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Isoelectric points of erabutoxins and monoacyl derivatives of erabutoxin b. Estimation of the pK values of amino groups in erabutoxins by using isoelectric-focusing data.

ABSTRACT: The isoelectric points of erabutoxins a, b and c, neurotoxic proteins of a sea snake, Laticauda semifasciata, were determined by density-gradient isoelectric focusing. The same measurement was also made with monoacyl derivatives of erabutoxin b, in which each one of all amino groups had been either acetylated or propionylated. Erabutoxins a and b showed the same isoelectric point at pH 9.68. The values for ]1-N alpha-acetyl-arginine]-, [15-N6-acetyl-lysine]-, [27-N6-acetyl-lysine]-, [47-N6-propionyl-lysine]- and [51-N6-acetyl-lysine]-erabutoxin b were at pH 9.52, 9.31, 9.45, 9.22 and 9.09 respectively, being definitely different from each other and lower than the value for the unmodified molecule. The isoelectric point of erabutoxin c, which is [51-asparagine]-erabutoxin b, was the same as that of [51-N6-acetyl-lysine]erabutoxin b. Assuming that no change in pK occurs on monoacylation, the pK values of amino groups in erabutoxin b were calculated from the isoelectric-point data. It is indicated that the pK values of zeta-amino groups differ markedly from each other and that the value of alpha-amino group is anomalously high.

SUBMITTER: Ui N

PROVIDER: S-EPMC1158247 | biostudies-other | 1982 May

REPOSITORIES: biostudies-other

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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

Isoelectric points of erabutoxins and monoacyl derivatives of erabutoxin b. Estimation of the pK values of amino groups in erabutoxins by using isoelectric-focusing data.

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