Development of an AI-Coded Protein Quantitation Analyzer for Biochemistry Research and Instruction
Type of Presentation
Poster Session
Location
University Library
Start Date
4-10-2026 11:30 AM
End Date
4-10-2026 12:45 PM
Abstract
Colorimetric protein quantitation is a fundamental technique used in analytical, biochemistry and molecular biology labs across the globe. Although sophisticated and expensive software is available with advanced instruments, flexible computational tools for standard curve analysis and unknown concentration determination remain limited for academic research and educational settings, mostly relegated to spreadsheet and graphing programs like Excel. To address this gap, we developed a “Protein Quantitation Analyzer” application using AI-assisted coding (Claude, Anthropic), for both research-grade analysis and classroom instruction without the need for specialized software or hardware. The software, moreover, can run on virtually any operating system, including the tablets commonly used in laboratory environments. The application was built using natural-language prompts and is freely accessible as a web application through the Henne Lab at Governors State University, along with numerous other applications. The tool supports five widely used colorimetric assay types: Bradford (595 nm); Lowry (750 nm); BCA (562 nm); Biuret (540 nm); and A₂₈₀ (280 nm), as well as a custom wavelength option. Data entry is performed through an interactive interface with individually labeled wells, supporting flexible experimental designs including triplicates and mixed-plate layouts. Key features include automated blank subtraction, standard curve generation using linear or nonlinear regression, error bar display, percent coefficient of variation (% CV) reporting, and back-calculation of unknown protein concentrations. Absorbance is modeled according to the Beer-Lambert Law (A = εlc), and results can be exported in CSV format for analysis in other software packages. Customizable axis labels, plot titles, and color schemes are provided to support publication-ready figure generation. This protein quantitation tool has been successfully integrated into both undergraduate and graduate biochemistry laboratory courses (in addition to research to projects). Our report illustrates how AI-assisted software development can rapidly create open-access scientific tools for research laboratories worldwide.
Development of an AI-Coded Protein Quantitation Analyzer for Biochemistry Research and Instruction
University Library
Colorimetric protein quantitation is a fundamental technique used in analytical, biochemistry and molecular biology labs across the globe. Although sophisticated and expensive software is available with advanced instruments, flexible computational tools for standard curve analysis and unknown concentration determination remain limited for academic research and educational settings, mostly relegated to spreadsheet and graphing programs like Excel. To address this gap, we developed a “Protein Quantitation Analyzer” application using AI-assisted coding (Claude, Anthropic), for both research-grade analysis and classroom instruction without the need for specialized software or hardware. The software, moreover, can run on virtually any operating system, including the tablets commonly used in laboratory environments. The application was built using natural-language prompts and is freely accessible as a web application through the Henne Lab at Governors State University, along with numerous other applications. The tool supports five widely used colorimetric assay types: Bradford (595 nm); Lowry (750 nm); BCA (562 nm); Biuret (540 nm); and A₂₈₀ (280 nm), as well as a custom wavelength option. Data entry is performed through an interactive interface with individually labeled wells, supporting flexible experimental designs including triplicates and mixed-plate layouts. Key features include automated blank subtraction, standard curve generation using linear or nonlinear regression, error bar display, percent coefficient of variation (% CV) reporting, and back-calculation of unknown protein concentrations. Absorbance is modeled according to the Beer-Lambert Law (A = εlc), and results can be exported in CSV format for analysis in other software packages. Customizable axis labels, plot titles, and color schemes are provided to support publication-ready figure generation. This protein quantitation tool has been successfully integrated into both undergraduate and graduate biochemistry laboratory courses (in addition to research to projects). Our report illustrates how AI-assisted software development can rapidly create open-access scientific tools for research laboratories worldwide.