2/17/2024 0 Comments Annotation of an article![]() ![]() These available genome mining tools are powerful and have led to the identification of new enzymes, compounds, and elucidation of biosynthetic pathways. Examples are the “Secondary Metabolite Unknown Region Finder” (SMURF) 16, a web-based tool for the mining of fungal genome sequences for PKS, NRPS, hybrid PKS-NRPS, and DMATS gene clusters, and “antibiotics and Secondary Metabolite Analysis SHell” (antiSMASH) 17, 18, for the identification of secondary metabolite biosynthetic gene clusters in fungal and bacterial genomes. Other software search for complete SM gene clusters. Some of these tools are dedicated to specific classes of SMKEs, mostly PKS and/or NRPS 14, 15. Classically, genome mining approaches focus on the identification of genes encoding SMKEs, based on their sequence conservation. ![]() In the past years, several computational methods have been developed to help researchers in mining microorganism genomes for SM genes and clusters, and multiple reviews have been published comparing the algorithmic logic behind each software, as well as their advantages and limitations 10, 11, 12, 13. ![]() The annotation of secondary metabolism genes in fungal genomes is of great interest for the discovery of new bioactive compounds and for the understanding of their biosynthesis. The genes encoding key and accessory enzymes of a given SM pathway are usually physically linked into a gene cluster with a shared transcriptional control 8, 9. ![]() Most frequently accessory enzymes are glycosyl transferases, methyltransferases, reductases and oxidases, particularly cytochromes P450 oxidoreductases 6. Accessory enzymes act upstream or downstream of the essential stage of the biosynthetic pathway, either producing precursors used by the key enzyme or modifying the metabolite produced. The main families of fungal SM key enzymes (SMKEs) are (i) polyketide synthases (PKS), (ii) non-ribosomal peptide synthetases (NRPS), (iii) hybrid PKS-NRPS, (iv) dimethylallyl tryptophan synthases (DMATS), and (v) terpene synthases (TS) 6, 7. In the absence of this enzyme, the final metabolite is not produced. Key enzymes are involved in the essential step of the biosynthetic pathway, usually the first step in this pathway that leads to the synthesis of the metabolite skeleton 6. The genes encoding enzymes involved in the biosynthetic pathways producing secondary metabolites fall into two categories: genes encoding key enzymes and genes encoding accessory enzymes. In particular, the genome analysis of plant and insect pathogenic fungi has revealed their potential to produce a wide range of previously uncharacterized compounds 3, 4, 5. Indeed, the analysis of fungal genomes has revealed the presence of huge repertoires of genes involved in the biosynthesis of SM, indicating that these organisms have the capacity to produce many more compounds than those described to date 2. Although fungi have been exploited for decades for their potential in antibiotic and pharmaceutical production, the chemical diversity of fungal SM and their potential biological activities remain under-explored. These include antibiotics, immunosuppressants, and phytotoxins with a wide range of molecular targets 1, 2. Similar content being viewed by othersįungal secondary metabolites (SM), also known as specialized metabolites, are an important source of compounds of pharmaceutical and agrochemical interest. The highly configurable characteristics of this application makes it a generic tool, which allows the user to refine the function of predicted proteins, to extend detection to new enzymes families, and may also be applied to biological systems other than fungi and to other proteins than those involved in secondary metabolism. CusProSe was successfully used to identify, in fungal genomes, genes encoding key enzyme families involved in secondary metabolism, such as polyketide synthases (PKS), non-ribosomal peptide synthetases (NRPS), hybrid PKS-NRPS and dimethylallyl tryptophan synthases (DMATS), as well as to characterize distinct terpene synthases (TS) sub-families. IterHMMBuild allows the iterative construction of Hidden Markov Model (HMM) profiles for conserved domains of selected protein sequences, while ProSeCDA scans a proteome of interest against an HMM profile database, and annotates identified proteins using user-defined rules. It consists of two independent tools, IterHMMBuild and ProSeCDA. We report here a new application, CustomProteinSearch (CusProSe), whose purpose is to help users to search for proteins of interest based on their domain composition. ![]()
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