Prediction of amino acid side chain conformation using a deep neural network

Understand

A deep neural network based architecture was constructed to predict amino acid side chain conformation with unprecedented accuracy.

• Amino acid side chain conformation prediction is essential for protein homology modeling and protein design.
• Current widely-adopted methods use physics-based energy functions to evaluate side chain conformation.
• Here, using a deep neural network architecture without physics-based assumptions, we have demonstrated that side chain conformation prediction accuracy can be improved by more than 25%, especially for aromatic residues compared with current standard methods.