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Predicting Potential Active Chiroptera Cathelicidins Sequences From Sequences of Precursors Using Bioinformatic Tools
College of Science: Department of Chemistry and Biochemistry

Predicting Potential Active Chiroptera Cathelicidins Sequences From Sequences of Precursors Using Bioinformatic Tools

Abstract

Cathelicidins are a family of host defense peptides that play a large role in the innate immune system of many vertebrates, including humans, birds, and bats. This study looks at cathelicidins present in Chiroptera, commonly known as bats, because they have the ability to host viral pathogens that can cause significant human disease. Of the 30 cathelicidin sequences reported in the NCBI database, 27 unique cathelicidin remained after duplicate sequences were removed. Similarly, 6 unique sequences were compiled from the 7 sequences reported in UniProt; however, these analyses did not yield the active regions of the peptides. Using the multiple sequence alignment tool in Jalview, the combined 33 sequences were aligned and qualitatively analyzed to establish similarities in their respective sequences. Then, ScanProsite, from Prosite, confirmed the presence of the cathelicidin signature in a number of sequences by comparing them to the Prosite motif collection. The amino acid residues C-terminal to the cathelicidin signature were identified as the active amino acid regions. The predicted active regions were evaluated through the Antimicrobial Peptide Database, as well as CAMP R3. Both sites gave a strong indication that the peptides present antimicrobial properties. Through these analyses, 11 sequences from NCBI and 6 sequences from UniProt were identified to have active antimicrobial regions. Identifying the active regions of cathelicidins is crucial to their application as resources for therapeutic development.

How to Cite:

RAWAT, M., Carfagno, A. & Bishop, B., (2022) “Predicting Potential Active Chiroptera Cathelicidins Sequences From Sequences of Precursors Using Bioinformatic Tools”, Journal of Student-Scientists' Research 2. doi: https://doi.org/10.13021/jssr2020.2910

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