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] 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: 0.721)
  > 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: 0.659)
  > (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.

[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: 0.716)
  > 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] 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: 0.699)
  > 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.

[4] 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: 0.682)
  > 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.

[5] Phase-separating fusion proteins drive cancer by dysregulating transcription through ectopic condensates

• Authors: Nazanin Farahi, Tamas Lazar, P. Tompa, Bálint Mészáros, Rita Pancsa
• Year: 2023
• Venue: bioRxiv
• URL: https://www.semanticscholar.org/paper/57a63e3228a18d5d68d54eb8303eeb7c0ae29da6
• DOI: 10.1101/2023.09.20.558425
• Citations: 1
• Summary: It is found that proteins initiating LLPS are frequently implicated in somatic cancers, even surpassing their involvement in neurodegeneration, and protein regions driving condensate formation show an increased association with DNA- or chromatin-binding domains of transcription regulators within OFPs, indicating a common molecular mechanism underlying several soft tissue sarcomas and hematologic malignancies.
• Evidence snippets:
• Snippet 1 (score: 0.679)
  > We defined the subcellular localization for each protein in the human proteome by integrating data from Gene Ontology annotations in UniProt (GOA), UniProt annotations, the Human Transmembrane Proteome (HTP) 121 , MatrixDB 122 , and MatrisomeDB 123 . We divided the UniProt and the Gene Ontology annotations (GOA) into tier 1 (more reliable) and tier 2 (less reliable) annotations, depending on the attached evidence codes. For UniProt, annotations with the evidence codes ECO:0000269 or ECO:0000305 are considered as tier 1, while annotations with evidence codes ECO:0000250, ECO:0000255, or ECO:0000303 are tier 2. For Gene Ontology, annotations with evidence codes IDA, IMP, IPI, IGI, EXP, IBA, IKR, TAS, NAS, IC, or ND are tier 1, while annotations with evidence codes HDA, ISS, ISA, RCA, ISO, ISM, IGC, or IEA are tier 2. Based on these, each protein was assigned exactly one broad localization. It was considered to be a transmembrane protein (TMP), if it is assigned the 'integral component of membrane (GO:0016021)' GO term in tier 1 GOA annotations, or it is annotated as a TMP in HTP with a confidence score over 85, or is annotated in HTP as a TMP with a confidence score above 50 and is also annotated as a TMP in GOA (either tier).

[6] 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: 0.667)
  > 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.

[7] 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: 0.665)
  > 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.

[8] iDS372, a Phenotypically Reconciled Model for the Metabolism of Streptococcus pneumoniae Strain R6

• Authors: Óscar Dias, J. Saraiva, Cristiana Faria, M. Ramirez, F. Pinto et al.
• Year: 2019
• Venue: Frontiers in Microbiology
• URL: https://www.semanticscholar.org/paper/a9b961f66aa380f0d52504b7ae8ea9ba2e279fde
• DOI: 10.3389/fmicb.2019.01283
• PMID: 31293525
• PMCID: 6603136
• Citations: 18
• Summary: A high-quality GSM model for Streptococcus pneumoniae R6 model strain (iDS372), comprising 372 genes and 529 reactions, was developed to better understand the metabolism of this major pathogen, provide clues regarding new drug targets, and eventually design strategies for fighting infections by these bacteria.
• Evidence snippets:
• Snippet 1 (score: 0.655)
  > Gene annotations in which merlin's assignment matched the information retrieved from reviewed genes in UniProt can be regarded with higher confidence. In these cases, information was often replicated throughout the remaining databases, such as the case of gene Spr0021.
  > The annotation of genes with incomplete EC number, such as the cases of Spr0022 and Spr0064, required an in-depth analysis. Databases such as KEGG, UniProtKB, and CDD were consulted. In the case of Spr0064, the CDD classified the protein as a sugar isomerase. A search of the protein name assigned by BRENDA returned the EC number 5.3.1.26 which also belonged to the sugar isomerase superfamily. Due to the absence of literature support and information on other databases, the gene was annotated with this EC number and assigned the Label E. Regarding gene Spr0022, KEGG assigned it EC number 3.5.4.33 [tRNA(adenine34) deaminase], UniProtKB annotated the product as a hypothetical protein while BioCyc assigned the EC number 3.5.4.5 (cytidine deaminase) to the gene product. Analysis of the protein sequence on CDD revealed that the gene encoded a protein of the cytidine/deoxycytidilate deaminase superfamily and BLAST on UniProtKB revealed similarity to a gene that encodes a cytidine deaminase protein and search of this protein on BRENDA returned the EC number 3.5.4.5. Thus, this gene was assigned the Label E. Labels A, B, and C account for over 86% of the annotation, with approximately 33, 34, and 20% of the classifications, respectively. There were cases in which genes assigned with incomplete EC numbers (e.g., spr0068) were ultimately annotated with complete ones. Despite the classification as hypothetical protein by KEGG and UniProtKB, the analysis of the conserved domains as well as the availability of complete EC numbers that matched protein function by merlin increased reliability of the assigned function (uridine phosphorylase in this example).

[9] 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: 0.653)
  > 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.

[10] 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: 0.652)
  > 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.

[11] 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: 0.651)
  > 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.

[12] 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: 0.649)
  > 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.

[13] Network pharmacology-based strategic prediction and target identification of apocarotenoids and carotenoids from standardized Kashmir saffron (Crocus sativus L.) extract against polycystic ovary syndrome

• Authors: Anshul Tiwari, Siddharth J Modi, A. Girme, L. Hingorani
• Year: 2023
• Venue: Medicine
• URL: https://www.semanticscholar.org/paper/3e3253804574634d1968a0fd5b65dd1674bff6c6
• DOI: 10.1097/MD.0000000000034514
• PMID: 37565925
• PMCID: 10419424
• Citations: 2
• Summary: A network pharmacology-based method to determine the potential therapeutic pathways of phytoconstituents of UHPLC-PDA standardized stigma-based Crocus sativus extract for the management of PCOS revealed that the apocarotenoids and carotenoidal could act on various targets to regulate multiple pathways related to PCOS.
• Evidence snippets:
• Snippet 1 (score: 0.647)
  > The target protein name of the active ingredient was converted to the standard target gene name using the UniProt Knowledge Base (UniProtKB). UniProt KB is the central hub for the collection of functional information on proteins, with accurate, consistent, and rich annotation. The target protein names were uploaded into UniProtKB, with the organism restricted to "Homo sapiens," eventually gaining the official symbol. The potential targets obtained from the UniproKB are depicted in Figures 3 and 4.

[14] GOnet: a tool for interactive Gene Ontology analysis

• Authors: M. Pomaznoy, Brendan Ha, Bjoern Peters
• Year: 2018
• Venue: BMC Bioinformatics
• URL: https://www.semanticscholar.org/paper/d984d075cb08f6c39a48bcf1f32e36f333a423d9
• DOI: 10.1186/s12859-018-2533-3
• PMID: 30526489
• PMCID: 6286514
• Citations: 247
• Influential citations: 17
• Summary: The open-source GOnet web-application is created, which takes a list of gene or protein entries from human or mouse data and performs GO term annotation analysis and provides insight into the functional interconnection of the submitted entries.
• Evidence snippets:
• Snippet 1 (score: 0.636)
  > In a basic workflow, the GOnet application receives a list of gene symbols, protein symbols, or protein IDs (UniProt IDs) as an input, and outputs a graph (an example given in Fig. 1). There are various input parameters which will affect the actual structure of the graph visualized and its appearance. The first main user choice is which GO terms the genes are annotated against:
  > 1. GO terms statistically significantly over-represented in the gene list submitted. 2. A predefined subset (also known as 'GO slim'), or a user-supplied list of terms.
  > In the first case the analysis will be referred to as an 'enrichment' analysis, in the second as an 'annotation' analysis.
  > Input parameters 1) Gene list. A mandatory input parameter containing the genes/proteins of interest. Currently human and mouse data is supported. An example of a human gene list might look like this:
  > Fig. 1 Sample network output generated by GOnet application. Gene differentially expressed in CD4 Bulk Memory T cells in Latent TB patients compared to healthy controls were used as an example [22] The gene list can also be accompanied with a contrast value. For example, This contrast value can be any decimal number, such as the log-fold change of gene expression between two conditions. This is merely a visualization enhancement. If the value is supplied it can be used later to differentially color specific genes in the graph (note different colors of gene nodes in Fig. 1), and visually indicate up-or down-regulation of specific genes and gene clusters.
  > The application can process common gene symbols (like in the example above), UniProt IDs, and MGI Accession IDs (mouse only). The former type of ID (gene symbols), although is the most human friendly, can unfortunately be ambiguous. For example, AIM1 can mean 'absent in melanoma' (also called CRYBG1) or 'Aurora and Ipl1-like midbody-associated protein' (also known as AURKB). Due to this ambiguity UniProt IDs or MGI accession IDs (for mouse) are preferred.
  > 2) GO namespace. Can be any of 'biological process', 'molecular function' or 'cellular component'.

[15] Role of histone-lysine N-methyltransferase 2D (KMT2D) in MEK-ERK signaling-mediated epigenetic regulation: a phosphoproteomics perspective

• Authors: Sreeshma Ravindran Kammarambath, Leona Dcunha, Athira Perunelly Gopalakrishnan, Amal Fahma, N. Krishna et al.
• Year: 2025
• Venue: Frontiers in Bioinformatics
• URL: https://www.semanticscholar.org/paper/0ac0729148aff3d839e6a15984e11532e9e740f9
• DOI: 10.3389/fbinf.2025.1683469
• PMID: 41341998
• PMCID: 12669113
• Citations: 3
• Summary: The phosphoregulatory network of Histone-lysine N-methyltransferase 2D is delineated, positioning it as a dynamic epigenetic effector modulated by MEK-ERK signaling, with broader implications for cancer and developmental disorders.
• Evidence snippets:
• Snippet 1 (score: 0.634)
  > Each protein was mapped to its corresponding gene symbol based on the HGNC (downloaded on 30.05.2023) and to its corresponding UniProt (13.04.2023) (UniProt, 2023) accessions using our in-built mapping tool to ensure consistent and standardized annotation. We conducted the analysis using the methodologies outlined in (Sanjeev et al., 2024). The overall workflow used in this study is outlined in Figure 1.

[16] The USDA-ARS Ag100Pest Initiative: High-Quality Genome Assemblies for Agricultural Pest Arthropod Research

• Authors: Anna K. Childers, S. Geib, S. Sim, Monica F. Poelchau, B. Coates et al.
• Year: 2021
• Venue: Insects
• URL: https://www.semanticscholar.org/paper/a33d31da6f5aee18501cfc2332ff50d5b7f23508
• DOI: 10.3390/insects12070626
• PMID: 34357286
• PMCID: 8307976
• Citations: 41
• Influential citations: 2
• Summary: It is shown that the Ag100Pest Initiative will greatly expand the diversity of publicly available arthropod genome assemblies and demonstrate the high quality of preliminary contig assemblies, which should help other researchers attain similarly high-quality assemblies.
• Evidence snippets:
• Snippet 1 (score: 0.634)
  > Structural annotation refers to the prediction of gene structures on a genome assembly, including the positions of transcripts, exons, introns, coding sequences, and other features [49]. Functional annotation provides information about the gene's biological role(s), for example, gene ontologies [50], pathways, functional domains, and names. Model organism databases can manually assign biological function to genes by accumulating evidence from the scientific literature and structuring it in human and machine-readable formats. In contrast, for non-model organisms such as those in the Ag100Pest Initiative, most, if not all, functional annotation is performed computationally, as (1) gene function in very few genes have been established experimentally for these non-model species, and (2) the capacity for literature-based curation of gene function does not yet exist for these species.
  > Most of the genome assemblies generated by the Ag100Pest project are being annotated using the NCBI eukaryotic annotation pipeline [51]. This pipeline relies on Gnomon [52] for gene prediction and uses genome assembly, RNA sequencing (RNA-Seq) alignments, transcripts, and protein alignments as inputs. The resulting gene predictions are given an accession number and made publicly available. Gene names are assigned based on homology to proteins in SwissProt [53,54]. The NCBI eukaryotic annotation pipeline requires both the genome assembly and associated RNA-Seq evidence to be publicly available in the NCBI's GenBank and Sequence Read Archive, respectively (SRA; see [55]). In the event that an Ag100Pest species lacks sufficient RNA-Seq evidence in SRA, additional data will be generated, as appropriate, and submitted to aid with NCBI gene structure prediction and annotation.
  > NCBI does not currently generate additional functional annotations. Proteins deposited in GenBank or generated by RefSeq should eventually be functionally annotated by UniProt [53].

[17] MultiLoc2: integrating phylogeny and Gene Ontology terms improves subcellular protein localization prediction

• Authors: Torsten Blum, S. Briesemeister, O. Kohlbacher
• Year: 2009
• Venue: BMC Bioinformatics
• URL: https://www.semanticscholar.org/paper/c2f00f9a94fe72eeeadc54a37a816731f329bfa4
• DOI: 10.1186/1471-2105-10-274
• PMID: 19723330
• PMCID: 2745392
• Citations: 294
• Influential citations: 36
• Summary: MultiLoc2 is an extensive high-performance subcellular protein localization prediction system that outperforms other prediction systems in two benchmarks studies and yields higher accuracies compared to its previous version.
• Evidence snippets:
• Snippet 1 (score: 0.634)
  > The Gene Ontology (GO) is a controlled vocabulary for uniformly describing gene products in terms of biological processes, cellular components and molecular function across all organisms [46]. It has been shown that GO terms can be used to improve the performance of subcellular protein localization prediction methods [47,48]. In the literature to date, there are three possibilities for obtaining GO annotation terms for a query sequence. If the UniProt [49] accession number is known, one can simply extract the GO annotation from the UniProt database [50]. However, this procedure fails for novel proteins without accession number. Another possibility is to search for homologous proteins annotated with GO terms using BLAST [28,29]. This becomes difficult in cases where proteins have no close homolog or proteins have many homologs, because no GO term can be obtained or GO terms might be ambiguous. A further method of inferring GO terms is InterProScan [51] used, for example, by Chou and Cai [52]. Given a protein sequence, the tool scans against various pattern and signature data sources collected by the InterPro project [53]. InterPro also provides a mapping of the detected protein domains and functional sites to GO terms.
  > Our subpredictor GOLoc is based on GO terms calculated using InterProScan. Since the GO terms are derived directly from the query sequence, we avoid the drawbacks of using accession numbers or BLAST. The input of GOLoc is a binary-coded vector which represents all GO terms of the training sequences (see Fig. 2). GO terms present in the query sequence are set to 1 in the vector and to 0 otherwise [see Additional file 1].

[18] 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: 0.630)
  > 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/]

[19] 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: 0.630)
  > 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.

Notes

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

Citations

1. 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
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. 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
4. 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
5. Nazanin Farahi, Tamas Lazar, P. Tompa, Bálint Mészáros, Rita Pancsa (2023). Phase-separating fusion proteins drive cancer by dysregulating transcription through ectopic condensates. bioRxiv. https://www.semanticscholar.org/paper/57a63e3228a18d5d68d54eb8303eeb7c0ae29da6
6. 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
7. 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
8. Óscar Dias, J. Saraiva, Cristiana Faria, M. Ramirez, F. Pinto et al. (2019). iDS372, a Phenotypically Reconciled Model for the Metabolism of Streptococcus pneumoniae Strain R6. Frontiers in Microbiology. https://www.semanticscholar.org/paper/a9b961f66aa380f0d52504b7ae8ea9ba2e279fde
9. 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
10. 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
11. 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
12. 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
13. Anshul Tiwari, Siddharth J Modi, A. Girme, L. Hingorani (2023). Network pharmacology-based strategic prediction and target identification of apocarotenoids and carotenoids from standardized Kashmir saffron (Crocus sativus L.) extract against polycystic ovary syndrome. Medicine. https://www.semanticscholar.org/paper/3e3253804574634d1968a0fd5b65dd1674bff6c6
14. M. Pomaznoy, Brendan Ha, Bjoern Peters (2018). GOnet: a tool for interactive Gene Ontology analysis. BMC Bioinformatics. https://www.semanticscholar.org/paper/d984d075cb08f6c39a48bcf1f32e36f333a423d9
15. Sreeshma Ravindran Kammarambath, Leona Dcunha, Athira Perunelly Gopalakrishnan, Amal Fahma, N. Krishna et al. (2025). Role of histone-lysine N-methyltransferase 2D (KMT2D) in MEK-ERK signaling-mediated epigenetic regulation: a phosphoproteomics perspective. Frontiers in Bioinformatics. https://www.semanticscholar.org/paper/0ac0729148aff3d839e6a15984e11532e9e740f9
16. Anna K. Childers, S. Geib, S. Sim, Monica F. Poelchau, B. Coates et al. (2021). The USDA-ARS Ag100Pest Initiative: High-Quality Genome Assemblies for Agricultural Pest Arthropod Research. Insects. https://www.semanticscholar.org/paper/a33d31da6f5aee18501cfc2332ff50d5b7f23508
17. Torsten Blum, S. Briesemeister, O. Kohlbacher (2009). MultiLoc2: integrating phylogeny and Gene Ontology terms improves subcellular protein localization prediction. BMC Bioinformatics. https://www.semanticscholar.org/paper/c2f00f9a94fe72eeeadc54a37a816731f329bfa4
18. 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
19. 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

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: Functional Annotation of tpiA (UniProt Q88DV4, locus PP_4715) in Pseudomonas putida KT2440

Executive summary

The gene tpiA in Pseudomonas putida KT2440 (ordered locus name PP_4715) encodes triosephosphate isomerase (TIM/TPI; EC 5.3.1.1), a highly efficient cytosolic enzyme that reversibly interconverts dihydroxyacetone phosphate (DHAP) and glyceraldehyde-3-phosphate (G3P/D-GAP), balancing the pool of triose phosphates in central carbon metabolism. In KT2440, genetic evidence shows that loss of tpiA prevents growth on both glucose and succinate, indicating a critical role under both glycolytic and gluconeogenic regimes, despite the organism’s atypical glucose catabolism dominated by Entner–Doudoroff (ED) reactions. (nikel2015pseudomonasputidakt2440 pages 6-7)

1. Identity verification (critical disambiguation)

Target confirmed: In a detailed KT2440 central carbon metabolism study, tpiA is explicitly annotated as PP_4715 encoding “triose phosphate isomerase” (TpiA) and is included among mutant derivatives evaluated, confirming that the KT2440 gene symbol tpiA corresponds to triosephosphate isomerase rather than an unrelated “tpiA” in other organisms. (nikel2015pseudomonasputidakt2440 pages 15-17)

Visual evidence from the same work includes (i) a pathway schematic where TpiA catalyzes DHAP ↔ G3P and (ii) plate-growth results including the ΔtpiA strain. (nikel2015pseudomonasputidakt2440 media 6f85cc6f, nikel2015pseudomonasputidakt2440 media 0edc1cf4)

2. Key concepts and definitions (current understanding)

2.1 Enzyme reaction and substrate specificity

Triosephosphate isomerase (TIM/TPI) catalyzes the reversible aldose–ketose isomerization:

• DHAP ⇄ D-GAP (G3P)

TIM is described as stereospecific in converting DHAP to D-GAP and does not require a cofactor or metal ion. (wierenga2010triosephosphateisomerasea pages 1-3)

Substrate binding strongly depends on recognition of the phosphate dianion in a specialized pocket formed by multiple active-site loops; this tight binding helps enforce productive catalysis and suppresses side reactions such as phosphate elimination. (wierenga2010triosephosphateisomerasea pages 6-8, wierenga2010triosephosphateisomerasea pages 1-3)

2.2 Catalytic mechanism and conserved residues

TIM is a paradigmatic “near-perfect” enzyme. Mechanistically, TIM catalysis proceeds through an enediolate-type intermediate, enabled by a conserved active-site architecture and loop motions that close the active site to exclude bulk solvent. (wierenga2010triosephosphateisomerasea pages 3-5, wierenga2010triosephosphateisomerasea pages 1-3)

Key catalytic residues in the canonical numbering scheme include:
- Asn11, Lys13, His95, and Glu167 (catalytic base) (wierenga2010triosephosphateisomerasea pages 3-5, wierenga2010triosephosphateisomerasea pages 1-3)

A 2023 expert review on TPI in eukaryotes uses slightly different numbering (e.g., K14, H96, E166) but refers to the same conserved catalytic core. (myers2023newlydiscoveredroles pages 1-2)

2.3 Enzyme efficiency and kinetics (quantitative)

TIM is frequently cited as diffusion-limited or near diffusion-limited in catalytic efficiency. Reported values include:
- kcat/Km ≈ 1 × 10^9 M−1 s−1 (diffusion-limited range) in the D-GAP → DHAP direction (wierenga2010triosephosphateisomerasea pages 3-5)
- kcat ~500 s−1 for DHAP → D-GAP and ~5,000 s−1 for D-GAP → DHAP (wierenga2010triosephosphateisomerasea pages 3-5)
- Km ~1.2 mM for DHAP and ~0.25 mM for D-GAP (with additional notes about the unhydrated species) (wierenga2010triosephosphateisomerasea pages 3-5)

These parameters reflect an enzyme optimized to rapidly equilibrate intracellular DHAP and G3P pools. (wierenga2010triosephosphateisomerasea pages 3-5, wierenga2010triosephosphateisomerasea pages 19-20)

2.4 Oligomeric state

TIM is typically dimeric, and only the dimer is fully active; engineered monomeric variants show orders-of-magnitude lower catalytic performance (e.g., kcat ~1 s−1, Km ~5 mM), illustrating that dimerization contributes to active-site rigidity and solvent exclusion. (wierenga2010triosephosphateisomerasea pages 10-12)

2.5 Subcellular localization (bacterial expectation)

TIM is generally a cytosolic enzyme in most organisms, consistent with its canonical role in central carbon metabolism; specialized compartmentalization is described mainly for specific eukaryotes (e.g., glycosomes), not typical bacteria. (wierenga2010triosephosphateisomerasea pages 3-5, kursula2003crystallographicstudieson pages 34-37)

3. Pseudomonas putida KT2440: pathway context and functional role

3.1 Central carbon metabolism architecture in KT2440

P. putida KT2440 is well known for glucose catabolism that is strongly routed through periplasmic oxidation and ED pathway reactions, and lacks a “standard” fully operational Embden–Meyerhof–Parnas (EMP) glycolysis due to low/absent canonical phosphofructokinase activity (as framed in the study’s pathway description). (nikel2015pseudomonasputidakt2440 pages 15-17)

Nevertheless, the KT2440 network uses ED/EMP/PP reactions in an integrated cycle for glucose processing, and triose-phosphate balancing remains crucial. (nikel2015pseudomonasputidakt2440 pages 6-7, nikel2015pseudomonasputidakt2440 pages 15-17)

Quantitative flux/yield context reported for KT2440 on glucose includes:
- Lag phase ~1.2 ± 0.5 h on fresh glucose
- Periplasmic oxidation yields: gluconate yG/S = 0.34 ± 0.02 and 2-ketogluconate yK/S = 0.11 ± 0.01 (C-mol/C-mol)
- >80% of glucose influx routed via periplasmic oxidation
- About 25% of fructose-6-phosphate (F6P) formed through the PP pathway
- An ~50% relative flux contribution of ED pathway to pyruvate formation

These values highlight the metabolic background in which triose-phosphate interconversion is embedded. (nikel2015pseudomonasputidakt2440 pages 6-7)

3.2 Genetic evidence for essentiality/criticality of tpiA in KT2440

In KT2440, deleting/disrupting tpiA produced a striking phenotype: the authors report “lack of growth of a tpiA mutant in either glucose or succinate.” (nikel2015pseudomonasputidakt2440 pages 6-7)

They also emphasize that this was “unexpected,” because ED metabolism produces G3P and pyruvate and thus, in principle, the TpiA reaction might be thought less critical for growth on glucose; the observed non-growth indicates that triose-phosphate interconversion is indispensable for KT2440 physiology under the tested conditions. (nikel2015pseudomonasputidakt2440 pages 6-7)

Figure-based evidence shows the ΔtpiA mutant included among strains evaluated on glucose vs succinate minimal media plates. (nikel2015pseudomonasputidakt2440 media 6f85cc6f, nikel2015pseudomonasputidakt2440 media 0edc1cf4)

3.3 Enzymology in KT2440 extracts and metabolite context

Enzyme assays in cell-free extracts indicate that Fbp, Fda, and TpiA (EMP-associated enzymes) were “equally active” in both glucose and succinate cultures, consistent with a role beyond “classical” glycolysis-only contexts. (nikel2015pseudomonasputidakt2440 pages 5-6)

For quasi in vivo enzymatic assays, intracellular metabolite levels included G3P ~140 µM, providing a quantitative anchor for the triose-phosphate pool under the studied conditions. (nikel2015pseudomonasputidakt2440 pages 5-6)

4. Recent developments (prioritizing 2023–2024)

4.1 2024 Nature Communications: engineered KT2440 adaptation to D-xylose

A 2024 Nature Communications study on synthetically primed adaptation of KT2440 to D-xylose explicitly remarks that operation of Fbp, Fba, and TpiA would be redundant to pentose phosphate pathway (PPP) activity in the engineered xylose-grown strain, reflecting modern interpretations of how central carbon flux can be rewired in P. putida and when EMP/gluconeogenic steps become unnecessary. (dvorak2024syntheticallyprimedadaptationof pages 4-5)

This provides recent, high-authority evidence that TpiA’s functional necessity can be context-dependent in engineered metabolic states, even though it is strongly required for growth in the baseline KT2440 conditions tested in earlier work. (dvorak2024syntheticallyprimedadaptationof pages 4-5, nikel2015pseudomonasputidakt2440 pages 6-7)

Publication details: March 2024; URL: https://doi.org/10.1038/s41467-024-46812-9 (dvorak2024syntheticallyprimedadaptationof pages 4-5)

4.2 2024 Microbial Cell Factories: anoxic-electrogenic cultivation and multi-omics

A 2024 multi-omics study of electrogenic (bio-electrochemical) cultivation of KT2440 reports that tpiA (within “triose recycling” genes) was largely unchanged or slightly downregulated under anoxic-electrogenic fermentation conditions, suggesting that core triose-phosphate balancing may not be strongly transcriptionally induced in that specific non-growth/maintenance-like regime (at least at the qualitative level provided in the excerpt). (weimer2024systemsbiologyof pages 8-9)

Publication details: September 2024; URL: https://doi.org/10.1186/s12934-024-02509-8 (weimer2024systemsbiologyof pages 8-9)

4.3 2023 expert review: expanding conceptual landscape of TPI (moonlighting)

A 2023 review synthesizes evidence that TPI can have “moonlighting” functions (including nuclear roles) in eukaryotes and disease contexts. While this is not direct evidence for bacterial P. putida tpiA moonlighting, it is relevant as an expert caution: TPI proteins can participate in cellular processes beyond glycolysis in some systems, and catalytic loss may not fully explain phenotypes in all organisms. (myers2023newlydiscoveredroles pages 1-2, myers2023newlydiscoveredroles pages 2-4)

Publication details: January 2023; URL: https://doi.org/10.1186/s10020-023-00612-x (myers2023newlydiscoveredroles pages 1-2)

5. Current applications and real-world implementations

Because tpiA encodes a core central-carbon enzyme, it is primarily leveraged indirectly in applications that engineer P. putida central metabolism:

• Substrate scope expansion and metabolic rewiring: In engineered KT2440 strains adapted to new substrates (e.g., xylose), pathway designs and evolved flux states can make steps like TpiA (together with Fbp/Fba) redundant relative to PPP-driven carbon processing, informing design choices for strain engineering. (dvorak2024syntheticallyprimedadaptationof pages 4-5)

• Bioelectrochemical and non-standard bioprocessing: In anoxic-electrogenic systems aimed at producing oxidized sugars such as 2-ketogluconate, KT2440 shows broad transcriptome remodeling while some central “triose recycling” genes including tpiA are not strongly induced, illustrating how industrially relevant cultivation modes may alter central-carbon gene utilization patterns. (weimer2024systemsbiologyof pages 8-9)

6. Expert opinions and analysis (authoritative synthesis)

• TIM as an archetypal optimized enzyme: The detailed structural and mechanistic review characterizes TIM as a “highly evolved biocatalyst” with catalytic performance approaching diffusion limits, emphasizing loop closure, phosphate recognition, and catalytic-base chemistry as key design principles. (wierenga2010triosephosphateisomerasea pages 1-3, wierenga2010triosephosphateisomerasea pages 19-20)

• KT2440 network-level interpretation: Nikel et al. interpret the unexpected non-growth of ΔtpiA as evidence that EMP-associated reactions, despite the unusual architecture of glucose catabolism in P. putida, are functionally necessary and active in vivo, helping define the integrated ED/EMP/PP cycling picture for KT2440. (nikel2015pseudomonasputidakt2440 pages 6-7, nikel2015pseudomonasputidakt2440 pages 5-6)

7. Evidence map (compact)

Table: This table compiles validated organism-specific facts for Pseudomonas putida KT2440 tpiA/PP_4715 together with core triosephosphate isomerase biochemistry. It is useful as a compact evidence map for function, pathway role, localization, and key quantitative properties.

8. Functional annotation conclusion for Q88DV4 (tpiA/PP_4715)

Recommended functional annotation (supported by evidence):
- Protein: Triosephosphate isomerase (TIM/TPI)
- EC: 5.3.1.1
- Reaction: DHAP ⇄ G3P (D-GAP)
- Primary role in KT2440: Balances triose-phosphate pools in central carbon metabolism; required for growth on both glucose and succinate in the tested conditions.
- Localization: Cytosolic enzyme (bacterial expectation) supporting central metabolism.

All major claims above are supported by the cited primary literature and reviews listed in the references.

References (URLs and publication dates)

1. Nikel PI, Chavarría M, Fuhrer T, Sauer U, de Lorenzo V. Pseudomonas putida KT2440 metabolizes glucose through a cycle formed by ED, EMP, and PP enzymes. Journal of Biological Chemistry. Oct 2015. https://doi.org/10.1074/jbc.M115.687749 (nikel2015pseudomonasputidakt2440 pages 6-7)
2. Wierenga RK, Kapetaniou EG, Venkatesan R. Triosephosphate isomerase: a highly evolved biocatalyst. Cellular and Molecular Life Sciences. Aug 2010. https://doi.org/10.1007/s00018-010-0473-9 (wierenga2010triosephosphateisomerasea pages 1-3)
3. Myers TD, Palladino MJ. Newly discovered roles of triosephosphate isomerase including functions within the nucleus. Molecular Medicine. Jan 2023. https://doi.org/10.1186/s10020-023-00612-x (myers2023newlydiscoveredroles pages 1-2)
4. Dvořák P, et al. Synthetically-primed adaptation of Pseudomonas putida to a non-native substrate D-xylose. Nature Communications. Mar 2024. https://doi.org/10.1038/s41467-024-46812-9 (dvorak2024syntheticallyprimedadaptationof pages 4-5)
5. Weimer A, et al. Systems biology of electrogenic Pseudomonas putida: multi-omics insights and metabolic engineering for enhanced 2-ketogluconate production. Microbial Cell Factories. Sep 2024. https://doi.org/10.1186/s12934-024-02509-8 (weimer2024systemsbiologyof pages 8-9)

References

1. (nikel2015pseudomonasputidakt2440 pages 6-7): Pablo I. Nikel, Max Chavarría, Tobias Fuhrer, Uwe Sauer, and Víctor de Lorenzo. Pseudomonas putida kt2440 strain metabolizes glucose through a cycle formed by enzymes of the entner-doudoroff, embden-meyerhof-parnas, and pentose phosphate pathways. Journal of Biological Chemistry, 290:25920-25932, Oct 2015. URL: https://doi.org/10.1074/jbc.m115.687749, doi:10.1074/jbc.m115.687749. This article has 440 citations and is from a domain leading peer-reviewed journal.

2. (nikel2015pseudomonasputidakt2440 pages 15-17): Pablo I. Nikel, Max Chavarría, Tobias Fuhrer, Uwe Sauer, and Víctor de Lorenzo. Pseudomonas putida kt2440 strain metabolizes glucose through a cycle formed by enzymes of the entner-doudoroff, embden-meyerhof-parnas, and pentose phosphate pathways. Journal of Biological Chemistry, 290:25920-25932, Oct 2015. URL: https://doi.org/10.1074/jbc.m115.687749, doi:10.1074/jbc.m115.687749. This article has 440 citations and is from a domain leading peer-reviewed journal.

3. (nikel2015pseudomonasputidakt2440 media 6f85cc6f): Pablo I. Nikel, Max Chavarría, Tobias Fuhrer, Uwe Sauer, and Víctor de Lorenzo. Pseudomonas putida kt2440 strain metabolizes glucose through a cycle formed by enzymes of the entner-doudoroff, embden-meyerhof-parnas, and pentose phosphate pathways. Journal of Biological Chemistry, 290:25920-25932, Oct 2015. URL: https://doi.org/10.1074/jbc.m115.687749, doi:10.1074/jbc.m115.687749. This article has 440 citations and is from a domain leading peer-reviewed journal.

4. (nikel2015pseudomonasputidakt2440 media 0edc1cf4): Pablo I. Nikel, Max Chavarría, Tobias Fuhrer, Uwe Sauer, and Víctor de Lorenzo. Pseudomonas putida kt2440 strain metabolizes glucose through a cycle formed by enzymes of the entner-doudoroff, embden-meyerhof-parnas, and pentose phosphate pathways. Journal of Biological Chemistry, 290:25920-25932, Oct 2015. URL: https://doi.org/10.1074/jbc.m115.687749, doi:10.1074/jbc.m115.687749. This article has 440 citations and is from a domain leading peer-reviewed journal.

5. (wierenga2010triosephosphateisomerasea pages 1-3): R. K. Wierenga, E. G. Kapetaniou, and R. Venkatesan. Triosephosphate isomerase: a highly evolved biocatalyst. Cellular and Molecular Life Sciences, 67:3961-3982, Aug 2010. URL: https://doi.org/10.1007/s00018-010-0473-9, doi:10.1007/s00018-010-0473-9. This article has 284 citations and is from a domain leading peer-reviewed journal.

6. (wierenga2010triosephosphateisomerasea pages 6-8): R. K. Wierenga, E. G. Kapetaniou, and R. Venkatesan. Triosephosphate isomerase: a highly evolved biocatalyst. Cellular and Molecular Life Sciences, 67:3961-3982, Aug 2010. URL: https://doi.org/10.1007/s00018-010-0473-9, doi:10.1007/s00018-010-0473-9. This article has 284 citations and is from a domain leading peer-reviewed journal.

7. (wierenga2010triosephosphateisomerasea pages 3-5): R. K. Wierenga, E. G. Kapetaniou, and R. Venkatesan. Triosephosphate isomerase: a highly evolved biocatalyst. Cellular and Molecular Life Sciences, 67:3961-3982, Aug 2010. URL: https://doi.org/10.1007/s00018-010-0473-9, doi:10.1007/s00018-010-0473-9. This article has 284 citations and is from a domain leading peer-reviewed journal.

8. (myers2023newlydiscoveredroles pages 1-2): Tracey D. Myers and Michael J. Palladino. Newly discovered roles of triosephosphate isomerase including functions within the nucleus. Molecular Medicine, Jan 2023. URL: https://doi.org/10.1186/s10020-023-00612-x, doi:10.1186/s10020-023-00612-x. This article has 51 citations and is from a peer-reviewed journal.

9. (wierenga2010triosephosphateisomerasea pages 19-20): R. K. Wierenga, E. G. Kapetaniou, and R. Venkatesan. Triosephosphate isomerase: a highly evolved biocatalyst. Cellular and Molecular Life Sciences, 67:3961-3982, Aug 2010. URL: https://doi.org/10.1007/s00018-010-0473-9, doi:10.1007/s00018-010-0473-9. This article has 284 citations and is from a domain leading peer-reviewed journal.

10. (wierenga2010triosephosphateisomerasea pages 10-12): R. K. Wierenga, E. G. Kapetaniou, and R. Venkatesan. Triosephosphate isomerase: a highly evolved biocatalyst. Cellular and Molecular Life Sciences, 67:3961-3982, Aug 2010. URL: https://doi.org/10.1007/s00018-010-0473-9, doi:10.1007/s00018-010-0473-9. This article has 284 citations and is from a domain leading peer-reviewed journal.

11. (kursula2003crystallographicstudieson pages 34-37): I Kursula. Crystallographic studies on the structure-function relationships in triosephosphate isomerase. Unknown journal, 2003.

12. (nikel2015pseudomonasputidakt2440 pages 5-6): Pablo I. Nikel, Max Chavarría, Tobias Fuhrer, Uwe Sauer, and Víctor de Lorenzo. Pseudomonas putida kt2440 strain metabolizes glucose through a cycle formed by enzymes of the entner-doudoroff, embden-meyerhof-parnas, and pentose phosphate pathways. Journal of Biological Chemistry, 290:25920-25932, Oct 2015. URL: https://doi.org/10.1074/jbc.m115.687749, doi:10.1074/jbc.m115.687749. This article has 440 citations and is from a domain leading peer-reviewed journal.

13. (dvorak2024syntheticallyprimedadaptationof pages 4-5): Pavel Dvořák, Barbora Burýšková, Barbora Popelářová, Birgitta Elisabeth Ebert, Tibor Botka, Dalimil Bujdoš, Alberto Sánchez-Pascuala, Hannah Schöttler, Heiko Hayen, Víctor de Lorenzo, Lars M. Blank, and Martin Benešík. Synthetically-primed adaptation of pseudomonas putida to a non-native substrate d-xylose. Nature Communications, Mar 2024. URL: https://doi.org/10.1038/s41467-024-46812-9, doi:10.1038/s41467-024-46812-9. This article has 37 citations and is from a highest quality peer-reviewed journal.

14. (weimer2024systemsbiologyof pages 8-9): Anna Weimer, Laura Pause, Fabian Ries, Michael Kohlstedt, Lorenz Adrian, Jens Krömer, Bin Lai, and Christoph Wittmann. Systems biology of electrogenic pseudomonas putida - multi-omics insights and metabolic engineering for enhanced 2-ketogluconate production. Microbial Cell Factories, Sep 2024. URL: https://doi.org/10.1186/s12934-024-02509-8, doi:10.1186/s12934-024-02509-8. This article has 7 citations and is from a peer-reviewed journal.

15. (myers2023newlydiscoveredroles pages 2-4): Tracey D. Myers and Michael J. Palladino. Newly discovered roles of triosephosphate isomerase including functions within the nucleus. Molecular Medicine, Jan 2023. URL: https://doi.org/10.1186/s10020-023-00612-x, doi:10.1186/s10020-023-00612-x. This article has 51 citations and is from a peer-reviewed journal.

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Citations

1. wierenga2010triosephosphateisomerasea pages 1-3
2. myers2023newlydiscoveredroles pages 1-2
3. wierenga2010triosephosphateisomerasea pages 3-5
4. wierenga2010triosephosphateisomerasea pages 10-12
5. dvorak2024syntheticallyprimedadaptationof pages 4-5
6. weimer2024systemsbiologyof pages 8-9
7. wierenga2010triosephosphateisomerasea pages 6-8
8. wierenga2010triosephosphateisomerasea pages 19-20
9. kursula2003crystallographicstudieson pages 34-37
10. myers2023newlydiscoveredroles pages 2-4
11. https://doi.org/10.1038/s41467-024-46812-9
12. https://doi.org/10.1186/s12934-024-02509-8
13. https://doi.org/10.1186/s10020-023-00612-x
14. https://doi.org/10.1074/jbc.M115.687749
15. https://doi.org/10.1007/s00018-010-0473-9
16. https://doi.org/10.1074/jbc.m115.687749,
17. https://doi.org/10.1007/s00018-010-0473-9,
18. https://doi.org/10.1186/s10020-023-00612-x,
19. https://doi.org/10.1038/s41467-024-46812-9,
20. https://doi.org/10.1186/s12934-024-02509-8,