Phospho;#8208;dependent signaling during the general stress response by the atypical response regula
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Schwartz, Jacob↗
SEARCH · Engineering Papers
Results for “Protein sequence analysis”
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Snekmer: a scalable pipeline for protein sequence fingerprinting based on amino acid recoding
Abstract Motivation The vast expansion of sequence data generated from single organisms and microbiomes has precipitated the need for faster and more sensitive methods to assess evolutionary and functional relationships between proteins. Representing proteins as sets of short peptide sequences (kmers) has been used for rapid, accurate classification of proteins into functional categories; however, this approach employs an exact-match methodology and thus may be limited in terms of sensitivity and coverage. We have previously used similarity groupings, based on the chemical properties of amino acids, to form reduced character sets and recode proteins. This amino acid recoding (AAR) approach simplifies the construction of protein representations in the form of kmer vectors, which can link sequences with distant sequence similarity and provide accurate classification of problematic protein families. Results Here, we describe Snekmer, a software tool for recoding proteins into AAR kmer vectors and performing either (i) construction of supervised classification models trained on input protein families or (ii) clustering for de novo determination of protein families. We provide examples of the operation of the tool against a set of nitrogen cycling families originally collected using both standard hidden Markov models and a larger set of proteins from Uniprot and demonstrate that our method accurately differentiates these sequences in both operation modes. Availability and implementation Snekmer is written in Python using Snakemake. Code and data used in this article, along with tutorial notebooks, are available at http://github.com/PNNL-CompBio/Snekmer under an open-source BSD-3 license. Supplementary information Supplementary data are available at Bioinformatics Advances online.
59 BASIC BIOLOGICAL SCIENCES↗
NGPINT V3: a containerized orchestration Python software for discovery of next-generation protein–protein interactions
Abstract Summary Batch yeast two-hybrid (Y2H) assays, leveraged with next-generation sequencing, have afforded successful innovations for the analysis of protein–protein interactions. NGPINT is a Conda-based software designed to process the millions of raw sequencing reads resulting from Y2H–next-generation interaction screens. Over time, increasing compatibility and dependency issues have prevented clean NGPINT installation and operation. A system-wide update was essential to continue effective use with its companion software, Y2H-SCORES. We present NGPINT V3, a containerized implementation built with both Singularity and Docker, allowing accessibility across virtually any operating system and computing environment. Availability and implementation This update includes streamlined dependencies and container images hosted on Sylabs (https://cloud.sylabs.io/library/schuyler/ngpint/ngpint) and Dockerhub (https://hub.docker.com/r/schuylerds/ngpint), facilitating easier adoption and integration into high-throughput and cloud-computing workflows. Full instructions and software can be also found in the GitHub repository https://github.com/Wiselab2/NGPINT_V3 and Zenodo https://doi.org/10.5281/zenodo.15256036.
Biochemistry & Molecular Biology↗
Sequence Design of Random Heteropolymers as Protein Mimics
Random heteropolymers (RHPs) have been computationally designed and experimentally shown to recapitulate protein-like phase behavior and function. However, unlike proteins, RHP sequences are only statistically defined and cannot be sequenced. Recent developments in reversible-deactivation radical polymerization allowed simulated polymer sequences based on the well-established Mayo–Lewis equation to more accurately reflect ground-truth sequences that are experimentally synthesized. This led to opportunities to perform bioinformatics-inspired analysis on simulated sequences to guide the design, synthesis, and interpretation of RHPs. We compared batches on the order of 10000 simulated RHP sequences that vary by synthetically controllable and measurable RHP characteristics such as chemical heterogeneity and average degree of polymerization. Our analysis spans across 3 levels: segments along a single chain, sequences within a batch, and batch-averaged statistics. We discuss simulator fidelity and highlight the importance of robust segment definition. Examples are presented that demonstrate the use of simulated sequence analysis for in-silico iterative design to mimic protein hydrophobic/hydrophilic segment distributions in RHPs and compare RHP and protein sequence segments to explain experimental results of RHPs that mimic protein function. To facilitate the community use of this workflow, the simulator and analysis modules have been made available through an open source toolkit, the RHPapp.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Purification and sequence analysis of two rat tissue inhibitors of metalloproteinases
Two protein inhibitors of metalloproteinases (TIMP) were isolated from medium conditioned by the clonal rat osteosarcoma line UMR 106-01. Initial purification of both a 30-kDa inhibitor and a 20-kDa inhibitor was accomplished using heparin-Sepharose chromatography with dextran sulfate elution followed by DEAE-Sepharose and CM-Sepharose chromatography. Purification of the 20-kDa inhibitor to homogeneity was completed with reverse-phase high-performance liquid chromatography. The 20-kDa inhibitor was identified as rat TIMP-2. The 30-kDa inhibitor, although not purified to homogeneity, was identified as rat TIMP-1. Amino terminal amino acid sequence analysis of the 30-kDa inhibitor demonstrated 86% identity to human TIMP-1 for the first 22 amino acids while the sequence of the 20-kDa inhibitor was identical to that of human TIMP-2 for the first 22 residues. Treatment with peptide:N-glycosidase F indicated that the 30-kDa rat inhibitor is glycosylated while the 20-kDa inhibitor is apparently unglycosylated. Inhibition of both rat and human interstitial collagenase by rat TIMP-2 was stoichiometric, with a 1:1 molar ratio required for complete inhibition. Exposure of UMR 106-01 cells to 10(-7) M parathyroid hormone resulted in approximately a 40% increase in total inhibitor production over basal levels.
NASA Discipline Musculoskeletal↗
Structural and Biophysical Properties of a [4Fe-4S] Ferredoxin-Like Protein from Synechocystis sp. PCC 6803 with a Unique Two Domain Structure
Electron carrier proteins (ECPs), binding iron-sulfur clusters, are vital components within the intricate network of metabolic and photosynthetic reactions. They play a crucial role in the distribution of reducing equivalents. In Synechocystis sp. PCC 6803, the ECP network includes at least nine ferredoxins. Previous research, including global expression analyses and protein binding studies, has offered initial insights into the functional roles of individual ferredoxins within this network. This study primarily focuses on Ferredoxin 9 (slr2059). Through sequence analysis and computational modeling, Ferredoxin 9 emerges as a unique ECP with a distinctive two-domain architecture. It consists of a C-terminal iron-sulfur binding domain and an N-terminal domain with homology to Nil-domain proteins, connected by a structurally rigid 4-amino acid linker. Notably, in contrast to canonical [2Fe-2S] ferredoxins exemplified by PetF (ssl0020), which feature highly acidic surfaces facilitating electron transfer with photosystem I reaction centers, models of Ferredoxin 9 reveal a more neutral to basic protein surface. Using a combination of electron paramagnetic resonance spectroscopy and square-wave voltammetry on heterologously produced Ferredoxin 9, this study demonstrates that the protein coordinates 2x[4Fe-4S]2+/1+ redox-active and magnetically interacting clusters, with measured redox potentials of -420 +/- 9 mV and -516 +/- 10 mV vs SHE. A more in-depth analysis of Fdx9's unique structure and protein sequence suggests that this type of Nil-2[4Fe-4S] multi-domain ferredoxin is well conserved in cyanobacteria, bearing structural similarities to proteins involved in homocysteine synthesis in methanogens.
cyanobacteria↗
Genes encoding calmodulin-binding proteins in the Arabidopsis genome
Analysis of the recently completed Arabidopsis genome sequence indicates that approximately 31% of the predicted genes could not be assigned to functional categories, as they do not show any sequence similarity with proteins of known function from other organisms. Calmodulin (CaM), a ubiquitous and multifunctional Ca(2+) sensor, interacts with a wide variety of cellular proteins and modulates their activity/function in regulating diverse cellular processes. However, the primary amino acid sequence of the CaM-binding domain in different CaM-binding proteins (CBPs) is not conserved. One way to identify most of the CBPs in the Arabidopsis genome is by protein-protein interaction-based screening of expression libraries with CaM. Here, using a mixture of radiolabeled CaM isoforms from Arabidopsis, we screened several expression libraries prepared from flower meristem, seedlings, or tissues treated with hormones, an elicitor, or a pathogen. Sequence analysis of 77 positive clones that interact with CaM in a Ca(2+)-dependent manner revealed 20 CBPs, including 14 previously unknown CBPs. In addition, by searching the Arabidopsis genome sequence with the newly identified and known plant or animal CBPs, we identified a total of 27 CBPs. Among these, 16 CBPs are represented by families with 2-20 members in each family. Gene expression analysis revealed that CBPs and CBP paralogs are expressed differentially. Our data suggest that Arabidopsis has a large number of CBPs including several plant-specific ones. Although CaM is highly conserved between plants and animals, only a few CBPs are common to both plants and animals. Analysis of Arabidopsis CBPs revealed the presence of a variety of interesting domains. Our analyses identified several hypothetical proteins in the Arabidopsis genome as CaM targets, suggesting their involvement in Ca(2+)-mediated signaling networks.
NASA Discipline Plant Biology↗
Genome sequence, phylogenetic analysis, and structure-based annotation reveal metabolic potential of Chlorella sp. SLA-04
Algae are a broad class of photosynthetic eukaryotes that are phylogenetically and physiologically diverse. Most of the phylogenetic diversity has been inferred from 18S rDNA sequencing since there are only a few complete genomes available in public databases. Here we use ultra-long-read Nanopore sequencing to determine a gapless, telomere-to-telomere complete genome sequence of Chlorella sp. SLA-04, previously described as Chlorella sorokiniana SLA-04. Chlorella sp. SLA-04 is a green alga that grows to high cell density in a wide variety of environments - high and neutral pH, high and low alkalinity, and high and low salinity. SLA-04's ability to grow in high pH and high alkalinity media without external CO 2 supply is favorable for large-scale algal biomass production. Phylogenetic analysis performed using ribosomal DNA and conserved protein sequences consistently reveal that Chlorella sp. SLA-04 forms a distinct lineage from other strains of Chlorella sorokiniana. We complement traditional genome annotation methods with high throughput structural predictions and demonstrate that this approach expands functional prediction of the SLA-04 proteome. Genomic analysis of the SLA-04 genome identifies the genes capable of utilizing TCA cycle intermediates to replenish cytosolic acetyl-CoA pools for lipid production. We also identify a complete metabolic pathway for sphingolipid anabolism that may allow SLA-04 to readily adapt to changing environmental conditions and facilitate robust cultivation in mass production systems. Altogether, this work clarifies the phylogeny of Chlorella sp. SLA-04 within Trebouxiophyceae and demonstrates how structural predictions can be used to improve annotation beyond sequencebased methods.
59 BASIC BIOLOGICAL SCIENCES↗
Distributed Many-to-Many Protein Sequence Alignment using Sparse Matrices
Identifying similar protein sequences is a core step in many computational biology pipelines such as detection of homologous protein sequences, generation of similarity protein graphs for downstream analysis, functional annotation, and gene location. Performance and scalability of protein similarity search have proven to be a bottleneck in many bioinformatics pipelines due to increase in cheap and abundant sequencing data. This work presents a new distributed-memory software PASTIS. PASTIS relies on sparse matrix computations for efficient identification of possibly similar proteins. We use distributed sparse matrices for scalability and show that the sparse matrix infrastructure is a great fit for protein similarity search when coupled with a fully-distributed dictionary of sequences that allow remote sequence requests to be fulfilled. Our algorithm incorporates the unique bias in amino acid sequence substitution in search without altering basic sparse matrix model, and in turn, achieves ideal scaling up to millions of protein sequences.
97 MATHEMATICS AND COMPUTING↗
A novel beta-glucosidase from the cell wall of maize (Zea mays L.): rapid purification and partial characterization
Plants have a variety of glycosidic conjugates of hormones, defense compounds, and other molecules that are hydrolyzed by beta-glucosidases (beta-D-glucoside glucohydrolases, E.C. 3.2.1.21). Workers have reported several beta-glucosidases from maize (Zea mays L.; Poaceae), but have localized them mostly by indirect means. We have purified and partly characterized a 58-Ku beta-glucosidase from maize, which we conclude from a partial sequence analysis, from kinetic data, and from its localization is not identical to any of those already reported. A monoclonal antibody, mWP 19, binds this enzyme, and localizes it in the cell walls of maize coleoptiles. An earlier report showed that mWP19 inhibits peroxidase activity in crude cell wall extracts and can immunoprecipitate peroxidase activity from these extracts, yet purified preparations of the 58 Ku protein had little or no peroxidase activity. The level of sequence similarity between beta-glucosidases and peroxidases makes it unlikely that these enzymes share epitopes in common. Contrary to a previous conclusion, these results suggest that the enzyme recognized by mWP19 is not a peroxidase, but there is a wall peroxidase closely associated with the 58 Ku beta-glucosidase in crude preparations. Other workers also have co-purified distinct proteins with beta-glucosidases. We found no significant charge in the level of immunodetectable beta-glucosidase in mesocotyls or coleoptiles that precedes the red light-induced changes in the growth rate of these tissues.
Non-NASA Center↗
Phaseolus vulgaris SUT1.1 is a high affinity sucrose–proton co–transporter
Plant sucrose transporters are required for phloem loading, and therefore are essential for plant growth and development. In common beans (Phaseolus vulgaris) there are only two sucrose transporters functionally characterized. Through a previous RNA-seq study, we identified a putative sucrose transporter in common bean, which we hypothesize to function in import of sucrose into plant cells. In silico analysis revealed that PvSUT1.1 is a putative sucrose-proton co-transporter distinct from other characterized sucrose transporters in common bean indicating that this is a previously undescribed transporter protein in beans. Further analysis revealed that PvSUT1.1 shares high protein sequence homology to the phloem loader Arabidopsis SUC2; both have 12 transmembrane domains, a typical characteristic of plant sucrose transporters. Heterologous expression in yeast further showed PvSUT1.1 to be functional and it imported sucrose into yeast cells with a K m of 0.7 mM sucrose. Import of sucrose through PvSUT1.1 is also pH-dependent with highest uptake at pH 4.0, and activity is lost in the presence of the uncoupler carbonyl cyanide 3-chlorophenylhydrazone. Consistent with identification of PvSUT1.1 as a Type I transporter, PvSUT1.1 also transports esculin. Finally, PvSUT1.1 showed expression in multiple tissues and the protein was localized to the plasma membrane. The results show that PvSUT1.1 is a sucrose transporter that is probably involved in the uptake of sucrose into source and sink cells. The potential role of PvSUT1.1 in leaf phloem loading of sucrose in common beans and its importance in heat tolerance of reproductive tissues are further discussed.
59 BASIC BIOLOGICAL SCIENCES↗
EC-Bench: A Benchmark for Enzyme Commission Number Prediction
Enzymes are proteins that catalyze specific biochemical reactions in cells. Enzyme Commission (EC) numbers are used to annotate enzymes in a four-level hierarchy that classifies enzymes based on the specific chemical reactions they catalyze. Accurate EC number prediction is essential for understanding enzyme functions. Despite the availability of numerous methods for predicting EC numbers from protein sequences, there is no unified framework for evaluating and studying such methods systematically. This gap limits the ability of the community to identify the most effective approaches for enzyme annotation. We introduce EC-Bench, a benchmark for EC number prediction, consisting of 1) an initial representative set of existing methods (including homology-based, deep learning, contrastive learning, and language model methods), 2) existing and novel accuracy and efficiency performance metrics, and 3) selected datasets to allow for comprehensive comparative study. EC-Bench is open-source and provides a framework for researchers to not only compare among existing methods objectively under uniform conditions, but also to introduce and effectively evaluate performance of new methods in a comparative framework. To demonstrate the utility of EC-Bench, we perform extensive experimentation to compare the existing EC number prediction methods and establish their advantages and disadvantages in a variety of prediction tasks, namely “exact EC number prediction”, “EC number completion” and (partial or additional) “EC number recommendation”. We find wide variation in the performance of different methods, but also subtle but potentially useful differences in the performance of different methods across tasks and for different parts of the EC hierarchy.
59 BASIC BIOLOGICAL SCIENCES↗
Post-genomic science: converting primary structure into physiological function
No abstract available
NASA Discipline Exobiology↗
Optimizing metaproteomics database construction: lessons from a study of the vaginal microbiome
Metaproteomics, a method for untargeted, high-throughput identification of proteins in complex samples, provides functional information about microbial communities and can tie functions to specific taxa. Metaproteomics often generates less data than other omics techniques, but analytical workflows can be improved to increase usable data in metaproteomic outputs. Identification of peptides in the metaproteomic analysis is performed by comparing mass spectra of sample peptides to a reference database of protein sequences. Although these protein databases are an integral part of the metaproteomic analysis, few studies have explored how database composition impacts peptide identification. Here, we used cervicovaginal lavage (CVL) samples from a study of bacterial vaginosis (BV) to compare the performance of databases built using six different strategies. We evaluated broad versus sample-matched databases, as well as databases populated with proteins translated from metagenomic sequencing of the same samples versus sequences from public repositories. Smaller sample-matched databases performed significantly better, driven by the statistical constraints on large databases. Additionally, large databases attributed up to 34% of significant bacterial hits to taxa absent from the sample, as determined orthogonally by 16S rRNA gene sequencing. We also tested a set of hybrid databases which included bacterial proteins from NCBI RefSeq and translated bacterial genes from the samples. These hybrid databases had the best overall performance, identifying 1,068 unique human and 1,418 unique bacterial proteins, ~30% more than a database populated with proteins from typical vaginal bacteria and fungi. Our findings can help guide the optimal identification of proteins while maintaining statistical power for reaching biological conclusions.
59 BASIC BIOLOGICAL SCIENCES↗
Unusual modifications of protein biomarkers expressed by plasmid, prophage, and bacterial host of pathogenic Escherichia coli identified using top‐down proteomic analysis
Rationale Pathogenic bacteria often carry prophage (bacterial viruses) and plasmids (small circular pieces of DNA) that may harbor toxin, antibacterial, and antibiotic resistance genes. Proteomic characterization of pathogenic bacteria should include the identification of host proteins and proteins produced by prophage and plasmid genomes. Methods Protein biomarkers of two strains of Shiga toxin–producingEscherichia coli(STEC) were identified using antibiotic induction, matrix‐assisted laser desorption/ionization tandem time‐of‐flight (MALDI‐TOF‐TOF) tandem mass spectrometry (MS/MS) with post‐source decay (PSD), top‐down proteomic (TDP) analysis, and plasmid sequencing. Alphafold2 was also used to compare predicted in silico structures of the identified proteins to prominent fragment ions generated using MS/MS‐PSD. Strain samples were also analyzed with and without chemical reduction treatment to detect the attachment of pendant groups bound by thioester or disulfide bonds. Results Shiga toxin was detected and/or identified in both STEC strains. For the first time, we also identified the osmotically inducible protein (OsmY) whose sequence unexpectedly had two forms: a full and a truncated sequence. The truncated OsmY terminates in the middle of an α‐helix as determined by Alphafold2. A plasmid‐encoded colicin immunity protein was also identified with and without attachment of an unidentified cysteine‐bound pendant group (~307 Da). Plasmid sequencing confirmed top‐down analysis and the identification of a promoter upstream of the immunity gene that is activated by antibiotic induction, that is, SOS box. Conclusions TDP analysis, coupled with other techniques (e.g., antibiotic induction, chemical reduction, plasmid sequencing, and in silico protein modeling), is a powerful tool to identify proteins (and their modifications), including prophage‐ and plasmid‐encoded proteins, produced by pathogenic microorganisms.
Biochemistry & Molecular Biology↗
Unraveling the functional dark matter through global metagenomics
Metagenomes encode an enormous diversity of proteins, reflecting a multiplicity of functions and activities1,2. Exploration of this vast sequence space has been limited to a comparative analysis against reference microbial genomes and protein families derived from those genomes. Here, to examine the scale of yet untapped functional diversity beyond what is currently possible through the lens of reference genomes, we develop a computational approach to generate reference-free protein families from the sequence space in metagenomes. We analyse 26,931 metagenomes and identify 1.17 billion protein sequences longer than 35 amino acids with no similarity to any sequences from 102,491 reference genomes or the Pfam database3. Using massively parallel graph-based clustering, we group these proteins into 106,198 novel sequence clusters with more than 100 members, doubling the number of protein families obtained from the reference genomes clustered using the same approach. We annotate these families on the basis of their taxonomic, habitat, geographical and gene neighbourhood distributions and, where sufficient sequence diversity is available, predict protein three-dimensional models, revealing novel structures. Overall, our results uncover an enormously diverse functional space, highlighting the importance of further exploring the microbial functional dark matter.
54 ENVIRONMENTAL SCIENCES↗
Characterization of the proteins comprising the integral matrix of Strongylocentrotus purpuratus embryonic spicules
In the present study, we enumerate and characterize the proteins that comprise the integral spicule matrix of the Strongylocentrotus purpuratus embryo. Two-dimensional gel electrophoresis of [35S]methionine radiolabeled spicule matrix proteins reveals that there are 12 strongly radiolabeled spicule matrix proteins and approximately three dozen less strongly radiolabeled spicule matrix proteins. The majority of the proteins have acidic isoelectric points; however, there are several spicule matrix proteins that have more alkaline isoelectric points. Western blotting analysis indicates that SM50 is the spicule matrix protein with the most alkaline isoelectric point. In addition, two distinct SM30 proteins are identified in embryonic spicules, and they have apparent molecular masses of approximately 43 and 46 kDa. Comparisons between embryonic spicule matrix proteins and adult spine integral matrix proteins suggest that the embryonic 43-kDa SM30 protein is an embryonic isoform of SM30. An adult 49-kDa spine matrix protein is also identified as a possible adult isoform of SM30. Analysis of the SM30 amino acid sequences indicates that a portion of SM30 proteins is very similar to the carbohydrate recognition domain of C-type lectin proteins.
Non-NASA Center↗
Functional and evolutionary relationships between bacteriorhodopsin and halorhodopsin in the archaebacterium, halobacterium halobium
The archaebacteria occupy a unique place in phylogenetic trees constructed from analyses of sequences from key informational macromolecules, and their study continues to yield interesting ideas on the early evolution and divergence of biological forms. It is now known that the halobacteria among these species contain various retinal-proteins, resembling eukaryotic rhodopsins, but with different functions. Two of these pigments, located in the cytoplasmic membranes of the bacteria, are bacteriorhodopsin (a light-driven proton pump) and halorhodopsin (a light-driven chloride pump). Comparison of these systems is expected to reveal structure/function relationships in these simple (primitive?) energy transducing membrane components and evolutionary relationships which had produced the structural features which allow the divergent functions. Findings indicate that very different primary structures are needed for these proteins to accomplish their different functions. Indeed, analysis of partial amino acid sequences from halo-opsin shows already that few if any long segments exist which are homologous to bacterio-opsin. Either these proteins diverged a very long time ago to allow for the observed differences, or the evolutionary clock in the halobacteria runs faster than usual.
Lanyi, J. K.↗