Abstract
Protein-protein interactions are key to many biological processes. Computational methodologies devised to predict protein-protein interaction (PPI) sites on protein surfaces are important tools in providing insights into the biological functions of proteins and in developing therapeutics targeting the protein-protein interaction sites. One of the general features of PPI sites is that the core regions from the two interacting protein surfaces are complementary to each other, similar to the interior of proteins in packing density and in the physicochemical nature of the amino acid composition. In this work, we simulated the physicochemical complementarities by constructing three-dimensional probability density maps of non-covalent interacting atoms on the protein surfaces. The interacting probabilities were derived from the interior of known structures. Machine learning algorithms were applied to learn the characteristic patterns of the probability density maps specific to the PPI sites. The trained predictors for PPI sites were cross-validated with the training cases (consisting of 432 proteins) and were tested on an independent dataset (consisting of 142 proteins). The residue-based Matthews correlation coefficient for the independent test set was 0.423; the accuracy, precision, sensitivity, specificity were 0.753, 0.519, 0.677, and 0.779 respectively. The benchmark results indicate that the optimized machine learning models are among the best predictors in identifying PPI sites on protein surfaces. In particular, the PPI site prediction accuracy increases with increasing size of the PPI site and with increasing hydrophobicity in amino acid composition of the PPI interface; the core interface regions are more likely to be recognized with high prediction confidence. The results indicate that the physicochemical complementarity patterns on protein surfaces are important determinants in PPIs, and a substantial portion of the PPI sites can be predicted correctly with the physicochemical complementarity features based on the non-covalent interaction data derived from protein interiors.
Highlights
Proteins perform essential functions in biological systems through recognizing their protein partners and by forming permanent or transient protein complexes
The physicochemical complementarities around the protein surface atom i were simulated with the probability density maps (PDMs) of non-covalent interacting atoms and were described with the 32 numerical features calculated with Equation (2) (i.e., Ai,j for interacting atom type j = 1,31 as shown in Table 1; j = 32 derived from protein surface geometry)
The protein-protein interaction (PPI) interface features (Figure 1) predicted with the PDMs and those derived from surveys of PPI interfaces implies that both protein folding and binding are governed by similar energetic principles
Summary
Proteins perform essential functions in biological systems through recognizing their protein partners and by forming permanent or transient protein complexes. The core interface regions are tightly packed as in protein interior with key residues that are mostly hydrophobic in nature (except for Arg, which is frequently observed in PPI sites) [8,9,10,11]. The trends in physicochemical and geometrical complementarity in the PPI interfaces have been demonstrated in many analyses [10], identifying clear determinants that correlate with the surface regions mediating PPIs remains challenging [3,4]. This is true for the protein surfaces mediating non-obligated protein-protein interactions [15]
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