All Notebooks | Help | Support | About
Archives
Authors
Sections
Tools
Show/Hide Keys
16th June 2015 @ 14:07

The pharmacophore model produced in Pharmacophore Modelling of the Malaria Box PfATP4 Active Compounds. was used to screen the Maybridge library to select a small and diverse selection of molecules to test in the PfATP4 assay.

Using Accelrys Discovery Studio, the search 3D database protocol was ran.

Input Files

Protocol.pr_xml
exclusion10_01.chm

The results were then manually filtered based on fit value, pose, shape, fit with "cavity feature" and diversity.

The final selection included 18 compounds with fitValues ranging between 3.88 and 3.66.

selected_and_ordered.pdf
round01_18ordered.sdf
Attached Files
16th June 2015 @ 13:05

The following 28 Malaria Box compounds, found to cause disruption against Na+ regulation in PfATP4  were used to search for common pharmacophore features.
 
Accerlys Discovery Studio was used running the Common Feature Pharmacophore Generation protocol.
 
Input Files
 
Protocol Parameter File
 
Protocol.pr_xml
 
Input SD file
 
Malaria_Box_active_clean.sd
 
Input SMILES and InChI codes
 
input_smiles.txt
 
 
The protocol produced 10 four-feature models, upon inspection of poses and score the following model was selected to be taken forward.
 
.CHM file
 
Malaria_Box_active_clean_03.chm
 
 
 

Fine tuning the model

The 28 active compounds aligned to the pharmacophore were used to create a shape feature that could be used to manually predict the shape of the active site.  10 exclusion features were then added in areas where high scoring, inactive ligands penetrated out of this shape.  The shape feature itself was never used as a query feature and was never intended to be used as such. 

 

03.png

04.png

05.png

06.png

Final CHM file

exclusion10_01.chm

 

 

 

 

 
 
Linked Entries
Attached Files
24th February 2013 @ 10:51

As previously shown, a four common feature pharmacophore model (excluding the hydrophobic features for simplicity) can be used to map both the arylpyrrole (OSM-S-5) and near neighbour (NN) (OSM-S-35) series

Looking at some less active examples it can be seen that both aromatic features are required for potent activity as show by OSM-S-54.  Also, substituting the ester link for an amide (OSM-S-19) or a oxazole (OSM-S-62) kills activity. 

Not illustrated here but OSM-S-103 only showed weak activity suggesting that the ester linkage is essential for potent activity in the arylpyrrole series.

Wanted List

From the Wanted list the oxadiazole doesn’t look too promising based on the synthesised oxazoles.  The ether has potential but it does not appear to map to well and I’d predict it would have a similar activity to the carbonyl derivative OSM-S-103.

The sulphonamides look good and it’d definitely worth trying to make these.  In addition, the sulphonate also looks promising.  (At the time of writing this I’m not sure in the sulfonate is synthetically possible)

OSM-S-106

OSM-S-106 showed great potency but the mapped pose is flipped to as what was expected where the N3 would map onto the first acceptor feature.  This could either suggest the OSM-S-106 is acting in a different manner or it could mean that a meta-substituted sulfonamie is hitting a previously untouched pocket in the active site.


This theory could be easily tested by making a small number of derivatives (A-E).  Mapped are the phenyl substituted version on OSM-S-106 (A) and it’s morpholine analog (B)– this would potentially hit the second acceptor feature.  Finally the para-substituted version of OSM-S-106 is mapped (C), the sulfonamaide hits both the acceptor features but a the expense of the top aromatic feature.  This is worth making, again to validate the model and the poses calculated.  Compounds D and E would also help to confirm the binding of OSM-S-106.

 

 

In addition, the NN derivative with a meta-sulphonamide (F) could be a useful compound to make if the NN series is to be continued.  The sulphonamide group also greatly reduced the logP of the compound.

 

Attached Files
8th January 2013 @ 03:36
The four feature hypothesis was derived from OSM-S-5 in the following conformer generated using Phase and the following training set.
based_on_AARR7.sdf
training_set_01.sdf


The coordinates for the features are as follows.
phase_hypoAARR7_Maybridge2008_db_default_run2_find_matches.xyz


2 A 6.464370 -0.041014 -3.706010
1 A 5.981490 1.362750 0.140407
8 R 2.615530 0.583573 0.505623
9 R -0.945719 -1.127960 0.005613

The Maybridge library was then screened to find compounds hitting all features. Phase returned the top 1000 hits which the top 200 were manually checked and filtered to produce the following selection of compounds. Compounds with little variation were not selected after the first occurrence therefore if any of the below structures are selected for purchase then a simple substructure search should be carried out to see other members of the group.

23 compounds were selected then clustered by substructure into 6 groups.

Group A - Pyrazole and Imidazole containing compounds. Based on the OSM-S pyrazole compounds already synthesised it could be assumed that this series would be inactive although no imidazole like compounds have been tested.

Group B - Thiophene containing compounds.

Group C - Thiazol-acetamide containing compounds. Similar to the pyrazoles and therefore likely to be inactive.

A-C.png

Group D - Thienopyrimidine containing compounds. Similar to compounds being re-synthesised as part of the OSM-S strategy. Very interesting seeing these map onto the hypothesis.

Group E - Thiophene-sulfonamide compounds. Similar to Group B

Group F - Singleton compounds from the selected 23.

D.png
Attached Files
12th December 2012 @ 07:25
Continuing on from the previous post and using the CheMBL substructure search feature the GSK TCAMS Database was filtered to give 17 compounds containing the aryl pyrrole substructure and with accompanying pIC50 data. This was combined with a manually filtered data set of OSM compounds with IC50 data to give this training set.

The structures were prepared using the default settings-
Clean Structure
Ionise at pH7

and conformers were generated using the default settings.

The activity threshold was set to PIC50 = 4.5 (30uM) and sites were created with the following parameters

Find common pharmacophore – Max sites 6
Min sites 5

With the constraints to find only 1 hydrophobe and 2 aromatic rings.

The resulting hypotheses were scored for actives and inactive's using the default settings and then clustered to give 6 hypotheses.

All hypotheses were viewed and manually assessed. The selected map had the features at the following co-ordinates.

0 A 8.855380 -8.074250 1.459800
4 A 4.492750 -7.101520 -0.342651
8 H 11.881400 -4.989210 0.281807
9 R 9.213580 -4.790410 0.496400
10 R 10.737100 -1.309760 -0.741114

The sites were based on the GSK compound 535,023 in this conformer. I've manually produced a .chm file in DS3.5 visualizer for download. This has not been confirmed though - if there are any problems with it can you please let me know.
Hypo160.chm


The actives in the training set can be clustered into 5 groups and all map onto the above hypothesis very well

clustered_actives.png

Phase071212.jpg

Viewing these poses and with the new data available for compounds up OSM-S-5 a few conclusions can be made.

Seeing as all 5 clustered active groups map onto this hypothesis it was predicted that OSM-S-103 should be active. It turned out that 103 was only slightly active and from this two things can be suggested. First of all it looks like an ester group at this position is the most favoured ad as such the feature at this site need modified. Maybe a carbothioate at this position could be investigated?

It has also been attempted to map the ester derivative of OSM-S-5 onto this hypothesis with no great success. It could therefore be predicted that this analog will be no more active than OSM-S-103.

Re-mapping the active compounds onto the hypothesis with it limited to only give results when hitting all 5 features.

phase_AAHHR2_match_all_5-56_hits.sdf


56 compounds were returned - 33 of which are active. The original input file had 47 active compounds. The actives that did not match are contained within this file.

phase_AAHHR2_non_matched.sdf


The inactive compounds can be filtered it with further feature mapping.

phase_AAHHR2_inactive_hits.sdf


- Pyrazoles are totally inactive so can be filtered out.
- A Nitrogen appears not to be accepted in the area of 6.53, -6.28, 1.52 however an oxygen here if favoured.
- A size exclusion sphere should be added in the region of 4.53, -5.9, 2.99

Unfortunately, this pipe-lining or advanced screening is beyond my capabilities with the software I'm using here. This is a great opportunity for someone else to get involved in the project.

Finally, The activity of OSM-S-5 and it's primary amide feature have not been mapped due to flex-abilities in the side chain. More examples from the GSK data may help this process but time constraints have not allowed this at the moment
Attached Files