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

Jiajia Li1, Lanlan Qin2, Haijun Feng3

  • 1School of Chemistry and Chemical Engineering, Guangdong Provincial Key Lab for Green Chemical Product Technology, South China University of Technology, Guangzhou 510640, P. R. China.

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Summary

This study introduces a machine learning framework to predict protein adsorption on surfaces. Interpretable models accurately forecast adsorption amounts and behaviors, aiding in surface engineering and biomaterial design.

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Area of Science:

  • Surface Science
  • Biomaterials Engineering
  • Computational Chemistry

Background:

  • Protein adsorption is complex, influenced by protein, surface, and environmental factors.
  • Predicting and controlling protein adsorption remains a significant challenge in surface science.

Purpose of the Study:

  • To develop a data-driven machine learning framework for predicting protein adsorption.
  • To systematically evaluate determinants of protein adsorption and their impact on adsorption behavior.

Main Methods:

  • Utilized the AutoGluon framework with seven critical descriptors.
  • Constructed three optimal models, with WeightedEnsemble_L2 (WE_L2) showing superior performance.
  • Employed SHAP analysis to quantify descriptor contributions and their effects on protein adsorption.

Main Results:

  • The WE_L2 model accurately predicted Bovine Serum Albumin (BSA) adsorption on self-assembled monolayer (SAM) surfaces.
  • The model captured complex Human Serum Albumin (HSA) adsorption behaviors, including bilayer formation and electrostatic effects.
  • SHAP analysis revealed promoting and inhibiting effects of descriptors on protein adsorption.

Conclusions:

  • Interpretable machine learning models enable precise prediction of protein adsorption amounts.
  • This framework offers a powerful tool for regulating interfacial protein adsorption in biomaterial applications.