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Investigation into the role of sequence-driven-features for prediction of protein structural classes

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2 Author(s)
Sundeep Singh Nanuwa ; Bio-Health Informatics Research Team at the Centre for Computational Intelligence, School of Computing, De Montfort University, Leicester, UK, LE1 9BH ; Huseyin Seker

There have been a number of techniques developed for the prediction of protein structural classes, however, they show various degrees of accuracies over different assessment procedures and, in particular, the role of sequence-driven-features (SDF) not rigorously investigated. Therefore, the aim of this study is to carry out the largest comprehensive and consistent investigation on approximately 1500 protein sequence-driven-features that form 65 subsets in order to develop a robust predictive model and identify how well these feature(s) are at predicting protein structural classes. For evaluation of the features, two high quality 40% (or less) homology datasets that contain over 7000 protein sequences were extracted from proteomic databases. As a predictive technique, an optimum K-Nearest Neighbour Classifier, namely multiple-K-NN (MKNN) was developed, which not only records MKNN results, but also a predictive accuracy for each K nearest neighbourhood for K=1 to 11. In order to make the analyses consistent, three different cross-validation test procedures, 10-fold, leave-one-out and independent set, were used for all data sets and methods implemented. Over 5000 individual predictive results obtained, no firm consensus found on which features are highly associated with protein structural classes. However, interestingly, the best subsets of the features are found to be traditional AAC (48.62%) for 10-fold and (50.09%) for LOO, and dipeptide composition (85.91%) for independent set. The results appear to suggest that the AAC features are one of the best two subsets over 65 different subsets. Interestingly, in particular, with pseudo-amino-acid composition (PseAAC), unlike other research results presented in the literature, this investigation finds that there is no statistical improvement obtained from the sequence-order effect aspect (lamda) of PseAAC, which averaged 39.15%. The results also suggest that most of its predictive power comes from the AAC part that averaged- - at 46.84%, and the overall average predictive accuracy for PseAAC is 47.86%. This information appears to suggest that this feature set, which is claimed to better capture sequence order, yields almost no improvement and can be considered a redundant and noisy feature set. It should be noted that overall outcome of this comprehensive study sheds light not only in structural class prediction, but also other proteomic studies.

Published in:

BioInformatics and BioEngineering, 2008. BIBE 2008. 8th IEEE International Conference on

Date of Conference:

8-10 Oct. 2008