Dataset Information

Using isoelectric point to determine the pH for initial protein crystallization trials.

ABSTRACT:

Motivation

The identification of suitable conditions for crystallization is a rate-limiting step in protein structure determination. The pH of an experiment is an important parameter and has the potential to be used in data-mining studies to help reduce the number of crystallization trials required. However, the pH is usually recorded as that of the buffer solution, which can be highly inaccurate.

Results

Here, we show that a better estimate of the true pH can be predicted by considering not only the buffer pH but also any other chemicals in the crystallization solution. We use these more accurate pH values to investigate the disputed relationship between the pI of a protein and the pH at which it crystallizes.

Availability and implementation

Data used to generate models are available as Supplementary Material.

Contact

julie.wilson@york.ac.uk

Supplementary information

Supplementary data are available at Bioinformatics online.

SUBMITTER: Kirkwood J

PROVIDER: S-EPMC4410668 | biostudies-literature | 2015 May

REPOSITORIES: biostudies-literature

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Publications

Using isoelectric point to determine the pH for initial protein crystallization trials.

Kirkwood Jobie J Hargreaves David D O'Keefe Simon S Wilson Julie J

Bioinformatics (Oxford, England) 20150107 9

<h4>Motivation</h4>The identification of suitable conditions for crystallization is a rate-limiting step in protein structure determination. The pH of an experiment is an important parameter and has the potential to be used in data-mining studies to help reduce the number of crystallization trials required. However, the pH is usually recorded as that of the buffer solution, which can be highly inaccurate.<h4>Results</h4>Here, we show that a better estimate of the true pH can be predicted by cons ...[more]

PMID: 25573921

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Project description:We previously described the photoactivated depot or PAD approach that allows for the light control of therapeutic protein release. This approach relies on the ability to use light to change a protein's solubility. Traditionally this was accomplished by linking the protein to an insoluble but injectable polymer via a light cleaved linker. This allows the injected material to remain at the site of injection, until transcutaneous irradiation breaks the link between polymer and protein, permitting the protein to be absorbed. However, there are multiple problems associated with polymer based approaches: The polymer makes up a majority of the material, making it inefficient. In addition, after protein release, the polymer has to be cleared from the body, a significant design challenge. In this work, we create materials that form photoactivated depots of insulin without the need for polymers, by linking photolysis to an isoelectric point shift, which itself is linked to a solubility shift. Specifically, we linked basic groups to insulin via a light cleaved linker. These shift the normal pI of insulin from 5.4 to approximately 7. The result of this incorporation are materials that are completely soluble in mildly acidic solutions but precipitate upon injection into a pH 7 environment, i.e., the skin. We successfully synthesized four such modified insulins, demonstrating that their pI values were shifted in the expected manner. We then analyzed one of them, P2-insulin, in detail, demonstrating that it behaves as designed: It is soluble in a formulation pH of 4, but precipitates at pH 7.2, its approximate pI value. Upon irradiation, the photocleavable link to insulin is broken, and completely native and soluble insulin is released from the depot in a well behaved, first order fashion. These materials are 90% therapeutic, form completely soluble and injectable formulations in mildly acidic conditions, form insoluble depots at neutral pH, efficiently release soluble protein from these depots when irradiated, and leave behind only small easily absorbed molecules after irradiation. As such they approach ideality for photoactivated depot materials.

Project description:In any macromolecular polyprotic system-for example protein, DNA or RNA-the isoelectric point-commonly referred to as the pI-can be defined as the point of singularity in a titration curve, corresponding to the solution pH value at which the net overall surface charge-and thus the electrophoretic mobility-of the ampholyte sums to zero. Different modern analytical biochemistry and proteomics methods depend on the isoelectric point as a principal feature for protein and peptide characterization. Protein separation by isoelectric point is a critical part of 2-D gel electrophoresis, a key precursor of proteomics, where discrete spots can be digested in-gel, and proteins subsequently identified by analytical mass spectrometry. Peptide fractionation according to their pI is also widely used in current proteomics sample preparation procedures previous to the LC-MS/MS analysis. Therefore accurate theoretical prediction of pI would expedite such analysis. While such pI calculation is widely used, it remains largely untested, motivating our efforts to benchmark pI prediction methods.Using data from the database PIP-DB and one publically available dataset as our reference gold standard, we have undertaken the benchmarking of pI calculation methods. We find that methods vary in their accuracy and are highly sensitive to the choice of basis set. The machine-learning algorithms, especially the SVM-based algorithm, showed a superior performance when studying peptide mixtures. In general, learning-based pI prediction methods (such as Cofactor, SVM and Branca) require a large training dataset and their resulting performance will strongly depend of the quality of that data. In contrast with Iterative methods, machine-learning algorithms have the advantage of being able to add new features to improve the accuracy of prediction.yperez@ebi.ac.ukThe software and data are freely available at https://github.com/ypriverol/pIRSupplementary information: Supplementary data are available at Bioinformatics online.

Dataset Information

Using isoelectric point to determine the pH for initial protein crystallization trials.

Motivation

Results

Availability and implementation

Contact

Supplementary information

Publications

Using isoelectric point to determine the pH for initial protein crystallization trials.

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