Abstract
Cell-free biosynthesis (CFB) is an efficient and environmentally friendly method to synthesize molecules such as pharmaceuticals, biochemicals, and biofuels through the in vitro use of enzyme cascades. These enzymes often require redox cofactors to drive chemical reactions. Natural redox cofactors (NAD(P)H) are expensive to isolate, motivating synthetic nicotinamide cofactor biomimetics (NCBs) as a cost-effective solution. A select handful of NCBs have been identified as potential NAD(P)H alternatives with comparable or improved redox capabilities, however they display a tendency to degrade in common buffers. In this study, a library of 132 NCB candidates is systematically generated, over 85% of which have not been characterized in the literature, to expand the diversity of currently explored NCBs. The decomposition mechanism of NCBs in phosphate is evaluated using density functional theory (DFT), revealing protonation at the nicotinamide C5 position as a reporter of cofactor stability. Based on this result, we trained a linear regression model on DFT calculated descriptors to predict NCB stability in phosphate buffer, achieving mean absolute error (MAE) and root mean squared error (RMSE) values within computational accuracy. Analysis of key atomic descriptors and qualitative trends in our dataset informed the design of novel NCB candidates we propose with optimized stability. This work enables researchers to predict the relative stability of NCBs before synthesis, thereby streamlining the process to make CFB more affordable and viable at industry scales.
Supplementary materials
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Supporting Information
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Further details regarding NCB generation, conformational sampling, high-throughput DFT calculations, and predictive modeling; structures of the full NCB library investigated in this work; comparisons with experimental data for NCB stability, including density functional benchmarking results; full data regarding kinetic reaction barriers for select NCB structures; description of all semi-empirical descriptors considered for model training and semi-empirical model results; comparison of different model architectures for DFT-level data; relationships between DFT descriptors used for model training and stability; PFI scores for each model and model performance with varying numbers of descriptors; MLR equations for each model from standardized and raw descriptor data; Spearman rank-order correlations for each model.
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Supporting Information and Code
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Scripts used for NCB structure generation; scripts used to gather DFT descriptors, select NCBs to calculate decomposition barriers, and calculate the Spearman rank-order correlations for our models; calculation workflow for this work; scripts used to create figures in Python; sdf-coordinate files for all NCB structures; predictive modeling inputs and results.
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