Proteomic analysis aims to get overall information of proteins generated by an organism via studying its peptide mixture following enzymatic digestion. Peptide mixtures may contain millions of peptides and are an extremely complex matrix. Conventional LC-MS/MS approaches are far from ideal for the large-scale detection. A tims-ToF spectrometer, is an instrument that couples a trapped ion mobility spectrometer between the LC and MS regions, and grants an additional separation and analysis dimension depending on the varying collision cross sections (CCS) of peptides. This technology greatly improves proteomic analysis and makes it possible to identify thousands of proteins with microliters of sample injection using short gradient times. Here, we focus on investigating the effect of five gradient types (linear, logarithm-like, exponent-like, stepwise, and steplinear) and timecourse (30 min, 60 min, 90 min) on the number of identified protein groups and unique peptides in Hela digest. The results indicate that the steplinear gradient with a 60 min gradient generates optimal performance with over 3,800 protein groups identified (³ 1 unique peptide, FRD £ 1%). Two data search engines, PEAKS Studio and MSFragger were also compared under the same conditions. The results show that MSFragger (3897 identified protein groups) works faster and has more proteins identified than PEAKS Studio (3869 identified proteins). We will discuss the differences in gradient types and data analysis methods to obtain optimum proteomic results.

Effect of gradient type and duration on the number of identified proteins in HeLa digest by NanoLC-NSI-timsToF Pro

ACS Spring 2022

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