ArticleInternational journal of molecular sciences2012
Correlation between protein sequence similarity and crystallization reagents in the biological macromolecule crystallization database.
Article in International journal of molecular sciences, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Shoebill: an interpretable AlphaFold2-informed predictor of protein crystallization propensity using XGBoost.Briefings in bioinformatics · 2026Article
- Using AlphaFold and Symmetrical Docking to Predict Protein-Protein Interactions for Exploring Potential Crystallization Conditions.Proteins · 2025Article
- Computational crystallization.Archives of biochemistry and biophysics · 2016Review
- Analysis of crystallization data in the Protein Data Bank.Acta crystallographica. Section F, Structural biology communications · 2015Article
- A drunken search in crystallization space.Acta crystallographica. Section F, Structural biology communications · 2014Article
- An investigation of the effects of self-assembled monolayers on protein crystallisation.International journal of molecular sciences · 2013Article
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Authors and funding
5 authors.
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Abstract
The protein structural entries grew far slower than the sequence entries. This is partly due to the bottleneck in obtaining diffraction quality protein crystals for structural determination using X-ray crystallography. The first step to achieve protein crystallization is to find out suitable chemical reagents. However, it is not an easy task. Exhausting trial and error tests of numerous combinations of different reagents mixed with the protein solution are usually necessary to screen out the pursuing crystallization conditions. Therefore, any attempts to help find suitable reagents for protein crystallization are helpful. In this paper, an analysis of the relationship between the protein sequence similarity and the crystallization reagents according to the information from the existing databases is presented. We extracted information of reagents and sequences from the Biological Macromolecule Crystallization Database (BMCD) and the Protein Data Bank (PDB) database, classified the proteins into different clusters according to the sequence similarity, and statistically analyzed the relationship between the sequence similarity and the crystallization reagents. The results showed that there is a pronounced positive correlation between them. Therefore, according to the correlation, prediction of feasible chemical reagents that are suitable to be used in crystallization screens for a specific protein is possible.
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