1. Prediction of FMN Binding Sites in Electron Transport Chains Based on 2-D CNN and PSSM Profiles
- Author
-
Binh P. Nguyen and Nguyen-Quoc-Khanh Le
- Subjects
Flavin Mononucleotide ,0206 medical engineering ,02 engineering and technology ,Flavin group ,Computational biology ,Convolutional neural network ,Electron Transport ,Deep Learning ,FMN binding ,Genetics ,Position-Specific Scoring Matrices ,Binding site ,Binding Sites ,business.industry ,Chemistry ,Applied Mathematics ,Deep learning ,Computational Biology ,Electron transport chain ,Neural Networks, Computer ,Artificial intelligence ,business ,Algorithms ,020602 bioinformatics ,Biotechnology - Abstract
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.
- Published
- 2021
- Full Text
- View/download PDF