Unveiling Encrypted Antimicrobial Peptides from Cephalopods' Salivary Glands: A Proteolysis-Driven Virtual Approach

12 December 2023, Version 1
This content is a preprint and has not undergone peer review at the time of posting.

Abstract

Antimicrobial peptides (AMPs), with their versatile actions, offer promise against antimicrobial resistance and as templates for novel therapeutic agents. While existing AMP databases primarily feature AMPs from terrestrial eukaryotes, marine sources are gaining attention, with cephalopods emerging as a promising but still underexplored source. This study unveils the potential reservoir of AMPs encrypted within the proteome of cephalopods’ salivary glands using in silico proteolysis. A composite protein database comprising canonical and non-canonical proteins from cephalopods' salivary apparatus was used as the substrate for five proteases involved in three digestion protocols. The resulting millions of peptides were screened using machine learning, deep learning, multi-query similarity-based models, and complex networks. The screening prioritizes antimicrobial activity, the absence of haemolytic and toxic attributes, and structural distinctiveness compared to characterized AMPs. Diverse publicly accessible AMP datasets are produced, catering to various research needs, ranging from those focused solely on antimicrobial activity to refined datasets of non-haemolytic and non-toxic AMPs. Comparative analyses and network science principles were applied to identify singular and representative subsets from non-haemolytic and non-toxic AMPs. All these sets of AMPs and the proposed mining tools serve as valuable assets for peptide drug developers.

Keywords

Cephalopods
salivary glands
omics data
virtual screening
complex networks
AMPs datasets

Supplementary materials

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Figure 1SM
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Venn diagrams representing the prediction results from the three evaluated models. The consensus prediction for 1A- AMPs detection, 1B- Non-haemolytic AMPs and 1C- Non-haemolytic/non-toxic AMPs.
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Figure 2SM
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Distribution of global peptide features (length, amino acid (AA) frequency, isoelectric point (pI), global charge, global hydrophobicity, and global hydrophobic moment) within each peptide library class. 2A- AMPs consensus, 2B- Non-haemolytic AMPs, 2C- Non-haemolytic/non-toxic AMPs
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Figure 3SM
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Histograms of pairwise sequence identity for datasets sharing similarity with StarPepDB at following identity percentage ranges: 40-50, 50-60, 60-70, 70-80, and greater than 80
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File 1SM
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Raw prediction results for AMPs detection on the 13 individual peptidomes by each of the three models.
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File 2SM
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Raw prediction results for non-haemolytic AMPs detection on the 13 individual peptidomes by each of the three models.
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File 3SM
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Raw prediction results for non-haemolytic AMPs deprived of toxic signatures detection (non-haemolytic/non-toxic AMPs) on the 13 individual peptidomes by each of the three models.
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File 4SM
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Similarity clusters resulting from the comparison between 68,694 non-haemolytic/non-toxic AMPs from cephalopods versus StarPepDB members at different identity cutoffs. It also contains FASTA sequences from cephalopods extracted from similarity clusters at different identity cutoffs.
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File 5SM
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HSPN projecting the clustering of CSPs with StarPepDB. Clusters composition and their characterization through peptide length, charge, pI, hydrophobicity, amphiphilicity, Boman Index.
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File 6SM
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HSPN that projects the chemical/sequence space of the 5,466 CSPs at 0.75 of similarity cutoff. HSPN properties and CSPs’ representative subsets extracted with network centralities
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