Dear Tegar,

I believe that the alignment process is necessary in Structure-Based Pharmacophore study.

I suggest that you look through the following publication entitled "Combining structure-based pharmacophore modeling, virtual screening, and in silico ADMET analysis to discover novel tetrahydro-quinoline based pyruvate kinase isozyme M2 activators with antitumor activity " which describes in details the generation of structure-based pharmacophore models.

I have copied an important text from the article for quick view:

Generation of structure-based pharmacophore models

A set of seven crystal structures of PKM2 in complex with diverse ligands were obtained from the Protein Data Bank (PDB). The crystal structures with FBP, the natural ligand of PKM2, were not used in the analyses in order to avoid the unnecessary noise likely to be introduced into the pharmacophore model.The generation procedures of the structure-based pharmacophore models were referenced to our previous reports. The whole process of generation and utilization of the structure-based pharmacophore models were illustrated in Figure 2 and detailed as follows. Briefly, the coordinates of all PKM2-ligand X-ray crystal structures were transformed into a common reference frame by using “Multiple Structure Alignment (Modeller)” module within Discovery Studio (DS). The complex-based pharmacophore generation module embedded in DS was used to generate seven individual complex-based pharmacophore models based on the previously aligned structures. All pharmacophore features identified by DS were clustered according to their interaction pattern with the PKM2 protein. The obtained model was further refined by the modification of the constraint tolerance of the spheres in accordance with the default values of pharmacophore modules embedded in DS.

Pharmacophore-based virtual screening

The ligand-based procedure was applied to the ZINC database supplied via the worldwide web, which contained about 35 million commercially available compounds.The ZINC database provides files corresponding to substances distributed from different vendors, and the entire database was processed by using the SMILES compounds’ codes. Among this large set of compounds the tetrahydro-quinoline scaffold was found only in approximately 12,000 compounds, which were further investigated. In the present study, structure-based pharmacophore model comprising of six chemical features was used as a query for searching tetrahydro-quinoline based chemical library. We performed all database searching experiments using the “Best” conformation generation option and the “Flexible” fitting method option in the “pharmacophore” protocols of DS software.The molecules in the tetrahydro-quinoline based chemical library which fit with all the features of the pharmacophore model were retained as hits. The criterion for screening for further validation was high fit values, which indicate good matches.

Molecular docking study

Molecular docking studies were performed to generate the bioactive binding poses of PKM2 activators in the active site of proteins by using the LibDock module in DS software. LibDock uses protein site features, referred to as hot spots, consisting of two types (polar and apolar). In the current study, the CHARMm Force Field was used for energy minimization of the ligands. The binding sphere was primarily defined as all residues of the target within 10 Å from the ligand binding site. Conformer Algorithm based on Energy Screening and Recursive build-up (CAESAR) was used for generating conformations. Then, the smart minimizer was used for in situ ligand minimization. All other parameters used were utilized default settings. To better understand the interactions between protein residues and bound ligands, the Ligplot program was used for generating the 2D diagrams that helped to identify the binding site residue, including amino acid residues, waters, and ligand compounds.

Fingerprint based similarity analysis

The chemical similarity search was conducted with DS using the standard protocol. The MACCS structural keys were utilized with the Tanimoto Coefficient (Tc) as the similarity metric.

In silico ADMET analysis

ADMET refers to the absorption, distribution, metabolism, excretion, and toxicity properties of a molecule within an organism, which were predicted using the ADMET module and the TOPKAT module embedded in DS software. In the ADMET module, six mathematical models (aqueous solubility, blood–brain barrier penetration, CYP2D6 inhibition, hepatotoxicity, human intestinal absorption, and plasma protein binding) were used to quantitatively predict properties of a set of rules that specify ADMET characteristics of the chemical structure of the molecules. The hit compounds possessing satisfactory ADMET properties were chosen according to TOPKAT calculation. The TOPKAT predictions help in optimizing therapeutic ratios of lead compounds for further development and assessing their potential safety concerns. They will help in evaluating intermediates, metabolites, and pollutants, along with setting dose range for animal assays.

To view the full publication, please use the following link:

http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4159224/

Hoping this will be helpful,

Rafik

Dear Prof Karaman

Thank you for your answer and publication you included. However, in the example above they mentioned the utilization of several receptor-ligand model. While the receptor i use only have 1 model in PDB. Furthermore, the main problem i faced is how to validate the pharmacophore hypotheses. Specifically how to make it recognise the native ligand which i include in training set.

Regards

If you know the receptor-bound geometry for certain ligands of your training set, this geometry should remain fixed throughout the alignment process. Other ligands may be allowed to change their geometries, unless you have specific hypotheses about how their active conformers should look like.

How you are using the native ligand? i think you would have extracted the bound ligand from the crystal structure. Native ligand should be considered as template.

Sorry for my very late response. Thank you Prof. Kireev and Dr. Kumar for the answers. I think my mistake is to built the native ligand from scratch instead of extracting it straight from the complexes. I also just found out that Ligand Scout needs Omega conformer generator in which we don't have the license, so we use free tool for conformer generation. Once again thank you very much