DeepETC (Deep learning Electron Transport Classification) is a database created to explore the universe of the electron transport proteins, which are essential for life because of the importance of electron transfer in bioenergetics and other processes. Here you can browse this universe, view molecular functions, and submit your own sequences to predict the electron transport proteins.
The electron transport chain is a number of protein complexes embedded inside the inner membrane of the mitochondria. The below figure indicates the electron transport chain system.
Electrons captured from donor molecules are transferred via these complexes. These complexes are organized into Complex I, Complex II, Complex III, Complex IV, and ATP synthase (which may be called Complex V). Each complex includes numerous specific electron carriers with different molecular functions. At the mitochondrial inner membrane, electrons from nicotinamide adenine dinucleotide (NADH) and succinate bypass through the electron transport chain to oxygen. The most famous molecular function in complex I and complex II are NADH dehydrogenase and succinate dehydrogenase, respectively. Electrons bypass from complex I to a carrier (coenzyme Q) that embeds itself inside the membrane. From coenzyme Q, electrons are handed to complex III (cytochrome b, c1 complex). The pathway from complex III ends in cytochrome c then to complex IV (cytochrome oxidase complex). At the end, the proton electrochemical gradient allows ATP synthase to apply the flow of H+ to generate ATP. The identification of the molecular functions in the electron transport chain is vital for helping biologists understand the electron transport chain process and energy production in cells. We suggest that our study could be a power model for determining new proteins that belongs into which molecular function of electron transport proteins.
DeepETC contains 2 main function:
A database containing information on the electron transport proteins with their corresponding complexes. The content has been generated using the UniProt and GeneOntology annotation. The users can freely retrieve all the electron transport proteins with the experimental assertion.
• The identification and classification model of the electron transport proteins based on the protein sequence. The identification and classification has been evaluated by the deep learning via convolutional neural networks and position specific scoring matrices.
Method
We approached a novel using CNN and PSSM profiles to identify the electron transport proteins from transport proteins with high performance. The flowchart of the study includes four sub processes in each phase: data collection, feature set generation, CNN generation, and model evaluation.
Dataset
All the dataset using in this web server are retrieved from UniProt and GeneOntoly. If you would like to build a model and evaluate our model, we provide the dataset here.
Members
Yu-Yen Ou Assistant Professor
Department of Computer Science and Engineering
Yuan Ze University
135 Yuan-Tung Road, Chung-Li, Taiwan 32003, R.O.C.
Nguyen-Quoc-Khanh Le Research Scholar
Department of Computer Science and Engineering
Yuan Ze University
135 Yuan-Tung Road, Chung-Li, Taiwan 32003, R.O.C.
Quang-Thai Ho Research Scholar
Department of Computer Science and Engineering
Yuan Ze University
135 Yuan-Tung Road, Chung-Li, Taiwan 32003, R.O.C.
Contact us
Yuan Ze University
Department of Computer Science and Engineering
Graduate Program in Biomedical Informatics
Bioinformatics Laboratory (R1607B)
Address: No. 135, Yuandong Road, Chungli City, Taoyuan County, Taiwan R.O.C .32003
Tel: (03) 463-8800
