A Hierarchical Approach to Protein Fold Prediction

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doi doi:10.2390/biecoll-jib-2011-185
submission May 03, 2011
last revision August 18, 2011
published October 19, 2011
NCBI PubMed PubMed ID 22008449

Tabrez Anwar Shamim Mohammad and Hampapathalu Adimurthy Nagarajaram

Correspondence should be addressed to:
Hampapathalu Nagarajaram
Laboratory of Computational Biology, CDFD, Bldg.7, Gruhakalpa, Nampally, Hyderabad 500 001 India


Fold recognition, assigning novel proteins to known structures, forms an important component of the overall protein structure discovery process. The available methods for protein fold recognition are limited by the low fold-coverage and/or low prediction accuracies. We describe here a new Support Vector Machine (SVM)-based method for protein fold prediction with high prediction accuracy and high fold-coverage. The new method of fold prediction with high fold-coverage was developed by training and testing on a large number of folds in order to make the method suitable for large scale fold predictions. However, presence of large number of folds in the training set made the classification task difficult as a consequence of increased complexity involved in binary classifications of SVMs. In order to overcome this complexity we adopted a hierarchical approach where fold-prediction is made in two steps. At the first step structural class of the query is predicted and at the second step fold is predicted within the predicted structural class. This decreased the complexity of the classification problem and also improved the overall fold prediction accuracy. To the best of our knowledge this is the first taxonomic fold recognition method to cover over 700 protein-folds and gives prediction accuracy of around 70% on a benchmark dataset. Since the new method gives rise to state of the art prediction performance and hence can be very useful for structural characterization of proteins discovered in various genomes.


Tabrez Anwar Shamim Mohammad and Hampapathalu Adimurthy Nagarajaram. A Hierarchical Approach to Protein Fold Prediction. Journal of Integrative Bioinformatics, 8(1):185, 2011. Online Journal: http://journal.imbio.de/index.php?paper_id=185
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