Iterative Optimal TM_Score and Z_Score Guided Sampling Significantly Improves Model Topology

Protein structure prediction is the major goal to match up the count of known protein sequences and solved protein structures. Current template based modelling methodologies (TBM) rely on selection of structural folds or evolutionary related templates from already solved experimental structures. Even when the first predicted model conformation from correctly selected template(s) through any of the employed modelling algorithm is correct, the increased model sampling seems ruining the initial model’s topology.Increased sampling is not biased towards the correct nearnative state for a target sequence. Model assessment measures employed during model sampling also pose a huge problem in the reliable selection of the most accurate decoy among the generated ones for a protein sequence. Such persisting model sampling issues are thus carefully studied and streamlined to consistently yield highly accurate models for the majority of protein sequences. A TM and Z score guided sampling algorithm is designed to solve this problem in a logically efficient manner to make the predictions come closer to the actual native conformation for a target sequence. Our sampling methodology yields an average GDT-HA,TM_Score improvement of 4.802 and 0.031 respectively for 21 CASP8 TBM-HA targets (35 Domains), against their best predicted CASP8 models, and thereby our models are found accurate not only for the individual domain(s) but also for the complete overall conformation of the target sequence.

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