Image processing techniques represent innovative tools for comparative analysis of proteins

Abstract

Different bioinformatic and data-mining approaches have been used for the analysis of proteins. Here, we describe a novel, robust, and reliable approach for comparative analysis of a large number of proteins by combining Image Processing Techniques and Convolutional Deep Neural Network (IPT-CNN). As proof of principle, we used IPT-CNN to predict different subtypes of Influenza A virus (IAV). Over 8000 sequences of surface proteins haemagglutinin (HA) and neuraminidase (NA) from different IAV subtypes were used to create polynomial or binary vector datasets. The datasets were then converted into binary images. Analysis of these images enabled the classification of IAV subtypes with 100% accuracy and, compared to non-image-based approaches, within a shorter time frame. The proteome-based IPT-CNN approach described here may be used for analysis and proteome-based classification of other proteins.