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Secondary Metabolism

Q: What are some common examples of secondary metabolites?

Some commonly known examples of secondary metabolites include alkaloids, terpenoids, and phenolics. These diverse chemical compounds are produced by a wide range of living organisms, from plants to microbes, and exhibit a vast array of structures and functionalities.

Secondary metabolism refers to the production of diverse chemical compounds by living organisms, often beyond the basic requirements for growth and development.
These compounds, such as alkaloids, terpenoids, and phenolics, can serve a variety of functions, including defense, signaling, and structural roles.
Undestanding the complexities of secondary metabolism is crucial for applications in fields like pharmaceuticals, agriculture, and bioenergy.
Reserch in this area involves the identification, characterization, and optimization of metabolic pathways and regulatory mechanisms.
By leveraging advanced AI techonologies, researchers can streamline their workflow and enhance their secondary metabolism studies, leading to new discoveries and innovative applications.

Most cited protocols related to «Secondary Metabolism»

Metabolite Extraction from Plant Tissues

All solvents used for sample preparation and analyses were of LC/MS-grade quality (CHROMASOLV, Fluka). A list of standard compounds used for the recovery experiment including sum formulas, molar masses, PubChem IDs and suppliers can be found in the Supplemental Information S3. L-Tryptophan-2′,4′,5′,6′,7′-D₅ (98%) was purchased from Cambridge Isotope Laboratories. Arabidopsis thaliana (ecotype Col-0) was grown for six weeks on a soil/vermiculite mixture (3/2) in a growth cabinet with 8 h light ( 150 μE m⁻²s⁻¹) at 22°C and 16 h dark at 20°C. Seeds of Brassica napus, Brassica oleracera and Brassica rapa were kindly provided by D. Strack, Department of Secondary Metabolism, Leibniz Institute of Plant Biochemistry, Halle. All other seeds were obtained from local distributors. Procedures for extraction of leaf and seed material are provided in Supplemental Information S4.

Kuhl C., Tautenhahn R., Böttcher C., Larson T.R, & Neumann S. (2011). CAMERA: An integrated strategy for compound spectra extraction and annotation of LC/MS data sets. Analytical chemistry, 84(1), 283-289.

Publication 2011

11-dehydrocorticosterone Arabidopsis thalianas Brassica Brassica napus Brassica rapa Diet, Formula Ecotype Isotopes Light Molar Plant Leaves Plants Secondary Metabolism Solvents Tryptophan vermiculite

Expanded Secondary Metabolite Detection in antiSMASH 2.0

In addition to the secondary metabolite cluster types supported in the original release of antiSMASH (type I, II and III polyketides, non-ribosomal peptides, terpenes, lantipeptides, bacteriocins, aminoglycosides/aminocyclitols, β-lactams, aminocoumarins, indoles, butyrolactones, ectoines, siderophores, phosphoglycolipids, melanins and a generic class of clusters encoding unusual secondary metabolite biosynthesis genes), version 2.0 adds support for oligosaccharide antibiotics, phenazines, thiopeptides, homoserine lactones, phosphonates and furans. The cluster detection uses the same pHMM rule-based approach as the initial release (17 (link)): in short, the pHMMs are used to detect signature proteins or protein domains that are characteristic for the respective secondary metabolite biosynthetic pathway. Some pHMMs were obtained from PFAM or TIGRFAM. If no suitable pHMMs were available from these databases, custom pHMMs were constructed based on manually curated seed alignments (Supplementary Table S1). These are composed of protein sequences of experimentally characterized biosynthetic enzymes described in literature, as well as their close homologs found in gene clusters from the same type. The models were curated by manually inspecting the output of searches against the non-redundant (nr) database of protein sequences. The seed alignments are available online at http://antismash.secondarymetabolites.org/download.html#extras. After scanning the genome with the pHMM library, antiSMASH evaluates all hits using a set of rules (Supplementary Table S2) that describe the different cluster types. Unlike the hard-coded rules in the initial release of antiSMASH, the detection rules and profile lists are now located in editable TXT files, making it easy for users to add and modify cluster rules in the stand-alone version, e.g. to accommodate newly discovered or proprietary compound classes without code changes. The results of gene cluster predictions by antiSMASH are continuously checked on new data arising from research performed throughout the natural products community, and pHMMs and their cut-offs are regularly updated when either false positives or false negatives become apparent.
The profile-based detection of secondary metabolite clusters has now been augmented by a tighter integration of the generalized PFAM (22 (link)) domain-based ClusterFinder algorithm (Cimermancic et al., in preparation) already included in version 1.0 of antiSMASH. This algorithm performs probabilistic inference of gene clusters by identifying genomic regions with unusually high frequencies of secondary metabolism-associated PFAM domains, and it was designed to detect ‘classical’ as well as less typical and even novel classes of secondary metabolite gene clusters. While antiSMASH 1.0 only generated the output of this algorithm in a static image, version 2.0 displays these additional putative gene clusters along with the other gene clusters in the HTML output. A key advantage of this is that these putative gene clusters will now also be included in the subsequent (Sub)ClusterBlast analyses.

Blin K., Medema M.H., Kazempour D., Fischbach M.A., Breitling R., Takano E, & Weber T. (2013). antiSMASH 2.0—a versatile platform for genome mining of secondary metabolite producers. Nucleic Acids Research, 41(Web Server issue), W204-W212.

Publication 2013

Amino Acid Sequence Aminocoumarins Aminoglycosides Anabolism Antibiotics Bacteriocins Biosynthetic Pathways Childbirth Classes Enzymes Furans Gene Clusters Generic Drugs Genes Genome Genomic Library homoserine lactone Indoles Lactams Melanins Natural Products Oligosaccharides Peptides Phenazines Phosphonates Polyketides Prognosis Protein Domain Proteins Ribosomes Secondary Metabolism Siderophores Terpenes

Automated Bioinformatic Annotation of Biosynthetic Domains

KS and C domains were extracted from select PKS and NRPS genes associated with experimentally characterized biosynthetic pathways using the online program NRPS-PKS (http://www.nii.res.in/searchall.html) [19] (link), [21] (link). The pathways selected include representatives of the currently known enzyme architectures and functions associated with type I and II PKSs and NRPSs and thus this database is not meant to be comprehensive. The biochemical function and enzyme architecture of each domain was manually confirmed by analysis of the associated domain string and secondary metabolic product. Based on these results, each sequence was preliminarily assigned to a domain class. The compound produced by the associated pathway, the literature reference including PubMed ID, and the gene accession number was also recorded for each domain.

Ziemert N., Podell S., Penn K., Badger J.H., Allen E, & Jensen P.R. (2012). The Natural Product Domain Seeker NaPDoS: A Phylogeny Based Bioinformatic Tool to Classify Secondary Metabolite Gene Diversity. PLoS ONE, 7(3), e34064.

Publication 2012

Biosynthetic Pathways Enzymes Genes Secondary Metabolism

Comprehensive Microbial Secondary Metabolite Prediction

We used PRISM 4 and antiSMASH 5 to predict the chemical structures of secondary metabolites encoded within 3759 complete bacterial genomes and 6362 metagenome-assembled genomes (MAGs). All bacterial genomes with an assembly level of ‘Complete’ were downloaded from NCBI Genome, and a set of dereplicated genomes as determined by the Genome Taxonomy Database^{15 (link)} were retained to mitigate the impact of highly similar genomes on our analysis. Similarly, a set of 7902 MAGs^{23 (link)} was obtained from NCBI BioProject (accession PRJNA348753) and the subset of dereplicated genomes was retained. Detected BGCs were matched between PRISM and antiSMASH if their nucleotide sequence overlapped over any range. A small number of PRISM BGC types were mapped to more than one antiSMASH BGC type, including aminoglycosides (‘amglyccycl’ and ‘oligosaccharide’), type I polyketides (‘t1pks’ and ‘transatpks’), and RiPPs (‘bottromycin’, ‘cyanobactin’, ‘glycocin’, ‘head_to_tail’, ‘LAP’, ‘lantipeptide’, ‘lassopeptide’, ‘linaridin’, ‘microviridin’, ‘proteusin’, ‘sactipeptide’, and ‘thiopeptide’). The “hybrid” category encompassed all BGCs assigned any combination of two or more cluster types, i.e., it was not limited to hybrid NRPS-PKS BGCs. The “other” category encompassed aryl polyenes, bacteriocins, butyrolactones, ectoines, furans, homoserine lactones, ladderanes, melanins, N-acyl amino acids, NRPS-independent siderophores, phenazines, phosphoglycolipids, resorcinols, stilbenes, terpenes, and type III polyketides. Producing organism taxonomy was based on genome phylogeny and retrieved from the Genome Taxonomy Database^{15 (link)}.
Cheminformatic metrics, including molecular weight, number of hydrogen bond donors and acceptors, octanol-water partition coefficients, and Bertz topological complexity, were calculated in RDKit. Both platforms occasionally generated very small, non-specific structure predictions (for example, a single unspecified amino acid or a single malonyl unit) that did not provide actionable information about the chemical structure of the encoded product; to remove these from consideration, we applied a molecular weight filter to remove structures under 100 Da output by either platform. To evaluate the internal structural diversity of each set of predicted structures, we computed the distribution of pairwise Tcs for each set⁴⁵, taking the median pairwise Tc instead of the mean as a summary statistic to ensure robustness against outliers. Structural similarity to known natural products was assessed using the RDKit implementation of the ‘natural product-likeness’ score^{22 (link)}, and by the median Tc between predicted structures and the known secondary metabolite structures deposited in the NP Atlas database^{46 (link)}.

Skinnider M.A., Johnston C.W., Gunabalasingam M., Merwin N.J., Kieliszek A.M., MacLellan R.J., Li H., Ranieri M.R., Webster A.L., Cao M.P., Pfeifle A., Spencer N., To Q.H., Wallace D.P., Dejong C.A, & Magarvey N.A. (2020). Comprehensive prediction of secondary metabolite structure and biological activity from microbial genome sequences. Nature Communications, 11, 6058.

Publication 2020

Amino Acids Aminoglycosides Bacteriocins Base Sequence bottromycin cyanobactins Donors Furans Genome Genome, Bacterial Head homoserine lactone Hybrids Hydrogen Bonds Melanins Metagenome Natural Products Octanols Oligosaccharides Phenazines Polyenes Polyketides prisma Prokaryotic Cells Resorcinols Secondary Metabolism Siderophores Stilbenes Tail Terpenes

Annotating Aspergillus Genes with GO Terms

To augment the annotations for all genes, including secondary metabolism related genes, we used manual and domain-based GO annotations to annotate the predicted orthologs that lacked direct experimental characterization. Ortholog predictions for A. nidulans, A. fumigatus, A. niger and A. oryzae were made based on the characterized proteins of S. cerevisiae, S. pombe and the other Aspergillus species in AspGD. Candidate GO annotations to be used as the basis for these inferences are limited to those with experimental evidence, that is, with evidence codes of IDA (Inferred from Direct Assay), IPI (Inferred from Physical Interaction), IGI (Inferred from Genetic Interaction) or IMP (Inferred from Mutant Phenotype). Annotations that are themselves predicted in S. cerevisiae, S. pombe or in Aspergillus, either based on sequence similarity or by some other methods, are excluded from this group to avoid transitive propagation of predictions. Also excluded from the predicted annotation set are annotations that are redundant with existing, manually curated annotations or those that assign a related but less specific GO term. The orthology-based GO assignments are given the evidence code IEA (Inferred from Electronic Annotation) and displayed with the source species and name of the gene from which they were derived, along with a hyperlink to the appropriate gene page at AspGD, SGD or PomBase. The new annotations that have been manually assigned or electronically transferred from S. cerevisiae and S. pombe to A. nidulans, A. fumigatus, A. niger and A. oryzae, and between the Aspergillus species are summarized in Table 3.
Domain-based GO transfers were assigned to a lower precedence than orthology-based transfers. IprScan predicts InterPro domains based on protein sequences [56 (link)]. The Interpro2go mapping file (http://www.ebi.ac.uk/interpro) was used to map GO annotations to genes with the corresponding domain predictions. A domain-based GO prediction was made only if it was not redundant with an existing manually-curated or orthology-based GO term, or one of its parental terms, that was already assigned to an orthologous protein.
Finally, descriptions for genes lacking manual or GO-based annotations were constructed from the manual GO terms assigned to characterized orthologs. GO annotations were included with the following precedence: BP, followed by MF, and then CC. For genes that lacked experimental characterization and characterized orthologs, but had functionally characterized InterPro domains, descriptions were generated from the domain-based GO annotations. The same precedence rules applied as to the descriptions generated using orthology-based GO information. For genes that lacked experimental characterization and characterized orthologs, and without functionally characterized InterPro domains, but had uncharacterized orthologs, the descriptions simply list the orthology relationship because no inferred GO information was available.

Inglis D.O., Binkley J., Skrzypek M.S., Arnaud M.B., Cerqueira G.C., Shah P., Wymore F., Wortman J.R, & Sherlock G. (2013). Comprehensive annotation of secondary metabolite biosynthetic genes and gene clusters of Aspergillus nidulans, A. fumigatus, A. niger and A. oryzae. BMC Microbiology, 13, 91.

Publication 2013

Amino Acid Sequence Aspergillus Base Sequence Biological Assay Gene Annotation Genes Parent Phenotype Physical Examination Protein S Proteins Reproduction Schizosaccharomyces pombe Secondary Metabolism

Most recents protocols related to «Secondary Metabolism»

Terpene Synthase Genes in Psidium cattleyanum

In this study, we first used terpene synthase protein sequences from fully sequenced genomes of A. thaliana¹⁰⁰ and E. grandis^{29 (link)}, to classify the putative genes found in P. cattleyanum according to the previous classification in the subfamilies TPS-a,-b,-c,-e/f, and -g by sequence similarity^{26 (link)}.
To examine the evolutionary history of TPS genes, a second analysis including more species (E. grandis, E. globulus, A. thaliana, P. trichocarpa, V. vinifera, C. citriodora, and M. alternifolia) was carried out. We generated a tree with TPS sequences related to primary metabolism (subfamilies -c, -e, and -f) with a total of 45 sequences and a second tree related to secondary metabolism (subfamilies a, b, g) including 360 sequences^{29 (link),32 (link),55 (link)}.
The functionally characterized pinene (RtTPS1 and RtTPS2 accession number AXY92166 and AXY92167, respectively) and caryophyllene synthases (RtTPS3 and RtTPS4 accession numbers AXY92168 and AXY92169) from Rhodomyrtus tomentosa^{52 (link)}, pinene synthase (EpTPS1 accession number MK873024) and 1,8-cineole synthases (EpTPS2 and EpTPS3 accession numbers MK873025 and QCQ05478) from Eucalyptus polybractea^{56 (link)}, beta cayophyllene synthase (Eucgr. J01451) from E. grandis^{29 (link)}, myrcene synthase from Antirrhium majus (AAO41727)^{101 (link)}, two isoprene synthase genes from E. globulus (EglobTPS106), E. grandis (Eucgr. K00881)^{29 (link)} and five linalool synthases from Oenothera californica (AAD19841)^{63 (link)}, Clarkia breweri (AAD19840), Clarkia concinna (AAD19839), and Fragaria x ananassa (CAD57106)^{102 (link)} were also included in the phylogenetic analysis to assess the homology of known TPS to Psidium genes.
For each dataset used to construct the trees, we first aligned the amino acid sequences of putative TPS genes using ClustalW implemented within MEGA v7.0 software package^{103 (link)}. Due to high levels of variation and variable exon counts between taxa, we trimmed the alignment using Gblocks^{104 (link)} with the following parameters: smaller final blocks, gap positions within the final blocks, and less strict flanking positions. We used the maximum-likelihood method implemented in PhyML v2.4.4^{105 (link)} online web server^{106 (link)} to perform the phylogenetic analysis. The JTT + G + F was the best-fit substitution model selected with ModelGenerator for protein analyses^{107 (link)}. The confidence values in the tree topology were assessed by running 100 bootstrap replicates. Trees were visualized using Figtree v1.4.4¹⁰⁸.

Canal D., Escudero F.L., Mendes L.A., da Silva Ferreira M.F, & Turchetto-Zolet A.C. (2023). Genome-wide identification, expression profile and evolutionary relationships of TPS genes in the neotropical fruit tree species Psidium cattleyanum. Scientific Reports, 13, 3930.

Publication 2023

Amino Acid Sequence caryophyllene Clarkia Eucalyptol Eucalyptus Evolution, Molecular Exons Fragaria Genes Genome isoprene synthase linalool Metabolism myrcene Nitric Oxide Synthase Oenothera Proteins Psidium Secondary Metabolism terpene synthase Trees

Modeling Antibiotic Production Dynamics

Antibiotic production rate is modeled as an increasing function A of the number of antibiotic genes a a bacterium has. In analogy with the function derived for replication, we model antibiotic production as a Hill function. At the same time, production is strongly inhibited by growth‐promoting genes—a function Ig with g the number of growth‐promoting genes in the genome, in accordance with a likely trade‐off between growth and secondary metabolism (Zhang et al, 2020b (link)). Antibiotic production rate, per time step, is the function:

k_{ab production} = A (a) I (g)

with

\begin{array}{l} A (a) = α_{a} \frac{a}{a + h_{a}}, \\ I (g) = \exp (- β_{g} g) . \end{array}

According to this function, the trade‐off becomes rapidly steeper with larger βg. We expect that results would not qualitatively differ with if I was written in terms of a decreasing hyperbolic function, e.g., as I=1/1+βgg. For simplicity, we assume that antibiotics are deposited at a random location within a circle of radius ra around the producing bacterium. Moreover, we do not take into account concentrations of antibiotics and only model the presence/absence of an antibiotic type in a spatial location (antibiotics of different types can be in the same location). Analogous to the Gillespie algorithm (Gillespie, 1976 ; see also methods in Takeuchi & Hogeweg, 2007 (link); for the spatially extended version), antibiotic production is a first‐order (linear) process. As kabproduction takes values in 0,1, we treat it as the probability of producing an antibiotic over one time step. We normalize the probability of antibiotic production by the area Ara, i.e., the number of lattice sites in the circle of radius ra around the bacterium, and we draw the number of antibiotics deposited from a binomial distribution with parameters (Ara, kabproduction/Ara). This makes antibiotic production independent from the deposition radius and allows us to compare results across simulations with different ra. If a bacterium has multiple antibiotic genes, each antibiotic deposited is chosen randomly from the antibiotic genes with uniform probability.

Colizzi E.S., van Dijk B., Merks R.M., Rozen D.E, & Vroomans R.M. (2023). Evolution of genome fragility enables microbial division of labor. Molecular Systems Biology, 19(3), e11353.

Publication 2023

Antibiotics Antibiotics, Antitubercular Bacteria DNA Replication Genes Genes, vif Genome Multiple Birth Offspring One-Step dentin bonding system Radius Secondary Metabolism STEEP1 protein, human

Comprehensive Genomic Analysis of Penicillium Species

Genes related to cellulose and hemicellulose degradation: Carbohydrate-active enzymes (CAZymes) in the three Penicillium species were annotated using BLAST (Johnson M. et al., 2008 (link)). The dbCAN annotation program HMMER 3 (Finn et al., 2011 (link)) was used to search against the CAZy (carbohydrate-active enzyme) database (Lombard et al., 2014 (link)). The results were combined when e values ≤1e–5. The class II peroxidases and dye-decolorizing peroxidases were further confirmed by BLAST searches against PeroxiBase (Fawal et al., 2012 (link)).
Secondary metabolism genes: Candidate transporter genes in the three Penicillium species were identified based on searches of the Transporter Classification Database (TCDB) (Saier et al., 2014 (link)) with e values ≤1e–5 and identity values ≥40%. The secondary metabolism biosynthesis genes and gene clusters in the genomes of the three Penicillium species were predicted with AntiSMASH 6.0 (Blin et al., 2021 (link)). The Comprehensive Antibiotic Research Database (CARD) (McArthur et al., 2013 (link)) was used to compare coding genes (of the three Penicillium species) involved in antimicrobial resistance.
Virulence associated genes: Candidate pathogen-host interactions (PHI) genes within the genome of the three Penicillium species were identified using BLASTp to search against PHI-base v4.3⁷, and protein alignments were performed to identify putative virulence-associated genes in the three Penicillium species with identity ≥40% and query coverage ≥70%.

Guo W., Wang W., Tang J., Li T, & Li X. (2023). Genome analysis and genomic comparison of a fungal cultivar of the nonsocial weevil Euops chinensis reveals its plant decomposition and protective roles in fungus-farming mutualism. Frontiers in Microbiology, 14, 1048910.

Publication 2023

Anabolism Antibiotics Carbohydrates Cellulose Enzymes Genes Genome hemicellulose Host-Pathogen Interactions Membrane Transport Proteins Microbicides Penicillium Peroxidases Proteins Secondary Metabolism Virulence

Phylogenetic Analysis of WRKY TFs

A phylogenetic tree was constructed based on the sequences of WRKY TFs that regulate secondary metabolisms and some WRKYs that regulate stress tolerance (Table S6). The phylogenetic tree was constructed using the default settings of MEGA v.6 and using the neighbor join method. The bootstrap value was calculated from 1000 repetitions [54 (link)].

Wen C., Zhang Z., Shi Q., Duan X., Du J., Wu C, & Li X. (2023). Methyl Jasmonate- and Salicylic Acid-Induced Transcription Factor ZjWRKY18 Regulates Triterpenoid Accumulation and Salt Stress Tolerance in Jujube. International Journal of Molecular Sciences, 24(4), 3899.

Publication 2023

Base Sequence Immune Tolerance Secondary Metabolism

Assessing Antifungal Activity and Gene Expression of Mup1 Mutants

Dual confrontations between TAWT, OEMup1–5, OEMup1–6, ΔMup1–1 and ΔMup1–10 and the pathogens (Fusarium graminearum) were carried out as previously described method [19 (link)]. A 5 mm diameter of the agar plug of TAWT, OEMup1–5, OEMup1–6, ΔMup1–1 and ΔMup1–10 and the pathogens (F. graminearum) was inoculated on either side of the PDA agar and incubated for 5 days at 28 °C. Single culture of TAWT, OEMup1–5, OEMup1–6, ΔMup1–1, ΔMup1–10 and F. graminearum was used as a control. The percentage growth inhibition of the pathogens (F. graminearum) was calculated as in the previous study [19 (link)].
The expression of secondary metabolites genes related to mycoparasitism in TAWT, OEMup1–5, OEMup1–6, ΔMup1–1, and ΔMup1–10 was studied using a mock antagonism assay [19 (link)]. Briefly, the TAWT, OEMup1–5, OEMup1–6, ΔMup1–1, ΔMup1–10 was prepared in PDB medium, washed and transferred into 4-days-old F. graminearum (wheat head blight and root rot pathogen) culture grown in the PDB medium. After 24 h incubation with shaking, the cultures were harvested, frozen, and the RNA was extracted. Induction of the secondary-metabolism-related genes related to mycoparasitism activity (non-ribosomal peptide synthetase (NP1 and NP2), Putative ferrichrome synthetase (NP3), Cytochrome P450 (Tri 13) 1, O-methyl transferase (OMT) and Polyketide synthetase (PK1 and PK2)) was assessed by quantitative RT-PCR. The results were the average of three independent biological replicates with qPCR technical triplicates.
The culture filtrate was used as the crude protein extract to determine the enzyme activity. Chitinase activity was assessed using a chitinase enzyme Activity Determination Kit (Shanghai Cablebridge Biotechnology Co., Ltd., Shanghai, China). Activities of β-1,3-glucanase and cellulase were measured using a β-1,3-glucanase and cellulase enzyme Activity Determination Kit (Shanghai Cablebridge Biotechnology Co., Ltd., Shanghai, China), respectively, according to the manufacturer’s instruction.

Karuppiah V., Zhang C., Liu T., Li Y, & Chen J. (2023). Transcriptome Analysis of T. asperellum GDFS 1009 Revealed the Role of MUP1 Gene on the Methionine-Based Induction of Morphogenesis and Biological Control Activity. Journal of Fungi, 9(2), 215.

Publication 2023

Agar antagonists Biological Assay Biopharmaceuticals Cellulase Chitinases Cytochrome P450 enzyme activity ferrichrome synthetase Freezing Fusarium graminearum Gene Expression Head Induction, Genetic Ligase non-ribosomal peptide synthase Pathogenicity Plant Roots Polyketides Proteins Psychological Inhibition Reverse Transcriptase Polymerase Chain Reaction Secondary Metabolism Transferase Triticum aestivum

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What are the main functions of secondary metabolites in living organisms?

Secondary metabolites serve a variety of important functions beyond basic growth and development requirements. These include roles in defense against predators and pathogens, signaling and communication, and providing structural support. Understanding the complexities of secondary metabolism is crucial for applications in fields like pharmaceuticals, agriculture, and bioenergy.

What are some common examples of secondary metabolites?

How can PubCompare.ai assist with secondary metabolism research?

PubCompare.ai allows researchers to screen protocol literature more efficiently and leverage AI to pinpoint critical insights. The platform can help identify the most effective protocols related to secondary metabolism for your specific research goals. PubCompare.ai's AI-driven analysis can highlight key differences in protocol effectiveness, enabling you to choose the best option for reproducibility and accuracy in your studies.

What are some common challenges in studying secondary metabolism?

One of the key challenges in studying secondary metabolism is the complexity and diversity of the metabolic pathways and regulatory mechanisms involved. Researchers must often navigate a vast landscape of literature and protocols to identify the most effective approaches. Additionaly, optimizing secondary metabolism for desired applications can be a time-consuming and iterative process. PubCompare.ai can help streamline this workflow and enhance research outcomes.

How does secondary metabolism differ from primary metabolism?

Unlike primary metabolism, which focuses on the core biochemical processes essential for an organsims's basic survival and reproduction, secondary metabolism involves the production of a wide range of specialized chemical compounds. While primary metabolism is universal across living beings, secondary metabolism is often more variable and adapted to specific environmental conditions and evolutionary pressures. This distinction is imporant to consider when studying and applying secondary metabolic pathways.

More about "Secondary Metabolism"

Secondary metabolism, also known as specialized or specialized metabolism, refers to the production of diverse chemical compounds by living organisms, often beyond the basic requirements for growth and development.
These compounds, such as alkaloids, terpenoids, and phenolics, can serve a variety of functions, including defense, signaling, and structural roles.
Understanding the complexities of secondary metabolism is crucial for applications in fields like pharmaceuticals, agriculture, and bioenergy.
Research in this area involves the identification, characterization, and optimization of metabolic pathways and regulatory mechanisms.
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By leveraging advanced AI technologies, researchers can streamline their workflow and enhance their secondary metabolism studies, leading to new discoveries and innovative applications.
PubCompare.ai, an AI-driven platform, can help researchers easily locate and compare protocols from literature, pre-prints, and patents to identify the best approaches, ultimately optimizing their secondary metabolism research and unlocking the full potential of this fascinating field of study.