title: PDP-CON: prediction of domain/linker residues in protein sequences using a consensus approach.
abstract: The prediction of domain/linker residues in protein sequences is a crucial task in the functional classification of proteins, homology-based protein structure prediction, and high-throughput structural genomics. In this work, a novel consensus-based machine-learning technique was applied for residue-level prediction of the domain/linker annotations in protein sequences using ordered/disordered regions along protein chains and a set of physicochemical properties. Six different classifiers-decision tree, Gaussian naïve Bayes, linear discriminant analysis, support vector machine, random forest, and multilayer perceptron-were exhaustively explored for the residue-level prediction of domain/linker regions. The protein sequences from the curated CATH database were used for training and cross-validation experiments. Test results obtained by applying the developed PDP-CON tool to the mutually exclusive, independent proteins of the CASP-8, CASP-9, and CASP-10 databases are reported. An n-star quality consensus approach was used to combine the results yielded by different classifiers. The average PDP-CON accuracy and F-measure values for the CASP targets were found to be 0.86 and 0.91, respectively. The dataset, source code, and all supplementary materials for this work are available at https://cmaterju.org/cmaterbioinfo/ for noncommercial use.
authors.affiliation: Department of Computer Science and Engineering, Netaji Subhash Engineering College, Garia, Kolkata, 700152, India.
authors.forename: Piyali
authors.initials: P
authors.lastname: Chatterjee
authors.affiliation: Department of Computer Science and Engineering, Jadavpur University, Kolkata, 700032, India. subhadip@cse.jdvu.ac.in.
authors.forename: Subhadip
authors.initials: S
authors.lastname: Basu
authors.affiliation: Institute of Computer Science, Polish Academy of Sciences, Warsaw, Poland.|Center of New Technologies, University of Warsaw, Banacha 2c, 02-097, Warsaw, Poland.
authors.forename: Julian
authors.initials: J
authors.lastname: Zubek
authors.affiliation: Department of Computer Science and Engineering, Jadavpur University, Kolkata, 700032, India.
authors.forename: Mahantapas
authors.initials: M
authors.lastname: Kundu
authors.affiliation: Department of Computer Science and Engineering, Jadavpur University, Kolkata, 700032, India.
authors.forename: Mita
authors.initials: M
authors.lastname: Nasipuri
authors.affiliation: Center of New Technologies, University of Warsaw, Banacha 2c, 02-097, Warsaw, Poland. d.plewczynski@cent.uw.edu.pl.|Faculty of Pharmacy, Medical University of Warsaw, Warsaw, Poland. d.plewczynski@cent.uw.edu.pl.
authors.forename: Dariusz
authors.identifier: http://orcid.org/0000-0002-3840-7610
authors.initials: D
authors.lastname: Plewczynski
mesh_terms: D001499:Bayes Theorem; D053455:Caspase 10 / Q000737:chemistry*; D053181:Caspase 8 / Q000737:chemistry*; D053453:Caspase 9 / Q000737:chemistry*; D019295:Computational Biology / Q000379:methods*; D030562:Databases, Protein; D003663:Decision Trees; D016002:Discriminant Analysis; D006801:Humans; D016571:Neural Networks, Computer; D000072417:Protein Domains; D020539:Sequence Analysis, Protein; D040681:Structural Homology, Protein; D060388:Support Vector Machine
chemical_list: C505530:CASP8 protein, human; C506433:CASP9 protein, human; D053455:Caspase 10; D053181:Caspase 8; D053453:Caspase 9; C506438:CASP10 protein, human
keywords: Consensus strategy; Domain boundary prediction; Machine-learning approaches; Ordered-disordered regions in protein sequence; Physicochemical properties; Protein domain/linker prediction