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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 181 records · Page 10
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

HNRNPUL1 inhibits cisplatin sensitivity of esophageal squamous cell carcinoma through regulating the formation of circMAN1A2

Highlights: • HNRNPUL1 is an independent prognostic factor of ESCC patients receiving platinum-based chemotherapy. • Knockdown of HNRNPUL1 enhances cisplatin sensitivity of ESCC cells. • HNRNPUL1 inhibits the cisplatin sensitivity of ESCC cells by regulating the formation of circMAN1A2. Cisplatin (CDDP) is widely used for chemotherapy of esophageal squamous cell carcinoma (ESCC) but the drug resistance limits its therapeutic benefit. Heterogeneous nuclear ribonucleoprotein U-like 1 (HNRNPUL1) belongs to the family of RNA-binding proteins (RBPs) and is involved in DNA damage repair. To investigate whether and how HNRNPUL1 affects CDDP resistance of ESCC, we evaluated the expression of HNRNPUL1 and found that it was associated with recurrence in ESCC patients receiving postoperative platinum-based chemotherapy and was an independent prognostic factor for disease-free survival (DFS). Besides, we showed that the reduced expression of HNRNPUL1 enhanced the CDDP sensitivity of ESCC cells. Furthermore, RNA immunoprecipitation coupled with high-throughput sequencing (RIP-seq) were performed and a range of HNRNPUL1-binding RNAs influenced by CDDP treatment were identified followed by bioinformatics analysis. In terms of mechanism, we found that HNRNPUL1 inhibited CDDP sensitivity of ESCC cells by regulating the CDDP sensitivity-inhibited circular RNA (circRNA) MAN1A2 formation. Taken together, our results first demonstrated the role of HNRNPUL1 in CDDP resistance of ESCC and suggested that HNRNPUL1 may be a potential target of ESCC chemotherapy.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

A goldilocks computational protocol for inhibitor discovery targeting DNA damage responses including replication-repair functions

While many researchers can design knockdown and knockout methodologies to remove a gene product, this is mainly untrue for new chemical inhibitor designs that empower multifunctional DNA Damage Response (DDR) networks. Here, we present a robust Goldilocks (GL) computational discovery protocol to efficiently innovate inhibitor tools and preclinical drug candidates for cellular and structural biologists without requiring extensive virtual screen (VS) and chemical synthesis expertise. By computationally targeting DDR replication and repair proteins, we exemplify the identification of DDR target sites and compounds to probe cancer biology. Our GL pipeline integrates experimental and predicted structures to efficiently discover leads, allowing early-structure and early-testing (ESET) experiments by many laboratories. By employing an efficient VS protocol to examine protein-protein interfaces (PPIs) and allosteric interactions, we identify ligand binding sites beyond active sites, leveraging in silico advances for molecular docking and modeling to screen PPIs and multiple targets. A diverse 3,174 compound ESET library combines Diamond Light Source DSI-poised, Protein Data Bank fragments, and FDA-approved drugs to span relevant chemotypes and facilitate downstream hit evaluation efficiency for academic laboratories. Two VS per library and multiple ranked ligand binding poses enable target testing for several DDR targets. This GL library and protocol can thus strategically probe multiple DDR network targets and identify readily available compounds for early structural and activity testing to overcome bottlenecks that can limit timely breakthrough drug discoveries. By testing accessible compounds to dissect multi-functional DDRs and suggesting inhibitor mechanisms from initial docking, the GL approach may enable more groups to help accelerate discovery, suggest new sites and compounds for challenging targets including emerging biothreats and advance cancer biology for future precision medicine clinical trials.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Applying the ATOM drug discovery platform to small-molecule antivirals (Annual Report)

The objective of this project is to advance our understanding of the ML approaches needed to find the most potent inhibitor through an AI guided search that simultaneously optimizes for limited off-target safety activity and desirable pharmacokinetics (PK) properties. The methods will be tested for inhibiting SARS-CoV-2 activity through inhibition of the main protease as a demonstration, but the methodology will be developed to apply to any biothreat target using a small-molecule protein binding-based intervention, with limited experimental data. Computational experiments are conducted using our ATOM generative molecular design (GMD) loop software, which is the implementation of our AI/ML drug design pipeline.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Ultra-large-scale ab initio quantum chemical computation of bio-molecular systems: The case of spike protein of SARS-CoV-2 virus

The COVID-19 pandemic poses a severe threat to human health with an unprecedented social and economic disruption. Spike (S) glycoprotein of the SARS-CoV-2 virus is pivotal in understanding the virus anatomy, since it initiates the first contact with the ACE2 receptor in the human cell. We report results of ab initio computation of the spike protein, the largest ab initio quantum chemical computation to date on any bio-molecular system, using a divide and conquer strategy by focusing on individual structural domains. In this approach we divided the S-protein into seven structural domains: N-terminal domain (NTD), receptor binding domain (RBD), subdomain 1 (SD1), subdomain 2 (SD2), fusion peptide (FP), heptad repeat 1 with central helix (HR1-CH) and connector domain (CD). The entire Chain A has 14,488 atoms including the hydrogen atoms but excluding the amino acids with missing coordinates based on the PDB data (ID: 6VSB). The results include structural refinement, ab initio calculation of intramolecular bonding mechanism, 3- dimensional non-local inter-amino acid interaction with implications for the inter-domain interaction. Details of the electronic structure, interatomic bonding, partial charge distribution and the role played by hydrogen bond network are discussed. In the interaction among structural domains, we present new insights for crucial hinge-like movement and fusion process. Extension of such calculation to the interface between the S-protein binding domain and ACE2 receptor can provide a pathway for computational understanding of mutations and the design of therapeutic drugs to combat the COVID-19 pandemic.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Targeting a proteolytic neoepitope on CUB domain containing protein 1 ( CDCP1 ) for RAS -driven cancers

Extracellular proteolysis is frequently dysregulated in disease and can generate proteoforms with unique neoepitopes not found in healthy tissue. Here, we demonstrate that Abs that selectively recognize a proteolytic neoepitope on CUB domain containing protein 1 (CDCP1) could enable more effective and safer treatments for solid tumors. CDCP1 is highly overexpressed in RAS-driven cancers, and its ectodomain is cleaved by extracellular proteases. Biochemical, biophysical, and structural characterization revealed that the 2 cleaved fragments of CDCP1 remain tightly associated with minimal proteolysis-induced conformational change. Using differential phage display, we generated recombinant Abs that are exquisitely selective to cleaved CDCP1 with no detectable binding to the uncleaved form. These Abs potently targeted cleaved CDCP1-expressing cancer cells as an Ab-drug conjugate, an Ab-radionuclide conjugate, and a bispecific T cell engager. In a syngeneic pancreatic tumor model, these cleaved-specific Abs showed tumor-specific localization and antitumor activity with superior safety profiles compared with a pan-CDCP1 approach. Targeting proteolytic neoepitopes could provide an orthogonal "AND" gate for improving the therapeutic index.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

The FBXW7-binding sites on FAM83D are potential targets for cancer therapy

Increasing evidence shows the oncogenic function of FAM83D in human cancer, but how FAM83D exerts its oncogenic function remains largely unclear. Here, we investigated the importance of FAM83D/FBXW7 interaction in breast cancer (BC). We systematically mapped the FBXW7-binding sites on FAM83D through a comprehensive mutational analysis together with co-immunoprecipitation assay. Mutations at the FBXW7-binding sites on FAM83D led to that FAM83D lost its capability to promote the ubiquitination and proteasomal degradation of FBXW7; cell proliferation, migration, and invasion in vitro; and tumor growth and metastasis in vivo, indicating that the FBXW7-binding sites on FAM83D are essential for its oncogenic functions. A meta-evaluation of FAM83D revealed that the prognostic impact of FAM83D was independent on molecular subtypes. The higher expression of FAM83D has poorer prognosis. Moreover, high expression of FAM83D confers resistance to chemotherapy in BCs, which is experimentally validated in vitro. We conclude that identification of FBXW7-binding sites on FAM83D not only reveals the importance for FAM83D oncogenic function, but also provides valuable insights for drug target.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

Identification of a Covalent Molecular Inhibitor of Anti-apoptotic BFL-1 by Disulfide Tethering

The BCL-2 family is composed of anti- and pro-apoptotic members that respectively protect or disrupt mitochondrial integrity. Anti-apoptotic overexpression can promote oncogenesis by trapping the BCL-2 homology 3 (BH3) “killer domains” of pro-apoptotic proteins in a surface groove, blocking apoptosis. Groove inhibitors, such as the relatively large BCL-2 drug venetoclax (868 Da), have emerged as cancer therapies. BFL-1 remains an undrugged oncogenic protein and can cause venetoclax resistance. Having identified a unique C55 residue in the BFL-1 groove, we performed a disulfide tethering screen to determine if C55 reactivity could enable smaller molecules to block BFL-1's BH3-binding functionality. We found that a disulfide-bearing N-acetyltryptophan analog (304 Da adduct) effectively targeted BFL-1 C55 and reversed BFL-1-mediated suppression of mitochondrial apoptosis. Structural analyses implicated the conserved leucine-binding pocket of BFL-1 as the interaction site, resulting in conformational remodeling. Thus, therapeutic targeting of BFL-1 may be achievable through the design of small, cysteine-reactive drugs.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Broadly neutralizing humanized SARS-CoV-2 antibody binds to a conserved epitope on Spike and provides antiviral protection through inhalation-based delivery in non-human primates

The COVID-19 pandemic represents a global challenge that has impacted and is expected to continue to impact the lives and health of people across the world for the foreseeable future. The rollout of vaccines has provided highly anticipated relief, but effective therapeutics are required to further reduce the risk and severity of infections. Monoclonal antibodies have been shown to be effective as therapeutics for SARS-CoV-2, but as new variants of concern (VoC) continue to emerge, their utility and use have waned due to limited or no efficacy against these variants. Furthermore, cumbersome systemic administration limits easy and broad access to such drugs. As well, concentrations of systemically administered antibodies in the mucosal epithelium, a primary site of initial infection, are dependent on neonatal Fc receptor mediated transport and require high drug concentrations. To reduce the viral load more effectively in the lung, we developed an inhalable formulation of a SARS-CoV-2 neutralizing antibody binding to a conserved epitope on the Spike protein, ensuring pan-neutralizing properties. Administration of this antibody via a vibrating mesh nebulization device retained antibody integrity and resulted in effective distribution of the antibody in the upper and lower respiratory tract of non-human primates (NHP). In comparison with intravenous administration, significantly higher antibody concentrations can be obtained in the lung, resulting in highly effective reduction in viral load post SARS-CoV-2 challenge. This approach may reduce the barriers of access and uptake of antibody therapeutics in real-world clinical settings and provide a more effective blueprint for targeting existing and potentially emerging respiratory tract viruses.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

Spatial Graph Attention and Curiosity-driven Policy for Antiviral Drug Discovery

We developed Distilled Graph Attention Policy Network (DGAPN), a reinforcement learning model to generate novel graph-structured chemical representations that optimize user-defined objectives by efficiently navigating a physically constrained domain. The framework is examined on the task of generating molecules that are designed to bind, noncovalently, to functional sites of SARS-CoV-2 proteins. We present a spatial Graph Attention (sGAT) mechanism that leverages self-attention over both node and edge attributes as well as encoding the spatial structure --- this capability is of considerable interest in synthetic biology and drug discovery. An attentional policy network is introduced to learn the decision rules for a dynamic, fragment-based chemical environment, and state-of-the-art policy gradient techniques are employed to train the network with stability. Exploration is driven by the stochasticity of the action space design and the innovation reward bonuses learned and proposed by random network distillation. In experiments, our framework achieved outstanding results compared to state-of-the-art algorithms, while reducing the complexity of paths to chemical synthesis.

Wu, Yulun↗
DOE OSTI2022

SPATIAL GRAPH ATTENTION AND CURIOSITY-DRIVEN POLICY FOR ANTIVIRAL DRUG DISCOVERY

We developed Distilled Graph Attention Policy Network (DGAPN), a reinforcement learning model to generate novel graph-structured chemical representations that optimize user-defined objectives by efficiently navigating a physically constrained domain. The framework is examined on the task of generating molecules that are designed to bind, noncovalently, to functional sites of SARS-CoV-2 proteins. We present a spatial Graph Attention (sGAT) mechanism that leverages self-attention over both node and edge attributes as well as encoding the spatial structure - this capability is of considerable interest in synthetic biology and drug discovery. An attentional policy network is introduced to learn the decision rules for a dynamic, fragment-based chemical environment, and state-of-the-art policy gradient techniques are employed to train the network with stability. Exploration is driven by the stochasticity of the action space design and the innovation reward bonuses learned and proposed by random network distillation. In experiments, our framework achieved outstanding results compared to state-of-the-art algorithms, while reducing the complexity of paths to chemical synthesis.

Wu, Y↗
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 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 OSTI2020

A 3.4-Å cryo-electron microscopy structure of the human coronavirus spike trimer computationally derived from vitrified NL63 virus particles

Human coronavirus NL63 (HCoV-NL63) is an enveloped pathogen of the family Coronaviridae that spreads worldwide and causes up to 10% of all annual respiratory diseases. HCoV-NL63 is typically associated with mild upper respiratory symptoms in children, elderly and immunocompromised individuals. It has also been shown to cause severe lower respiratory illness. NL63 shares ACE2 as a receptor for viral entry with SARS-CoV-1 and SARS-CoV-2. Here we present the in situ structure of HCoV-NL63 spike (S) trimer at 3.4-Å resolution by single-particle cryo-EM imaging of vitrified virions without chemical fixative. It is structurally homologous to that obtained previously from the biochemically purified ectodomain of HCoV-NL63 S trimer, which displays a 3-fold symmetric trimer in a single conformation. In addition to previously proposed and observed glycosylation sites, our map shows density at other sites, as well as different glycan structures. The domain arrangement within a protomer is strikingly different from that of the SARS-CoV-2 S and may explain their different requirements for activating binding to the receptor. Overall, this structure provides the basis for future studies of spike proteins with receptors, antibodies, or drugs, in the native state of the coronavirus particles.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Design, Synthesis, and Biological Activity of Novel Ornithine Decarboxylase (ODC) Inhibitors

We here describe the design, synthesis, and biological activity of novel ornithine decarboxylase (ODC) inhibitors that show significantly higher potency in vitro than α-difluoromethylornithine (DFMO), a U.S. Food and Drug Administration (FDA) approved drug. We report two X-ray structures of ODC complexed with new ODC inhibitors, computational docking, molecular dynamics, and binding free energy calculations to validate the experimental models. The X-ray structures reveal that covalent adducts with pyridoxal phosphate (PLP) are formed in the active site of the human ODC enzyme, as verified by their preparation and enzymatic testing. Finally, we verified that the cellular activity of endogenous ODC was inhibited, and polyamine levels were reduced. Given that ODC is a clinically validated target, combined with the fact that DFMO is currently the only ODC inhibitor in clinical use for several indications, the further development of more potent ODC inhibitors with superior activity and physical properties is warranted.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2026

Structural basis for the subtype-selectivity of K Ca 2.2 channel activators

Small-conductance (K Ca 2.2) and intermediate-conductance (K Ca 3.1) Ca 2+ -activated K + channels are gated by a Ca 2+ -calmodulin dependent mechanism. NS309 potentiates the activity of both K Ca 2.2 and K Ca 3.1, while rimtuzalcap selectively activates K Ca 2.2. Rimtuzalcap has been used in clinical trials for the treatment of spinocerebellar ataxia and essential tremor. We report cryo-electron microscopy structures of NS309-bound K Ca 2.2 and K Ca 3.1, in addition to structures of rimtuzalcap-bound K Ca 2.2 and mutant K Ca 3.1_R355K. The different conformations of calmodulin and the cytoplasmic HC helices in the two channels underlie the subtype-selectivity of rimtuzalcap for K Ca 2.2. NS309 binds to pre-existing pockets in both channels, while the bulkier rimtuzalcap binds in an induced-fit pocket in K Ca 2.2 requiring conformational changes. In K Ca 2.2, calmodulin’s N-lobes are sufficiently far apart to enable conformational changes to accommodate either NS309 or rimtuzalcap. In K Ca 3.1, calmodulin’s N-lobes are closer to each other and constrained by K Ca 3.1’s HC helices, which allows binding of NS309 but not rimtuzalcap. Replacement of arginine-355 in K Ca 3.1’s HB helix with lysine (K Ca 3.1_R355K) allows the binding of rimtuzalcap and renders the mutant channel sensitive to rimtuzalcap. These structures provide a framework for structure-based drug design targeting K Ca 2.2 channels.

Nam, Young-Woo [Chapman Univ., Irvine, CA (United ↗
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

Substituent size versus metal binding of inhibitors with variants of influenza endonuclease

The influenza virus causes a significant burden of illness each year. Although vaccination is the most effective method to prevent seasonal influenza infection, viral escape mechanisms make vaccine composition difficult to predict. Antivirals are crucial for decreasing rates of morbidity and mortality from influenza viral infection. The newest anti-influenza drugs target the RNA-dependent RNA polymerase acidic N-terminal (PA N ) endonuclease, a critical component of influenza viral replication machinery. This study examines the structure of inhibitors of PAN that utilize a hydroxypyridinone-based metal-binding pharmacophore (MBP). Specifically, this report explores how the size of substituent groups impacts the binding conformation and affinity of a series of compounds against both wild-type (WT) and resistance mutant strains, I38T and E23K. Co-crystal structures revealed that the distance between compounds and enzyme residue 38 was conserved to maintain strong interactions, resulting in deviations from ideal coordination geometries at the active site metal centers. This suggests the interactions with residue 38 with each compound is important and can impact inhibitor potency as a consequence of distortions in the metal binding geometry of the compounds.

Endonuclease↗