利用监督机器学习为 ToF-SIMS 光谱开发多肽识别系统

IF 3.1 2区 化学 Q2 BIOCHEMICAL RESEARCH METHODS
Satoka Aoyagi, Miya Fujita, Hidemi Itoh, Hiroto Itoh, Takaharu Nagatomi, Masayuki Okamoto, Tomikazu Ueno
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引用次数: 0

摘要

飞行时间二次离子质谱(ToF-SIMS)对有机材料的数据解读非常复杂,因为每个分子都会产生各种碎片离子,而且不同分子的某些质量峰会重叠。SIMS 中的碎片机制非常复杂,因为不同的溅射和电离过程可能同时发生。因此,需要一个能识别样品中物质的预测系统。我们开发了一种基于 ToF-SIMS 和基于氨基酸的教学信息(标签)的新型肽预测系统,用于监督机器学习。要开发适用于一般有机材料的预测系统,材料的注释对于为监督学习创建有效标签至关重要。肽由 20 个氨基酸残基组成,可用作标签。我们之前使用随机森林(一种有监督的机器学习方法)开发了一个肽预测系统。然而,该系统只预测了目标肽所含的氨基酸,而无法假设氨基酸序列。在本研究中,通过在标签中添加相邻两个氨基酸的信息来确定测试肽的氨基酸序列。一旦预测系统学会了目标肽谱,就能识别新获得的 ToF-SIMS 图谱中的肽。新的预测系统还为未知肽的鉴定提供了有用的信息。预测结果表明,两个相邻氨基酸的排列组合是表达多肽氨基酸序列的有效教学信息。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Development of Peptide Identification System for ToF-SIMS Spectra Using Supervised Machine Learning.

Time-of-flight secondary ion mass spectrometry (ToF-SIMS) data interpretation for organic materials is complicated because of various fragment ions produced from each molecule and the overlapping of certain mass peaks from different molecules. Fragmentation mechanisms in SIMS are complex because different sputtering and ionization processes can simultaneously occur. Therefore, a prediction system that can identify materials in a sample is required. A novel prediction system for peptides based on ToF-SIMS and amino-acid-based teaching information (labels) for supervised machine learning was developed. To develop the prediction system for general organic materials, the annotation of materials is crucial to creating effective labels for supervised learning. Peptides are composed of 20 amino acid residues, which can be used as labels. We previously developed a peptide prediction system using Random Forest, a supervised machine-learning method. However, only the amino acids contained in the target peptide were predicted, and the amino acid sequence was unable to be assumed. In this study, the amino acid sequence of the test peptide was determined by adding the information on two adjacent amino acids to the labels. Once the prediction system learned the target peptide spectra, the peptides in the newly obtained ToF-SIMS spectra could be identified. The new prediction system also provides useful information for the identification of unknown peptides. The prediction results indicate that two adjacent permutations of amino acids are effective pieces of teaching information for expressing the amino acid sequence of a peptide.

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来源期刊
CiteScore
5.50
自引率
9.40%
发文量
257
审稿时长
1 months
期刊介绍: The Journal of the American Society for Mass Spectrometry presents research papers covering all aspects of mass spectrometry, incorporating coverage of fields of scientific inquiry in which mass spectrometry can play a role. Comprehensive in scope, the journal publishes papers on both fundamentals and applications of mass spectrometry. Fundamental subjects include instrumentation principles, design, and demonstration, structures and chemical properties of gas-phase ions, studies of thermodynamic properties, ion spectroscopy, chemical kinetics, mechanisms of ionization, theories of ion fragmentation, cluster ions, and potential energy surfaces. In addition to full papers, the journal offers Communications, Application Notes, and Accounts and Perspectives
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