Jianzhao Gao, Zhonghua Wu, Gang Hu, Kui Wang, Jiangning Song, Andrzej Joachimiak* and Lukasz Kurgan* Pages 200 - 210 ( 11 )
Selection of proper targets for the X-ray crystallography will benefit biological research community immensely. Several computational models were proposed to predict propensity of successful protein production and diffraction quality crystallization from protein sequences. We reviewed a comprehensive collection of 22 such predictors that were developed in the last decade. We found that almost all of these models are easily accessible as webservers and/or standalone software and we demonstrated that some of them are widely used by the research community. We empirically evaluated and compared the predictive performance of seven representative methods. The analysis suggests that these methods produce quite accurate propensities for the diffraction-quality crystallization. We also summarized results of the first study of the relation between these predictive propensities and the resolution of the crystallizable proteins. We found that the propensities predicted by several methods are significantly higher for proteins that have high resolution structures compared to those with the low resolution structures. Moreover, we tested a new meta-predictor, MetaXXC, which averages the propensities generated by the three most accurate predictors of the diffraction-quality crystallization. MetaXXC generates putative values of resolution that have modest levels of correlation with the experimental resolutions and it offers the lowest mean absolute error when compared to the seven considered methods. We conclude that protein sequences can be used to fairly accurately predict whether their corresponding protein structures can be solved using X-ray crystallography. Moreover, we also ascertain that sequences can be used to reasonably well predict the resolution of the resulting protein crystals.
X-ray crystallography, diffraction quality crystallization, protein production, resolution of protein crystals, meta prediction, protein structure, prediction.
School of Mathematical Sciences and LPMC, Nankai University, Tianjin, School of Mathematical Sciences and LPMC, Nankai University, Tianjin, School of Mathematical Sciences and LPMC, Nankai University, Tianjin, School of Mathematical Sciences and LPMC, Nankai University, Tianjin, Infection and Immunity Program, Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University, Melbourne, Midwest Center for Structural Genomics, Argonne, IL, Department of Computer Science, Virginia Commonwealth University, Richmond, VA