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Marginal protein stability drives subcellular proteome isoelectric point.

ABSTRACT: There exists a positive correlation between the pH of subcellular compartments and the median isoelectric point (pI) for the associated proteomes. Proteins in the human lysosome-a highly acidic compartment in the cell-have a median pI of ?6.5, whereas proteins in the more basic mitochondria have a median pI of ?8.0. Proposed mechanisms reflect potential adaptations to pH. For example, enzyme active site general acid/base residue pKs are likely evolved to match environmental pH. However, such effects would be limited to a few residues on specific proteins, and might not affect the proteome at large. A protein model that considers residue burial upon folding recapitulates the correlation between proteome pI and environmental pH. This correlation can be fully described by a neutral evolution process; no functional selection is included in the model. Proteins in acidic environments incur a lower energetic penalty for burying acidic residues than basic residues, resulting in a net accumulation of acidic residues in the protein core. The inverse is true under alkaline conditions. The pI distributions of subcellular proteomes are likely not a direct result of functional adaptations to pH, but a molecular spandrel stemming from marginal stability.

SUBMITTER: Loell K

PROVIDER: S-EPMC6243250 | biostudies-literature | 2018 Nov

REPOSITORIES: biostudies-literature

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Marginal protein stability drives subcellular proteome isoelectric point.

Loell Kaiser K Nanda Vikas V

Proceedings of the National Academy of Sciences of the United States of America 20181101 46

There exists a positive correlation between the pH of subcellular compartments and the median isoelectric point (pI) for the associated proteomes. Proteins in the human lysosome-a highly acidic compartment in the cell-have a median pI of ∼6.5, whereas proteins in the more basic mitochondria have a median pI of ∼8.0. Proposed mechanisms reflect potential adaptations to pH. For example, enzyme active site general acid/base residue p<i>K</i>s are likely evolved to match environmental pH. However, s ...[more]

PMID: 30385634

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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

Marginal protein stability drives subcellular proteome isoelectric point.

Publications

Marginal protein stability drives subcellular proteome isoelectric point.

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