![]() ![]() Approximately three-quarters of the initial sample volume passed through the membrane, while a quarter remained in the upper fraction. The bottom fraction was transferred to the new test tube, while the upper portion was moved to the new centrifugal unit, placed upside down, and spun for 3 min at 1000× g. It was centrifuged for 8 min at 12,400× g using 0.5 mL Microcon ®-30 kDa centrifugal filter unit with Ultracel ®-30 membrane (Merck Millipore, Cork, Ireland). 1:80 ( v/ v) aliquot was directly taken to further analysis as a dilution of crude venom, while a 100-fold dilution sample was subjected to the additional step of pre-fractionation. Ĭrude venom was diluted at a 1:80 and 1:100 ( v/ v) ratios with 50 mM ammonium bicarbonate pH 8. If we then add several various procedures for further data processing, based on distinct concepts and algorithms, it is extremely difficult to draw accurate and reliable conclusions from comparing proteomic data between different studies. Application of different techniques for the preparation, decomplexation, and measurement of the venom samples leads to the fact that the data obtained by different research teams may significantly differ. Therefore, very often, different research groups decide to rely on methods that are faster and available to them. This workflow, however, consists of cysteine mapping, N-terminal sequencing, and MS analysis of fractions separated by RP-HPLC and SDS-PAGE respectively, is very time-consuming and requires an application of techniques that are not routinely used in every laboratory. The ideal example of such an approach is the snake venomics protocol, firstly introduced in 2004 by Calvete’s team, which is still considered as the gold standard in proteomic research of venoms. įor these reasons, to estimate venom proteome with sufficient fidelity, we are forced to apply a sophisticated research workflow consisting of many individual techniques, each providing unique information about the sample. On the other hand, top-down approaches are still in the early stages of development and it seems that we still have to wait until MS analysis of intact proteins have the chance to become a method of choice in venom studies. Issues related to the presence of shared peptides, the problem with missing data due to the high dynamic range of venoms and paucity of comprehensive protein databases are the hindrances that currently impede the development of peptide-centric methods. However, even with such great scientific progress, there is still no single technique capable to unambiguously assign protein identity to every venom component. The emergence of new research concepts, technological advances as well as the implementation of appropriate bioinformatics platforms, are the factors that made it possible to solve many of the problems that venomics have been struggling with for years. ![]() Over the past decades, we have witnessed continuous progress in venom research, which has resulted in the gathering of a substantial amount of invaluable data on the biochemical composition of many venoms, the mechanism of their action, or their potential use in medicine to design drugs or improve current therapies to treat snakebite envenomation. Our results underline the necessary caution in the interpretation of data based on a comparative analysis of data derived from different studies. ![]() Moreover, we reported a rapid and straightforward technique for the separation of the fraction of proteins from the three-finger toxin family. We were able to provide new information regarding the protein composition of this venom but also present the qualitative and quantitative limitations of currently used proteomic methods. #DECOY DATABASE FORMAT PEPTIDESHAKER SOFTWARE#We applied two software solutions (PeptideShaker and MaxQuant) to process data from shotgun LC-MS/MS analysis of Naja ashei venom and collate it with the previous report concerning this species. In this article, we aimed to show the possible differences that can arise, in the final results of the proteomic experiment, while using different research workflows. The information contained therein is often used for comparisons between different datasets and to draw biological conclusions therefrom. The dynamic development of venomics in recent years has resulted in a significant increase in publicly available proteomic data. ![]()
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