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• W2328398973 abstract "Relatively low success rates of X-ray crystallography, which is the most popular method for solving proteins structures, motivate development of novel methods that support selection of tractable protein targets. This aspect is particularly important in the context of the current structural genomics efforts that allow for a certain degree of flexibility in the target selection. We propose CRYSpred, a novel in-silico crystallization propensity predictor that uses a set of 15 novel features which utilize a broad range of inputs including charge, hydrophobicity, and amino acid composition derived from the protein chain, and the solvent accessibility and disorder predicted from the protein sequence. Our method outperforms seven modern crystallization propensity predictors on three, independent from training dataset, benchmark test datasets. The strong predictive performance offered by the CRYSpred is attributed to the careful design of the features, utilization of the comprehensive set of inputs, and the usage of the Support Vector Machine classifier. The inputs utilized by CRYSpred are well-aligned with the existing rules-of-thumb that are used in the structural genomics studies. Keywords: crystallization, crystallization propensity prediction, protein structure, structural genomics, target selection, X-ray crystallography, CRYSpred, benchmark test datasets, rules-of-thumb, PDB, SG, TargetDB, PSI-BLAST, ROC, MCC" @default.
• W2328398973 created "2016-06-24" @default.
• W2328398973 creator A5040777177 @default.
• W2328398973 creator A5088423732 @default.
• W2328398973 date "2012-01-01" @default.
• W2328398973 modified "2023-10-16" @default.
• W2328398973 title "CRYSpred: Accurate Sequence-Based Protein Crystallization Propensity Prediction Using Sequence-Derived Structural Characteristics" @default.
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• W2328398973 doi "https://doi.org/10.2174/092986612798472910" @default.
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• W2328398973 hasPublicationYear "2012" @default.
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