Probabilistic divergence of a TBM methodology from the ideal protocol

Abstract

AbstractProtein structural information is essential for the detailed mapping of a functional protein network. For a higher modelling accuracy and quicker implementation, template based algorithms have been extensively deployed and redefined. The methods only assess the predicted structure against its native state/template, and do not estimate the accuracy for each modelling step. A divergence measure is postulated to estimate the modelling accuracy against its theoretical optimal benchmark. By freezing the domain boundaries, the divergence measures are predicted for the most crucial steps of a modelling algorithm. To precisely refine the score using weighting constants, big data analysis could further be deployed.