D2N: Distance to the native

• Development of distance to native (D2N) metric for predicting RMSD, TM and GDT
• Prediction does not use native structure.
• Correlation coefficient reached 0.90 between predicted and original RMSDs.
• D2N captures the best model among top 5 in 95% of cases.

Root-mean-square-deviation (RMSD), of computationally-derived protein structures from experimentally determined structures, is a critical index to assessing protein-structure-prediction-algorithms (PSPAs). The development of PSPAs to obtain 0 Å RMSD from native structures is considered central to computational biology. However, till date it has been quite challenging to measure how far a predicted protein structure is from its native — in the absence of a known experimental/native structure. In this work, we report the development of a metric “D2N” (distance to the native) — that predicts the “RMSD” of any structure without actually knowing the native structure. By combining physico-chemical properties and known universalities in spatial organization of soluble proteins to develop D2N, we demonstrate the ability to predict the distance of a proposed structure to within ± 1.5 Ǻ error with a remarkable average accuracy of 93.6% for structures below 5 Ǻ from the native. We believe that this work opens up a completely new avenue towards assigning reliable structures to whole proteomes even in the absence of experimentally determined native structures. The D2N tool is freely available at http://www.scfbio-iitd.res.in/software/d2n.jsp.