Abstract

Hydrogenases are a group of enzymes that catalyze the reversible reduction of protons to hydrogen. They are extremely efficient, with turnover frequencies that rival the most efficient metal catalysts (Pt or Pd), using earth abundant metals (Ni or Fe) at their active sites. As such, hydrogenases have attracted attention as inspiration for catalyst design in scaled-up alternative fuel development. Researchers who use Fourier Transform infrared (FTIR) spectroscopy to investigate catalytic mechanisms of enzymes spend a significant amount of time in processing data. At present, the data processing workflow takes 3 person hours per sample, 1.5 hours per researcher, with 2 people processing the data to ensure accuracy. ProSpecPy is a Python package which streamlines and automates data processing of such FTIR spectroscopy data. The resulting workflow offers a standardized, easily documented, and reproducible workflow for the manipulation of these data, through Jupyter Notebooks. The current version reduces processing time per sample from 90 to 15 minutes, a ~78% reduction.