Protein Subcellular Localization Prediction Model Based on Graph Convolutional Network

Zhang, Tianhao; Gu, Jian; Wang, Zeyu; Wu, Chunguo; Shi, Xiaohu

doi:10.1007/s12539-022-00529-9

Cited by 7 publications

(1 citation statement)

References 33 publications

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“…To address this issue, we use interpretable machine learning (ML)-based approach to characterize the surfaces of HAC proteins by quantifying the contribution of the surface descriptors. Over the past few decades, ML techniques have been increasingly applied to predict protein–protein interactions [ 12 ], protein–ligand molecular docking [ 13 ], protein subcellular localization [ 14 ], and the 3D structure of proteins [ 15 ]. Despite significant advances in these areas, identifying protein surface characteristics using only a few representative physicochemical, structural, and geometrical descriptors remains challenging.…”

Section: Introductionmentioning

confidence: 99%

Application of Machine Learning in the Quantitative Analysis of the Surface Characteristics of Highly Abundant Cytoplasmic Proteins: Toward AI-Based Biomimetics

Moon,

Hu,

Hayashi

2024

Biomimetics

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Proteins in the crowded environment of human cells have often been studied regarding nonspecific interactions, misfolding, and aggregation, which may cause cellular malfunction and disease. Specifically, proteins with high abundance are more susceptible to these issues due to the law of mass action. Therefore, the surfaces of highly abundant cytoplasmic (HAC) proteins directly exposed to the environment can exhibit specific physicochemical, structural, and geometrical characteristics that reduce nonspecific interactions and adapt to the environment. However, the quantitative relationships between the overall surface descriptors still need clarification. Here, we used machine learning to identify HAC proteins using hydrophobicity, charge, roughness, secondary structures, and B-factor from the protein surfaces and quantified the contribution of each descriptor. First, several supervised learning algorithms were compared to solve binary classification problems for the surfaces of HAC and extracellular proteins. Then, logistic regression was used for the feature importance analysis of descriptors considering model performance (80.2% accuracy and 87.6% AUC) and interpretability. The HAC proteins showed positive correlations with negatively and positively charged areas but negative correlations with hydrophobicity, the B-factor, the proportion of beta structures, roughness, and the proportion of disordered regions. Finally, the details of each descriptor could be explained concerning adaptative surface strategies of HAC proteins to regulate nonspecific interactions, protein folding, flexibility, stability, and adsorption. This study presented a novel approach using various surface descriptors to identify HAC proteins and provided quantitative design rules for the surfaces well-suited to human cellular crowded environments.

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