Journal
COMPUTERS IN BIOLOGY AND MEDICINE
Volume 131, Issue -, Pages -Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.compbiomed.2021.104258
Keywords
FAD binding Site; Electron transport chain; BERT; Natural language processing; Deep learning; Position specific scoring matrix
Categories
Funding
- Ministry of Science and Technology, Taiwan, R.O.C. [MOST 109-2221-E 155-045, MOST 109-2811-E 155-505]
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The study introduces a new approach based on pre-trained BERT models to predict FAD-binding sites in transport proteins found in nature recently. The proposed method achieves an accuracy of 85.14% and improves accuracy by 11% compared to the previous method on the same independent set.
The electron transport chain is a series of protein complexes embedded in the process of cellular respiration, which is an important process to transfer electrons and other macromolecules throughout the cell. Identifying Flavin Adenine Dinucleotide (FAD) binding sites in the electron transport chain is vital since it helps biological researchers precisely understand how electrons are produced and are transported in cells. This study distills and analyzes the contextualized word embedding from pre-trained BERT models to explore similarities in natural language and protein sequences. Thereby, we propose a new approach based on Pre-training of Bidirectional Encoder Representations from Transformers (BERT), Position-specific Scoring Matrix profiles (PSSM), Amino Acid Index database (AAIndex) to predict FAD-binding sites from the transport proteins which are found in nature recently. Our proposed approach archives 85.14% accuracy and improves accuracy by 11%, with Matthew's correlation coefficient of 0.39 compared to the previous method on the same independent set. We also deploy a web server that identifies FAD-binding sites in electron transporters available for academics at http://1 40.138.155.216/fadbert/.
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