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Abstract

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.