Critical evaluation of bioinformatics tools for the prediction of protein crystallization propensity.
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.
Department of Chemical Biology, College of Chemistry and Chemical Engineering, Xiamen University, China.
Huilin
H
Wang
NMR Center, Xiamen University, China.
Liubin
L
Feng
Monash Centre for Data Science, Faculty of Information Technology, Monash University, Australia.
Geoffrey I
GI
Webb
Department of Computer Science, Virginia Commonwealth University, USA.
Lukasz
L
Kurgan
Department of Biochemistry and Molecular Biology, Monash University, Australia.
Jiangning
J
Song
Department of Chemical Biology, College of Chemistry and Chemical Engineering, Xiamen University, China.
Donghai
D
Lin
D000465:Algorithms; D000595:Amino Acid Sequence; D019295:Computational Biology / Q000379:methods*; D003460:Crystallization / Q000379:methods* / Q000706:statistics & numerical data; D018360:Crystallography, X-Ray; D030562:Databases, Protein / Q000706:statistics & numerical data; D006801:Humans; D011506:Proteins / Q000737:chemistry / Q000235:genetics / Q000302:isolation & purification*; D012984:Software
D011506:Proteins