Pknb Study
This posting is to give you the idea about recent research i have done in Mtb PknB. Previously i have reported some of the predicted inhibitors. Now this time i am again updating some of the research done in identifying more inhibitors.I am here in India from June 21 to august 20 th 2012 under
IUSSTF science and technology scholarship at Birla Institute of technology hyderabad campus.
Previously some work had been done here on PknB inhibitors.(paper under publication),with collaboration with OSDD.They have identified some inhibitors under 1micro molar concentration . Well quite impressive!!
But the problem with the PknB is that the inhibitors are not capable enough to inhibit the growth in cell line study.
Why is this so?
Is it due to the mycolic acids barrier which don't let compounds to enter the membrane.Not sure about this But more research needs to be done on the branch of drug permeation.
I set my target as PknB which i had been doing earlier and a recent
paper by Lougheed team helped me to study the compounds well and also it's pharmacophores.Previously they also reported another group of inhibitors in a
paper which was not so much potent as the current ones.But the study done by me helped me to identify the main pharmacophore behind these structures.
PknB a kinase so what it could be a typical kinase like pharmacophore. having hydrogen bond donor's and acceptors and hyrophobic moeity for example class I type inhibitors. Thanks to
Jae hong shin for good presentation on kinase inhibitors which helped me to study the inhibitors of PknB.
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| Kinase Inhibitors and its pharmacophoric positions. |
From a review paper by Zhang etal
Targeting cancer with small molecule kinase inhibitors given in the pic above we can see most of inhibitors and its scaffolds the basic pharmacophore looks like which is given below having 2 donors and one acceptors and big hydrophobic moiety.
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| Different types of Kinase pharmacophores. |
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| List of published PknB Inhibitors. |
The features of the kinase pharmacophore completely resemble inhibitors identified the Lougheed team. I collected 62 Inhibitors with IC 50 values and generated E-pharmacophore based on the compound VIII given in Figure below.E -Pharmacophores are based on the Energetic contribution of the group of atoms to the receptors .It is generated from the Glide XP descriptors. After docking the 62 compounds compound I showed the best docking score followed by compound VIII and then it was Mitoxantrone. I generated pharmacophores for three compounds.
The pharmacophore for compounds was resembling the typical kinase class-I type pharmacophore. I did some enrichment studies and BEDROC scores and AUC studies and found the pharmacophore for compound VIII was most appropiate for PknB till now from the dataset I collected. If anyone wants the dataset of 62 compounds email me at abseal@indiana.edu.For enrichment study 1000 decoys were collected from Schrodinger's website. The pharmacophore was having a high enrichment at 1% ,2% and 5% of the database hits.
The paper by
Salam etal on E-pharmacophore mention the choice of sites which have scores greater than -1 kcal/mol but i have a different scenario here . The ring aromatics score was less than -1 Kcal.mol and one extra donor was having less than -1kcal/mol score. Still my selected pharmacohore was giving a very good enrichment results.
Not sure anywhere does selection of sites based on the energetic contirbution or not.But i selected the sites based on the structure of a typical kinase pharmacophore. The picture given below represents my pharmacophore .The previous work done not published didn't considered the extra donor D6 on the bottom right side. This donor has some special effect for most of the compounds when this donor site is present it brings down the IC 50 value below 0.1 micro molar which indicates that this is one of the important sites.
The docking pose of the compound is also given below for compound VIII along with the pharmacophore
I have done data fusion using the structure and ligand based methods pharmacophore ,glide and rocs and using sum score, sum rank and reciprocal rank . Well reciprocal rank is amazingly performing well giving me one of the best enrichment scores along with a BEDROC value of 0.875 and RIE 12.73.Also the AUC for 1% ,2% and 5% was 0.71,0.75 and 0.81. The next datafusion algorithm worked very well was sum score method with BEDROC of 0.785. Both the data fusion methods performance was better than usual Virtual screening methods.
I have done some screening using the datafusion methods using the Asinex screening library. Will post some materials in next post.
Happy reading my post.
