Thunder-DDA-PASEF enables high-coverage immunopeptidomics and is boosted by MS2Rescore with novel MS2PIP timsTOF fragmentation prediction model

ABSTRACT: Human leukocyte antigen (HLA) class I peptide ligands (HLAIps) are key targets for developing vaccines and immunotherapies against infectious pathogens or cancer cells. Identifying HLAIps is challenging due to their high diversity, low abundance, and patient individuality. Here, we developed a highly sensitive method for identifying HLAIps using liquid chromatography-ion mobility-tandem mass spectrometry (LC-IMS-MS/MS). In addition, we trained a novel timsTOF-specific peak intensity MS2PIP model for tryptic and non-tryptic peptides and implemented it in MS2Rescore (v3) together with the CCS predictor from ionmob. The optimized method, Thunder-DDA-PASEF, semi-selectively fragments singly and multiply charged HLAIps based on their IMS and m/z. Moreover, the method employs the high sensitivity mode and extended IMS resolution with fewer MS/MS frames (300 ms TIMS ramp, 3 MS/MS frames), doubling the coverage of immunopeptidomics analyses, compared to the proteomics-tailored DDA-PASEF (100 ms TIMS ramp, 10 MS/MS frames). Additionally, rescoring boosted the HLAIps identification by 41.7% to 33%. This enabled in-depth profiling of HLAIps from diverse human cell lines and human plasma. Finally, profiling JY and Raji cells transfected to express the SARS-CoV-2 spike protein resulted in 16 spike HLAIps, thirteen of which had been reported to elicit immune responses in human patients.

ORGANISM(S): Homo Sapiens (human)

SUBMITTER: Prof. Dr. Stefan Tenzer 

PROVIDER: PXD040385 | JPOST Repository | Mon Mar 18 00:00:00 GMT 2024

REPOSITORIES: jPOST