Application of spectral library prediction for parallel reaction monitoring of viral peptides_PRM_data

Project description:

A major part of the analysis of parallel reaction monitoring (PRM) data is the comparison of observed fragment ion intensities to a library spectrum. Classically, these libraries are generated by data-dependent acquisition (DDA). Here we test Prosit, a published deep neural network algorithm, for its applicability in predicting spectral libraries for PRM. For this purpose, we targeted 1,529 precursors derived from synthetic viral peptides and analyzed the data with Prosit and DDA-derived libraries. Additionally, we used a spectral library predicted by Prosit and a DDA library to identify SARS-CoV-2 peptides from a simulated oropharyngeal swab.

Sample processing protocol:

A total of 1,569 crude synthetic viral peptides were ordered in six pools from JPT (Berlin, Germany). Synthetic peptides were separated on a 200 cm μPAC™ column (PharmaFluidics) by using an EASY-nLC1200 system (Thermo Fisher Scientific) equipped with a μPAC™ trapping column (PharmaFluidics). The flow rate was set to 300 nL/min and a stepped linear 160 min gradient was applied: 3-10% B in 22 min, 10-33%B in 95 min, 33-49% B in 23 min, 49-80% B in 10 min and 80% B for 10 min. Solvent A was 0.1% (v/v) formic acid (FA) in water, solvent B consisted of 80% (v/v) acetonitrile in 0.1% (v/v) FA. The column temperature was set to 50 °C. The Q Exactive Plus (Thermo Fisher Scientific) operated in Full MS/dd-MS2 or unscheduled PRM mode. For MS/dd-MS2 the following parameters were used. MS1 resolution was 70.000 with an AGC target of 3x106, max. injection time of 20 ms and a scan range of 300-1650 m/z. MS2 resolution was 17.500 with an AGC target of 105, max. injection time of 50 ms and an isolation window of 2 m/z. The analysis parameters in PRM mode were set as follows. MS1 parameters were identical to DDA. MS2 resolution was 17.500 with an AGC target of 106, max. injection time of 55 ms and an isolation window of 1.4 m/z.

Potential SARS-CoV-2 target peptides belonging to the N protein were identified by DDA of SARS-CoV-2 infected Calu-3 cells. Peptides were diluted in 0.1% TFA (0.2 µg/µL) and 5 µL were separated on a 50 cm μPAC™ column (PharmaFluidics) using an EASY-nLC1200 system (Thermo Fisher Scientific). The flow rate was set to 800 nL/min and a stepped 30 min gradient was applied: 6-11% B in 2:58 min, 11-30% B in 17:10 min, 30-35% B in 2:41 min, 35-47% B in 3:11 min, 47-80% B for 0:10 min, 80% B for 1:50 min, 80-0% B in 0:10 min and 100% A for 1:50 min. Solvent A was 0.1% (v/v) formic acid (FA) in water, solvent B consisted of 80% (v/v) acetonitrile in 0.1% (v/v) FA. The column temperature was set to 50 °C. The Q Exactive HF (Thermo Fisher Scientific) operated in Full MS/dd-MS2 (Top20) using the following parameters. MS1 resolution was 60.000 with an AGC target of 3x106, max. injection time of 20 ms and a scan range of 300-1650 m/z. MS2 resolution was 17.500 with an AGC target of 105, max. injection time of 50 ms and an isolation window of 2 m/z.

To simulate a SARS-CoV-2 positive patient sample, we spiked cell-culture derived virus in a negative oropharyngeal swab and targeted the N protein by PRM. LC parameters were identical to DDA analysis of SARS-CoV-2 infected Calu-3 cells. The PRM parameters of the The Q Exactive HF (Thermo Fisher Scientific) were set as follows. MS1 parameters were identical to DDA. MS2 resolution was 45.000 with an AGC target of 106, max. injection time of 100 ms and an isolation window of 1.4 m/z.

Data processing protocol:

DDA Raw files were searched with MaxQuant against the respective virus database (UniProt) with a peptide FDR of 1%. Detailed MaxQuant parameters can be found in the parameters.txt files of the according results. MaxQuant .msms output files were used to generate spectral libraries with BiblioSpec implemented in the Skyline environment using a cut-off score of 0.95. Peptide identification of PRM runs was done in Skyline using the top 6 fragment ions of the DDA spectral library or according Prosit derived library (Prosit_2020_intensity_model).