Prediction of biological protein–protein interactions using atom‐type and amino acid properties

Abstract

Identification and analysis of types of biological protein-protein interactions and their interfaces to predict obligate and non-obligate complexes is a problem that has drawn the attention of the research community in the past few years. In this paper, we propose a prediction approach to predict these two types of complexes. We use desolvation energies - amino acid and atom type - of the residues present in the interface. The prediction is performed via two state-of-the-art classification techniques, namely linear dimensionality reduction (LDR) and support vector machines (SVM). The results on a newly compiled data set, namely BPPI, which is a joint and modified version of two well-known data sets consisting of 213 obligate and 303 non-obligate complexes, show that the best prediction is achieved with SVM (76.94% accuracy) when using desolvation energies of atom-type features. Also, the proposed approach outperforms the previous solvent accessible area-based approaches using SVM (75% accuracy) and LDR (73.06% accuracy). Moreover, a visual analysis of desolvation energies in obligate and non-obligate complexes shows that a few atom-type pairs are good descriptors for these types of complexes.

A model to predict and analyze protein-protein interaction types using electrostatic energies

“…We compare our approach with prediction of obligate and non-obligate using desolvation energies as properties [7]. Their work was based on the ZH and MW datasets.…”

“…A model that uses solvent accessible surface area and other interface properties for prediction of types (obligate and nonobligate) was reported in [8]. A very recent work presented in [7] shows the use of desolvation energies to predict obligate and non-obligate complexes using SVM and linear dimensionality reduction (LDR).…”

“…Interacting regions can be characterized by diverse sets of physiochemical properties [1] and other sequence features. In addition, other properties have been used for PPI prediction, such as analysis of solvent accessibility [3], geometry, hydrophobicity, sequence-based features and desolvation energy [7]. Based on interface properties such as interface area and ratio of area [3], Zhu et al predicted biological and crystal packing interactions using a support vector machine (SVM).…”

A model based on minimotifs for classification of stable protein-protein complexes

“…In addition, other properties have been used for prediction of PPIs, such as analysis of solvent accessibility [6], geometry, hydrophobicity, sequence-based features, and desolvation energy [7]. Based on interface properties such as interface area and ratio of area [6], Zhu et al predicted biological and crystal packing interactions using a support vector machine (SVM).…”

Using desolvation energies of structural domains to predict stability of protein complexes

“…Features are the observed properties of each sample that are used for prediction. Some studies on PPIs consider a wide range of features for predicting obligate and non-obligate complexes, including solvent accessibility (Shanahan and Thornton 2005;Zhu et al 2006), geometry (Lawrence and Colman 1993), hydrophobicity (Young 1994;Glaser et al 2001), sequencebased features (Mintseris and Weng 2003), desolvation energy Hall et al 2012;Rueda et al 2010a, b;Aziz et al 2011) and, more recently, electrostatic energies (Vasudev and Rueda 2012). In this study, we use desolvation energies, which have been shown to be very efficient for PPI prediction (Rueda et al 2010a, b).…”