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Advanced Precursor Ion Selection Algorithms for Increased Depth of Bottom-Up Proteomic Profiling.

ABSTRACT: Conventional TopN data-dependent acquisition (DDA) LC-MS/MS analysis identifies only a limited fraction of all detectable precursors because the ion-sampling rate of contemporary mass spectrometers is insufficient to target each precursor in a complex sample. TopN DDA preferentially targets high-abundance precursors with limited sampling of low-abundance precursors and repeated analyses only marginally improve sample coverage due to redundant precursor sampling. In this work, advanced precursor ion selection algorithms were developed and applied in the bottom-up analysis of HeLa cell lysate to overcome the above deficiencies. Precursors fragmented in previous runs were efficiently excluded using an automatically aligned exclusion list, which reduced overlap of identified peptides to ?10% between replicates. Exclusion of previously fragmented high-abundance peptides allowed deeper probing of the HeLa proteome over replicate LC-MS runs, resulting in the identification of 29% more peptides beyond the saturation level achievable using conventional TopN DDA. The gain in peptide identifications using the developed approach translated to the identification of several hundred low-abundance protein groups, which were not detected by conventional TopN DDA. Exclusion of only identified peptides compared with the exclusion of all previously fragmented precursors resulted in an increase of 1000 (?10%) additional peptide identifications over four runs, suggesting the potential for further improvement in the depth of proteomic profiling using advanced precursor ion selection algorithms.

SUBMITTER: Kreimer S

PROVIDER: S-EPMC5557716 | biostudies-literature | 2016 Oct

REPOSITORIES: biostudies-literature

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Advanced Precursor Ion Selection Algorithms for Increased Depth of Bottom-Up Proteomic Profiling.

Kreimer Simion S Belov Mikhail E ME Danielson William F WF Levitsky Lev I LI Gorshkov Mikhail V MV Karger Barry L BL Ivanov Alexander R AR

Journal of proteome research 20160907 10

Conventional TopN data-dependent acquisition (DDA) LC-MS/MS analysis identifies only a limited fraction of all detectable precursors because the ion-sampling rate of contemporary mass spectrometers is insufficient to target each precursor in a complex sample. TopN DDA preferentially targets high-abundance precursors with limited sampling of low-abundance precursors and repeated analyses only marginally improve sample coverage due to redundant precursor sampling. In this work, advanced precursor ...[more]

PMID: 27569903

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Project description:Ant species have specialized venom systems developed to sting and inoculate a biological cocktail of organic compounds, including peptide and polypeptide toxins, for the purpose of predation and defense. The genus Dinoponera comprises predatory giant ants that inoculate venom capable of causing long-lasting local pain, involuntary shaking, lymphadenopathy, and cardiac arrhythmias, among other symptoms. To deepen our knowledge about venom composition with regard to protein toxins and their roles in the chemical-ecological relationship and human health, we performed a bottom-up proteomics analysis of the crude venom of the giant ant D. quadriceps, popularly known as the "false" tocandiras. For this purpose, we used two different analytical approaches: (i) gel-based proteomics approach, wherein the crude venom was resolved by denaturing sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE) and all protein bands were excised for analysis; (ii) solution-based proteomics approach, wherein the crude venom protein components were directly fragmented into tryptic peptides in solution for analysis. The proteomic data that resulted from these two methodologies were compared against a previously annotated transcriptomic database of D. quadriceps, and subsequently, a homology search was performed for all identified transcript products. The gel-based proteomics approach unequivocally identified nine toxins of high molecular mass in the venom, as for example, enzymes [hyaluronidase, phospholipase A1, dipeptidyl peptidase and glucose dehydrogenase/flavin adenine dinucleotide (FAD) quinone] and diverse venom allergens (homologous of the red fire ant Selenopsis invicta) and venom-related proteins (major royal jelly-like). Moreover, the solution-based proteomics revealed and confirmed the presence of several hydrolases, oxidoreductases, proteases, Kunitz-like polypeptides, and the less abundant inhibitor cysteine knot (ICK)-like (knottin) neurotoxins and insect defensin. Our results showed that the major components of the D. quadriceps venom are toxins that are highly likely to damage cell membranes and tissue, to cause neurotoxicity, and to induce allergic reactions, thus, expanding the knowledge about D. quadriceps venom composition and its potential biological effects on prey and victims.

Dataset Information

Advanced Precursor Ion Selection Algorithms for Increased Depth of Bottom-Up Proteomic Profiling.

Publications

Advanced Precursor Ion Selection Algorithms for Increased Depth of Bottom-Up Proteomic Profiling.

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