Figure: Side chain flexible docking model (in green) enables the chimera design of two ligand fragments (in yellow and pink).
A bisubstrate nicotinamide N-methyltransferase (NNMT) inhibitor was developed with the aid of Accutar Biotech’s platform recently. The binding pose of the designed inhibitor with human recombinant NNMT was predicted by Accutar Biotech’s side chain flexible docking method. The docking result was further validated by crystallization experiments.
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.
Using a deep neural network architecture without physics-based assumptions, Accutar Biotech has demonstrated that side chain conformation prediction accuracy can be improved by more than 25%, especially for aromatic residues compared with current standard methods.
