Abstract
BackgroundThe correct determination of protein–protein interaction interfaces is important for understanding disease mechanisms and for rational drug design. To date, several computational methods for the prediction of protein interfaces have been developed, but the interface prediction problem is still not fully understood. Experimental evidence suggests that the location of binding sites is imprinted in the protein structure, but there are major differences among the interfaces of the various protein types: the characterising properties can vary a lot depending on the interaction type and function. The selection of an optimal set of features characterising the protein interface and the development of an effective method to represent and capture the complex protein recognition patterns are of paramount importance for this task.ResultsIn this work we investigate the potential of a novel local surface descriptor based on 3D Zernike moments for the interface prediction task. Descriptors invariant to roto-translations are extracted from circular patches of the protein surface enriched with physico-chemical properties from the HQI8 amino acid index set, and are used as samples for a binary classification problem. Support Vector Machines are used as a classifier to distinguish interface local surface patches from non-interface ones. The proposed method was validated on 16 classes of proteins extracted from the Protein–Protein Docking Benchmark 5.0 and compared to other state-of-the-art protein interface predictors (SPPIDER, PrISE and NPS-HomPPI).ConclusionsThe 3D Zernike descriptors are able to capture the similarity among patterns of physico-chemical and biochemical properties mapped on the protein surface arising from the various spatial arrangements of the underlying residues, and their usage can be easily extended to other sets of amino acid properties. The results suggest that the choice of a proper set of features characterising the protein interface is crucial for the interface prediction task, and that optimality strongly depends on the class of proteins whose interface we want to characterise. We postulate that different protein classes should be treated separately and that it is necessary to identify an optimal set of features for each protein class.
Highlights
The correct determination of protein–protein interaction interfaces is important for understanding disease mechanisms and for rational drug design
Since the classification labels depend on the selected features, key properties which drive protein interactions in the current protein class will have many of their features selected by the algorithm
In this study we introduced a novel method for the prediction of Protein–protein interactions (PPIs) interface regions based on 3D Zernike descriptors, HQI8 amino acid index set and Support Vector Machine (SVM)
Summary
The correct determination of protein–protein interaction interfaces is important for understanding disease mechanisms and for rational drug design. Experimental evidence suggests that the location of binding sites is imprinted in the protein structure, but there are major differences among the interfaces of the various protein types: the characterising properties can vary a lot depending on the interaction type and function. The selection of an optimal set of features characterising the protein interface and the development of an effective method to represent and capture the complex protein recognition patterns are of paramount importance for this task. Dysfunction or malfunction of pathways and alterations in protein interactions have shown to be the cause of several diseases such as neurodegenerative disorders [3] and cancer [4], and the identification of the exact location on a protein’s surface where it is likely to bind to its partners, i.e. the binding interface, has become one of the most popular targets for rational. PPI interface predictions can greatly aid protein–protein docking algorithms by being used in scoring functions or to constrain the available search space [6,7,8]
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