The technology presents general methods for generating large, combinatorial libraries of structurally diverse macrocyclic compounds with a hybrid peptide/non-peptidic backbone.
The methodologies of this invention can be used for the discovery and development of small molecules with tailored binding affinity for therapeutic or biotechnological applications. Specifically, the methods of the invention can be applied for generating very large libraries of structurally diverse compounds by taking advantage of both biosynthetic and synthetic combinatorial methods. Such libraries can be easily screened and deconvoluted for identifying ligands that possess tailored binding affinity and selectivity toward a biomolecule of interest (e.g. a protein, an enzyme, a nucleic acid molecule. Accordingly, the methods of the invention can be useful to identify capturing agents for biomolecules (e.g. proteins, nucleic acids) for application such as affinity purification of biomolecules from complex mixtures, in vivo and in vitro labeling of biomolecules via affinity ligands, protein capturing for proteomic analysis. In addition, they can be applied to identify small molecule inhibitors (or activators) of biomolecular interactions such as protein-protein, protein-nucleic acid, and nucleic acid-nucleic acid interactions, which are notoriously challenging drug targets.
Macrocyclic peptide-based molecules constitute promising scaffolds for the development of bioactive molecules. Compared to linear peptides, they exhibit enhanced proteolytic stability, more favorable membrane-crossing properties, and improved binding affinity. Compared to conventional small molecules (< 500 Da), they are much better suited for targeting biomolecular interactions (i.e. protein-protein, protein-nucleic acid, and nucleic acid-nucleic acid interactions), owing to their greater size and functional complexity. This invention presents a method to generate vast libraries of macrocyclic peptide-based molecules, which integrates the advantages of biologically-encoded peptide libraries (large library size, rapid deconvolution) with those of synthetic combinatorial methods (broader spectrum of functionally and structurally diverse building blocks). As such, these methods can greatly accelerate and facilitate the discovery of bioactive compounds as potential drug molecules or the identification of lead structures for the development of new drugs.
URV Reference Number: 1-11033-10030