CMS College researchers are developing a model to predict cross-species transmission of the virus

Researchers at CMS College in Kottayam have developed a computational framework capable of predicting the transmission of viruses from animals to humans.

The model, called SPHAK (Sequence-based Prediction of Host spillover by Analysis of k-mers), analyzes viral protein sequences to identify signals indicating the virus’ ability to cross species barriers. It examines short amino acid patterns, or k-mers, in viral proteins to detect host-associated signatures and generates a “spillover prediction” (SP) score. The finding was published in Nature Scientific Reports.

According to the researchers, SPHAK differs from traditional methods that rely on whole-genome analysis by targeting viral proteins that act as molecular “keys” interacting with host cell receptors. By working with a 20-amino acid protein alphabet, the system captures subtle evolutionary changes that may indicate the virus’s adaptation to a new host.

An SP score above 0.5 is used to indicate viruses with a higher probability of cross-species transmission, allowing early prioritization of samples for further study without the need for complete genome sequencing.

Accuracy over 97%.

The model was tested on more than 950 animal virus species and achieved more than 97% accuracy in identifying virus families. It also found early warning signals consistent with known zoonotic sources, with viruses from birds, bats and pigs showing strong spillover potential. In validation tests, SPHAK identified all known human-infecting influenza strains with 100% sensitivity and identified the bat-derived HKU5 coronavirus as a potential zoonotic threat.

The framework has also been extended to plant viruses to assess the risk of crop disease spread. The system targeted the capsid proteins that protect viral genetic material and was able to identify high-risk signatures that could help in early containment of plant infections.

The researchers say the tool could serve as an early warning system for both animal and plant disease outbreaks, helping to prioritize surveillance and preventative measures. The study was led by Vibin Ipe Thomas, assistant professor of chemistry, and involved a team including assistant professor of biotechnology Prisho Mariam Paul, student researchers Vinny NG, Ananya Prakash, S. Kavya and C. Rajalakshmi.

Published – 17 May 2026 20:11 IST