Pretata: predicting TATA binding proteins with novel features and dimensionality reduction strategy.
BACKGROUND
It is necessary and essential to discovery protein function from the novel primary sequences. Wet lab experimental procedures are not only time-consuming, but also costly, so predicting protein structure and function reliably based only on amino acid sequence has significant value. TATA-binding protein (TBP) is a kind of DNA binding protein, which plays a key role in the transcription regulation. Our study proposed an automatic approach for identifying TATA-binding proteins efficiently, accurately, and conveniently. This method would guide for the special protein identification with computational intelligence strategies.


RESULTS
Firstly, we proposed novel fingerprint features for TBP based on pseudo amino acid composition, physicochemical properties, and secondary structure. Secondly, hierarchical features dimensionality reduction strategies were employed to improve the performance furthermore. Currently, Pretata achieves 92.92% TATA-binding protein prediction accuracy, which is better than all other existing methods.


CONCLUSIONS
The experiments demonstrate that our method could greatly improve the prediction accuracy and speed, thus allowing large-scale NGS data prediction to be practical. A web server is developed to facilitate the other researchers, which can be accessed at http://server.malab.cn/preTata/ .
School of Computer Science and Technology, Tianjin University, Tianjin, China.
Quan
Q
Zou
School of Computer Science and Technology, Tianjin University, Tianjin, China.|Key Laboratory of Network Oriented Intelligent Computation, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, Guangdong, 518055, China.
Shixiang
S
Wan
School of Information Science and Engineering, Xiamen University, Xiamen, China.
Ying
Y
Ju
School of Computer Science and Technology, Tianjin University, Tianjin, China.|School of Computational Science and Engineering, University of South Carolina, Columbia, USA.
Jijun
J
Tang
School of Information Science and Engineering, Xiamen University, Xiamen, China. xzeng@xmu.edu.cn.
Xiangxiang
X
Zeng
D000595:Amino Acid Sequence; D055598:Chemical Phenomena; D019295:Computational Biology / Q000379:methods*; D011485:Protein Binding; D017433:Protein Structure, Secondary; D012984:Software; D060388:Support Vector Machine; D035181:TATA-Box Binding Protein / Q000737:chemistry / Q000378:metabolism*
D035181:TATA-Box Binding Protein
Dimensionality reduction; Machine learning; Protein sequence features; Support vector machine; TATA binding protein