Author: Serena H. Chen; M. Todd Young; John Gounley; Christopher Stanley; Debsindhu Bhowmik
Title: Distinct Structural Flexibility within SARS-CoV-2 Spike Protein Reveals Potential Therapeutic Targets Document date: 2020_4_18
ID: klb8oe9q_20
Snippet: To further understand the molecular structures of different human coronavirus S proteins and the oligomeric state of SARS-CoV-2 S protein, we deployed a custom-built deep learning architecture, a convolutional variational autoencoder (CVAE), to encode the high dimensional protein structures from the MD simulations into lower dimensional latent spaces. The goal of our AI method is to reduce the high dimensionality of the molecular system while pre.....
Document: To further understand the molecular structures of different human coronavirus S proteins and the oligomeric state of SARS-CoV-2 S protein, we deployed a custom-built deep learning architecture, a convolutional variational autoencoder (CVAE), to encode the high dimensional protein structures from the MD simulations into lower dimensional latent spaces. The goal of our AI method is to reduce the high dimensionality of the molecular system while preserving the inherent characteristics of the system and learning novel behavior in a latent space that is normally distributed. The direct comparison between the decoded and original input data ensures the accuracy of the latent space representation. This customized CVAE approach has been successfully applied to study the folding pathways of small proteins and structural clustering of biomolecules [26] - [28] .
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