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Results for “library screening”

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 OSTI2023

Discovering polyelemental nanostructures with redistributed plasmonic modes through combinatorial synthesis

Coupling plasmonic and functional materials provides a promising way to generate multifunctional structures. However, finding plasmonic nanomaterials and elucidating the roles of various geometric and dielectric configurations are tedious. This work describes a combinatorial approach to rapidly exploring and identifying plasmonic heteronanomaterials. Symmetry-broken noble/non-noble metal particle heterojunctions (~100 nanometers) were synthesized on multiwindow silicon chips with silicon nitride membranes. The metal types and the interface locations were controlled to establish a nanoparticle library, where the particle morphology and scattering color can be rapidly screened. By correlating structural data with near- and far-field single-particle spectroscopy data, we found that certain low-energy plasmonic modes could be supported across the heterointerface, while others are localized. Furthermore, we found a series of triangular heteronanoplates stabilized by epitaxial Moiré superlattices, which show strong plasmonic responses despite largely comprising a lossy metal (~70 atomic %). These architectures can become the basis for multifunctional and cost-effective plasmonic devices.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
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↗
NASA NTRS2009

Catalysts for Efficient Production of Carbon Nanotubes

Several metal alloys have shown promise as improved catalysts for catalytic thermal decomposition of hydrocarbon gases to produce carbon nanotubes (CNTs). Heretofore almost every experiment on the production of carbon nanotubes by this method has involved the use of iron, nickel, or cobalt as the catalyst. However, the catalytic-conversion efficiencies of these metals have been observed to be limited. The identification of better catalysts is part of a continuing program to develop means of mass production of high-quality carbon nanotubes at costs lower than those achieved thus far (as much as $100/g for purified multi-wall CNTs or $1,000/g for single-wall CNTs in year 2002). The main effort thus far in this program has been the design and implementation of a process tailored specifically for high-throughput screening of alloys for catalyzing the growth of CNTs. The process includes an integral combination of (1) formulation of libraries of catalysts, (2) synthesis of CNTs from decomposition of ethylene on powders of the alloys in a pyrolytic chemical-vapor-decomposition reactor, and (3) scanning- electron-microscope screening of the CNTs thus synthesized to evaluate the catalytic efficiencies of the alloys. Information gained in this process is put into a database and analyzed to identify promising alloy compositions, which are to be subjected to further evaluation in a subsequent round of testing. Some of these alloys have been found to catalyze the formation of carbon nano tubes from ethylene at temperatures as low as 350 to 400 C. In contrast, the temperatures typically required for prior catalysts range from 550 to 750 C.

Sun, Ted X.↗
DOE OSTI2024

Discovery of highly potent and ALK2/ALK1 selective kinase inhibitors using DNA-encoded chemistry technology

Activin receptor type 1 (ACVR1; ALK2) and activin receptor like type 1 (ACVRL1; ALK1) are transforming growth factor beta family receptors that integrate extracellular signals of bone morphogenic proteins (BMPs) and activins into Mothers Against Decapentaplegic homolog 1/5 (SMAD1/SMAD5) signaling complexes. Several activating mutations in ALK2 are implicated in fibrodysplasia ossificans progressiva (FOP), diffuse intrinsic pontine gliomas, and ependymomas. The ALK2 R206H mutation is also present in a subset of endometrial tumors, melanomas, non-small lung cancers, and colorectal cancers, and ALK2 expression is elevated in pancreatic cancer. Using DNA-encoded chemistry technology, we screened 3.94 billion unique compounds from our diverse DNA-encoded chemical libraries (DECLs) against the kinase domain of ALK2. Off-DNA synthesis of DECL hits and biochemical validation revealed nanomolar potent ALK2 inhibitors. Further structure-activity relationship studies yielded center for drug discovery (CDD)-2789, a potent [NanoBRET (NB) cell IC50: 0.54 μM] and metabolically stable analog with good pharmacological profile. Crystal structures of ALK2 bound with CDD-2281, CDD-2282, or CDD-2789 show that these inhibitors bind the active site through Van der Waals interactions and solvent-mediated hydrogen bonds. CDD-2789 exhibits high selectivity toward ALK2/ALK1 in KINOMEscan analysis and NB K192 assay. In cell-based studies, ALK2 inhibitors effectively attenuated activin A and BMP-induced Phosphorylated SMAD1/5 activation in fibroblasts from individuals with FOP in a dose-dependent manner. Thus, CDD-2789 is a valuable tool compound for further investigation of the biological functions of ALK2 and ALK1 and the therapeutic potential of specific inhibition of ALK2.

Jimmidi, Ravikumar↗
DOE OSTI2020

Combining mechanistic and machine learning models for predictive engineering and optimization of tryptophan metabolism

Abstract Through advanced mechanistic modeling and the generation of large high-quality datasets, machine learning is becoming an integral part of understanding and engineering living systems. Here we show that mechanistic and machine learning models can be combined to enable accurate genotype-to-phenotype predictions. We use a genome-scale model to pinpoint engineering targets, efficient library construction of metabolic pathway designs, and high-throughput biosensor-enabled screening for training diverse machine learning algorithms. From a single data-generation cycle, this enables successful forward engineering of complex aromatic amino acid metabolism in yeast, with the best machine learning-guided design recommendations improving tryptophan titer and productivity by up to 74 and 43%, respectively, compared to the best designs used for algorithm training. Thus, this study highlights the power of combining mechanistic and machine learning models to effectively direct metabolic engineering efforts.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Metagenomics harvested genus-specific single-stranded DNA-annealing proteins improve and expand recombineering in Pseudomonas species

The widespread Pseudomonas genus comprises a collection of related species with remarkable abilities to degrade plastics and polluted wastes and to produce a broad set of valuable compounds, ranging from bulk chemicals to pharmaceuticals. Pseudomonas possess characteristics of tolerance and stress resistance making them valuable hosts for industrial and environmental biotechnology. However, efficient and high-throughput genetic engineering tools have limited metabolic engineering efforts and applications. To improve their genome editing capabilities, we first employed a computational biology workflow to generate a genus-specific library of potential single-stranded DNA-annealing proteins (SSAPs). Assessment of the library was performed in different Pseudomonas using a high-throughput pooled recombinase screen followed by Oxford Nanopore NGS analysis. Among different active variants with variable levels of allelic replacement frequency (ARF), efficient SSAPs were found and characterized for mediating recombineering in the four tested species. New variants yielded higher ARFs than existing ones in Pseudomonas putida and Pseudomonas aeruginosa, and expanded the field of recombineering in Pseudomonas taiwanensisand Pseudomonas fluorescens. These findings will enhance the mutagenesis capabilities of these members of the Pseudomonas genus, increasing the possibilities for biotransformation and enhancing their potential for synthetic biology applications.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Multivariate chemogenomic screening prioritizes new macrofilaricidal leads

Development of direct acting macrofilaricides for the treatment of human filariases is hampered by limitations in screening throughput imposed by the parasite life cycle. In vitro adult screens typically assess single phenotypes without prior enrichment for chemicals with antifilarial potential. We developed a multivariate screen that identified dozens of compounds with submicromolar macrofilaricidal activity, achieving a hit rate of >50% by leveraging abundantly accessible microfilariae. Adult assays were multiplexed to thoroughly characterize compound activity across relevant parasite fitness traits, including neuromuscular control, fecundity, metabolism, and viability. Seventeen compounds from a diverse chemogenomic library elicited strong effects on at least one adult trait, with differential potency against microfilariae and adults. Our screen identified five compounds with high potency against adults but low potency or slow-acting microfilaricidal effects, at least one of which acts through a novel mechanism. We show that the use of microfilariae in a primary screen outperforms model nematode developmental assays and virtual screening of protein structures inferred with deep learning. These data provide new leads for drug development, and the high-content and multiplex assays set a new foundation for antifilarial discovery.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Development of a web-based screening tool for ground source heat pump applications

Ground source heat pump (GSHP) technology has great potential to help the nation meet its energy and decarbonization goals, but several barriers hinder the wide application of GSHP. Important barriers include the lack of a coherent toolset for analyzing the technical feasibility and economic viability of the GSHP application. The current design and analysis methods are ineffective and require significant expertise to apply. Although building energy modeling is increasingly important in designing buildings, the tools for GSHP modeling and simulation are lacking. A web-based free-to-use tool is being developed for quick techno-economic analysis of GSHP applications in nearly any building in the United States. This tool is enabled by improvements in the calculation methodology to allow rapid sizing of borehole configurations that provide significant cost savings. The screening tool currently uses US Department of Energy (DOE) prototype building models and an extended g-function library to size ground heat exchangers and simulate the performance of GSHP systems. The team is integrating with DOE's Oak Ridge National Laboratory's AutoBEM program to automatically create a building model based on user inputs. This paper introduces the structure, components, features, and results of the web-based screening tool for GSHP applications. Future directions for further developing the tool are also discussed.

Liu, Xiaobing↗
DOE OSTI2021

Masitinib is a broad coronavirus 3CL inhibitor that blocks replication of SARS-CoV-2

Targeting the main protease of SARS-CoV-2 Inside host cells, the RNA genome of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is translated into two polyproteins that are cleaved to give the individual viral proteins. The main viral protease, known as Mpro or 3CLpro, plays a key role in these cleavages, making it an important drug target. Drayman et al . identified eight drugs that target 3CLpro from a library of 1900 clinically safe drugs. Because of the challenge of working with SARS-CoV-2, they started by screening for drugs that inhibit the replication of a human coronavirus that causes the common cold. They then evaluated the top hits for inhibiting SARS-CoV-2 replication and for inhibiting 3CLpro. Masitinib, a broad antiviral, inhibited the main proteases of coronaviruses and picornaviruses and was effective in reducing SARS-CoV-2 replication in mice.

Drayman, Nir↗
DOE OSTI2021

Click-Chemistry-Based Free Azide versus Azido Sugar Detection Enables Rapid In Vivo Screening of Glycosynthase Activity

Engineering of carbohydrate-active enzymes such as glycosynthases to enable chemoenzymatic synthesis of bespoke oligosaccharides has been limited by the lack of suitable ultrahigh-throughput screening methods capable of robustly detecting either starting substrates or end-products of the glycosidic bond formation reaction. Currently, there are limited screening methods available for rapid and highly sensitive single-cell-based screening of glycosynthase enzymes employing azido sugars as activated donor glycosyl substrates. Here, we report a fluorescence-based approach employing click-chemistry for the selective detection of glycosyl azides as substrates versus free inorganic azides as reaction products that facilitated an ultrahigh-throughput in vivo single-cell-based assay of glycosynthase activity. This assay was developed based on the distinct differences observed in relative fluorescence intensity of the triazole-containing fluorophore product formed during the click-chemistry reaction of organic glycosyl azides versus inorganic azides. This discovery formed the basis for proof of concept validation of a directed evolution methodology for screening and sorting glycosynthase mutants capable of synthesis of targeted fucosylated oligosaccharides. Our screening approach facilitated fluorescence-activated cell sorting of an error-prone polymerase chain reaction-based mutant library of fucosynthases expressed in Escherichia coli to identify several novel mutants that showed increased activity for ß-fucosyl azide-activated donor sugars toward desired acceptor sugars (e.g., pNP-xylose and lactose). Finally, we discuss avenues for improving this proof of concept in vivo assay method to identify better glycosynthase mutants and further demonstrate the broader applicability of this screening methodology for synthesis of bespoke glycans.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Symmetry relation database and its application to ferroelectric materials discovery

To investigate the displacive phase transition at the atomic scale, we have implemented a numerical algorithm to automate the detection of the symmetry relations between any two candidate crystal structures. Using this algorithm, here we systematically screen all possible polar–nonpolar structure pairs from the Materials Project database and establish a library of ~4500 pairs that can be connected through a continuous phase transition with small atomic displacements. From this database, we identify several new ferroelectric materials. In addition, the database may also be used in other areas, such as material structure prediction and new materials discovery.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Small molecule microarray identifies inhibitors of tyrosyl-DNA phosphodiesterase 1 that simultaneously access the catalytic pocket and two substrate binding sites

Tyrosyl-DNA phosphodiesterase 1 (TDP1) is a member of the phospholipase D family of enzymes, which catalyzes the removal of both 3'- and 5'-DNA phosphodiester adducts. Importantly, it is capable of reducing the anticancer effects of type I topoisomerase (TOP1) inhibitors by repairing the stalled covalent complexes of TOP1 with DNA. It achieves this by promoting the hydrolysis of the phosphodiester bond between the Y723 residue of human TOP1 and the 3'-phosphate of its DNA substrate. Blocking TDP1 function is an attractive means of enhancing the efficacy of TOP1 inhibitors and overcoming drug resistance. Previously, we reported the use of an X-ray crystallographic screen of more than 600 fragments to identify small molecule variations on phthalic acid and hydroxyquinoline motifs that bind within the TDP1 catalytic pocket. Yet, the majority of these compounds showed limited (millimolar) TDP1 inhibitory potencies. We now report examining a 21 000-member library of drug-like Small Molecules in Microarray (SMM) format for their ability to bind Alexa Fluor 647 (AF647)-labeled TDP1. The screen identified structurally similar N,2-diphenylimidazo[1,2-a]pyrazin-3-amines as TDP1 binders and catalytic inhibitors. We then explored the core heterocycle skeleton using one-pot Groebke–Blackburn–Bienayme multicomponent reactions and arrived at analogs having higher inhibitory potencies. Solving TDP1 co-crystal structures of a subset of compounds showed their binding at the TDP1 catalytic site, while mimicking substrate interactions. Although our original fragment screen differed significantly from the current microarray protocol, both methods identified ligand–protein interactions containing highly similar elements. Importantly inhibitors identified through the SMM approach show competitive inhibition against TDP1 and access the catalytic phosphate-binding pocket, while simultaneously providing extensions into both the substrate DNA and peptide-binding channels. As such, they represent a platform for further elaboration of trivalent ligands, that could serve as a new genre of potent TDP1 inhibitors.

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

In Vitro Evolution and Computational Approaches to Predict, Prevent and Control Future Pandemics

The natural tendency of virus to mutate and the under-sampling of the environment makes it difficult to become aware of the emergence of new viral strains with pandemic potential. Being able to predict what mutations make a virus more infective might allow to spot such strains with minimal sampling and potentially allow to predict/prevent the next pandemic. The team attempted to mimic natural viral mutations and recombination through computational and experimental methods producing a variety of mutants of a SARS-COV-2 protein (receptor binding domain, RBD, of spike protein) responsible for viral entry in mammalian cells. The library of mutants was then interrogated for ability and lack-there-of to interact with the host cell receptor ushering viral entry, Angiotensin-converting enzyme 2 (ACE2). The negative and positive data set is intended to “teach the rules” of virus-host receptor interaction. Additionally, the positive clones were used to screen a set of antibody mutants designed to widen the breadth of viral mutants recognition, to demonstrate that this kind of libraries could also be a tool to produce antibody therapeutics impervious to viral mutation, even before a pandemic strain is discovered.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Transcriptional Regulatory Networks Involved in C3–C4 Alcohol Stress Response and Tolerance in Yeast

Alcohol toxicity significantly impacts the titer and productivity of industrially produced biofuels. To overcome this limitation, we must find and use strategies to improve stress tolerance in production strains. Previously, we developed a multiplex navigation of a global regulatory network (MINR) library that targeted 25 regulatory genes that are predicted to modify global regulation in yeast under different stress conditions. Herein, we expanded this concept to target the active sites of 47 transcriptional regulators using a saturation mutagenesis library. The 47 targeted regulators interact with more than half of all yeast genes. We then screened and selected for C3–C4 alcohol tolerance. We identified specific mutants that have resistance to isopropanol and isobutanol. Notably, the WAR1_K110N variant improved tolerance to both isopropanol and isobutanol. In addition, we investigated the mechanisms for improvement of isopropanol and isobutanol stress tolerance and found that genes related to glycolysis play a role in tolerance to isobutanol, while changes in ATP synthesis and mitochondrial respiration play a role in tolerance to both isobutanol and isopropanol. Overall, this work sheds light on basic mechanisms for isopropanol and isobutanol toxicity and demonstrates a promising strategy to improve tolerance to C3–C4 alcohols by perturbing the transcriptional regulatory network.

09 BIOMASS FUELS↗
DOE OSTI2020

An automatically curated first-principles database of ferroelectrics

Ferroelectric materials have technological applications in information storage and electronic devices. The ferroelectric polar phase can be controlled with external fields, chemical substitution and size-effects in bulk and ultrathin film form, providing a platform for future technologies and for exploratory research. In this work, we integrate spin-polarized density functional theory (DFT) calculations, crystal structure databases, symmetry tools, workflow software, and a custom analysis toolkit to build a library of known, previously-proposed, and newly-proposed ferroelectric materials. With our automated workflow, we screen over 67,000 candidate materials from the Materials Project database to generate a dataset of 255 ferroelectric candidates, and propose 126 new ferroelectric materials. We benchmark our results against experimental data and previous first-principles results. The data provided includes atomic structures, output files, and DFT values of band gaps, energies, and the spontaneous polarization for each ferroelectric candidate. Finally, we contribute our workflow and analysis code to the open-source python packages atomate and pymatgen so others can conduct analogous symmetry driven searches for ferroelectrics and related phenomena.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Identification of proteins influencing CRISPR-associated transposases for enhanced genome editing

CRISPR-associated transposases (CASTs) hold tremendous potential for microbial genome editing because of their ability to integrate large DNA cargos in a programmable, site-specific manner. However, their widespread application has been hindered by poorly understood host factor requirements for transposition. To address this gap, we conducted the first genome-wide screen for host factors affecting Vibrio cholerae CAST (VchCAST) activity using an Escherichia coli RB-TnSeq library and identified 15 genes affecting VchCAST transposition. Of these, seven factors were validated to improve VchCAST activity, and two were inhibitory. Guided by the identification of homologous recombination effectors, RecD and RecA, we tested the λ-Red recombineering system in our VchCAST editing vectors and increased editing efficiency by 55.2-fold in E. coli, 5.6-fold in Pseudomonas putida, and 10.8-fold in Klebsiella michiganensis while maintaining high target specificity and similar insertion arrangements. This study improves the understanding of factors affecting VchCAST activity and enhances its efficiency as a bacterial genome editor.

Song, Leo C T↗
DOE OSTI2020

A Phenotarget Approach for Identifying an Alkaloid Interacting with the Tuberculosis Protein Rv1466

In recent years, there has been a revival of interest in phenotypic-based drug discovery (PDD) due to target-based drug discovery (TDD) falling below expectations. Both PDD and TDD have their unique advantages and should be used as complementary methods in drug discovery. The PhenoTarget approach combines the strengths of the PDD and TDD approaches. Phenotypic screening is conducted initially to detect cellular active components and the hits are then screened against a panel of putative targets. This PhenoTarget protocol can be equally applied to pure compound libraries as well as natural product fractions. Here we described the use of the PhenoTarget approach to identify an anti-tuberculosis lead compound. Fractions from Polycarpa aurata were identified with activity against Mycobacterium tuberculosis H37Rv. Native magnetic resonance mass spectrometry (MRMS) against a panel of 37 proteins from Mycobacterium proteomes showed that a fraction from a 95% ethanol re-extraction specifically formed a protein-ligand complex with Rv1466, a putative uncharacterized Mycobacterium tuberculosis protein. The natural product responsible was isolated and characterized to be polycarpine. The molecular weight of the ligand bound to Rv1466, 233 Da, was half the molecular weight of polycarpine less one proton, indicating that polycarpine formed a covalent bond with Rv1466.

phenotarget approach, MRMS, polycarpine, drug disc↗
DOE OSTI2023

Integrated data-driven and experimental approaches to accelerate lead optimization targeting SARS-CoV- 2 main protease

Identification of potential therapeutic candidates can be expedited by integrating computational modeling with domain aware machine learning (ML) approaches followed by experimental validation. Generative deep learning models have been recently developed that can generate thousands of new candidates, but their physiochemical properties are typically not optimized. Using our deep learning models and a scaffold as a starting point, we generated tens of thousands of compounds for SARS-CoV-2 M pro that preserve the core scaffold. Here we utilized and implemented several computational tools such as structural alert and toxicity analysis, high throughput virtual screening, ML-based 3D quantitative structure–activity relationships, multi-parameter optimization, and graph neural networks on libraries of generated candidates to predict biological activity and binding affinity a priori. From these collective computational results, eight promising candidates were identified and tested experimentally using Native Mass Spectrometry (MS) and FRET-based functional assays. Two compounds, with quinazoline-2-thiol and acetylpiperidine core moiety showed IC 50 values in the low micromolar range: 2.95±0.0017 µM and 3.41±0.0015 µM, respectively. The molecular dynamics simulations further highlight that binding of these compounds results in allosteric modulations in the chain B and the interface domains of the M pro . The key fragments from these top hits can be used as input for closed loop lead optimization in the integrated pipeline.

60 APPLIED LIFE SCIENCES↗