A number of computational methods have been developed in this context but most of the existing PLI prediction heavily depends on 2D protein sequence data. Here, we present a novel parallel graph neural network (GNN) to integrate knowledge representation and reasoning for PLI prediction to perform deep learning guided by expert knowledge and informed by 3D structural data. We develop two distinct GNN architectures: $$\hbox {GNN}_{\mathrm{F}}$$ GNN F is the base implementation that employs distinct featurization to enhance domain-awareness, while $$\hbox {GNN}_{\mathrm{P}}$$ GNN P is a novel implementation that can predict with no prior knowledge of the intermolecular interactions. The comprehensive evaluation demonstrated that GNN can successfully capture the binary interactions between ligand and protein’s 3D structure with 0.979 test accuracy for $$\hbox {GNN}_{\mathrm{F}}$$ GNN F and 0.958 for $$\hbox {GNN}_{\mathrm{P}}$$ GNN P for predicting activity of a protein–ligand complex. These models are further adapted for regression tasks to predict experimental binding affinities and $$\hbox {pIC}_{\mathrm{50}}$$ pIC 50 crucial for compound’s potency and efficacy. We achieve a Pearson correlation coefficient of 0.66 and 0.65 on experimental affinity and 0.50 and 0.51 on $$\hbox {pIC}_{\mathrm{50}}$$ pIC 50 with $$\hbox {GNN}_{\mathrm{F}}$$ GNN F and $$\hbox {GNN}_{\mathrm{P}}$$ GNN P , respectively, outperforming similar 2D sequence based models. Our method can serve as an interpretable and explainable artificial intelligence (AI) tool for predicted activity, potency, and biophysical properties of lead candidates. To this end, we show the utility of $$\hbox {GNN}_{\mathrm{P}}$$ GNN P on SARS-Cov-2 protein targets by screening a large compound library and comparing the prediction with the experimentally measured data.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Inter-cofactor protein remodeling rewires short-circuited transmembrane electron transfer

Intraprotein electron transfer (ET) requires explicit local control of the environment of cofactors to influence their intermolecular distances, relative orientations, and redox properties. Efficient, longer-range ET often utilizes molecular orbitals of aromatic residues present in the intervening space. Here, revitalization of a vestigial ET pathway in the bacterial photosynthetic reaction center is achieved by scanning with tryptophans to uncover markedly improved routes of electron conduction in a key stabilizing step spanning 15 Å between tetrapyrrole and quinone cofactors. This ET event is maximally enhanced by pairing one or more tryptophans with a threonine to influence quinone binding and/or redox potential. Synergistic effects of these substitutions increase the yield of that ET step to ~95%. Joining these substitutions with mutant residues that improve initial ET steps dramatically enhances transmembrane charge separation via this redesigned version of a pathway that is quantitatively inactive in the native protein-cofactor complex.

Biophysical chemistry↗
DOE OSTI2020

Steering Molecular Dynamics Simulations of Membrane-Associated Proteins with Neutron Reflection Results

Herein, we present a novel method to incorporate structural results from surface-sensitive scattering, such as X-ray or neutron reflectometry, into molecular dynamics simulations. While reflectometry techniques generally provide a means to determine the molecular-scale structures of organized interfacial films, they were recently shown to offer the capability to characterize the structures of protein-membrane complexes supported by a solid substrate. One-dimensional information inherent in the experimental results is used in the form of component volume occupancy (CVO) profiles, which describe the distribution of molecular components within an interfacial architecture, to construct real-space constraints in the form of a biasing potential for the simulation that vanishes when the simulated and experimental profiles agree. This approach improves the correspondence between simulation and experiment, as shown in the re-evaluation of an neutron-reflection-derived structure which was approximated by an independent molecular dynamics simulation in earlier work, and it also leads to faster equilibration of ensemble structures. We further show that time averaging the CVO profile that develops in the simulation while biasing with this approach permits fluctuations about the average that are necessary for conformational exploration of the system. This method is particularly valuable for studies of proteins at interfaces that contain disordered regions since the conformation of such regions is difficult to judge from the analysis of one-dimensional experimental profiles and may take prohibitively long to equilibrate in simulations.

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

Mechanism of Protein Transport on the Twin Arginine Translocation Pathway

This project was awarded in September 2018 and ran for one year. The overarching goals of this multi-award project was to understand the mechanism and energetics of the Tat protein transport system responsible to the transport of proteins into the chloroplast thylakoid lumen. This pathway is responsible for the delivery of a number of proteins required for chloroplast biogenesis and homeostasis, including subunits of the photosynthetic oxygen-evolving complex and of the three large protein complexes of the photosynthetic electron transport chain. The manner through which proteins are transported on this pathway is highly unusual, and through the research supported by his set of DOE grants are leading to a mechanism of action that is previously unknown for protein trafficking. During the course of this short project we have continued to uncover evidence that the Tat pathway allows proteins to cross membranes through lipid-lined toroidal pores. The experiments undertaken during this project have been continued during the subsequent on-going DOE award period.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Tau and Membranes: Interactions That Promote Folding and Condensation

Tau misfolding and assembly is linked to a number of neurodegenerative diseases collectively described as tauopathies, including Alzheimer’s disease (AD) and Parkinson’s disease. Anionic cellular membranes, such as the cytosolic leaflet of the plasma membrane, are sites that concentrate and neutralize tau, primarily due to electrostatic interactions with tau’s microtubule binding repeat domain (RD). In addition to electrostatic interactions with lipids, tau also has interactions with membrane proteins, which are important for tau’s cellular functions. Tau also interacts with lipid tails to facilitate direct translocation across the membrane and can form stable protein-lipid complexes involved in cell-to-cell transport. Concentrated tau monomers at the membrane surface can form reversible condensates, change secondary structures, and induce oligomers, which may eventually undergo irreversible crosslinking and fibril formation. These β-sheet rich tau structures are capable of disrupting membrane organization and are toxic in cell-based assays. Given the evidence for relevant membrane-based tau assembly, we review the emerging hypothesis that polyanionic membranes may serve as a site for phase-separated tau condensation. Membrane-mediated phase separation may have important implications for regulating tau folding/misfolding, and may be a powerful mechanism to spatially direct tau for native membrane-mediated functions.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Speed vs Accuracy: Effect on Ligand Pose Accuracy of Varying Box Size and Exhaustiveness in AutoDock Vina

Abstract Structure‐based virtual high‐throughput screening involves docking chemical libraries to targets of interest. A parameter pertinent to the accuracy of the resulting pose is the root mean square deviation (RMSD) from a known crystallographic structure, i. e., the ‘docking power’. Here, using a popular algorithm, Autodock Vina, as a model program, we evaluate the effects of varying two common docking parameters: the box size (the size of docking search space) and the exhaustiveness of the global search (the number of independent runs starting from random ligand conformations) on the RMSD from the PDBbind v2017 refined dataset of experimental protein‐ligand complexes. Although it is clear that exhaustiveness is an important parameter, there is wide variation in the values used, with variation between 1 and >100. We, therefore, evaluated a combination of cubic boxes of different sizes and five exhaustiveness values (1, 8, 25, 50, 75, 100) within the range of those commonly adopted. The results show that the default exhaustiveness value of 8 performs well overall for most box sizes. In contrast, for all box sizes, but particularly for large boxes, an exhaustiveness value of 1 led to significantly higher median RMSD (mRMSD) values. The docking power was slightly improved with an exhaustiveness of 25, but the mRMSD changes little with values higher than 25. Therefore, although low exhaustiveness is computationally faster, the results are more likely to be far from reality, and, conversely, values >25 led to little improvement at the expense of computational resources. Overall, we recommend users to use at least the default exhaustiveness value of 8 for virtual screening calculations.

Agarwal, Rupesh↗
DOE OSTI2021

Using diverse potentials and scoring functions for the development of improved machine-learned models for protein–ligand affinity and docking pose prediction

The advent of computational drug discovery holds the promise of significantly reducing the effort of experimentalists, along with monetary cost. More generally, predicting the binding of small organic molecules to biological macromolecules has far-reaching implications for a range of problems, including metabolomics. However, problems such as predicting the bound structure of a protein–ligand complex along with its affinity have proven to be an enormous challenge. In recent years, machine learning-based methods have proven to be more accurate than older methods, many based on simple linear regression. Nonetheless, there remains room for improvement, as these methods are often trained on a small set of features, with a single functional form for any given physical effect, and often with little mention of the rationale behind choosing one functional form over another. Moreover, it is not entirely clear why one machine learning method is favored over another. Here, we endeavor to undertake a comprehensive effort towards developing high-accuracy, machine-learned scoring functions, systematically investigating the effects of machine learning method and choice of features, and, when possible, providing insights into the relevant physics using methods that assess feature importance. Here, we show synergism among disparate features, yielding adjusted R 2 with experimental binding affinities of up to 0.871 on an independent test set and enrichment for native bound structures of up to 0.913. When purely physical terms that model enthalpic and entropic effects are used in the training, we use feature importance assessments to probe the relevant physics and hopefully guide future investigators working on this and other computational chemistry problems.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Solar water splitting Pt-nanoparticle photosystem I thylakoid systems: Catalyst identification, location and oligomeric structure

In this study, photosynthetic conversion of light energy into chemical energy occurs in sheet-like membrane-bound compartments called thylakoids and is mediated by large integral membrane protein-pigment complexes called reaction centers (RCs). Oxygenic photosynthesis of higher plants, cyanobacteria and algae requires the symbiotic linking of two RCs, photosystem II (PSII) and photosystem I (PSI), to split water and assimilate carbon dioxide. Worldwide there is a large research investment in developing RC-based hybrids that utilize the highly evolved solar energy conversion capabilities of RCs to power catalytic reactions for solar fuel generation. Of particular interest is the solar-powered production of H 2 , a clean and renewable energy source that can replace carbonbased fossil fuels and help provide for ever-increasing global energy demands. Recently, we developed thylakoid membrane hybrids with abiotic catalysts and demonstrated that photosynthetic Z-scheme electron flow from the light-driven water oxidation at PSII can drive H 2 production from PSI. One of these hybrid systems was created by self-assembling Pt-nanoparticles (PtNPs) with the stromal subunits of PSI that extend beyond the membrane plane in both spinach and cyanobacterial thylakoids. Using PtNPs as site-specific probe molecules, we report the electron microscopic (EM) imaging of oligomeric structure, location and organization of PSI in thylakoid membranes and provide the first direct visualization of photosynthetic Z-scheme solar water-splitting biohybrids for clean H 2 production.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Structural basis of heme scavenging by the ChtA and HtaA hemophores in Corynebacterium diphtheriae

Corynebacterium diphtheriae causes diphtheria, a potentially fatal infectious disease that damages tissues in the upper respiratory tract. In order to proliferate, this pathogen acquires the essential nutrient iron from heme (iron-protoporphyrin IX) primarily found in human hemoglobin (Hb). C. diphtheriae secretes ChtA and HtaA hemophore proteins that bind ferric heme (hemin) via conserved region (CR) domains. Here, we demonstrate that their CR domains scavenge hemin after it is spontaneously released from Hb, and define the structural basis of hemin binding to ChtA and the N-terminal CR domain from HtaA by determining X-ray crystal structures of their protein-hemin complexes. Resonance Raman and electron paramagnetic resonance experiments demonstrate that the CR domains from ChtA and HtaA engage in pentacoordinate hemin binding through a conserved iron-tyrosyl linkage, though variations in their hemin pockets alter the way they stabilize the axial tyrosine and mask hemin’s metal. The importance of these interactions is probed using isothermal titration calorimetry experiments, which represent the first quantitative assessment of CR-hemin affinity and reveal that ChtA binds hemin via an enthalpically driven process. Hemin partitioning experiments using native mass spectrometry demonstrate that the cohort of CR domains within C. diphtheriae ’s hemin-uptake system have dissociation constants for hemin between 0.8 and 22 nM, raising the possibility that affinity differences contribute to the directional flow of hemin into the cell. Collectively, the results of this work provide insight into how C. diphtheriae and other pathogenic and commensal corynebacterium species utilize CR domains to scavenge iron rich hemin from their environment.

Corynebacterium diphtheriae↗
DOE OSTI2021

Role of Aberrant Spontaneous Neurotransmission in SNAP25-Associated Encephalopathies

SNARE (soluble N-ethylmaleimide sensitive factor attachment protein receptor) complex, composed of synaptobrevin, syntaxin, and SNAP25, forms the essential fusion machinery for neurotransmitter release. Recent studies have reported several mutations in the gene encoding SNAP25 as a causative factor for developmental and epileptic encephalopathies of infancy and childhood with diverse clinical manifestations. However, it remains unclear how SNAP25 mutations give rise to these disorders. In this work, we show that although structurally clustered mutations in SNAP25 give rise to related synaptic transmission phenotypes, specific alterations in spontaneous neurotransmitter release are a key factor to account for disease heterogeneity. Importantly, we identified a single mutation that augments spontaneous release without altering evoked release, suggesting that aberrant spontaneous release is sufficient to cause disease in humans.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

Preclinical studies of a PARP targeted, Meitner-Auger emitting, theranostic radiopharmaceutical for metastatic ovarian cancer

Advanced ovarian cancer currently has few therapeutic options. Poly(ADP-ribose) polymerase (PARP) inhibitors bind to nuclear PARP and trap the protein-inhibitor complex to DNA. This work investigates a theranostic PARP inhibitor for targeted radiopharmaceutical therapy of ovarian cancer in vitro and PET imaging of healthy mice in vivo. Methods: [ 77 Br]RD1 was synthesized and assessed for pharmacokinetics and cytotoxicity in human and murine ovarian cancer cell lines. [ 76 Br]RD1 biodistribution and organ uptake in healthy mice were quantified through longitudinal PET/CT imaging and ex vivo radioactivity measurements. Organ-level dosimetry following [ 76/77 Br]RD1 administration was calculated using RAPID, an in-house platform for absorbed dose in mice, and OLINDA for equivalent and effective dose in human. Results: The maximum specific binding (B max ), equilibrium dissociation constant (K d ), and nonspecific binding slope (NS) were calculated for each cell line. These values were used to calculate the cell specific activity uptake for cell viability studies. The half maximal effective concentration (EC 50 ) was measured as 0.17 (95 % CI: 0.13–0.24) nM and 0.46 (0.13–0.24) nM for PARP(+) and PARP(–) expressing cell lines, respectively. The EC 50 was 0.27 (0.21–0.36) nM and 0.30 (0.22–0.41) nM for BRCA1(–) and BRCA1(+) expressing cell lines, respectively. When measuring the EC 50 as a function of cellular activity uptake and nuclear dose, the EC 50 ranges from 0.020 to 0.039 Bq/cell and 3.3–9.2 Gy, respectively. Excretion through the hepatobiliary and renal pathways were observed in mice, with liver uptake of 2.3 ± 0.4 %ID/g after 48 h, contributing to estimated absorbed dose values in mice of 19.3 ± 0.3 mGy/MBq and 290 ± 10 mGy/MBq for [ 77 Br]RD1 and [ 76 Br]RD1, respectively. Conclusion: [ 77 Br]RD1 cytotoxicity was dependent on PARP expression and independent of BRCA1 status. Finally, the in vitro results suggest that [ 77 Br]RD1 cytotoxicity is driven by the targeted Meitner-Auger electron (MAe) radiotherapeutic effect of the agent. Further studies investigating the theranostic potential, organ dose, and tumor uptake of [ 76/77 Br]RD1 are warranted.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2022

Hit Expansion of a Noncovalent SARS-CoV-2 Main Protease Inhibitor

Inhibition of the SARS-CoV-2 main protease (M pro ) is a major focus of drug discovery efforts against COVID-19. Here we report a hit expansion of non-covalent inhibitors of M pro . Starting from a recently discovered scaffold (The COVID Moonshot Consortium. Open Science Discovery of Oral Non-Covalent SARS-CoV-2 Main Protease Inhibitor Therapeutics. bioRxiv 2020.10.29.339317) represented by an isoquinoline series, we searched a database of over a billion compounds using a cheminformatics molecular fingerprinting approach. We identified and tested 48 compounds in enzyme inhibition assays, of which 21 exhibited inhibitory activity above 50% at 20 μM. Among these, four compounds with IC 50 values around 1 μM were found. Interestingly, despite the large search space, the isoquinolone motif was conserved in each of these four strongest binders. Room-temperature X-ray structures of co-crystallized protein–inhibitor complexes were determined up to 1.9 Å resolution for two of these compounds as well as one of the stronger inhibitors in the original isoquinoline series, revealing essential interactions with the binding site and water molecules. Molecular dynamics simulations and quantum chemical calculations further elucidate the binding interactions as well as electrostatic effects on ligand binding. The results help explain the strength of this new non-covalent scaffold for M pro inhibition and inform lead optimization efforts for this series, while demonstrating the effectiveness of a high-throughput computational approach to expanding a pharmacophore library.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

SLAB: simultaneous labeling and binding affinity prediction for protein–ligand structures

Machine learning models are often used as scoring functions to predict the binding affinity of a protein–ligand complex. These models are trained with limited amounts of data with experimentally measured binding affinity values. A large number of compounds are labeled inactive through single-concentration screens without measuring binding affinities. These inactive compounds, along with the active ones, can be used to train binary classification models, while regression models are trained using compounds with binding affinities only. However, the classification and regression tasks are often handled separately, without sharing the learned feature representations. In this paper, we propose a novel model architecture that jointly performs regression and classification objectives, aiming to maximize data utilization and improve predictive performance by leveraging two complementary tasks. In our setup, the regression yields the binding affinity, whereas the classification task yields the label as active or inactive. We demonstrate our method using PDBbind, the standard 3D structure database, as well as a dataset of flavivirus protease compounds with binding affinity data. Our experiments show that the new joint training strategy improves the accuracy of the model, increasing applicability in various practical drug screening scenarios.

Biological and medical sciences↗
DOE OSTI2024

Lanthanide binding peptide surfactants at air–aqueous interfaces for interfacial separation of rare earth elements

Rare earth elements (REEs) are critical materials to modern technologies. They are obtained by selective separation from mining feedstocks consisting of mixtures of their trivalent cation. We are developing an all-aqueous, bioinspired, interfacial separation using peptides as amphiphilic molecular extractants. Lanthanide binding tags (LBTs) are amphiphilic peptide sequences based on the EF-hand metal binding loops of calcium-binding proteins which complex selectively REEs. We study LBTs optimized for coordination to Tb 3+ using luminescence spectroscopy, surface tensiometry, X-ray reflectivity, and X-ray fluorescence near total reflection, and find that these LBTs capture Tb 3+ in bulk and adsorb the complex to the interface. Molecular dynamics show that the binding pocket remains intact upon adsorption. We find that, if the net negative charge on the peptide results in a negatively charged complex, excess cations are recruited to the interface by nonselective Coulombic interactions that compromise selective REE capture. If, however, the net negative charge on the peptide is −3, resulting in a neutral complex, a 1:1 surface ratio of cation to peptide is achieved. Surface adsorption of the neutral peptide complexes from an equimolar mixture of Tb 3+ and La 3+ demonstrates a switchable platform dictated by bulk and interfacial effects. The adsorption layer becomes enriched in the favored Tb 3+ when the bulk peptide is saturated, but selective to La 3+ for undersaturation due to a higher surface activity of the La 3+ complex.

Ortuno Macias, Luis E. (ORCID:0000000284342192)↗