After a model is built, it is important to check it for possible errors. The quality of a model can be approximately predicted from the sequence similarity between the target and the template. Sequence identity above 30% is a relatively good predictor of the expected accuracy of a model. However, other factors, including the environment, can strongly influence the accuracy of a model. For example, some calcium-binding proteins undergo large conformational changes when bound to calcium. If a calcium-free template is used to model the calcium-bound state of a target, it is likely that the model will be incorrect irrespective of the target-template similarity. This estimate also uses to determination of protein structure by experiment; a structure must be determined in the functionally meaningful environment. If the target-template sequence identity falls below 30%, the sequence identity becomes significantly less reliable as a measure of expected accuracy of a single model. The reason is that below 30% sequence identity, models are often obtained that deviate significantly, in both directions, from the average accuracy. It is in such cases that model evaluation methods are most informative.
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;
Stuctural Analysis and Verification Server (SAVS) is present at http://nihserver.mbi.ucla.edu/SAVES/ SAVS, uses following servers to check the quality of the protein structures:
Procheck: It checks the stereochemical quality of a protein structure by analyzing residue-by-residue geometry and overall geometry of structure.
What_Check: It is derived from a subset of protein verification tools from the WHATIF program, this does extensive checking of many sterochemical parameters of the residues in the model.
ERRAT: Analyzes the statistics of non-bonded interactions between different atom types 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.
Verify3D: Determines the compatibility of an atomic model (3D) with its own amino acid sequence (1D) by assigned a structural class based on its location and environment (alpha, beta, loop, polar, nonpolar etc) and comparing the results to good structures.
Prove: Calculates the volumes of atoms in macromolecules using an algorithm which treats the atoms like hard spheres and calculates a statistical Z-score deviation for the model from highly resolved (2.0 Å or better) and refined (R-factor of 0.2 or better) PDB-deposited structures.
Ramachandran plot computed by PROCHECK module of SAVES showed that only fractional value in percentage of residues existed in disallowed regions confirming the quality of protein model predicted to be highly significant.