It has recently been reported that ribosomes from erythromycin-resistant Escherichia coli strains, when isolated in S30 extracts and incubated with chemically mis-acylated tRNA, can incorporate certain β-amino acids into full length DHFR in vitro. Here we report that wild-type E. coli EF-Tu and phenylalanyl-tRNA synthetase collaborate with these mutant ribosomes and others to incorporate β(3)-Phe analogs into full length DHFR in vivo. E. coli harboring the most active mutant ribosomes are robust, with a doubling time only 14% longer than wild-type. These results reveal the unexpected tolerance of E. coli and its translation machinery to the β(3)-amino acid backbone and should embolden in vivo selections for orthogonal translational machinery components that incorporate diverse β-amino acids into proteins and peptides. E. coli harboring mutant ribosomes may possess the capacity to incorporate many non-natural, non-α-amino acids into proteins and other sequence-programmed polymeric materials.

Molecular shape defines function in both biological and material settings, and chemists have developed an ever-increasing vernacular to describe these shapes. Noncanonical atropisomers—shape-defined molecules that are formally topologically trivial but are interconvertible only by complex, nonphysical multibond torsions—form a unique subset of atropisomers that differ from both canonical atropisomers (e.g., binaphthyls) and topoisomers (i.e., molecules that have identical connectivity but nonidentical molecular graphs). Small molecules, in contrast to biomacromolecules, are not expected to exhibit such ambiguous shapes. Using total synthesis, we found that the peptidic alkaloid tryptorubin A can be one of two noncanonical atropisomers. We then devised a synthetic strategy that drives the atropospecific synthesis of a noncanonical atrop-defined small molecule.

Research in natural products, the genetically encoded small molecules produced by organisms in an idiosyncratic fashion, deals with molecular structure, biosynthesis, and biological activity. Bioinformatics analyses of microbial genomes can successfully reveal the genetic instructions, biosynthetic gene clusters, that produce many natural products. Genes to molecule predictions made on biosynthetic gene clusters have revealed many important new structures. There is no comparable method for genes to biological activity predictions. To address this missing pathway, we developed a machine learning bioinformatics method for predicting a natural product’s antibiotic activity directly from the sequence of its biosynthetic gene cluster. We trained commonly used machine learning classifiers to predict antibacterial or antifungal activity based on features of known natural product biosynthetic gene clusters. We have identified classifiers that can attain accuracies as high as 80% and that have enabled the identification of biosynthetic enzymes and their corresponding molecular features that are associated with antibiotic activity.

Epidermal growth factor receptor (EGFR) interacts through its extracellular domain with seven different growth factors. These factors induce different structures within the cytoplasmic juxtamembrane (JM) segment of the dimeric receptor and propagate different growth factor-dependent signals to the cell interior. How this process occurs is unknown. Here we apply diverse experimental and computational tools to show that growth factor identity is encoded by the EGFR transmembrane (TM) helix into discrete helix dimer populations that differ in both cross-location and cross-angle. Helix dimers with smaller cross-angles at multiple cross locations are decoded to induce an EGF-type coiled coil in the adjacent JM, whereas helix dimers with larger cross-angles at fewer cross locations induce the TGF-α-type coiled coil. We propose an updated model for how conformational coupling across multiple EGFR domains results in growth factor-specific information transfer, and demonstrate that this model applies to both EGFR and the related receptor ErbB2.

Despite significant progress in the design of receptors and sensors for simple polyols and monosaccharides, few synthetic receptors discriminate among multiple saccharide units simultaneously, especially under physiological conditions. Described here is the three-dimensional structure of a supramolecular complex-a β-peptide bundle-designed for the potential to interact simultaneously with as many as eight discrete monosaccharide units. The preliminary evaluation of this construct as a vehicle for polyol binding is also presented.