Prediction of cross-species infection propensities of viruses with receptor similarity.

Studies of host factors that affect susceptibility to viral infections have led to the possibility of determining the risk of emerging infections in potential host organisms. In this study, we constructed a computational framework to estimate the probability of virus transmission between potential hosts based on the hypothesis that the major barrier to virus infection is differences in cell-receptor sequences among species. Information regarding host susceptibility to virus infection was collected to classify the cross-species infection propensity between hosts. Evolutionary divergence matrices and a sequence similarity scoring program were used to determine the distance and similarity of receptor sequences. The discriminant analysis was validated with cross-validation methods. The results showed that the primary structure of the receptor protein influences host susceptibility to cross-species viral infections. Pair-wise distance, relative distance, and sequence similarity showed the best accuracy in identifying the susceptible group. Based on the results of the discriminant analysis, we constructed ViCIPR (http://lcbb3.snu.ac.kr/ViCIPR/home.jsp), a server-based tool to enable users to easily extract the cross-species infection propensities of specific viruses using a simple two-step procedure. Our sequence-based approach suggests that it may be possible to identify virus transmission between hosts without requiring complex structural analysis. Due to a lack of available data, this method is limited to viruses whose receptor use has been determined. However, the significant accuracy of predictive variables that positively and negatively influence virus transmission suggests that this approach could be improved with further analysis of receptor sequences.