Unveiling the peptidases of parasites from the office chair - The endothelin-converting enzyme case study
Language English Country Great Britain, England Media print-electronic
Document type Journal Article, Review
PubMed
39448189
DOI
10.1016/bs.apar.2024.05.003
PII: S0065-308X(24)00032-0
Knihovny.cz E-resources
- Keywords
- Bioinformatic tools, Biological databases, Computational analysis, Endothelin converting enzyme, High-throughput methodologies, Next-generation sequencing, Omics, Parasite, Parasitic peptidases, Peptidase, Schistosoma mansoni, ECE, metallopeptidase,
- MeSH
- Endothelin-Converting Enzymes genetics MeSH
- Humans MeSH
- Peptide Hydrolases genetics metabolism MeSH
- Proteomics MeSH
- Schistosoma mansoni enzymology genetics MeSH
- Computational Biology * MeSH
- Animals MeSH
- Check Tag
- Humans MeSH
- Animals MeSH
- Publication type
- Journal Article MeSH
- Review MeSH
- Names of Substances
- Endothelin-Converting Enzymes MeSH
- Peptide Hydrolases MeSH
The emergence of high-throughput methodologies such as next-generation sequencing and proteomics has necessitated significant advancements in biological databases and bioinformatic tools, therefore reshaping the landscape of research into parasitic peptidases. In this review we outline the development of these resources along the -omics technologies and their transformative impact on the field. Apart from extensive summary of general and specific databases and tools, we provide a general pipeline on how to use these resources effectively to identify candidate peptidases from these large datasets and how to gain as much information about them as possible without leaving the office chair. This pipeline is then applied in an illustrative case study on the endothelin-converting enzyme 1 homologue from Schistosoma mansoni and attempts to highlight the contemporary capabilities of bioinformatics. The case study demonstrate how such approach can aid to hypothesize enzyme functions and interactions through computational analysis alone effectively and emphasizes how such virtual investigations can guide and optimize subsequent wet lab experiments therefore potentially saving precious time and resources. Finally, by showing what can be achieved without traditional wet laboratory methods, this review provides a compelling narrative on the use of bioinformatics to bridge the gap between big data and practical research applications, highlighting the key role of these technologies in furthering our understanding of parasitic diseases.
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