Determination of binding affinity upon mutation for type I dockerin–cohesin complexes from C lostridium thermocellum and C lostridium cellulolyticum using deep sequencing

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.

Pro region engineering of nerve growth factor by deep mutational scanning enables a yeast platform for conformational epitope mapping of anti‐NGF monoclonal antibodies

“…This signal:noise ratio was calculated by measuring the ratio of sample MFI over the MFI in the absence of biotinylated antibody (“background”) for the subset of cmyc + and fsc/ssc + cells (to ensure measurement of individual yeast cells). Since similar experiments from our research group show 50 to over 100‐fold above background for diverse protein‐protein interactions (Kowalsky, Faber, et al, ; Kowalsky & Whitehead, ), we conclude that cNGF surface displays in a mostly misfolded form.…”

“…cNGF and pro‐cNGF displayed on the yeast surface but in mostly misfolded forms. This adds to a growing body of literature showing that while the quality control machinery in the S. cerevisiae endoplasmic reticulum (Ellgaard & Helenius, ) can impact the overall amount of protein displayed on the surface (Klesmith et al, ; Whitehead, Chevalier, Song, & Dreyfus, ), for any given grossly misfolded protein some will still pass through quality control checkpoints in the secretory pathway and display on the yeast surface (Kowalsky, Faber, et al, ; Kowalsky & Whitehead, ; Park et al, ; Whitehead et al, ).…”

Finding the ΔΔG spot: Are predictors of binding affinity changes upon mutations in protein–protein interactions ready for it?

“…[37][38][39] The Critical Assessment of Prediction of Interactions (CAPRI), a community-wide experiment on the comparative evaluation of protein-protein docking for structure prediction, has run a ΔΔG prediction challenge based on experimental data from deep sequencing. 40 Deep sequencing was also exploited for two specific complexes, type I dockerin-cohesin 36 and TCR-pepMHC, 41 to study the impact of mutations and train ΔΔG predictors.…”

Generating quantitative binding landscapes through fractional binding selections, deep sequencing and data normalization

“…One of the limitations of previous approaches for binding landscape generation was that DDGbind showed linear dependence on the NGS-enrichment value only in the narrow range of DDGbind values close to zero 26 . The methodology could not previously discriminate between different highly destabilizing mutations since such mutations were characterized with the same enrichment values.…”

“…Additional studies showed that DDGbind could be inferred from the NGS-based enrichment values only in the narrow range of energies from -0.8 to +0.5 kcal/mol 25,26 , preventing construction of quantitative binding landscapes for all of the explored mutations with broader range of target affinities. In addition, the above methodology set a requirement on the concentration of the target protein in the selection experiment; the concentration should be similar to the interaction Kd, thus limiting the application of the approach to only subset of all PPIs with medium affinities.…”