title: Crysalis: an integrated server for computational analysis and design of protein crystallization.
abstract: The failure of multi-step experimental procedures to yield diffraction-quality crystals is a major bottleneck in protein structure determination. Accordingly, several bioinformatics methods have been successfully developed and employed to select crystallizable proteins. Unfortunately, the majority of existing in silico methods only allow the prediction of crystallization propensity, seldom enabling computational design of protein mutants that can be targeted for enhancing protein crystallizability. Here, we present Crysalis, an integrated crystallization analysis tool that builds on support-vector regression (SVR) models to facilitate computational protein crystallization prediction, analysis, and design. More specifically, the functionality of this new tool includes: (1) rapid selection of target crystallizable proteins at the proteome level, (2) identification of site non-optimality for protein crystallization and systematic analysis of all potential single-point mutations that might enhance protein crystallization propensity, and (3) annotation of target protein based on predicted structural properties. We applied the design mode of Crysalis to identify site non-optimality for protein crystallization on a proteome-scale, focusing on proteins currently classified as non-crystallizable. Our results revealed that site non-optimality is based on biases related to residues, predicted structures, physicochemical properties, and sequence loci, which provides in-depth understanding of the features influencing protein crystallization. Crysalis is freely available at http://nmrcen.xmu.edu.cn/crysalis/.
authors.affiliation: Department of Chemical Biology, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, China.
authors.forename: Huilin
authors.initials: H
authors.lastname: Wang
authors.affiliation: Department of Chemical Biology, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, China.
authors.forename: Liubin
authors.initials: L
authors.lastname: Feng
authors.affiliation: State Key Laboratory of Agrobiotechnology, College of Biological Sciences, China Agricultural University, Beijing 100193, China.
authors.forename: Ziding
authors.initials: Z
authors.lastname: Zhang
authors.affiliation: Monash Centre for Data Science, Faculty of Information Technology, Monash University, Melbourne, VIC 3800, Australia.
authors.forename: Geoffrey I
authors.initials: GI
authors.lastname: Webb
authors.affiliation: Department of Chemical Biology, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, China.
authors.forename: Donghai
authors.initials: D
authors.lastname: Lin
authors.affiliation: Monash Centre for Data Science, Faculty of Information Technology, Monash University, Melbourne, VIC 3800, Australia.|Department of Microbiology, School of Biomedical Sciences, Monash University, Melbourne, VIC 3800, Australia.|National Engineering Laboratory for Industrial Enzymes, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin 300308, China.
authors.forename: Jiangning
authors.initials: J
authors.lastname: Song
mesh_terms: D000595:Amino Acid Sequence; D019295:Computational Biology; D003460:Crystallization; D018360:Crystallography, X-Ray; D030562:Databases, Protein; D000069550:Machine Learning; D058977:Molecular Sequence Annotation; D011506:Proteins / Q000737:chemistry*; D012984:Software
chemical_list: D011506:Proteins