Machine Learning Enables Comprehensive Prediction of the Relative Protein Abundance of Multiple Proteins on the Protein Corona

Overview

Journal Research (Wash D C)

Specialty Biology

Date 2024 Sep 26

PMID 39324017

Authors

Xiuhao Fu

Chao Yang

Yunyun Su

Chunling Liu

Haoye Qiu

Yanyan Yu

Gaoxing Su

Qingchen Zhang

Leyi Wei

Feifei Cui

Quan Zou

Zilong Zhang

Affiliations

Soon will be listed here.

Abstract

Understanding protein corona composition is essential for evaluating their potential applications in biomedicine. Relative protein abundance (RPA), accounting for the total proteins in the corona, is an important parameter for describing the protein corona. For the first time, we comprehensively predicted the RPA of multiple proteins on the protein corona. First, we used multiple machine learning algorithms to predict whether a protein adsorbs to a nanoparticle, which is dichotomous prediction. Then, we selected the top 3 performing machine learning algorithms in dichotomous prediction to predict the specific value of RPA, which is regression prediction. Meanwhile, we analyzed the advantages and disadvantages of different machine learning algorithms for RPA prediction through interpretable analysis. Finally, we mined important features about the RPA prediction, which provided effective suggestions for the preliminary design of protein corona. The service for the prediction of RPA is available at http://www.bioai-lab.com/PC_ML.

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