Distance geometry generates native‐like folds for small helical proteins using the consensus distances of predicted protein structures

For successful ab initio protein structure prediction, a method is needed to identify native‐like structures from a set containing both native and non‐native protein‐like conformations. In this regard, the use of distance geometry has shown promise when accurate inter‐residue distances are available. We describe a method by which distance geometry restraints are culled from sets of 500 protein‐like conformations for four small helical proteins generated by the method of Simons et al. (1997). A consensus‐based approach was applied in which every inter‐Cα distance was measured, and the most frequently occurring distances were used as input restraints for distance geometry. For each protein, a structure with lower coordinate root‐mean‐square (RMS) error than the mean of the original set was constructed; in three cases the topology of the fold resembled that of the native protein. When the fold sets were filtered for the best scoring conformations with respect to an all‐atom knowledge‐based scoring function, the remaining subset of 50 structures yielded restraints of higher accuracy. A second round of distance geometry using these restraints resulted in an average coordinate RMS error of 4.38 Å.