CARDIO-PRED: an in silico tool for predicting cardiovascular-disorder associated proteins

Abstract

Interactions between proteins largely govern cellular processes and this has led to numerous efforts culminating in enormous information related to the proteins, their interactions and the function which is determined by their interactions. The main concern of the present study is to present interface analysis of cardiovascular-disorder (CVD) related proteins to shed lights on details of interactions and to emphasize the importance of using structures in network studies. This study combines the network-centred approach with three dimensional studies to comprehend the fundamentals of biology. Interface properties were used as descriptors to classify the CVD associated proteins and non-CVD associated proteins. Machine learning algorithm was used to generate a classifier based on the training set which was then used to predict potential CVD related proteins from a set of polymorphic proteins which are not known to be involved in any disease. Among several classifying algorithms applied to generate models, best performance was achieved using Random Forest with an accuracy of 69.5 %. The tool named CARDIO-PRED, based on the prediction model is present at http://www.genomeinformatics.dce.edu/CARDIO-PRED/. The predicted CVD related proteins may not be the causing factor of particular disease but can be involved in pathways and reactions yet unknown to us thus permitting a more rational analysis of disease mechanism. Study of their interactions with other proteins can significantly improve our understanding of the molecular mechanism of diseases.

Keywords: 3-Dimensional structure; Cardiovascular-disorder; Interface properties; Machine learning; Network analysis.

Fig. 1

Flow chart of the methodology carried out

Fig. 2

Overview of CARDIO-PRED web page interface (a) a screenshot of home page of CARDIO-PRED, (b) the Tool page of CARDIO-PRED representing the attributes—interface area, % charged residues, gap volume index, no. of hydrogen bonds, gap volume, secondary structure at interface, disulphide bonds, no. of salt bridges, (c) an example: using “FGG”and selecting different attributes to be displayed, (d) result page showing the output for “FGG”

Fig. 3

Network chart of PPI with structures mapped on the network

Fig. 4

Interaction between two proteins along with the interface residues

Fig. 5

Bar graph with different classifiers and the statistical values