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About ProQ2
Protein Quality Predictors

Computational modeling of three-dimensional structure of protein molecules is an important research area that has the potential to dramatically accelerate the determination of protein structures, both in terms of purely predicted structures and as part of an otherwise experimental pipeline. An increase in the number of proteins for which high quality structural data is available would greatly enhance our ability to understand biological function, redesign proteins of interest and develop new drugs, which might even be peptides themselves (ProQ2) . The score presented in the result list is the ProQ2 score.

ProQ2 predicts the so called S-score for each individual residue (You will get a plot over this score when you click the link on each score.) The S-score is a transformation of the normal RMSD for each residue using the following formula: S_i=(1/sqrt(1+RMSD_i^2/9)), where RMSD_i is the local RMSD deviation for residue i based on a global superposition trying to maximize essentially the sum of S-score over the whole model (in reality the superposition is a trade off between getting a high sum of S-score and the length of the structural alignment).

   
References
Improved model quality assessment using ProQ2. Arjun Ray, Erik Lindahl and Björn Wallner. BMC Bioinformatics 2012, 13:224, doi:10.1186/1471-2105-13-224 PDF.

 
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