Improved Prediction of Protein Crystallization, Purification and Production Propensity Using Hybrid Sequence Representation

Title:Improved Prediction of Protein Crystallization, Purification and Production Propensity Using Hybrid Sequence Representation

Volume: 9 Issue: 1

Author(s): Jianzhao Gao, Gang Hu, Zhonghua Wu, Jishou Ruan, Shiyi Shen, Michelle Hanlon and Kui Wang

Affiliation:

Keywords: Protein crystallization, protein purification, X-ray crystallography.

Abstract: Production of high-quality crystals is one of the main bottlenecks in X-ray crystallography-based protein structure determination. In this paper we introduce PPCinter, a novel method to predict the propensity for production of diffraction-quality crystals, production of crystals, purification and production of protein material.

PPCinter utilizes not only intra-molecular factors, but considers inter-molecular factors as well. Our method outperforms several current crystallization predictors, obtaining an overall accuracy of 57.5% and an average MCC of 0.39. Our method also reveals several factors that influence the success of the crystallization process, including the unfold-based index, energy-based, solvent accessibility, and hydrophobicity-based indices, amino acid composition, the isoelectric point and disorder-based features.

The proposed method, PPCinter, could provide useful input for the target selection procedures utilized by structural genomics centers.

Cite this article as:

Gao Jianzhao, Hu Gang, Wu Zhonghua, Ruan Jishou, Shen Shiyi, Hanlon Michelle and Wang Kui, Improved Prediction of Protein Crystallization, Purification and Production Propensity Using Hybrid Sequence Representation, Current Bioinformatics 2014; 9 (1) . https://dx.doi.org/10.2174/15748936113080990006