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On-line amino acid-based capillary isoelectric focusing-ESI-MS/MS for protein digests analysis.

ABSTRACT: Six amino acids with pIs that ranged from 3.2 to 9.7 were used as ampholytes to establish a pH gradient in capillary isoelectric focusing. This amino acid-based capillary isoelectric focusing (cIEF) was coupled with ESI-MS/MS using an electrokinetically pumped sheath-flow interface for peptide analysis. Amino acid-based isoelectric focusing generates a two-order of magnitude lower background signal than commercial ampholytes in the important m/z range of 300-1800. Good focusing was achieved for insulin receptor, which produced ~10 s peak width. For 0.1 mg mL(-1) bovine serum albumin (BSA) digests, 24±1 peptides (sequence coverage 47±4%) were identified in triplicate analysis. As expected, the BSA peptides were separated according to their pI. The concentration detection limit for the BSA digests is 7 nM and the mass detection limit is 7 fmole. A solution of six bovine protein tryptic digests spanning 5 orders of magnitude in concentration was analyzed by amino acid based cIEF-ESI-MS/MS. Five proteins with a concentration range spanning 4 orders of magnitude were identified in triplicate runs. Using amino acid based cIEF-ESI-MS/MS, 112 protein groups and 303 unique peptides were identified in triplicate runs of a RAW 264.7 cell homogenate protein digest. In comparison with ampholyte based cIEF-ESI-MS/MS, amino acid based cIEF-ESI-MS/MS produces higher resolution of five acidic peptides, much cleaner mass spectra, and higher protein spectral counts.

SUBMITTER: Zhu G

PROVIDER: S-EPMC3474973 | biostudies-literature | 2012 Oct

REPOSITORIES: biostudies-literature

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On-line amino acid-based capillary isoelectric focusing-ESI-MS/MS for protein digests analysis.

Zhu Guijie G Sun Liangliang L Yang Ping P Dovichi Norman J NJ

Analytica chimica acta 20120427

Six amino acids with pIs that ranged from 3.2 to 9.7 were used as ampholytes to establish a pH gradient in capillary isoelectric focusing. This amino acid-based capillary isoelectric focusing (cIEF) was coupled with ESI-MS/MS using an electrokinetically pumped sheath-flow interface for peptide analysis. Amino acid-based isoelectric focusing generates a two-order of magnitude lower background signal than commercial ampholytes in the important m/z range of 300-1800. Good focusing was achieved for ...[more]

PMID: 23062442

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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

On-line amino acid-based capillary isoelectric focusing-ESI-MS/MS for protein digests analysis.

Publications

On-line amino acid-based capillary isoelectric focusing-ESI-MS/MS for protein digests analysis.

Similar Datasets

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