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The Augmenting Effects of Desolvation and Conformational Energy Terms on the Predictions of Docking Programs against mPGES-1.

Gupta A, Chaudhary N, Kakularam KR, Pallu R, Polamarasetty A - PLoS ONE (2015)

Bottom Line: Later, rescoring method was developed as empirical sum of normalised values of docking scores, LogP and Nrotb.The results clearly indicated that LogP and Nrotb recuperate the predictions of these docking programs.The accurate prediction of binding affinities for analogues of the same compounds is a major challenge for many of the existing docking programs; in the present study the high correlation obtained for experimental and predicted pIC50 values for the test set compounds validates the efficiency of the scoring method.

View Article: PubMed Central - PubMed

Affiliation: Centre for Computational Biology and Bioinformatics, School of Life Sciences, Central University of Himachal Pradesh, Dharamshala, Himachal Pradesh- 176215, India.

ABSTRACT
In this study we introduce a rescoring method to improve the accuracy of docking programs against mPGES-1. The rescoring method developed is a result of extensive computational study in which different scoring functions and molecular descriptors were combined to develop consensus and rescoring methods. 127 mPGES-1 inhibitors were collected from literature and were segregated into training and external test sets. Docking of the 27 training set compounds was carried out using default settings in AutoDock Vina, AutoDock, DOCK6 and GOLD programs. The programs showed low to moderate correlation with the experimental activities. In order to introduce the contributions of desolvation penalty and conformation energy of the inhibitors various molecular descriptors were calculated. Later, rescoring method was developed as empirical sum of normalised values of docking scores, LogP and Nrotb. The results clearly indicated that LogP and Nrotb recuperate the predictions of these docking programs. Further the efficiency of the rescoring method was validated using 100 test set compounds. The accurate prediction of binding affinities for analogues of the same compounds is a major challenge for many of the existing docking programs; in the present study the high correlation obtained for experimental and predicted pIC50 values for the test set compounds validates the efficiency of the scoring method.

No MeSH data available.


Structure of training set compounds.
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pone.0134472.g001: Structure of training set compounds.

Mentions: For this study 127 inhibitors of mPGES-1 were selected randomly from literature and BRENDA [24] database. All the structures were prepared in Accelrys Draw and optimized initially using HF method in R.E.D server [25–29] and further optimized using DFT based method i.e. B3LYP/6-31G(d) [30, 31] in Gaussian09 [29] to get the lowest energy conformations. The lowest energy conformations from Gaussian were further used for docking. The dataset was further segregated into training set (27 compounds) (Fig 1) and external test set (100 compounds) (Fig A,B,C in S1 File).


The Augmenting Effects of Desolvation and Conformational Energy Terms on the Predictions of Docking Programs against mPGES-1.

Gupta A, Chaudhary N, Kakularam KR, Pallu R, Polamarasetty A - PLoS ONE (2015)

Structure of training set compounds.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4549307&req=5

pone.0134472.g001: Structure of training set compounds.
Mentions: For this study 127 inhibitors of mPGES-1 were selected randomly from literature and BRENDA [24] database. All the structures were prepared in Accelrys Draw and optimized initially using HF method in R.E.D server [25–29] and further optimized using DFT based method i.e. B3LYP/6-31G(d) [30, 31] in Gaussian09 [29] to get the lowest energy conformations. The lowest energy conformations from Gaussian were further used for docking. The dataset was further segregated into training set (27 compounds) (Fig 1) and external test set (100 compounds) (Fig A,B,C in S1 File).

Bottom Line: Later, rescoring method was developed as empirical sum of normalised values of docking scores, LogP and Nrotb.The results clearly indicated that LogP and Nrotb recuperate the predictions of these docking programs.The accurate prediction of binding affinities for analogues of the same compounds is a major challenge for many of the existing docking programs; in the present study the high correlation obtained for experimental and predicted pIC50 values for the test set compounds validates the efficiency of the scoring method.

View Article: PubMed Central - PubMed

Affiliation: Centre for Computational Biology and Bioinformatics, School of Life Sciences, Central University of Himachal Pradesh, Dharamshala, Himachal Pradesh- 176215, India.

ABSTRACT
In this study we introduce a rescoring method to improve the accuracy of docking programs against mPGES-1. The rescoring method developed is a result of extensive computational study in which different scoring functions and molecular descriptors were combined to develop consensus and rescoring methods. 127 mPGES-1 inhibitors were collected from literature and were segregated into training and external test sets. Docking of the 27 training set compounds was carried out using default settings in AutoDock Vina, AutoDock, DOCK6 and GOLD programs. The programs showed low to moderate correlation with the experimental activities. In order to introduce the contributions of desolvation penalty and conformation energy of the inhibitors various molecular descriptors were calculated. Later, rescoring method was developed as empirical sum of normalised values of docking scores, LogP and Nrotb. The results clearly indicated that LogP and Nrotb recuperate the predictions of these docking programs. Further the efficiency of the rescoring method was validated using 100 test set compounds. The accurate prediction of binding affinities for analogues of the same compounds is a major challenge for many of the existing docking programs; in the present study the high correlation obtained for experimental and predicted pIC50 values for the test set compounds validates the efficiency of the scoring method.

No MeSH data available.