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Predicting transmembrane ▫$\beta-strands$▫ of membrane protein using a chemometric approach
Roy Choudhury, Amrita ; Novič, Marjana
Despite the importance of transmembrane proteins and growing interest in them, the vast majority of the membrane proteins remains underexplored owing to experimental difficulties. To fill this ...knowledge gap, several in-silico methods are developed aiming to predict the transmembrane regions, topology and structure. Although prediction for [alpha]-helical transmembrane regions can be made with considerable accuracy, it is not so in case of transmembrane [beta]-strands. The shorter and less hydrophobic transmembrane [beta]-strands are much harder to predict. The [beta]-barrel transmembrane proteins are present in the outer membrane of bacteria, celi organelles like mitochondria and chloroplasts. They function as ion transporters and play rolein passive nutrient uptake. In this work, we present a data-driven prediction model of J3-strand transmembrane region. The prediction is done based on amino acid sequence information without using any evolutionary data from multiple sequence alignments. Data on [beta]-barrel transmembrane proteins with atomic resolution structures and known transmembrane region is collected from public domain databases PDB and PDBTM. The protein sequences are separated into their transmembrane and non-transmembrane regions. The model is developed based on non-linear counter-propagation artificial neural network using mathematical descriptors defining the transmembrane protein sequences. The model shows 83% prediction accuracy when tested with external validation set. To further improve the prediction for unknown protein sequences and successfully eliminate false positives and negatives, statistical data on amino acid distribution in transmembrane [beta]-strands is incorporated in the final prediction. Finally, we did a benchmarking study com pa ring our developed prediction method with other algorithmic techniques and predictors available.
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