Fragment ion intensity prediction improves the identification rate of non-tryptic peptides in TimsTOF

Abstract

AbstractImmunopeptidomics plays a crucial role in identifying targets for immunotherapy and vaccine development. Because the generation of immunopeptides from their parent proteins does not adhere to clear-cut rules, rather than being able to use known digestion patterns, every possible protein subsequence within HLA class-specific length restrictions needs to be considered. This leads to an inflation of the search space and results in lower spectrum annotation rates. Rescoring is a powerful enhancement of standard sequence database searching that boosts the spectrum annotation performance. In the field of immunopeptidomics low abundant peptides often occur, which is why the highly sensitive timsTOF instruments are increasingly gaining popularity. To improve rescoring for immunopeptides measured using timsTOF instruments, we trained a deep learning-based fragment ion intensity prediction model. Over 300,000 synthesized non-tryptic peptides from the ProteomeTools project were analyzed on a timsTOF-Pro to generate a dataset that was used to fine-tune an existing Prosit model. By applying our fragment ion intensity prediction model, we demonstrate up to 3-fold improvement in the identification of immunopeptides. Furthermore, our approach increased detection of immunopeptides even from low input samples.