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Authors: Jonny A. Uribe 1 ; Julián D. Arias-Londoño 1 and Alexandre Perera-Lluna 2

Affiliations: 1 Universidad de Antioquia, Colombia ; 2 Universitat Politècnica de Catalunya, Spain

ISBN: 978-989-758-214-1

Keyword(s): Intrinsically Disordered Proteins, Intrinsically Disordered Regions, Entropy Measures, Kullback-Leibler Divergence, Dihedral Torsion Angles, Ramachandran Plot, Conditional Random Fields.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Bioinformatics ; Biomedical Engineering ; Computational Intelligence ; Data Mining and Machine Learning ; Genomics and Proteomics ; Pattern Recognition, Clustering and Classification ; Soft Computing

Abstract: This paper addresses the problem of order/disorder prediction in protein sequences from alignment free methods. The proposed approach is based on a set of 11 information theory measures estimated from the distribution of the dihedral torsion angles in the amino acid chain. The aim is to characterize the energetically allowed regions for amino acids in the protein structures, as a way of measuring the rigidity/flexibility of every amino acid in the chain, and the effect of such rigidity on the disorder propensity. The features are estimated from empirical Ramachandran Plots obtained using the Protein Geometry Database. The proposed features are used in conjunction with well-established features in the state of the art for disorder prediction. The classification is performed using two different strategies: one based on conventional supervised methods and the other one based on structural learning. The performance is evaluated in terms of AUC (Area Under the ROC Curve), and thre e suitable performance metrics for unbalanced classification problems. The results show that the proposed scheme using conventional supervised methods is able to achieve results similar than well-known alignment free methods for disorder prediction. Moreover, the scheme based on structural learning outperforms the results obtained for all the methods evaluated, including three alignment-based methods. (More)

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Paper citation in several formats:
Uribe J., Arias-Londoño J. and Perera-Lluna A. (2017). Protein Disorder Prediction using Information Theory Measures on the Distribution of the Dihedral Torsion Angles from Ramachandran Plots.In Proceedings of the 10th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 3: BIOINFORMATICS, (BIOSTEC 2017) ISBN 978-989-758-214-1, pages 43-51. DOI: 10.5220/0006140500430051

@conference{bioinformatics17,
author={Jonny A. Uribe and Julián D. Arias-Londoño and Alexandre Perera-Lluna},
title={Protein Disorder Prediction using Information Theory Measures on the Distribution of the Dihedral Torsion Angles from Ramachandran Plots},
booktitle={Proceedings of the 10th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 3: BIOINFORMATICS, (BIOSTEC 2017)},
year={2017},
pages={43-51},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006140500430051},
isbn={978-989-758-214-1},
}

TY - CONF

JO - Proceedings of the 10th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 3: BIOINFORMATICS, (BIOSTEC 2017)
TI - Protein Disorder Prediction using Information Theory Measures on the Distribution of the Dihedral Torsion Angles from Ramachandran Plots
SN - 978-989-758-214-1
AU - Uribe J.
AU - Arias-Londoño J.
AU - Perera-Lluna A.
PY - 2017
SP - 43
EP - 51
DO - 10.5220/0006140500430051

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