A protein identification method for proteomics using amino acid composition analysis with IoT-based remote control

Kazuyuki Kato; Yasutake Mukawa; Shoichi Uemura; Masataka Okayama; Zentaro Kadota; Chika Hosozawa; Sayaka Kumamoto; Shun Furuta; Michio Iwaoka; Tomohiro Araki; Hiroshi Yamaguchi

doi:10.1016/j.ab.2022.114904

A protein identification method for proteomics using amino acid composition analysis with IoT-based remote control

Anal Biochem. 2022 Nov 15:657:114904. doi: 10.1016/j.ab.2022.114904. Epub 2022 Sep 21.

Authors

Affiliations

¹ Hitachi High-Tech Fielding Corporation, 1-17-1 Toranomon, Minato-ku, Tokyo, 105-6410, Japan.
² Hitachi High-Tech Corporation, 1-17-1 Toranomon, Minato-ku, Tokyo, 105-6409, Japan.
³ Graduate School of Agriculture, Tokai University, 9-1-1 Toroku, Higashi-ku, Kumamoto, Kumamoto, 862-8652, Japan.
⁴ Department of Chemistry, School of Science, Tokai University, 4-1-1 Kitakaname, Hiratsuka, Kanagawa, 259-1292, Japan; Institute of Advanced Biosciences, Tokai University, 4-1-1 Kitakaname, Hiratsuka, Kanagawa, 259-1292, Japan.
⁵ Graduate School of Agriculture, Tokai University, 9-1-1 Toroku, Higashi-ku, Kumamoto, Kumamoto, 862-8652, Japan; Research Institute of Agriculture, Tokai University, 9-1-1 Toroku, Higashi-ku, Kumamoto, Kumamoto, 862-8652, Japan.
⁶ Graduate School of Agriculture, Tokai University, 9-1-1 Toroku, Higashi-ku, Kumamoto, Kumamoto, 862-8652, Japan; Department of Food and Life Science, School of Agriculture, Tokai University, 9-1-1 Toroku, Higashi-ku, Kumamoto, Kumamoto, 862-8652, Japan. Electronic address: yamahiro@tokai-u.jp.

PMID: 36152875
DOI: 10.1016/j.ab.2022.114904

Abstract

In the present study, we developed a protein identification method using low-cost and easy-to-operate amino acid composition analysis. The identification program automatically compares the quantitative result for each amino acid concentration obtained from the amino acid analysis to the amino acid composition data retrieved from the UniProt protein database. We found that the accuracy of protein identification using amino acid composition analysis was comparable to that of mass spectrometry analysis. The method was able to distinguish and identify differences in amino acid substitutions of several residues between proteins with high sequence homology. The identification accuracy of proteins was also improved by correcting the concentrations in the program for Cys, Trp, and Ile residues, which cannot be quantified by general sample preparation for amino acid analysis. Moreover, the amino acid analyzer was remotely controlled in accordance with the growing demand for remote work. The measured amino acid data were automatically uploaded to the IoT portal within a few minutes of each measurement, allowing researchers to download data and analyze them using the identification program anywhere and at any time by connecting to a network. The results indicated that the present method is useful for protein identification.

Keywords: Amino acid analysis; IoT portal; Protein identification; Proteomics; Remote control.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Amino Acids* / chemistry
Databases, Protein
Mass Spectrometry
Proteins / chemistry
Proteomics* / methods

Substances

Amino Acids
Proteins