  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. More
strikingly, the prediction method presented here is robust enough to identify
individual conformational outliers from high resolution structures in a protein
data bank without providing its structural factors. We envisage that our amino
acid side chain predictor could be used as a quality check step for future
protein structure model validation and many other potential applications such
as side chain assignment in Cryo-electron microscopy, crystallography model
auto-building, protein folding and small molecule ligand docking.
