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

Area of Science:

• Biochemistry
• Proteomics
• Analytical Chemistry

Background:

• Traditional protein identification methods can be expensive and complex.
• There is a need for accessible and accurate protein analysis techniques.
• Advancements in remote work necessitate flexible data accessibility.

Purpose of the Study:

• To develop and validate a novel protein identification method based on amino acid composition analysis.
• To assess the accuracy and capabilities of this method compared to existing techniques.
• To integrate remote operation and data accessibility features into the protein identification workflow.

Main Methods:

• Quantitative amino acid composition analysis of protein samples.
• Development of an identification program comparing sample data to the UniProt database.
• Implementation of remote control for the amino acid analyzer and IoT-based data uploading.
• Correction for unquantifiable amino acids (Cys, Trp, Ile) to improve accuracy.

Main Results:

• The amino acid composition analysis achieved protein identification accuracy comparable to mass spectrometry.
• The method successfully distinguished proteins with high sequence homology and amino acid substitutions.
• Remote control and automatic data uploading via an IoT portal were successfully implemented.
• Improved identification accuracy was observed after correcting for specific amino acid concentrations.

Conclusions:

• Amino acid composition analysis provides a viable, low-cost alternative for protein identification.
• The developed method is accurate, capable of resolving subtle protein differences, and user-friendly.
• Remote accessibility enhances research flexibility and collaboration, making the method highly useful.