PIGMENTS AND PROTEINS: AUTOMATING STRUCTURE-BASED OPTICAL SPECTRA PREDICTION

Plant and algae-based biofuels offer a compelling green energy solution, but the utility of biological photosynthesis is limited by inefficiency in energy production. Without the connection between the protein structure and optical spectra, it is challenging to modify optical properties for improving energy production. Chlorophyll proteins (CPs) are the primary work-horses of biological photosynthesis that absorb and transfer solar energy to chemically active reaction centers and control the chemical energy storage process. While the CP structures are well-known, predicting their optical and electronic properties remains a serious challenge. There are computational complications for treating large, electronically coupled molecular pigments embedded in a dynamically structured protein environment. In this work, we aim to address some prominent issues with structure-based optical spectra predictions and the limitations in applying the Frenkel exciton model.