Affinity selection-mass spectrometry with linearizable macrocyclic peptide libraries.

Area of Science:

• Biochemistry and Medicinal Chemistry
• Peptide Synthesis and Drug Discovery

Background:

• Large synthetic macrocyclic libraries are crucial for ligand discovery but have been challenging to prepare.
• Macrocycles offer unique structural properties for therapeutic applications.

Purpose of the Study:

• To develop a method for generating large, diverse macrocyclic libraries for efficient ligand discovery.
• To identify novel macrocyclic binders for therapeutic targets like cadherin-2 and antibodies.

Main Methods:

• Generation of 100-million-membered macrocyclic libraries using natural and nonnatural amino acids.
• Facilitation of disulfide bond formation via rapid iodine oxidation and reduction with dithiothreitol.
• Affinity selection and structure-activity relationship studies to identify and optimize binders.

Main Results:

• Successful discovery of macrocyclic binders to cadherin-2 and anti-hemagglutinin antibody.
• Identification of key affinity-driving residues (hotspots) and mutation-tolerant residues (coldspots) in a cadherin-binding peptide (CBP).
• Development of a high-affinity ligand (NCBP-4) with a K_d of 29 nanomolar through derivatization of coldspots.

Conclusions:

• The developed macrocyclic library platform enables efficient discovery of high-affinity peptide ligands.
• Derivatization of specific residues in macrocycles can significantly enhance binding affinity.
• This approach holds promise for advancing therapeutic peptide development.