Abstract
BackgroundAccurate estimation of the isoelectric point (pI) based on the amino acid sequence is useful for many analytical biochemistry and proteomics techniques such as 2-D polyacrylamide gel electrophoresis, or capillary isoelectric focusing used in combination with high-throughput mass spectrometry. Additionally, pI estimation can be helpful during protein crystallization trials.ResultsHere, I present the Isoelectric Point Calculator (IPC), a web service and a standalone program for the accurate estimation of protein and peptide pI using different sets of dissociation constant (pKa) values, including two new computationally optimized pKa sets. According to the presented benchmarks, the newly developed IPC pKa sets outperform previous algorithms by at least 14.9 % for proteins and 0.9 % for peptides (on average, 22.1 % and 59.6 %, respectively), which corresponds to an average error of the pI estimation equal to 0.87 and 0.25 pH units for proteins and peptides, respectively. Moreover, the prediction of pI using the IPC pKa’s leads to fewer outliers, i.e., predictions affected by errors greater than a given threshold.ConclusionsThe IPC service is freely available at http://isoelectric.ovh.org Peptide and protein datasets used in the study and the precalculated pI for the PDB and some of the most frequently used proteomes are available for large-scale analysis and future development.ReviewersThis article was reviewed by Frank Eisenhaber and Zoltán GáspáriElectronic supplementary materialThe online version of this article (doi:10.1186/s13062-016-0159-9) contains supplementary material, which is available to authorized users.
Highlights
Accurate estimation of the isoelectric point based on the amino acid sequence is useful for many analytical biochemistry and proteomics techniques such as 2-D polyacrylamide gel electrophoresis, or capillary isoelectric focusing used in combination with high-throughput mass spectrometry
Comparison to other algorithms To compare the performance of Isoelectric Point Calculator 15, other Dissociation constant (pKa) sets and two programs based on support vector machines (SVM) and artificial neural networks (ANN) were tested
In most cases the order of the method’s performance on both training and testing datasets is similar; for instance the change in the order on the protein dataset can be seen for the Dawson and Bjellqvist pKa sets, which is within the error margin
Summary
Accurate estimation of the isoelectric point (pI) based on the amino acid sequence is useful for many analytical biochemistry and proteomics techniques such as 2-D polyacrylamide gel electrophoresis, or capillary isoelectric focusing used in combination with high-throughput mass spectrometry. The procedure relies on physicochemical properties of amino acids such as a molecular mass or a charge. But still widely used technique is 2-D polyacrylamide gel electrophoresis (2D-PAGE) [1, 2], where proteins are separated in two dimensions on a gel and identified using estimated molecular weight and isoelectric point (pI is the pH value at which the net charge of a macromolecule is zero, and its electrophoretic mobility is stopped). 2D-PAGE has been today replaced in many cases by gel-free techniques such as
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