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20 results for “amino acid sequence”
Darwin: an amino acid sequence collection of complete proteomes from eukaryotes with different phylogenetic affinities (v. 03_2020_137)
<p><strong>Background</strong></p> <p>Every time we find an interesting gene in an organism of interest, the first question is often “how widely is this gene distributed in the eukaryotic kingdom?”. Naturally, one could use NCBI BLAST search against the non-redundant sequence database provided by GenBank to answer this question. However, it can be cumbersome to parse the results and assign them to taxonomic units. It is also not straightforward to get an overview of which eukaryotic groups are represented in the results. Top BLAST hits can be crowded with sequences from closely-related organisms making it difficult gain an overview of the overall distribution across eukaryotes. To streamline this process, we developed an in-house database of complete eukaryotic proteomes. We tagged each sequence with a eukaryotic group handle (two-character symbol) and combined them into a single data set searchable by standalone BLAST on one’s own computer. We named this data set “Darwin” to reflect the diverse nature of the sequences it contains. </p> <p><strong>Methods</strong></p> <p>We downloaded predicted proteomes in FASTA format from different sources such as GenBank, Joint Genome Institute (Depart of Energy, USA), Broad Institute (Massachusetts Institute of Technology, USA), Phytozome and a number of other specialized websites catering for a specific organism such as the Arabidopsis Information Resource (TAIR), or the Saccharomyces Genome Database (SGD). All the organisms we included in Darwin are listed in Table 1. To reduce redundancy, we took care not to include the same species more than once unless subspecies were known to show wide diversity. Each sequence header was tagged with a eukaryotic group handle composed of two-character symbols (based on Keeling <em>et al</em>., 2005). These handles clearly appear in BLAST output and can be parsed easily. We combined sequences from all proteomes into a single data set and named it “Darwin”.</p> <p><strong>Results</strong></p> <p>The current version of Darwin (v. 03_2020_137) contains 2,601,132 amino acid sequences from 137 eukaryotes (Table 1, Data file 1). The sizes of the proteomes were diverse, ranging from ~4000 sequences in some alveolates to 60,000-76,000 in plants. Darwin represents most of the supergroups of eukaryotic kingdom described in Keeling <em>et al.,</em> (2005) except those in Rhizaria whose genomes were not available at the time of data set construction. The data set contains larger numbers of proteomes from fungi and plants reflecting areas of interest in our group. </p> <p><strong>Conclusions</strong></p> <p>Darwin is provided as a text fasta file that can be formatted for BLAST searches on standalone computers. The results from the BLAST searches can be parsed to determine how widely a gene of interest is distributed among different eukaryotes. Simple counting of the eukaryotic group handles would also yield an overview of the distribution across taxa. Darwin is also useful for rapidly finding out whether a gene is missing in particular taxa.</p> <p><strong>Reference</strong></p> <p>Keeling PJ, Burger G, Durnford DG, Lang BF, Lee RW, Pearlman RE, Roger AJ, Gray MW (2005) The tree of eukaryotes. <em>Trends Ecol. Evol.</em> <strong>20:</strong> 670-676</p>
Control T-cell receptor (TCR) alpha and beta chain nucleotide and amino acid sequences from human and mouse
<p>A dataset of pooled T-cell receptor (TCR) sequences for TCR alpha and beta chains of human and mouse.</p> <p>Sequences are obtained from various samples of healthy individuals/mice using our conventional protocols: see for example [Britanova et al "Dynamics of individual T cell repertoires: from cord blood to centenarians" The Journal of Immunology 2016] and [Izraelson et al. "Comparative analysis of murine T‐cell receptor repertoires." Immunology 2018].</p> <p>The sequences are stored as gzipped clonotype tables in VDJtools format, see [https://vdjtools-doc.readthedocs.io/en/master/input.html#vdjtools-format].</p> <p>This control dataset can be used as a proxy for a generative VDJ rearrangement model to estimate the expected frequency distribution of TCRs and check for enrichment of rare TCR clonotypes and groups of similar TCR sequences. For the implementation of the enrichment analysis, please see CalcDegreeStats routine from VDJtools software, see [https://vdjtools-doc.readthedocs.io/en/master/annotate.html#calcdegreestats].</p> <p>Files named "human.tra.strict.txt.gz", etc are pools of random/naive TCR clonotypes containing unique V/J/CDR3 nucleotide sequence combinations observed in data. The pools.zip file is used for TCR motif inference in VDJdb database [https://github.com/antigenomics/vdjdb-motifs], it contains human.tra.aa.txt, etc files that contain random/naive TCR clonotypes grouped by CDR3 amino acid sequence with the most frequent representative V and J.</p>
FIGURE 4 Deduced amino-acid sequences for the Gnrh2 in Differential expression of HPG-axis genes in autotetraploids derived from red crucian carp Carassius auratus red var., × blunt snout bream Megalobrama amblycephala,
FIGURE 4 Deduced amino-acid sequences for the Gnrh2 () and Lhr () genes in Carassius auratus red var. (RCC) and autotetraploid C. auratus red var. ♀ × Megalobrama amblycephala ♂ (4nRR)
Phylogenetic analysis of policistronic amino-acid sequences encoded by 116 flavivirus genomes
<p><span>Recently, Genome Biology and Evolution (11:3341-3352) published three statistical tests for testing whether alignments of sequence data violate the phylogenetic assumption of evolution under homogeneous conditions. The tests extend the matched-pairs tests of symmetry, marginal symmetry, and internal symmetry for pairs of aligned homologous sequences to the case where a whole alignment is considered. Here we reveal that the new tests are misleading. We explain why this is so, reveal how the tests of whole alignments may be done, and release new bioinformatics tools that implement statistically sound methods of dealing with multiple comparisons (i.e., by controlling the family-wise error rate or the false discovery rate). Using the new software to analyse an alignment of amino acids encoded by 116 flavivirus genomes, we reveal, for the first time, that these genomes are unlikely to have evolved under stationary, reversible, and homogeneous Markovian conditions.</span></p>
Data file: Amino acid sequence Cm28; a scorpion toxin
<p>The Data set contains the amino acid sequence of Cm28, a peptide toxin found in the venom of <em>Centruroides margaritatus</em>. The peptide sequence and functional data are reported in an article in the Journal of General Physiology (JGP) under this DOI 10.1085/jgp.202213146 and this data will appear in the UniProt Knowledgebase under the accession<br> number C0HM22.</p>
RefSeq bacterial protein (amino acid) sequences
<p><strong>Bacteria_Protein.fas.gz</strong></p><p>151,835,459 protein (amino acid) sequences extracted from 44,831 randomly selected bacterial genomes from NCBI's RefSeq (release 220). Sequences are named by their accession number, followed by "|" and their PGAP predicted function ("protein" tag). For example, the first sequence is named:</p><blockquote><p>WP_125174066.1|iron ABC transporter permease</p></blockquote><p>The process of creating the file involved the following steps.<br><strong>Step 1.</strong> Download 318,613 faa and fna files associated with a bacterial assembly in RefSeq. The following query was used:<br><i>esearch -db assembly -query '"Bacteria"[Organism] AND "latest refseq"[properties] AND "refseq has annotation"[properties]' | esummary | xtract -pattern DocumentSummary -element FtpPath_RefSeq</i><br><strong>Step 2.</strong> Verify all protein coding sequences match the expected protein sequence lengths within three codons, otherwise skip the assembly.<br><strong>Step 3.</strong> Remove all redundant protein coding or protein sequences in a genome. Only exact duplicates were removed, but they were removed from both nucleotides and proteins. Hence, a duplicated amino acid sequence would be discarded along with its coding sequence even if the coding sequence was unique. This was done to keep the two sets of sequences consistent.<br><strong>Step 4.</strong> Name sequences by their accession and PGAP predicted function, separated by a "|" character. The PGAP predicted function is generally uniform, although there are subtle difference between some taxon specific predictions. The predicted function is reasonably dependable but certainly not perfect.<br><strong>Step 5.</strong> Discard any sequences without a predicted function ("hypothetical protein"). These were discarded under the assumption that the protein's function would be required for downstream uses of the sequences.<br><strong>Step 6.</strong> Append protein and protein coding (nucleotide) sequences from randomly ordered assemblies to separate gzipped FASTA formatted files until the Zenodo file size limit was met for either file. Hence, there are many exact duplicate sequences in the set, but none for the sequences from each genome.</p><p>The final sets of sequences are intended to provide large sets of matched protein coding (nucleotide) and protein (amino acid) sequences with consistent labels. The FASTA descriptions in both files are identical. Note, the protein coding sequences do not exactly translate into the protein sequences because of slight differences in length (typically inclusion/exclusion of the first or last codon), as well as use of different translation tables depending on the organism.</p><p>See <i>Related works</i> for the companion file of protein coding (nucleotide) sequences (DOI: 10.5281/zenodo.10031801).</p>
Amino acid sequences of annotated genes in Pelargonium zonale
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NSRC-Search: Efficient searching for similar protein sequences of non-standard amino acid composition
<p>This research was funded by the National Science Centre in Poland (grant number 2021/41/N/ST6/01919)</p>
Amino acid sequences of RWP-RK domain containing proteins used for the construction of phylogenetic tree shown in Fig. 1
<p><span>The RWP-RK protein family is a group</span><span> of transcription factors containing </span><span>the RWP-RK DNA-binding domain. The RWP-RK DNA-binding domain is an ancient motif that emerged before the establishment of the Viridiplantae (green plants), which consist of green algae and land plants. This domain is mostly absent in other kingdoms but widely distributed in Viridiplantae. In green algae, a liverwort, and several angiosperms, RWP-RK proteins play essential roles in nitrogen responses and sexual reproduction-associated processes, which</span><span> </span><span>are seemingly unrelated phenomena but possible interdependent processes</span><span> </span><span>in autotrophs. Consistent with</span><span> related but diversified roles of the RWP-RK proteins in these organisms, the RWP-RK protein family appears to have expanded intensively, but independently, in the algal and land plant lineages. Therefore, bryophyte RWP-RK proteins occupy a unique position in the evolutionary process of establishing the RWP-RK protein family. In this review, we summarize current knowledge about the RWP-RK protein family in the Viridiplantae, and discuss the significance of bryophyte RWP-RK proteins in clarifying the relationship between diversification in the RWP-RK protein family and </span><span>procurement</span><span> of sophisticated mechanisms for adaptation to the terrestrial environment.</span></p>
Amino acid sequences of the proteins predicted from the whole genome of hilsa shad (Tenualosa ilisha) of the Bay of Bengal
<p>Gene prediction was performed by AUGUSTUS (Stanke et al., 2006) from the whole genome sequence of <em>T. ilisha</em> (<a href="https://www.ncbi.nlm.nih.gov/bioproject/400122">PRJNA400122</a>). The data contain amino acid sequences of 37,450 predicted protein coding genes.</p>
Artificial intelligence method to design and fold alpha-helical structural proteins from the primary amino acid sequence
<p>Dataset for paper: Z. Qin, L. Wu, H. Sun, S. Huo, T. Ma, E. Lim, P.-Y. Chen, B. Marelli, M.J. Buehler, Artificial intelligence method to design and fold alpha-helical structural proteins from the primary amino acid sequence, Extreme Mechanics Letters, Vol. 36, 100652, 2020. <a href="https://doi.org/10.1016/j.eml.2020.100652">https://doi.org/10.1016/j.eml.2020.100652</a>.</p> <p>Code: https://github.com/lamm-mit/MNNN/ </p>
Amino acid sequences of RWP-RK domain containing proteins used for the construction of phylogenetic tree shown in Fig. 1
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RTPR_rnrdb_tara_full-length_amino_acid_sequences
<p>RTPR amino acid sequences used as reference for framshift corrections</p>
Fig. 4. Amino acid sequences alignment between TCS1 and candidate N in Discovery and Biochemical Characterization of N-methyltransferase Genes Involved in Purine Alkaloid Biosynthetic Pathway of Camellia gymnogyna Hung T.Chang (Theaceae) from Dayao Mountain
Fig. 4. Amino acid sequences alignment between TCS1 and candidate N-methyltransferase genes (GCS1, GCS2, and GCS3).
Data from: Sequence entropy of folding and the absolute rate of amino acid substitutions
Adequate representations of protein evolution should consider how the acceptance of mutations depends on the sequence context in which they arise. However, epistatic interactions among sites in a protein result in hererogeneities in the substitution rate, both temporal and spatial, that are beyond the capabilities of current models. Here we use parallels between amino acid substitutions and chemical reaction kinetics to develop an improved theory of protein evolution. We constructed a mechanistic framework for modelling amino acid substitution rates that uses the formalisms of statistical mechanics, with principles of population genetics underlying the analysis. Theoretical analyses and computer simulations of proteins under purifying selection for thermodynamic stability show that substitution rates and the stabilization of resident amino acids (the 'evolutionary Stokes shift') can be predicted from biophysics and the effect of sequence entropy alone. Furthermore, we demonstrate that substitutions predominantly occur when epistatic interactions result in near neutrality; substitution rates are determined by how often epistasis results in such nearly neutral conditions. This theory provides a general framework for modelling protein sequence change under purifying selection, potentially explains patterns of convergence and mutation rates in real proteins that are incompatible with previous models, and provides a better null model for the detection of adaptive changes.
294-fold mini all-α protein library encoded by 7,350 amino-acid sequences (project "flood of fold" )
<p>This repository includes 3 compressed archive files for the “flood-of-fold” mini-protein library project. </p> <ol> <li>FloodOfFolds_294_backbone_models.tar.gz includes 294 mini all-α backbone models showing distinct topologies (folds). They are poly-VAL models. </li> <li>FloodOfFolds_7350_designs_MODEL.tar.gz includes 7,350 design protein models in the pdb format for the 294 mini-protein library. 25 amino-acid sequences were designed for each backbone model (25 x 294 = 7,350). </li> <li>FloodOfFolds_7350_designs_FASTA.tar.gz includes 7,350 fasta files derived from the pdb files in FloodOfFolds_7350_designs_MODEL.tar.gz.<br> </li> </ol> <p>See also here for results of folding simulations: https://zenodo.org/record/5526849#.YWRhFBBBw1I</p> <p>Acknowledgement: K.S. and S.M. would like to deeply thank Koga laboratory at Institute for Molecular Science providing computational resources. Most of the computations for model building and folding simulations were performed using the facilities at the Research Center for Computational Science, Okazaki, Japan.</p>
Data from: Sequence entropy of folding and the absolute rate of amino acid substitutions
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Predicting biophysical characteristics of proteins from their amino acid sequence
<p>As part of the Galaxy Training Material, this is the tutorial workflow's input file in FASTA format. It contains the amino acid sequence of protein P04156 ("PRIO_HUMAN OS=Homo sapiens OX=9606 GN=PRNP PE=1 SV=1").</p>
RNA-sequencing data of mouse macrophages exposed to polyphenols or polyphenol-amino acid adducts
GEO Series GSE206519. Mus. 18 samples. Type: Expression profiling by high throughput sequencing.
Nucleotide and Amino Acid sequences of shortlisted antigens
<p>This file contains the nucleotide sequences and amino acid sequences of forty five antigens. These are fish specific orphan (unknown) antigens that are predicted to be secretory or surface-expressed proteins identified in the transcriptomes of PKD infected trout kidneys.</p>
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International Brain Laboratory public data
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OpenNeuro
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