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: 20
• Snippets retrieved: 20

Relevant Papers

[1] 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: 0.741)
  > 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.

[2] 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: 0.727)
  > 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.

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

• Authors: Samuel J. Modlin, A. Elghraoui, Deepika Gunasekaran, Alyssa M Zlotnicki, N. Dillon et al.
• Year: 2021
• Venue: mSystems
• URL: https://www.semanticscholar.org/paper/76ff9a62b36b32cc10e46e71ffd4dd90344e4706
• DOI: 10.1128/mSystems.00673-21
• PMID: 34726489
• PMCID: 8562490
• Citations: 15
• Summary: This work systematically updates the functional genome annotation of Mycobacterium tuberculosis virulent type strain H37Rv and identifies hundreds of high-confidence candidates for mechanisms of antibiotic resistance, virulence factors, and basic metabolism and other functions key in clinical and basic tuberculosis research.
• Evidence snippets:
• Snippet 1 (score: 0.713)
  > 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.

[4] 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: 0.692)
  > 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/]

[5] 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: 0.683)
  > 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.

[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: 0.681)
  > 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] 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: 0.680)
  > 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.

[8] 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: 0.680)
  > 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.

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

• Authors: Yanfei Liu, Yue Liu, Wantong Zhang, Mingyue Sun, Weiliang Weng et al.
• Year: 2020
• Venue: Evidence-based Complementary and Alternative Medicine : eCAM
• URL: https://www.semanticscholar.org/paper/fadeff691eb41dff82e019cc3d38b846fdc605c4
• DOI: 10.1155/2020/8508491
• PMID: 32802136
• PMCID: 7403930
• Citations: 8
• Summary: The study found that flavonoids (quercetin, luteolin, and kaempferol) and beta-sitosterol and the top eight candidate targets, namely, PTGS2, PPARG, DPP4, GSK3B, CCNA2, AR, MAPK14, and ESR1, were selected as the main therapeutic targets of EGb.
• Evidence snippets:
• Snippet 1 (score: 0.678)
  > e validated target proteins of the active components were obtained from the TCMSP database. e target protein name of the active ingredient was converted to the standard target gene name through the UniProt Knowledge Base (UniProtKB, http://www. uniprot.org/). e UniProtKB is the central hub for the collection of functional information on proteins, with accurate, consistent, and rich annotation. e target protein names were inputted into UniProtKB, with the organism restricted to "Homo sapiens," eventually gaining the official symbol.

[10] 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: 0.674)
  > 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.

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

• Authors: Wisam Taher Muslim, L. J. Mohammad, Munaf M. Naji, Isaac Karimi, Matheel D. Al-Sabti et al.
• Year: 2025
• Venue: Frontiers in Pharmacology
• URL: https://www.semanticscholar.org/paper/659ab502877a1d6b5ab7ce45fa51f0f9a13dcf24
• DOI: 10.3389/fphar.2024.1511627
• PMID: 39830340
• PMCID: 11738930
• Summary: Acute leukemic T-cells were one of the top predicted tumor cell lines for these ligands and the possible antileukemic effects of synthesized xanthone derivatives are potentially very interesting and warrant further studies.
• Evidence snippets:
• Snippet 1 (score: 0.674)
  > The UniProt accession identification of target kinases was converted to gene symbols for humans using the SynGO gene set analysis tool (Koopmans et al., 2019), and pooled together, and submitted to GeneMANIA to construct target kinase network. GeneMANIA is a handy web interface for acquiring gene ontology, scrutinizing gene lists, and highlighting genes for functional assays (Warde-Farley et al., 2010). After choosing Homo sapiens from the list of optional organisms, the genes of interest in the previous step were entered into the search bar and the results were collated and high-scored genes were culled for further discussion. Moreover, the protein-protein network was also constructed in STRING ver. 12 launched at https://string-db.org, and submitted to Cytoscape ver. 3.10.2 for network analysis using a novel Cytoscape plugin cytoHubba and visualization (Shannon et al. , 2003).

[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: 0.670)
  > 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] Discovery of Simple Sequence Repeat Markers through Transcriptome Analysis of Baccaurea motleyana

• Authors: K. Nasir, Muhammad Fairuz Mohd Yusof, M. S. F. A. Razak, Siti Norsaidah Ibrahim, Mira Farzana Mohamad Moktar et al.
• Year: 2020
• Venue: Journal of Food Science and Engineering
• URL: https://www.semanticscholar.org/paper/f99fe2940881ec45ecbd8ba3da7f10b4fb22fc3b
• DOI: 10.17265/2159-5828/2020.02.001
• Summary: Baccaurea motleyana (rambai) is underutilized fruits that are native to Malaysia, Indonesia and Thailand and used for simple sequence repeat (SSR) analysis by MIcroSAtellite (MISA).
• Evidence snippets:
• Snippet 1 (score: 0.663)
  > To get comprehensive gene function of rambai genes, gene annotation to seven databases, namely National Center for Biotechnology Information (NCBI) non-redundant protein sequences (NR), NCBI nucleotide sequences (NT), Kyoto Encyclopedia of Genes and Genome Ortholog (KO), SwissProt, Protein family (Pfam), Gene Ontology (GO) and Cluster of Orthologous Groups (KOG), was used as reference.
  > The NCBI non-redundant protein sequences (NR), include protein sequence information from GenBank, Protein Data Bank (PDB), SwissProt, Protein Information Resource (PIR) and Protein Research Foundation (PRF). The NCBI nucleotide sequences (NT) are the nucleotide sequence database that includes nucleotide sequence from GenBank of the European Bioinformatics Institute (EMBL) and DNA Data Bank of Japan (DDBJ). KEGG is a database resource for understanding high-level functions and utilities of the biological system, such as cell, organism and ecosystem, from molecular-level information, especially for large-scale molecular datasets generated by genome sequencing and other high-throughput experimental technologies. KEGG is an established Cluster of Orthologous (KO) annotation system that can accomplish the function annotation of the genome/transcriptome of a newly sequenced species. SwissProt is a manual annotated and reviewed protein sequence database that has a high-quality protein sequence database from experimental results, computed features and scientific conclusions. Pfam is comprehensive collection of protein domains and families, represented as multiple sequence alignments and as profile of hidden Markov models. Many proteins are composed of structural domains, and the protein sequence of a specific structural domain possesses a certain degree of conservative property. GO is the established standard for the functional annotation of gene products and controlled vocabulary used to classify the functional attributes of gene products of a biological process, a molecular function and a cellular component.

[14] 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: 0.662)
  > 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).

[15] 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: 0.660)
  > 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'.

[16] 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: 0.658)
  > 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.

[17] Protein-coding genes in humans and model mammals (mouse, rat and pig): gene identifiers and disambiguation of gene nomenclature retrieved from the Ensembl genome browser

• Authors: Grzegorz R. Juszczak, C. Pareek, U. Czarnik, M. Pierzchała
• Year: 2025
• Venue: BMC Genomics
• URL: https://www.semanticscholar.org/paper/d9089dfc889d790deb49cbc5b4617bda55bcc4da
• DOI: 10.1186/s12864-025-12329-8
• PMID: 41408139
• PMCID: 12822150
• Citations: 2
• Summary: An R script is developed that performs a gene symbol update to current official versions combined with identification of ambiguous symbols and retrieval of other IDs from the Ensembl database and provides a single list of updated symbols with annotation about their ambiguity.
• Evidence snippets:
• Snippet 1 (score: 0.649)
  > Gene nomenclature contains current official symbols and various numbers of synonyms, which pose a challenge to integrating genomic data and increase the probability that different genes share the same symbol. Therefore, we retrieved identifiers assigned to all protein-coding genes in human, mouse, rat and pig genomes that are available in the Ensembl genome browser (release 113) to assess the number of genes, compare species and identify ambiguous symbols. Results: Our analysis revealed that the total number of symbols, both official symbols and synonyms, used to identify protein-coding genes ranges from 16,600 in pigs to 64,580 in mice. Furthermore, the gene nomenclature is not complete because there are also genes without an assigned symbol, which indicates gaps in understanding protein-coding genes, especially in pigs. We also found a large number of gene symbols that map to more than one gene. These symbols might complicate the identification of about 10% of rat and mouse genes and 18% of human protein-coding genes. A simple solution for this problem is the usage of stable gene IDs assigned by scientific institutions and committees (Ensembl, NCBI, RGD, HGNC and VGNC) provided that the genomic information associated with these IDs is retrieved directly from proprietary databases containing the most accurate data. Finally, although gene symbols may pose a problem with unequivocal identification of genes, there are instances when no other identifiers are available in the literature. Therefore, we have developed an R script performing search of the Ensembl database and integrating data to provide a single list of updated symbols with annotation about their ambiguity. Conclusions: Gene symbols are not always reliable and should be reported together with stable IDs to enable unequivocal identification of genes. Therefore, data containing only gene symbols should be used cautiously to avoid misidentification of genes. A solution for this problem is our R script REgeness that performs a gene symbol update to current official versions combined with identification of ambiguous symbols and retrieval of other IDs from the Ensembl database.

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

• Authors: H. Kumar, Zikang Chen, Abayomi Adegunlehin, Loren Trowbridge, Leonardo Aguilar et al.
• Year: 2025
• Venue: Briefings in Bioinformatics
• URL: https://www.semanticscholar.org/paper/d212d0a2c8e4a7cedcd2c1e4fa11ed6de23345f7
• DOI: 10.1093/bib/bbaf259
• PMID: 40471992
• PMCID: 12140011
• Summary: A novel in silico pipeline for predicting 3D structures of kinase fusion proteins and performing structure-based virtual screening, which revealed that most predicted structures showed high pLDDT scores (pLDDT >70) within conserved kinase domains.
• Evidence snippets:
• Snippet 1 (score: 0.647)
  > To assign the functional or gene categories, we integrated cancer genes, tumor suppressors, epigenetic regulators, DNA damage repair genes, human essential genes, kinases, and transcription factors. In each gene group, we checked the retention and ORFs of the main protein functional features. There are 13 features belonging to the 'region' category, including 'calcium binding', Figure 1. Overview of KinaseFusionDB and its role in understanding kinase fusion proteins. (a) I. The top section identifies therapeutic targets for oncogenic activation of kinases, including wild-type kinases activated by overexpression or genomic amplification, mutated kinases with gain-offunction mutations, and kinase fusion proteins resulting from chromosomal rearrangements. II. The middle section highlights the functionalities of KinaseFusionDB, such as providing functional annotations, mRNA/protein expression data, drug binding information, and relevant literature reviews. III. The third section addresses the current limitations in targeting kinase fusion proteins, particularly the lack of comprehensive 3D structures for these proteins. It emphasizes the partial knowledge of kinase fusion protein structures and the need for further drug screening. IV. The bottom section discusses how computational approaches, such as predicted 3D structures (e.g. from AlphaFold2), can fill these gaps, supporting drug screening and filtering based on consistent binding among multiple fusion protein isoforms. (b) Kinase domain-retained human kinase fusion proteins.
  > 'coiled coil', 'compositional bias', 'DNA binding', 'domain', 'intramembrane', 'motif', 'nucleotide binding', 'region', 'repeat', 'topological domain', 'transmembrane', and 'zinc finger'. To perform the protein functional feature retention search, we first downloaded the GFF (General Feature Format) format protein information of 10 651 UniProt [26] accessions from UniProt for 10 619 genes involved in 15 030 fusion genes [27]. UniProt provides the loci information of 39 protein features, including 6 molecule processing features, 13 region features, 4 site features, 6 amino acid modification features, 2 natural variation features, 5 experimental info features, and 3 secondary structure features.

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

• Authors: Xiao Yang, Shyamasree Saha, Aravind Venkatesan, Santosh Tirunagari, Vid Vartak et al.
• Year: 2023
• Venue: Scientific Data
• URL: https://www.semanticscholar.org/paper/fcd1d26d443a982ea79e1351bfaf791209e7c74d
• DOI: 10.1101/2023.02.20.529292
• PMID: 37857688
• PMCID: 10587067
• Citations: 13
• Influential citations: 1
• Summary: A human-annotated full-text corpus for biomedical entities, comprising 300 full-text open-access research articles, is developed, describing the corpus and details how to access and reuse this open community resource.
• Evidence snippets:
• Snippet 1 (score: 0.647)
  > Examples are for illustrative purposes only and specific to each case, hence not all the entities are shown and highlighted. RED: Gene/Protein BLUE: Disease GREEN: Organism a. Biomedical concepts Gene/Protein: Annotations could be specific gene/protein names or classes/family names of gene/proteins. In particular, very broad concepts like "protein", "gene", "enzyme", "receptors", "kinase", "cytokine", "transcription regulators/factors" are out of the scope of annotations. However, family/subtype names of those concepts are considered for the annotations, such as "amylolytic enzyme", "antioxidant enzyme", "map kinase p38", because these terms narrow the concepts to specific families of gene/protein, enzyme.
  > Annotators can refer to Uniprot and Protein Ontology.

[20] Protein Localization Analysis of Essential Genes in Prokaryotes

• Authors: Chong Peng, Feng Gao
• Year: 2014
• Venue: Scientific Reports
• URL: https://www.semanticscholar.org/paper/69181762648fd77a085b2f93618a71b43b62cf76
• DOI: 10.1038/srep06001
• PMID: 25105358
• PMCID: 4126397
• Citations: 27
• Summary: A comprehensive protein localization analysis of essential genes in 27 prokaryotes including 24 bacteria, 2 mycoplasmas and 1 archaeon has been performed and shows that proteins encoded by essential genes are enriched in internal location sites, while exist in cell envelope with a lower proportion compared with non-essential ones.
• Evidence snippets:
• Snippet 1 (score: 0.644)
  > Bioinformatics Databases. DEG is a database of essential genes (http://www. essentialgene.org/). The newly released DEG 10 has been developed to accommodate the quantitative and qualitative advancements brought by the progressive identification methods. Currently available records of both essential and nonessential genes among a wide range of organisms can be downloaded from DEG 10, making it possible to compare the two different types of genes in many aspects 21 .
  > 27 prokaryotic organisms including 24 bacteria, 2 mycoplasmas and Methanococcus maripaludis S2, the only record of the Archaea domain were selected to analyze the protein localization and GO distribution of the essential and nonessential genes. There are 31 bacterial records corresponding to 27 organisms in the database in total and 26 sets of data were selected in the current study. Streptococcus pneumonia was not chosen for the lack of non-essential genes. Since the essential genes were not genome-widely identified, it's not reasonable to regard the complementary set of essential genes as non-essential genes in Streptococcus pneumonia 29,30 . In the case of multiple records for one organism, the one with the most convincing experimental methods was chosen. The non-essential genes in Methanococcus maripaludis S2 and 13 bacteria such as Escherichia coli MG1655 are obtained based on the original literatures, while non-essential genes in other 12 organisms such as Bacillus subtilis 168 are the complementary set of essential genes. The information of the organisms used in the current study are displayed in Table 1.
  > The three model genomes' subcellular location information and the Gene Ontology (GO) terms used for the analysis in the current study were downloaded from the Universal Protein Resource (UniProt; http://www.uniprot.org). Maintained by the UniProt Consortium, UniProt is committed to providing biologists with a comprehensive, high-quality and freely accessible resource of protein sequences and functional annotation 27 . Among the wealth of annotation data, detailed GO annotation statements are included.

Notes

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

Citations

1. 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
2. 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
3. Samuel J. Modlin, A. Elghraoui, Deepika Gunasekaran, Alyssa M Zlotnicki, N. Dillon et al. (2021). Structure-Aware Mycobacterium tuberculosis Functional Annotation Uncloaks Resistance, Metabolic, and Virulence Genes. mSystems. https://www.semanticscholar.org/paper/76ff9a62b36b32cc10e46e71ffd4dd90344e4706
4. 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
5. 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
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. 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
8. 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
9. Yanfei Liu, Yue Liu, Wantong Zhang, Mingyue Sun, Weiliang Weng et al. (2020). Network Pharmacology-Based Strategy to Investigate the Pharmacological Mechanisms of Ginkgo biloba Extract for Aging. Evidence-based Complementary and Alternative Medicine : eCAM. https://www.semanticscholar.org/paper/fadeff691eb41dff82e019cc3d38b846fdc605c4
10. 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
11. Wisam Taher Muslim, L. J. Mohammad, Munaf M. Naji, Isaac Karimi, Matheel D. Al-Sabti et al. (2025). Synthesis, characterization, and computational evaluation of some synthesized xanthone derivatives: focus on kinase target network and biomedical properties. Frontiers in Pharmacology. https://www.semanticscholar.org/paper/659ab502877a1d6b5ab7ce45fa51f0f9a13dcf24
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. K. Nasir, Muhammad Fairuz Mohd Yusof, M. S. F. A. Razak, Siti Norsaidah Ibrahim, Mira Farzana Mohamad Moktar et al. (2020). Discovery of Simple Sequence Repeat Markers through Transcriptome Analysis of Baccaurea motleyana. Journal of Food Science and Engineering. https://www.semanticscholar.org/paper/f99fe2940881ec45ecbd8ba3da7f10b4fb22fc3b
14. 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
15. M. Pomaznoy, Brendan Ha, Bjoern Peters (2018). GOnet: a tool for interactive Gene Ontology analysis. BMC Bioinformatics. https://www.semanticscholar.org/paper/d984d075cb08f6c39a48bcf1f32e36f333a423d9
16. 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
17. Grzegorz R. Juszczak, C. Pareek, U. Czarnik, M. Pierzchała (2025). Protein-coding genes in humans and model mammals (mouse, rat and pig): gene identifiers and disambiguation of gene nomenclature retrieved from the Ensembl genome browser. BMC Genomics. https://www.semanticscholar.org/paper/d9089dfc889d790deb49cbc5b4617bda55bcc4da
18. H. Kumar, Zikang Chen, Abayomi Adegunlehin, Loren Trowbridge, Leonardo Aguilar et al. (2025). KinaseFusionDB: an integrative knowledge of kinase fusion proteins in multi-scales. Briefings in Bioinformatics. https://www.semanticscholar.org/paper/d212d0a2c8e4a7cedcd2c1e4fa11ed6de23345f7
19. Xiao Yang, Shyamasree Saha, Aravind Venkatesan, Santosh Tirunagari, Vid Vartak et al. (2023). Europe PMC annotated full-text corpus for gene/proteins, diseases and organisms. Scientific Data. https://www.semanticscholar.org/paper/fcd1d26d443a982ea79e1351bfaf791209e7c74d
20. Chong Peng, Feng Gao (2014). Protein Localization Analysis of Essential Genes in Prokaryotes. Scientific Reports. https://www.semanticscholar.org/paper/69181762648fd77a085b2f93618a71b43b62cf76

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: pgk (PP_4963; UniProt Q88D64) in Pseudomonas putida KT2440

0) Target verification (critical identity check)

The target gene symbol pgk in Pseudomonas putida KT2440 is explicitly annotated as phosphoglycerate kinase with locus tag PP_4963 in a KT2440 central carbon metabolism map, matching the provided UniProt identity (Q88D64; PGK family). Pgk is placed at the 1,3-bisphosphoglycerate ↔ 3-phosphoglycerate step (canonical PGK step) in the pathway diagram for KT2440. (poblete‐castro2017hostorganismpseudomonas pages 1-3, poblete‐castro2017hostorganismpseudomonas media daeb7766)

1) Key concepts, definitions, and current understanding

1.1 Enzyme identity and primary biochemical function

Phosphoglycerate kinase (PGK; EC 2.7.2.3) is a conserved, typically ~45 kDa enzyme that catalyzes a reversible phosphoryl-transfer reaction between the triose-derived acyl-phosphate metabolite 1,3-bisphosphoglycerate (1,3-BPG) and ADP:

• 1,3-BPG + ADP ⇌ 3-phosphoglycerate (3-PG) + ATP

In the glycolytic direction, PGK performs substrate-level phosphorylation to produce ATP. In the gluconeogenic direction, it runs in reverse to generate 1,3-BPG for upstream biosynthesis. (serimbetov2018thestructureand pages 56-63, rojaspirela2020phosphoglyceratekinasestructural pages 4-5)

1.2 Substrate specificity

Across organisms, the canonical PGK reaction couples adenine nucleotide (ADP/ATP) chemistry with the phosphorylated glycerate intermediates 1,3-BPG/3-PG. The available KT2440-focused excerpts do not report organism-specific deviations in substrate specificity for Pgk (PP_4963); thus, functional annotation is best supported by strong family conservation and the pathway placement of Pgk at the 1,3-BPG ↔ 3-PG step. (poblete‐castro2017hostorganismpseudomonas pages 1-3, poblete‐castro2017hostorganismpseudomonas media daeb7766, rojaspirela2020phosphoglyceratekinasestructural pages 4-5)

1.3 Mechanism and structural determinants (expert-level current view)

PGK is a classic two-domain hinge-bending enzyme:

• Architecture: two Rossmann-like α/β domains separated by a cleft; the N-terminal domain binds 3-PG/1,3-BPG and the C-terminal domain binds MgADP/MgATP. (serimbetov2018thestructureand pages 26-32, rojaspirela2020phosphoglyceratekinasestructural pages 4-5)
• Catalytic motion: catalysis requires large domain closure, reported as ~56° hinge rotation, bringing substrates from ~16 Å apart to ~4 Å, and PGK spends only ~7% of its time in a fully closed conformation during turnover. (serimbetov2018thestructureand pages 26-32)
• Electrostatics/transition-state stabilization: Mg2+ coordination is central to activity, helping shield negative charge and stabilize the phosphoryl-transfer transition state; conserved basic residues in the N-terminal “basic patch” contribute to binding/stabilization of the phosphorylated glycerate substrate. (rojaspirela2020phosphoglyceratekinasestructural pages 7-8)

These properties provide a mechanistic basis for functional inference for Pgk (PP_4963) in P. putida KT2440.

2) P. putida KT2440 pathway context: biological processes and where Pgk acts

2.1 Central carbon metabolism in KT2440: incomplete EMP and strong ED/peripheral oxidation

A key systems-level fact in KT2440 is that the classical Embden–Meyerhof–Parnas (EMP) glycolytic route is incomplete in the forward glycolytic direction because KT2440 lacks 6-phosphofructokinase (Pfk), rendering the EMP glycolytic pathway nonfunctional as a full glucose-to-pyruvate route. (poblete‐castro2017hostorganismpseudomonas pages 1-3)

Accordingly, KT2440 glucose catabolism heavily relies on:
- Peripheral periplasmic oxidation (e.g., glucose → gluconate; gluconate → 2-ketogluconate), and
- Entner–Doudoroff (ED) and pentose phosphate (PP) pathway connections. (poblete‐castro2017hostorganismpseudomonas pages 1-3, chen2024gnurrepressesthe pages 1-3)

Within this architecture, Pgk (PP_4963) is still present and positioned in the lower part of the EMP / gluconeogenesis module at the 1,3-BPG ↔ 3-PG step (Figure evidence). (poblete‐castro2017hostorganismpseudomonas pages 1-3, poblete‐castro2017hostorganismpseudomonas media daeb7766)

2.2 Biological process interpretation

Given its reaction, Pgk contributes to:
- Energy metabolism via substrate-level phosphorylation (ATP generation when operating in the glycolytic direction) and
- Gluconeogenic biosynthesis via reverse flux to produce 1,3-BPG when building upper glycolytic intermediates. (serimbetov2018thestructureand pages 56-63, rojaspirela2020phosphoglyceratekinasestructural pages 4-5)

In KT2440 specifically, because glucose flux preferentially enters via periplasmic oxidation and ED (and EMP is incomplete in the forward direction), Pgk’s physiological importance is best viewed as supporting lower-glycolysis segment function, energy balance, and gluconeogenic flux rather than supporting a full canonical EMP glycolysis. (poblete‐castro2017hostorganismpseudomonas pages 1-3, chen2024gnurrepressesthe pages 1-3)

3) Cellular localization: where the Pgk protein acts

Pgk is depicted as part of the intracellular central carbon network (lower glycolysis/gluconeogenesis) in KT2440 pathway maps. The periplasmic oxidation steps (glucose→gluconate→2-ketogluconate) are explicitly distinguished as periplasmic processes in KT2440-focused discussions, while Pgk sits in the cytosolic/lower central carbon portion of the network. However, the retrieved excerpts do not provide a direct experimental localization assay for Pgk; thus, localization is inferred from metabolic role and pathway compartmentation presented in the KT2440 pathway map. (poblete‐castro2017hostorganismpseudomonas pages 1-3, poblete‐castro2017hostorganismpseudomonas media daeb7766, chen2024gnurrepressesthe pages 1-3)

4) Gene-specific evidence in P. putida KT2440: expression and proteomics

4.1 Transcriptomics (direct Pgk/PP_4963 measurement)

In transcriptome profiling connected to PHA-oriented metabolic engineering, Pgk (PP4963) is listed under “glycolysis/gluconeogenesis” and shows fold-change ~1.0 and 1.1 in two engineered strains versus wild type, i.e., Pgk transcription was essentially unchanged in those genetic backgrounds/conditions. (pobletecastro2013insilicodrivenmetabolicengineering pages 6-7)

4.2 Proteomics under anoxic bioelectrochemical conditions

In a bioelectrochemical system (BES) study of anoxic electrode-driven fructose catabolism, proteomics detected 2,377 proteins (~43.5% of predicted ORFs). Many glycolytic enzymes were downregulated, while proteins of lower central carbon metabolism generally increased; however, the authors explicitly list phosphoglycerate kinase (Pgk) among a small set of exceptions at peak current—implying Pgk was not significantly upregulated (using fold-change ≥2 and p ≤ 0.05) in that condition/time point. (nguyen2021theanoxicelectrode‐driven pages 4-5)

Interpretation: Pgk appears to be a “stable core” enzyme in some contexts (transcriptome unchanged in certain engineered strains; not strongly induced in BES fructose proteomics), consistent with its role as a conserved housekeeping enzyme in central metabolism, though the exact regulation may be condition-specific.

5) Recent developments (2023–2024 prioritized): regulation, fluxes, and electrogenic applications

5.1 Regulatory discoveries controlling upstream supply to the Pgk-containing lower network (2024)

A 2024 study dissected regulation of glucose/gluconate catabolism in KT2440 and defined the GnuR regulon, reporting that GnuR directly represses genes involved in the ED pathway and peripheral glucose/gluconate metabolism, and proposing an incoherent feedforward regulatory motif. (chen2024gnurrepressesthe pages 1-3)

This matters for Pgk functional context because the ED/peripheral modules determine carbon flow into the lower central carbon “trunk” where Pgk resides, especially in a bacterium with incomplete forward EMP. (chen2024gnurrepressesthe pages 1-3)

Quantitative pathway split reported in this work underscores the peripheral bias:
- ~90% of periplasmic glucose is oxidized to gluconate,
- ~10% is transported directly into the cytoplasm,
- ~11% of periplasmic gluconate is oxidized to 2-ketogluconate. (chen2024gnurrepressesthe pages 1-3)

5.2 Fluxomics and energy accounting under electrogenic/anoxic conditions (2024)

A 2024 BES study quantified central carbon fluxes and ATP generation under electrogenic conditions (WT and uptake-route mutants). Key quantitative results include:
- Estimated maximum ATP generation in WT: 147 μmol ATP·gCDW⁻1·h⁻1.
- Very low ED flux to pyruvate under BES: 3.4, 6.5, 3.9 μmol·gCDW⁻1·h⁻1 (WT, KT-GL, KT-KG).
- Acetate production ~8–14 μmol·gCDW⁻1·h⁻1.
- Acetate 13C-enrichment (SFL) ~26%–50% depending on strain.
This study explicitly frames substrate-level phosphorylation “at the level of phosphoglycerate and pyruvate kinase” as central to ATP supply, directly implicating the PGK step conceptually in the energy balance even when gene-level Pgk values are not reported. (pause2024anaerobicglucoseuptake pages 9-11)

5.3 Real-world implementation: electrogenic bioproduction of 2-ketogluconate (2KG) (2024)

Multi-omics and metabolic engineering studies in 2024 show KT2440 can sustain long-term metabolic activity in an anoxic BES, oxidizing glucose predominantly in the periplasm and producing valuable oxidized sugars (notably 2-ketogluconate). (weimer2024systemsbiologyof pages 1-2)

Quantitative engineering outcomes reported include:
- Acetate pathway deletions (e.g., ΔaldBI ΔaldBII) reduced acetate by ~80%, doubled glucose conversion with complete consumption in ~200 h, and achieved 2KG yield = 0.96 mol·mol⁻1 (glucose) with minimal gluconate accumulation. (weimer2024systemsmetabolicengineering pages 79-83, weimer2024systemsmetabolicengineeringa pages 79-83)
- In peer-reviewed multi-omics reporting, the best mutant produced 2KG nearly twice as fast and with fivefold less acetate, also reporting 0.96 mol·mol⁻1 yield. (weimer2024systemsbiologyof pages 12-14)
- OprF overexpression improved BES performance: early current 1.0 mA vs 0.44 mA, glucose consumption 0.089 vs 0.048 mM·h⁻1, peak current 2.83 vs 1.93 mA, and gluconate accumulation rate 0.051 vs 0.015 mM·h⁻1 (3.5-fold) before gluconate re-consumption and 2KG predominance. (weimer2024systemsmetabolicengineering pages 101-105)

Relevance to Pgk: these implementations highlight that KT2440’s productivity under anoxic electrogenic conditions depends on how carbon is routed through peripheral oxidation/ED and how ATP is balanced between respiration and substrate-level phosphorylation steps that include the Pgk reaction. (pause2024anaerobicglucoseuptake pages 9-11, chen2024gnurrepressesthe pages 1-3)

6) Expert synthesis and functional annotation for Pgk (PP_4963)

6.1 Most defensible functional statement

Based on direct KT2440 pathway annotation and strong enzyme-family conservation, the primary function of Pgk (PP_4963; UniProt Q88D64) in P. putida KT2440 is:

• Catalyze the reversible phosphoryl transfer between 1,3-bisphosphoglycerate and ADP to produce 3-phosphoglycerate and ATP (and the reverse in gluconeogenesis), contributing to substrate-level phosphorylation and supporting central carbon flux in the lower glycolysis/gluconeogenesis segment. (poblete‐castro2017hostorganismpseudomonas pages 1-3, poblete‐castro2017hostorganismpseudomonas media daeb7766, serimbetov2018thestructureand pages 56-63, rojaspirela2020phosphoglyceratekinasestructural pages 4-5)

6.2 Pathway placement and physiological framing in KT2440

In KT2440, Pgk should be annotated within central carbon metabolism (lower glycolysis / gluconeogenesis), with an explicit note that:
- KT2440’s forward EMP is incomplete due to missing Pfk, and carbon commonly enters the central network through peripheral oxidation and ED, not through a canonical full glycolysis chain. (poblete‐castro2017hostorganismpseudomonas pages 1-3, chen2024gnurrepressesthe pages 1-3)

6.3 Regulation and condition dependence

Available evidence suggests pgk may not be among the most dynamically regulated central enzymes under certain perturbations:
- Transcript levels ~unchanged in some engineered strains (fold-change ~1.0–1.1). (pobletecastro2013insilicodrivenmetabolicengineering pages 6-7)
- In BES fructose proteomics, Pgk is mentioned as an “exception” to the general upregulation of lower central carbon proteins at peak current, implying it was not significantly induced (FC ≥2). (nguyen2021theanoxicelectrode‐driven pages 4-5)

This pattern is consistent with Pgk functioning as a housekeeping enzyme whose control may occur more through substrate availability, allostery, and network-level flux partitioning than through large transcriptional swings (though this conclusion should be treated as provisional because it is drawn from limited contexts and the evidence does not provide Pgk-specific fold changes in proteomics). (nguyen2021theanoxicelectrode‐driven pages 4-5)

7) Evidence map (high-value summary table)

The following table compiles the most directly relevant, citable evidence for identity, function, pathway context, and quantitative findings.

Table: This table compiles the verified identity, biochemical role, mechanistic features, pathway context, and key quantitative findings relevant to Pgk (PP_4963; UniProt Q88D64) in Pseudomonas putida KT2440. It is useful as a compact evidence map distinguishing direct Pgk-specific evidence from broader central-metabolism context.

8) Key evidence gaps and what remains unresolved

• Pgk-specific kinetics in P. putida KT2440 (Km/kcat for 1,3-BPG, 3-PG, ADP/ATP; metal dependence) were not found in the retrieved sources; structural/mechanistic parameters used here come from general PGK literature. (rojaspirela2020phosphoglyceratekinasestructural pages 7-8, serimbetov2018thestructureand pages 26-32)
• Direct localization assays (e.g., fractionation, fluorescent fusions) for Pgk (PP_4963) were not present in the retrieved excerpts; cytosolic action is inferred from pathway context. (poblete‐castro2017hostorganismpseudomonas media daeb7766, chen2024gnurrepressesthe pages 1-3)
• Gene essentiality for pgk/PP_4963 in KT2440 was not obtained from accessible transposon/essentiality datasets in this run.

9) References (URLs and publication dates)

• Poblete-Castro et al. “Host organism: Pseudomonas putida.” (Nov 2017). https://doi.org/10.1002/9783527807796.ch8 (poblete‐castro2017hostorganismpseudomonas pages 1-3, poblete‐castro2017hostorganismpseudomonas media daeb7766)
• Poblete-Castro et al. “In-silico-driven metabolic engineering of Pseudomonas putida for enhanced production of PHAs.” Metabolic Engineering (Jan 2013). https://doi.org/10.1016/j.ymben.2012.10.004 (pobletecastro2013insilicodrivenmetabolicengineering pages 6-7)
• Rojas-Pirela et al. “Phosphoglycerate kinase: structural aspects and functions…” Open Biology (Nov 2020). https://doi.org/10.1098/rsob.200302 (rojaspirela2020phosphoglyceratekinasestructural pages 7-8, rojaspirela2020phosphoglyceratekinasestructural pages 8-9, rojaspirela2020phosphoglyceratekinasestructural pages 4-5)
• Nguyen et al. “The anoxic electrode-driven fructose catabolism of P. putida KT2440.” Microbial Biotechnology (Jun 2021). https://doi.org/10.1111/1751-7915.13862 (nguyen2021theanoxicelectrode‐driven pages 4-5)
• Chen et al. “GnuR represses the expression of glucose and gluconate catabolism in P. putida KT2440.” Microbial Biotechnology (Nov 2024). https://doi.org/10.1111/1751-7915.70059 (chen2024gnurrepressesthe pages 1-3)
• Pause et al. “Anaerobic glucose uptake in P. putida KT2440 in a bioelectrochemical system.” Microbial Biotechnology (Nov 2024). https://doi.org/10.1111/1751-7915.14375 (pause2024anaerobicglucoseuptake pages 9-11)
• Weimer et al. “Systems biology of electrogenic Pseudomonas putida… for enhanced 2-ketogluconate production.” Microbial Cell Factories (Sep 2024). https://doi.org/10.1186/s12934-024-02509-8 (weimer2024systemsbiologyof pages 1-2, weimer2024systemsbiologyof pages 12-14)

References

1. (poblete‐castro2017hostorganismpseudomonas pages 1-3): Ignacio Poblete‐Castro, José M. Borrero‐de Acuña, Pablo I. Nikel, Michael Kohlstedt, and Christoph Wittmann. Host organism: pseudomonas putida. ArXiv, pages 299-326, Nov 2017. URL: https://doi.org/10.1002/9783527807796.ch8, doi:10.1002/9783527807796.ch8. This article has 49 citations.

2. (poblete‐castro2017hostorganismpseudomonas media daeb7766): Ignacio Poblete‐Castro, José M. Borrero‐de Acuña, Pablo I. Nikel, Michael Kohlstedt, and Christoph Wittmann. Host organism: pseudomonas putida. ArXiv, pages 299-326, Nov 2017. URL: https://doi.org/10.1002/9783527807796.ch8, doi:10.1002/9783527807796.ch8. This article has 49 citations.

3. (serimbetov2018thestructureand pages 56-63): Z Serimbetov. The structure and dynamics of phosphoglycerate kinase along its catalytic cycle. Unknown journal, 2018.

4. (rojaspirela2020phosphoglyceratekinasestructural pages 4-5): Maura Rojas-Pirela, Diego Andrade-Alviárez, Verónica Rojas, Ulrike Kemmerling, Ana J. Cáceres, Paul A. Michels, Juan Luis Concepción, and Wilfredo Quiñones. Phosphoglycerate kinase: structural aspects and functions, with special emphasis on the enzyme from kinetoplastea. Open Biology, Nov 2020. URL: https://doi.org/10.1098/rsob.200302, doi:10.1098/rsob.200302. This article has 91 citations and is from a peer-reviewed journal.

5. (serimbetov2018thestructureand pages 26-32): Z Serimbetov. The structure and dynamics of phosphoglycerate kinase along its catalytic cycle. Unknown journal, 2018.

6. (rojaspirela2020phosphoglyceratekinasestructural pages 7-8): Maura Rojas-Pirela, Diego Andrade-Alviárez, Verónica Rojas, Ulrike Kemmerling, Ana J. Cáceres, Paul A. Michels, Juan Luis Concepción, and Wilfredo Quiñones. Phosphoglycerate kinase: structural aspects and functions, with special emphasis on the enzyme from kinetoplastea. Open Biology, Nov 2020. URL: https://doi.org/10.1098/rsob.200302, doi:10.1098/rsob.200302. This article has 91 citations and is from a peer-reviewed journal.

7. (chen2024gnurrepressesthe pages 1-3): Wenbo Chen, Rao Ma, Yong Feng, Yunzhu Xiao, Agnieszka Sekowska, Antoine Danchin, and Conghui You. Gnur represses the expression of glucose and gluconate catabolism in pseudomonas putida kt2440. Microbial Biotechnology, Nov 2024. URL: https://doi.org/10.1111/1751-7915.70059, doi:10.1111/1751-7915.70059. This article has 2 citations and is from a peer-reviewed journal.

8. (pobletecastro2013insilicodrivenmetabolicengineering pages 6-7): Ignacio Poblete-Castro, Danielle Binger, Andre Rodrigues, Judith Becker, Vitor A.P. Martins dos Santos, and Christoph Wittmann. In-silico-driven metabolic engineering of pseudomonas putida for enhanced production of poly-hydroxyalkanoates. Metabolic engineering, 15:113-23, Jan 2013. URL: https://doi.org/10.1016/j.ymben.2012.10.004, doi:10.1016/j.ymben.2012.10.004. This article has 205 citations and is from a domain leading peer-reviewed journal.

9. (nguyen2021theanoxicelectrode‐driven pages 4-5): Anh Vu Nguyen, Bin Lai, Lorenz Adrian, and Jens O. Krömer. The anoxic electrode‐driven fructose catabolism of pseudomonas putida kt2440. Microbial Biotechnology, 14:1784-1796, Jun 2021. URL: https://doi.org/10.1111/1751-7915.13862, doi:10.1111/1751-7915.13862. This article has 13 citations and is from a peer-reviewed journal.

10. (pause2024anaerobicglucoseuptake pages 9-11): Laura Pause, Anna Weimer, Nicolas T. Wirth, Anh Vu Nguyen, Claudius Lenz, Michael Kohlstedt, Christoph Wittmann, Pablo I. Nikel, Bin Lai, and Jens O. Krömer. Anaerobic glucose uptake in pseudomonas putida kt2440 in a bioelectrochemical system. Microbial Biotechnology, Nov 2024. URL: https://doi.org/10.1111/1751-7915.14375, doi:10.1111/1751-7915.14375. This article has 11 citations and is from a peer-reviewed journal.

11. (weimer2024systemsbiologyof pages 1-2): 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.

12. (weimer2024systemsmetabolicengineering pages 79-83): ALA Weimer. Systems metabolic engineering of electrogenic anaerobic pseudomonas putida for enhanced 2-ketogluconate production. Unknown journal, 2024.

13. (weimer2024systemsmetabolicengineeringa pages 79-83): ALA Weimer. Systems metabolic engineering of electrogenic anaerobic pseudomonas putida for enhanced 2-ketogluconate production. Unknown journal, 2024.

14. (weimer2024systemsbiologyof pages 12-14): 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. (weimer2024systemsmetabolicengineering pages 101-105): ALA Weimer. Systems metabolic engineering of electrogenic anaerobic pseudomonas putida for enhanced 2-ketogluconate production. Unknown journal, 2024.

16. (rojaspirela2020phosphoglyceratekinasestructural pages 8-9): Maura Rojas-Pirela, Diego Andrade-Alviárez, Verónica Rojas, Ulrike Kemmerling, Ana J. Cáceres, Paul A. Michels, Juan Luis Concepción, and Wilfredo Quiñones. Phosphoglycerate kinase: structural aspects and functions, with special emphasis on the enzyme from kinetoplastea. Open Biology, Nov 2020. URL: https://doi.org/10.1098/rsob.200302, doi:10.1098/rsob.200302. This article has 91 citations and is from a peer-reviewed journal.

17. (weimer2024systemsmetabolicengineering pages 65-69): ALA Weimer. Systems metabolic engineering of electrogenic anaerobic pseudomonas putida for enhanced 2-ketogluconate production. Unknown journal, 2024.

18. (weimer2024systemsbiologyof pages 10-12): 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.

Artifacts

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Citations

1. serimbetov2018thestructureand pages 26-32
2. rojaspirela2020phosphoglyceratekinasestructural pages 7-8
3. pobletecastro2013insilicodrivenmetabolicengineering pages 6-7
4. chen2024gnurrepressesthe pages 1-3
5. pause2024anaerobicglucoseuptake pages 9-11
6. weimer2024systemsbiologyof pages 1-2
7. weimer2024systemsbiologyof pages 12-14
8. weimer2024systemsmetabolicengineering pages 101-105
9. weimer2024systemsbiologyof pages 10-12
10. serimbetov2018thestructureand pages 56-63
11. rojaspirela2020phosphoglyceratekinasestructural pages 4-5
12. weimer2024systemsmetabolicengineering pages 79-83
13. weimer2024systemsmetabolicengineeringa pages 79-83
14. rojaspirela2020phosphoglyceratekinasestructural pages 8-9
15. weimer2024systemsmetabolicengineering pages 65-69
16. https://doi.org/10.1002/9783527807796.ch8
17. https://doi.org/10.1098/rsob.200302
18. https://doi.org/10.1002/9783527807796.ch8;
19. https://doi.org/10.1111/1751-7915.70059
20. https://doi.org/10.1016/j.ymben.2012.10.004
21. https://doi.org/10.1186/s12934-024-02509-8
22. https://doi.org/10.1111/1751-7915.14375;
23. https://doi.org/10.1111/1751-7915.13862
24. https://doi.org/10.1111/1751-7915.14375
25. https://doi.org/10.1002/9783527807796.ch8,
26. https://doi.org/10.1098/rsob.200302,
27. https://doi.org/10.1111/1751-7915.70059,
28. https://doi.org/10.1016/j.ymben.2012.10.004,
29. https://doi.org/10.1111/1751-7915.13862,
30. https://doi.org/10.1111/1751-7915.14375,
31. https://doi.org/10.1186/s12934-024-02509-8,