Paper Title
Prioritization of Modelled Protein Structures using Pareto Points and Topsis
Abstract
It is conceivable to produce large number of models for a given protein sequence. Ranking the predicted models
accurately and choosing the best predicted model from the hopeful pool stay as challenging assignments. The
physiochemical properties of the protein affect the nature of their structure, these properties are used to distinguish native or
native-like structure. In this study, Pareto points technique and TOPSIS method are used. These methods are analyzed with
four qualitative parameters i.e. Root-mean-square deviation for the entire target structure (RMS_CA), Template Modelling
Score (TM-Score), Global Distance Test Total Score (GDT_TS) and Z-Score[D] to determine the TOPSIS Score for ranking
these modelled protein structures.
In similar work, protein structure prediction center can predict the ranking of modelled protein structures according to only
one qualitative parameter i.e. GDT_TS Score but in the present work four qualitative parameters are used for ranking these
modelled protein structures. This research work mainly focuses on the selection of the best modelled protein structures from
the pool of decoy in the absence of its true native structure using Pareto points technique and TOPSIS method, Pareto points
technique is used for finding the optimal modelled protein structures and the output of Pareto points technique is further used
by TOPSIS method for find the best modelled protein structure from the decoy of optimal modelled protein structures.
TOPSIS method uses four qualitative parameters to generate a single score value that is used to rank the modelled protein
structures and select the best modelled protein structures from the decoy.
Index Terms - Protein Structure Prediction, Pareto points and TOPSIS Method.