Enzyme CoMFA

The biggest limitation of the CoMFA method is the alignment step. The algorithm superimposes the portions of the inhibitors that are of similar stmcture, assuming that they bind with similar orientations in the active site of the enzyme, which is not necessarily the case. Also, because of a problem with alignment, a CoMFA may fail when a few molecules are very dissimilar from all others in the series. Like QSAR, CoMFA does not require a stmcture of the relevant biological receptor, but does require knowledge about a series of inhibitory compounds. 

DePriest SA, Mayer D, Naylor CB, Marshall GR. 3D-QSAR of angiotensinconverting enzyme and thermolysin inhibitors a comparison of CoMFA models based on deduced and experimentally determined active site geometries. J Am Chem Soc 1993 115 5372-84. 

Cavalli A, Greco G, Novellino E, Recanatini M. Linking CoMFA and protein homology models of enzyme-inhibitor interactions an application to nonsteroidal aromatase inhibitors. Bioorg Med Chem 2000 8 2771-80. 

Sipila J, Hood AM, Coughtrie MW, Taskinen J. CoMFA modeling of enzyme... 

The same assumptions apply to CoMFA as to ordinary Hansch analysis. These are additivity of effects and the availability of structurally similar (congeneric) molecules. The method does not account for pharmacokinetic effects, such as distribution, elimination, transport and metabolization. A prospective drug may appear to bind well to the receptor or enzyme, but may not reach the target site due to undesirable pharmacokinetic properties [8]. 

Comparative molecular Leld analysis (CoMFA) is another promising approach developed in recent years for QSAR study. CoMFA is a molecular modeling technique forthe determination of molecular stericand electrostatic force Lelds (Tripos, 1992). It has been successfully used in deriving molecular descriptors for prediction of the bioactivity of steroids (Cramer etal., 1988), molecular Lux through a polymer membrane (Liu and Matheson, 1994), and metabolism and cytochrome p450 enzyme activities (Long and V felker, 2003). 

Additionally, computational chemists often use the resulting output alignment of the molecules as input for 3D-QSAR modeling. As already stated, most field-based 3D-QSAR approaches (such as CoMFA) need a pre-aligned set of molecules and the pharmacophore method is certainly one of the best ways to obtain an objective alignment of the compounds. Klabunde et al., for instance, have recently reported the use of a pharmacophore model of human liver glycogen phosphorylase inhibitors together with 3D information from inhibitor-enzyme complexes to derive a predictive CoMFA model [98]. 

Molecules are characterized by potential hydrogen bonding, polar, hydrophobic, and electrostatic interactions in 3D space, using 3D molecular fields. Techniques such as Comparative Molecular Field Analysis (CoMFA), which considers the 3D distribution of electrostatic and steric fields, have been applied to congeneric series of enzyme substrates or inhibitors generating 3D QSAR equations. Most examples of such applications are to modeling CYP substrate and inhibitor specificity and these have been extensively reviewed in the literature (Ekins et al., 2000 2001 Ter Laak and Vermeulen, 2001 Ter Laak et al., 2002). 

In an earlier GRID/PCA study. Matter and Schwab [4] presented a detailed comparison of MMP3 and MMPS. There, the main selectivity difference was attributed to differences in the ST pocket the identified contour regions were in the vicinity of amino acid differences between the two enzymes. This analysis was supported by parallel CoMFA and CoMSIA analyses, which produced a consistent picture explaining the experimental affinity and selectivity of a series of MMP3 and MMPS inhibitors. 

De Priest, S. A., et al. 3D-QSAR of Angiotensin-Converting Enzyme and Thermolysin Inhibitors A Comparison of CoMFA Models Based on Deduced and Experimentally Determined Active-Site Geometries,/. Am. Chem. Soe. 

Moreover GRID can be used to understand the structural differences related to enzyme selectivity, a fundamental field in the rational design of drugs. GRID maps can also be used as descriptor input in statistical procedures like CoMFA, GOLPE or SIMCA for QSAR or 3D-QSAR analyses. 

Waller, C.L. and Marshall, G.R. (1993). Three Dimensional Quantitative Structure-Activity Relationship of Angiotesin Converting Enzyme and Thermolysin Inhibitors. 2. A Comparison of CoMFA Models Incorporating Molecular Orbital Fields and Desolvation Free Energies Based on Active Analog and Complementary Receptor Field Alignment Rules. JMeeLChem., 36,2390-2403. 

Fig. 6 Correlation plot for CoMFA and CoMSIA models with experimental enzyme inhibition data on human KMO enzyme...

Figure 13.8. Comparison of pairwise biological distances versus descriptor differences for 138 angiotensinconverting enzyme (ACE) inhibitors (upper panel) as example for neighborhood plots (a) CoMFA ster-ic fields (b) 2D fingerprints (c) molecular weight.

Molecular Field Analysis (CoMFA) and Catalyst. An early metabolic study using CoMFA involved the monoamine oxidase-catalyzed oxidation of MPTP analogs (185). A more recent study using the Catalyst algorithm described the pharmacophoric features characteristic of CYP2B6 substrates (186), a simplified representation ofwhich is given in Fig. 13.36. Pharmacophoric features of inhibitors have also been obtained for a number of CYP enzymes using 3D-QSAR methods (187-189). 

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