期刊
IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS
卷 18, 期 6, 页码 2189-2197出版社
IEEE COMPUTER SOC
DOI: 10.1109/TCBB.2019.2932416
关键词
Proteins; Deep learning; Neural networks; Bioinformatics; Microsoft Windows; Computer architecture; Feature extraction; FMN binding site; electron transport chains; position specific scoring matrix; convolutional neural networks; deep learning
类别
资金
- School of Mathematics and Statistics, Victoria University of Wellington, New Zealand [220257, 220735]
- NVIDIA Corporation
Flavin mono-nucleotides (FMNs) are crucial cofactors in cellular respiration, responsible for electron transport chain; understanding their functions can provide insights into human diseases and drug targets. A deep learning model was proposed to identify FMN interacting residues with high accuracy and sensitivity, outperforming previous studies and showing potential for broader applications in bioinformatics and computational biology.
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 percent, specificity of 99.2 percent, accuracy of 98.2 percent, 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.
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