Antidiabetic Properties of Azardiracta indica and Bougainvillea spectabilis: In Vivo Studies in Murine Diabetes Model

Diabetes mellitus is a metabolic syndrome characterized by an increase in the blood glucose level. Treatment of diabetes is complicated due to multifactorial nature of the disease. Azadirachta indica Adr. Juss and Bougainvillea spectabilis are reported to have medicinal values including antidiabetic properties. In the present study using invivo diabetic murine model, A. indica and B. spectabilis chloroform, methanolic and aqueous extracts were investigated for the biochemical parameters important for controlling diabetes. It was found that A. indica chloroform extract and B. spectabilis aqueous, methanolic extracts showed a good oral glucose tolerance and significantly reduced the intestinal glucosidase activity. Interestingly, A. indica chloroform and B. spectabilis aqueous extracts showed significant increase in glucose-6-phosphate dehydrogenase activity and hepatic, skeletal muscle glycogen content after 21 days of treatment. In immunohistochemical analysis, we observed a regeneration of insulin-producing cells and corresponding increase in the plasma insulin and c-peptide levels with the treatment of A. indica chloroform and B. spectabilis aqueous, methanolic extracts. Analyzing the results, it is clear that A. indica chloroform and B. spectabilis aqueous extracts are good candidates for developing new neutraceuticals treatment for diabetes

A novel knowledge based conformation sampling algorithm and applications in drug discovery

Computational approaches have become important tools in drug discovery. Computational technologies have been developed for application in all aspects of the drug discovery process including target identification, lead compound discovery, and lead compound optimization. Interactions of drugs with a protein targets depend on the ability to adopt a three-dimensional structure that is complementary. Complete and rapid prediction of conformational space is important for the success of computational drug discovery technologies. A novel knowledge based conformation sampling algorithm was implemented which derives a database of frequently sampled small molecule fragments within the Cambridge Structure Database and the Protein Data Bank. Likely fragment conformations or ‘rotamers’ are used for rapid sampling of molecular conformational space. The ‘rotamer’ approach has allowed integration of the algorithm into computational biology programs like ROSETTA and FOLDIT, the online science game. Computational methods like homology modelling, docking and virtual high throughput screening were applied in the discovery of novel inhibitors of Discoidin Domain Receptor kinase domain which led to the identification of at least two novel scaffolds

BCL::Conf – Improved Open-Source Knowledge-Based Conformation Sampling using the Crystallographic Open Database

This paper describes recent improvements made to the BCL::Conf rotamer generation algorithm and comparison of its performance against other freely available and commercial conformer generation software. We demonstrate that BCL::Conf, with the use of rotamers derived from the COD, more effectively recovers crystallographic ligand-binding conformations seen in the PDB than other commercial and freely available software. BCL::Conf is now distributed with the COD-derived rotamer library, free for academic use. The BCL can be downloaded at http://meilerlab.org/ bclcommons for Windows, Linux, or Apple operating systems.</div

Quantitative Structure–Activity Relationship Modeling of Kinase Selectivity Profiles

The discovery of selective inhibitors of biological target proteins is the primary goal of many drug discovery campaigns. However, this goal has proven elusive, especially for inhibitors targeting the well-conserved orthosteric adenosine triphosphate (ATP) binding pocket of kinase enzymes. The human kinome is large and it is rather difficult to profile early lead compounds against around 500 targets to gain an upfront knowledge on selectivity. Further, selectivity can change drastically during derivatization of an initial lead compound. Here, we have introduced a computational model to support the profiling of compounds early in the drug discovery pipeline. On the basis of the extensive profiled activity of 70 kinase inhibitors against 379 kinases, including 81 tyrosine kinases, we developed a quantitative structure–activity relation (QSAR) model using artificial neural networks, to predict the activity of these kinase inhibitors against the panel of 379 kinases. The model’s performance in predicting activity ranges from 0.6 to 0.8 depending on the kinase, from the area under the curve (AUC) of the receiver operating characteristics (ROC). The profiler is available online at http://www.meilerlab.org/index.php/servers/show?s_id=23

BCL::Conf – Improved Open-Source Knowledge-Based Conformation Sampling using the Crystallographic Open Database

&lt;div&gt;This paper describes recent improvements made to the BCL::Conf rotamer generation algorithm and comparison of its performance against other freely available and commercial conformer generation software. We demonstrate that BCL::Conf, with the use of rotamers derived from the COD, more effectively recovers crystallographic ligand-binding conformations seen in the PDB than other commercial and freely available software. BCL::Conf is now distributed with the COD-derived rotamer library, free for academic use. The BCL can be downloaded at &lt;a href="http://meilerlab.org/index.php/bclcommons/show/b_apps_id/1"&gt;http://meilerlab.org/ bclcommons&lt;/a&gt; for Windows, Linux, or Apple operating systems.&lt;br&gt;&lt;/div&gt;</jats:p

MOESM1 of BCL::Conf: small molecule conformational sampling using a knowledge based rotamer library

Additional file 1. Supplementary data and protocol capture describing steps to reproduce data

Quantitative Structure–Activity Relationship Modeling of Kinase Selectivity Profiles

The discovery of selective inhibitors of biological target proteins is the primary goal of many drug discovery campaigns. However, this goal has proven elusive, especially for inhibitors targeting the well-conserved orthosteric adenosine triphosphate (ATP) binding pocket of kinase enzymes. The human kinome is large and it is rather difficult to profile early lead compounds against around 500 targets to gain an upfront knowledge on selectivity. Further, selectivity can change drastically during derivatization of an initial lead compound. Here, we have introduced a computational model to support the profiling of compounds early in the drug discovery pipeline. On the basis of the extensive profiled activity of 70 kinase inhibitors against 379 kinases, including 81 tyrosine kinases, we developed a quantitative structure–activity relation (QSAR) model using artificial neural networks, to predict the activity of these kinase inhibitors against the panel of 379 kinases. The model’s performance in predicting activity ranges from 0.6 to 0.8 depending on the kinase, from the area under the curve (AUC) of the receiver operating characteristics (ROC). The profiler is available online at http://www.meilerlab.org/index.php/servers/show?s_id=23