Predicting the Success of Fmoc-Based Peptide Synthesis

Synthetic peptides are commonly used in biomedical science for many applications in basic and translational research. While peptide synthesis is generally easy and reliable, the chemical nature of some amino acids as well as the many steps and chemical compounds involved can render the synthesis of some peptide sequences difficult. Identification of these problematic sequences and mitigation of issues they may present can be important for the reliable use of peptide reagents in several contexts. Here, we assembled a large dataset of peptides that were synthesized using standard Fmoc chemistry and whose identity was validated using mass spectrometry. We analyzed the mass spectra to identify errors in peptide syntheses and sought to develop a computational tool to predict the likelihood that any given peptide sequence would be synthesized accurately. Our model, named Peptide Synthesis Score (PepSySco), is able to predict the likelihood that a peptide will be successfully synthesized based on its amino acid sequence.

Automated summary

Synthetic peptides are widely used in biomedical science for research purposes. However, the synthesis of certain peptide sequences can be challenging due to the chemical nature of some amino acids and the complexity of the synthesis process. This study analyzed a dataset of synthesized peptides and developed a computational tool to predict the likelihood of successful synthesis based on the peptide sequence.