| Autor: |
Melendez C; Department of Genome Sciences, University of Washington, Seattle, Washington 98195, United States., Sanders J; Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, Washington 98195, United States., Yilmaz M; Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, Washington 98195, United States., Bittremieux W; Department of Computer Science, University of Antwerp, 2020 Antwerp, Belgium., Fondrie WE; Talus Bioscience, Seattle, Washington 98195, United States., Oh S; Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, Washington 98195, United States., Noble WS; Department of Genome Sciences, University of Washington, Seattle, Washington 98195, United States.; Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, Washington 98195, United States. |
| Abstrakt: |
A key parameter of any bottom-up proteomics mass spectrometry experiment is the identity of the enzyme that is used to digest proteins in the sample into peptides. The Casanovo de novo sequencing model was trained using data that was generated with trypsin digestion; consequently, the model prefers to predict peptides that end with the amino acids "K" or "R". This bias is desirable when Casanovo is used to analyze data that was also generated using trypsin but can be problematic if the data was generated using some other digestion enzyme. In this work, we modify Casanovo to take as input the identity of the digestion enzyme alongside each observed spectrum. We then train Casanovo with data generated by using several different enzymes, and we demonstrate that the resulting model successfully learns to capture enzyme-specific behavior. However, we find, surprisingly, that this new model does not yield a significant improvement in sequencing accuracy relative to a model trained without enzyme information but using the same training set. This observation may have important implications for future attempts to make use of experimental metadata in de novo sequencing models. |
