Peptide nucleic acids (PNAs) are potential antisense therapies for genetic, acquired, and viral diseases. Efficiently selecting candidate PNA sequences for synthesis and evaluation from a genome containing hundreds to thousands of options can be challenging. To facilitate this process, we leverage here machine learning (ML) algorithms and automated synthesis technology to predict PNA synthesis efficiency and guide rational PNA sequence design. The training data was collected from individual fluorenylmethyloxycarbonyl (Fmoc) deprotection reactions performed on a fully automated PNA synthesizer. Our optimized ML model allows for 93% prediction accuracy and 0.97 Pearson’s r. The predicted synthesis scores were validated to be correlated with the experimental HPLC crude purities (correlation coefficient R2 = 0.95). Furthermore, we demonstrated a general applicability of ML through designing synthetically accessible antisense PNA sequences from 102,315 predicted candidates targeting exon 44 of the human dystrophin gene, SARS-CoV-2, HIV, as well as selected genes associated with cardiovascular diseases, type II diabetes, and various cancers. Collectively, ML provides an accurate prediction of PNA synthesis quality and serves as a useful computational tool for rational PNA sequence design.
Materials and Methods, Supplementary Text, Figures S1 to S5, Table S1 to S2, Synthetic UV-vis Traces, HPLC, LC-MS traces