Determination of binding affinity upon mutation for type I dockerin-cohesin complexes from Clostridium thermocellum and Clostridium cellulolyticum using deep sequencing.

TitleDetermination of binding affinity upon mutation for type I dockerin-cohesin complexes from Clostridium thermocellum and Clostridium cellulolyticum using deep sequencing.
Publication TypeJournal Article
Year of Publication2016
AuthorsKowalsky CA, Whitehead TA
JournalProteins
Volume84
Issue12
Pagination1914-1928
Date Published2016 Dec
ISSN1097-0134
KeywordsArea Under Curve, Bacterial Proteins, Binding Sites, Cell Cycle Proteins, Chromosomal Proteins, Non-Histone, Cloning, Molecular, Clostridium cellulolyticum, Clostridium thermocellum, Escherichia coli, Gene Expression, High-Throughput Nucleotide Sequencing, Membrane Proteins, Point Mutation, Protein Binding, Protein Interaction Domains and Motifs, Protein Structure, Secondary, Recombinant Proteins, ROC Curve, Software, Thermodynamics, Two-Hybrid System Techniques
Abstract

The comprehensive sequence determinants of binding affinity for type I cohesin toward dockerin from Clostridium thermocellum and Clostridium cellulolyticum was evaluated using deep mutational scanning coupled to yeast surface display. We measured the relative binding affinity to dockerin for 2970 and 2778 single point mutants of C. thermocellum and C. cellulolyticum, respectively, representing over 96% of all possible single point mutants. The interface ΔΔG for each variant was reconstructed from sequencing counts and compared with the three independent experimental methods. This reconstruction results in a narrow dynamic range of -0.8-0.5 kcal/mol. The computational software packages FoldX and Rosetta were used to predict mutations that disrupt binding by more than 0.4 kcal/mol. The area under the curve of receiver operator curves was 0.82 for FoldX and 0.77 for Rosetta, showing reasonable agreements between predictions and experimental results. Destabilizing mutations to core and rim positions were predicted with higher accuracy than support positions. This benchmark dataset may be useful for developing new computational prediction tools for the prediction of the mutational effect on binding affinities for protein-protein interactions. Experimental considerations to improve precision and range of the reconstruction method are discussed. Proteins 2016; 84:1914-1928. © 2016 Wiley Periodicals, Inc.

DOI10.1002/prot.25175
Alternate JournalProteins
PubMed ID27699856