Prediction of Protein Secondary Structures with a Novel Kernel Density Estimator

* To whom correspondence should be addressed. E-mail: [email protected], Tel: +886-2-33664888 ext. 431, Fax: +886-2-23688675. Abstract Though prediction of protein secondary structures has been an active research issue in bioinformatics for quite a few years and many approaches have been proposed, a new challenge emerges as the sizes of contemporary protein structure databases such as the Protein Data Bank (PDB) continue to grow exponentially. The new challenge concerns how to effectively exploit the huge amount of structural information deposited in large protein structure databases and deliver ever-improving accuracy as the sizes of the databases continue to grow. This new challenge is addressed in this article by resorting to a kernel density estimation based approach. The kernel density estimator proposed in this article is distinctive in that the pointwise MSE (mean square error) of its basic form converges at O(n -2/3 ) regardless of the dimension of the vector space, where n is the number of instances in the training dataset. In addition, just like many conventional kernel density estimators, it features average O(nlogn) time complexity for generating the approximation function. The experimental results show that with the novel kernel density estimator the proposed predictor has been able to outperform the state-of-art predictors currently available. Experimental results further reveal that prediction accuracy delivered by the proposed predictor will continue to increase in the future as the size of the protein structure database keeps growing.