Shape Analysis MSA

The second method to be discussed, MSA, was introduced in 1980. It explicitly assumes that the shapes of the molecules provide information about the shape of the receptor cavity.Pairwise similarities and dissimilarities are calculated between a reference structure and the other compounds of the data set. Typically, biological potency is correlated with one shape index plus conventional QSAR descriptors for lipophilicity and electronic effects. 

MSA usually begins with a fixed-valence (frozen bond lengths and bond angles) conformational search and energy minimization on each compound to produce a set of candidate conformations. If the data set consists of congeners, the molecules are superimposed on their common skeleton otherwise, they are superimposed by a pharmacophore hypothesis as discussed in the section on molecular alignment. 

To choose the reference shape for MSA, each available conformation is used in turn as a reference to calculate the pairwise molecular similarity to all other conformations of all other molecules. The conformation of each molecule that has the highest overlap volume with the current reference is used as the similarity measure for that reference. Thus, given M conformations in the database, there will be M MSA parameters that describe the shapes of the compounds. In a 1994 study, the overlapped structures of four molecules were merged to define a reference shape. 

MSA parameters appear in a regression equation as linear or quadratic terms. Positive correlations with V(, emphasize that the greater the similarity between a given molecule and the reference (the most potent compound), the higher the expected potency of the former. Alternatively, negative correlations with NV() emphasize that potency tends to decrease as the dissimilarity between the molecule and the reference shape increases. 

Equations that have only linear terms in MSA descriptors suggest that there are no compounds more potent than the reference, because by definition there can be no compound that has a V value, or forecast potency, higher than that of the reference. To minimize the nonoverlap steric volume NV, the best we can do is to use the reference shape for which NVq will be zero and forecast potency equal to that of the reference. These problems result from lumping multiple regions of 3D space into one shape descriptor. Even when the most potent compound in the training set is close to the ideal, other 3D-QSAR methodologies might reveal bad steric contacts that could be removed to produce an increase in potency. 

---

With the development of accurate computational methods for generating 3D conformations of chemical structures, QSAR approaches that employ 3D descriptors have been developed to address the problems of 2D QSAR techniques, e.g., their inability to distinguish stereoisomers. The examples of 3D QSAR include molecular shape analysis (MSA) [34], distance geometry [35,36], and Voronoi techniques [37]. 

The adenosine A3 receptor antagonistic activity of these compounds have been further analyzed [104] in 3D-QSAR using molecular shape analysis (MSA) and molecular field analysis (MFA) techniques in Cerius2 (version 4.8) software [50]. hi this, Jurs atomic charge descriptors were used for the MSA study and H+ point charges and CH3 derived steric fields were used for the MFA study. In this 3D-QSAR study, MSA resulted in Eqs. 12 and 13 and MFA led to Eq. 14. 

They are - molecular descriptors or - substituent descriptors calculated by difference between the considered compound (or functional group, fragment) and a -> reference structure or a - hyperstructure. Examples of differential descriptors are those obtained by the -> minimal topological difference (MTD) and - molecular shape analysis (MSA). 

Spherical harmonics provide a parameterization of three-dimensional shape which is especially useful for protein structure description (4). Overlap volume comparisons are the basis for the Molecular Shape Analysis (MSA) method of Hopfinger (5,6). TTiis technique has been extended to include a quantification of the steric and electrostatic fields surrounding a molecule (7). A further refinement of field analysis, which merges statistical and molecular modeling techniques, is the COMparative Molecular Field Analysis method (COMFA) of Cramer (8). These latter approaches seek to encode information about more than just steric bulk or form. They express multivariate information about the structure, so they might be considered multidimensional shape descriptors. 

The hyperstructure approaches MSD and MTD (minimal steric difference and minimal topological difference) and molecular shape analysis (MSA) are discussed in chapter 4.6, the use of similarity indices is discussed in chapter 9.4. 

Ligand structures can be represented by molecular fields (electrostatic or steric), which contain enthalpic contributions to binding when implemented by conventional comparative molecular field analysis (CoMFA) (see Comparative Molecular Field Analysis (CoMFA)). Steric volume incorporated in molecular shape analysis (MSA) (Figure la) is another representation commonly used in SAR studies (see Molecular Surface and Volume) Alternatively, in the hypothetical active site lattice (HASL) approach, molecules are represented by a three-dimensional grid of points (lattice) associated with discrete electronic properties. ... 

D searching and pharmacophore development. They have been extended by the addition of lipophilicity to the standard steric and electrostatic field descriptors. Another 3D QSAR technique which can be used in conjunction with 3D searching is molecular shape analysis (MSA). Use of the genetic function algorithm within a molecular field analy.sis (MFA) has also been demonstrated to be a useful alternative to CoMFA and MSA. These approaches depend on proper superposition of the structures that are being compared. This... 

MSA descriptors molecular shape analysis descriptors -> molecular shape analysis... 

D-QSAR = three-dimensional quantitative structure-activity relationship 3D-SAR = three-dimensional structure-activity relationship CoMFA = comparative molecular fields analysis DG = distance geometry HASL = hypothetical active-site lattice MLR = multiple linear regression MSA = molecular shape analysis PLS = partial least-squares. 

ACD = Available Chemicals Directory CSD = Cambridge Structural Database MFA = molecular field analysis MSA = molecular shape analysis PDB = Brookhaven Protein Data Bank. 

Tlie main assumption of this approach is that the shape of the molecule is closely related to the shape of the - binding site cavity and, as a consequence, to the biological activity. Therefore, a shape reference compound is chosen which represents the binding site cavity, and the similarity (or commonality) measured between the reference shape and the shape of other compounds is used to determine the biological activity of these compounds. As well as the shape similarity measures, other molecular descriptors such as those in - Hansch analysis can be used to evaluate the biological response. The MSA model is thus defined as ... 

Figure 2 Structural alignment methods with continuous properties. Methods are based on continuous properties using the algorithms implemented in MSA (I), HASL (II), and CoMFA (III). A conformational analysis (optional) can be used to select candidate conformations ( bioactive conformation, or alternatively a set of biologically relevant conformations that are used for the alignment). The structure representation is based on molecular shape (I), a four-dimensional lattice (II). or molecular fields (III). The different algorithms for the alignment require that a reference be cho.sen so that comparisons between each ligand and the reference can be made. A statistical measure of molecular similarity is performed to identify the alignment that maximizes the molecular similarity between the ligands in term.s of three-dimensional overlap (MSA, HASL) or electrostatic potential distributions...

---