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

Nguyen-Quoc-Khanh Le, Binh Phu Nguyen

Research output: Contribution to journalArticlepeer-review

41 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)2189-2197
Number of pages9
JournalIEEE/ACM Transactions on Computational Biology and Bioinformatics
Issue number6
Publication statusPublished - 2021


Dive into the research topics of 'Prediction of FMN Binding Sites in Electron Transport Chains based on 2-D CNN and PSSM Profiles'. Together they form a unique fingerprint.

Cite this