Understanding protein–protein interactions (PPIs) are critical for advancements in drug discovery, enzyme engineering, and systems biology. However, translating the structural insights provided by models like AlphaFold into actionable annotations remains a significant challenge. This paper introduces Structural-Linguistic Protein Interaction Modeling (SLPIM), an innovative approach that bridges 3D protein structure modeling with the interpretative capabilities of large language models (LLMs). By leveraging multimodal learning, SLPIM provides enriched annotations and insights, fostering a deeper understanding of PPIs and their implications. The approach is validated on well-documented datasets, showcasing its potential for expanding the horizons of protein interaction research.