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Background: It is necessary and essential to discovery protein function fromthe novel primary sequences. Wet lab experimental procedures are not onlytime-consuming, but also costly, so predicting protein structure and functionreliably based only on amino acid sequence has significant value. TATA-bindingprotein (TBP) is a kind of DNA binding protein, which plays a key role in thetranscription regulation. Our study proposed an automatic approach foridentifying TATA-binding proteins efficiently, accurately, and conveniently.This method would guide for the special protein identification withcomputational intelligence strategies. Results: Firstly, we proposed novelfingerprint features for TBP based on pseudo amino acid composition,physicochemical properties, and secondary structure. Secondly, hierarchicalfeatures dimensionality reduction strategies were employed to improve theperformance furthermore. Currently, Pretata achieves 92.92% TATA- bindingprotein prediction accuracy, which is better than all other existing methods.Conclusions: The experiments demonstrate that our method could greatly improvethe prediction accuracy and speed, thus allowing large-scale NGS dataprediction to be practical. A web server is developed to facilitate the otherresearchers, which can be accessed at http://server.malab.cn/preTata/.

Pretata: predicting TATA binding proteins with novel features and dimensionality reduction strategy