Modeling Human Protein Physical Interactions Involved in HIV Attachment In Silico

The human immunodeficiency virus (HIV) remains a major global health challenge. A promising therapeutic strategy involves identifying human proteins capable of physically blocking viral entry by interacting with key components of the HIV attachment system. To address this challenge systematically, we developed a computational pipeline for prioritizing protein-protein interaction and applied it to identify host proteins interacting with the viral glycoprotein gp120 and cellular receptors (CD4, CCR5, CXCR4, CCR2). Our approach combined large-scale interaction modeling using AlphaFold 3 with a comprehensive comparative analysis framework. We screened a panel of 55 candidate human proteins selected through integrated bioinformatics analysis. The pipeline incorporated model confidence assessment, quantitative contact analysis, and normalization against reference interactions to generate a robust ranking of candidates. Key findings reveal several important patterns. Chemokine CCL27 uniquely demonstrated high binding potential to both CCR5 co-receptor and viral gp120, suggesting its potential for dual-blockade capability. Analysis of natural ligand interactions with chemokine receptors showed marked disparity: CC-chemokine family members exhibited significantly greater binding capacity for CCR5 and CCR2 receptors compared to CXC-family ligand interactions with CXCR4. This binding imbalance may potentially drive selective viral pressure and influence tropism evolution during disease progression. We also identified potential interactions between HIV entry components and neuropeptides including PNOC and NPY, as well as various membrane receptors beyond classical coreceptors. Furthermore, cluster analysis revealed clear separation between receptor-type and ligand-type interactors, supporting the biological plausibility of our predictions. While acknowledging limitations related to model refinement, this study provides a systematically ranked set of candidate targets for HIV therapeutic development. Beyond identifying specific HIV interaction candidates, this study establishes a generalizable computational pipeline for the prioritization of protein-protein interaction in pathogen-host systems, effectively bridging large-scale modeling.