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

ABSTRACT: Computer-assisted methods, which hold the promise to transform synthetic organic chemistry, are often limited by experimental data lacking in quality, diversity, and quantity. In solid-phase peptide synthesis (SPPS), automated flow chemistry is well-suited to deliver such data, which is key for prediction and optimization of sequence-dependent "difficult couplings", and insights obtained in flow-SPPS can be transferred to batch-SPPS. The current data analysis techniques rely on the height and the width of fluorenylmethoxycarbonyl (Fmoc) deprotection peaks and perform well under standard conditions. Yet any deviation in parameters (e.g. temperature, flow rate, resin loading) leads to incomplete capture of information and exclusion from the dataset. Here, we present a flexible and rob

SUBMITTER: Tamas B 

PROVIDER: S-EPMC10966953 | biostudies-literature | 2024 Mar

REPOSITORIES: biostudies-literature