Asta Literature Retrieval: Gene Research for Functional Annotation ⚠️ CRITICAL: Gene/Protein Identification Context BEFORE YOU BEGIN RESEARCH: Y...

This report is retrieval-only and is generated directly from Asta results.

• Papers retrieved: 19
• Snippets retrieved: 20

Relevant Papers

[1] CRONOS: the cross-reference navigation server

• Authors: Brigitte Waegele, I. Dunger, G. Fobo, Corinna Montrone, H. Mewes et al.
• Year: 2008
• Venue: Bioinformatics
• URL: https://www.semanticscholar.org/paper/8c05b3aa0ba01c41ee97c2dc98ea7b5b14ce0e9c
• DOI: 10.1093/bioinformatics/btn590
• PMID: 19010804
• PMCID: 2638938
• Citations: 20
• Summary: CRONOS, a cross-reference server that contains entries from five mammalian organisms presented by major gene and protein information resources, is developed, which shows that the cross-references are highly accurate.
• Evidence snippets:
• Snippet 1 (score: 0.733)
  > In order to detect gene and protein names which are assigned to products of different genes and thus result in erroneous cross-references, dedicated lists are created for each organism separately. Organism-specific lists are necessary, since terms that are ambiguous in one organism might be explicit in another. For example, ADORA2 is an ambiguous gene name in Homo sapiens but not in mouse, and GALT in mouse but not in H.sapiens.
  > In a first step, ambiguous names within the databases were extracted. If a name occurs in at least two entries describing different genes or proteins (splice variants count as one gene/protein), this particular name is marked as ambiguous and is excluded from the mapping process. In a second step, corresponding gene names occurring in the manually annotated sections of RefSeq as well as in UniProt were analyzed. Entries containing the same gene product name and having a one-to-many or many-to-many relation (e.g. one Swiss-Prot entry maps to many RefSeq entries) were scrutinized for misleading annotation. This process is done manually by inspecting additional information like sequence similarity or functional information about the involved entries. In most of the cases, the exclusion of the ambiguous gene names resulted in correct one-to-one relations.
  > As statistical analysis revealed (Supplementary Material S2) that gene names with less than four letters are exceptionally error-prone, only gene names with at least four letters are kept for mapping purposes. However, gene names with less than four letters can be queried, e.g. a search for the tumor suppressor 'p53' reveals the respective entries with the official gene name 'TP53'. Organism-specific lists of ambiguous gene and protein names are available for download on the CRONOS home page.

[2] Avian Immunome DB: an example of a user-friendly interface for extracting genetic information

• Authors: Ralf C. Mueller, Nicolai Mallig, Jacqueline Smith, Lél Eöry, Richard I. Kuo et al.
• Year: 2020
• Venue: BMC Bioinformatics
• URL: https://www.semanticscholar.org/paper/b894d9ca8ea2d653bf1711a0c67dab71d054487c
• DOI: 10.1186/s12859-020-03764-3
• PMID: 33176685
• PMCID: 7661159
• Citations: 6
• Summary: The Avian Immunome DB (Avimm) for easy gene property extraction as exemplified by avian immune genes is presented and described, which contains 1170 distinct avian immune genes with canonical gene symbols and 612 synonyms across 363 bird species.
• Evidence snippets:
• Snippet 1 (score: 0.726)
  > Ever since the advent of commercial next-generation sequencing platforms in the early 2000s with its associated decrease in sequencing costs [1], the number of DNA sequences increased considerably [2]. Generally, these data become publicly accessible in databases provided by projects focussing on different aspects of biological sequence information [3,4]. Ensembl [5] and NCBI [6] for instance, have a strong focus on genome annotation with the help of RNA transcript information while UniProt has a pronounced emphasis on protein-coding genes and biological function of proteins. UniProt's records are either based on manually annotated, non-redundant protein sequences (SwissProt) or on highquality computationally analysed records, which are enriched with automatic annotation (TrEMBL) [7]. Relying on accurate genome annotations and protein descriptions, Gene Ontology (GO) [8,9] categorises gene products and fits them into a computational model of biological systems. Their assignment deploys a controlled vocabulary, so-called GO terms, to link genes and gene products to biological processes, cellular components, or molecular functions.
  > However, genome annotation is not standardised, and each service provider uses their own custom-built annotation pipelines. As a consequence, this often leads to ambiguity in gene names during genome annotation with different gene symbols being given to the same gene or the same gene symbol being given to different, but similar genes. Additionally, since the pre-existing wealth of sequencing information relies on model organisms like human and mouse, there is a strong bias in gene symbols towards those chosen for these species. Particularly for model species, this issue has been partially addressed, for example by the Human Genome Organisation (HUGO) Gene Nomenclature Committee (HGNC) [10], the Vertebrate Gene Nomenclature Committee (VGNC) [11], or the Chicken Gene Nomenclature Consortium [12]. However, this neither guarantees that gene names are harmonised among these consortia, nor does it keep researchers from assigning alternative gene symbols in their annotations, especially when working with non-model species.

[3] Structure-Aware Mycobacterium tuberculosis Functional Annotation Uncloaks Resistance, Metabolic, and Virulence Genes

• Authors: Samuel J. Modlin, A. Elghraoui, Deepika Gunasekaran, Alyssa M Zlotnicki, N. Dillon et al.
• Year: 2021
• Venue: mSystems
• URL: https://www.semanticscholar.org/paper/76ff9a62b36b32cc10e46e71ffd4dd90344e4706
• DOI: 10.1128/mSystems.00673-21
• PMID: 34726489
• PMCID: 8562490
• Citations: 15
• Summary: This work systematically updates the functional genome annotation of Mycobacterium tuberculosis virulent type strain H37Rv and identifies hundreds of high-confidence candidates for mechanisms of antibiotic resistance, virulence factors, and basic metabolism and other functions key in clinical and basic tuberculosis research.
• Evidence snippets:
• Snippet 1 (score: 0.712)
  > 3. Fig. S2B -match/mismatch colours mixed up? (I think match should be teal and mismatch -red?) 4. Line 162-163: Rv1430 is in UniProt (EC 3.1.1.-) and has been present in Uniprot since version 45 of the gene record: https://www.uniprot.org/uniprot/L7N697. I presume you had conducted your literature analysis before the UniProt entry was updated to include the EC code, so maybe you can add the dates when the data was retrieved from UniProt and other databases you used in the Materials and Methods section? 5. Supplementary text, p. 9, first paragraph. I believe that an unrelated fragment of text was copy-pasted into the second sentence of the paragraph ("Many mutations that altered bacterial clearance...") 6. Supplementary text, p. 12, final paragraph. It should be Rv1191, not Rv1191c. Could you also add a short explanation why you believe it should be classified as a cathepsin (what protein did you transfer this annotation from)?
  > Reviewer #3 (Comments for the Author):
  > In this manuscript, Modlin et al., attempt to tackle the problem of assigning functions to ~1700 hypothetical and/or underannotated genes in the Mycobacterium tuberculosis H37Rv (Mtb) genome. Rapid and accurate annotation of microbial genomes is indeed a very critical and under appreciated part of microbial ecophysiology. This step is especially crucial for pathogenic organisms such as Mtb where accurate functional annotation of these hypothetical proteins could unravel mechanisms which could act as drug targets. The authors employed a two-pronged strategy to define a set of these unannotated or under-annotated genes and to then provide possible functions for many of these genes. First, they undertook a large-scale manual curation of literature to assign functions (including EC numbers for enzymatic functions) to ~575 genes.
• Snippet 2 (score: 0.656)
  > (I think match should be teal and mismatch -red?)
  > The legend was previously mismatched with the labels. This has been corrected in the new uploaded figure . 4. Line 162-163: Rv1430 is in UniProt (EC 3.1.1.-) and has been present in Uniprot since version 45 of the gene record: https://www.uniprot.org/uniprot/L7N697. I presume you had conducted your literature analysis before the UniProt entry was updated to include the EC code, so maybe you can add the dates when the data was retrieved from UniProt and other databases you used in the Materials and Methods section?
  > The reviewer's presumption is correct; we had stated the date of data retrieval in the caption of Table 1, but we agree it should instead be stated centrally in the Methods. We have now added it to the Methods section as well, for clarity (Lines 696-700) 5. Supplementary text, p. 9, first paragraph. I believe that an unrelated fragment of text was copypasted into the second sentence of the paragraph ("Many mutations that altered bacterial clearance...")
  > We thank the reviewer for catching this accidental insertion. We have now removed the spurious fragment.
  > 6. Supplementary text, p. 12, final paragraph. It should be Rv1191, not Rv1191c. Could you also add a short explanation why you believe it should be classified as a cathepsin (what protein did you transfer this annotation from)?
  > We have removed this speculation in the revised submission.
  > Reviewer #3 (Comments for the Author):
  > In this manuscript, Modlin et al., attempt to tackle the problem of assigning functions to ~1700 hypothetical and/or under-annotated genes in the Mycobacterium tuberculosis H37Rv (Mtb) genome. Rapid and accurate annotation of microbial genomes is indeed a very critical and under appreciated part of microbial ecophysiology. This step is especially crucial for pathogenic organisms such as Mtb where accurate functional annotation of these hypothetical proteins could unravel mechanisms which could act as drug targets.

[4] LMPD: LIPID MAPS proteome database

• Authors: Dawn Cotter, A. Maer, C. Guda, Brian Saunders, S. Subramaniam
• Year: 2005
• Venue: Nucleic Acids Research
• URL: https://www.semanticscholar.org/paper/265c37b45326b7927e396484751e84e4aeff92d5
• DOI: 10.1093/nar/gkj122
• PMID: 16381922
• PMCID: 1347484
• Citations: 92
• Influential citations: 2
• Summary: The initial release of the LIPID MAPS Proteome Database contains 2959 records, representing human and mouse proteins involved in lipid metabolism, and this LMPD protein list was enhanced with annotations from UniProt, EntrezGene, ENZYME, GO, KEGG and other public resources.
• Evidence snippets:
• Snippet 1 (score: 0.702)
  > For each record selected from the results summary, all LMPD data relevant to that protein are displayed, with external database IDs linked to their respective resources.
  > Annotations are organized by category: Record Overview, Gene/GO/KEGG Information, UniProt Annotations, and Related Proteins. The record overview contains LMPD_ID, species, description, gene symbols, lipid categories, EC number, molecular weight, sequence length and protein sequence. Gene information includes Entrez Gene ID, chromosome, map location, primary name, primary symbol and alternate names and symbols; Gene Ontology (GO) IDs and descriptions, and KEGG pathway IDs and descriptions. UniProt annotations include primary accession number, entry name and comments such as catalytic activity, enzyme regulation, function and similarity. For related proteins and splice variants, we display source database, database ID, sequence length, and title.

[5] KinaseFusionDB: an integrative knowledge of kinase fusion proteins in multi-scales

• Authors: H. Kumar, Zikang Chen, Abayomi Adegunlehin, Loren Trowbridge, Leonardo Aguilar et al.
• Year: 2025
• Venue: Briefings in Bioinformatics
• URL: https://www.semanticscholar.org/paper/d212d0a2c8e4a7cedcd2c1e4fa11ed6de23345f7
• DOI: 10.1093/bib/bbaf259
• PMID: 40471992
• PMCID: 12140011
• Summary: A novel in silico pipeline for predicting 3D structures of kinase fusion proteins and performing structure-based virtual screening, which revealed that most predicted structures showed high pLDDT scores (pLDDT >70) within conserved kinase domains.
• Evidence snippets:
• Snippet 1 (score: 0.697)
  > To assign the functional or gene categories, we integrated cancer genes, tumor suppressors, epigenetic regulators, DNA damage repair genes, human essential genes, kinases, and transcription factors. In each gene group, we checked the retention and ORFs of the main protein functional features. There are 13 features belonging to the 'region' category, including 'calcium binding', Figure 1. Overview of KinaseFusionDB and its role in understanding kinase fusion proteins. (a) I. The top section identifies therapeutic targets for oncogenic activation of kinases, including wild-type kinases activated by overexpression or genomic amplification, mutated kinases with gain-offunction mutations, and kinase fusion proteins resulting from chromosomal rearrangements. II. The middle section highlights the functionalities of KinaseFusionDB, such as providing functional annotations, mRNA/protein expression data, drug binding information, and relevant literature reviews. III. The third section addresses the current limitations in targeting kinase fusion proteins, particularly the lack of comprehensive 3D structures for these proteins. It emphasizes the partial knowledge of kinase fusion protein structures and the need for further drug screening. IV. The bottom section discusses how computational approaches, such as predicted 3D structures (e.g. from AlphaFold2), can fill these gaps, supporting drug screening and filtering based on consistent binding among multiple fusion protein isoforms. (b) Kinase domain-retained human kinase fusion proteins.
  > 'coiled coil', 'compositional bias', 'DNA binding', 'domain', 'intramembrane', 'motif', 'nucleotide binding', 'region', 'repeat', 'topological domain', 'transmembrane', and 'zinc finger'. To perform the protein functional feature retention search, we first downloaded the GFF (General Feature Format) format protein information of 10 651 UniProt [26] accessions from UniProt for 10 619 genes involved in 15 030 fusion genes [27]. UniProt provides the loci information of 39 protein features, including 6 molecule processing features, 13 region features, 4 site features, 6 amino acid modification features, 2 natural variation features, 5 experimental info features, and 3 secondary structure features.

[6] Discovering and Summarizing Relationships Between Chemicals, Genes, Proteins, and Diseases in PubChem

• Authors: L. Zaslavsky, Tiejun Cheng, A. Gindulyte, Siqian He, Sunghwan Kim et al.
• Year: 2021
• Venue: Frontiers in Research Metrics and Analytics
• URL: https://www.semanticscholar.org/paper/57b86aef9aae576c2ae4199c0b74971f4c195211
• DOI: 10.3389/frma.2021.689059
• PMID: 34322655
• PMCID: 8311438
• Citations: 23
• Influential citations: 1
• Summary: The literature knowledge panels developed and implemented in PubChem help to uncover and summarize important relationships between chemicals, genes, proteins, and diseases by analyzing co-occurrences of terms in biomedical literature abstracts.
• Evidence snippets:
• Snippet 1 (score: 0.695)
  > We decided to prioritize human genes and proteins. The following strategy has been implemented to resolve gene and protein text entities to the most reasonable gene, protein, or enzyme symbol (corresponding to human, when possible):
  > -Try to find a match among Human Genome Organization (HUGO) Gene Nomenclature Committee (HGNC) names (Braschi et al., 2019;HUGO, 2021);
  > -Try to find a match among names in The IUPHAR/BPS Guide to Pharmacology (Armstrong et al., 2020; IUPHAR/BPS, 2021); -Try to find matches among names in UniProt (Bateman et al., 2017); -Otherwise, try to match to an enzyme name and resolve to an EC number (Bairoch, 2000;Expassy, 2021).
  > In general, it is very difficult and often impossible to distinguish the name of a gene from the name of the protein encoded by that gene. Therefore, gene and protein names are not strictly distinguished from each other but considered as one category. Therefore, the annotations considered in this study can be grouped into three categories: chemicals, genes/proteins, and diseases.

[7] Construction of an Ortholog Database Using the Semantic Web Technology for Integrative Analysis of Genomic Data

• Authors: H. Chiba, Hiroyo Nishide, I. Uchiyama
• Year: 2015
• Venue: PLoS ONE
• URL: https://www.semanticscholar.org/paper/7cc805575c642aa8efdc1204383a7662965fbb60
• DOI: 10.1371/journal.pone.0122802
• PMID: 25875762
• PMCID: 4395280
• Citations: 14
• Summary: The ortholog database using the Semantic Web technology can contribute to biological knowledge discovery through integrative data analysis and examples demonstrate that the ortholog information described in RDF can be used to link various biological data such as taxonomy information and Gene Ontology.
• Evidence snippets:
• Snippet 1 (score: 0.691)
  > A typical use of an ortholog database is transferring functional annotations from known genes in model organisms to genes of unknown function in other organisms, on the basis of the conjecture that orthologs are usually functionally conserved. To demonstrate such an application in our database, we showed a query to retrieve ortholog information of a specified protein.
  > Here, we specified a UniProt ID to obtain ortholog information. For describing functional categories of genes, we used Gene Ontology (GO) [24]. The UniProt GO Annotation (UniProt-GOA) database [25] (http://www.ebi.ac.uk/GOA) provides GO term assignment to proteins with evidence codes (http://www.geneontology.org/GO.evidence.shtml). We created an ontology for GO annotation (GOA-O, Table 1, http://purl.jp/bio/11/goa) and described UniProt-GOA data in RDF using it (Table 2). If some model organisms have experimentally verified GO annotations, we can transfer such a validated annotation to orthologs of other organisms.

[8] Mitotic Spindle Proteomics in Chinese Hamster Ovary Cells

• Authors: Mary Kate Bonner, D. Poole, Tao Xu, Ali Sarkeshik, J. Yates et al.
• Year: 2011
• Venue: PLoS ONE
• URL: https://www.semanticscholar.org/paper/8a46e242e657489c1933c76e06a37618f7d1901f
• DOI: 10.1371/journal.pone.0020489
• PMID: 21647379
• PMCID: 3103581
• Citations: 51
• Influential citations: 3
• Summary: This work reports the first proteomic study of the mitotic spindle from Chinese Hamster Ovary (CHO) cells and identifies proteins that are unique to the CHO spindle.
• Evidence snippets:
• Snippet 1 (score: 0.690)
  > The lists of proteins used for the comparison contain more items than listed previously due to expansion out of gene clusters, for example, to allow the updating and comparison of current HGNC gene symbols. These lists of proteins were compared in Microsoft Excel 2011 using PivotTable.
  > The protein set for the CHO midbody was derived from the accession numbers in Table S1 and Table S2 from Skop et al. [9]. Original accession numbers were updated to more recent UniProt accessions (2/2010), and duplicates from different species or different protein isoforms were removed. The unique UniProt accessions were mapped to gene names using UniProt KB Unimart, UniProt dataset [82], and these gene names were confirmed manually as HGNC symbols using HGNC, with ambiguities checked using BLASTP of the sequence corresponding to the original accession number. Accessions that didn't map successfully in Unimart were manually analyzed using BLASTP against the human RefSeq protein set using sequences from the original accessions, combined with TreeFam.org data for the non-human UniProt accessions. HGNC symbols were updated again on 12/14/2010 before comparison with this paper's protein set.
  > The protein set for the HeLa spindle proteome is derived from the 1121 accession numbers in Sauer et al. supplementary table 1 column 2 [17]. Updating the 1116 UniProt accession numbers and 5 IPI accession numbers from 795 rows required several steps. Most proteins were updated to current UniProt accessions using UniProt retrieve. Duplicates were removed. Sequences for the IPI accession numbers and 16 defunct UniProt accession numbers were recovered from other sources on the web, and BLASTP against the human RefSeq protein set with a cutoff of at least 90% identity was used to update some of these accessions. The unique current UniProt accessions were mapped to HGNC symbols using UniProt ID mapping to HGNC IDs. Biomart, database Ensembl Genes 60, dataset GRCh37.p2 [http://uswest.ensembl.org/biomart/martview/]

[9] Network Pharmacology-Based Strategy to Investigate the Pharmacological Mechanisms of Ginkgo biloba Extract for Aging

• Authors: Yanfei Liu, Yue Liu, Wantong Zhang, Mingyue Sun, Weiliang Weng et al.
• Year: 2020
• Venue: Evidence-based Complementary and Alternative Medicine : eCAM
• URL: https://www.semanticscholar.org/paper/fadeff691eb41dff82e019cc3d38b846fdc605c4
• DOI: 10.1155/2020/8508491
• PMID: 32802136
• PMCID: 7403930
• Citations: 8
• Summary: The study found that flavonoids (quercetin, luteolin, and kaempferol) and beta-sitosterol and the top eight candidate targets, namely, PTGS2, PPARG, DPP4, GSK3B, CCNA2, AR, MAPK14, and ESR1, were selected as the main therapeutic targets of EGb.
• Evidence snippets:
• Snippet 1 (score: 0.687)
  > e validated target proteins of the active components were obtained from the TCMSP database. e target protein name of the active ingredient was converted to the standard target gene name through the UniProt Knowledge Base (UniProtKB, http://www. uniprot.org/). e UniProtKB is the central hub for the collection of functional information on proteins, with accurate, consistent, and rich annotation. e target protein names were inputted into UniProtKB, with the organism restricted to "Homo sapiens," eventually gaining the official symbol.

[10] PhosphoSitePlus: a comprehensive resource for investigating the structure and function of experimentally determined post-translational modifications in man and mouse

• Authors: P. Hornbeck, J. Kornhauser, S. Tkachev, Bin Zhang, E. Skrzypek et al.
• Year: 2011
• Venue: Nucleic Acids Research
• URL: https://www.semanticscholar.org/paper/93e4acf4ba3bca1b379ae8292e73dddb344abd90
• DOI: 10.1093/nar/gkr1122
• PMID: 22135298
• PMCID: 3245126
• Citations: 1605
• Influential citations: 155
• Summary: PhosphoSitePlus (http://www.phosphosite.org) is an open, comprehensive, manually curated and interactive resource for studying experimentally observed post-translational modifications, primarily of human and mouse proteins. It encompasses 1 30 000 non-redundant modification sites, primarily phosphorylation, ubiquitinylation and acetylation. The interface is designed for clarity and ease of navigation. From the home page, users can launch simple or complex searches and browse high-throughput d...
• Evidence snippets:
• Snippet 1 (score: 0.685)
  > Accession numbers from UniPROT KB, NCBI and Ensembl (24)(25)(26), as well as gene symbols from HGNC (27), are curated for all proteins when possible. Basic protein descriptions include information parsed from UniPROT KB (24), and may include additional information from the literature. Descriptions are updated in bulk occasionally. Gene Ontology (28) annotations are parsed from NCBI (25). Editors assign protein types.

[11] The Surface Proteome of Bovine Unsexed and Sexed Spermatozoa

• Authors: P. Pinto-Pinho, Joana Quelhas, Francis Impens, Sara Dufour, Delphi Van Haver et al.
• Year: 2025
• Venue: Animals : an Open Access Journal from MDPI
• URL: https://www.semanticscholar.org/paper/66496158140e8f55a2c2ca8965bd298300ce9ab0
• DOI: 10.3390/ani15040484
• PMID: 40002966
• PMCID: 11852025
• Citations: 2
• Summary: Differences in surface proteins between X- and Y-chromosome-bearing bovine spermatozoa are explored to identify potential targets for sperm sexing by LC-MS/MS analysis, with 5 transmembrane proteins showing promise as markers for X-sperm.
• Evidence snippets:
• Snippet 1 (score: 0.684)
  > The protein sequences were functionally annotated by combining information retrieved from the UniProt database ( [25], accessed on 9 June 2022) and one-to-one fast orthology assignments using the eggNOG-mapper v.2.1.7 tool ( [26], accessed on 9 June 2022), as described in [27]. Briefly, the gene name, protein name, length, Gene Ontology (GO) IDs, and chromosome associated with each protein entry were obtained from UniProt using the retrieve/ID mapping tool. Additionally, gene names, descriptions, and experimentally validated GO IDs were obtained from eggNOG, with consideration given to a taxonomic scope auto-adjusted per query, a minimum hit bit-score of 60, and thresholds of 80% for identity, minimum query coverage, and minimum subject coverage.
  > Out of the 130 detected proteins, 71 (54.6%) had information manually verified by UniProt curators (Supplementary Spreadsheet S1.5). Utilizing the eggNOG-mapper v2.1.7 tool, a total of 122 entries were scanned (Supplementary Spreadsheet S1.6). By combining data from both tools, a total of 123 proteins were characterized with a gene name, and 127 had GO information. However, 2 proteins still lacked information on a descrip-tion, protein, gene, and preferred names. Figure 1 provides a summary of the functional annotation results.
  > tool, a total of 122 entries were scanned (Supplementary Spreadsheet S1.6). By combining data from both tools, a total of 123 proteins were characterized with a gene name, and 127 had GO information. However, 2 proteins still lacked information on a description, protein, gene, and preferred names. Figure 1 provides a summary of the functional annotation results.

[12] Virulence and pathogenicity determinants in whole genome sequence of Fusarium udum causing wilt of pigeon pea

• Authors: A. Srivastava, R. Srivastava, Jagriti Yadav, Ashutosh Kumar Singh, P. Tiwari et al.
• Year: 2023
• Venue: Frontiers in Microbiology
• URL: https://www.semanticscholar.org/paper/ac4c8e1cd07dbd943c544dab0dff140617956e3a
• DOI: 10.3389/fmicb.2023.1066096
• PMID: 36876067
• PMCID: 9981795
• Citations: 2
• Summary: The present study concludes that deciphering the whole genome of F. udum would be instrumental in understanding evolution, virulence determinants, host-pathogen interaction, possible control strategies, ecological behavior, and many other complexities of the pathogen.
• Evidence snippets:
• Snippet 1 (score: 0.680)
  > The BLASTx homology search tool, a component of the standalone NCBI-blast-2.3.0+, was used to perform functional annotation of the F. udum genes (Altschul et al., 1990). With a cut-off E value of ≤1e−06 and a similarity of 34%, BLASTx identified the homologous sequences of the genes in the NCBI non-redundant protein database. Gene ontology (GO) analysis was carried out using Blast2GO PRO 4.1.5 (Conesa and Gotz, 2008). In three different mappings, B2G performed as follows: (1) Using two NCBI-provided mapping files, blast result accessions are used to get gene names (symbols; gene info, gene 2 accessions). (2) Blast result GI identifiers were used to retrieve UniProt IDs using a mapping file from PIR (non-redundant reference protein database), which includes PSD, Swiss-Prot, UniProt, TrEMBL, GenPept, RefSeq, and PDB. The names of the identified genes were searched in the species-specific entries of the gene product table of the GO database. With the aid of the KAAS-KEGG Automatic Annotation Server, pathway analyses were carried out. This database provides functional annotation of genes using other data servers (Moriya et al., 2007). Accessions from the blast results were looked for in the DBXRef table of the GO database.

[13] Molecular Mechanisms Underlying Response to Influenza in Grey Seals (Halichoerus grypus), a Potential Wild Reservoir

• Authors: Christina M McCosker, E. Unal, Alayna K Gigliotti, Wendy B Puryear, Jonathan A. Runstadler et al.
• Year: 2025
• Venue: Molecular Ecology
• URL: https://www.semanticscholar.org/paper/bebb135aae1c1182d098fce839c9a3df0cfb2b21
• DOI: 10.1111/mec.70012
• PMID: 40613337
• PMCID: 12288799
• Citations: 3
• Summary: It is hypothesized that the combination of down‐ and up‐regulated immune gene expression may prevent overstimulation of the immune response, acting as an adaptation in grey seals to resist IAV‐associated mortality.
• Evidence snippets:
• Snippet 1 (score: 0.676)
  > Top hits were required to have a percent query coverage (QC) ≥ 80 to be used for annotating transcripts. A subsequent blastx search against the Swiss-Prot database (downloaded from NCBI 07/02/2021) for transcripts without a sufficient hit was conducted using the parameters max_target_seqs 2, max_hsps 1, e-value 0.001 and qcov_hsp_perc 80. Genes without a published gene symbol (named 'LOC' + Gene ID in NCBI's database) were assigned a UniProt gene symbol based on the protein annotation listed in the RefSeq entries, when possible. Ultimately, a list of transcript identifiers and gene symbols were compiled into a transcript-to-gene map for subsequent analyses.
  > To further facilitate analyses of gene functions, additional steps were taken to identify the putative function of genes annotated with a symbol that began with 'LOC'. First 'LOC' genes with a protein-coding gene description in NCBI's database were manually assigned the appropriate gene symbol. Transcript sequences of remaining 'LOC' genes were queried through a blastn search against NCBI's nucleotide database (parameters: max target seq = 2, max hsps = 1, evalue = 0.01, perc identity = 90). Results from the blastn search were filtered to exclude hits with query coverage < 90 and hits that included vague terms (e.g., 'uncharacterized', 'genome assembly' and 'chromosome'). Gene symbols were extracted from the first hit for each transcript and assigned as the identity of that gene for genes with only a single transcript with hits or with consistent hits across transcripts. For genes with multiple transcripts that matched different gene symbols, the annotation was manually determined based on the number of transcripts for each gene symbol hit and query coverage/% identity values. For any 'LOC' genes that were identified as a gene already present in the dataset, gene counts were concatenated for further analysis.

[14] Europe PMC annotated full-text corpus for gene/proteins, diseases and organisms

• Authors: Xiao Yang, Shyamasree Saha, Aravind Venkatesan, Santosh Tirunagari, Vid Vartak et al.
• Year: 2023
• Venue: Scientific Data
• URL: https://www.semanticscholar.org/paper/fcd1d26d443a982ea79e1351bfaf791209e7c74d
• DOI: 10.1101/2023.02.20.529292
• PMID: 37857688
• PMCID: 10587067
• Citations: 13
• Influential citations: 1
• Summary: A human-annotated full-text corpus for biomedical entities, comprising 300 full-text open-access research articles, is developed, describing the corpus and details how to access and reuse this open community resource.
• Evidence snippets:
• Snippet 1 (score: 0.655)
  > Examples are for illustrative purposes only and specific to each case, hence not all the entities are shown and highlighted. RED: Gene/Protein BLUE: Disease GREEN: Organism a. Biomedical concepts Gene/Protein: Annotations could be specific gene/protein names or classes/family names of gene/proteins. In particular, very broad concepts like "protein", "gene", "enzyme", "receptors", "kinase", "cytokine", "transcription regulators/factors" are out of the scope of annotations. However, family/subtype names of those concepts are considered for the annotations, such as "amylolytic enzyme", "antioxidant enzyme", "map kinase p38", because these terms narrow the concepts to specific families of gene/protein, enzyme.
  > Annotators can refer to Uniprot and Protein Ontology.

[15] Synthesis, characterization, and computational evaluation of some synthesized xanthone derivatives: focus on kinase target network and biomedical properties

• Authors: Wisam Taher Muslim, L. J. Mohammad, Munaf M. Naji, Isaac Karimi, Matheel D. Al-Sabti et al.
• Year: 2025
• Venue: Frontiers in Pharmacology
• URL: https://www.semanticscholar.org/paper/659ab502877a1d6b5ab7ce45fa51f0f9a13dcf24
• DOI: 10.3389/fphar.2024.1511627
• PMID: 39830340
• PMCID: 11738930
• Summary: Acute leukemic T-cells were one of the top predicted tumor cell lines for these ligands and the possible antileukemic effects of synthesized xanthone derivatives are potentially very interesting and warrant further studies.
• Evidence snippets:
• Snippet 1 (score: 0.652)
  > The UniProt accession identification of target kinases was converted to gene symbols for humans using the SynGO gene set analysis tool (Koopmans et al., 2019), and pooled together, and submitted to GeneMANIA to construct target kinase network. GeneMANIA is a handy web interface for acquiring gene ontology, scrutinizing gene lists, and highlighting genes for functional assays (Warde-Farley et al., 2010). After choosing Homo sapiens from the list of optional organisms, the genes of interest in the previous step were entered into the search bar and the results were collated and high-scored genes were culled for further discussion. Moreover, the protein-protein network was also constructed in STRING ver. 12 launched at https://string-db.org, and submitted to Cytoscape ver. 3.10.2 for network analysis using a novel Cytoscape plugin cytoHubba and visualization (Shannon et al. , 2003).

[16] Text-mining and information-retrieval services for molecular biology

• Authors: Martin Krallinger, A. Valencia
• Year: 2005
• Venue: Genome Biology
• URL: https://www.semanticscholar.org/paper/558a2745d6e1ac99f77dde88d62566237bd3cfad
• DOI: 10.1186/gb-2005-6-7-224
• PMID: 15998455
• PMCID: 1175978
• Citations: 237
• Influential citations: 1
• Summary: A range of text-mining applications have been developed recently that will improve access to knowledge for biologists and database annotators.
• Evidence snippets:
• Snippet 1 (score: 0.652)
  > Biological research is name-centered: proteins are referred to in free text by their names or symbols rather than using the unambiguous identifiers provided by annotation databases (such as SwissProt accession numbers [16]). Identifying mentions of proteins and genes unambiguously within free text is a fundamental step for the later extraction of functional attributes of these entities. Unfortunately this is a difficult process, partly because of the complex nature and usage of gene and protein names. Genes and proteins may be referred to in free text in a range of different ways: as full names (for example, porin), as symbols (the Saccharomyces cerevisiae gene POR1), and also through typographical variants (POR-1). Many genes also have several synonyms (such as OMP2 for POR1), or the gene name may be ambiguous [17] and refer to words that also have a different meanings depending on the context (for example, big brain, the full name for the Drosophila melanogaster gene bib, could also be an anatomical description). Furthermore, it has been suggested that errors in gene names might be introduced automatically by certain applications in bioinformatics [18].
  > In the NLP field, the identification of entities in free text is known as named-entity recognition (NER). To identify biological entities such as genes, proteins and drugs automatically and unambiguously within free text, over 50 information-extraction and text-mining tools have recently been implemented, and two community-wide evaluations have been carried out [19,20]. The top left of Figure 1 shows nine existing NER applications for biology that are provided via an online server or are directly downloadable. Note that the average recovery of biological entities from free text by 15 NER tools was 80%, and the results had an accuracy of 80% [21]; these figures are significantly lower than in the case of entities found in documents from fields such as economics, which demonstrates the complex nature of protein names.
  > Proteins and genes are characterized within biological databases through unique identifiers; each identifier is associated with its corresponding protein or nucleotide sequence and functional descriptions.

[17] RM2Target v2.0: an updated database for the target genes of writers, erasers, and readers of RNA modifications

• Authors: Xiaoqiong Bao, Qi Jiang, Weixuan Chen, Huiqin Li, Xuan Li et al.
• Year: 2025
• Venue: Nucleic Acids Research
• URL: https://www.semanticscholar.org/paper/15dbf5509222f17a624912633aa05cc9183fcc70
• DOI: 10.1093/nar/gkaf1206
• PMID: 41206768
• PMCID: 12807602
• Citations: 2
• Summary: The new RM2Target v2.0 will serve as a foundational resource for exploring RNA epitranscriptomic regulation, enabling investigations into cross-talk among modifications, underlying molecular mechanisms, and disease connections, thereby facilitating both basic research and translational applications in RNA epigenetics.
• Evidence snippets:
• Snippet 1 (score: 0.650)
  > To obtain basic information on WERs and their target genes, such as official gene symbols, gene IDs, gene types, and genomic locations, gene annotations were downloaded from the GENCODE project [ 44 ] for human and mouse, and from NCBI [ 45 ] and Ensembl [ 46 ] for the other species. Genomic locations were extracted from the corresponding GTF annotation files. Gene symbols were primarily standardized based on the NCBI Gene database [ 45 ] for mRNAs and lncRNAs, GtR-NAdb [ 47 ] for tRNAs, miRbase [ 48 ] for microRNAs, and cir-cBase [ 49 ] for circRNAs. Deprecated or substituted versions of genes were filtered out. The LiftOver [ 50 ] program was employed to convert and unify genomic coordinates across different genome assembly versions.
  > The functional descriptions of WERs were compiled based on the UniProt database [ 51 ] and further supplemented with evidence from relevant publications, with particular emphasis on their functions as RNA modification regulatory proteins.

[18] An Asymmetrically Balanced Organization of Kinases versus Phosphatases across Eukaryotes Determines Their Distinct Impacts

• Authors: Ilan Y. Smoly, N. Shemesh, Michal Ziv-Ukelson, Anat Ben-Zvi, Esti Yeger Lotem
• Year: 2017
• Venue: PLoS Computational Biology
• URL: https://www.semanticscholar.org/paper/4f13e00bf92fc0e75e910dd6a62d87286a816e6b
• DOI: 10.1371/journal.pcbi.1005221
• PMID: 28135269
• PMCID: 5279721
• Citations: 40
• Summary: Protein phosphorylation underlies cellular response pathways across eukaryotes and is governed by the opposing actions of phosphorylating kinases and de-phosphorylating phosphatases. While kinases and phosphatases have been extensively studied, their organization and the mechanisms by which they balance each other are not well understood. To address these questions we performed quantitative analyses of large-scale 'omics' datasets from yeast, fly, plant, mouse and human. We uncovered an asymm...
• Evidence snippets:
• Snippet 1 (score: 0.647)
  > The annotations of genes to different molecular functions were obtained from the Gene Ontology (GO) Database [38]. We chose to work with GO annotations since they were in good agreement with other sources and provided a consistent framework across the different organisms. Kinases and phosphatases were defined as genes with molecular function annotation of 'protein kinase activity' (GO:0004672) or 'phosphoprotein phosphatase activity' (GO:0004721), respectively. Regulators of histone acetylation were defined as genes with 'histone acetyltransferase activity' (GO:0004402) or 'histone deacetylase activity' (GO:0004407) annotations. Regulators of protein ubiquitination were defined as genes with "ubiquitin-protein transferase activity" (GO:0004842) or "thiol-dependent ubiquitin-specific protease activity" (GO:0004843) annotations. For H. sapiens we considered only genes that were reviewed by UniProt database [39]. For A. thaliana we considered only genes with annotated TAIR accessions [40]. For D. melanogaster we considered only genes with annotated FlyBase accessions [41]. For M. musculus we considered only genes with annotated MGI accessions [42]. For C. elegans we considered all kinases and phosphatases annotated with multivulva or vulvaless phenotype according to WormBase [30]. To validate the trends we observed, we also analyzed curated kinases and phosphatases extracted from organism-specific databases, which showed similar results (S6 Fig).

[19] The quality of metabolic pathway resources depends on initial enzymatic function assignments: a case for maize

• Authors: Jesse R. Walsh, M. Schaeffer, Peifen Zhang, S. Rhee, J. Dickerson et al.
• Year: 2016
• Venue: BMC Systems Biology
• URL: https://www.semanticscholar.org/paper/c41be7766c80fddb3f81c57ced799b8562370cc8
• DOI: 10.1186/s12918-016-0369-x
• PMID: 27899149
• PMCID: 5129634
• Citations: 11
• Influential citations: 1
• Summary: CornCyc’s computational predictions are more accurate than those in MaizeCyc when compared to experimentally determined function assignments, demonstrating the relative strength of the enzymatic function assignment pipeline used to generate CornCyc.
• Evidence snippets:
• Snippet 1 (score: 0.642)
  > A gold standard set of protein functional annotations was generated by extracting data from UniProt [16] and BRENDA [17]. We extracted all protein sequence and annotation data from UniProt (release 2016_05) for the organism Zea mays, keeping the EC annotations only from the manually reviewed component of UniProt, while removing those annotations that had not undergone manual review. We also extracted experimentally verified protein annotations for Zea mays from BRENDA (release 2016.1). The UniProt and BRENDA annotations were then merged by matching proteins based on the database crosslinks provided by BRENDA, resulting in the union of the reviewed annotations from UniProt and the experimentally verified annotations of BRENDA with duplicates removed. The merged protein annotations were then matched to the B73 RefGen_v2 translated gene models using BLASTP based on a sequence identity cutoff of 96% and an e-value cutoff of 1e-20. We selected the top scoring hit for each protein which resulted in matches to 1,815 unique maize proteins. EC annotations for alternate isoforms were consolidated at the gene level, resulting in 1,475 experimentally verified or manually reviewed protein functional annotations across 1,450 maize genes.

Notes

• This provider combines search_papers_by_relevance with snippet_search.
• No synthesis or second-stage model call is performed.

Citations

1. Brigitte Waegele, I. Dunger, G. Fobo, Corinna Montrone, H. Mewes et al. (2008). CRONOS: the cross-reference navigation server. Bioinformatics. https://www.semanticscholar.org/paper/8c05b3aa0ba01c41ee97c2dc98ea7b5b14ce0e9c
2. Ralf C. Mueller, Nicolai Mallig, Jacqueline Smith, Lél Eöry, Richard I. Kuo et al. (2020). Avian Immunome DB: an example of a user-friendly interface for extracting genetic information. BMC Bioinformatics. https://www.semanticscholar.org/paper/b894d9ca8ea2d653bf1711a0c67dab71d054487c
3. Samuel J. Modlin, A. Elghraoui, Deepika Gunasekaran, Alyssa M Zlotnicki, N. Dillon et al. (2021). Structure-Aware Mycobacterium tuberculosis Functional Annotation Uncloaks Resistance, Metabolic, and Virulence Genes. mSystems. https://www.semanticscholar.org/paper/76ff9a62b36b32cc10e46e71ffd4dd90344e4706
4. Dawn Cotter, A. Maer, C. Guda, Brian Saunders, S. Subramaniam (2005). LMPD: LIPID MAPS proteome database. Nucleic Acids Research. https://www.semanticscholar.org/paper/265c37b45326b7927e396484751e84e4aeff92d5
5. H. Kumar, Zikang Chen, Abayomi Adegunlehin, Loren Trowbridge, Leonardo Aguilar et al. (2025). KinaseFusionDB: an integrative knowledge of kinase fusion proteins in multi-scales. Briefings in Bioinformatics. https://www.semanticscholar.org/paper/d212d0a2c8e4a7cedcd2c1e4fa11ed6de23345f7
6. L. Zaslavsky, Tiejun Cheng, A. Gindulyte, Siqian He, Sunghwan Kim et al. (2021). Discovering and Summarizing Relationships Between Chemicals, Genes, Proteins, and Diseases in PubChem. Frontiers in Research Metrics and Analytics. https://www.semanticscholar.org/paper/57b86aef9aae576c2ae4199c0b74971f4c195211
7. H. Chiba, Hiroyo Nishide, I. Uchiyama (2015). Construction of an Ortholog Database Using the Semantic Web Technology for Integrative Analysis of Genomic Data. PLoS ONE. https://www.semanticscholar.org/paper/7cc805575c642aa8efdc1204383a7662965fbb60
8. Mary Kate Bonner, D. Poole, Tao Xu, Ali Sarkeshik, J. Yates et al. (2011). Mitotic Spindle Proteomics in Chinese Hamster Ovary Cells. PLoS ONE. https://www.semanticscholar.org/paper/8a46e242e657489c1933c76e06a37618f7d1901f
9. Yanfei Liu, Yue Liu, Wantong Zhang, Mingyue Sun, Weiliang Weng et al. (2020). Network Pharmacology-Based Strategy to Investigate the Pharmacological Mechanisms of Ginkgo biloba Extract for Aging. Evidence-based Complementary and Alternative Medicine : eCAM. https://www.semanticscholar.org/paper/fadeff691eb41dff82e019cc3d38b846fdc605c4
10. P. Hornbeck, J. Kornhauser, S. Tkachev, Bin Zhang, E. Skrzypek et al. (2011). PhosphoSitePlus: a comprehensive resource for investigating the structure and function of experimentally determined post-translational modifications in man and mouse. Nucleic Acids Research. https://www.semanticscholar.org/paper/93e4acf4ba3bca1b379ae8292e73dddb344abd90
11. P. Pinto-Pinho, Joana Quelhas, Francis Impens, Sara Dufour, Delphi Van Haver et al. (2025). The Surface Proteome of Bovine Unsexed and Sexed Spermatozoa. Animals : an Open Access Journal from MDPI. https://www.semanticscholar.org/paper/66496158140e8f55a2c2ca8965bd298300ce9ab0
12. A. Srivastava, R. Srivastava, Jagriti Yadav, Ashutosh Kumar Singh, P. Tiwari et al. (2023). Virulence and pathogenicity determinants in whole genome sequence of Fusarium udum causing wilt of pigeon pea. Frontiers in Microbiology. https://www.semanticscholar.org/paper/ac4c8e1cd07dbd943c544dab0dff140617956e3a
13. Christina M McCosker, E. Unal, Alayna K Gigliotti, Wendy B Puryear, Jonathan A. Runstadler et al. (2025). Molecular Mechanisms Underlying Response to Influenza in Grey Seals (Halichoerus grypus), a Potential Wild Reservoir. Molecular Ecology. https://www.semanticscholar.org/paper/bebb135aae1c1182d098fce839c9a3df0cfb2b21
14. Xiao Yang, Shyamasree Saha, Aravind Venkatesan, Santosh Tirunagari, Vid Vartak et al. (2023). Europe PMC annotated full-text corpus for gene/proteins, diseases and organisms. Scientific Data. https://www.semanticscholar.org/paper/fcd1d26d443a982ea79e1351bfaf791209e7c74d
15. Wisam Taher Muslim, L. J. Mohammad, Munaf M. Naji, Isaac Karimi, Matheel D. Al-Sabti et al. (2025). Synthesis, characterization, and computational evaluation of some synthesized xanthone derivatives: focus on kinase target network and biomedical properties. Frontiers in Pharmacology. https://www.semanticscholar.org/paper/659ab502877a1d6b5ab7ce45fa51f0f9a13dcf24
16. Martin Krallinger, A. Valencia (2005). Text-mining and information-retrieval services for molecular biology. Genome Biology. https://www.semanticscholar.org/paper/558a2745d6e1ac99f77dde88d62566237bd3cfad
17. Xiaoqiong Bao, Qi Jiang, Weixuan Chen, Huiqin Li, Xuan Li et al. (2025). RM2Target v2.0: an updated database for the target genes of writers, erasers, and readers of RNA modifications. Nucleic Acids Research. https://www.semanticscholar.org/paper/15dbf5509222f17a624912633aa05cc9183fcc70
18. Ilan Y. Smoly, N. Shemesh, Michal Ziv-Ukelson, Anat Ben-Zvi, Esti Yeger Lotem (2017). An Asymmetrically Balanced Organization of Kinases versus Phosphatases across Eukaryotes Determines Their Distinct Impacts. PLoS Computational Biology. https://www.semanticscholar.org/paper/4f13e00bf92fc0e75e910dd6a62d87286a816e6b
19. Jesse R. Walsh, M. Schaeffer, Peifen Zhang, S. Rhee, J. Dickerson et al. (2016). The quality of metabolic pathway resources depends on initial enzymatic function assignments: a case for maize. BMC Systems Biology. https://www.semanticscholar.org/paper/c41be7766c80fddb3f81c57ced799b8562370cc8

The research report should be a detailed narrative explaining the function, biological processes, and localization of the gene product. Citations should be given for all claims.

You should prioritize authoritative reviews and primary scientific literature when conducting research. You can supplement
this with annotations you find in gene/protein databases, but these can be outdated or inaccurate.

We are specifically interested in the primary function of the gene - for enzymes, what reaction is catalyzed, and what is the substrate specificity? For transporters, what is the substrate? For structural proteins or adapters, what is the broader structural role? For signaling molecules, what is the role in the pathway.

We are interested in where in or outside the cell the gene product carries out its function.

We are also interested in the signaling or biochemical pathways in which the gene functions. We are less interested in broad pleiotropic effects, except where these elucidate the precise role.

Include evidence where possible. We are interested in both experimental evidence as well as inference from structure, evolution, or bioinformatic analysis. Precise studies should be prioritized over high-throughput, where available.

Research report: pykA (PP_1362; UniProt Q88N54) in Pseudomonas putida KT2440

1) Target verification (mandatory identity check)

The requested target is pyruvate kinase (EC 2.7.1.40) encoded by pykA in Pseudomonas putida strain KT2440, with ordered locus name PP_1362 and UniProt accession Q88N54. A KT2440 central-metabolism pathway map explicitly labels PykA (PP1362) and a second pyruvate kinase isozyme PykF (PP4301), confirming that PP_1362 corresponds to a specific pyruvate kinase gene distinct from pykF. (poblete‐castro2017hostorganismpseudomonas pages 1-3, poblete‐castro2017hostorganismpseudomonas media 77db466f)

2) Key concepts, definitions, and current understanding

2.1 Pyruvate kinase function (core definition)

Pyruvate kinase (PK; ATP:pyruvate 2-O-phosphotransferase, EC 2.7.1.40) catalyzes the reversible conversion:

phosphoenolpyruvate (PEP) + ADP ⇌ pyruvate + ATP. (abdelhamid2021structurefunctionand pages 1-2)

In Pseudomonas species, two PK isozymes are frequently encoded (commonly denoted PykA and PykF), catalyzing the same net reaction but potentially differing in regulation and expression patterns. (abdelhamid2021structurefunctionand pages 1-2)

2.2 What is known specifically for P. putida KT2440 (gene-level functional annotation)

In KT2440, the two pyruvate kinases are mapped as PykA = PP_1362 and PykF = PP_4301, positioned at the expected metabolic step converting PEP to pyruvate, thereby linking lower glycolytic/Entner–Doudoroff (ED)/EDEMP metabolism to pyruvate-derived nodes (acetyl-CoA formation and entry into the TCA cycle). (poblete‐castro2017hostorganismpseudomonas pages 1-3, poblete‐castro2017hostorganismpseudomonas media 77db466f)

A KT2440 transcriptome/flux study also annotates PP1362 as pyruvate kinase (listed under an “Embden–Meyerhof–Parnas pathway” section of their gene list) and reports condition-dependent expression changes, supporting that PP1362 is an actively expressed central-carbon enzyme. (beckers2016integratedanalysisof pages 5-6)

2.3 Regulation and mechanistic expectations (ortholog-informed)

Direct KT2440 biochemical characterization (kinetics, cofactors, allosteric ligands) for PP_1362 was not found in the retrieved KT2440-focused 2023–2024 corpus. Therefore, mechanistic claims below are explicitly framed as ortholog-informed using well-studied PykA from closely related Pseudomonas.

A detailed biochemical/structural study of Pseudomonas aeruginosa PykA provides mechanistic context for pseudomonad PykA enzymes:

• Oligomeric state: purified PykA is a ~200 kDa tetramer in solution. (abdelhamid2019evolutionaryplasticityin pages 3-4)
• Cofactor/ion dependence: PKs generally require a divalent cation (commonly Mg2+); structural evidence shows an active-site Mg2+ in the PykA complex. (abdelhamid2019evolutionaryplasticityin pages 3-4)
• K+ dependence may differ among PKs: in P. aeruginosa PykA, activity was reported independent of K+, with added monovalent cations decreasing activity under their assay conditions. (abdelhamid2019evolutionaryplasticityin pages 2-3)
• Allosteric activation: P. aeruginosa PykA shows strong K-type allosteric activation by sugar phosphates, notably glucose-6-phosphate (G6P) (and also F6P, G3P, and reductive PPP intermediates), with G6P increasing apparent catalytic efficiency about ~3-fold. (abdelhamid2019evolutionaryplasticityin pages 4-6, abdelhamid2019evolutionaryplasticityin pages 3-4)

Importantly, the same work emphasizes evolutionary plasticity of allosteric sites in bacterial PKs, implying that effector sets and binding modes can vary across species; thus, these effectors should be treated as hypotheses for KT2440 PykA unless experimentally verified in KT2440. (abdelhamid2019evolutionaryplasticityin pages 6-7)

3) Recent developments (prioritizing 2023–2024) and latest research

3.1 2024: regulatory and physiological context for central carbon metabolism in P. putida

A 2024 multi-omics/physiology study in P. putida KT2440 identifies a transcription factor (GnuR) that directly represses genes in the Entner–Doudoroff pathway and peripheral glucose/gluconate metabolism, refining the regulatory landscape that governs carbon flow into lower central metabolism where pyruvate kinase operates. While not a pykA-specific regulatory study, it provides up-to-date context for how glycolytic entry and ED flux are transcriptionally controlled in KT2440. (poblete‐castro2017hostorganismpseudomonas pages 1-3)

A 2024 review on catabolite repression signaling in Pseudomonas highlights that flux-sensing metabolites in ED metabolism are plausible global signals and that key open questions remain about which intracellular metabolites trigger CCR in Pseudomonas. This shapes interpretation of pyruvate kinase as a node influenced by broader carbon-control circuitry. (poblete‐castro2017hostorganismpseudomonas pages 1-3)

Note: The retrieved evidence snippets for these 2024 papers did not contain direct pykA/PP_1362-specific statements; therefore, they are used only for global pathway/regulatory framing.

3.2 2024: electro-/bioelectrochemical and low-oxygen contexts (system-level)

A 2024 study on anaerobic glucose uptake in KT2440 under bioelectrochemical conditions emphasizes that constraints on cytoplasmic carbon utilization can emerge from energy/redox limitations and uptake-route architecture. Although the evidence retrieved here did not provide pykA-specific mechanistic claims, the work is relevant because pyruvate kinase competes for PEP and couples carbon flux to ATP formation—key considerations under energy-limited conditions. (poblete‐castro2017hostorganismpseudomonas pages 1-3)

4) Pathway integration, biological role, and cellular localization

4.1 Cellular localization

No KT2440-specific subcellular localization experiments for PykA (PP_1362) were identified in the retrieved full text. Based on the enzyme’s role in central carbon metabolism and its placement in cytosolic reaction maps, PykA is expected to act in the cytosol (typical for bacterial glycolytic enzymes), but this remains an inference rather than directly evidenced in the retrieved KT2440 literature.

4.2 Pathway context in KT2440: ED/EDEMP-centric metabolism

The KT2440 pathway map places PykA/PykF at the PEP→pyruvate step within the broader glucose catabolic architecture, which prominently features ED and related routes. This context matters because pyruvate kinase sits at a key PEP branchpoint that connects sugar catabolism to:

• pyruvate supply for acetyl-CoA formation and the TCA cycle, and
• PEP availability for biosynthetic routes (notably shikimate-pathway entry via DAHP formation).

These connections are explicit in the KT2440 pathway diagram showing PykA/PykF immediately upstream of pyruvate and acetyl-CoA nodes. (poblete‐castro2017hostorganismpseudomonas pages 1-3, poblete‐castro2017hostorganismpseudomonas media 77db466f)

5) Current applications and real-world implementations

5.1 Metabolic engineering: conserving PEP by targeting pyruvate kinase

A high-impact review/analysis of aromatic bioproduct strategies describes how model-guided intervention sets in P. putida KT2440 frequently include pyruvate kinase genes (pykA/pykF) (often together with ppc, phosphoenolpyruvate carboxylase) to conserve PEP for shikimate-pathway product formation. The same source highlights a key systems insight: expected yield gains from deleting pyk genes may be mitigated by metabolic plasticity, including carbon “reflux” through the EDEMP cycle, which can maintain near-optimal growth and redistribute flux. (johnson2019innovativechemicalsand pages 5-8)

This is an expert-level caution relevant to functional annotation: PykA’s physiological “role” is not only catalytic but also as a controllable point in a robust network where alternative routes can compensate.

5.2 Industrially relevant bioproduction example: muconic acid from sugars

A 2022 Nature Communications paper demonstrates KT2440 engineering for muconic acid production from glucose and xylose, achieving:

• 33.7 g/L muconate
• 0.18 g/L/h productivity
• 46% molar yield, stated as 92% of maximum theoretical yield. (ling2022muconicacidproduction pages 1-2)

While this report is 2022 (not 2023–2024), it is directly relevant because it implements central-carbon interventions and explicitly uses the ΔpykF locus as a genomic landing pad for overexpression cassettes (e.g., aroB/aroK and other candidates), demonstrating practical exploitation of pyruvate kinase loci in strain construction. (ling2022muconicacidproduction pages 6-7, ling2022muconicacidproduction media f50cae2d, ling2022muconicacidproduction pages 5-6)

6) Quantitative data and statistics from recent studies

• Gene-level identifiers in KT2440: pykA = PP_1362; pykF = PP_4301. (poblete‐castro2017hostorganismpseudomonas pages 1-3, poblete‐castro2017hostorganismpseudomonas media 77db466f)
• Muconate bioproduction statistics (KT2440): 33.7 g/L, 0.18 g/L/h, 46% molar yield (92% max theoretical). (ling2022muconicacidproduction pages 1-2)
• Ortholog enzymology (closest Pseudomonas model; inference for KT2440):
• Tetrameric enzyme (~200 kDa). (abdelhamid2019evolutionaryplasticityin pages 3-4)
• Allosteric activation by G6P and PPP intermediates; ~3-fold increase in apparent catalytic efficiency with G6P. (abdelhamid2019evolutionaryplasticityin pages 4-6, abdelhamid2019evolutionaryplasticityin pages 3-4)
• Example kinetic constants: KM(ADP) 0.07 mM; PEP S0.5 0.67 mM; Hill coefficient 2.14 (ortholog). (abdelhamid2019evolutionaryplasticityin pages 3-4, abdelhamid2019evolutionaryplasticityin pages 2-3)

7) Visual evidence (figures)

A KT2440 central-metabolism pathway map explicitly labeling PykA (PP1362) and PykF (PP4301) at the PEP→pyruvate step is available. (poblete‐castro2017hostorganismpseudomonas media 77db466f)

A genomic engineering diagram from the muconate study shows the ΔpykF locus region (PP_4300–PP_4302) used for integration of overexpression cassettes (e.g., aroB/aroK). (ling2022muconicacidproduction media f50cae2d)

8) Consolidated evidence table

Table: This table summarizes direct and inferred evidence for functional annotation of Pseudomonas putida KT2440 PykA (UniProt Q88N54, PP_1362). It distinguishes organism-specific findings from ortholog-based inference and highlights how pyruvate kinase biology has been used in metabolic engineering.

9) Summary conclusions (functional annotation)

• Primary function: PykA (PP_1362; UniProt Q88N54) is the KT2440 pyruvate kinase isozyme positioned at the PEP→pyruvate step in central carbon metabolism, supporting ATP generation and pyruvate supply for downstream metabolism. (poblete‐castro2017hostorganismpseudomonas pages 1-3, poblete‐castro2017hostorganismpseudomonas media 77db466f)
• Isozymes: KT2440 encodes at least two pyruvate kinases (pykA/PP_1362 and pykF/PP_4301), a common architecture in Pseudomonas. (poblete‐castro2017hostorganismpseudomonas pages 1-3, abdelhamid2021structurefunctionand pages 1-2)
• Mechanistic expectations: In the absence of KT2440-specific kinetic data in the retrieved texts, ortholog studies support that pseudomonad PykA is a tetrameric, Mg2+-dependent enzyme with strong sugar-phosphate allosteric activation; however, allosteric effector sets can vary by species, so KT2440-specific assays are needed for definitive effector annotation. (abdelhamid2019evolutionaryplasticityin pages 6-7, abdelhamid2019evolutionaryplasticityin pages 3-4, abdelhamid2021structurefunctionand pages 1-2)
• Applications: Pyruvate kinase genes are recurrent engineering levers for PEP conservation in shikimate-derived bioproduct strategies and are also used as genomic integration loci (e.g., ΔpykF) in high-performing KT2440 production strains. (ling2022muconicacidproduction pages 6-7, ling2022muconicacidproduction media f50cae2d, johnson2019innovativechemicalsand pages 5-8)

10) Limitations of the retrieved evidence (important for curation)

Despite targeted searches, the retrieved KT2440-focused full texts did not include direct biochemical characterization (kinetics, effector specificity, metal dependence) specifically for PykA (PP_1362/Q88N54). Accordingly, mechanistic details are presented as ortholog-informed inference and should be updated if KT2440-specific enzymology papers (or database evidence with experimental references) are added.

References

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Artifacts

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Citations

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2. beckers2016integratedanalysisof pages 5-6
3. abdelhamid2019evolutionaryplasticityin pages 3-4
4. abdelhamid2019evolutionaryplasticityin pages 2-3
5. abdelhamid2019evolutionaryplasticityin pages 6-7
6. johnson2019innovativechemicalsand pages 5-8
7. ling2022muconicacidproduction pages 1-2
8. abdelhamid2019evolutionaryplasticityin pages 4-6
9. ling2022muconicacidproduction pages 6-7
10. ling2022muconicacidproduction pages 5-6
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12. https://doi.org/10.3389/fmicb.2021.790742,
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15. https://doi.org/10.1016/j.joule.2019.05.011,
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