Ribosomes can produce proteins in minutes and are largely constrained to proteinogenic amino acids. Here, we report highly efficient chemistry matched with an automated fast-flow instrument for the direct manufacturing of peptide chains up to 164 amino acids long over 327 consecutive reactions. The machine is rapid: Peptide chain elongation is complete in hours. We demonstrate the utility of this approach by the chemical synthesis of nine different protein chains that represent enzymes, structural units, and regulatory factors. After purification and folding, the synthetic materials display biophysical and enzymatic properties comparable to the biologically expressed proteins. High-fidelity automated flow chemistry is an alternative for producing single-domain proteins without the ribosome.
Rapid development of antisense therapies can enable on-demand responses to new viral pathogens and make personalized medicine for genetic diseases practical. Antisense phosphorodiamidate morpholino oligomers (PMOs) are promising candidates to fill such a role, but their challenging synthesis limits their widespread application. To rapidly prototype potential PMO drug candidates, we report a fully automated flow-based oligonucleotide synthesizer. Our optimized synthesis platform reduces coupling times by up to 22-fold compared to previously reported methods. We demonstrate the power of our automated technology with the synthesis of milligram quantities of three candidate therapeutic PMO sequences for an unserved class of Duchenne muscular dystrophy (DMD). To further test our platform, we synthesize a PMO that targets the genomic mRNA of SARS-CoV-2 and demonstrate its antiviral effects. This platform could find broad application not only in designing new SARS-CoV-2 and DMD antisense therapeutics, but also for rapid development of PMO candidates to treat new and emerging diseases.
Antisense peptide nucleic acids (PNAs) have yet to translate to the clinic because of poor cellular uptake, limited solubility, and rapid elimination. Cell-penetrating peptides (CPPs) covalently attached to PNAs may facilitate clinical development by improving uptake into cells. We report an efficient technology that utilizes a fully automated fast-flow instrument to manufacture CPP-conjugated PNAs (PPNAs) in a single shot. The machine is rapid, with each amide bond being formed in 10 s. Anti-IVS2-654 PPNA synthesized with this instrument presented threefold activity compared to transfected PNA in a splice-correction assay. We demonstrated the utility of this approach by chemically synthesizing eight anti-SARS-CoV-2 PPNAs in 1 day. A PPNA targeting the 5′ untranslated region of SARS-CoV-2 genomic RNA reduced the viral titer by over 95% in a live virus infection assay (IC 50 = 0.8 μM). Our technology can deliver PPNA candidates to further investigate their potential as antiviral agents.
Mirror-image biological systems have the potential for broad-reaching impact in health and diagnostics, but their study has been greatly limited by the lack of routine access to synthetic D-proteins. We demonstrate that automated fast flow peptide synthesis (AFPS) can reliably produce novel mirror-image protein targets without prior sequence engineering. We synthesized 12 D-proteins, along with their L-counterparts. All 24 synthetic proteins were folded into active structures in vitro, and characterized using biochemical and biophysical techniques. From these chiral protein pairs, we chose MDM2 and CHIP to carry forward into mirror-image phage display screens, and identified macrocyclic D-peptides that bind the recombinant targets. We report 6 mirror-image peptide ligands with unique binding modes: three to MDM2, and three to CHIP, each confirmed with X-ray co-crystal structures. Reliable production of mirror-image proteins with AFPS stands to enable not only the discovery of D-peptide drug leads, but to the study of mirror-image biological systems more broadly.
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, this work leverages machine learning (ML) algorithms and automated synthesis technology to predict PNA synthesis efficiency and guide rational PNA sequence design. The training data is collected from individual fluorenylmethyloxycarbonyl (Fmoc) deprotection reactions performed on a fully automated PNA synthesizer. The optimized ML model allows for 93% prediction accuracy and 0.97 Pearson's r. The predicted synthesis scores are validated to be correlated with the experimental high-performance liquid chromatography (HPLC) crude purities (correlation coefficient R 2 = 0.95). Furthermore, a general applicability of ML is demonstrated 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 informing PNA sequence design.
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