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

TL;DR

Pretata is a novel computational method that accurately predicts TATA-binding proteins using new features and a dimensionality reduction strategy, significantly outperforming existing methods and facilitating large-scale genomic data analysis.

Contribution

The paper introduces novel fingerprint features and a hierarchical dimensionality reduction approach for TATA-binding protein prediction.

Findings

Achieved 92.92% prediction accuracy, surpassing existing methods.

Developed a web server for accessible large-scale predictions.

Enhanced prediction speed and accuracy for protein function analysis.

Abstract

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/.