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Results for “Sequence Function Data”

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 OSTI2021

Identification and characterization of proteins of unknown function (PUFs) in Clostridium thermocellum DSM 1313 strains as potential genetic engineering targets

Abstract Background Mass spectrometry-based proteomics can identify and quantify thousands of proteins from individual microbial species, but a significant percentage of these proteins are unannotated and hence classified as proteins of unknown function (PUFs). Due to the difficulty in extracting meaningful metabolic information, PUFs are often overlooked or discarded during data analysis, even though they might be critically important in functional activities, in particular for metabolic engineering research. Results We optimized and employed a pipeline integrating various “guilt-by-association” (GBA) metrics, including differential expression and co-expression analyses of high-throughput mass spectrometry proteome data and phylogenetic coevolution analysis, and sequence homology-based approaches to determine putative functions for PUFs in Clostridium thermocellum . Our various analyses provided putative functional information for over 95% of the PUFs detected by mass spectrometry in a wild-type and/or an engineered strain of C. thermocellum . In particular, we validated a predicted acyltransferase PUF (WP_003519433.1) with functional activity towards 2-phenylethyl alcohol, consistent with our GBA and sequence homology-based predictions. Conclusions This work demonstrates the value of leveraging sequence homology-based annotations with empirical evidence based on the concept of GBA to broadly predict putative functions for PUFs, opening avenues to further interrogation via targeted experiments.

09 BIOMASS FUELS↗
DOE OSTI2018

PDGF family function and prognostic value in tumor biology

Highlights: • Large scale transcriptomic data analysis reveals tumor-specific roles of PDGF signaling. • Functional gene sets associated with PDGF expression are conserved between PDGF family members. • PDGF related gene signatures have increased prognostic value over each single gene. The development and progression of a tumor depends on the close interaction of malignant cells and the supportive and suppressive tumor microenvironment. Paracrine signaling enables tumor cells to shape the surrounding tissue in order to decrease recognition by the immune system, attract blood vessels to fuel growth, change metabolic programs, and induce wound healing programs. In this study, we investigate the role of the platelet-derived growth factor (PDGF) family members PDGFA, PDGFB, PDGFC and PDGFD and their cognate tyrosine kinase receptors PDGFRA and PDGFRB, using publicly available data from The Cancer Genome Atlas and the Human Protein Atlas. Large scale analysis of expression correlation in RNA sequencing data from 7616 samples derived from 16 tumor types, revealed conserved functional programs in PDGF signaling in the majority of solid tumor types. Besides the well-known effects of PDGF signaling in mesenchymal cells, our analyses revealed a potential role of PDGF signaling in the composition of the immune microenvironment. We furthermore derived gene signatures with increased prognostic value for each PDGF family member. This study emphasizes the potential to impinge on specific paracrine signaling events to interfere with the crosstalk between malignant cells and their microenvironment.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

RNA language models predict mutations that improve RNA function

Structured RNA lies at the heart of many central biological processes, from gene expression to catalysis. RNA structure prediction is not yet possible due to a lack of high-quality reference data associated with organismal phenotypes that could inform RNA function. We present GARNET (Gtdb Acquired RNa with Environmental Temperatures), a new database for RNA structural and functional analysis anchored to the Genome Taxonomy Database (GTDB). GARNET links RNA sequences to experimental and predicted optimal growth temperatures of GTDB reference organisms. Using GARNET, we develop sequence- and structure-aware RNA generative models, with overlapping triplet tokenization providing optimal encoding for a GPT-like model. Leveraging hyperthermophilic RNAs in GARNET and these RNA generative models, we identify mutations in ribosomal RNA that confer increased thermostability to the Escherichia coli ribosome. The GTDB-derived data and deep learning models presented here provide a foundation for understanding the connections between RNA sequence, structure, and function.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Multi-Omics Driven Metabolic Network Reconstruction and Analysis of Lignocellulosic Carbon Utilization in Rhodosporidium toruloides

An oleaginous yeast Rhodosporidium toruloides is a promising host for converting lignocellulosic biomass to bioproducts and biofuels. In this work, we performed multi-omics analysis of lignocellulosic carbon utilization in R. toruloides and reconstructed the genome-scale metabolic network of R. toruloides . High-quality metabolic network models for model organisms and orthologous protein mapping were used to build a draft metabolic network reconstruction. The reconstruction was manually curated to build a metabolic model using functional annotation and multi-omics data including transcriptomics, proteomics, metabolomics, and RB-TDNA sequencing. The multi-omics data and metabolic model were used to investigate R. toruloides metabolism including lipid accumulation and lignocellulosic carbon utilization. The developed metabolic model was validated against high-throughput growth phenotyping and gene fitness data, and further refined to resolve the inconsistencies between prediction and data. We believe that this is the most complete and accurate metabolic network model available for R. toruloides to date.

09 BIOMASS FUELS↗
DOE OSTI2019

California condor microbiomes: Bacterial variety and functional properties in captive-bred individuals

Around the world, scavenging birds such as vultures and condors have been experiencing drastic population declines. Scavenging birds have a distinct digestive process to deal with higher amounts of bacteria in their primary diet of carcasses in varying levels of decay. These observations motivate us to present an analysis of captive and healthy California condor (Gymnogyps californianus) microbiomes to characterize a population raised together under similar conditions. Shotgun metagenomic DNA sequences were analyzed from fecal and cloacal samples of captive birds. Classification of shotgun DNA sequence data with peptide signatures using the Sequedex package provided both phylogenetic and functional profiles, as well as individually annotated reads for targeted confirmatory analysis. We observed bacterial species previously associated with birds and gut microbiomes, including both virulent and opportunistic pathogens such as Clostridium perfringens, Propionibacterium acnes, Shigella flexneri, and Fusobacterium mortiferum, common flora such as Lactobacillus johnsonii, Lactobacillus ruminus, and Bacteroides vulgatus, and mucosal microbes such as Delftia acidovorans, Stenotrophomonas maltophilia, and Corynebacterium falsnii. Classification using shotgun metagenomic reads from phylogenetic marker genes was consistent with, and more specific than, analysis based on 16S rDNA data. Classification of samples based on either phylogenetic or functional profiles of genomic fragments differentiated three types of samples: fecal, mature cloacal and immature cloacal, with immature birds having approximately 40% higher diversity of microbes.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2018

RNAcentral: a hub of information for non-coding RNA sequences

RNAcentral is a comprehensive database of non-coding RNA (ncRNA) sequences, collating information on ncRNA sequences of all types from a broad range of organisms. We have recently added a new genome mapping pipeline that identifies genomic locations for ncRNA sequences in 296 species. We have also added several new types of functional annotations, such as tRNA secondary structures, Gene Ontology annotations, and miRNA-target interactions. A new quality control mechanism based on Rfam family assignments identifies potential contamination, incomplete sequences, and more. The RNAcentral database has become a vital component of many workflows in the RNA community, serving as both the primary source of sequence data for academic and commercial groups, as well as a source of stable accessions for the annotation of genomic and functional features. These examples are facilitated by an improved RNAcentral web interface, which features an updated genome browser, a new sequence feature viewer, and improved text search functionality.

Sweeney, Blake A.↗
DOE OSTI2019

Interrogating the Grainyhead-like 2 ($Grhl2$) genomic locus identifies an enhancer element that regulates palatogenesis in mouse

The highly-conserved Grainyhead-like (Grhl) transcription factors are critical regulators of embryogenesis that regulate cellular survival, proliferation, migration and epithelial integrity, especially during the formation of the craniofacial skeleton. Family member Grhl2 is expressed throughout epithelial tissues during development, and loss of Grhl2 function leads to significant defects in neurulation, abdominal wall closure, formation of the face and fusion of the maxilla/palate. Whereas numerous downstream target genes of Grhl2 have been identified, very little is known about how this crucial developmental transcription factor itself is regulated. Using in silico and in utero expression analyses and functional deletion in mice, we have identified a novel 2.4 kb enhancer element (mm1286) that drives reporter gene expression in a pattern that strongly recapitulates endogenous Grhl2 in the craniofacial primordia, modulates Grhl2expression in these tissues, and augments Grhl2-mediated closure of the secondary palate. Deletion of this genomic element, in the context of inactivation of one allele of Grhl (through generation of double heterozygous Grhl2 +/- ;mm1286 +/- mice), results in a significant predisposition to palatal clefting at birth. Moreover, we found that a highly conserved 325 bp region of mm1286 is both necessary and sufficient for mediating the craniofacial-specific enhancer activity of this region, and that an extremely well-conserved 12-bp sequence within this element (CTGTCAAACAGGT) substantially determines full enhancer function. Together, these data provide valuable new insights into the upstream genomic regulatory landscape responsible for transcriptional control of Grhl2 during palatal closure.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Efficient Probabilistic Prediction and Uncertainty Quantification of Tropical Cyclone–Driven Storm Tides and Inundation

Abstract This study proposes and assesses a methodology to obtain high-quality probabilistic predictions and uncertainty information of near-landfall tropical cyclone–driven (TC-driven) storm tide and inundation with limited time and resources. Forecasts of TC track, intensity, and size are perturbed according to quasi-random Korobov sequences of historical forecast errors with assumed Gaussian and uniform statistical distributions. These perturbations are run in an ensemble of hydrodynamic storm tide model simulations. The resulting set of maximum water surface elevations are dimensionality reduced using Karhunen–Loève expansions and then used as a training set to develop a polynomial chaos (PC) surrogate model from which global sensitivities and probabilistic predictions can be extracted. The maximum water surface elevation is extrapolated over dry points incorporating energy head loss with distance to properly train the surrogate for predicting inundation. We find that the surrogate constructed with third-order PCs using elastic net penalized regression with leave-one-out cross validation provides the most robust fit across training and test sets. Probabilistic predictions of maximum water surface elevation and inundation area by the surrogate model at 48-h lead time for three past U.S. landfalling hurricanes (Irma in 2017, Florence in 2018, and Laura in 2020) are found to be reliable when compared to best track hindcast simulation results, even when trained with as few as 19 samples. The maximum water surface elevation is most sensitive to perpendicular track-offset errors for all three storms. Laura is also highly sensitive to storm size and has the least reliable prediction. Significance Statement The purpose of this study is to develop and evaluate a methodology that can be used to provide high-quality probabilistic predictions of hurricane-induced storm tide and inundation with limited time and resources. This is important for emergency management purposes during or after the landfall of hurricanes. Our results show that sampling forecast errors using quasi-random sequences combined with machine learning techniques that fit polynomial functions to the data are well suited to this task. The polynomial functions also have the benefit of producing exact sensitivity indices of storm tide and inundation to the forecasted hurricane properties such as path, intensity, and size, which can be used for uncertainty estimation. The code implementing the presented methodology is publicly available on GitHub.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Metagenomic clustering links specific metabolic functions to globally relevant ecosystems

ABSTRACT Metagenomic sequencing has advanced our understanding of biogeochemical processes by providing an unprecedented view into the microbial composition of different ecosystems. While the amount of metagenomic data has grown rapidly, simple-to-use methods to analyze and compare across studies have lagged behind. Thus, tools expressing the metabolic traits of a community are needed to broaden the utility of existing data. Gene abundance profiles are a relatively low-dimensional embedding of a metagenome’s functional potential and are, thus, tractable for comparison across many samples. Here, we compare the abundance of KEGG Ortholog Groups (KOs) from 6,539 metagenomes from the Joint Genome Institute’s Integrated Microbial Genomes and Metagenomes (JGI IMG/M) database. We find that samples cluster into terrestrial, aquatic, and anaerobic ecosystems with marker KOs reflecting adaptations to these environments. For instance, functional clusters were differentiated by the metabolism of antibiotics, photosynthesis, methanogenesis, and surprisingly GC content. Using this functional gene approach, we reveal the broad-scale patterns shaping microbial communities and demonstrate the utility of ortholog abundance profiles for representing a rapidly expanding body of metagenomic data. IMPORTANCE Metagenomics, or the sequencing of DNA from complex microbiomes, provides a view into the microbial composition of different environments. Metagenome databases were created to compile sequencing data across studies, but it remains challenging to compare and gain insight from these large data sets. Consequently, there is a need to develop accessible approaches to extract knowledge across metagenomes. The abundance of different orthologs (i.e., genes that perform a similar function across species) provides a simplified representation of a metagenome’s metabolic potential that can easily be compared with others. In this study, we cluster the ortholog abundance profiles of thousands of metagenomes from diverse environments and uncover the traits that distinguish them. This work provides a simple to use framework for functional comparison and advances our understanding of how the environment shapes microbial communities.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

PersGNN: Applying Topological Data Analysis and Geometric Deep Learning to Structure-Based Protein Function Prediction

Understanding protein structure-function relationships is a key challenge in computational biology, with applications across the biotechnology and pharmaceutical industries. While it is known that protein structure directly impacts protein function, many functional prediction tasks use only protein sequence. In this work, we isolate protein structure to make functional annotations for proteins in the Protein Data Bank in order to study the expressiveness of different structure-based prediction schemes. We present PersGNN - an end-to-end trainable deep learning model that combines graph representation learning with topological data analysis to capture a complex set of both local and global structural features. While variations of these techniques have been successfully applied to proteins before, we demonstrate that our hybridized approach, PersGNN, outperforms either method on its own as well as a baseline neural network that learns from the same information. PersGNN achieves a 9.3% boost in area under the precision recall curve (AUPR) compared to the best individual model, as well as high F1 scores across different gene ontology categories, indicating the transferability of this approach.

Swenson, Nicolas↗
DOE OSTI2020

Soybean ( Glycine max ) Haplotype Map (GmHapMap): a universal resource for soybean translational and functional genomics

Here, we describe a worldwide haplotype map for soybean (GmHapMap) constructed using whole-genome sequence data for 1007 Glycine max accessions and yielding 14.9 million variants as well as 4.3 M tag single-nucleotide polymorphisms (SNPs). When sampling random subsets of these accessions, the number of variants and tag SNPs plateaued beyond approximately 800 and 600 accessions, respectively. This suggests extensive coverage of diversity within the cultivated soybean. GmHapMap variants were imputed onto 21 618 previously genotyped accessions with up to 96% success for common alleles. A local association analysis was performed with the imputed data using markers located in a 1-Mb region known to contribute to seed oil content and enabled us to identify a candidate causal SNP residing in the NPC1 gene. We determined gene-centric haplotypes (407 867 GCHs) for the 55 589 genes and showed that such haplotypes can help to identify alleles that differ in the resulting phenotype. Finally, we predicted 18 031 putative loss-of-function (LOF) mutations in 10 662 genes and illustrated how such a resource can be used to explore gene function. The GmHapMap provides a unique worldwide resource for applied soybean genomics and breeding.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Robust Power System Stability Assessment Against Adversarial Machine Learning-Based Cyberattacks via Online Purification

The increasing complexity associated with renewable generation brings more challenges to power system stability assessment (SA). Data-driven approaches based on machine learning (ML) techniques for stability assessment have received significant research interest and shown their promising performance. However, ML-based models are recognized to be vulnerable to adversarial disturbances, where a slight perturbation to power system measurements could lead to unacceptable errors. To address this issue, this paper develops a novel lightweight mitigation strategy, i.e., robust online stability assessment (ROSA), to enhance the ML-based assessment model against both white-box and the black-box adversarial disturbances (i.e., purification) in the online implementation. The ROSA involves a supervised learning-based module for the primary stability assessment and a self-supervised learning-based module. Further, the two modules are trained jointly with different objective (loss) functions and implemented in sequence. A suitable purification objective and various time-series data augmentation methods are designed for SA applications to tackle adversarial disturbances adaptively. Case studies are performed, and the comparative results have clearly illustrated the competitive, robust accuracy against various adversarial scenarios and verified the effectiveness of the proposed online purification strategy.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

Applying Noether's theorem to matter in the Milky Way: evidence for external perturbations and non-steady-state effects from Gaia Data Release 2

We apply Noether's theorem to observations of main-sequence stars from the Gaia Data Release 2 archive to probe the matter distribution function of the Galaxy. That is, we examine the axial symmetry of stars at vertical heights $z$, $0.2 \le |z| \le 3$ kpc, to probe the quality of the angular momentum $L_z$ as an integral of motion. The failure of this symmetry test would speak to a Milky Way, in both its visible and dark matter, that is not isolated and/or not in steady state. The left-right symmetry-breaking pattern we have observed, north and south, reveals both effects, with a measured deviation from symmetry of typically 0.5%. We show that a prolate form of the gravitational distortion of the Milky Way by the Large Magellanic Cloud, determined from fits to the Orphan stream by Erkal et al., 2019, is compatible with the size and sign of the axial-symmetry-breaking effects we have discovered in our sample of up to 14.4 million main-sequence stars, speaking to a distortion of an emergent, rather than static, nature.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2016

The main sequences of star-forming galaxies and active galactic nuclei at high redshift

We provide a novel, unifying physical interpretation on the origin, average shape, scatter, and cosmic evolution for the main sequences of star-forming galaxies and active galactic nuclei (AGNs) at high redshift z≳1. We achieve this goal in a model-independent way by exploiting: (i) the redshift-dependent star formation rate functions based on the latest UV/far-IR data from HST/Herschel, and related statistics of strong gravitationally lensed sources; (ii) deterministic evolutionary tracks for the history of star formation and black hole accretion, gauged on a wealth of multiwavelength observations including the observed Eddington ratio distribution. We further validate these ingredients by showing their consistency with the observed galaxy stellar mass functions and AGN bolometric luminosity functions at different redshifts via the continuity equation approach. Our analysis of the main sequence for high-redshift galaxies and AGNs highlights that the present data are consistently interpreted in terms of an in situ coevolution scenario for star formation and black hole accretion, envisaging these as local, time-coordinated processes.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

A Route to Design Novel Functional Peptides by Applying a Denoising Diffusional Model to mRNA Display Libraries

In vitro directed evolution techniques, such as mRNA display, enable peptide ligand discovery and optimization. However, physical libraries that rely on a genetic code can only search a small fraction of sequence space due to inherent biases in the genetic code and experimental limitations. To address this challenge, denoising diffusion implicit models (DDIMs) are applied to generate novel peptide ligands against B‐cell lymphoma extra‐large (Bcl‐x L ), a key cancer target. Starting with high‐throughput sequencing data from previous selections, a DDIM is trained to produce novel sequences with high affinity binding. Experimental validation confirms that most generated sequences are functionally equivalent to the original library members for Bcl‐x L binding and demonstrated comparable binding kinetics and affinity relative to the wildtype and nearest original neighbors. Importantly, this approach generated rare sequences not easily accessible via mutation and directed evolution. These results indicate that DDIMs can complement and expand directed evolution data, efficiently exploring underrepresented regions of sequence space. This approach provides a broadly applicable framework for accelerating ligand discovery and optimizing molecular properties across diverse targets.

Qi, Pearl [Mork Family Department of Chemical Engi↗
DOE OSTI2016

Unification of [FeFe]-hydrogenases into three structural and functional groups

[FeFe]-hydrogenases (Hyd) are structurally diverse enzymes that catalyze the reversible oxidation of hydrogen (H 2 ). Recent biochemical data demonstrate new functional roles for these enzymes, including those that function in electron bifurcation where an exergonic reaction is coupled with an endergonic reaction to drive the reversible oxidation/production of H 2 . To identify the structural determinants that underpin differences in enzyme functionality, a total of 714 homologous sequences of the catalytic subunit, HydA, were compiled. Bioinformatics approaches informed by biochemical data were then used to characterize differences in inferred quaternary structure, HydA active site protein environment, accessory iron-sulfur clusters in HydA, and regulatory proteins encoded in HydA gene neighborhoods. HydA homologs were clustered into one of three classification groups, Group 1 (G1), Group 2 (G2), and Group 3 (G3). G1 enzymes were predicted to be monomeric while those in G2 and G3 were predicted to be multimeric and include HydB, HydC (G2/G3) and HydD (G3) subunits. Variation in the HydA active site and accessory iron-sulfur clusters did not vary by group type. Group-specific regulatory genes were identified in the gene neighborhoods of both G2 and G3 Hyd. Analyses of purified G2 and G3 enzymes by mass spectrometry strongly suggests that they are post-translationally modified by phosphorylation. In conclusion, these results suggest that bifurcation capability is dictated primarily by the presence of both HydB and HydC in Hyd complexes, rather than by variation in HydA.

09 BIOMASS FUELS↗
DOE OSTI2019

Unified rational protein engineering with sequence-based deep representation learning

Rational protein engineering requires a holistic understanding of protein function. Here, we apply deep learning to unlabeled amino-acid sequences to distill the fundamental features of a protein into a statistical representation that is semantically rich and structurally, evolutionarily and biophysically grounded. We show that the simplest models built on top of this unified representation (UniRep) are broadly applicable and generalize to unseen regions of sequence space. Our data-driven approach predicts the stability of natural and de novo designed proteins, and the quantitative function of molecularly diverse mutants, competitively with the state-of-the-art methods. UniRep further enables two orders of magnitude efficiency improvement in a protein engineering task. Lastly, UniRep is a versatile summary of fundamental protein features that can be applied across protein engineering informatics.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2018

Myotube-derived factor promotes oligodendrocyte precursor cell proliferation

Highlights: • Myotube-derived factors promote proliferation of oligodendrocyte precursor cells (OPCs). • Myotube-derived factors do not promote OPC migration and differentiation. • Candidate genes which promote OPC proliferation were identified by RNA sequence analysis. • Molecules released by myotubes may be involved in OPC proliferation. Muscle cells secrete numerous molecules that function as endocrine hormones and regulate the functions of distant organs. Myelination in the central nervous system (CNS) is regulated by peripheral hormones. However, the effects of muscle-derived molecules on myelination have not been sufficiently analyzed. In this study, we show that muscle-releasing factors promote proliferation of oligodendrocyte precursor cells (OPCs), which is an element of myelination process. Supernatants of mouse myotube cultures stimulated bromodeoxyuridine (BrdU) incorporation into mouse OPCs. Mouse myotube supernatants did not enhance mouse OPC transmigration and myelin basic protein (MBP) expression. RNA sequencing identified candidate genes with hormonal functions that were expressed in mouse myotubes. These data support the possibility that hormonal molecules secreted by myotubes contribute to OPC proliferation and myelination.

60 APPLIED LIFE SCIENCES↗