Predict MS2 spectrum based on protein sequence by Deep Convolutional Neural Networks
| Autor: | Lin, Yang-Ming, 林洋名 |
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| Rok vydání: | 2018 |
| Druh dokumentu: | 學位論文 ; thesis |
| Popis: | 106 Mass spectrometry allows biologists to identify and quantify protein samples in the form of digested peptide sequences. Tandem mass spectrometry (MS2) provides a tool to match signal observations with the chemical process. A peak in MS2 spectrum indicates the presence of a peptide fragmented ion with a specific mass and charge. Thus, it is useful to develop the predictor of MS2 signal peak intensity. In this thesis, we proposed a regression model, MS2CNN, based on a deep learning algorithm - deep convolutional neural network. MS2CNN is trained on the National Institute of Standards and Technology MS2 spectrum dataset and evaluated on a publicly available independent test dataset of human HeLa cell lysate from LC-MS experiment. For this dataset, MS2CNN achieved a cosine similarity (COS) in the range of 0.57 and 0.79 for peptides of 2+ and a COS in the range of 0.59 and 0.74 for peptides of 3+. In five-fold cross-validation, the COS and PCC of training, validation and testing is 0.93, 0.86, 0.83 and 0.91, 0.83, 0.79, respectively. In independent set test, our model shows better COS and PCC (0.69 and 0.64) than the ones of MS2PIP (0.66 and 0.61). We showed that MS2CNN performs better than MS2PIP, specially in short peptide (i.e., sequence length less than 19). The results suggest incorporating more data for deep learning model for longer peptides can potentially improve the performance. |
| Databáze: | Networked Digital Library of Theses & Dissertations |
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