DyeDactic workflow to predict halochromism of biosynthetic colourants

Textile dyeing using microorganisms is a step towards sustainable manufacturing. Computational design offers the prospect of new biosynthetic colourants with better dyeing performance, greater photostability, reduced toxicity, and desired colour. We present a workflow (DyeDactic) to predict halochromism, i.e. colour at different pH values. We filter compound libraries using a graph neural network model to estimate the relevant electronic transition energies of potential colourants. The absorption spectra in the visible region and the colours of the resultant molecules are calculated using time-dependent density functional theory. The populations of protonated and deprotonated species are estimated at different pH values. A weighted sum of their computed absorption spectra gives the predicted colour. The DyeDactic workflow is applied to four natural colourants: emodin, quinalizarin, biliverdin, and orcein, followed by experimental validation. As an illustration we also investigated the molecular mechanism of a red to blue colour change when microbial culture containing polyketide bikaverin is autoclaved. The workflow represents a useful tool to guide chemoenzymatic modifications to achieve industrial applicability.