Critical evaluation of bioinformatics tools for the prediction of protein crystallization propensity. (27th February 2017)

• Record Type: Journal Article
• Authors: Wang, Huilin; Feng, Liubin; Webb, Geoffrey I; Kurgan, Lukasz; Song, Jiangning; Lin, Donghai
• Is Part Of: Briefings in bioinformatics. Volume 19:Number 5(2018:Sep.)
• Page Start: 838
• Page End: 852
• Publication Date: 2017-02-27
• Subjects: structural genomics -- protein crystallization propensity -- target selection -- bioinformatics -- sequence analysis -- machine learning; Genetics -- Data processing -- Periodicals; Molecular biology -- Data processing -- Periodicals; Genomes -- Data processing -- Periodicals; 572.80285
• DOI: 10.1093/bib/bbx018
• Languages: English
• ISSNs: 1467-5463

Abstract

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.