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      Critical evaluation of bioinformatics tools for the prediction of protein crystallization propensity

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          Abstract

          X-ray crystallography is the main tool for structural determination of proteins. Yet, the underlying crystallization process is costly, has a high attrition rate and involves a series of trial-and-error attempts to obtain diffraction-quality crystals. The Structural Genomics Consortium aims to systematically solve representative structures of major protein-fold classes using primarily high-throughput X-ray crystallography. The attrition rate of these efforts can be improved by selection of proteins that are potentially easier to be crystallized. In this context, bioinformatics approaches have been developed to predict crystallization propensities based on protein sequences. These approaches are used to facilitate prioritization of the most promising target proteins, search for alternative structural orthologues of the target proteins and suggest designs of constructs capable of potentially enhancing the likelihood of successful crystallization. We reviewed and compared nine predictors of protein crystallization propensity. Moreover, we demonstrated that integrating selected outputs from multiple predictors as candidate input features to build the predictive model results in a significantly higher predictive performance when compared to using these predictors individually. Furthermore, we also introduced a new and accurate predictor of protein crystallization propensity, Crysf, which uses functional features extracted from UniProt as inputs. This comprehensive review will assist structural biologists in selecting the most appropriate predictor, and is also beneficial for bioinformaticians to develop a new generation of predictive algorithms.

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          Author and article information

          Journal
          Brief Bioinform
          Brief. Bioinformatics
          bib
          Briefings in Bioinformatics
          Oxford University Press
          1467-5463
          1477-4054
          September 2018
          27 February 2017
          27 February 2018
          : 19
          : 5
          : 838-852
          Affiliations
          [1 ]Department of Chemical Biology, College of Chemistry and Chemical Engineering, Xiamen University, China
          [2 ]NMR Center, Xiamen University, China
          [3 ]Monash Centre for Data Science, Faculty of Information Technology, Monash University, Australia
          [4 ]Department of Computer Science, Virginia Commonwealth University, USA
          [5 ]Department of Biochemistry and Molecular Biology, Monash University, Australia
          Author notes
          Corresponding authors: Lukasz Kurgan, Department of Computer Science, Virginia Commonwealth University, USA. Tel.: +1 804-827-3986; Email: lkurgan@ 123456vcu.edu ; Jiangning Song, Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University, Melbourne, VIC 3800, Australia. Tel.: +61-3-9902-9304; Fax: +61-3-9902-9500; Email: Jiangning.Song@ 123456monash.edu ; Donghai Lin, The Key Laboratory for Chemical Biology of Fujian Province, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, China. Tel.: +86-592-2186078; Fax: +86-592-2186078; Email: dhlin@ 123456xmu.edu.cn
          Article
          PMC6171492 PMC6171492 6171492 bbx018
          10.1093/bib/bbx018
          6171492
          28334201
          31756f0d-2cc1-491b-b86b-ac4d13d15ee8
          © The Author 2017. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com

          This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model ( https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model)

          History
          : 19 October 2016
          : 19 January 2017
          Page count
          Pages: 15
          Funding
          Funded by: National Key Research and Development Program of China
          Award ID: 2016YFA0500600
          Funded by: National Natural Science Foundation of China 10.13039/501100001809
          Award ID: 31670741
          Award ID: 61202167
          Award ID: 61303169
          Award ID: 81661138005
          Funded by: National Health and Medical Research Council 10.13039/501100000925
          Award ID: 490989
          Funded by: National Institutes of Health 10.13039/100000002
          Award ID: AI111965
          Categories
          Paper

          protein crystallization propensity,machine learning,sequence analysis,bioinformatics,target selection,structural genomics

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