TY - JOUR
T1 - Mapping protein selectivity landscapes using multi-target selective screening and next-generation sequencing of combinatorial libraries
AU - Naftaly, Si
AU - Cohen, Itay
AU - Shahar, Anat
AU - Hockla, Alexandra
AU - Radisky, Evette S.
AU - Papo, Niv
N1 - Funding Information:
The authors thank Vered Caspi (BGU), Matan Shemer (BGU), and Jonathan Barlev (Weizmann Institute of Science, Israel) for their helpful discussions. We thank Dr. Uzi Hadad for his technical assistance. FACS experiments were performed at the Ilse Katz Institute for Nanoscale Science & Technology, BGU. N.P. acknowledges support from the European Research Council “Ideas program” ERC-2013-StG (contract grant number: 336041). N.P. and E.S.R. acknowledge support from the US-Israel Binational Science Foundation (BSF). E.S.R. acknowledges support from the United States National Institutes of Health grant number R01CA154387. The structural studies were performed on beamline ID30-B at the European Synchrotron Radiation Facility (ESRF), Grenoble, France. We are grateful to Christoph Mueller-Dieckmann for providing assistance in using this beamline. We would like to thank Prof. Kay Diederichs and Dr. Ronan Keegan for their help and contribution in the structure determination during the 1st CCP4/BGU Structure Solution Workshop, which took place at Ben-Gurion University of Negev during February 2018.
Publisher Copyright:
© 2018, The Author(s).
PY - 2018/12/1
Y1 - 2018/12/1
N2 - 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.
AB - 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.
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U2 - 10.1038/s41467-018-06403-x
DO - 10.1038/s41467-018-06403-x
M3 - Article
C2 - 30258049
AN - SCOPUS:85054056989
SN - 2041-1723
VL - 9
JO - Nature Communications
JF - Nature Communications
IS - 1
M1 - 3935
ER -