(9g) High Quality Protein Structure Prediction Using Equivariant Convoluted Networks with Applications in Drug Design and Next Generation Biomaterials
AIChE Annual Meeting
2020
2020 Virtual AIChE Annual Meeting
Materials Engineering and Sciences Division
Biomaterials and Life Sciences Engineering: Faculty Candidates I
Monday, November 16, 2020 - 9:15am to 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.