Mapping protein selectivity landscapes using multi-target selective screening and next-generation sequencing of combinatorial libraries
Characterizing the binding selectivity landscape of interacting proteins is crucial both for elucidating the underlying mechanisms of their interaction and for developing selective inhibitors. However, current mapping methods are laborious and cannot provide a sufficiently comprehensive description of the landscape. Here, we introduce a novel and efficient strategy for comprehensively mapping the binding landscape of proteins using a combination of experimental multi-target selective library screening and in silico next-generation sequencing analysis. We map the binding landscape of a non-selective trypsin inhibitor, the amyloid protein precursor inhibitor (APPI), to human serine proteases including kallikrein-6 and mesotrypsin. We then use this map to dissect and improve the affinity and selectivity of APPI variants toward each of the proteases. Our strategy can be used as a platform for the development of a new generation of target-selective probes and therapeutic agents based on selective protein–protein interactions. Overall design: This study investigates the binding selectivity of human amyloid protein precursor inhibitor (APPI) variants toward the human serine proteases: kallikrein-6 (KLK6 ) and mesotrypsin. A yeast surface display (YSD) library containing 3.5 million APPI-3M variants, each with 0–2 amino acid mutations, was generated. These variants were subjected to pairwise selective screening using fluorescence-activated cell sorting (FACS) and labeled protease pairs to isolate variants with enhanced or diminished selectivity for specific proteases. The sorted variants were analyzed using next-generation sequencing (NGS) on the Illumina Miseq platform to identify mutations associated with differential binding selectivity.