Comparative Proteomic Analysis of Venom from Three Viper Taxa: Evaluating Software-Specific Protein and Peptide Profiles

Abstract

Snake venom is increasingly recognised in biomedical research as a potential source of relevant proteins that are still relatively unknown in various species. In this experiment, we performed proteomic quantification and identification of the venomic profile of three viper taxa: Montivipera blugardaghica subsp. bulgardaghica (MB), Vipera ammodytes subsp. montandoni (VA) and Vipera kaznakovi (KV); and compared the performance of three peptide identification software: PEAKS, MaxQuant and Proteome Discoverer. Overall, PEAKS identified 19 unique proteins (19 in MB, 11 in VA and 19 for KV) and 125 unique peptides (55 in MB, 35 in VA and 63 for KV); MaxQuant identified 577 unique proteins (234 in MB, 275 in VA and 297 for KV) and 1233 unique peptides (518 in MB, 647 in VA and 642 for KV); Proteome Discoverer identified 621 unique proteins (310 in MB, 248 for VA and 346 for VK) and 1657 unique peptides (894 in MB, 830 in VA and 1041 for VK). The three software shared 5 identified proteins and 67 peptides; PEAKS shared 6 proteins and 69 peptides with MaxQuant and 6 proteins and 79 peptides with Proteome Discoverer; MaxQuant shared 139 proteins and 781 peptides with Proteome Discoverer. All identified proteins were categorised into families for each taxon and then compared with the existing literature. This revealed significant discrepancies in the results between the software and the reviewed literature. Overall, PEAKS performed very poorly, while MaxQuant and Proteome Discoverer performed best for both protein and peptide identification, with the latter software being particularly noteworthy.