Decoding classical swine fever virus-swine protein interactions: a bioinformatics approach to targeted viral control.

Abstract

This study mapped and analyzed protein-protein interactions (PPIs) between classical swine fever virus (CSFV) proteins and 19 swine host proteins to uncover key molecular mechanisms underlying viral infection and pathogenesis. An integrated bioinformatics workflow was implemented in Google Colab, combining structural modeling, binding affinity estimation, and network analysis to predict and evaluate interactions involving CSFV proteins (E1, E2, NS5A, protein C, etc.) and host proteins such as signalosome subunit 2, IQGAP1, and MYO1B. Predicted interaction scores (in silico KD equivalents in the range of 10⁻⁷-10⁻¹⁰ M) were used to rank the strength of viral-host complexes. These values represent computational estimates and should not be interpreted as absolute biophysical constants. CSFV proteins exhibited a hierarchical interaction network, with E2, E1, NS5A, and protein C showing high predicted interaction scores with central host proteins. Signalosome subunit 2 interacted with 12 viral proteins, indicating its potential pivotal role in viral replication and immune evasion. IQGAP1 and MYO1B also emerged as major hubs in the predicted network. Multivariable and network analyses revealed predicted interaction patterns within this curated set of host proteins. The Colab-based computational approach provided a scalable platform for large-scale PPI prediction and analysis, showing strong inter-protein agreement across models. The study presents an in silico interaction map of selected CSFV-swine host proteins, identifying signalosome subunit 2 and IQGAP1 as high-centrality candidate targets that warrant experimental validation.