DeepETC: a deep convolutional neural network architecture for investigating and classifying electron transport chain's complexes

Abstract

An electron transport chain is a series of protein complexes embedded in the transport protein, which is an important process to transfer electrons and other macromolecules throughout the cell. It is the primary process to extract energy via redox reactions in the case of oxidation of sugars in cellular respiration. According to the molecular functions, the components of the electron transport chain could be formed with five complexes and with several different electron carriers. The functional loss of a specific molecular function in electron transport chain has been implicated in a variety of human diseases such as diabetes, neurodegenerative disorders, Parkinson, and Alzheimer's disease. Therefore, creating a precise model to identify its functions is pertinent to the understanding of human diseases and designing of drug targets. Previous bioinformatics studies have almost exclusively focused on the electron transport proteins without information on the five complexes. Here we present DeepETC, a deep learning model that uses a two-dimensional convolutional neural network and position-specific scoring matrices profiles to classify electron transport proteins into the five complexes. DeepETC can classify the electron transporters with the independent test accuracy of 99.6%, 99.7%, 99.7%, 99.1% and 99.8% for complex I, II, III, IV, and V, respectively. Our performance results are significantly more accurate than the state-of-the-art traditional neural networks in all typical measurement metrics. Throughout the proposed study, we provide an effective tool for investigating electron transport proteins and our achievement could promote the use of deep learning in bioinformatics and computational biology. DeepETC can be freely accessible via http://www.biologydeep.com/deepetc/.