Data-Driven Machine Learning Framework for the Regulation of Protein Adsorption on Surfaces

Protein adsorption on surfaces is a highly complex process, governed by intricate interactions between protein, surface and surrounding environment. However, accurately predicting protein adsorption amounts and precisely controlling adsorption behavior at surfaces remain significant challenges. In this work, we present a data-driven machine learning framework that systematically evaluates the determinants of protein adsorption, thereby enabling robust prediction of adsorption amount and adsorption behavior. Seven critical descriptors were selected and used in the AutoGluon framework to construct three optimal models, with the WeightedEnsemble_L2 (WE_L2) model exhibiting superior performance. Furthermore, SHAP analysis quantified and evaluated the contributions of the seven descriptors, revealing their respective promoting or inhibiting effects on protein adsorption along with their percentage impacts. Moreover, the WE_L2 model accurately predicts BSA adsorption on various SAM surfaces, demonstrating excellent agreement with experimental values. It also effectively captures the complex adsorption behavior of HSA, including bilayer formation, electrostatic shielding effects, and the influence of surface hydrophobicity. These findings demonstrate that interpretable machine learning models not only facilitate precise predictions of protein adsorption amounts, but also provide a powerful tool for regulating interfacial protein adsorption.