Characterizing Changes in the Rate of Protein-Protein Dissociation upon Interface Mutation Using Hotspot Energy and Organization
Figure 3
Hotspot and molecular descriptors for estimating change in off-rate.
The hotspot descriptors designed in this work are benchmarked against a set of 110 molecular descriptors; both in their ability to estimate Δlog10(koff) and in their ability to detect stabilizing mutations of Δlog10(koff) <−1. The performance measures shown here enable us to assess the raw predictive power of the descriptors independent of any learning models. Green and black bars highlight descriptors from the hotspot and molecular descriptor sets respectively. (A) Comparison of the distribution of the absolute PCC values for the hotspot descriptors designed in this work against that for the molecular descriptors. The related list of descriptor names and their respective PCCs is found in Text S5. (B) Top 10 hotspot descriptors and top 10 molecular descriptor according to absolute PCC with experimental Δlog10(koff). (C) Mann Whitney U-Test rankings for all descriptors where values are ranked according to −log10(pval) and represent the discrimination ability of the descriptors for the detection of stabilizing mutants (Δlog10(koff) <−1) from neutral to destabilizing mutants (Δlog10(koff) >0) (Referred to as CDS1). This dataset contains 31 stabilizing mutants and 503 neutral to destabilizing mutants. (D) Matthew's Correlation Coefficient (MCC) rankings for all descriptors on same dataset. (E) and (F) are identical to (C) and (D) except that results are for off-rates that satisfy |Δlog10(koff)| >1. This dataset contains 31 stabilizing mutants and 213 destabilizing mutants (referred to as CDS2).