Please note: This master’s thesis presentation will take place online.
Zhenbo Li, Master’s candidate
David R. Cheriton School of Computer Science
Supervisors: Professors Bin Ma, Yang Lu
To improve the quality of spectral library search, several MS/MS spectrum predictors have been developed in the last decades. After success in various fields, deep learning techniques are adopted by MS/MS spectrum predictors to increase the accuracy of predicted spectra. However, the quality and quantity of the training set are both required to train a deep learning model. Due to the less representation of MHC-associated peptides in most spectral libraries, current MS/MS spectrum predictors provide less accurate predicted spectra for MHC-associated peptides than their performance for other peptides.
In this thesis, we built several MHC-associated peptide spectral libraries for training and evaluation purposes. We selected PredFull as our base model and performed transfer learning with these MHC-associated peptide libraries, which are much smaller than common tryptic spectral libraries. The result showed that the fine-tuned model outperformed the original model significantly when predicting MHC-associated peptides.