Survey of Predictors of Propensity for Protein Production and Crystallization with Application to Predict Resolution of Crystal Structures

Gao, Jianzhao; Wu, Zhonghua; Hu, Gang; Wang, Kui; Song, Jiangning; Joachimiak, Andrzej; Kurgan, Lukasz

doi:10.2174/1389203718666170921114437

Title: Survey of Predictors of Propensity for Protein Production and Crystallization with Application to Predict Resolution of Crystal Structures

Abstract

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 errormore »« less

Authors:

Gao, Jianzhao ^[1]; Wu, Zhonghua ^[1]; Hu, Gang ^[1]; Wang, Kui ^[1]; Song, Jiangning ^[2]; Joachimiak, Andrzej ^[3]; Kurgan, Lukasz ^[4]

Nankai University, Tianjin (China)
Monash Univ., Melbourne, VIC (Australia)
Midwest Center for Structural Genomics, Argonne, IL (United States); Argonne National Lab. (ANL), Argonne, IL (United States); Univ. of Chicago, IL (United States)
Virginia Commonwealth Univ., Richmond, VA (United States)

Publication Date:: Mon Dec 18 00:00:00 EST 2017

Research Org.:: Argonne National Lab. (ANL), Argonne, IL (United States)

Sponsoring Org.:: National Natural Science Foundation of China (NSFC); National Institutes of Health (NIH); Specialized Research Fund for the Doctoral Program of Higher Education (SRFDP); International Development Research Centre (IDRC); Virginia Commonwealth University - Qimonda Endowed Chair; USDOE

OSTI Identifier:: 1438826

Grant/Contract Number:: AC02-06CH11357; 20130031120001; 11101226; 11701296; 104519-010; GM115586

Resource Type:: Accepted Manuscript

Journal Name:: Current Protein & Peptide Science

Additional Journal Information:: Journal Volume: 19; Journal Issue: 2; Journal ID: ISSN 1389-2037

Publisher:: Bentham Science Publishers

Country of Publication:: United States

Language:: English

Subject:: 59 BASIC BIOLOGICAL SCIENCES; prediction; protein structure; X-ray crystallography; diffraction quality crystallization; meta prediction; protein production; resolution of protein crystals

Citation Formats


                    Gao, Jianzhao, Wu, Zhonghua, Hu, Gang, Wang, Kui, Song, Jiangning, Joachimiak, Andrzej, and Kurgan, Lukasz. Survey of Predictors of Propensity for Protein Production and Crystallization with Application to Predict Resolution of Crystal Structures.  United States: N. p., 2017. 
Web.  doi:10.2174/1389203718666170921114437.

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                    Gao, Jianzhao, Wu, Zhonghua, Hu, Gang, Wang, Kui, Song, Jiangning, Joachimiak, Andrzej, & Kurgan, Lukasz. Survey of Predictors of Propensity for Protein Production and Crystallization with Application to Predict Resolution of Crystal Structures.  United States.  https://doi.org/10.2174/1389203718666170921114437

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                    Gao, Jianzhao, Wu, Zhonghua, Hu, Gang, Wang, Kui, Song, Jiangning, Joachimiak, Andrzej, and Kurgan, Lukasz. Mon .  
"Survey of Predictors of Propensity for Protein Production and Crystallization with Application to Predict Resolution of Crystal Structures".  United States.  https://doi.org/10.2174/1389203718666170921114437.  https://www.osti.gov/servlets/purl/1438826.

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@article{osti_1438826,

  title        = {Survey of Predictors of Propensity for Protein Production and Crystallization with Application to Predict Resolution of Crystal Structures},

  author       = {Gao, Jianzhao and Wu, Zhonghua and Hu, Gang and Wang, Kui and Song, Jiangning and Joachimiak, Andrzej and Kurgan, Lukasz},

  abstractNote = {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. Here, 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.},

  doi          = {10.2174/1389203718666170921114437},

  journal      = {Current Protein & Peptide Science},

  number       = 2,

  volume       = 19,

  place        = {United States},

  year         = {Mon Dec 18 00:00:00 EST 2017},

  month        = {Mon Dec 18 00:00:00 EST 2017}

}

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Figures / Tables:

Table 1: Summary of protein sequence-based predictors of the propensity for protein cloning, material production, purification, crystallization and structure determination using X-ray crystallography (diffraction-quality crystallization). Bold font identifies methods that were used to perform empirical analysis.

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