Introduction: The Nipah virus, first identified in Malaysia in 1999, has been responsible for recurrent outbreaks across Asia, particularly in Bangladesh and India, with reported case fatality rates ranging from 40% to 75%. This places NiV among the most lethal zoonotic viruses known. According to WHO data, over 700 confirmed human cases and 400 deaths have been recorded globally. NiV poses a significant public health threat due to its dual transmission modes (zoonotic and human-to-human), high virulence, potential for international spread via travel, absence of approved therapeutics, and lack of population-level immunity. These factors underscore its potential to cause widespread epidemics. Objective: This study aimed to develop computational approaches for the structural and functional characterization of Nipah virus envelope glycoproteins, enabling their differentiation from other protein families. Methods: We implemented a bioinformatics pipeline integrating computational genomics tools to evaluate intrinsic disorder propensity in glycoprotein sequences. Specifically, we analyzed protein intrinsic disorder profiles (PIDP) (glycoprotein intrinsic disorder predisposition) and polarity patterns using PIM 3.0v (polarity index method). Results: Comparative analysis of PIM 3.0v and PIDP outputs revealed distinct structural signatures in NiV glycoproteins. These profiles facilitated the identification of conserved morphological and biophysical traits, highlighting unique features that distinguish NiV glycoproteins from other viral proteins. Conclusion: Our findings demonstrated PIM 3.0v as an effective tool for the computational discrimination of Nipah virus glycoproteins. This work can contribute to molecular virology and epidemic preparedness by providing a framework for rapid pathogen characterization. Further validation could enhance its utility in diagnostics and vaccine design.
Polanco et al. (Sun,) studied this question.