In the last two decades, a group of proteins whose mutations are associated with a disease manifested by episodes of muscle weakness (periodic paralysis), changes in heart rhythm (arrhythmia), and developmental abnormalities has been under constant study. This malady is known as Andersen-Tawil syndrome, with ~60% of cases of this syndrome being caused by 16 mutations in the KCNJ2 gene [UniProt ID: P63252-01-P63252-17]. In this work, we present a computational study designed to obtain a fingerprint of Andersen-Tawil mutated proteins and differentiate them from mutated proteins associated with Brugada syndrome and from functional groups of proteins belonging to APD3, UniProt, and CPPsite databases. We show here that Andersen-Tawil mutated proteins are characterized by specific features that can be used to differentiate, with a high level of certainty (90%), proteins carrying these mutations from similar functional groups, such as mutated proteins associated with Brugada syndrome, and from different functional protein and peptide groups, such as antimicrobial peptides, Cell-Penetrating Peptides, and intrinsically disorder proteins. Therefore, our main results allow us to conjecture that it is possible to identify the group of the Andersen-Tawil mutated proteins by their "PIM profile". Furthermore, when we applied this "fingerprint PIM profile" on the UniProt database, we observed that one protein found in humans [UniProt ID: Q9NZV8], and six of all "reviewed" proteins found in living organisms, possess a very similar PIM profile as the Andersen-Tawil mutated protein group. The bioinformatics "fingerprint" of the Andersen-Tawil mutated proteins was retrieved using the in-house bioinformatics system named Polarity Index Method® and supported-at residues level- by the algorithms for the prediction of intrinsic disorder predisposition, such as PONDR® FIT, PONDR® VLXT, PONDR® VSL2, PONDR® VL3, FoldIndex, IUPred, and TopIDP.
Keywords: Andersen–Tawil syndrome; Ion channels; PIM profile; bioinformatics; channelopathies; high performance computing; mutations; proteins; structural bioinformatics.