title: Prediction of FMN-binding residues with three-dimensional probability distributions of interacting atoms on protein surfaces.
abstract: Flavin mono-nucleotide (FMN) is a cofactor which is involved in many biological reactions. The insights on protein-FMN interactions aid the protein functional annotation and also facilitate in drug design. In this study, we have established a new method, making use of an encoding scheme of the three-dimensional probability density maps that describe the distributions of 40 non-covalent interacting atom types around protein surfaces, to predict FMN-binding sites on protein surfaces. One machine learning model was trained for each of the 30 protein atom types to predict tentative FMN-binding sites on protein structures. The method's capability was evaluated by five-fold cross-validation on a dataset containing 81 non-redundant FMN-binding protein structures and further tested on independent datasets of 30 and 15 non-redundant protein structures respectively. These predictions achieved an accuracy of 0.94, 0.94 and 0.96 with the Matthews correlation coefficient (MCC) of 0.53, 0.53 and 0.65 respectively for the three protein structure sets. The prediction capability is superior to the existing method. This is the first structure-based approach that does not rely on evolutionary information for predicting FMN-interacting residues. The webserver for the prediction is available at http://ismblab.genomics.sinica.edu.tw/.
authors.affiliation: Genomics Research Center, Academia Sinica, 128 Academia Rd., Sec. 2, Nankang Dist., Taipei 115, Taiwan. Electronic address: rajasekaran.mahalingam@case.edu.
authors.forename: Rajasekaran
authors.initials: R
authors.lastname: Mahalingam
authors.affiliation: Genomics Research Center, Academia Sinica, 128 Academia Rd., Sec. 2, Nankang Dist., Taipei 115, Taiwan; Institute of Biomedical Informatics, National Yang-Ming University, Taipei 11221, Taiwan; Bioinformatics Program, Taiwan International Graduate Program, Institute of Information Science, Academia Sinica, Taipei 115, Taiwan.
authors.forename: Hung-Pin
authors.initials: HP
authors.lastname: Peng
authors.affiliation: Genomics Research Center, Academia Sinica, 128 Academia Rd., Sec. 2, Nankang Dist., Taipei 115, Taiwan. Electronic address: yangas@gate.sinica.edu.tw.
authors.forename: An-Suei
authors.initials: AS
authors.lastname: Yang
mesh_terms: D000465:Algorithms; D000595:Amino Acid Sequence; D000596:Amino Acids / Q000378:metabolism*; D001185:Artificial Intelligence; D001665:Binding Sites; D030562:Databases, Protein; D005486:Flavin Mononucleotide / Q000378:metabolism*; D008958:Models, Molecular; D011336:Probability; D011506:Proteins / Q000737:chemistry* / Q000378:metabolism*; D012372:ROC Curve; D017386:Sequence Homology, Amino Acid
chemical_list: D000596:Amino Acids; D011506:Proteins; D005486:Flavin Mononucleotide
keywords: Computational method; Drug discovery; Functional annotation; Machine learning; Structure-based