Search NASA⌕ Search

SEARCH · Search NASA

Results for “Sequence alignment”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
NASA NTRS2019

Size and Structure of the Sequence Space of Repeat Proteins

The coding space of protein sequences is shaped by evolutionary constraints set by requirements of function and stability. We show that the coding space of a given protein family— the total number of sequences in that family—can be estimated using models of maximum entropy trained on multiple sequence alignments of naturally occurring amino acid sequences. We analyzed and calculated the size of three abundant repeat proteins families, whose members are large proteins made of many repetitions of conserved portions of *30 amino acids. While amino acid conservation at each position of the alignment explains most of the reduction of diversity relative to completely random sequences, we found that correlations between amino acid usage at different positions significantly impact that diversity. We quantified the impact of different types of correlations, functional and evolutionary, on sequence diversity. Analysis of the detailed structure of the coding space of the families revealed a rugged landscape, with many local energy minima of varying sizes with a hierarchical structure, reminiscent of frustrated energy landscapes of spin glass in physics. This clustered structure indicates a multiplicity of subtypes within each family and suggests new strategies for protein design.

Jacopo Marchi↗
DOE OSTI2024

Genomic and morphological characterization of Knufia obscura isolated from the Mars 2020 spacecraft assembly facility

Members of the family Trichomeriaceae, belonging to the Chaetothyriales order and the Ascomycota phylum, are known for their capability to inhabit hostile environments characterized by extreme temperatures, oligotrophic conditions, drought, or presence of toxic compounds. The genus Knufia encompasses many polyextremophilic species. In this report, the genomic and morphological features of the strain FJI-L2-BK-P2 presented, which was isolated from the Mars 2020 mission spacecraft assembly facility located at the Jet Propulsion Laboratory in Pasadena, California. The identification is based on sequence alignment for marker genes, multi-locus sequence analysis, and whole genome sequence phylogeny. The morphological features were studied using a diverse range of microscopic techniques (bright field, phase contrast, differential interference contrast and scanning electron microscopy). The phylogenetic marker genes of the strain FJI-L2-BK-P2 exhibited highest similarities with type strain of Knufia obscura (CBS 148926 T ) that was isolated from the gas tank of a car in Italy. To validate the species identity, whole genomes of both strains (FJI-L2-BK-P2 and CBS 148926 T ) were sequenced, annotated, and strain FJI-L2-BK-P2 was confirmed as K. obscura. The morphological analysis and description of the genomic characteristics of K. obscura FJI-L2-BK-P2 may contribute to refining the taxonomy of Knufia species. Key morphological features are reported in this K. obscura strain, resembling microsclerotia and chlamydospore-like propagules. These features known to be characteristic features in black fungi which could potentially facilitate their adaptation to harsh environments.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Inventory of Composable Elements (ICE) v6.0.0

The Inventory of Composable Elements (ICE) is an open source registry software platform for managing information about biological parts. It is capable of recording information about plasmids, microbial host strains and seeds, as well as DNA parts. Includes features such as DNA sequence visualization, editing and annotation, auto-aligning sequencing trace files against reference templates, SBOL XML/RDF support, and web-of-registries functionality. The web of registries functionality provides strong support for distributed interconnected use and enables sharing and transfer of biological parts across various independent ICE instances. ICE adopts modern software development principles, leveraging component-base frameworks, offering a REST API for convenient third-party integration and emphasizing scalability, security, and service integrations for dynamic content availability. The source code is hosted at https://github.com/JBEI/ice. A public instance is available at public-registry.jbei.org, where users can try out features, upload parts or simply use it for their projects.

Plahar, Hector↗
NASA NTRS2000

Cloning and characterization of ftsZ and pyrF from the archaeon Thermoplasma acidophilum

To characterize cytoskeletal components of archaea, the ftsZ gene from Thermoplasma acidophilum was cloned and sequenced. In T. acidophilum ftsZ, which is involved in cell division, was found to be in an operon with the pyrF gene, which encodes orotidine-5'-monophosphate decarboxylase (ODC), an essential enzyme in pyrimidine biosynthesis. Both ftsZ and pyrF from T. acidophilum were expressed in Escherichia coli and formed functional proteins. FtsZ expression in wild-type E. coli resulted in the filamentous phenotype characteristic of ftsZ mutants. T. acidophilum pyrF expression in an E. coli mutant lacking pyrF complemented the mutation and rescued the strain. Sequence alignments of ODCs from archaea, bacteria, and eukarya reveal five conserved regions, two of which have homology to 3-hexulose-6-phosphate synthase (HPS), suggesting a common substrate recognition and binding motif. Copyright 2000 Academic Press.

Bacterial Proteins/chemistry/genetics/metabolism↗
DOE OSTI2024

Poplar

SAND2025-00683O Poplar is a software tool that generates a phylogenetic tree from input gene and genome sequences. It integrates established tools to identify genes within genomes, group sequences, construct gene trees, and infer a species tree. Poplar processes nucleotide sequences, identifies similar sequences using Nucleotide BLAST, groups them with DBSCAN, aligns sequences with MAFFT, constructs gene trees with RAxML-NG, and infers a species tree using ASTRAL-Pro3. This pipeline provides a structured approach to phylogenetic analysis, facilitating the study of evolutionary relationships among species. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

Krishnakumar, Raga↗
DOE OSTI2023

NMPFamsDB: a database of novel protein families from microbial metagenomes and metatranscriptomes

Abstract The Novel Metagenome Protein Families Database (NMPFamsDB) is a database of metagenome- and metatranscriptome-derived protein families, whose members have no hits to proteins of reference genomes or Pfam domains. Each protein family is accompanied by multiple sequence alignments, Hidden Markov Models, taxonomic information, ecosystem and geolocation metadata, sequence and structure predictions, as well as 3D structure models predicted with AlphaFold2. In its current version, NMPFamsDB hosts over 100 000 protein families, each with at least 100 members. The reported protein families significantly expand (more than double) the number of known protein sequence clusters from reference genomes and reveal new insights into their habitat distribution, origins, functions and taxonomy. We expect NMPFamsDB to be a valuable resource for microbial proteome-wide analyses and for further discovery and characterization of novel functions. NMPFamsDB is publicly available in http://www.nmpfamsdb.org/ or https://bib.fleming.gr/NMPFamsDB.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

From sequence to protein structure and conformational dynamics with artificial intelligence/machine learning

The 2024 Nobel Prize in Chemistry was awarded in part for de novo protein structure prediction using AlphaFold2, an artificial intelligence/machine learning (AI/ML) model trained on vast amounts of sequence and three-dimensional structure data. AlphaFold2 and related models, including RoseTTAFold and ESMFold, employ specialized neural network architectures driven by attention mechanisms to infer relationships between sequence and structure. At a fundamental level, these AI/ML models operate on the long-standing hypothesis that the structure of a protein is determined by its amino acid sequence. More recently, AlphaFold2 has been adapted for the prediction of multiple protein conformations by subsampling multiple sequence alignments. Herein, we provide an overview of the deterministic relationship between sequence and structure, which was hypothesized over half a century ago with profound implications for the biological sciences ever since. We postulate that protein conformational dynamics are also determined, at least in part, by amino acid sequence and that this relationship may be leveraged for construction of AI/ML models dedicated to predicting protein conformational ensembles. Accordingly, we describe a conceptual model architecture, which may be trained on sequence data in combination with conformationally sensitive structural information, coming primarily from nuclear magnetic resonance (NMR) spectroscopy. Notwithstanding certain limitations in this context, NMR offers abundant structural heterogeneity conducive to conformational ensemble prediction. As NMR and other data continue to accumulate, sequence-informed prediction of protein structural dynamics with AI/ML has the potential to emerge as a transformative capability across the biological sciences.

Artificial intelligence↗
NASA NTRS1978

Origins of prokaryotes, eukaryotes, mitochondria, and chloroplasts

A computer branching model is used to analyze cellular evolution. Attention is given to certain key amino acids and nucleotide residues (ferredoxin, 5s ribosomal RNA, and c-type cytochromes) because of their commonality over a wide variety of cell types. Each amino acid or nucleotide residue is a sequence in an inherited biological trait; and the branching method is employed to align sequences so that changes reflect substitution of one residue for another. Based on the computer analysis, the symbiotic theory of cellular evolution is considered the most probable. This theory holds that organelles, e.g., mitochondria and chloroplasts invaded larger bodies, e.g., bacteria, and combined functions to form eucaryotic cells.

Schwartz, R. M.↗
DOE OSTI2020

Major Revisions in Arthropod Phylogeny Through Improved Supermatrix, With Support for Two Possible Waves of Land Invasion by Chelicerates

Deep phylogeny involving arthropod lineages is difficult to recover because the erosion of phylogenetic signals over time leads to unreliable multiple sequence alignment (MSA) and subsequent phylogenetic reconstruction. One way to alleviate the problem is to assemble a large number of gene sequences to compensate for the weakness in each individual gene. Such an approach has led to many robustly supported but contradictory phylogenies. A close examination shows that the supermatrix approach often suffers from two shortcomings. The first is that MSA is rarely checked for reliability and, as will be illustrated, can be poor. The second is that, to alleviate the problem of homoplasy at the third codon position of protein-coding genes due to convergent evolution of nucleotide frequencies, phylogeneticists may remove or degenerate the third codon position but may do it improperly and introduce new biases. We performed extensive reanalysis of one of such “big data” sets to highlight these two problems, and demonstrated the power and benefits of correcting or alleviating these problems. Our results support a new group with Xiphosura and Arachnopulmonata (Tetrapulmonata + Scorpiones) as sister taxa. This favors a new hypothesis in which the ancestor of Xiphosura and the extinct Eurypterida (sea scorpions, of which many later forms lived in brackish or freshwater) returned to the sea after the initial chelicerate invasion of land. Our phylogeny is supported even with the original data but processed with a new “principled” codon degeneration. We also show that removing the 1673 codon sites with both AGN and UCN codons (encoding serine) in our alignment can partially reconcile discrepancies between nucleotide-based and AA-based tree, partly because two sequences, one with AGN and the other with UCN, would be identical at the amino acid level but quite different at the nucleotide level.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Evaluating the Impact of gRNA SNPs in CasRx Activity for Reducing Viral RNA in HCoV-OC43

Viruses within a given family often share common essential genes that are highly conserved due to their critical role for the virus’s replication and survival. In this work, we developed a proof-of-concept for a pan-coronavirus CRISPR effector system by designing CRISPR targets that are cross-reactive among essential genes of different human coronaviruses (HCoV). We designed CRISPR targets for both the RNA-dependent RNA polymerase (RdRp) gene as well as the nucleocapsid (N) gene in coronaviruses. Using sequencing alignment, we determined the most highly conserved regions of these genes to design guide RNA (gRNA) sequences. In regions that were not completely homologous among HCoV species, we introduced mismatches into the gRNA sequence and tested the efficacy of CasRx, a Cas13d type CRISPR effector, using reverse transcription quantitative polymerase chain reaction (RT-qPCR) in HCoV-OC43. We evaluated the effect that mismatches in gRNA sequences has on the cleavage activity of CasRx and found that this CRISPR effector can tolerate up to three mismatches while still maintaining its nuclease activity in HCoV-OC43 viral RNA. This work highlights the need to evaluate off-target effects of CasRx with gRNAs containing up to three mismatches in order to design safe and effective CRISPR experiments.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

A deep dilated convolutional residual network for predicting interchain contacts of protein homodimers

Abstract Motivation Deep learning has revolutionized protein tertiary structure prediction recently. The cutting-edge deep learning methods such as AlphaFold can predict high-accuracy tertiary structures for most individual protein chains. However, the accuracy of predicting quaternary structures of protein complexes consisting of multiple chains is still relatively low due to lack of advanced deep learning methods in the field. Because interchain residue–residue contacts can be used as distance restraints to guide quaternary structure modeling, here we develop a deep dilated convolutional residual network method (DRCon) to predict interchain residue–residue contacts in homodimers from residue–residue co-evolutionary signals derived from multiple sequence alignments of monomers, intrachain residue–residue contacts of monomers extracted from true/predicted tertiary structures or predicted by deep learning, and other sequence and structural features. Results Tested on three homodimer test datasets (Homo_std dataset, DeepHomo dataset and CASP-CAPRI dataset), the precision of DRCon for top L/5 interchain contact predictions (L: length of monomer in a homodimer) is 43.46%, 47.10% and 33.50% respectively at 6 Å contact threshold, which is substantially better than DeepHomo and DNCON2_inter and similar to Glinter. Moreover, our experiments demonstrate that using predicted tertiary structure or intrachain contacts of monomers in the unbound state as input, DRCon still performs well, even though its accuracy is lower than using true tertiary structures in the bound state are used as input. Finally, our case study shows that good interchain contact predictions can be used to build high-accuracy quaternary structure models of homodimers. Availability and implementation The source code of DRCon is available at https://github.com/jianlin-cheng/DRCon. The datasets are available at https://zenodo.org/record/5998532#.YgF70vXMKsB. Supplementary information Supplementary data are available at Bioinformatics online.

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

Sequence-structure-function characterization of the emerging tetracycline destructase family of antibiotic resistance enzymes

Tetracycline destructases (TDases) are flavin monooxygenases which can confer resistance to all generations of tetracycline antibiotics. The recent increase in the number and diversity of reported TDase sequences enables a deep investigation of the TDase sequence-structure-function landscape. Here, we evaluate the sequence determinants of TDase function through two complementary approaches: (1) constructing profile hidden Markov models to predict new TDases, and (2) using multiple sequence alignments to identify conserved positions important to protein function. Using the HMM-based approach we screened 50 high-scoring candidate sequences in Escherichia coli, leading to the discovery of 13 new TDases. The X-ray crystal structures of two new enzymes from Legionella species were determined, and the ability of anhydrotetracycline to inhibit their tetracycline-inactivating activity was confirmed. Using the MSA-based approach we identified 31 amino acid positions 100% conserved across all known TDase sequences. The roles of these positions were analyzed by alanine-scanning mutagenesis in two TDases, to study the impact on cell and in vitro activity, structure, and stability. These results expand the diversity of TDase sequences and provide valuable insights into the roles of important residues in TDases, and flavin monooxygenases more broadly.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2026

nf-core/proteinfamilies: a scalable pipeline for the generation of protein families

The growth of metagenomics-derived amino acid sequence data has transformed our understanding of protein function, microbial diversity, and evolutionary relationships. However, the vast majority of these proteins remain functionally uncharacterized. Grouping the millions of such uncharacterized sequences with the few experimentally characterized ones allows the transfer of annotations, while the inspection of conserved residues with multiple sequence alignments can provide clues to function, even in the absence of existing functional information. To address the challenges associated with this data surge and the need to group sequences, we present a scalable, open-source, parametrizable Nextflow pipeline (nf-core/proteinfamilies) that generates nascent protein families or assigns new proteins to existing families. The computational benchmarks demonstrated that resource usage scales approximately linearly with input size, and the biological benchmarks showed that the generated protein families closely resemble manually curated families in widely used databases.

Nextflow↗
DOE OSTI2020

Structural characterization and computational analysis of PDZ domains in Monosiga brevicollis

Abstract Identification of the molecular networks that facilitated the evolution of multicellular animals from their unicellular ancestors is a fundamental problem in evolutionary cellular biology. Choanoflagellates are recognized as the closest extant nonmetazoan ancestors to animals. These unicellular eukaryotes can adopt a multicellular‐like “rosette” state. Therefore, they are compelling models for the study of early multicellularity. Comparative studies revealed that a number of putative human orthologs are present in choanoflagellate genomes, suggesting that a subset of these genes were necessary for the emergence of multicellularity. However, previous work is largely based on sequence alignments alone, which does not confirm structural nor functional similarity. Here, we focus on the PDZ domain, a peptide‐binding domain which plays critical roles in myriad cellular signaling networks and which underwent a gene family expansion in metazoan lineages. Using a customized sequence similarity search algorithm, we identified 178 PDZ domains in the Monosiga brevicollis proteome. This includes 11 previously unidentified sequences, which we analyzed using Rosetta and homology modeling. To assess conservation of protein structure, we solved high‐resolution crystal structures of representative M. brevicollis PDZ domains that are homologous to human Dlg1 PDZ2, Dlg1 PDZ3, GIPC, and SHANK1 PDZ domains. To assess functional conservation, we calculated binding affinities for mbGIPC, mbSHANK1, mbSNX27, and mbDLG‐3 PDZ domains from M. brevicollis . Overall, we find that peptide selectivity is generally conserved between these two disparate organisms, with one possible exception, mbDLG‐3. Overall, our results provide novel insight into signaling pathways in a choanoflagellate model of primitive multicellularity.

Gao, Melody↗
DOE OSTI2024

Improved deep learning prediction of antigen–antibody interactions

Identifying antibodies that neutralize specific antigens is crucial for developing effective immunotherapies, but this task remains challenging for many target antigens. The rise of deep learning–based computational approaches presents a promising avenue to address this challenge. Here, we assess the performance of a deep learning approach through two benchmark tests aimed at predicting antibodies for the receptor-binding domain of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spike protein. Three different strategies for constructing input sequence alignments are employed for predicting structural models of antigen–antibody complexes. In our initial testing set, which comprises known experimental structures, these strategies collectively yield a significant top-ranked prediction for 61% of cases and a success rate of 47%. Notably, one strategy that utilizes the sequences of known antigen binders outperforms the other two, achieving a precision of 90% in a subsequent test set of ~1,000 antibodies, balanced between true and control antibodies for the antigen, albeit with a lower recall of 25%. Our results underscore the potential of integrating deep learning methods with single B cell sequencing techniques to enhance the prediction accuracy of antigen–antibody interactions.

Science & Technology - Other Topics↗
NASA NTRS2003

Search-based optimization

The problem of determining the minimum cost hypothetical ancestral sequences for a given cladogram is known to be NP-complete (Wang and Jiang, 1994). Traditionally, point estimations of hypothetical ancestral sequences have been used to gain heuristic, upper bounds on cladogram cost. These include procedures with such diverse approaches as non-additive optimization of multiple sequence alignment, direct optimization (Wheeler, 1996), and fixed-state character optimization (Wheeler, 1999). A method is proposed here which, by extending fixed-state character optimization, replaces the estimation process with a search. This form of optimization examines a diversity of potential state solutions for cost-efficient hypothetical ancestral sequences and can result in greatly more parsimonious cladograms. Additionally, such an approach can be applied to other NP-complete phylogenetic optimization problems such as genomic break-point analysis. c2003 The Willi Hennig Society. Published by Elsevier Science (USA). All rights reserved.

Non-NASA Center↗
DOE OSTI2023

TopPICR: A Companion R Package for Top-Down Proteomics Data Analysis

Top-down proteomics is the analysis of proteins in their intact form without proteolysis, thus preserving valuable information about post-translational modifications, isoforms, and proteolytic processing. However, it is still a developing field due to limitations in the instrumentation, difficulties with interpretation of complex mass spectra, and a lack of well-established quantification approaches. TopPIC is one of the popular tools for proteoform identification. Here we extended its capabilities into label-free proteoform quantification by developing a companion R package (TopPICR). Key steps in the TopPICR pipeline include filtering identifications, inferring a minimal set of protein accessions explaining the observed sequences, aligning retention times, recalibrating measured masses, clustering features across datasets, and finally compiling feature intensities using the match-between-runs approach. The output of the pipeline is an MSnSet object which makes downstream data analysis seamlessly compatible with packages from the Bioconductor project. It also provides the capability for visualizing proteoforms within the context of the parent protein sequence. The functionality of TopPICR is demonstrated on top-down LC-MS/MS datasets of 10 human-in-mouse xenografts of luminal and basal breast tumor samples.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Instruction Roofline: An insightful visual performance model for GPUs

The Roofline performance model provides an intuitive approach to identify performance bottlenecks and guide performance optimization. However, the classic FLOP-centric approach is inappropriate for the emerging applications that perform more integer operations than floating point operations. In this article, we reintroduce our Instruction Roofline Model on NVIDIA GPUs and expand our evaluation of it. The Instruction Roofline incorporates instructions and memory transactions across all memory hierarchies together, and provides more performance insights than the FLOP-oriented Roofline Model, that is, instruction throughput, stride memory access patterns, bank conflicts, and thread predication. We use our Instruction Roofline methodology to analyze eight proxy applications: HPGMG from AMReX, Matrix Transpose benchmarks, ADEPT from MetaHipMer's sequence alignment phase, EXTENSION from MetaHipMer's local assembly phase, CUSP, cuSPARSE, cudaTensorCoreGemm, and cuBLAS. We demonstrate the ability of our methodology to understand various aspects of performance and performance bottlenecks on NVIDIA GPUs and motivate code optimizations.

Ding, N↗