NLM DIR Seminar Schedule

Abstract:

Mass spectrometry–based proteomics enables the identification and quantification of peptides and proteins by matching observed fragmentation spectra to candidate peptides from a protein database. Incorporating computationally predicted spectra into this process can substantially improve peptide identification. While recently proposed deep learning–based spectrum prediction methods achieve high performance, they are computationally expensive and thus unsuitable for some applications. In this talk, I introduce FastSpel, a simple, accurate, and efficient peptide spectrum prediction method that achieves performance comparable to state-of-the-art deep learning–based approaches at a fraction of the computational cost. FastSpel is therefore well suited for applications that require, or benefit from, on-the-fly predictions. Moreover, unlike deep learning-based methods, FastSpel includes easily interpretable parameters and thus may provide new insights into the peptide fragmentation process.