(9g) High Quality Protein Structure Prediction Using Equivariant Convoluted Networks with Applications in Drug Design and Next Generation Biomaterials
- Conference: AIChE Annual Meeting
- Year: 2020
- Proceeding: 2020 Virtual AIChE Annual Meeting
- Group: Materials Engineering and Sciences Division
- Time: Monday, November 16, 2020 - 9:15am-9:30am
However, none of these methods are able to resolve (a) loop structures, and (b) cis/ trans orientations of amino acids (because it is difficult two learn from the bi-modal distribution of w (omega), which is 0° for cis and 180° for trans amino acids). We hereby propose a novel method which will learn rotations and torsions, in addition to inter-residue distances and dihedrals to predict a distogram which not only encodes information between residue i and i+1, but also information about all possible NC2 information using an equivariant neural network (see Figure 1). Subsequently, the best fit Ca-trace would be obtained that meet the distance, dihedral, rotational, and torsional constraints. Finally, in-built functions of PyRosetta would be used to build a PDB structure of the protein with appropriate rotamer-repacking to obtain a lowest energy structure.
As potential application of this tool, I envision applications ranging from therapeutic drug design, to next generation materials for CAR-T cell therapy or for precise bioseparations and drug delivery using stable ensembles of block copolymers and designed channel proteins.