PDP-CON: prediction of domain/linker residues in protein sequences using a consensus approach

• DOI: 10.1007/s00894-016-2933-0
• Publication Date: 2016-03-11T01:51:30Z
• Authors: Piyali Chatterjee; Subhadip Basu; Julian Zubek; Mahantapas Kundu; Mita Nasipuri; Dariusz Plewczynski
• Keywords: 0301 basic medicine; Original Paper; Caspase 8; Support Vector Machine; Organic Chemistry; Decision Trees; Computational Biology; Discriminant Analysis; Bayes Theorem; Catalysis; Caspase 9; Computer Science Applications; Inorganic Chemistry; 03 medical and health sciences; Computational Theory and Mathematics; Protein Domains; Sequence Analysis, Protein; Structural Homology, Protein; Humans; Neural Networks, Computer; Physical and Theoretical Chemistry; Caspase 10; Databases, Protein

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.