IPC 2.0: prediction of isoelectric point and pKa dissociation constants

The isoelectric point is the pH at which a particular molecule is electrically neutral due to the equilibrium of positive and negative charges. In proteins and peptides, this depends on the dissociation constant (pK(a)) of charged groups of seven amino acids and NH+ and COO- groups at polypeptide termini. Information regarding isoelectric point and pK(a) is extensively used in two-dimensional gel electrophoresis (2D-PAGE), capillary isoelectric focusing (cIEF), crystallisation, and mass spectrometry. Therefore, there is a strong need for the in silico prediction of isoelectric point and pK(a) values. In this paper, I present Isoelectric Point Calculator 2.0 (IPC 2.0), a web server for the prediction of isoelectric points and pK(a) values using a mixture of deep learning and support vector regression models. The prediction accuracy (RMSD) of IPC 2.0 for proteins and peptides outperforms previous algorithms: 0.848 versus 0.868 and 0.222 versus 0.405, respectively. Moreover, the IPC 2.0 prediction of pK(a) using sequence information alone was better than the prediction from structure-based methods (0.576 versus 0.826) and a few folds faster. The IPC 2.0 webserver is freely available at www.ipc2-isoelectric-point.org

Automated summary

The isoelectric point is the pH at which a molecule is electrically neutral, dependent on factors like charged groups from amino acids in proteins and peptides, and is used in experiments like 2D gel electrophoresis and computational predictions. IPC 2.0, utilizing a mix of deep learning and support vector regression models, outperforms previous algorithms in predicting proteins and peptides' isoelectric points with higher accuracy. Additionally, the web server's prediction of pK(a) values using sequence data alone is better and faster than structure-based methods.