Mapping protein selectivity landscapes using multi-target selective screening and next-generation sequencing of combinatorial libraries.
Mapping protein selectivity landscapes using multi-target selective screening and next-generation sequencing of combinatorial libraries.
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DOI：
10.1038/s41467-018-06403-x
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发表时间：
2018-09-26
影响因子：
16.6
通讯作者：
Papo N
中科院分区：
文献类型：
--
作者：
Naftaly S;Cohen I;Shahar A;Hockla A;Radisky ES;Papo N
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 each of the four human serine proteases (kallikrein-6, mesotrypsin, and anionic and cationic trypsins). We then use this map to dissect and improve the affinity and selectivity of APPI variants toward each of the four 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. Characterizing the binding selectivity landscape of interacting proteins is crucial in protein engineering. Here the authors use multi-target selective library screening and in silico next-generation sequencing to map the binding landscape of proteins and produce improved proteases inhibitors.
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