Department of Chemical Biology, College of Chemistry and Chemical Engineering, Xiamen University, China.
NMR Center, Xiamen University, China.
Brief Bioinform. 2018 Sep 28;19(5):838-852. doi: 10.1093/bib/bbx018.
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.
X 射线晶体学是蛋白质结构测定的主要工具。然而，基础的结晶过程成本高昂、淘汰率高，并且需要进行一系列反复尝试才能获得具有衍射质量的晶体。结构基因组学联盟旨在使用主要的高通量 X 射线晶体学系统地解决主要蛋白质折叠类别的代表性结构。通过选择潜在更容易结晶的蛋白质，可以提高这些努力的淘汰率。在这种情况下，已经开发了基于蛋白质序列预测结晶倾向的生物信息学方法。这些方法用于促进最有前途的靶蛋白的优先级排序，寻找靶蛋白的替代结构同源物，并建议设计能够潜在提高结晶成功率的构建体。我们回顾和比较了九个蛋白质结晶倾向预测器。此外，我们证明，将多个预测器的选定输出集成作为候选输入特征来构建预测模型，与单独使用这些预测器相比，可显著提高预测性能。此外，我们还引入了一个新的、准确的蛋白质结晶倾向预测器 Crysf，它使用从 UniProt 提取的功能特征作为输入。本综述将有助于结构生物学家选择最合适的预测器，也有利于生物信息学家开发新一代预测算法。
