title: Binding Activity Prediction of Cyclin-Dependent Inhibitors.
abstract: The Cyclin-Dependent Kinases (CDKs) are the core components coordinating eukaryotic cell division cycle. Generally the crystal structure of CDKs provides information on possible molecular mechanisms of ligand binding. However, reliable and robust estimation of ligand binding activity has been a challenging task in drug design. In this regard, various machine learning techniques, such as Support Vector Machine, Naive Bayesian classifier, Decision Tree, and K-Nearest Neighbor classifier, have been used. The performance of these heterogeneous classification techniques depends on proper selection of features from the data set. This fact motivated us to propose an integrated classification technique using Genetic Algorithm (GA), Rotational Feature Selection (RFS) scheme, and Ensemble of Machine Learning methods, named as the Genetic Algorithm integrated Rotational Ensemble based classification technique, for the prediction of ligand binding activity of CDKs. This technique can automatically find the important features and the ensemble size. For this purpose, GA encodes the features and ensemble size in a chromosome as a binary string. Such encoded features are then used to create diverse sets of training points using RFS in order to train the machine learning method multiple times. The RFS scheme works on Principal Component Analysis (PCA) to preserve the variability information of the rotational nonoverlapping subsets of original data. Thereafter, the testing points are fed to the different instances of trained machine learning method in order to produce the ensemble result. Here accuracy is computed as a final result after 10-fold cross validation, which also used as an objective function for GA to maximize. The effectiveness of the proposed classification technique has been demonstrated quantitatively and visually in comparison with different machine learning methods for 16 ligand binding CDK docking and rescoring data sets. In addition, the best possible features have been reported for CDK docking and rescoring data sets separately. Finally, the Friedman test has been conducted to judge the statistical significance of the results produced by the proposed technique. The results indicate that the integrated classification technique has high relevance in predicting of protein-ligand binding activity.
authors.affiliation: †Centre of New Technologies, University of Warsaw, 02-097 Warsaw, Poland.|‡Institute of Informatics and Telematics, National Research Council, 56124 Pisa, Italy.|§Institute of Computer Science, University of Wroclaw, 50-383 Wroclaw, Poland.
authors.forename: Indrajit
authors.initials: I
authors.lastname: Saha
authors.affiliation: †Centre of New Technologies, University of Warsaw, 02-097 Warsaw, Poland.
authors.forename: Benedykt
authors.initials: B
authors.lastname: Rak
authors.affiliation: ∥Department of Computer Science and Engineering, Jadavpur University, Kolkata-700032, West Bengal, India.|⊥Department of Informatics, University of Evora, Evora 7004-516, Portugal.
authors.forename: Shib Sankar
authors.initials: SS
authors.lastname: Bhowmick
authors.affiliation: ∥Department of Computer Science and Engineering, Jadavpur University, Kolkata-700032, West Bengal, India.
authors.forename: Ujjwal
authors.initials: U
authors.lastname: Maulik
authors.affiliation: ∥Department of Computer Science and Engineering, Jadavpur University, Kolkata-700032, West Bengal, India.
authors.forename: Debotosh
authors.initials: D
authors.lastname: Bhattacharjee
authors.affiliation: □Lead Discovery Center, Emil-Figge-Strasse 76a, 44227 Dortmund, Germany.
authors.forename: Uwe
authors.initials: U
authors.lastname: Koch
authors.affiliation: †Centre of New Technologies, University of Warsaw, 02-097 Warsaw, Poland.
authors.forename: Michal
authors.initials: M
authors.lastname: Lazniewski
authors.affiliation: †Centre of New Technologies, University of Warsaw, 02-097 Warsaw, Poland.|△The Jackson Laboratory for Genomic Medicine, c/o University of Connecticut Health Center, Administrative Services Building-Call Box 901, 263 Farmington Avenue, Farmington, Connecticut 06030, United States.|¶Yale University, New Haven, Connecticut 06520, United States.
authors.forename: Dariusz
authors.initials: D
authors.lastname: Plewczynski
mesh_terms: D000465:Algorithms; D001499:Bayes Theorem; D002875:Chromosomes / Q000235:genetics; D018844:Cyclin-Dependent Kinases / Q000037:antagonists & inhibitors* / Q000737:chemistry / Q000378:metabolism*; D003663:Decision Trees; D000069550:Machine Learning; D008958:Models, Molecular; D011485:Protein Binding; D011487:Protein Conformation; D047428:Protein Kinase Inhibitors / Q000378:metabolism* / Q000494:pharmacology*; D060388:Support Vector Machine
chemical_list: D047428:Protein Kinase Inhibitors; D018844:Cyclin-Dependent Kinases