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.