Characterizing Changes in the Rate of Protein-Protein Dissociation upon Interface Mutation Using Hotspot Energy and Organization

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Abstract
Predicting the effects of mutations on the kinetic rate constants of protein-protein interactions is central to both the modeling of complex diseases and the design of effective peptide drug inhibitors. However, while most studies have concentrated on the determination of association rate constants, dissociation rates have received less attention. In this work we take a novel approach by relating the changes in dissociation rates upon mutation to the energetics and architecture of hotspots and hotregions, by performing alanine scans pre- and post-mutation. From these scans, we design a set of descriptors that capture the change in hotspot energy and distribution. The method is benchmarked on 713 kinetically characterized mutations from the SKEMPI database. Our investigations show that, with the use of hotspot descriptors, energies from single-point alanine mutations may be used for the estimation of off-rate mutations to any residue type and also multi-point mutations. A number of machine learning models are built from a combination of molecular and hotspot descriptors, with the best models achieving a Pearson's Correlation Coefficient of 0.79 with experimental off-rates and a Matthew's Correlation Coefficient of 0.6 in the detection of rare stabilizing mutations. Using specialized feature selection models we identify descriptors that are highly specific and, conversely, broadly important to predicting the effects of different classes of mutations, interface regions and complexes. Our results also indicate that the distribution of the critical stability regions across protein-protein interfaces is a function of complex size more strongly than interface area. In addition, mutations at the rim are critical for the stability of small complexes, but consistently harder to characterize. The relationship between hotregion size and the dissociation rate is also investigated and, using hotspot descriptors which model cooperative effects within hotregions, we show how the contribution of hotregions of different sizes, changes under different cooperative effects. Within a cell, protein-protein interactions vary considerably in their degree of stickiness. Mutations at protein interfaces can alter the interaction between protein pairs, causing them to dissociate faster or slower. This may lead to an alteration in the dynamics of the cellular networks in which these proteins are involved. Therefore, the calculation and interpretation of mutants, which affect the rate of dissociation, is critical to our understanding of complex networks and disease. A key characteristic of protein–protein interfaces is that a subset of residues are responsible for most of the binding energy, such residues are called hotspots and effectively represent the sticky points of the interaction. In this work, we exploit both hotspot energies and organization and use them for the calculation of off-rate changes upon mutations. The insights gained provide us with a clearer understanding of the critical regions of stability and how they change for complexes of different sizes. Moreover, we provide a comprehensive map of the key determinants responsible for the accurate characterization of different classes of mutations, complexes and interface regions. This paves the way for more intelligent computational-interface-design algorithms and provides new insight into the interpretation of destabilizing mutations involved in complex diseases.