title: Prediction of FAD binding sites in electron transport proteins according to efficient radial basis function networks and significant amino acid pairs.
abstract: BACKGROUND
  Cellular respiration is a catabolic pathway for producing adenosine triphosphate (ATP) and is the most efficient process through which cells harvest energy from consumed food. When cells undergo cellular respiration, they require a pathway to keep and transfer electrons (i.e., the electron transport chain). Due to oxidation-reduction reactions, the electron transport chain produces a transmembrane proton electrochemical gradient. In case protons flow back through this membrane, this mechanical energy is converted into chemical energy by ATP synthase. The convert process is involved in producing ATP which provides energy in a lot of cellular processes. In the electron transport chain process, flavin adenine dinucleotide (FAD) is one of the most vital molecules for carrying and transferring electrons. Therefore, predicting FAD binding sites in the electron transport chain is vital for helping biologists understand the electron transport chain process and energy production in cells.
  
  
  RESULTS
  We used an independent data set to evaluate the performance of the proposed method, which had an accuracy of 69.84 %. We compared the performance of the proposed method in analyzing two newly discovered electron transport protein sequences with that of the general FAD binding predictor presented by Mishra and Raghava and determined that the accuracy of the proposed method improved by 9-45 % and its Matthew's correlation coefficient was 0.14-0.5. Furthermore, the proposed method enabled reducing the number of false positives significantly and can provide useful information for biologists.
  
  
  CONCLUSIONS
  We developed a method that is based on PSSM profiles and SAAPs for identifying FAD binding sites in newly discovered electron transport protein sequences. This approach achieved a significant improvement after we added SAAPs to PSSM features to analyze FAD binding proteins in the electron transport chain. The proposed method can serve as an effective tool for predicting FAD binding sites in electron transport proteins and can help biologists understand the functions of the electron transport chain, particularly those of FAD binding sites. We also developed a web server which identifies FAD binding sites in electron transporters available for academics.
authors.affiliation: Department of Computer Science and Engineering, Yuan Ze University, Chung-Li, Taiwan. khanhlee87@gmail.com.
authors.forename: Nguyen-Quoc-Khanh
authors.initials: NQ
authors.lastname: Le
authors.affiliation: Department of Computer Science and Engineering, Yuan Ze University, Chung-Li, Taiwan. yienou@gmail.com.
authors.forename: Yu-Yen
authors.initials: YY
authors.lastname: Ou
mesh_terms: D000465:Algorithms; D000595:Amino Acid Sequence; D000596:Amino Acids / Q000737:chemistry; D019540:Area Under Curve; D001665:Binding Sites; D045222:Electron Transport Chain Complex Proteins / Q000737:chemistry / Q000378:metabolism*; D005182:Flavin-Adenine Dinucleotide / Q000737:chemistry / Q000378:metabolism*; D020407:Internet; D011485:Protein Binding; D012372:ROC Curve; D014584:User-Computer Interface
chemical_list: D000596:Amino Acids; D045222:Electron Transport Chain Complex Proteins; D005182:Flavin-Adenine Dinucleotide
keywords: Annotation; Electron transport protein; FAD binding site; Feature selection; Position specific scoring matrix; Significant amino acid pairs; Transporter