De Novo Protein Structure Prediction - Successful de Novo Modeling Requirements

Successful De Novo Modeling Requirements

De novo conformation predictors usually function by producing candidate conformations (decoys) and then choosing amongst them based on their thermodynamic stability and energy state. Most successful predictors will have the following three factors in common:

1) An accurate energy function that corresponds the most thermodynamically stable state to the native structure of a protein

2) An efficient search method capable of quickly identify low-energy states through conformational search

3) The ability to select native-like models from a collection of decoy structures

De novo programs will search three dimensional space and, in the process, produce candidate protein confirmations. As a protein approaches its correctly folded, native state, entropy and free energy will decrease. Using this information, de novo predictors can discriminate amongst decoys. Specifically, de novo programs will select possible confirmations with lower free energies – which are more likely to be correct than those structures with higher free energies. As stated by David A. Baker in regards to how his de novo Rosetta predictor works, “during folding, each local segment of the chain flickers between a different subset of local conformations…folding to the native structure occurs when the conformations adopted by the local segments and their relative orientations allow…low energy features of native protein structures. In the Rosetta algorithm…the program then searches for the combination of these local conformations that has the lowest overall energy.”