DyeDactic workflow to predict halochromism of biosynthetic colourants

Area of Science:

• Biotechnology
• Computational Chemistry
• Materials Science

Background:

• Microbial dyeing offers a sustainable alternative for textile manufacturing.
• Computational design can create novel biosynthetic colorants with improved properties like photostability and reduced toxicity.
• Predicting halochromism (color change with pH) is crucial for dye application.

Purpose of the Study:

• To present the DyeDactic workflow for predicting halochromism in potential colorants.
• To utilize computational methods for designing new biosynthetic dyes with enhanced performance.
• To guide chemoenzymatic modifications for industrial applicability of microbial colorants.

Main Methods:

• A graph neural network model filters compound libraries to estimate electronic transition energies.
• Time-dependent density functional theory (TD-DFT) calculates absorption spectra and molecular colors.
• Protonation states at different pH values are estimated, and spectra are combined to predict overall color.

Main Results:

• The DyeDactic workflow was applied to natural colorants (emodin, quinalizarin, biliverdin, orcein) with experimental validation.
• The molecular mechanism behind a pH-induced color change in bikaverin was investigated.
• The workflow successfully predicts halochromism for various compounds.

Conclusions:

• The DyeDactic workflow is a valuable tool for the rational design of biosynthetic colorants.
• This approach facilitates the development of sustainable dyes with tunable properties for industrial applications.
• Computational prediction of halochromism aids in achieving desired color and performance in microbial dyes.