Predicting the protein-protein interactions using primary structures with predicted protein surface

Abstract

BackgroundMany biological functions involve various protein-protein interactions (PPIs). Elucidating such interactions is crucial for understanding general principles of cellular systems. Previous studies have shown the potential of predicting PPIs based on only sequence information. Compared to approaches that require other auxiliary information, these sequence-based approaches can be applied to a broader range of applications.ResultsThis study presents a novel sequence-based method based on the assumption that protein-protein interactions are more related to amino acids at the surface than those at the core. The present method considers surface information and maintains the advantage of relying on only sequence data by including an accessible surface area (ASA) predictor recently proposed by the authors. This study also reports the experiments conducted to evaluate a) the performance of PPI prediction achieved by including the predicted surface and b) the quality of the predicted surface in comparison with the surface obtained from structures. The experimental results show that surface information helps to predict interacting protein pairs. Furthermore, the prediction performance achieved by using the surface estimated with the ASA predictor is close to that using the surface obtained from protein structures.ConclusionThis work presents a sequence-based method that takes into account surface information for predicting PPIs. The proposed procedure of surface identification improves the prediction performance with an F-measure of 5.1%. The extracted surfaces are also valuable in other biomedical applications that require similar information.

Protein-Protein Interaction Prediction using PCA and SVR-PHCS

“…For instance, some of them use gene data [12], including gene neighborhood [13], gene fusion [1,14], phylogenetic profile [15] and mirror-tree [16]. Some other methods employ structural information [17,18], protein sequence [19][20][21][22][23] and domain *Address correspondence to this author at the Electrical and Computer Engineering Faculty, Tarbiat Modares University, Tehran, Iran; E-mail: charkari@modares.ac.ir information [24][25][26]. Since each of these datasets provides partial information about the interacting pairs, many researchers have attempted to integrate several data source for predicting PPIs with more reliability [27][28][29].…”

Combining Phylogenetic Profiling-Based and Machine Learning-Based Techniques to Predict Functional Related Proteins

“…ML techniques have been widely used to predict protein relations in many studies [15], [16], [17], [18], in which several techniques have been developed to capture the important features of protein pairs. Shen et al proposed the “conjoint triad” feature, which employs the frequency of three continuous amino acids to encoded protein sequences into feature vectors [15].…”

“…Guo et al adopted the SVM and proposed an auto cross covariance-based mechanism to encode proteins [16]. Chang et al showed that the features extracted from the protein surface are critical in predicting protein interactions [17]. They used the relaxed variable kernel density estimator (RVKDE) [20] to construct the abstract model.…”

Computational Approaches to Predict Protein Interaction

“…In this regard, efficient ML algorithms with acceptable accuracy are reasonable alternatives to SVM. The relaxed variable kernel density estimation (RVKDE) algorithm (Oyang, Hwang et al 2005) has been practically used in recent interaction studies (Chang, Syu et al 2010;Yu, Chou et al 2010). The time complexity of RVKDE is an order faster than SVM.…”

“…This led to more complicated relations than that among co-occurrence-based methods. For example, Shen et al (Shen, Zhang et al 2007) proposed to use a composition of short sequences as protein features and a following work by Chang et al (Chang, Syu et al 2010) combined these features with protein surface information. In addition to the overlap of features among different MLbased methods, they may use identical or different ML techniques.…”