A robust data analytical method to investigate sequence dependence in flow-based peptide synthesis

Abstract

temperature, flow rate, resin loading) leads to incomplete capture of information and exclusion from the dataset. Here, we present a flexible and robust processing and analysis method that is based on the Gaussian shape of the deprotection peaks to overcome these challenges, which drastically increases the interpretable size of our data set. Using this straightforward method retains the full information and data quality while the generation of hazardous dimethylformamide solvent waste is reduced by 50%. Overall, this work highlights how the interplay between synthetic and computational analysis enables the collection of high-quality data even under non-ideal, non-standard conditions.

Topics & Concepts