Predicting Antigenic Distance from Genetic Data for PRRSV-Type 1: Applications of Machine Learning.

ABSTRACT: The control of porcine reproductive and respiratory syndrome (PRRS) remains a significant challenge due to the genetic and antigenic variability of the causative virus (PRRSV). Predominantly, PRRSV management includes using vaccines and live virus inoculations to confer immunity against PRRSV on farms. While understanding cross-protection among strains is crucial for the continued success of these interventions, understanding how genetic diversity translates to antigenic diversity remains elusive. We developed machine learning algorithms to estimate antigenic distance in silico, based on genetic sequence data, and identify differences in specific amino acid sites associated with antigenic differences between viruses. First, we obtained antigenic distance estimates derived from serum

SUBMITTER: Makau DN 

PROVIDER: S-EPMC9927307 | biostudies-literature | 2023 Feb

REPOSITORIES: biostudies-literature