The ﬁnal homology model has to be evaluated to make sure that the structural features of the model are consistent with the physicochemical rules. This involves checking anomalies in φ–ψ angles, bond lengths, close contacts, and so on. Another way of checking the quality of a protein model is to implicitly take these stereochemical properties into account. This is a method that detects errors by compiling statistical proﬁles of spatial features and interaction energy from experimentally determined structures. By comparing the statistical parameters with the constructed model, the method reveals which regions of a sequence appear to be folded normally and which regions do not. If structural irregularities are found, the region is considered to have errors and has to be further reﬁned.
The three-dimensional (3D) profile of a protein structure is a table computed from the atomic coordinates of the structure that can be used to score the compatibility of the 3D structure model with any amino acid sequence. Three-dimensional profiles computed from correct protein structures match their own sequences with high scores. An incorrectly modeled segment in an otherwise correct structure can be identified by examining the profile score in a moving-window scan. Thus, the correctness of a protein model can be verified by its 3D profile, regardless of whether the model has been derived by X-ray, nuclear magnetic resonance (NMR), or computational procedures. For this reason, 3D profiles are useful in the evaluation of undetermined protein models, based on low-resolution electron-density maps, on NMR spectra with inadequate distance constraints, or on computational procedures. An advantage of using 3D profiles for testing models is that profiles have not themselves been used in the determination of the structure. Traditional R-factor tests in X-ray analysis depend on the comparison of observed properties—that is, the X-ray structure factor magnitudes with the same property calculated from the final protein model.
Verify3D classifies each residue in the protein into one of the 18 classes according to the residue’s structural environment in the input model. The propensity of each amino acid to exist in each such structural environment class is calculated according to statistics collected from structures in the PDB, and the final score given to the protein structure is the sum of propensities of the individual residues.
Verify3D is another server using the statistical approach. It uses a pre-computed database containing eighteen environmental proﬁles based on secondary structures and solvent exposure, compiled from high-resolution protein structures. To assess the quality of a protein model, the secondary structure and solvent exposure propensity of each residue are calculated. If the parameters of a residue fall within one of the proﬁles, it receives a high score, otherwise a low score. The result is a two-dimensional graph illustrating the folding quality of each residue of the protein structure. The threshold value is normally set at zero. Residues with scores below zero are considered to have an unfavorable environment.
VERIFY3D process by assigning a structural class based on the location and environment of each residue position and by comparing the results to good structures. Environments of residues correspond to three parameters: the local secondary structure, the area of the residue that is buried and the fraction of side-chain area covered by polar atoms.