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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 649 records · Page 36
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Accelerating Biological Insight for Understudied Genes

Synopsis The rapid expansion of genome sequence data is increasing the discovery of protein-coding genes across all domains of life. Annotating these genes with reliable functional information is necessary to understand evolution, to define the full biochemical space accessed by nature, and to identify target genes for biotechnology improvements. The majority of proteins are annotated based on sequence conservation with no specific biological, biochemical, genetic, or cellular function identified. Recent technical advances throughout the biological sciences enable experimental research on these understudied protein-coding genes in a broader collection of species. However, scientists have incentives and biases to continue focusing on well documented genes within their preferred model organism. This perspective suggests a research model that seeks to break historic silos of research bias by enabling interdisciplinary teams to accelerate biological functional annotation. We propose an initiative to develop coordinated projects of collaborating evolutionary biologists, cell biologists, geneticists, and biochemists that will focus on subsets of target genes in multiple model organisms. Concurrent analysis in multiple organisms takes advantage of evolutionary divergence and selection, which causes individual species to be better suited as experimental models for specific genes. Most importantly, multisystem approaches would encourage transdisciplinary critical thinking and hypothesis testing that is inherently slow in current biological research.

Zoology↗
DOE OSTI2021

Metabolic patterns in Septoria canker resistant and susceptible Populus trichocarpa genotypes 24 hours post-inoculation

Sphaerulina musiva is an economically and ecologically important fungal pathogen that causes Septoria stem canker and leaf spot disease of Populus species. Here, to bridge the gap between genetic markers and structural barriers previously found to be linked to Septoria (Sphaerulina musiva) canker disease resistance in poplar, we employed hydrophilic interaction liquid chromatography (HILIC) and tandem mass spectrometry (MS/MS) to identify and quantify metabolites involved with signaling and cell wall re-modelling. Fluctuations in signaling molecules, organic acids, amino acids, sterols, phenolics, and saccharides in resistant and susceptible Populus trichocarpa inoculated with Sphaerulina musiva, were observed. The patterns of 222 metabolites in the resistant host implicates systemic acquired resistance (SAR), cell wall apposition, and lignin deposition as modes of resistance to this hemibiotrophic pathogen. This pattern is consistent with the expected response to the biotrophic phase of S. musiva colonization during the first 24 hours post-inoculation. The fungal pathogen metabolized key regulatory signals of SAR, other phenolics, and precursors of lignin biosynthesis which were depleted in the susceptible host. This is the first study to characterize metabolites associated with the response to initial colonization by S. musiva between resistant and susceptible hosts.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Autoclave treatment of the classical scrapie agent US No. 13-7 and experimental inoculation to susceptible VRQ/ARQ sheep via the oral route results in decreased transmission efficiency

Scrapie, a prion disease of sheep, is highly resistant to conventional deactivation. Numerous methods to deactivate scrapie have been tested in laboratory animal models, and adequate autoclave treatment can reduce or remove the infectivity of some classical scrapie strains depending on the heating parameters used. In this study, we autoclaved brain homogenate from a sheep with US scrapie strain 13–7 for 30 minutes at 121°C. Genetically susceptible VRQ/ARQ sheep were orally inoculated with 3 grams of the autoclaved brain homogenate. For comparison, a second group of sheep was inoculated with a non-autoclaved brain homogenate. Rectal biopsies were used to assess antemortem scrapie disease progression throughout the study. Five out of ten (5/10) sheep that received autoclaved inoculum ultimately developed scrapie after an experimental endpoint of 72 months. These sheep had a mean incubation period of 26.99 months. Two out of five (2/5) positive sheep had detectable PrP Sc in antemortem rectal biopsies, and two (2/5) other sheep had PrP Sc in postmortem rectal tissue. A single sheep (1/5) was positive for scrapie in the CNS, small intestine, and retropharyngeal lymph node but had negative rectal tissue. All of the sheep (10/10) that received non-autoclaved inoculum developed scrapie with a mean incubation period of 20.2 months and had positive rectal biopsies at the earliest timepoint (14.7 months post-inoculation). These results demonstrate that sheep are orally susceptible to US derived classical scrapie strain 13–7 after autoclave treatment at 121°C for 30 minutes. Differences in incubation periods and time interval to first positive rectal biopsies indicate a partial reduction in infectivity titers for the autoclaved inoculum group.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

High-throughput protein characterization by complementation using DNA barcoded fragment libraries

Abstract Our ability to predict, control, or design biological function is fundamentally limited by poorly annotated gene function. This can be particularly challenging in non-model systems. Accordingly, there is motivation for new high-throughput methods for accurate functional annotation. Here, we used co mplementation of aux otrophs and DNA barcode seq uencing (Coaux-Seq) to enable high-throughput characterization of protein function. Fragment libraries from eleven genetically diverse bacteria were tested in twenty different auxotrophic strains of Escherichia coli to identify genes that complement missing biochemical activity. We recovered 41% of expected hits, with effectiveness ranging per source genome, and observed success even with distant E. coli relatives like Bacillus subtilis and Bacteroides thetaiotaomicron . Coaux-Seq provided the first experimental validation for 53 proteins, of which 11 are less than 40% identical to an experimentally characterized protein. Among the unexpected function identified was a sulfate uptake transporter, an O-succinylhomoserine sulfhydrylase for methionine synthesis, and an aminotransferase. We also identified instances of cross-feeding wherein protein overexpression and nearby non-auxotrophic strains enabled growth. Altogether, Coaux-Seq’s utility is demonstrated, with future applications in ecology, health, and engineering.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

DLK1 is a GATA1s-driven dependency and therapeutic target in Down syndrome–associated myeloid leukemia

Children with Down syndrome have a markedly increased risk of developing myeloid leukemia. Although having an excellent prognosis, 10% to 20% develop relapsed or refractory disease with poor survival, highlighting the need for new targeted approaches. The pathogenesis of myeloid leukemia of Down syndrome (ML-DS) is tightly linked to fetal hematopoiesis and mutations in GATA1, generating the truncated GATA1 short (GATA1s) isoform. We identified Delta-like noncanonical Notch ligand 1 (DLK1) as a direct GATA1s target. DLK1, a paternally imprinted transmembrane protein, is highly expressed in fetal liver CD34 + cells but absent in adult hematopoiesis, making it an attractive immunotherapeutic target. Chromatin profiling revealed GATA1s occupancy at a distal enhancer within the DLK1-DIO3 locus, driving aberrant DLK1 upregulation in ML-DS. Functional studies demonstrated that DLK1 is a leukemia dependency, as its genetic ablation impaired proliferation and engraftment, induced apoptosis, and altered Notch and β-catenin signaling. Therapeutically, a DLK1-directed antibody-drug conjugate–induced selective cytotoxicity, abrogated colony formation, and significantly prolonged survival in refractory ML-DS patient-derived xenograft (PDX) models, achieving durable remissions at higher doses. These findings establish DLK1 as a leukemia-specific vulnerability and provide preclinical proof-of-concept for DLK1-targeted therapies in ML-DS and other leukemias with fetal-like expression programs.

Biological and medical sciences↗
DOE OSTI2026

Converting methoxy groups on lignin-derived aromatics from a toxic hurdle to a useful resource: a systems-driven approach

Lignin conversion to bioproducts is challenging for several reasons, including the toxicity generated from the abundant methoxy groups released as formaldehyde. Consequently, our project used the best understood methylotroph – for which there is growing knowledge of formaldehyde stress responses – to explore how genetic factors and single-cell phenotypic heterogeneity contribute to growth and production. Our major goals were to (1) understand the fundamentals of Methylobacterium growth on aromatics, (2) identify and understand the basis of physiological basis of single-cell heterogeneity, and (3) develop quantitative models that relate heterogeneity to physiological and evolutionary outcomes.

Source record↗
DOE OSTI2020

Predictive Inverse Model for Advective Heat Transfer in a Short–Circuited Fracture: Dimensional Analysis, Machine Learning, and Field Demonstration

Identifying fluid flow maldistribution in planar geometries is a well–established problem in subsurface science/engineering. Of particular importance to the thermal performance of enhanced (or “engineered”) geothermal systems is identifying the existence of nonuniform (i.e., heterogeneous) permeability and subsequently predicting advective heat transfer. Here, machine learning via a genetic algorithm (GA) identifies the spatial distribution of an unknown permeability field in a two–dimensional Hele–Shaw geometry (i.e., parallel plates). The inverse problem is solved by minimizing the L2 norm between simulated residence time distribution (RTD) and measurements of an inert tracer breakthrough curve (BTC) (C–Dot nanoparticle). Principal component analysis (PCA) of spatially correlated permeability fields enabled reduction of the parameter space by more than a factor of 10 and restricted the inverse search to reservoir–scale permeability variations. Thermal experiments and tracer tests conducted at the mesoscale Altona Field Laboratory (AFL) demonstrate that the method accurately predicts the effects of extreme flow channeling on heat transfer in a single bedding–plane rock fracture. However, this is only true when the permeability distributions provide adequate matches to both tracer RTD and frictional pressure loss. Without good agreement to frictional pressure loss, it is still possible to match a simulated RTD to measurements, but subsequent predictions of heat transfer are grossly inaccurate. Here, the results of this study suggest that it is possible to anticipate the thermal effects of flow maldistribution, but only if both simulated RTDs and frictional pressure loss between fluid inlets and outlets are in good agreement with measurements.

42 ENGINEERING↗
DOE OSTI2022

Quantifying shifts in natural selection on codon usage between protein regions: a population genetics approach

Codon usage bias (CUB), the non-uniform usage of synonymous codons, occurs across all domains of life. Adaptive CUB is hypothesized to result from various selective pressures, including selection for efficient ribosome elongation, accurate translation, mRNA secondary structure, and/or protein folding. Given the critical link between protein folding and protein function, numerous studies have analyzed the relationship between codon usage and protein structure. The results from these studies have often been contradictory, likely reflecting the differing methods used for measuring codon usage and the failure to appropriately control for confounding factors, such as differences in amino acid usage between protein structures and changes in the frequency of different structures with gene expression. Here we take an explicit population genetics approach to quantify codon-specific shifts in natural selection related to protein structure in S. cerevisiae and E. coli. Unlike other metrics of codon usage, our approach explicitly separates the effects of natural selection, scaled by gene expression, and mutation bias while naturally accounting for a region’s amino acid usage. Bayesian model comparisons suggest selection on codon usage varies only slightly between helix, sheet, and coil secondary structures and, similarly, between structured and intrinsically-disordered regions. Similarly, in contrast to previous findings, we find selection on codon usage only varies slightly at the termini of helices in E. coli. Using simulated data, we show this previous work indicating “non-optimal” codons are enriched at the beginning of helices in S. cerevisiae was due to failure to control for various confounding factors (e.g. amino acid biases, gene expression, etc.), and rather than selection to modulate cotranslational folding. Our results reveal a weak relationship between codon usage and protein structure, indicating that differences in selection on codon usage between structures are slight. In addition to the magnitude of differences in selection between protein structures being slight, the observed shifts appear to be idiosyncratic and largely codon-specific rather than systematic reversals in the nature of selection. Overall, our work demonstrates the statistical power and benefits of studying selective shifts on codon usage or other genomic features from an explicitly evolutionary approach. Limitations of this approach and future potential research avenues are discussed.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Detect and correct bias in multi-site neuroimaging datasets

The desire to train complex machine learning algorithms and to increase the statistical power in association studies drives neuroimaging research to use ever-larger datasets. The most obvious way to increase sample size is by pooling scans from independent studies. However, simple pooling is often ill-advised as selection, measurement, and confounding biases may creep in and yield spurious correlations. In this work, we combine 35,320 magnetic resonance images of the brain from 17 studies to examine bias in neuroimaging. In the first experiment, Name That Dataset, we provide empirical evidence for the presence of bias by showing that scans can be correctly assigned to their respective dataset with 71.5% accuracy. Given such evidence, we take a closer look at confounding bias, which is often viewed as the main shortcoming in observational studies. In practice, we neither know all potential confounders nor do we have data on them. Hence, we model confounders as unknown, latent variables. Kolmogorov complexity is then used to decide whether the confounded or the causal model provides the simplest factorization of the graphical model. Finally, we present methods for dataset harmonization and study their ability to remove bias in imaging features. In particular, we propose an extension of the recently introduced ComBat algorithm to control for global variation across image features, inspired by adjusting for unknown population stratification in genetics. Overall, our results demonstrate that harmonization can reduce dataset-specific information in image features. Further, confounding bias can be reduced and even turned into a causal relationship. However, harmonization also requires caution as it can easily remove relevant subject-specific information. Code is available at https://github.com/ai-med/Dataset-Bias.

42 ENGINEERING↗
DOE OSTI2022

Novel cis-regulatory elements as synthetic promoters to drive recombinant protein expression from the Chlamydomonas reinhardtii nuclear genome

Eukaryotic green microalgae represent a sustainable, photosynthetic biotechnology platform for generating high-value products. The model green alga Chlamydomonas reinhardtii has already been used to generate high value bioproducts such as recombinant proteins and terpenoids. However, low, unstable, and variable nuclear transgene expression has limited the ease and speed of metabolic engineering and recombinant protein expression in this system. Here, novel genetic devices for transgene expression in C. reinhardtii have been developed by identifying cis-regulatory DNA elements capable of driving high transgene expression in C. reinhardtii promoters using de novo motif discovery informatics approaches. Thirteen putative motifs were synthesized as concatemers, linked to a common minimal basal promoter, and assayed for their activity to drive expression of a yellow fluorescent protein reporter gene. Following transformation of the vectors into C. reinhardtii by electroporation, in vivo measurements of yellow fluorescent protein expression by flow cytometry revealed that five of the DNA motifs analyzed displayed significantly higher reporter expression compared to the basal promoter control. Two of the concatemerized motifs, despite being much smaller minimal cis-regulatory elements, drove reporter expression at levels approaching that of the conventionally-used AR1 promoter. This analysis provides insight into C. reinhardtii promoter structure and gene regulation, and provides a new toolbox of cis-regulatory elements that can be used to drive transgene expression at a variety of expression levels.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Optimization of the Reax force field for the lithium–oxygen system using a high fidelity charge model

Studies using molecular dynamics (MD) have long struggled to simulate the failure modes of materials, predicting unrealistically high ductility and failing to capture brittle fracture. The primary cause of this shortcoming is an inadequate description of bond breaking. While reactive force fields such as ReaxFF show improvements compared to traditional force fields, the charge models used yield unphysical partial charges, especially during dissociation of ionic bonds. This flaw may be remedied by using the atom-condensed Kohn–Sham density functional theory (DFT) approximated to a second order (ACKS2) charge model for determining partial charges. In this work, we present a new ACKS2-enabled Reax force field for fracture simulations of lithium oxide systems, which was obtained by training against an extensive set of DFT, multireference configuration interaction (MRCI), and MRCI+Q reference data using genetic optimization techniques. This new force field significantly improves the bond breaking behavior, but still cannot fully capture the brittle fracture in MD simulations, suggesting more research is needed to improve simulation of brittle fracture.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Cortical cell size regulates root metabolic cost

SUMMARY It has been hypothesized that vacuolar occupancy in mature root cortical parenchyma cells regulates root metabolic cost and thereby plant fitness under conditions of drought, suboptimal nutrient availability, and increased soil mechanical impedance. However, the mechanistic role of vacuoles in reducing root metabolic cost was unproven. Here we provide evidence to support this hypothesis. We first show that root cortical cell size is determined by both cortical cell diameter and cell length. Significant genotypic variation for both cortical cell diameter (~1.1‐ to 1.5‐fold) and cortical cell length (~ 1.3‐ to 7‐fold) was observed in maize and wheat. GWAS and QTL analyses indicate cortical cell diameter and length are heritable and under independent genetic control. We identify candidate genes for both phenes. Empirical results from isophenic lines contrasting for cortical cell diameter and length show that increased cell size, due to either diameter or length, is associated with reduced root respiration, nitrogen content, and phosphorus content.RootSlice, a functional‐structural model of root anatomy, predicts that an increased vacuolar: cytoplasmic ratio per unit cortical volume causes reduced root respiration and nutrient content. Ultrastructural imaging of cortical parenchyma cells with varying cortical diameter and cortical cell length confirms thein silicopredictions and shows that an increase in cell size is correlated with increased vacuolar volume and reduced cytoplasmic volume. Vacuolar occupancy and its relationship with cell size merits further investigation as a phene for improving crop adaptation to edaphic stress.

Plant Sciences↗
DOE OSTI2022

Parametric Modeling and Economic Analysis of a 2MW th 3-Stream sCO 2 Heat Exchanger

Here, this paper presents the design and cost optimization of a novel 2MW th 3-stream sCO2 plate-fin heat exchanger. This heat exchanger design is unique in that it uses reduced metal oxide particle-to-sCO2 heat exchanger for cost-effective energy storage applications. The design uses low velocity, laminar air as the re-oxidizing reactant to transfer the heat of the re-oxidizing reaction to a sCO2 power loop. The design of the heat exchanger is based on a 2-D, 3-fluid plate/fin heat transfer model. The model parameterizes the size, shape, and number of passages of the heat exchanger to calculate the temperature profile, pressure drop, and fluid velocities of all three fluids. Global heat exchanger parameters such as the effectiveness and total heat transferred to the sCO2 are then calculated for overall performance. Due to the value and increased use of sCO2 heat exchangers in power cycles, a cost model of the system based on the unique high temperature/high pressure operating conditions was created using quotes from reference projects and market analysis. These quoted air-to-sCO2 heat exchangers are then processed using multiple weighting factors pertinent to heat exchanger design, including heat exchanger type, maximum temperature, differential pressures, fluids, duty, and more. These factors are then used in an exponential function in order to generate a parameterized cost curve. The design and cost of the heat exchanger are then optimized using the SMPSO genetic algorithm in Python. The optimization objectives for the system are to maximize the overall system effectiveness, including an air recuperator for preheating, and to minimize unit costs. Additional constraints are added to the system for the sCO2 and air pressure drops, air velocity to reduce particle entrainment, and the length and volume of the heat exchanger.

Cost Model↗
DOE OSTI2020

FeGenie: a comprehensive tool for the identification of iron genes and iron gene neighborhoods in genomes and metagenome assemblies

Iron is a micronutrient for nearly all life on Earth. It can be used as an electron donor and electron acceptor by iron-oxidizing and iron-reducing microorganisms and is used in a variety of biological processes, including photosynthesis and respiration. While it is the fourth most abundant metal in the Earth’s crust, iron is often limiting for growth in oxic environments because it is readily oxidized and precipitated. Much of our understanding of how microorganisms compete for and utilize iron is based on laboratory experiments. However, the advent of next-generation sequencing and surge in publicly available sequence data has made it possible to probe the structure and function of microbial communities in the environment. To bridge the gap between our understanding of iron acquisition, iron redox cycling, iron storage, and magnetosome formation in model microorganisms and the plethora of sequence data available from environmental studies, we have created a comprehensive database of hidden Markov models (HMMs) based on genes related to iron acquisition, storage, and reduction/oxidation in Bacteria and Archaea. Along with this database, we present FeGenie, a bioinformatics tool that accepts genome and metagenome assemblies as input and uses our comprehensive HMM database to annotate provided datasets with respect to iron-related genes and gene neighborhood. An important contribution of this tool is the efficient identification of genes involved in iron oxidation and dissimilatory iron reduction, which have been largely overlooked by standard annotation pipelines. We validated FeGenie against a selected set of 28 isolate genomes and showcase its utility in exploring iron genes present in 27 metagenomes, 4 isolate genomes from human oral biofilms, and 17 genomes from candidate organisms, including members of the candidate phyla radiation. We show that FeGenie accurately identifies iron genes in isolates. Furthermore, analysis of metagenomes using FeGenie demonstrates that the iron gene repertoire and abundance of each environment is correlated with iron richness. While this tool will not replace the reliability of culture-dependent analyses of microbial physiology, it provides reliable predictions derived from the most up-to-date genetic markers. FeGenie’s database will be maintained and continually updated as new genes are discovered.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Structural and biochemical analyses of the nuclear IκBζ protein in complex with the NF-κB p50 homodimer

As part of the efforts to understand nuclear IκB function in NF-κB-dependent gene expression, we report an X-ray crystal structure of the IκBζ ankyrin repeat domain in complex with the dimerization domain of the NF-κB p50 homodimer. IκBζ possesses an N-terminal α helix that conveys domain folding stability. Affinity and specificity of the complex depend on a small portion of p50 at the nuclear localization signal. The model suggests that only one p50 subunit supports binding with IκBζ, and biochemical experiments confirm that IκBζ associates with DNA-bound NF-κB p50:RelA heterodimers. Comparisons of IκBζ:p50 and p50:κB DNA complex crystallographic models indicate that structural rearrangement is necessary for ternary complex formation of IκBζ and p50 with DNA.

Cell Biology↗
DOE OSTI2023

From Optimization to Sampling Through Gradient Flows

Optimization and sampling algorithms play a central role in science and engineering as they enable finding optimal predictions, policies, and recommendations, as well as expected and equilibrium states of complex systems. The notion of “optimality” is formalized by the choice of an objective function, while the notion of an “expected” state is specified by a probabilistic model for the distribution of states. Optimizing rugged objective functions and sampling multimodal distributions is computationally challenging, especially in high-dimensional problems. Here, for this reason, many optimization and sampling methods have been developed by researchers working in disparate fields such as Bayesian statistics, molecular dynamics, genetics, quantum chemistry, machine learning, weather forecasting, econometrics, and medical imaging.

Trillos, N. García↗
DOE OSTI2026

Towards modeling phage therapy

Patients infected with life-threatening multi-drug resistant (MDR) bacteria have been treated with cocktails of bacteriophages. This is a complicated form of personalized medicine as the phages given to a patient have to be selected beforehand on the basis of their lytic capacity of the infecting bacteria. Because bacteria rapidly become resistant, the evolution of resistance to a diverse cocktail of phages is a complicated dynamical process, during which competing bacterial strains replace one another by accumulating several resistance mechanisms, each of which may involve a fitness cost. As a consequence, it is typically not known why a particular phage therapy succeeded or failed, and how one can optimize the composition of the cocktails to maximize the rate of success. To improve upon this, we extend an existing in vivo -calibrated mouse model into a novel mathematical model for the human situation, and include multiple phages infecting multiple bacterial strains, differing in their resistance to each of the phages. We adjust several parameter estimates of the bacterial model to the human situation, and use the model to describe a successful case of phage therapy involving several cocktails, each containing several phages. In the model, treatment success crucially depended on pretreatment resistance levels, and on the diversity and the timing of the cocktails. Once an appropriate cocktail is found, it is less important to further optimize the infection rates of the phages. Resistant bacterial strains expand rapidly when sensitive strains decline, and the higher the infectivity of the phages, the faster resistant strains expand. Because resistance evolves rapidly, it is best to provide a diverse set of phages right from the start of therapy, i.e., to hit hard and early, and create a high genetic barrier to bacterial resistance.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Structural Characterization of the Reaction and Substrate Specificity Mechanisms of Pathogenic Fungal Acetyl-CoA Synthetases

Acetyl CoA synthetases (ACSs) are Acyl-CoA/NRPS/Luciferase (ANL) superfamily enzymes that couple acetate with CoA to generate acetyl CoA, a key component of central carbon metabolism in eukaryotes and prokaryotes. Normal mammalian cells are not dependent on ACSs, while tumor cells, fungi, and parasites rely on acetate as a precursor for acetyl CoA. Consequently, ACSs have emerged as a potential drug target. As part of a program to develop antifungal ACS inhibitors, we characterized fungal ACSs from five diverse human fungal pathogens using biochemical and structural studies. ACSs catalyze a two-step reaction involving adenylation of acetate followed by thioesterification with CoA. Our structural studies captured each step of these two half-reactions including the acetyl-adenylate intermediate of the first half-reaction in both the adenylation conformation and the thioesterification conformation and thus provide a detailed picture of the reaction mechanism. We also used a systematic series of increasingly larger alkyl adenosine esters as chemical probes to characterize the structural basis of the exquisite ACS specificity for acetate over larger carboxylic acid substrates. Consistent with previous biochemical and genetic data for other enzymes, structures of fungal ACSs with these probes bound show that a key tryptophan residue limits the size of the alkyl binding site and forces larger alkyl chains to adopt high energy conformers, disfavoring their efficient binding. Together, our analysis provides highly detailed structural models for both the reaction mechanism and substrate specificity that should be useful in designing selective inhibitors of eukaryotic ACSs as potential anticancer, antifungal, and antiparasitic drugs.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗