Accurate estimation of isoelectric point of protein and peptide based on amino acid sequences

Audain, Enrique, Ramos, Yassel, Hermjakob, Henning, Flower, Darren R. and Perez-Riverol, Yasset (2016). Accurate estimation of isoelectric point of protein and peptide based on amino acid sequences. Bioinformatics, 32 (6), pp. 821-827.

Abstract

Motivation: 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. Results: 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. Contact: yperez@ebi.ac.uk Availability and Implementation: The software and data are freely available at https://github.com/ypriverol/pIR. Supplementary information: Supplementary data are available at Bioinformatics online.

Publication DOI: https://doi.org/10.1093/bioinformatics/btv674
Divisions: Life & Health Sciences > Pharmacy
Life & Health Sciences
Life & Health Sciences > Chronic and Communicable Conditions
Additional Information: © The Author 2015. Published by Oxford University Press. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited. Funding: BBSRC ‘PROCESS’ grant (BB/K01997X/1). Supplementary data available on the journal website.
Uncontrolled Keywords: Biochemistry,Molecular Biology,Computational Theory and Mathematics,Computer Science Applications,Computational Mathematics,Statistics and Probability
Full Text Link: http://bioinfor ... ontent/32/6/821
Related URLs: http://www.scop ... tnerID=8YFLogxK (Scopus URL)
Published Date: 2016-03-15
Authors: Audain, Enrique
Ramos, Yassel
Hermjakob, Henning
Flower, Darren R. ( 0000-0002-8542-7067)
Perez-Riverol, Yasset

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