Determinants of protein corona adsorption and abundance revealed by interpretable machine learning across nanoparticle systems.

Nanoparticles (NPs) hold significant potential in biotechnology, including molecular sensing, controlled release systems, and therapeutic applications. However, their behavior in biological environments remains difficult to predict because proteins rapidly absorb onto NP surfaces, forming a protein corona (PC) that reshapes their surface properties and determines their biological identity, transport, and cellular interactions. In this study, we developed large-scale deep neural network (DNN) models to predict both protein adsorption (binary classification) and relative protein abundance (regression) on NP surfaces. We utilized a well-curated and comprehensive PC dataset comprising data from 83 peer-reviewed studies, 817 NP-PC samples, and 2,497 proteins, substantially expanding the scale and diversity compared with prior studies. Then, we employed a prevalence-based filtering strategy to mitigate sparsity and batch noise and trained over 200 machine learning models across proteins. The adsorption classification models achieved high discriminative performance (AUC = 0.96), while the abundance models achieved a pooled R² of 0.67 and an average per-protein R