Abstract

Coronaviruses have emerged as a global health threat due to their ability to cause severe respiratory infections, as exemplified by the COVID-19 pandemic. Understanding the molecular mechanisms underlying viral transmission is crucial for developing effective strategies to control outbreaks. In this article, we explore the role of protein intrinsic disorder in coronaviruses and its potential impact on viral transmission. We discuss the use of bioinformatics tools to predict intrinsic disorder in viral proteins and its implications for viral replication, host interactions, and immune evasion. By gaining insights into the structural dynamics of coronaviruses, we hope to provide a basis for developing targeted therapies and preventive measures against future coronavirus outbreaks.

This categorization enables quick identification of viruses with similar behaviors in transmission, regardless of genetic proximity. Based on this analysis, an empirical model for predicting the viral transmission behavior is developed. This model is able to explain some behavioral aspects of important coronaviruses that previously were not fully understood. The new predictor can be a useful tool for better epidemiological, clinical, and structural understanding of behavior of both newly emerging viruses and viruses that have been known for a long time.