Mining of protein contact maps for protein fold prediction

Authors:
Durga Bhavani S;Suvarnavani K;Somdatta Sinha
Affiliations:
Department of Computer and Information Sciences, University of Hyderabad, Hyderabad, India;Department of Computer Science and Engineering, V.R. Siddhartha Engineering College, Vijayawada, Andhra Pradesh, India;Centre for Cellular and Molecular Biology, Hyderabad, India
Venue:
Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery
Year:
2011

Citing 9
Cited 2

Introduction to Machine Learning (Adaptive Computation and Machine Learning)

Introduction to Machine Learning (Adaptive Computation and Machine Learning)
Assortative mixing in Protein Contact Networks and protein folding kinetics

Bioinformatics
Support Vector Machine-based classification of protein folds using the structural properties of amino acid residues and amino acid residue pairs

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A Demonstration of Clustering in Protein Contact Maps for Alpha Helix Pairs

ICANNGA '07 Proceedings of the 8th international conference on Adaptive and Natural Computing Algorithms, Part I
Prediction of Inter-residue Contact Clusters from Hydrophobic Cores

ICMLA '08 Proceedings of the 2008 Seventh International Conference on Machine Learning and Applications
Fast SCOP Classification of Structural Class and Fold Using Secondary Structure Mining in Distance Matrix

PRIB '09 Proceedings of the 4th IAPR International Conference on Pattern Recognition in Bioinformatics
Letters: Ensemble of classifiers for protein fold recognition

Neurocomputing
Machine learning for multi-class protein fold classification based on neural networks with feature gating

ICANN/ICONIP'03 Proceedings of the 2003 joint international conference on Artificial neural networks and neural information processing
Data Mining: Concepts and Techniques

Data Mining: Concepts and Techniques

Short-Range interactions and decision tree-based protein contact map predictor

EvoBIO'12 Proceedings of the 10th European conference on Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics
Using short-range interactions and simulated genetic strategy to improve the protein contact map prediction

MCPR'12 Proceedings of the 4th Mexican conference on Pattern Recognition

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Abstract

The three-dimensional structure of proteins is useful to carry out the biophysical and biochemical functions in a cell. Approaches to protein structure/fold prediction typically extract amino acid sequence features, and machine learning approaches are then applied to classification problem. Protein contact maps are two-dimensional representations of the contacts among the amino acid residues in the folded protein structure. This paper highlights the need for a systematic study of these contact networks. Mining of contact maps to derive features pertaining to fold information offers a new mechanism for fold discovery from the protein sequence via the contact maps. These ideas are explored in the structural class of all-alpha proteins to identify structural elements. A simple and computationally inexpensive algorithm based on triangle subdivision method is proposed to extract additional features from the contact map. The method successfully characterizes the off-diagonal interactions in the contact map for predicting specific ‘folds’. The decision tree classification results show great promise in developing a new and simple tool for the challenging problem of fold prediction. © 2011 John Wiley & Sons, Inc. WIREs Data Mining Knowl Discov 2011 1 362–368 DOI: 10.1002/widm.35