Combinatorial libraries

Structural analysis, antibiotic selection, and deep sequencing of CTX-M-14 mutants identify residues required for β-lactamase activity.

Chemical libraries with skeleton diversity are important for drug discovery. Here, the authors establish a synthetic methodology-based compound library (SMBL), and apply it to identify a small-molecule inhibitor to interrupt a challenging target:  the protein–protein interaction (PPI) of GIT1/β-Pix.

Prediction of material properties is crucial for early stages of material research, but current experimental data-based strategies possess limited accuracy. Here, the authors develop a machine learning-based semi-automated material exploration scheme to predict the solubility of tetraphenylporphyrin derivatives with an accuracy above 0.8.

Alanine substitution in peptides is crucial for studying peptide-based inhibitors of protein–protein interactions, but limited information is obtained from single alanine substitution. Here, the authors develop a label-free combinatorial alanine affinity selection platform to establish multi-alanine mutational tolerance and provide structure-activity relationships.

Fluorescence proximity sensing enables high throughput determination of binding affinities and kinetics of peptide inhibitors with varying valency and multivalent architecture.

DNA-templated compound library screening and structure-guided hit optimization resulted in the identification of selective macrocyclic inhibitors of cyclophilin isoforms CypD and CypE.