PDP-CON: prediction of domain/linker residues in protein sequences using a consensus approach
Piyali Chatterjee , Subhadip Basu , Julian Zubek , Mahantapas Kundu , Mita Nasipuri , Dariusz Plewczyński
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.
|Journal series||Journal of Molecular Modeling, ISSN 1610-2940, e-ISSN 0948-5023|
|ASJC Classification||; ; ; ; ;|
|Score|| = 20.0, 04-06-2020, ArticleFromJournal|
= 25.0, 04-06-2020, ArticleFromJournal
|Publication indicators||= 7; = 6; : 2016 = 0.531; : 2016 = 1.425 (2) - 2016=1.54 (5)|
* presented citation count is obtained through Internet information analysis and it is close to the number calculated by the Publish or Perish system.