ArticleActa crystallographica. Section D, Biological crystallography2014
Improving the chances of successful protein structure determination with a random forest classifier.
Article in Acta crystallographica. Section D, Biological crystallography, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.
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Who cites it
27 citing papers in PubMed.
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- A Structural Systems Biology Approach to High-Risk CG23 Klebsiella pneumoniae.Microbiology resource announcements · 2023Article
- Predictive Model of Functional Exercise Compliance of Patients with Breast Cancer Based on Decision Tree.International journal of women's health · 2023Article
- Sequence-Based Prediction of Transmembrane Protein Crystallization Propensity.Interdisciplinary sciences, computational life sciences · 2021Article
- Insights into the Structure of Rubisco from Dinoflagellates-In Silico Studies.International journal of molecular sciences · 2021Article
- Regioselectivity of hyoscyamine 6β-hydroxylase-catalysed hydroxylation as revealed by high-resolution structural information and QM/MM calculations.Dalton transactions (Cambridge, England : 2003) · 2020Article
- BCrystal: an interpretable sequence-based protein crystallization predictor.Bioinformatics (Oxford, England) · 2020Article
- Critical evaluation of bioinformatics tools for the prediction of protein crystallization propensity.Briefings in bioinformatics · 2018Review
- TMCrys: predict propensity of success for transmembrane protein crystallization.Bioinformatics (Oxford, England) · 2018Article
- A statistical model for improved membrane protein expression using sequence-derived features.The Journal of biological chemistry · 2018Article
- fDETECT webserver: fast predictor of propensity for protein production, purification, and crystallization.BMC bioinformatics · 2018Article
- Survey of Predictors of Propensity for Protein Production and Crystallization with Application to Predict Resolution of Crystal Structures.Current protein & peptide science · 2018Article
- Guidelines for the successful generation of protein-ligand complex crystals.Acta crystallographica. Section D, Structural biology · 2017Review
- The "Sticky Patch" Model of Crystallization and Modification of Proteins for Enhanced Crystallizability.Methods in molecular biology (Clifton, N.J.) · 2017Review
- Structural Analysis Provides Mechanistic Insight into Nicotine Oxidoreductase from Pseudomonas putida.Biochemistry · 2016Article
- Is unphosphorylated Rex, as multifunctional protein of HTLV-1, a fully intrinsically disordered protein? An in silico study.Biochemistry and biophysics reports · 2016Article
- Computational crystallization.Archives of biochemistry and biophysics · 2016Review
- Crysalis: an integrated server for computational analysis and design of protein crystallization.Scientific reports · 2016Article
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3 authors.
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Abstract
Obtaining diffraction quality crystals remains one of the major bottlenecks in structural biology. The ability to predict the chances of crystallization from the amino-acid sequence of the protein can, at least partly, address this problem by allowing a crystallographer to select homologs that are more likely to succeed and/or to modify the sequence of the target to avoid features that are detrimental to successful crystallization. In 2007, the now widely used XtalPred algorithm [Slabinski et al. (2007), Protein Sci. 16, 2472-2482] was developed. XtalPred classifies proteins into five `crystallization classes' based on a simple statistical analysis of the physicochemical features of a protein. Here, towards the same goal, advanced machine-learning methods are applied and, in addition, the predictive potential of additional protein features such as predicted surface ruggedness, hydrophobicity, side-chain entropy of surface residues and amino-acid composition of the predicted protein surface are tested. The new XtalPred-RF (random forest) achieves significant improvement of the prediction of crystallization success over the original XtalPred. To illustrate this, XtalPred-RF was tested by revisiting target selection from 271 Pfam families targeted by the Joint Center for Structural Genomics (JCSG) in PSI-2, and it was estimated that the number of targets entered into the protein-production and crystallization pipeline could have been reduced by 30% without lowering the number of families for which the first structures were solved. The prediction improvement depends on the subset of targets used as a testing set and reaches 100% (i.e. twofold) for the top class of predicted targets.
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