Sequence-Based Protein Crystallization Propensity Prediction for Structural Genomics: Review and Comparative Analysis

Structural genomics (SG) is an international effort that aims at solving three-dimensional shapes of important biological macro-molecules with primary focus on proteins. One of the main bottlenecks in SG is the ability to produce diffraction quality crystals for X-ray crystallography based protein structure determination. SG pipelines allow for certain flexibility in target selection which motivates development of insilico methods for sequence-based prediction/ assessment of the protein crystallization propensity. We overview existing SG databanks that are used to derive these predictive models and we discuss analytical results concerning protein sequence properties that were discovered to correlate with the ability to form crystals. We also contrast and empirically compare modern sequence-based predictors of crystallization propensity including OB-Score, ParCrys, XtalPred and CRYSTALP2. Our analysis shows that these methods provide useful and complimentary predictions. Although their average accuracy is similar at around 70%, we show that application of a simple majority-vote based ensemble improves accuracy to almost 74%. The best improvements are achieved by combining XtalPred with CRYSTALP2 while OB-Score and ParCrys methods overlap to a larger extend, although they still complement the other two predictors. We also demonstrate that 90% of the protein chains can be correctly predicted by at least one of these methods, which suggests that more accurate ensembles could be built in the future. We believe that current protein crystallization propensity predictors could provide useful input for the target selection procedures utilized by the SG centers.