Identification and engineering of potent cyclic peptides with selective or promiscuous binding through biochemical profiling and bioinformatic data analysis

Abstract

As our understanding of biological systems grows, so does the need to selectively target individual or multiple members of specific protein families in order to probe their function. Many targets of current biological and pharmaceutical interest are part of a large family of closely related proteins and achieving ligand selectivity often remains either an elusive or time-consuming endeavour. Cyclic peptides (CPs) occupy a key niche in ligand space, able to achieve high affinity and selectivity while retaining synthetic accessibility. De novo cyclic peptide ligands can be rapidly generated against a given target using mRNA display. In this study we harness mRNA display technology and the wealth of next generation sequencing (NGS) data generated to explore both experimental approaches and bioinformatic, statistical data analysis of peptide enrichment in cross-screen selections to rapidly generate high affinity CPs with differing intra-family protein selectivity profiles against fibroblast growth factor receptor (FGF-R) family proteins. Using these methods, CPs with distinct selectivity profiles can be generated which can serve as valuable tool compounds to decipher biological questions.