In-solution affinity selection (AS) of large synthetic peptide libraries affords identification of binders to protein targets through access to an expanded chemical space. Standard affinity selection methods, however, can be...

Rapid discovery and development of serum-stable, selective, and high affinity peptide-based binders to protein targets are challenging. Angiotensin converting enzyme 2 (ACE2) has recently been identified as a cardiovascular disease biomarker and the primary receptor utilized by the severe acute respiratory syndrome coronavirus 2. In this study, we report the discovery of high affinity peptidomimetic binders to ACE2 via affinity selection-mass spectrometry (AS-MS). Multiple high affinity ACE2-binding peptides (ABP) were identified by selection from canonical and noncanonical peptidomimetic libraries containing 200 million members (dissociation constant, KD = 19–123 nM). The most potent noncanonical ACE2 peptide binder, ABP N1 (KD = 19 nM), showed enhanced serum stability in comparison with the most potent canonical binder, ABP C7 (KD = 26 nM). Picomolar to low nanomolar ACE2 concentrations in human serum were detected selectively using ABP N1 in an enzyme-linked immunosorbent assay. The discovery of serum-stable noncanonical peptidomimetics like ABP N1 from a single-pass selection demonstrates the utility of advanced AS-MS for accelerated development of affinity reagents to protein targets.

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.

Recently,C uH-catalyzed reductive coupling processes involving carbonyl compounds and imines have become attractive alternatives to traditional methods for stereoselective addition because of their ability to use readily accessible and stable olefins as surrogates for organometallic nucleophiles. However,t he inability to use aldehydes,w hich usually reduce too rapidly in the presence of copper hydride complexes to be viable substrates,h as been am ajor limitation. Shownh ere is that by exploiting relative concentration effects through kinetic control, this intrinsic reactivity can be inverted and the reductive coupling of 1,3-dienes with aldehydes achieved. Using this method, both aromatic and aliphatic aldehydes can be transformed into synthetically valuable homoallylic alcohols with high levels of diastereo-and enantioselectivities,and in the presence of many useful functional groups.Furthermore, using ac ombination of theoretical (DFT) and experimental methods,i mportant mechanistic features of this reaction related to stereo-and chemoselectivities were uncovered.