Evaluation of Time-of-Flight Secondary Ion Mass Spectrometry Spectra of Peptides by Random Forest with Amino Acid Labels: Results from a Versailles Project on Advanced Materials and Standards Interlaboratory Study
| Autor: | Ichiro Mihara, Ian S. Gilmore, Toshihiko Tsuchiya, Shin-ichi Iida, Shigeaki Uemura, Zhanping Li, Tae Geol Lee, Masayuki Okamoto, Yung-Chen Wang, Andreas Luch, Kazuhiro Matsuda, Satoka Aoyagi, Yohei Murayama, Harald Jungnickel, Yukio Fujiwara, Yusheng Lang, Elke Tallarek, Hyun Kyong Shon, Reiko Ikeda, Akio Takano, Kunio Saiga, Ako Miisho, Takaharu Nagatomi, Jean-Luc Vorng, Birgit Hagenhoff |
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| Rok vydání: | 2021 |
| Předmět: |
chemistry.chemical_classification
Chemistry 010401 analytical chemistry Analytical chemistry Peptide Advanced materials 010402 general chemistry 01 natural sciences Spectral line 0104 chemical sciences Analytical Chemistry Random forest Amino acid Secondary ion mass spectrometry Time of flight Test sample |
| Zdroj: | Analytical Chemistry |
| ISSN: | 1520-6882 0003-2700 |
| Popis: | We report the results of a VAMAS (Versailles Project on Advanced Materials and Standards) interlaboratory study on the identification of peptide sample TOF-SIMS spectra by machine learning. More than 1000 time-of-flight secondary ion mass spectrometry (TOF-SIMS) spectra of six peptide model samples (one of them was a test sample) were collected using 27 TOF-SIMS instruments from 25 institutes of six countries, the U. S., the U. K., Germany, China, South Korea, and Japan. Because peptides have systematic and simple chemical structures, they were selected as model samples. The intensity of peaks in every TOF-SIMS spectrum was extracted using the same peak list and normalized to the total ion count. The spectra of the test peptide sample were predicted by Random Forest with 20 amino acid labels. The accuracy of the prediction for the test spectra was 0.88. Although the prediction of an unknown peptide was not perfect, it was shown that all of the amino acids in an unknown peptide can be determined by Random Forest prediction and the TOF-SIMS spectra. Moreover, the prediction of peptides, which are included in the training spectra, was almost perfect. Random Forest also suggests specific fragment ions from an amino acid residue Q, whose fragment ions detected by TOF-SIMS have not been reported, in the important features. This study indicated that the analysis using Random Forest, which enables translation of the mathematical relationships to chemical relationships, and the multi labels representing monomer chemical structures, is useful to predict the TOF-SIMS spectra of an unknown peptide. |
| Databáze: | OpenAIRE |
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