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

Piyali ChatterjeeSubhadip BasuJulian ZubekMahantapas KunduMita NasipuriDariusz Plewczyński
Published in: Journal of molecular modeling (2016)
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.
Keyphrases
  • machine learning
  • amino acid
  • protein protein
  • high throughput
  • binding protein
  • deep learning
  • small molecule
  • emergency department
  • adverse drug
  • quality improvement