Information-Entropy-Based Single Amino Acid Polymorphism Analysis Reveals Functional Variance of Enterovirus 2A Proteases

Enterovirus alphacoxsackie (EV-A) is a highly diverse viral species containing at least 25 serotypes with diverse biological and clinical characteristics. EV-A can cause diseases ranging from asymptomatic infections to severe neurological disorders, as well as mucocutaneous diseases such as hand, foot, and mouth disease. 2A, a cysteine protease expressed by EV-A, plays critical roles in virus–host interactions. Although 2A orthologs of different EV-A serotypes share consistent protease-catalytic motifs and cleavage patterns, they show functional diversity in interacting with cellular proteins, probably due to distinct protease-independent activities determined by the single amino acid polymorphisms (SAPs) among different 2A orthologs. However, routine sequence alignment and phylogenetic analysis can hardly identify the key SAP sites (kSAPs) contributing to the functional variance, mainly due to the high conservation of the proteins and the unequal weight of SAPs in determining protein function. Herein, we developed Single Amino Acid Polymorphism Statistics (SAAPS), an information-entropy (IE)-based algorithmic pipeline, to identify the functional kSAPs of EV-A 2A. The core principle of the algorithm is that the IE of the kSAPs can be neither too low (highly conserved sites not leading to variance) nor too high (random neutral mutations). Using SAAPS, we identified 56 kSAPs from 2A of 25 EV-A serotypes. Based on the kSAPs, the 2As can be clustered into three major groups with a few outliers, which was distinct from the clustering generated by phylogenetic analysis using the whole amino acid sequences. Functional verification with transcriptomic profiles of HEK-293T cells expressing different 2A variants revealed closer alignment of kSAP clustering than phylogenetic clustering. Notably, EV-A89, an outlier identified by kSAP clustering but not phylogenetic clustering, showed a unique expression pattern with an altered shift in the molecular weight, which suggested that it was related to three SAPs identified by SAAPS. This study presents SAAPS as a useful tool for prioritizing functionally relevant SAPs to guide mechanistic discovery and can be applied to highly conserved proteins like EV-A 2A.