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: 18
• Snippets retrieved: 20

Relevant Papers

[1] Putative Bifunctional Chorismate Mutase/Prephenate Dehydratase Contributes to the Virulence of Acidovorax citrulli

• Authors: Minyoung Kim, Jongchan Lee, Lynn Heo, Sang-Wook Han
• Year: 2020
• Venue: Frontiers in Plant Science
• URL: https://www.semanticscholar.org/paper/abb02fadc587ac78ec13ba9bdc11ae72b6f8f3b3
• DOI: 10.3389/fpls.2020.569552
• PMID: 33101336
• PMCID: 7546022
• Citations: 15
• Summary: The study provides new insights into the functions of a putative bifunctional protein related to virulence in Ac, and reveals that CmpAc is mostly involved in cell wall/membrane/envelop biogenesis.
• Evidence snippets:
• Snippet 1 (score: 0.806)
  > A Tn5-insertional library in the background of Ac strain KACC17005 belonging to group II was screened to identify genes involved in the virulence of Ac. We found one mutant that did not cause disease on watermelon and confirmed that a gene, which was annotated as chorismate mutase (Accession No. ATG94418; Locus tag, CQB05_10545), was disrupted by Tn5. The deduced amino acids of ATG94418 revealed that the protein possesses two domains: chorismate mutase type II family (15-91 aa) and prephenate dehydratase family (92-366 aa) (Figure 1A) , indicating that ATG94418 encodes a putative bifunctional chorismate mutase/ prephenate dehydratase protein. In agreement with our prediction, Zhang et al. reported that N-terminal and C-terminal of the P-protein, a bifunctional chorismate mutase/prephenate dehydratase protein, in E. coli are required for chorismate mutase and prephenate dehydratase activity, respectively (Zhang et al., 1998). In addition, CmpAc showed high homology with a putative chorismate mutase or prephenate dehydratase in other gram-negative bacteria (Figure 1B). ATG94418 showed 100% similarity with a putative prephenate dehydratase (Accession No. ABM33842) in Ac strain AAC00-1, 93% similarity with a putative chorismate mutase (Accession No. OGA85719) in Burkholderiales GWA2_64_37, and 92% similarity with a putative prephenate dehydratase (Accession No. TQK66165) in Nocardioides sp. SLBN-35. This suggests that the bifunctional chorismate mutase/ prephenate dehydratase protein is conserved in the genus as well as in other bacteria. Therefore, ATG94418 was named as CmpAc (bifunctional chorismate mutase/prephenate dehydratase in Ac).

[2] 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: 0.745)
  > 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: 0.682)
  > (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.

[3] 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: 0.733)
  > 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.

[4] 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: 0.698)
  > 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].

[5] 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: 0.691)
  > 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.

[6] 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/]

[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: 0.687)
  > 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.

[8] 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: 0.685)
  > 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.

[9] 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: 0.685)
  > 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.

[10] 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: 0.683)
  > 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.

[11] Prediction of Horizontally and Widely Transferred Genes in Prokaryotes

• Authors: Yoji Nakamura
• Year: 2018
• Venue: Evolutionary Bioinformatics Online
• URL: https://www.semanticscholar.org/paper/af7bde229b96609907924d1c68ea463af656efb2
• DOI: 10.1177/1176934318810785
• PMID: 30546254
• PMCID: 6287321
• Citations: 5
• Influential citations: 1
• Summary: A data-driven approach using massive sequence data may contribute to a broader understanding of HGT in prokaryotes, and predicted that six as-yet-uncharacterized genes were widely distributed HT genes, and therefore, will be interesting targets for evolutionary studies.
• Evidence snippets:
• Snippet 1 (score: 0.675)
  > Focusing on uncharacterized HT genes in the COG and KEGG databases, four gene groups (COG3209, COG1479, COG3291, and COG3791) were classified into "general function prediction only" (COG category code = R) or "function unknown" (S) according to the COG annotation. By adding two gene groups of "uncharacterized protein" (K08998 and K07062) from the KEGG annotation, a total of six were obtained as functionally ambiguous HT gene groups despite being distributed among more than 300 species. With reference to COG3209 (uncharacterized conserved protein RhaS, contains 28 RHS repeats), the genes encoded in E. coli have been suggested to be horizontally transferred from another organism. 43 Recently, RHS repeat-containing genes are reported to be involved in toxins against competitors 44 ; therefore, the genes in COG3209 could be considered as defense system genes. The functions of COG1479 (uncharacterized conserved protein, contains ParB-like and HNH nuclease domains), COG3291 (PKD repeat), and COG3791 (uncharacterized conserved protein) have yet to be examined. According to InterPro analysis, COG3791 genes have a domain of glutathione-dependent formaldehyde-activating enzyme (IPR006913); therefore, these genes might be related to formaldehyde detoxification. 45 The KO group, K08998, corresponds to COG0759 (membraneanchored protein YidD) of category M in the COG database that has previously been reported to be involved in the protein insertion process. 46 K07062 corresponds to COG1487 and is considered a toxic protein. 47 As a whole, it has to be said that the evolutionary significance of these six gene groups has not been fully realized. Conversely, these genes might be good targets for evolutionary studies in the context of HGT, providing an example of data-driven approaches from massive sequence data. 48

[12] 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: 0.675)
  > 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.

[13] Functional annotation of parasitic worm genomes, by assigning protein names and GO terms

• Authors: Avril Coghlan, M. Berriman
• Year: 2018
• Venue: Unknown venue
• URL: https://www.semanticscholar.org/paper/583c74a2e225dbff5fca04d36298e5b690491e82
• DOI: 10.1038/protex.2018.055
• Citations: 1
• Summary: A computational pipeline for assigning protein names and GO terms to predicted proteins in parasitic worm (nematode and platyhelminth) genomes, which transfers names and Go terms from orthologues in other species.
• Evidence snippets:
• Snippet 1 (score: 0.674)
  > Given a set of predicted protein-coding genes for a newly sequenced genome, functional annotation involves assigning putative functions to the predicted genes. Two ways in which this can be done are assigning protein names and Gene Ontology (GO;Gene Ontology Consortium, 2010) terms to the predicted proteins. Here we describe a computational pipeline for assigning protein names and GO terms to predicted proteins in parasitic worm (nematode and platyhelminth) genomes, which transfers names and GO terms from orthologues in other species.
  > When assigning protein names, UniProt protein naming rules (www.uniprot.org/docs/nameprot) are followed where possible. This recommends that a good and stable name for a protein is "as neutral as possible"; that a protein name "should be, as far as possible, unique and attributed to all orthologs"; and a protein name "should not contain a specific characteristic of the protein, and in particular it should not reflect the function or role of the protein, nor its subcellular location, its domain structure, its tissue specificity, its molecular weight or its species of origin".
  > In our protocol, a protein name is assigned to each predicted protein based on curated names in UniProt (Bairoch & Apweiler, 2000) for human, zebrafish, Drosophila melanogaster, Caenorhabditis elegans, and Schistosoma mansoni orthologues identified from a database of gene families (e.g. built using Ensembl Compara; Vilella et al. 2009), or (if no information is found from orthologues) based on InterPro (Hunter et al. 2012) domains. Figure 1 shows an example of using our protein naming pipeline for four Strongyloides ratti genes that belong to the tubulin polyglutamylase family (underlined in pink), where four different protein names were assigned to them (in pink), based on names of their C. elegans or human orthologues.
  > Since each of the S. ratti genes belonged to a different subfamily of the tubulin polyglutamylase family, they were assigned different names.

[14] 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: 0.673)
  > 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.

[15] Screening and Identification of Brown Planthopper Resistance Genes OsCM9 in Rice

• Authors: Jae-Ryoung Park, S. Yun, R. Jan, Kyung-Min Kim
• Year: 2020
• Venue: Agronomy
• URL: https://www.semanticscholar.org/paper/bc90d405b3398b6472ff12519822b55eaa87d6e8
• DOI: 10.3390/agronomy10121865
• Citations: 9
• Summary: The newly identified BPH-resistant gene OsCM9 can be used for the development of rice varieties that are capable of resisting sudden damage due to BPH, as the evolution of BPH due to climate change has had negative impacts on rice crops.
• Evidence snippets:
• Snippet 1 (score: 0.671)
  > BLAST results of the BPH-resistant gene CM showed that it has a high degree of sequence similarity with chorismate mutase (CM2). OsCM9 exhibited high levels of homology not only with rice, but also Arabidopsis thaliana, Zea mays, and Glycine max. Construction of a phylogenetic tree showed that the CM gene in Arabidopsis thaliana and Glycine max were highly similar (Figure 4). We predicted the functional partners using the domain of OsCM9. OsCM9 interacts with five proteins (CS; Chorismate synthase, ADCS; Aminodeoxychorismate synthase, ICS; Isochorismate synthase, CM; Chorismate mutase, PDT; Prephenate dehydratase) (Figure 4).
• Snippet 2 (score: 0.671)
  > BLAST results of the BPH-resistant gene CM showed that it has a high degree of sequence similarity with chorismate mutase (CM2). OsCM9 exhibited high levels of homology not only with rice, but also Arabidopsis thaliana, Zea mays, and Glycine max. Construction of a phylogenetic tree showed that the CM gene in Arabidopsis thaliana and Glycine max were highly similar (Figure 4). We predicted the functional partners using the domain of OsCM9. OsCM9 interacts with five proteins (CS; Chorismate synthase, ADCS; Aminodeoxychorismate synthase, ICS; Isochorismate synthase, CM; Chorismate mutase, PDT; Prephenate dehydratase) (Figure 4).

[16] A novel neural response algorithm for protein function prediction

• Authors: H. Yalamanchili, Quan-Wu Xiao, Junwen Wang
• Year: 2012
• Venue: BMC Systems Biology
• URL: https://www.semanticscholar.org/paper/0ae3a515fb360b8a4f225d623b23f86a63b5659c
• DOI: 10.1186/1752-0509-6-S1-S19
• PMID: 23046521
• PMCID: 3403322
• Citations: 7
• Summary: This work designed a novel automated protein functional assignment method based on the neural response algorithm, which simulates the neuronal behavior of the visual cortex in the human brain and gives it an edge over other available methods on annotation accuracy.
• Evidence snippets:
• Snippet 1 (score: 0.668)
  > Recent advances in high-throughput sequencing technologies have enabled the scientific community to sequence a large number of genomes. Currently there are 1,390 complete genomes [1] annotated in the KEGG genome repository and many more are in progress. However, experimental functional characterization of these genes cannot match the data production rate. Adding to this, more than 50% of functional annotations are enigmatic [2]. Even the well studied genomes, such as E. coli and C. elegans, have 51.17% and 87.92% ambiguous annotations (putative, probable and unknown) respectively [2]. To fill the gap between the number of sequences and their (quality) annotations, we need fast, yet accurate automated functional annotation methods. Such computational annotation methods are also critical in analyzing, interpreting and characterizing large complex data sets from high-throughput experimental methods, such as protein-protein interactions (PPI) [3] and gene expression data by clustering similar genes and proteins.
  > The definition of biological function itself is enigmatic in biology and highly context dependent [4][5][6]. This is part of the reason why more than 50% of functional annotations are ambiguous. The functional scope of a protein in an organism differs depending on the aspects under consideration. Proteins can be annotated based on their mode of action, i.e. Enzyme Commission (EC) number [7] (physiological aspect) or their association with a disease (phenotypic aspect). The lack of functional coherence increases the complexity of automated functional annotation. Another major barrier is the use of different vocabulary by different annotations. A function can be described differently in different organisms [8]. This problem can be solved by using ontologies, which serve as universal functional definitions. Enzyme Commission (E.C) [9], MIPS Functional Catalogue (FunCat) [10] and Gene Ontology (GO) [11] are such ontologies. With GO being the most recently and widely used, many automated annotation methods use GO for functional annotation.
  > Protein function assignment methods can be divided into two main categories -structure-based methods and sequence-based methods. A protein's function is highly related to its structure. Protein

[17] Purified Recombinant Hypothetical Protein Coded by Open Reading Frame Rv1885c of Mycobacterium tuberculosis Exhibits a Monofunctional AroQ Class of Periplasmic Chorismate Mutase Activity*

• Authors: Prachee Prakash, B. Aruna, A. A. Sardesai, S. Hasnain
• Year: 2005
• Venue: Journal of Biological Chemistry
• URL: https://www.semanticscholar.org/paper/554b9c10537aefc31251483596e4a3c8d7b80e6b
• DOI: 10.1074/jbc.m413026200
• PMID: 15737998
• Citations: 36
• Influential citations: 3
• Summary: It is demonstrated that unlike the corresponding proteins of E. coli, Mtb chorismate mutase does not have any associated prephenate dehydratase or dehydrogenase activity, indicating its monofunctional nature.
• Evidence snippets:
• Snippet 1 (score: 0.667)
  > Institute Pasteur, as a conserved hypothetical protein with some similarity to the monofunctional chorismate mutases of Erwinia herbicola (28.6% identity in a 133-aa overlap). Rv0948c has also been annotated as a conserved hypothetical protein, equivalent to a conserved hypothetical protein (105 aa) from M. leprae (NP_301237.1 NC_002677) that is also similar to the N terminus of some chorismate mutase/prephenate dehydratase enzymes.
  > We intended to study whether these two hypothetical proteins indeed show in vitro chorismate mutase activity and, if they do, how they are related to the two known classes of chorismate mutases (AroQ and AroH). Our approach involved expressing the Mtb ORFs Rv1885c and Rv0948c in E. coli and determining the biochemical and biophysical properties of the encoded proteins. Whereas more extensive studies were carried out with the protein coded by ORF Rv1885c, we were also able to demonstrate that Rv0948 also possesses chorismate mutase activity, although with a reduced turnover. Kinetic and regulatory studies of rRv1885c indicate several unique properties of the enzyme that include feedback regulation by pathway-specific as well as cross-pathway-specific ligands in the same manner. We have also used a gene fusion approach for functional characterization of the predicted N-terminal signal sequence of Mtb chorismate mutase. Our study provides sufficient evidence to conclusively place the protein coded by ORF Rv1885c of Mtb in the AroQ class of periplasmic chorismate mutases.

[18] Chorismate Mutase-Prephenate Dehydratase from Escherichia coli

• Authors: Shenmin Zhang, G. Pohnert, P. Kongsaeree, D. Wilson, J. Clardy et al.
• Year: 1998
• Venue: The Journal of Biological Chemistry
• URL: https://www.semanticscholar.org/paper/8c4e58374739b035a79c1a2d9ecc27640979cb10
• DOI: 10.1074/jbc.273.11.6248
• PMID: 9497350
• Citations: 69
• Influential citations: 6
• Summary: The bifunctional P-protein, which plays a central role in Escherichia coli phenylalanine biosynthesis, contains two catalytic domains (chorismate mutase and prephenate dehydratase activities) as well as one R-domain (for feedback inhibition by phenylAlanine) that were shown to reside in discrete domains of the P- protein.
• Evidence snippets:
• Snippet 1 (score: 0.664)
  > The bifunctional P-protein, which plays a central role in Escherichia coli phenylalanine biosynthesis, contains two catalytic domains (chorismate mutase and prephenate dehydratase activities) as well as one R-domain (for feedback inhibition by phenylalanine). Six genes coding for P-protein domains or subdomains were constructed and successfully expressed. Proteins containing residues 1–285 and residues 1–300 retained full mutase and dehydratase activity, but exhibited no feedback inhibition. Proteins containing residues 101–386 and residues 101–300 retained full dehydratase activity, but lacked mutase activity. Fluorescence emission spectra and binding assays indicated that residues 286–386 were crucial for phenylalanine binding. The mutase (residues 1–109), dehydratase (residues 101–285), and regulatory (residues 286–386) activities were thus shown to reside in discrete domains of the P-protein. Both the mutase domain and the native P-protein formed dimers. Deletion of the mutase domain diminished phenylalanine binding to the regulatory site as well as prephenate binding to the dehydratase domain, both through cooperative effects. Besides eliminating feedback inhibition, removal of the R-domain decreased the affinity of chorismate mutase for chorismate.

Notes

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

Citations

1. Minyoung Kim, Jongchan Lee, Lynn Heo, Sang-Wook Han (2020). Putative Bifunctional Chorismate Mutase/Prephenate Dehydratase Contributes to the Virulence of Acidovorax citrulli. Frontiers in Plant Science. https://www.semanticscholar.org/paper/abb02fadc587ac78ec13ba9bdc11ae72b6f8f3b3
2. 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
3. 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
4. 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
5. 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
6. 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
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. 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
9. 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
10. 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
11. Yoji Nakamura (2018). Prediction of Horizontally and Widely Transferred Genes in Prokaryotes. Evolutionary Bioinformatics Online. https://www.semanticscholar.org/paper/af7bde229b96609907924d1c68ea463af656efb2
12. 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
13. Avril Coghlan, M. Berriman (2018). Functional annotation of parasitic worm genomes, by assigning protein names and GO terms. https://www.semanticscholar.org/paper/583c74a2e225dbff5fca04d36298e5b690491e82
14. 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
15. Jae-Ryoung Park, S. Yun, R. Jan, Kyung-Min Kim (2020). Screening and Identification of Brown Planthopper Resistance Genes OsCM9 in Rice. Agronomy. https://www.semanticscholar.org/paper/bc90d405b3398b6472ff12519822b55eaa87d6e8
16. H. Yalamanchili, Quan-Wu Xiao, Junwen Wang (2012). A novel neural response algorithm for protein function prediction. BMC Systems Biology. https://www.semanticscholar.org/paper/0ae3a515fb360b8a4f225d623b23f86a63b5659c
17. Prachee Prakash, B. Aruna, A. A. Sardesai, S. Hasnain (2005). Purified Recombinant Hypothetical Protein Coded by Open Reading Frame Rv1885c of Mycobacterium tuberculosis Exhibits a Monofunctional AroQ Class of Periplasmic Chorismate Mutase Activity*. Journal of Biological Chemistry. https://www.semanticscholar.org/paper/554b9c10537aefc31251483596e4a3c8d7b80e6b
18. Shenmin Zhang, G. Pohnert, P. Kongsaeree, D. Wilson, J. Clardy et al. (1998). Chorismate Mutase-Prephenate Dehydratase from Escherichia coli. The Journal of Biological Chemistry. https://www.semanticscholar.org/paper/8c4e58374739b035a79c1a2d9ecc27640979cb10

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: pheA (PP_1769; UniProt Q88M06) in Pseudomonas putida KT2440

0) Mandatory identity verification (disambiguation)

The target protein UniProt Q88M06 corresponds to Pseudomonas putida strain KT2440 PP_1769, annotated as pheA, encoding a bifunctional chorismate mutase/prephenate dehydratase (often termed the bacterial “P-protein”). This assignment is supported by direct genetic-functional analysis in KT2440 showing that insertions in PP_1769 confer phenylalanine auxotrophy and by operon mapping placing PP_1769 with other aromatic biosynthesis genes (molinahenares2009functionalanalysisof pages 6-7, molinahenares2009functionalanalysisof pages 1-2). This confirms that the gene symbol pheA matches the protein description provided (bifunctional CM/PDT enzyme).

1) Key concepts and definitions (current understanding)

1.1 The chorismate node and aromatic amino-acid branching

In bacteria, chorismate is a central branch-point metabolite of the shikimate pathway. One major fate is conversion into prephenate via chorismate mutase activity; prephenate is then processed toward phenylalanine and tyrosine (dosselaere2001ametabolicnode pages 3-5). The importance of this “chorismate node” is that multiple pathways compete for chorismate, making enzymes such as PheA key control points for flux partitioning (dosselaere2001ametabolicnode pages 3-5).

1.2 PheA (bifunctional chorismate mutase/prephenate dehydratase)

Bacterial PheA proteins are two-domain (bifunctional) enzymes that couple:
- Chorismate mutase (CM; EC 5.4.99.5): catalyzes chorismate → prephenate (a Claisen rearrangement) (dosselaere2001ametabolicnode pages 3-5).
- Prephenate dehydratase (PDT; EC 4.2.1.51): converts prephenate → phenylpyruvate (the keto-acid precursor of L-phenylalanine) (dosselaere2001ametabolicnode pages 3-5).

This functional definition is consistent with Pseudomonas KT2440 genetics: disrupting PP_1769 blocks phenylalanine biosynthesis (molinahenares2009functionalanalysisof pages 6-7, molinahenares2009functionalanalysisof pages 1-2).

1.3 Regulatory concept: phenylalanine feedback inhibition (ACT-like regulatory region)

A central property of many bacterial PheA enzymes is allosteric feedback inhibition by L-phenylalanine. Authoritative biochemical dissection of model bacterial PheA shows a separable C-terminal regulatory region required for full phenylalanine-mediated inhibition; truncation abolishes feedback inhibition and alters substrate affinity (zhang1998chorismatemutaseprephenatedehydratase pages 5-6). Reviews of chorismate-utilizing enzymes similarly describe PheA as allosterically inhibited by phenylalanine with conformational/oligomeric effects (dosselaere2001ametabolicnode pages 3-5).

2) Gene/protein function in Pseudomonas putida KT2440 (primary evidence)

2.1 Essential role in phenylalanine biosynthesis (mutant phenotypes)

A functional genetic analysis in P. putida KT2440 found multiple independent mini-Tn5 insertions in PP_1769 (pheA) (insertions reported at codons 6, 37, 42, and 120) that produced mutants whose growth was restored by phenylalanine supplementation but not by tyrosine, demonstrating phenylalanine auxotrophy and arguing against a tyrosine→phenylalanine bypass in this organism (molinahenares2009functionalanalysisof pages 6-7). This provides organism-specific evidence that PP_1769 is required for endogenous phenylalanine synthesis.

2.2 Operon/genomic context: linkage to aromatic biosynthesis genes

In KT2440, PP_1769 (pheA) is tightly linked to aromatic biosynthesis genes: PP_1769 overlaps serC by 6 nucleotides and lies 61 nucleotides from PP_1770, and RT-PCR supports that pheA forms an operon with serC and also with PP_1770 (tyrA; prephenate dehydrogenase) (molinahenares2009functionalanalysisof pages 6-7). This genomic organization strengthens the functional inference that PP_1769 is part of the aromatic amino-acid biosynthetic module in KT2440.

3) Regulation and substrate specificity (evidence and inference)

3.1 Catalytic specificity at the pathway level

The KT2440 mutant phenotypes demonstrate that disrupting pheA blocks phenylalanine synthesis upstream of phenylalanine formation (molinahenares2009functionalanalysisof pages 6-7). While the KT2440 papers retrieved here do not report purified-enzyme kinetics, authoritative biochemical studies of PheA family proteins show that PheA catalyzes the two-step CM/PDT route committing carbon toward phenylpyruvate (and thus L-phenylalanine) (dosselaere2001ametabolicnode pages 3-5, zhang1998chorismatemutaseprephenatedehydratase pages 3-5).

3.2 Feedback inhibition by aromatic amino acids (expert biochemical characterization)

Detailed biochemical domain studies of bacterial PheA show that phenylalanine is a strong inhibitor of the dehydratase activity and that the C-terminal regulatory region is required for full feedback control (zhang1998chorismatemutaseprephenatedehydratase pages 5-6, zhang1998chorismatemutaseprephenatedehydratase pages 3-5). A chorismate-node review further describes phenylalanine binding as causing conformational changes and shifts in oligomeric state linked to decreased activity (dosselaere2001ametabolicnode pages 3-5).

3.3 Pseudomonas-specific recent evidence: engineering feedback-insensitive PheA to increase phenylalanine supply (2024)

A 2024 study in Pseudomonas putida DOT-T1E (a different P. putida strain, but the same enzyme class and pathway role) identified intracellular L-phenylalanine as a limiting substrate for 2-phenylethanol (2-PE) production and introduced a chorismate mutase/prephenate dehydratase variant described as insensitive to feedback inhibition by aromatic amino acids. This intervention increased phenylalanine availability and improved 2-PE titers (godoy2024biosynthesisoffragrance pages 1-2). This provides recent, experimentally supported Pseudomonas evidence that PheA-like feedback control materially constrains flux toward phenylalanine-derived products.

4) Subcellular localization and site of action

4.1 Most likely localization of KT2440 PheA

No direct subcellular localization experiment for KT2440 PP_1769/PheA was found in the retrieved full texts. However, the biochemical role of PheA is within core cytosolic metabolism (shikimate pathway branch), and bifunctional PheA enzymes in bacteria are typically treated as cytosolic enzymes acting on intracellular chorismate and prephenate pools (supported indirectly by the genetic/physiological phenotypes in KT2440) (molinahenares2009functionalanalysisof pages 6-7, molinahenares2009functionalanalysisof pages 1-2).

4.2 Distinguishing from periplasmic chorismate mutases in Pseudomonas

Importantly, Pseudomonas can also encode periplasm-localized monofunctional AroQ chorismate mutases (“AroQ”), shown experimentally in Pseudomonas aeruginosa as periplasmic proteins (calhoun2001theemergingperiplasmlocalized pages 1-2). These are not* the same as the KT2440 PP_1769 bifunctional PheA (which is genetically tied to cytosolic aromatic biosynthesis genes and is required for phenylalanine prototrophy). This distinction reduces risk of misannotation when interpreting chorismate mutase activity in Pseudomonads (molinahenares2009functionalanalysisof pages 6-7, calhoun2001theemergingperiplasmlocalized pages 1-2).

5) Recent developments and latest research (emphasis on 2023–2024)

5.1 2024: phenylalanine supply as a bottleneck for phenylalanine-derived aromatics in Pseudomonas

Godoy et al. (published April 2024) engineered P. putida DOT‑T1E strains for fragrance 2‑phenylethanol production via the Ehrlich pathway and found phenylalanine supply limiting. Introducing a feedback-insensitive CM/PDT (PheA-class) enzyme increased phenylalanine and improved 2‑PE production, and further random mutagenesis increased titers (godoy2024biosynthesisoffragrance pages 1-2). URL: https://doi.org/10.1186/s13068-024-02498-1 (godoy2024biosynthesisoffragrance pages 1-2).

Although this is not KT2440 specifically, it is a close-strain P. putida demonstration aligning with the known regulatory role of PheA and is directly relevant for functional annotation in the Pseudomonas context (godoy2024biosynthesisoffragrance pages 1-2).

5.2 2023 gap note

Within the retrieved corpus, no 2023 primary paper directly characterizing KT2440 PP_1769/PheA biochemistry (e.g., kinetics/structure) was obtained. Therefore, KT2440-specific “latest research” in 2023 cannot be asserted here beyond engineering/physiology already cited.

6) Current applications and real-world implementations

6.1 Redirecting chorismate away from phenylalanine/tyrosine toward product formation (KT2440; PHBA)

A clear implementation in P. putida KT2440 is metabolic engineering for para-hydroxybenzoic acid (PHBA) production from glucose. In this strategy, pheA was deleted (along with other edits) specifically to remove competition for chorismate and redirect flux toward PHBA formation (yu2016metabolicengineeringof pages 1-3, yu2016metabolicengineeringof pages 5-6). In fed-batch fermentation, the best engineered strain achieved 1.73 g/L PHBA and 18.1% carbon yield (C-mol/C-mol) (yu2016metabolicengineeringof pages 5-6). URL: https://doi.org/10.3389/fbioe.2016.00090 (published Nov 2016) (yu2016metabolicengineeringof pages 5-6).

6.2 Increasing phenylalanine-derived product formation by relieving feedback inhibition (Pseudomonas; 2-PE)

Conversely, when the product goal is phenylalanine-derived aromatics (e.g., 2‑phenylethanol), engineering strategies can increase flux through PheA by relieving feedback control. In P. putida DOT-T1E, introduction of a feedback-insensitive CM/PDT enzyme increased 2‑PE production and enabled use of agricultural waste-derived sugar mixtures as substrates (godoy2024biosynthesisoffragrance pages 1-2).

7) Expert opinions and authoritative analysis (interpretation)

7.1 Why pheA is a “high-leverage” annotation target

Reviews of chorismate-utilizing enzymes emphasize that the chorismate node is a heavily competed metabolic branchpoint, and that enzymes like PheA combine catalytic commitment with tight allosteric regulation by end products such as phenylalanine (dosselaere2001ametabolicnode pages 3-5). This explains why pheA deletions can strongly redirect chorismate to alternative products (e.g., PHBA) and why feedback-insensitive variants can boost phenylalanine supply for downstream synthesis (yu2016metabolicengineeringof pages 5-6, godoy2024biosynthesisoffragrance pages 1-2).

7.2 Regulatory domain modularity

Biochemical domain dissection of PheA shows that the regulatory region is separable from catalytic fragments, and removal can abolish phenylalanine feedback inhibition (zhang1998chorismatemutaseprephenatedehydratase pages 5-6). This modularity underlies a common metabolic engineering tactic: creating or importing feedback-resistant variants to increase flux (as reflected in the 2024 Pseudomonas work) (godoy2024biosynthesisoffragrance pages 1-2, zhang1998chorismatemutaseprephenatedehydratase pages 5-6).

8) Relevant statistics and data points (from recent and key studies)

Key quantitative outcomes directly tied to manipulating the PheA node include:
- PHBA production (KT2440): 1.73 g/L maximum titer; 18.1% C-mol/C-mol carbon yield in a non-optimized fed-batch fermentation; engineering included deletion of pheA to reduce chorismate consumption by aromatic amino-acid biosynthesis (yu2016metabolicengineeringof pages 5-6, yu2016metabolicengineeringof pages 1-3).
- 2-phenylethanol production (P. putida DOT-T1E; 2024): baseline production with glucose about 50–60 ppm, increasing to about 100 ppm after introducing a feedback-insensitive CM/PDT variant, and up to 120 ppm after additional random mutagenesis (godoy2024biosynthesisoffragrance pages 1-2).

9) Consolidated functional-annotation summary (table)

The following table consolidates direct KT2440 evidence, family-level biochemical understanding, and application-level data.

Table: This table summarizes verified functional annotation evidence for Pseudomonas putida KT2440 pheA/PP_1769 (UniProt Q88M06), including catalytic role, operon/genetic evidence, regulation, and engineering applications. It is useful for quickly separating direct KT2440 evidence from broader family-level regulatory inference.

10) Evidence gaps and confidence statement

• High confidence (KT2440-specific): PP_1769 (pheA) is required for phenylalanine biosynthesis; disruption causes phenylalanine auxotrophy; gene is operon-linked with serC and tyrA-region genes (molinahenares2009functionalanalysisof pages 6-7, molinahenares2009functionalanalysisof pages 1-2).
• High confidence (family-level biochemistry): PheA catalyzes chorismate→prephenate and prephenate→phenylpyruvate and is feedback inhibited by phenylalanine via a separable regulatory region (dosselaere2001ametabolicnode pages 3-5, zhang1998chorismatemutaseprephenatedehydratase pages 5-6, zhang1998chorismatemutaseprephenatedehydratase pages 3-5).
• Moderate confidence (KT2440 localization): No KT2440-specific localization assay was retrieved; by pathway context it is most consistent with cytosolic function. Distinct periplasmic chorismate mutases exist in Pseudomonads but represent a different enzyme class (calhoun2001theemergingperiplasmlocalized pages 1-2).

Key references (publication dates and URLs)

• Molina-Henares MA et al. Dec 2009. Functional analysis of aromatic biosynthetic pathways in Pseudomonas putida KT2440. Microbial Biotechnology. https://doi.org/10.1111/j.1751-7915.2008.00062.x (molinahenares2009functionalanalysisof pages 6-7)
• Yu S et al. Nov 2016. Metabolic engineering of Pseudomonas putida KT2440 for the production of para-hydroxy benzoic acid. Frontiers in Bioengineering and Biotechnology. https://doi.org/10.3389/fbioe.2016.00090 (yu2016metabolicengineeringof pages 5-6)
• Godoy P et al. Apr 2024. Biosynthesis of fragrance 2-phenylethanol from sugars by Pseudomonas putida. Biotechnology for Biofuels and Bioproducts. https://doi.org/10.1186/s13068-024-02498-1 (godoy2024biosynthesisoffragrance pages 1-2)
• Dosselaere F, Vanderleyden J. Jan 2001. A metabolic node in action: chorismate-utilizing enzymes in microorganisms. Critical Reviews in Microbiology. https://doi.org/10.1080/20014091096710 (dosselaere2001ametabolicnode pages 3-5)
• Calhoun DH et al. Jul 2001. The emerging periplasm-localized subclass of AroQ chorismate mutases…. Genome Biology. https://doi.org/10.1186/gb-2001-2-8-research0030 (calhoun2001theemergingperiplasmlocalized pages 1-2)
• Zhang S et al. 1998. Chorismate mutase-prephenate dehydratase from E. coli: study of catalytic and regulatory domains… (domain/feedback analysis) (zhang1998chorismatemutaseprephenatedehydratase pages 5-6)

References

1. (molinahenares2009functionalanalysisof pages 6-7): M. A. Molina-Henares, Adela García‐Salamanca, A. Molina-Henares, J. de la Torre, M. C. Herrera, J. Ramos, and E. Duque. Functional analysis of aromatic biosynthetic pathways in pseudomonas putida kt2440. Microbial biotechnology, 2:91-100, Dec 2009. URL: https://doi.org/10.1111/j.1751-7915.2008.00062.x, doi:10.1111/j.1751-7915.2008.00062.x. This article has 32 citations and is from a peer-reviewed journal.

2. (molinahenares2009functionalanalysisof pages 1-2): M. A. Molina-Henares, Adela García‐Salamanca, A. Molina-Henares, J. de la Torre, M. C. Herrera, J. Ramos, and E. Duque. Functional analysis of aromatic biosynthetic pathways in pseudomonas putida kt2440. Microbial biotechnology, 2:91-100, Dec 2009. URL: https://doi.org/10.1111/j.1751-7915.2008.00062.x, doi:10.1111/j.1751-7915.2008.00062.x. This article has 32 citations and is from a peer-reviewed journal.

3. (dosselaere2001ametabolicnode pages 3-5): Filip Dosselaere and J. Vanderleyden. A metabolic node in action: chorismate-utilizing enzymes in microorganisms. Critical Reviews in Microbiology, 27:131-75, Jan 2001. URL: https://doi.org/10.1080/20014091096710, doi:10.1080/20014091096710. This article has 254 citations and is from a peer-reviewed journal.

4. (zhang1998chorismatemutaseprephenatedehydratase pages 5-6): S Zhang, G Pohnert, P Kongsaeree, and DB Wilson. Chorismate mutase-prephenate dehydratase from escherichia coli: study of catalytic and regulatory domains using genetically engineered proteins. Unknown journal, 1998.

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6. (godoy2024biosynthesisoffragrance pages 1-2): Patricia Godoy, Zulema Udaondo, Estrella Duque, and Juan L. Ramos. Biosynthesis of fragrance 2-phenylethanol from sugars by pseudomonas putida. Biotechnology for Biofuels and Bioproducts, Apr 2024. URL: https://doi.org/10.1186/s13068-024-02498-1, doi:10.1186/s13068-024-02498-1. This article has 11 citations and is from a domain leading peer-reviewed journal.

7. (calhoun2001theemergingperiplasmlocalized pages 1-2): David H Calhoun, Carol A Bonner, Wei Gu, Gary Xie, and Roy A Jensen. The emerging periplasm-localized subclass of aroq chorismate mutases, exemplified by those from salmonella typhimurium and pseudomonas aeruginosa. Genome Biology, 2:research0030.1-research0030.16, Jul 2001. URL: https://doi.org/10.1186/gb-2001-2-8-research0030, doi:10.1186/gb-2001-2-8-research0030. This article has 75 citations and is from a highest quality peer-reviewed journal.

8. (yu2016metabolicengineeringof pages 1-3): Shiqin Yu, Manuel R. Plan, Gal Winter, and Jens O. Krömer. Metabolic engineering of pseudomonas putida kt2440 for the production of para-hydroxy benzoic acid. Frontiers in Bioengineering and Biotechnology, Nov 2016. URL: https://doi.org/10.3389/fbioe.2016.00090, doi:10.3389/fbioe.2016.00090. This article has 76 citations.

9. (yu2016metabolicengineeringof pages 5-6): Shiqin Yu, Manuel R. Plan, Gal Winter, and Jens O. Krömer. Metabolic engineering of pseudomonas putida kt2440 for the production of para-hydroxy benzoic acid. Frontiers in Bioengineering and Biotechnology, Nov 2016. URL: https://doi.org/10.3389/fbioe.2016.00090, doi:10.3389/fbioe.2016.00090. This article has 76 citations.

Artifacts

• Edison artifact artifact-00

Citations

1. dosselaere2001ametabolicnode pages 3-5
2. zhang1998chorismatemutaseprephenatedehydratase pages 5-6
3. molinahenares2009functionalanalysisof pages 6-7
4. godoy2024biosynthesisoffragrance pages 1-2
5. calhoun2001theemergingperiplasmlocalized pages 1-2
6. yu2016metabolicengineeringof pages 5-6
7. molinahenares2009functionalanalysisof pages 1-2
8. zhang1998chorismatemutaseprephenatedehydratase pages 3-5
9. yu2016metabolicengineeringof pages 1-3
10. https://doi.org/10.1186/s13068-024-02498-1
11. https://doi.org/10.3389/fbioe.2016.00090
12. https://doi.org/10.1111/j.1751-7915.2008.00062.x
13. https://doi.org/10.1080/20014091096710
14. https://doi.org/10.1186/gb-2001-2-8-research0030
15. https://doi.org/10.1111/j.1751-7915.2008.00062.x,
16. https://doi.org/10.1080/20014091096710,
17. https://doi.org/10.1186/s13068-024-02498-1,
18. https://doi.org/10.1186/gb-2001-2-8-research0030,
19. https://doi.org/10.3389/fbioe.2016.00090,