Comfa Is A Powerful Tool For Performing 3-d Qsar

Introduction

The key milestone of drug action is its interaction with target. The mechanism of binding and compatibility of drug with the target predicts its activity and proficiency in combating the disease. Same is the case with any reaction which proceeds after binding of a compound to its target. In biological systems, the interactions of inhibitors like kinase inhibitor, apoptosis inhibitors etc. with target proteins or enzymes is beautifully controlled by stereochemistry. Most of the targets are specific for L or D isomers of compounds which have to interact with them. If two compounds have same chemical formula but different stereo-chemical arrangement, only one of them with targets compatible stereochemistry will be able to interact with the target and produce the effect. High throughput screening technique also involves the screening of compounds which bind the targets successfully and are thus characterized as successful hits. If such a method is available which can predict the 3-D structure of a compound, only stereo-compatible models can be used in HTS assays to save time and money. Unfortunately, general QSAR approach does not relate the effects of stereo-chemical interactions of compounds with the targets. However, CoMFA approach is a useful tool to build 3-D models of compounds and acquiring deep insight into their stereo-chemical properties thereby producing even more valid results.
Process of CoMFA

The general process of CoMFA can be split into four major steps given below:
1. Firstly, all of the test compounds are placed in a 3-dimensional grid which has a spacing of 2angstroms.
2. Secondly, steric energy (Leenard-Jones potential) and electrostatic energy are measured for every molecule on each grid point. Usually a positively charged sp3 hybridized carbon atom is used a probe for this method.
3. Thirdly, domination by large steric and electrostatic energies is minimized by setting a cut off value for all those energies which exceed a specific value (mostly 30kcal/mol).
4. Lastly, partial least square (PLS) analysis is performed to predict activity of compounds by using the energy values at grid points.

Figure: The general process of CoMFA [1]
Contour Maps Produced by CoMFA

The analysis of compounds by CoMFA produces colorful contour plots. Each color of the model signifies a specific property for instance, the green zones of space shows that occupancy by the substrates increases affinity, whereas yellow areas depict zones where occupancy decreases affinity. On the other hand the red color represents the region where a high electron density provided by ligand increases and blue color region means that high electron density provided by ligand decreases activity. Left side of following figure shows the fitting criterion used for alignment of a substrate, menthol, to the CoMFA model for the oxidation of alcohols. The model on the right side shows the colorful model which is relatively easy to interpret output of CoMFA.

The steric or electrostatic zones in the space occupied by substrate are analyzed to deduce useful information. This method has been useful to predict if menthol enantiomers will be oxidized by a particular strain and which particular enantiomer will be enantio-compatible to the target. The CoMFA model predicted that1-tetralol, 1S-1-phenyl ethanol, 1S-1-(2-furyl) ethanol, menthol and neo-menthol must be oxidized but not iso-menthol [2].

Conclusion

After the above discussion, it can be concluded that CoMFA is a powerful tool for performing 3-D QSAR. The results of CoMFA can provide insight into the pharmacological requirements of a compound to be a good inhibitor. It provides the deep insight into the details of 3-dimensional structure of compounds. It not only predicts the model for the target or inhibitor alone but can also build models for the target-inhibitor complex. CoMFA also provides information about the stereo-compatibility of the inhibitor to its target. This technique can be used to design analogs of various inhibitors like imatinib, vorinostat etc. All of this valuable information saves time and money in high throughput screening assays!
References

1. Zhang L, Tsai KC et al. How to generate reliable and predictive CoMFA models. Curr Med Chem 2011; 18(6): 923-30.
2. Carballeira JD, Quezada MA. High Throughput Screening and QSAR-3D/CoMFA: Useful Tools to Design Predictive Models of Substrate Specificity for Biocatalysts. Molecules 2004; 9: 673-693.
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