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 shows which regions of 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.
Two types of evaluation can be carried out. “Internal” evaluation of self-consistency checks whether or not a model satisfies the restraints used to calculate it. “External” evaluation depends on information that was not used in the calculation of the model.
Validation of developed structures can be done using tools such as;
ERRAT is a web application which intends to assist users in model-building or in structure checking. The application investigates the statistics of pairwise atomic interactions and is able to take into account six different noncovalently bonded atom-atom interactions: CC, CN, CO, NN, NO, and 00. ERRAT analyzes the statistics of non-bonded atom-atom interactions in the reported structure (compared to a database of reliable high-resolution structures and plots the value of the error function versus position of a 9-residue sliding window, calculated by a comparison with statistics from highly refined structures.
The plot for a final model is generated in it. Regions of the structure that can be rejected at the 95% confidence level are yellow; 5% of a good protein structure is expected to have an error value above this level. Regions that can be rejected at the 99% level are shown in red. According to the analysis by ERRAT, the final model is significantly improved relative to the initial model.
Generally speaking, the method is sensitive to smaller errors than 3-D Profile analysis, but is more forgiving than Procheck.
Once the 3D structures were generated, evaluation of the structure and stereochemical analysis is performed using different types of validation tools. A study of backbone conformation of all models was evaluated by the analysis of Ramachandran plot using RAMPAGE program. ERRAT tool finds the overall quality factor of the proteins. ERRAT plot gives statistics of the error in the structure for each residue in the 3D protein structure model.
The overall quality factor value should be above than 95% that shows the built model would have a resolution of not more than 3 Å.