ArticleScientific reports2016
Crysalis: an integrated server for computational analysis and design of protein crystallization.
Article in Scientific reports, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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Who cites it
22 citing papers in PubMed.
- MrtR ofProceedings of the National Academy of Sciences of the United States of America · 2026Article
- Shoebill: an interpretable AlphaFold2-informed predictor of protein crystallization propensity using XGBoost.Briefings in bioinformatics · 2026Article
- RP3Net: a deep learning model for predicting recombinant protein production in Escherichia coli.Bioinformatics (Oxford, England) · 2026Article
- PLMC: Language Model of Protein Sequences Enhances Protein Crystallization Prediction.Interdisciplinary sciences, computational life sciences · 2024Article
- Deep learning applications in protein crystallography.Acta crystallographica. Section A, Foundations and advances · 2024Review
- TLCrys: Transfer Learning Based Method for Protein Crystallization Prediction.International journal of molecular sciences · 2022Article
- Sequence-Based Prediction of Transmembrane Protein Crystallization Propensity.Interdisciplinary sciences, computational life sciences · 2021Article
- BCrystal: an interpretable sequence-based protein crystallization predictor.Bioinformatics (Oxford, England) · 2020Article
- Toward more accurate prediction of caspase cleavage sites: a comprehensive review of current methods, tools and features.Briefings in bioinformatics · 2019Article
- ccPDB 2.0: an updated version of datasets created and compiled from Protein Data Bank.Database : the journal of biological databases and curation · 2019Article
- 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
- Implications of Newly Identified Brain eQTL Genes and Their Interactors in Schizophrenia.Molecular therapy. Nucleic acids · 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
- PVP-SVM: Sequence-Based Prediction of Phage Virion Proteins Using a Support Vector Machine.Frontiers in microbiology · 2018Article
- Identification and Analysis of Blood Gene Expression Signature for Osteoarthritis With Advanced Feature Selection Methods.Frontiers in genetics · 2018Article
- PIP-EL: A New Ensemble Learning Method for Improved Proinflammatory Peptide Predictions.Frontiers in immunology · 2018Article
- Survey of Predictors of Propensity for Protein Production and Crystallization with Application to Predict Resolution of Crystal Structures.Current protein & peptide science · 2018Article
- Machine learning to design integral membrane channelrhodopsins for efficient eukaryotic expression and plasma membrane localization.PLoS computational biology · 2017Article
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
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/.
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