Machine Learning to Develop Peptide Catalysts-Successes, Limitations, and Opportunities.

Peptides have been established as modular catalysts for various transformations. Still, the vast number of potential amino acid building blocks renders the identification of peptides with desired catalytic activity challenging. Here, we develop a machine-learning workflow for the optimization of peptide catalysts. First-in a hypothetical competition-we challenged our workflow to identify peptide catalysts for the conjugate addition reaction of aldehydes to nitroolefins and compared the performance of the predicted structures with those optimized in our laboratory. On the basis of the positive results, we established a universal training set (UTS) containing 161 catalysts to sample an in silico library of ∼30,000 tripeptide members. Finally, we challenged our machine learning strategy to identify a member of the library as a stereoselective catalyst for an annulation reaction that has not been catalyzed by a peptide thus far. We conclude with a comparison of data-driven versus expert-knowledge-guided peptide catalyst optimization.

Overcoming Deactivation of Amine-Based Catalysts: Access to Fluoroalkylated γ-Nitroaldehydes.

Organocatalytic conjugate addition reactions of aldehydes to fluoroalkylated nitroolefins with chiral amine catalysts offer a straightforward stereoselective path to fluoroalkylated γ-nitroaldehydes and downstream derivatives. However, amine-based catalysts suffer from deactivation by reaction with electron-poor fluoroalkylated nitroolefin. Here, we show that catalyst deactivation can be overcome by catalysts that bear an intramolecular acid for protonation and release of the alkylated catalyst through ß-elimination of the nitroolefin. NMR spectroscopic, kinetic, and molecular modeling studies provided detailed structural and mechanistic insights into the factors that control reversible catalyst alkylation and facilitate efficient catalysis.

Chiral Recognition of Amino Acid Esters in Organic Solvents Using a Glucose-Based Receptor.

Due to the chemical and biological relevance of amino acids, efficient methods for the recognition and separation of their enantiomers are highly sought after. Chiral receptors based on extended molecular scaffolds are typically employed for this purpose. These receptors are often effective only in specific environments and towards a narrow scope of amino acid guests. Recently we reported a simple, glucose-based macrocycle capable of enantioselective binding of a broad range of amino acid methyl esters in water. Herein we demonstrate that the same receptor can be used for chiral recognition of amino acid esters in organic solvents. We show that the binding affinity and selectivity of the receptor are highly dependent on the coordinating strength of the solvent. An in-depth analysis of the receptor's conformation and its interactions with amino acid methyl esters allowed us to propose a binding mode of amino acids to the receptor in CDCl3. The binding modes in CDCl3 and D2O were then compared, highlighting the main interactions responsible for binding affinity and selectivity in each solvent. We envision that the insight provided by this study will facilitate the development of further amino acid receptors based on monosaccharides with improved binding affinities and both enantio- as well as chemoselectivities.

Chiral recognition of amino-acid esters by a glucose-derived macrocyclic receptor.

We report a glucose-based crown ether capable of chiral recognition of a wide range of amino-acid methyl esters in aqueous environments. The enantioselectivities towards amino-acids with extended hydrophobic side chains displayed by the glucose-derived macrocycle are among the highest observed for small molecule synthetic receptors to date.

Engineered Polymer Nanoparticles with Unprecedented Antimicrobial Efficacy and Therapeutic Indices against Multidrug-Resistant Bacteria and Biofilms.

The rapid emergence of antibiotic-resistant bacterial "superbugs" with concomitant treatment failure and high mortality rates presents a severe threat to global health. The superbug risk is further exacerbated by chronic infections generated from antibiotic-resistant biofilms that render them refractory to available treatments. We hypothesized that efficient antimicrobial agents could be generated through careful engineering of hydrophobic and cationic domains in a synthetic semirigid polymer scaffold, mirroring and amplifying attributes of antimicrobial peptides. We report the creation of polymeric nanoparticles with highly efficient antimicrobial properties. These nanoparticles eradicate biofilms with low toxicity to mammalian cells and feature unprecedented therapeutic indices against red blood cells. Most notably, bacterial resistance toward these nanoparticles was not observed after 20 serial passages, in stark contrast to clinically relevant antibiotics where significant resistance occurred after only a few passages.

Inter- and Intramolecular Electron Transfer in Copper Complexes: Electronic Entatic State with Redox-Active Guanidine Ligands.

Fast and efficient electron transfer in blue copper proteins is realized by a structural harmonization between the CuI and CuII complex pair ("entatic state" model). Herein, we present now a CuI /CuII complex pair with redox-active guanidine ligands showing almost perfect match between both redox states. By modifying the ligand electron donor strength, the redox chemistry of the copper complex can be controlled to be either metal-centered or to cross the borderline to ligand-centered. This work is the first systematic study of complexes with redox-active ligands within the concept of the entatic state.