Docking validation study: PDK1-kinase (oncology)

Following the classic thrombine study, we catch up with a more important target: PDK1 kinase.

Pyruvate dehydrogenase kinase, isozyme 1, also known as PDK1, is a human gene.It codes for an isozyme of pyruvate dehydrogenase kinase (PDK).Pyruvate dehydrogenase (PDH) is a part of a mitochondrial multienzyme complex that catalyzes the oxidative decarboxylation of pyruvate and is one of the major enzymes responsible for the regulation of homeostasis of carbohydrate fuels in mammals. The enzymatic activity is regulated by a phosphorylation/dephosphorylation cycle. Phosphorylation of PDH by a specific pyruvate dehydrogenase kinase (PDK) results in inactivation.

There are no as much known inhibitors as for thrombine. BindingDB gives a few more than 70 compounds with measured binding affinities, all relatively strong binders, many of them similar to each other. We run our QUANTUM software to perform docking and the affinity calculations. The results are represented on the graph and demonstrate a solid correlation. In fact the correlation shows QUANTUM's ability to identify strong binders and distinguish between similar compounds (selectivity).

Docking validation study: classic example, thrombine

The Figure on the left represents a docking study of more than 200 molecules with known activity on thrombin. The protein is a well known target ....

We have extracted the binding data from the BindingDB database and docked all the molecules onto a single (of a few available) 3D structure (2cn0 from the pdb databank).

The figure represents graphically the results of the research. The calculated and the measured activities are well correlated. Strong binders are indeed identified as strong binders (left bottom part of the graph). The accuracy of the predictions is quite good (see our discussion on the quality of the biological data here and here).

The results of the calculations can be conveniently summarized in terms of confidentiality matrix. Normally a first screen of novel compounds is performed at a certain concentration to distinguish between the active and non-active compounds. Let's take a standard, 1muM (~-35kJ/M) activity, as a separation cut-off. Then the confidence matrix has the following elements:

• Experimentally active, Predicted active: 29 molecules
• Experimentally n-active, Predicted active: 15 molecules (false positives)
  
• Experimentally active, Predicted n-active: 8 molecules (false negatives)
  
• Experimentally n-active, Predicted n-active: 156 molecules
  

Protein Flexibility and False Positives detection.

Standard hit identification procedure with QUANTUM software implies screening of a large compound library against a given protein target. An example of such procedure for a small set of compounds with known activities is discussed in another blog entry.

Let us show first that a calculation with flexible protein gives a reasonable prediction of the binding free energy. To do that we selected a set of ~200 protein - ligand complexes from the BindingDB database. The protein-ligand pairs were selected mainly so that the complex is small and therefore the whole calculation is fast. The results are represented on the Figure. The horizontal and the vertical axis represent the calculated and the experimental value of the binding free energy calculated from the complexed positions of the ligand within the protein.

The correlation is clearly there and in a few days I will show that the calculated values demonstrate not only the accuracy, but also a good selectivity.

The other Figure represents the correlation between the results of rigid receptor fast docking procedure (horizontal axis) and the fully flexible binding free energies (vertical axis). Although the rigid protein force field has a decent correlation, it fails to recognize electrostatic clashes and thus leads to a fairly large amount of false positives among the predicted ligands. Only about 10% of all the ligands, all originally predicted in the muM range survives as binders. The trend is also clear: all the binding energy values increase (fully flexible force field gives less binders than the rigid calculation would suggest).

Computer aided drug design video from Quantum Pharma

Molecular modelling software of Quantum Pharmaceuticals is used to dock small molecule to active site of target protein. The molecular docking on flexible protein is explored. The Quantum docking software is available for free use at LeadFinding.com, the online hit-to-lead optimization service to filter and profile chemical compounds in chemical database of ChemDiv - organic chemistry supplier.

Model of Intestinal Passive Absorption

Drug penetration from intestinum into blood can be divided into two processes: the drug diffusion to apical membrane of enterocytes and the drug diffusion through the membrane. Let C₀, C₁ are drug concentrations in the intestinal lumen and in close to intestinal wall, correspondingly;

h_d and h_m – are thickness of the diffusion layer adjacent to the intestinal wall and the enterocyte’s membrane;

D_w and D_L are diffusion coefficients of the drug in the intestinal lumen (can be approximately described by the diffusion coefficient in water), and in the drug membrane. D – distribution coefficient of the drug.

The drug diffusion to apical membrane of enterocytes and through it is described by Fick’s law:

dJ₁/dt = -D_w gradC = -D_w (C₁-C₀)/h_d

dJ₂/dt = -D_L gradC = -D_L C₁D/h_m.

(here we supposed the the blood flow is high that the drug concentration in the blood is zero)

In the steady-state
dJ₁/dt = dJ₂/dt = dJ/dt,
and

C₁ = (D_w/h_D)/ (D_LD/h_m + D_w/h_d) * C₀

Therefore apparent permeability of the drug is:

P_app = [dJ/dt] / ΔC = [dJ/dt] / C₀ = (D_w/h_d)/(1 + D_wh_m/(h_dD_LD)) (*)

The Figure represents experimental LogP_app plotted against LogD for drugs that are reported not to be subjected to active transport, active efflux, and paracellular diffusion (blue points); and model predictions (solid line). RMSD = 0.34 log units.

LD50 vs. MRDD: what's death for a mice is good enough for a man

Prediction of toxic properties of small drug like molecules is a big challenge both from theoretical and practical points of view. Quantitatively people use different measures of toxicity such as Maximum Recommended Daily Dose (MRDD) or Lethal Dose (LD50).

Accurate prediction of such endpoints is only possible if both quantities are "physical" characteristics of a compound, rather than signatures of ever changing views of regulating agencies.

The plot on the left represents the "correlation" between experimental values of MRDD (according to FDA) and LD50 (rat) taken from different sources. As you can see, both quantities have a reasonable degree of correlation for low or intermediate toxicity levels. As soon as toxic compounds are considered, the correlation is lost and apparently no good prediction starting from physical properties of a molecule can be done.

For a moderately toxic molecule we can derive an approximate relation:
-LogMRDD = -LogLD50+2.
In "a human language": the lethal and the maximum recommended dose are roughly two orders of magnitude different; a concentration killing a mice is in fact the maximum recommended for a human being.

q-hERG: QUANTUM's innovative approach to hERG binding calculations is finally released

QUANTUM hERG (q-hERG) screening assays is a unique and innovative computational approach, which allows you to predict from a molecule structures of compounds their inhibition constants (IC50) for hERG channels.

q-hEARG features:

• Output is pIC50 values (-logIC50) for the molecules. The accuracy of prediction is 1.1 pIC50 units;
• No training sets or QSAR methods applied;
• hERG inhibition prediction is made by docking of compound on Quantum Pharmaceuticals’ Proprietary Flexible 3D structure of hERG;
• Docking is based on quantum and molecular physics (see Quantum Science Core for an overview);
• Average correlation has RMSD=1.18 pIC50 unit, and correlation coefficient = 0.82;
• Easy to use user interface, no special hardware requirements, both Linux/Windows supported;
• You can also request services based on QUANTUM hERG Screening Assays.

q-hERG is an independent software module, sharing the user interface and basic usage concepts with our q-ADME: ADME/PK properties prediction software, q-Mol: physico-chemical properties calculator, and q-Tox: toxicological profiling software. More information, including q-hERG product booklet can be obtained from the Quantum Pharmaceuticals products site.

Please provide your email below to request your free q-hERG trial download link:

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