| STUDY |
With this information, Keating and Princeton University assistant computer science professor Mona Singh are now testing a machine-learning method for predicting interactions for these proteins. Keating said they've used the experimental bZIP data to train the prediction method, improving its abilities considerably. They also plan to use the experimental data to improve atomic-level models for coiled coil interactions. "These models will, in turn, improve our ability to do prediction and will also be useful for protein design calculations," Keating said. It's not yet entirely clear what characteristics make for good binding, she noted. Factors such as electrostatic charge complementarity, pairing of buried asparagine residues, and good hydrophobic packing at the helix-helix interface are known to be important. But Keating and Singh's computations suggest they're not the whole story. Rather, when the team considered interfacial residue-residue interactions (which have not traditionally been considered important), they were found to improve the performance of machine-learning algorithms |
| UPDATE | 10.03 |
| AUTHOR |
- Keating Amy E. (MIT) - Singh Mona (Princeton Uni.) |
| LITERATURE REF. | This data is not available for free |
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