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- W4292995108 abstract "Understanding the interfacial behaviors of biomolecules is crucial to applications in biomaterials and nanoparticle-based biosensing technologies. In this work, we utilized autoencoder-based graph clustering to analyze discontinuous molecular dynamics (DMD) simulations of lysozyme adsorption on a graphene surface. Our high-throughput DMD simulations integrated with a Go̅-like protein-surface interaction model makes it possible to explore protein adsorption at a large temporal scale with sufficient accuracy. The graph autoencoder extracts a low-dimensional feature vector from a contact map. The sequence of the extracted feature vectors is then clustered, and thus the evolution of the protein molecule structure in the absorption process is segmented into stages. Our study demonstrated that the residue-surface hydrophobic interactions and the π-π stacking interactions play key roles in the five-stage adsorption. Upon adsorption, the tertiary structure of lysozyme collapsed, and the secondary structure was also affected. The folding stages obtained by autoencoder-based graph clustering were consistent with detailed analyses of the protein structure. The combination of machine learning analysis and efficient DMD simulations developed in this work could be an important tool to study biomolecules' interfacial behaviors." @default.
- W4292995108 created "2022-08-25" @default.
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- W4292995108 date "2022-08-24" @default.
- W4292995108 modified "2023-09-23" @default.
- W4292995108 title "Graph Clustering Analyses of Discontinuous Molecular Dynamics Simulations: Study of Lysozyme Adsorption on a Graphene Surface" @default.
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- W4292995108 doi "https://doi.org/10.1021/acs.langmuir.2c01331" @default.
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