Prediction of FMN Binding Sites in Electron Transport Chains based on 2-D CNN and PSSM Profiles

Nguyen-Quoc-Khanh Le, Binh Phu Nguyen

研究成果: 雜誌貢獻文章同行評審

21 引文 斯高帕斯(Scopus)

摘要

Flavin mono-nucleotides (FMNs) are cofactors that hold responsibility for carrying and transferring electrons in the electron transport chain stage of cellular respiration. Without being facilitated by FMNs, energy production is stagnant due to the interruption in most of the cellular processes. Investigation on FMN's functions, therefore, can gain holistic understanding about human diseases and molecular information on drug targets. We proposed a deep learning model using a two-dimensional convolutional neural network and position specific scoring matrices that could identify FMN interacting residues with the sensitivity of 83.7%, specificity of 99.2%, accuracy of 98.2%, and Matthews correlation coefficients of 0.85 for an independent dataset containing 141 FMN binding sites and 1,920 non-FMN binding sites. The proposed method outperformed other previous studies using similar evaluation metrics. Our positive outcome can also promote the utilization of deep learning in dealing with various problems in bioinformatics and computational biology.
原文英語
頁(從 - 到)2189-2197
頁數9
期刊IEEE/ACM Transactions on Computational Biology and Bioinformatics
18
發行號6
DOIs
出版狀態已發佈 - 2021

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