A Prediction Model for Uncoating Receptor Usage in Human Enteroviruses Based on Amino Acid Sequences and a Naive Bayes Algorithm.

This study constructed a bioinformatics prediction algorithm for human enterovirus uncoating receptors based on amino acid sequences and physicochemical properties. Based on the availability of uncoating receptor information and three-dimensional (3D) structural data, human enterovirus serotypes were classified into training, validation, and prediction datasets. Using amino acid sequences of receptor-binding sites and their physicochemical properties as model features, a prediction model was constructed using the Naive Bayes algorithm and bioinformatic network analysis method. The results showed that both the training and validation datasets achieved a prediction accuracy of 100%. Among the 56 serotypes in the prediction dataset, the vast majority utilized seven known types of uncoating receptors (e.g., SCARB2, CAR, and ICAM-1), while a minority of serotypes may share the same novel, unknown receptor. This study indicates that uncoating receptors can be accurately predicted based on the amino acid sequences and physicochemical properties of human enteroviruses. Furthermore, the three-dimensional structural features at receptor-binding sites can be reflected through corresponding amino acid sequences and their physicochemical properties. This study facilitates a more in-depth investigations of enterovirus pathogenic mechanisms and provides important insights for the development of vaccines and antiviral drugs.