Abstract
The lack of a deep understanding of how proteins interact remains an important roadblock in advancing efforts to identify binding partners and uncover the corresponding regulatory mechanisms of the functions they mediate. Understanding protein-protein interactions is also essential for designing specific chemical modifications to develop new reagents and therapeutics. We explored the hypothesis of whether protein interaction sites serve as generic biding sites for non-cognate protein ligands, just as it has been observed for small-molecule-binding sites in the past. Using extensive computational docking experiments on a test set of 241 protein complexes, we found that indeed there is a strong preference for non-cognate ligands to bind to the cognate binding site of a receptor. This observation appears to be robust to variations in docking programs, types of non-cognate protein probes, sizes of binding patches, relative sizes of binding patches and full-length proteins, and the exploration of obligate and non-obligate complexes. The accuracy of the docking scoring function appears to play a role in defining the correct site. The frequency of interaction of unrelated probes recognizing the binding interface was utilized in a simple prediction algorithm that showed accuracy competitive with other state of the art methods.
Highlights
Specific protein-protein interactions are essential for maintaining a robust phenotype
The methods to predict protein interfaces can be grouped into two main approaches: (1) homology-based and (2) ab initio
We ranked the residues in the receptor protein based on the Residue Interface Frequency (RIF) score
Summary
Specific protein-protein interactions are essential for maintaining a robust phenotype. Homology-based predictions of interfaces rely on the knowledge of known protein complexes to infer the likely binding sites in similar proteins. These methods can be very powerful[6, 7], but their applicability is limited by the amount of known interfaces. Within the category of “ab initio” protein interface predictions a number of studies have attempted to identify distinctive features of interfaces[8,9,10,11,12,13,14] often employing various machine learning approaches These features include residue composition [15], residue conservation[16,17,18], hydrophobicity[19, 20], planarity[15], predicted secondary structural features[14, 21], electrostatics[22], accessible surface area, among others
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