GRID/CPCA

Rocha J.R. Freitas R.F. Montanari C.A. The GRID/CPCA approach in drug discovery. Expert Opinion on Drug Discovery, 2010, 5 (4), 333-346. 

In the last few years, a number of publications have demonstrated that the GRID/PCA or GRID/CPCA methods can be successfully applied to characterize the structural differences between protein binding sites, and to identify differences in the protein-ligand interactions as well as the regions on the target enzymes which mediate highly selective interactions [4—17]. 

In this chapter, we will first review the basic principles of GRID/PCA and GRID/CPCA, highlighting some important technical aspects necessary for their successful application. Some space will be given to the discussion of the differences between PGA and the (hierarchical) GPGA methods, before their application to various selectivity problems will be summarized. 

Figure 3.4. Data collection for GRID/CPCA Starting from the GRID calculations for one probe, a one-dimensional vector containing all interaction energies at the k grid points is constructed. Then the vectors for the m probes are compiled into one long vector which contains (kx m) data points. The final X matrix is built by stacking these vectors for every target protein.

More recently, Afzelius and co-workers used GRID/CPCA for a comparative analysis of protein structures of GYP2C9 and CYP2C5 from different sources crystal structures, homology models, and snapshots from molecular dynamics simulations [16]. The evaluation of molecular dynamics simulations by means of GRID/GPGA is an especially interesting new aspect in their publication. 

At about the same time, Terp et al. [11] used GRID/CPCA to analyze 10 MMPs with the intention of highlighting regions that could be potential sites for obtaining selectivity. Some of the structures were retrieved from the RCSB protein data bank [53], others were obtained through homology modeling [56]. To facilitate the analysis, the authors used the cut-out tool to focus on each of the six subsites in turn. 

The selectivity analysis for the SI pocket is complex as it is surrounded by a loop. Its length and amino acid composition differs between the individual MMPs, leading to different shapes and interaction patterns for this subsite. Here, computational techniques like GRID/CPCA are especially advantageous, as they allow an automated, unbiased view on the interactions and an abstraction from a discussion of differences in single amino acids. They address the sum of all interactions at once, and the distances in the score plot allow one to somewhat quantify the differences among the proteins. 

The differences found in the CPCA analysis mirror the experimental attempts to obtain selectivity, which concentrated mostly on the SI site. Especially differences in the size and shape of the SI subsite have been utilized, however, the GRID/CPCA calculations point to additional interactions in the SI pocket that could be used to distinguish between several of the MMPs. In the S2 and S3 subsites, a wide range of substituents are tolerated and modifications there have often been used to optimize oral bioavailability and solubility. 

A GRID/CPCA analysis of the three subtypes of Peroxisome Proliferator-Activated Receptors (PPARs) vas reported by Pirard [18] using three PPARa, eight PPARy, and three PPARd X-ray structures of the ligand binding domain (LED). 

E. Myshkin, B. Wang, Chemometrical classification of ephrin ligands and Eph kinases using GRID/CPCA approach,... 

I. T, Jorgensen, E. S. Structural Differences of Matrix Metalloproteinases with Potential Implications for Inhibitor Selectivity Examined by the GRID/ CPCA Approach,/. Med. Chem. 2002,... 

Key words Structural biology, Database mining, Classification of protein structures, Pharmacophore, Hit finding, Binding site mapping, GRID/CPCA... 

Four types of plots help to analyze the output of GRID/CPCA ... 

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