Abstract
BackgroundIn the backdrop of challenge to obtain a protein structure under the known limitations of both experimental and theoretical techniques, the need of a fast as well as accurate protein structure evaluation method still exists to substantially reduce a huge gap between number of known sequences and structures. Among currently practiced theoretical techniques, homology modelling backed by molecular dynamics based optimization appears to be the most popular one. However it suffers from contradictory indications of different validation parameters generated from a set of protein models which are predicted against a particular target protein. For example, in one model Ramachandran Score may be quite high making it acceptable, whereas, its potential energy may not be very low making it unacceptable and vice versa. Towards resolving this problem, the main objective of this study was fixed as to utilize a simple experimentally derived output, Surface Roughness Index of concerned protein of unknown structure as an intervening agent that could be obtained using ordinary microscopic images of heat denatured aggregates of the same protein.ResultIt was intriguing to observe that direct experimental knowledge of the concerned protein, however simple it may be, might give insight on acceptability of its particular structural model out of a confusion set of models generated from database driven comparative technique for structure prediction. The result obtained from a widely varying structural class of proteins indicated that speed of protein structure evaluation can be further enhanced without compromising with accuracy by recruiting simple experimental output.ConclusionIn this work, a semi-empirical methodological approach was provided for improving protein structure evaluation. It showed that, once structure models of a protein were obtained through homology technique, the problem of selection of a best model out of a confusion set of Pareto-optimal structures could be resolved by employing a structure agent directly obtainable through experiment with the same protein as experimental ingredient. Overall, in the backdrop of getting a reasonably accurate protein structure of pathogens causing epidemics or biological warfare, such approach could be of use as a plausible solution for fast drug design.
Highlights
In the backdrop of challenge to obtain a protein structure under the known limitations of both experimental and theoretical techniques, the need of a fast as well as accurate protein structure evaluation method still exists to substantially reduce a huge gap between number of known sequences and structures
To check coherency in protein structure validation parameters, results of calculation of above-referred validation parameters, Energy Score as Knowledge Based Scoring Function (KBSF), Ramachandran Score (RS), G factor (GF) and Verified 3D (% residue) (V3D) were shown in Table 2 for all the protein models obtained through Modeller along with their protein data bank (PDB) structures
The values of Surface Roughness Index (SRI) both calculated from known structure and predicted through experiment for the proteins were shown in Table 3 from which parameters DCMOD and DPMOD were calculated
Summary
In the backdrop of challenge to obtain a protein structure under the known limitations of both experimental and theoretical techniques, the need of a fast as well as accurate protein structure evaluation method still exists to substantially reduce a huge gap between number of known sequences and structures. Among currently practiced theoretical techniques, homology modelling backed by molecular dynamics based optimization appears to be the most popular one It suffers from contradictory indications of different validation parameters generated from a set of protein models which are predicted against a particular target protein. In search of such parameter, in this study first it was identified that Surface Roughness Index (SRI) of a protein as derived, calculated from its known structure by Singha et al [10] might be utilized as common structure parameter since it could be extracted through experiment on the same protein as depicted by Mishra et al [11] In this regard the role of predicted SRI was to serve as a standard parameter that can be compared for its closeness with the values calculated from the predicted models to pick the best structure solution under the premise: closest model was the best one. The final validation of the selected model was done by comparing root mean square deviation (RMSD) of backbones of all models with that of reported PDB structure of the target protein
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