Abstract
Current practice of validating predicted protein structural model is knowledge-based where scoring parameters are derived from already known structures to obtain decision on validation out of this structure information. For example, the scoring parameter, Ramachandran Score gives percentage conformity with steric-property higher value of which implies higher acceptability. On the other hand, Force-Field Energy Score gives conformity with energy-wise stability higher value of which implies lower acceptability. Naturally, setting these two scoring parameters as target objectives sometimes yields a set of multiple models for the same protein for which acceptance based on a particular parameter, say, Ramachandran score, may not satisfy well with the acceptance of the same model based on other parameter, say, energy score. The confusion set of such models can further be resolved by introducing some parameters value of which are easily obtainable through experiment on the same protein. In this piece of work it was found that the confusion regarding final acceptance of a model out of multiple models of the same protein can be removed using a parameter Surface Rough Index which can be obtained through semi-empirical method from the ordinary microscopic image of heat denatured protein.
Highlights
Protein structure validation is as important a task as to obtain its structure through either experiments like X-Ray Crystallography or NMR or by Homology or Threading based prediction methods
While utility of Surface Roughness Index (SRI) to SRI distance of models from original structure can be understood for finalizing acceptance of a single model, the difficulty of this formalism is that it requires SRI derived from original structure
In absence of experimentally evaluated original structure we require reference SRI that can be derived by some other simpler means
Summary
Protein structure validation is as important a task as to obtain its structure through either experiments like X-Ray Crystallography or NMR or by Homology or Threading based prediction methods. Importance and limitation of knowledgebased validation of protein structure is well documented in the review of Kihara et al (2009) [1]. In this context, Semiempirical validation model for protein structure is a new idea being introduced in this work. There exists reports on attempts based on semiempirical strategy to unearth structural information of many protein related events [2] worked on use of semiempirical methods for building geometric model of proteins. Möhle et al (2001) [3] showed utility of semi-empirical method to improve efficiency in deducing secondary structure of peptides and proteins. Huey et al (2007) [6] claimed successful development and testing of semiempirical force field for incorporation in AutoDock formalism
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