A fundamental problem in biochemistry and molecular biology is understanding the spatial structure of macromolecules and then analyzing their functions. In this study, the three-dimensional structure of a ribosome-inactivatiug protein luffin-α was predicted using a neural network method and molecular dynamics simulation. A feedforward neural network with the backpropagation learning algorithm were trained on model class of homologous proteins including trichosanthin and α-momorcharin. The distance constraints for the Cα atoms in the protein backbone were utilized to generate a folded crude conformation of luffin-α by model building and the steepest descent minimization approach. The crude conformation was refined by molecular dynamics techniques and a simulated annealing procedure. The interaction between luffin-α and its analogous substrate GAGA was also simulated to understand its action mechanism.
Scopus Subject Areas
- 3D structure
- neural network
- Ribosome-inactivating proteins
- substrate interaction