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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 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Omics-guided metabolic pathway discovery in plants: Resources, approaches, and opportunities

Plants produce a vast array of metabolites, the biosynthetic routes of which remain largely undetermined. Genome-scale enzyme and pathway annotations and omics technologies have revolutionized research to decrypt plant metabolism and produced a growing list of functionally characterized metabolic genes and pathways. However, what is known is still a tiny fraction of the metabolic capacity harbored by plants. Here, in this work, we review plant enzyme and pathway annotation resources and cutting-edge omics approaches to guide discovery and characterization of plant metabolic pathways. We also discuss strategies for improving enzyme function prediction by integrating protein 3D structure information and single cell omics. This review aims to serve as a primer for plant biologists to leverage omics datasets to facilitate understanding and engineering plant metabolism.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2019

Comparative and Functional Algal Genomics

Over 100 whole-genome sequences from algae are published or soon to be published. The rapidly increasing availability of these fundamental resources is precipitating a paradigm shift in the way we understand one of the most diverse, complex, and understudied groups of photosynthetic eukaryotes. Genome sequences provide a window into the functional potential of each disparate alga, with phylogenomics and functional genomics as tools for contextualizing and transferring knowledge from reference organisms into less well-characterized systems. Remarkably, over half of the proteins encoded by algal genomes are of unknown function, highlighting the volume of unique functional capabilities yet to be discovered. We provide an overview of publicly available algal genomes, their associated protein inventories, and their quality, with a summary of the statuses of protein function understanding and prediction.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2018

Genome-wide analysis of cytochrome P450s of Trichoderma spp.: annotation and evolutionary relationships

Background Cytochrome P450s form an important group of enzymes involved in xenobiotics degradation and metabolism, both primary and secondary. These enzymes are also useful in industry as biotechnological tools for bioconversion and a few are reported to be involved in pathogenicity. Trichoderma spp. are widely used in industry and agriculture and are known for their biosynthetic potential of a large number of secondary metabolites. For realising the full biosynthetic potential of an organism, it is important to do a genome-wide annotation and cataloguing of these enzymes. Results Here, we have studied the genomes of seven species (T. asperellum, T. atroviride, T. citrinoviride, T. longibrachiatum, T. reesei , T. harzianum and T. virens) and identified a total of 477 cytochrome P450s. We present here the classification, evolution and structure as well as predicted function of these proteins. This study would pave the way for functional characterization of these groups of enzymes and will also help in realization of their full economic potential. Conclusion Our CYPome annotation and evolutionary studies of the seven Trichoderma species now provides opportunities for exploration of research-driven strategies to select Trichoderma species for various applications especially in relation to secondary metabolism and degradation of environmental pollutants.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2016

Data from: Diversity and population structure of northern switchgrass as revealed through exome capture sequencing

Switchgrass (Panicum virgatum L.) is a polyploid, perennial grass species that is native to North America, and is being developed as a future biofuels feedstock crop. Switchgrass is present primarily in two ecotypes: a northern upland ecotype composed of tetraploid and octoploid accessions, and a southern lowland ecotype composed of primarily tetraploid accessions. We employed high-coverage exome capture sequencing (~2.4 Tb) to genotype 537 individuals from 45 upland and 21 lowland populations. From these data, we identified ~27 million single nucleotide polymorphisms (SNPs), of which 1,590,653 high confidence SNPs were used in downstream analyses of diversity within and between the populations. From the 66 populations, we identified five primary population groups within the upland and lowland ecotypes, a result that was further supported through genetic distance analysis. We identified conserved, ecotype restricted non-synonymous SNPs that are predicted to impact protein function in genes that encode CONSTANS (CO) and EARLY HEADING DATE 1 (EHD1), key genes involved in flowering which may contribute to the phenotypic differences between the two ecotypes. We also identified, relative to the near-reference Kanlow population, 17,228 up-copy number variants (CNVs), 112,630 down-CNVs, and 14,430 presence/absence variants (PAV) impacting a total of 9,979 genes, including two upland-specific CNV-clusters. In total, 45,719 genes were impacted by a SNP, CNV, or a PAV across the panel providing a firm foundation to identify functional variation associated with phenotypic traits of interest for biofuel feedstock production.

09 BIOMASS FUELS↗
DOE OSTI2022

Expansion of the global RNA virome reveals diverse clades of bacteriophages

High-throughput RNA sequencing offers broad opportunities to explore the Earth RNA virome. Mining 5,150 diverse metatranscriptomes uncovered >2.5 million RNA virus contigs. Analysis of >330,000 RNA-dependent RNA polymerases (RdRPs) shows that this expansion corresponds to a 5-fold increase of the known RNA virus diversity. Gene content analysis revealed multiple protein domains previously not found in RNA viruses and implicated in virus-host interactions. Extended RdRP phylogeny supports the monophyly of the five established phyla and reveals two putative additional bacteriophage phyla and numerous putative additional classes and orders. The dramatically expanded phylum Lenarviricota, consisting of bacterial and related eukaryotic viruses, now accounts for a third of the RNA virome. Identification of CRISPR spacer matches and bacteriolytic proteins suggests that subsets of picobirnaviruses and partitiviruses, previously associated with eukaryotes, infect prokaryotic hosts.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

MONACO: accurate biological network alignment through optimal neighborhood matching between focal nodes

Motivation: Alignment of protein–protein interaction networks can be used for the unsupervised prediction of functional modules, such as protein complexes and signaling pathways, that are conserved across different species. To date, various algorithms have been proposed for biological network alignment, many of which attempt to incorporate topological similarity between the networks into the alignment process with the goal of constructing accurate and biologically meaningful alignments. Especially, random walk models have been shown to be effective for quantifying the global topological relatedness between nodes that belong to different networks by diffusing node-level similarity along the interaction edges. However, these schemes are not ideal for capturing the local topological similarity between nodes. Results: Here, we propose MONACO, a novel and versatile network alignment algorithm that finds highly accurate pairwise and multiple network alignments through the iterative optimal matching of ‘local’ neighborhoods around focal nodes. Extensive performance assessment based on real networks as well as synthetic networks, for which the ground truth is known, demonstrates that MONACO clearly and consistently outperforms all other state-of-the-art network alignment algorithms that we have tested, in terms of accuracy, coherence and topological quality of the aligned network regions. Furthermore, despite the sharply enhanced alignment accuracy, MONACO remains computationally efficient and it scales well with increasing size and number of networks.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

SoyCSN: Soybean context-specific network analysis and prediction based on tissue-specific transcriptome data

The Soybean Gene Atlas project provides a comprehensive map for understanding gene expression patterns in major soybean tissues from flower, root, leaf, nodule, seed, and shoot and stem. The RNA-Seq data generated in the project serve as a valuable resource for discovering tissue-specific transcriptome behavior of soybean genes in different tissues. We developed a computational pipeline for Soybean context-specific network (SoyCSN) inference with a suite of prediction tools to analyze, annotate, retrieve, and visualize soybean context-specific networks at both transcriptome and interactome levels. BicMix and Cross-Conditions Cluster Detection algorithms were applied to detect modules based on co-expression relationships across all the tissues. Soybean context-specific interactomes were predicted by combining soybean tissue gene expression and protein–protein interaction data. Functional analyses of these predicted networks provide insights into soybean tissue specificities. For example, under symbiotic, nitrogen-fixing conditions, the constructed soybean leaf network highlights the connection between the photosynthesis function and rhizobium–legume symbiosis. SoyCSN data and all its results are publicly available via an interactive web service within the Soybean Knowledge Base (SoyKB) at http://soykb.org/SoyCSN. SoyCSN provides a useful web-based access for exploring context specificities systematically in gene regulatory mechanisms and gene relationships for soybean researchers and molecular breeders.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2016

Phloem proteomics reveals new lipid-binding proteins with a putative role in lipid-mediated signaling

Global climate changes inversely affect our ability to grow the food required for an increasing world population. To combat future crop loss due to abiotic stress, we need to understand the signals responsible for changes in plant development and the resulting adaptations, especially the signaling molecules traveling long-distance through the plant phloem. Using a proteomics approach, we had identified several putative lipid-binding proteins in the phloem exudates. Simultaneously, we identified several complex lipids as well as jasmonates. These findings prompted us to propose that phloem (phospho-) lipids could act as long-distance developmental signals in response to abiotic stress, and that they are released, sensed, and moved by phloem lipid-binding proteins (Benning et al., 2012). Indeed, the proteins we identified include lipases that could release a signaling lipid into the phloem, putative receptor components, and proteins that could mediate lipid-movement. To test this possible protein-based lipid-signaling pathway, three of the proteins, which could potentially act in a relay, are characterized here: (I) a putative GDSL-motif lipase (II) a PIG-P-like protein, with a possible receptor-like function; (III) and PLAFP (phloem lipid-associated family protein), a predicted lipid-binding protein of unknown function. Here we show that all three proteins bind lipids, in particular phosphatidic acid (PtdOH), which is known to participate in intracellular stress signaling. Genes encoding these proteins are expressed in the vasculature, a prerequisite for phloem transport. Cellular localization studies show that the proteins are not retained in the endoplasmic reticulum but surround the cell in a spotted pattern that has been previously observed with receptors and plasmodesmatal proteins. Abiotic signals that induce the production of PtdOH also regulate the expression of GDSL-lipase and PLAFP, albeit in opposite patterns. Our findings suggest that while all three proteins are indeed lipid-binding and act in the vasculature possibly in a function related to long-distance signaling, the three proteins do not act in the same but rather in distinct pathways. Furthermore, it points toward PLAFP as a prime candidate to investigate long-distance lipid signaling in the plant drought response.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2017

The SlZRT1 Gene Encodes a Plasma Membrane-Located ZIP (Zrt-, Irt-Like Protein) Transporter in the Ectomycorrhizal Fungus Suillus luteus

Zinc (Zn) is an essential micronutrient but may become toxic when present in excess. In Zn-contaminated environments, trees can be protected from Zn toxicity by their root-associated micro-organisms, in particular ectomycorrhizal fungi. The mechanisms of cellular Zn homeostasis in ectomycorrhizal fungi and their contribution to the host tree’s Zn status are however not yet fully understood. The aim of this study was to identify and characterize transporters involved in Zn uptake in the ectomycorrhizal fungus Suillus luteus, a cosmopolitan pine mycobiont. Zn uptake in fungi is known to be predominantly governed by members of the ZIP (Zrt/IrtT-like protein) family of Zn transporters. Four ZIP transporter encoding genes were identified in the S. luteus genome. By in silico and phylogenetic analysis, one of these proteins, SlZRT1, was predicted to be a plasma membrane located Zn importer. Heterologous expression in yeast confirmed the predicted function and localization of the protein. A gene expression analysis via RT-qPCR was performed in S. luteus to establish whether SlZRT1 expression is affected by external Zn concentrations. SlZRT1 transcripts accumulated almost immediately, though transiently upon growth in the absence of Zn. Exposure to elevated concentrations of Zn resulted in a significant reduction of SlZRT1 transcripts within the first hour after initiation of the exposure. Altogether, the data support a role as cellular Zn importer for SlZRT1 and indicate a key role in cellular Zn uptake of S. luteus. Further research is needed to understand the eventual contribution of SlZRT1 to the Zn status of the host plant.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

A roadmap for the functional annotation of protein families: a community perspective

Over the last 25 years, biology has entered the genomic era and is becoming a science of ‘big data’. Most interpretations of genomic analyses rely on accurate functional annotations of the proteins encoded by more than 500 000 genomes sequenced to date. By different estimates, only half the predicted sequenced proteins carry an accurate functional annotation, and this percentage varies drastically between different organismal lineages. Such a large gap in knowledge hampers all aspects of biological enterprise and, thereby, is standing in the way of genomic biology reaching its full potential. A brainstorming meeting to address this issue funded by the National Science Foundation was held during 3–4 February 2022. Bringing together data scientists, biocurators, computational biologists and experimentalists within the same venue allowed for a comprehensive assessment of the current state of functional annotations of protein families. Further, major issues that were obstructing the field were identified and discussed, which ultimately allowed for the proposal of solutions on how to move forward.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2015

Diversity and population structure of northern switchgrass as revealed through exome capture sequencing

Panicum virgatum L. (switchgrass) is a polyploid, perennial grass species that is native to North America, and is being developed as a future biofuel feedstock crop. Switchgrass is present primarily in two ecotypes: a northern upland ecotype, composed of tetraploid and octoploid accessions, and a southern lowland ecotype, composed of primarily tetraploid accessions. We employed high-coverage exome capture sequencing (~2.4 Tb) to genotype 537 individuals from 45 upland and 21 lowland populations. From these data, we identified ~27 million single-nucleotide polymorphisms (SNPs), of which 1 590 653 high-confidence SNPs were used in downstream analyses of diversity within and between the populations. From the 66 populations, we identified five primary population groups within the upland and lowland ecotypes, a result that was further supported through genetic distance analysis. We identified conserved, ecotype-restricted, non-synonymous SNPs that are predicted to affect the protein function of CONSTANS (CO) and EARLY HEADING DATE 1 (EHD1), key genes involved in flowering, which may contribute to the phenotypic differences between the two ecotypes. We also identified, relative to the near-reference Kanlow population, 17 228 genes present in more copies than in the reference genome (up-CNVs), 112 630 genes present in fewer copies than in the reference genome (down-CNVs) and 14 430 presence/absence variants (PAVs), affecting a total of 9979 genes, including two upland-specific CNV clusters. In total, 45 719 genes were affected by an SNP, CNV, or PAV across the panel, providing a firm foundation to identify functional variation associated with phenotypic traits of interest for biofuel feedstock production.

09 BIOMASS FUELS↗
DOE OSTI2024

Interactive tools for functional annotation of bacterial genomes

Automated annotations of protein functions are error-prone because of our lack of knowledge of protein functions. For example, it is often impossible to predict the correct substrate for an enzyme or a transporter. Furthermore, much of the knowledge that we do have about the functions of proteins is missing from the underlying databases. We discuss how to use interactive tools to quickly find different kinds of information relevant to a protein’s function. Many of these tools are available via PaperBLAST (http://papers.genomics.lbl.gov). Combining these tools often allows us to infer a protein’s function. Ideally, accurate annotations would allow us to predict a bacterium’s capabilities from its genome sequence, but in practice, this remains challenging. We describe interactive tools that infer potential capabilities from a genome sequence or that search a genome to find proteins that might perform a specific function of interest.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

A gated graph transformer for protein complex structure quality assessment and its performance in CASP15

Abstract Motivation Proteins interact to form complexes to carry out essential biological functions. Computational methods such as AlphaFold-multimer have been developed to predict the quaternary structures of protein complexes. An important yet largely unsolved challenge in protein complex structure prediction is to accurately estimate the quality of predicted protein complex structures without any knowledge of the corresponding native structures. Such estimations can then be used to select high-quality predicted complex structures to facilitate biomedical research such as protein function analysis and drug discovery. Results In this work, we introduce a new gated neighborhood-modulating graph transformer to predict the quality of 3D protein complex structures. It incorporates node and edge gates within a graph transformer framework to control information flow during graph message passing. We trained, evaluated and tested the method (called DProQA) on newly-curated protein complex datasets before the 15th Critical Assessment of Techniques for Protein Structure Prediction (CASP15) and then blindly tested it in the 2022 CASP15 experiment. The method was ranked 3rd among the single-model quality assessment methods in CASP15 in terms of the ranking loss of TM-score on 36 complex targets. The rigorous internal and external experiments demonstrate that DProQA is effective in ranking protein complex structures. Availability and implementation The source code, data, and pre-trained models are available at https://github.com/jianlin-cheng/DProQA.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2015

Serum Proteome Signature of Radiation Response: Upregulation of Inflammation-Related Factors and Downregulation of Apolipoproteins and Coagulation Factors in Cancer Patients Treated With Radiation Therapy—A Pilot Study

Purpose: Ionizing radiation affects the proteome of irradiated cells and tissue, yet data concerning changes induced during radiation therapy (RT) in human blood are fragmentary and inconclusive. We aimed to identify features of serum proteome and associated processes involved in response to partial body irradiation during cancer treatment. Methods and Materials: Twenty patients with head and neck squamous cell cancer (HNSCC) and 20 patients with prostate cancer received definitive intensity modulated RT. Blood samples were collected before RT, just after RT, and 1 month after the end of RT. Complete serum proteome was analyzed in individual samples, using a shotgun liquid chromatography-tandem mass spectrometry approach which allowed identification of approximately 450 proteins. Approximately 100 unique proteins were quantified in all samples after exclusion of immunoglobulins, and statistical significance of differences among consecutive samples was assessed. Processes associated with quantified proteins and their functional interactions were predicted using gene ontology tools. Results: RT-induced changes were marked in the HNSCC patient group: 22 upregulated and 33 downregulated proteins were detected in post-RT sera. Most of the changes reversed during follow-up, yet levels of some proteins remained affected 1 month after the end of RT. RT-upregulated proteins were associated with acute phase, inflammatory response, and complement activation. RT-downregulated proteins were associated with transport and metabolism of lipids (plasma apolipoproteins) and blood coagulation. RT-induced changes were much weaker in prostate cancer patients, which corresponded to differences in acute radiation toxicity observed in both groups. Nevertheless, general patterns of RT-induced sera proteome changes were similar in both of the groups of cancer patients. Conclusions: In this pilot study, we proposed to identify a molecular signature of radiation response, based on specific features of serum proteome. The signature included upregulation of factors involved in acute or inflammatory response but also downregulation of plasma apolipoproteins and factors involved in blood coagulation.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2024

Artificial Intelligence Transforming Post-Translational Modification Research

Post-Translational Modifications (PTMs) are covalent changes to amino acids that occur after protein synthesis, including covalent modifications on side chains and peptide backbones. Many PTMs profoundly impact cellular and molecular functions and structures, and their significance extends to evolutionary studies as well. In light of these implications, we have explored how artificial intelligence (AI) can be utilized in researching PTMs. Initially, rationales for adopting AI and its advantages in understanding the functions of PTMs are discussed. Then, various deep learning architectures and programs, including recent applications of language models, for predicting PTM sites on proteins and the regulatory functions of these PTMs are compared. Finally, our high-throughput PTM-data-generation pipeline, which formats data suitably for AI training and predictions is described. We hope this review illuminates areas where future AI models on PTMs can be improved, thereby contributing to the field of PTM bioengineering.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2015

Prediction of multi-drug resistance transporters using a novel sequence analysis method

There are many examples of groups of proteins that have similar function, but the determinants of functional specificity may be hidden by lack of sequencesimilarity, or by large groups of similar sequences with different functions. Transporters are one such protein group in that the general function, transport, can be easily inferred from the sequence, but the substrate specificity can be impossible to predict from sequence with current methods. In this paper we describe a linguistic-based approach to identify functional patterns from groups of unaligned protein sequences and its application to predict multi-drug resistance transporters (MDRs) from bacteria. We first show that our method can recreate known patterns from PROSITE for several motifs from unaligned sequences. We then show that the method, MDRpred, can predict MDRs with greater accuracy and positive predictive value than a collection of currently available family-based models from the Pfam database. Finally, we apply MDRpred to a large collection of protein sequences from an environmental microbiome study to make novel predictions about drug resistance in a potential environmental reservoir.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2016

Protein Sequence Annotation Tool (PSAT): a centralized web-based meta-server for high-throughput sequence annotations

Abstract Background Here we introduce the Protein Sequence Annotation Tool (PSAT), a web-based, sequence annotation meta-server for performing integrated, high-throughput, genome-wide sequence analyses. Our goals in building PSAT were to (1) create an extensible platform for integration of multiple sequence-based bioinformatics tools, (2) enable functional annotations and enzyme predictions over large input protein fasta data sets, and (3) provide a web interface for convenient execution of the tools. Results In this paper, we demonstrate the utility of PSAT by annotating the predicted peptide gene products of Herbaspirillum sp. strain RV1423, importing the results of PSAT into EC2KEGG, and using the resulting functional comparisons to identify a putative catabolic pathway, thereby distinguishing RV1423 from a well annotated Herbaspirillum species. This analysis demonstrates that high-throughput enzyme predictions, provided by PSAT processing, can be used to identify metabolic potential in an otherwise poorly annotated genome. Conclusions PSAT is a meta server that combines the results from several sequence-based annotation and function prediction codes, and is available at http://psat.llnl.gov/psat/ . PSAT stands apart from other sequence-based genome annotation systems in providing a high-throughput platform for rapid de novo enzyme predictions and sequence annotations over large input protein sequence data sets in FASTA. PSAT is most appropriately applied in annotation of large protein FASTA sets that may or may not be associated with a single genome.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2017

High-throughput simulations reveal membrane-mediated effects of alcohols on MscL gating

The mechanosensitive channels of large conductance (MscL) are bacterial membrane proteins that serve as last resort emergency release valves in case of severe osmotic downshock. Sensing bilayer tension, MscL channels are sensitive to changes in the bilayer environment and are, therefore, an ideal test case for exploring membrane protein coupling. Here, we use high-throughput coarse-grained molecular dynamics simulations to characterize MscL gating kinetics in different bilayer environments under the influence of alcohols. We performed over five hundred simulations to obtain sufficient statistics to reveal the subtle effects of changes in the membrane environment on MscL gating. MscL opening times were found to increase with the addition of the straight-chain alcohols ethanol, octanol, and to some extent dodecanol but not with hexadecanol. Increasing concentration of octanol increased the impeding effect, but only up to 10–20 mol %. Our in silico predictions were experimentally confirmed using reconstituted MscL in a liposomal fluorescent efflux assay. Our combined data reveal that the effect of alcohols on MscL gating arises not through specific binding sites but through a combination of the alcohol-induced changes to a number of bilayer properties and their alteration of the MscL–bilayer interface. Finally, our work provides a key example of how extensive molecular simulations can be used to predict the functional modification of membrane proteins by subtle changes in their bilayer environment.

59 BASIC BIOLOGICAL SCIENCES↗