title: Prediction of Nucleotide Binding Peptides Using Star Graph Topological Indices.
abstract: The nucleotide binding proteins are involved in many important cellular processes, such as transmission of genetic information or energy transfer and storage. Therefore, the screening of new peptides for this biological function is an important research topic. The current study proposes a mixed methodology to obtain the first classification model that is able to predict new nucleotide binding peptides, using only the amino acid sequence. Thus, the methodology uses a Star graph molecular descriptor of the peptide sequences and the Machine Learning technique for the best classifier. The best model represents a Random Forest classifier based on two features of the embedded and non-embedded graphs. The performance of the model is excellent, considering similar models in the field, with an Area Under the Receiver Operating Characteristic Curve (AUROC) value of 0.938 and true positive rate (TPR) of 0.886 (test subset). The prediction of new nucleotide binding peptides with this model could be useful for drug target studies in drug development.
authors.affiliation: Department of Information and Communication Technologies, Computer Science Faculty, University of A Coruna, Campus de Elviña s/n, 15071, A Coruña, Spain, phone/fax: +34-981167000/+34-981167160.|Faculty of Veterinary Medicine and Animal Science, Autonomous University of the State of Mexico, Toluca, 50090, México.|Key Laboratory of Subtropical Agro-ecological Engineering, Institute of Subtropical Agriculture, the Chinese Academy of Sciences, Changsha, Hunan, 410125, P. R. China.
authors.forename: Yong
authors.initials: Y
authors.lastname: Liu
authors.affiliation: Department of Information and Communication Technologies, Computer Science Faculty, University of A Coruna, Campus de Elviña s/n, 15071, A Coruña, Spain, phone/fax: +34-981167000/+34-981167160. crm.publish@gmail.com.
authors.forename: Cristian R
authors.initials: CR
authors.lastname: Munteanu
authors.affiliation: Department of Information and Communication Technologies, Computer Science Faculty, University of A Coruna, Campus de Elviña s/n, 15071, A Coruña, Spain, phone/fax: +34-981167000/+34-981167160.
authors.forename: Enrique
authors.initials: E
authors.lastname: Fernández Blanco
authors.affiliation: Key Laboratory of Subtropical Agro-ecological Engineering, Institute of Subtropical Agriculture, the Chinese Academy of Sciences, Changsha, Hunan, 410125, P. R. China.
authors.forename: Zhiliang
authors.initials: Z
authors.lastname: Tan
authors.affiliation: Department of Information and Communication Technologies, Computer Science Faculty, University of A Coruna, Campus de Elviña s/n, 15071, A Coruña, Spain, phone/fax: +34-981167000/+34-981167160.
authors.forename: Antonino
authors.initials: A
authors.lastname: Santos Del Riego
authors.affiliation: Department of Information and Communication Technologies, Computer Science Faculty, University of A Coruna, Campus de Elviña s/n, 15071, A Coruña, Spain, phone/fax: +34-981167000/+34-981167160.
authors.forename: Alejandro
authors.initials: A
authors.lastname: Pazos
mesh_terms: D000069550:Machine Learning; D008958:Models, Molecular; D009711:Nucleotides / Q000737:chemistry*; D010455:Peptides / Q000737:chemistry*
chemical_list: D009711:Nucleotides; D010455:Peptides
keywords: QSAR; Star Graph; nucleotide binding proteins; topological indice