Protein-protein interaction hotspots carved into sequences

Abstract

Protein-protein interactions, a key to almost any biological process, are mediated by molecular mechanisms that are not entirely clear. The study of these mechanisms often focuses on all residues at protein-protein interfaces. However, only a small subset of all interface residues is actually essential for recognition or binding. Commonly referred to as "hotspots," these essential residues are defined as residues that impede protein-protein interactions if mutated. While no in silico tool identifies hotspots in unbound chains, numerous prediction methods were designed to identify all the residues in a protein that are likely to be a part of protein-protein interfaces. These methods typically identify successfully only a small fraction of all interface residues. Here, we analyzed the hypothesis that the two subsets correspond (i.e., that in silico methods may predict few residues because they preferentially predict hotspots). We demonstrate that this is indeed the case and that we can therefore predict directly from the sequence of a single protein which residues are interaction hotspots (without knowledge of the interaction partner). Our results suggested that most protein complexes are stabilized by similar basic principles. The ability to accurately and efficiently identify hotspots from sequence enables the annotation and analysis of protein-protein interaction hotspots in entire organisms and thus may benefit function prediction and drug development. The server for prediction is available at http://www.rostlab.org/services/isis.

Figure 1. Protein–Protein Interfaces, Hotspots, and Predictions

Residues that are part of protein–protein interfaces often constitute a large fraction of the protein. Hotspot residues, namely residues that upon mutation hamper the interaction, are only a small fraction of these interface residues. Interestingly, methods designed to predict interface residues usually capture only a small fraction of them. (A) Human growth hormone (yellow) bound to the extracellular portion of its homodimeric receptor. (B) The chains of the receptor (gray) are 201 residues long. The protein–protein interface covers 31 of these residues (blue and red) on each of the chains. Mutating one of the six residues colored in red abrogates or severely hampers the interaction. (C) A prediction method (ISIS; see text) that was designed to identify all interface residues managed to capture only five of the interface residues (colored green).

Figure 2. Accuracy of Prediction of Hotspots

The ability to identify the residues that account for most of the energy of binding is assessed both on particular proteins and on a large dataset of alanine scans. (A) Alanine scans and predictions of essential interface residues in the V1 domain of CD4. The red rectangles (above sequence) mark positions that were shown to have significant effect on the affinity of the binding between CD4 to gp120 upon substitution to alanine [33]; the same residues are colored in red on the lower left surface representation of CD4 (PDB ID 1wiq_A). The green rectangles (below sequence) mark positions predicted to participate in a protein–protein interaction; these residues are also colored in violet on the lower right. Note that five of the residues predicted in interfaces were not mutated in the alanine scan. Thus, we cannot evaluate their correctness and left them out of this analysis. (B) Hotspots experimentally observed and predicted for the shaker voltage-gated potassium channel. All predictions and experimental substitutions [34] for this stretch are reported in this figure. (C) Accuracy versus coverage in predicting hotspots and interface residues. The performance of ISIS (green) and random assignment (red) using 296 alanine scans as gold standard. The data were compiled for a set of proteins that was not used for developing the method. The stronger the confidence in our prediction, the higher the accuracy and the lower the coverage (i.e., when we select the strongest predictions [moving upward in the figure], most of these are correct). With an accuracy of approximately 0.61 (righthand side of the plot), ISIS correctly predicted most of the interacting residues in our test set. The performance of ISIS in the task for which it was originally developed, namely predicting all interface residues (broken blue), is substantially worse than its performance on hotspots.

Figure 3. The Common Features of Hotspots Are Hard To Identify without Machine Learning

Physicochemical, structural, and evolutionary features differentiate hotspots from other residues. However, while each of these features is crucial for the success of the prediction, a simple, linear combination of them will not suffice. The distributions of residue conservation (x-axis, HSSP [46] conservation score) are compared between the entire sequences of the proteins in the dataset (black circles), hotspots (blue diamonds), and residues with no effect (measured by alanine scans) on protein–protein binding (red triangles). The y-axis gives the fraction of residues with a given level of conservation. The differences are marginal, but the overall effect of conservation on the prediction is substantial.

Figure 4. Accuracy of Prediction of Hotspots at Coverage Levels of 15%

Several approaches were introduced in the past for the prediction of interface residues. We applied methods that rely on different features to the task of predicting hotspots (to which none of them was optimized). The hydrophobic moment method represented the approach that relies exclusively on local physicochemical factors. The evolutionary approach was represented by the ET method, which relies on conservation to identify functionally important residues. A knowledge-based tool we introduced in the past represented the sequence-only approach. Finally, ProMate, a method for predicting interaction sites from unbound structures, represented the structure-based approach.