New chlorophylls designed by theoretical spectroscopy and machine learning.

The main pigment for oxygenic photosynthesis, chlorophyll (Chl) a, is structurally related to several other Chl variants, naturally occurring mostly with mono-oxidized substitutions. These include Chl b, Chl d and Chl f and divinyl chlorophylls (DVChls) a and b. In this contribution, we computationally explore an expanded set of over 250,000 Chl variants, looking for potentially interesting targets for synthetic biology. We focus on optical properties, employing a machine learning (ML) approach and subsequently verifying the corresponding predictions using time-dependent density functional theory (TD-DFT) and multireference DFT (DFT/MRCI). We find that (i) Chl f is the best monosubstituted red-shifted Chl, as no other Chl in our set exceeds Chl f in terms of both red shift and absorption intensity, (ii) Chl b is not the best Chl to harvest photons from the green region of the optical spectrum, as several other Chls with the same or better green absorbance were identified (most notably DVChl b) and (iii) the T1 energy of Chls can be slightly adapted. The latter would enable experiments to determine whether it is beneficial having the T1 transition energy located between the two lowest O2 singlet state transitions, as it is found for Chl a. This might be a prerequisite for stable, efficient oxygen generation. Our ML approach thus provides a comprehensive overview on an extensive subset of potential Chl modifications that could be used for tuning oxygenic photosynthesis, if suitable synthesis pathways can be found.