Unveiling protein corona composition:predicting with resampling embedding and machine learning  

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作  者:Rong Liao Yan Zhuang Xiangfeng Li Ke Chen Xingming Wang Cong Feng Guangfu Yin Xiangdong Zhu Jiangli Lin Xingdong Zhang 

机构地区:[1]College of Biomedical Engineering,National Engineering Research Centre for Biomaterials,Sichuan University,Chengdu,610065,China

出  处:《Regenerative Biomaterials》2024年第1期27-33,共7页再生生物材料(英文版)

基  金:sponsored by the National Key Research and Development Program of China(2021YFB3802100,2021YFB3802105);the Major Project of Sichuan Science and Technology Department(2022ZDZX0029);the Miaozi Project of Sichuan Science and Technology Department(2023JDRC0097)。

摘  要:Biomaterials with surface nanostructures effectively enhance protein secretion and stimulate tissue regeneration.When nanoparticles(NPs)enter the living system,they quickly interact with proteins in the body fluid,forming the protein corona(PC).The accurate prediction of the PC composition is critical for analyzing the osteoinductivity of biomaterials and guiding the reverse design of NPs.However,achieving accurate predictions remains a significant challenge.Although several machine learning(ML)models like Random Forest(RF)have been used for PC prediction,they often fail to consider the extreme values in the abundance region of PC absorption and struggle to improve accuracy due to the imbalanced data distribution.In this study,resampling embedding was introduced to resolve the issue of imbalanced distribution in PC data.Various ML models were evaluated,and RF model was finally used for prediction,and good correlation coefficient(R^(2))and root-mean-square deviation(RMSE)values were obtained.Our ablation experiments demonstrated that the proposed method achieved an R^(2) of 0.68,indicating an improvement of approximately 10%,and an RMSE of 0.90,representing a reduction of approximately 10%.Furthermore,through the verification of label-free quantification of four NPs:hydroxyapatite(HA),titanium dioxide(TiO_(2)),silicon dioxide(SiO_(2))and silver(Ag),and we achieved a prediction performance with an R^(2) value>0.70 using Random Oversampling.Additionally,the feature analysis revealed that the composition of the PC is most significantly influenced by the incubation plasma concentration,PDI and surface modification.

关 键 词:NANOPARTICLES protein corona machine learming resampling technique feature analysis 

分 类 号:TP181[自动化与计算机技术—控制理论与控制工程]

 

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