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Protein purification and crystallization artifacts: The tale usually not told.
Ewa Niedzialkowska1,2,3, Olga Gasiorowska1,3, Katarzyna B Handing1,3
1Department of Molecular Physiology and Biological Physics, University of Virginia School of Medicine, 1340 Jefferson Park Avenue, Jordan Hall, Room 4223, Charlottesville, Virginia, 22908.
Protein Science : a Publication of the Protein Society
|December 15, 2015
Summary
Protein sample misidentification and contamination can hinder experimental reproducibility. This study presents methods to identify such contaminants in crystallized proteins, aiding in troubleshooting and accurate structure determination.
Area of Science:
- Structural Biology
- Biochemistry
- Protein Science
Background:
- Protein sample misidentification or contamination is a significant cause of experimental irreproducibility.
- This issue can arise during recombinant protein expression, purification, or isolation from natural sources.
- Problems may remain undetected until late stages of structural determination or functional studies.
Purpose of the Study:
- To present reliable methods for identifying protein contaminants in crystallized samples.
- To provide a resource of common contaminant structures for use as search models.
- To offer troubleshooting guidance for phasing and model building challenges in structural biology.
Main Methods:
- Lattice parameter searching against known crystallographic structures.
- Sequence or fold identification from incomplete structural models.
- Molecular replacement using common contaminants as search templates.
Main Results:
- Successfully identified four instances of purification and crystallization artifacts using the described methods.
- Provided a curated list of common contaminant structures for use in molecular replacement.
- Demonstrated the utility of these methods in resolving structural determination issues.
Conclusions:
- The presented methods offer effective solutions for identifying protein contaminants in structural studies.
- Accurate protein identification is crucial for ensuring experimental reproducibility and data integrity.
- This work provides practical tools and strategies for researchers encountering phasing and model building difficulties.
