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Title
Japanese: 
English:Protein Model Quality Estimation using Molecular Dynamics Simulation 
Author
Japanese: 石田貴士, KURNIAWAN Jason.  
English: Takashi Ishida, Jason Kurniawan.  
Language English 
Journal/Book name
Japanese: 
English:ACS OMEGA 
Volume, Number, Page        
Published date July 5, 2022 
Publisher
Japanese: 
English:American Chemical Society 
Conference name
Japanese: 
English: 
Conference site
Japanese: 
English: 
Official URL https://pubs.acs.org/doi/full/10.1021/acsomega.2c01475
 
DOI https://doi.org/10.1021/acsomega.2c01475
Abstract The estimation of protein model quality remains a challenging task and is important for protein structural model utilization. In the last decade, existing methods that rely on machine learning to deep learning have been developed and shown progressive improvement. Despite utilizing more sophisticated techniques and introducing new features, none of these methods employ explicit protein structure stability information. Hypothetically, protein model quality might be indicated by its structural stability in an in silico system disclosed by the structural difference from its initial structure. One of the possible methods to exploit such information is by implementing molecular dynamics simulations that have shown successful applications in many research fields. We present a novel approach by introducing explicit protein structure stability information using molecular dynamics simulation. Despite using only simple features, small data with no training process required, and a short molecular dynamics simulation time, our method shows comparable performance to the state-of-the-art deep learning-based method.

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