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

Area of Science:

• Synthetic organic chemistry
• Computational chemistry
• Analytical chemistry

Background:

• Computer-assisted methods in synthetic chemistry require high-quality, diverse, and abundant experimental data.
• Automated flow chemistry in solid-phase peptide synthesis (SPPS) can generate crucial data for optimizing difficult couplings.
• Existing data analysis techniques for SPPS are sensitive to parameter deviations, limiting data interpretability.

Purpose of the Study:

• To develop a flexible and robust method for processing and analyzing SPPS data.
• To overcome limitations of current techniques that exclude data obtained under non-standard conditions.
• To increase the volume and quality of interpretable data for computational chemistry applications.

Main Methods:

• Developed a novel data processing and analysis method based on the Gaussian shape of fluorenylmethoxycarbonyl (Fmoc) deprotection peaks.
• Applied the method to data from automated flow SPPS, including experiments with varied parameters (temperature, flow rate, resin loading).
• Compared the new method's performance against traditional height and width analysis of Fmoc deprotection peaks.

Main Results:

• The Gaussian peak analysis method significantly increased the interpretable dataset size.
• The method successfully captured full information and maintained data quality even under non-ideal conditions.
• Hazardous dimethylformamide solvent waste was reduced by 50% compared to standard methods.

Conclusions:

• The Gaussian peak analysis offers a robust solution for handling SPPS data variability.
• This approach enhances the reliability of computational chemistry in optimizing peptide synthesis.
• The interplay between synthetic and computational analysis is key to generating high-quality data under diverse conditions.