Abstract

Establishing structure–activity relationships is crucial to understand and optimize the activity of peptide-based inhibitors of protein–protein interactions. Single alanine substitutions provide limited information on the residues that tolerate simultaneous modifications with retention of biological activity. To guide optimization of peptide binders, we use combinatorial peptide libraries of over 4,000 variants—in which each position is varied with either the wild-type residue or alanine—with a label-free affinity selection platform to study protein–ligand interactions. Applying this platform to a peptide binder to the oncogenic protein MDM2, several multi-alanine-substituted analogs with picomolar binding affinity were discovered. We reveal a non-additive substitution pattern in the selected sequences. The alanine substitution tolerances for peptide ligands of the 12ca5 antibody and 14-3-3 regulatory protein are also characterized, demonstrating the general applicability of this new platform. We envision that binary combinatorial alanine scanning will be a powerful tool for investigating structure–activity relationships.

Alanine substitution in peptides is crucial for studying peptide-based inhibitors of protein–protein interactions, but limited information is obtained from single alanine substitution. Here, the authors develop a label-free combinatorial alanine affinity selection platform to establish multi-alanine mutational tolerance and provide structure-activity relationships.

Details

Title
Binary combinatorial scanning reveals potent poly-alanine-substituted inhibitors of protein-protein interactions
Author
Ye, Xiyun 1   VIAFID ORCID Logo  ; Lee, Yen-Chun 2   VIAFID ORCID Logo  ; Gates, Zachary P. 3 ; Ling, Yingjie 4 ; Mortensen, Jennifer C. 4   VIAFID ORCID Logo  ; Yang, Fan-Shen 5 ; Lin, Yu-Shan 4 ; Pentelute, Bradley L. 6   VIAFID ORCID Logo 

 Massachusetts Institute of Technology, Department of Chemistry, Cambridge, USA (GRID:grid.116068.8) (ISNI:0000 0001 2341 2786) 
 Massachusetts Institute of Technology, Department of Chemistry, Cambridge, USA (GRID:grid.116068.8) (ISNI:0000 0001 2341 2786); National Cheng Kung University, Department of Chemistry, Tainan City, Taiwan (GRID:grid.64523.36) (ISNI:0000 0004 0532 3255) 
 Massachusetts Institute of Technology, Department of Chemistry, Cambridge, USA (GRID:grid.116068.8) (ISNI:0000 0001 2341 2786); Agency for Science, Technology and Research (A*STAR), Institute of Sustainability for Chemicals, Energy and Environment (ISCE2), Singapore, Singapore (GRID:grid.185448.4) (ISNI:0000 0004 0637 0221); Agency for Science, Technology and Research (A*STAR), Disease Intervention Technology Laboratory (DITL), Singapore, Singapore (GRID:grid.185448.4) (ISNI:0000 0004 0637 0221) 
 Tufts University, Department of Chemistry, Medford, USA (GRID:grid.429997.8) (ISNI:0000 0004 1936 7531) 
 National Tsing Hua University, Department of Chemistry and Frontier Research Center on Fundamental and Applied Sciences and Matters, Hsinchu, Taiwan (GRID:grid.38348.34) (ISNI:0000 0004 0532 0580) 
 Massachusetts Institute of Technology, Department of Chemistry, Cambridge, USA (GRID:grid.116068.8) (ISNI:0000 0001 2341 2786); Massachusetts Institute of Technology, The Koch Institute for Integrative Cancer Research, Cambridge, USA (GRID:grid.116068.8) (ISNI:0000 0001 2341 2786); Massachusetts Institute of Technology, Center for Environmental Health Sciences, Cambridge, USA (GRID:grid.116068.8) (ISNI:0000 0001 2341 2786); Broad Institute of MIT and Harvard, Cambridge, USA (GRID:grid.66859.34) (ISNI:0000 0004 0546 1623) 
Publication year
2022
Publication date
2022
Publisher
Nature Publishing Group
e-ISSN
23993669
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2724799485
Copyright
© The Author(s) 2022. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.