ArticleMethods in enzymology2022
In silico prediction and mass spectrometric characterization of botanical antimicrobial peptides.
Article in Methods in enzymology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
2 citing papers in PubMed, 4 citations in OpenAlex.
- Identification and Characterization of CC-AMP1-like and CC-AMP2-like Peptides inJournal of proteome research · 2024Article
- Creating optimized peptide libraries for AMP discovery via PepSAVI-MS.Methods in enzymology · 2022Article
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
Abstract
Antimicrobial peptides (AMPs) are promising compounds for the treatment of antibiotic-resistant bacteria and are found across all organisms, including plants. Unlike most antibiotics, AMPs tend to act on more generalized and multiple targets, making development of resistance more difficult. Conventional approaches toward AMP identification include bioactivity-guided fractionation and genome mining. Complementary methods leveraging bioactivity-guided fractionation, cysteine motif-guided in silico AMP prediction, and mass spectrometric approaches can be combined to expand botanical AMP discovery. Herein, we present an integrated workflow which serves to streamline implementation toward a robust botanical AMP discovery pipeline.
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Registered trials
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