3D molecular

In the late 1960s, Langridge and co-workers developed methods, first at Princeton, then at UC San Francisco, to visualize 3D molecular models on the screens of cathode-ray tubes. At the same time Marshall, at Washington University St. Louis, MO, USA, started visuaHzing protein structures on graphics screens. 

The program system COBRA [118, 119] can be regarded as a rule- and data-based approach, but also applies the principles of fragment-based (or template-based) methods extensively (for a detailed description sec Chapter 11, Sections 7.1 and 7.2 in the Handbook). COBRA uses a library of predefined, optimized 3D molecular fragments which have been derived from crystal structures and foi ce-field calculations. Each fi agment contains some additional information on... 

Figure 2-100. CORINA-generated 3D molecular model of a fullerene dendrlmer with 1278 atoms (762 non-hydrogen atoms).

Figure 2-106. 3D molecular triJCtLjre of a-conoto in PNI1 polypeptide (PDB ID Ipen),... 

In order to represent 3D molecular models it is necessary to supply structure files with 3D information (e.g., pdb, xyz, df, mol, etc.. If structures from a structure editor are used directly, the files do not normally include 3D data. Indusion of such data can be achieved only via 3D structure generators, force-field calculations, etc. 3D structures can then be represented in various display modes, e.g., wire frame, balls and sticks, space-filling (see Section 2.11). Proteins are visualized by various representations of helices, / -strains, or tertiary structures. An additional feature is the ability to color the atoms according to subunits, temperature, or chain types. During all such operations the molecule can be interactively moved, rotated, or zoomed by the user. 

As already mentioned in Section 2.9, automatic 3D structure t eneration has a long tradition in th.c field of chcmoinformatics. Varions algorithms and approaches to addressing the problem of automatically generating 3D molecular models have been developed and published in the literature since the early 1980s, Some of the basic concepts and methods arc discussed in Section 2,9 and a more detailed description is given in Chapter II, Section 7.1 in the Handbook. 

The chirality code of a molecule is based on atomic properties and on the 3D structure. Examples of atomic properties arc partial atomic charges and polarizabilities, which are easily accessible by fast empirical methods contained in the PETRA package. Other atomic properties, calculated by other methods, can in principle be used. It is convenient, however, if the chosen atomic property discriminates as much as possible between non-equivalent atoms. 3D molecular structures are easily generated by the GORINA software package (see Section 2.13), but other sources of 3D structures can be used as well. 

Molecules are usually represented as 2D formulas or 3D molecular models. WhOe the 3D coordinates of atoms in a molecule are sufficient to describe the spatial arrangement of atoms, they exhibit two major disadvantages as molecular descriptors they depend on the size of a molecule and they do not describe additional properties (e.g., atomic properties). The first feature is most important for computational analysis of data. Even a simple statistical function, e.g., a correlation, requires the information to be represented in equally sized vectors of a fixed dimension. The solution to this problem is a mathematical transformation of the Cartesian coordinates of a molecule into a vector of fixed length. The second point can... 

Rusinko A III, J M Skell, R Balducci, C M McGarity and R S Pearlman 1988. CONCORD A. Program fi the Rapid Generation of High Quality 3D Molecular Structures. St Louis, Missouri, The University < Texas at Austin and Tripos Associates. 

Microsoft Windows integrated program allowing for 3D molecular visualization and the generation of high quality rendered images. http //www.msi.com/download/index.html... 

Computationally deriving a 3D molecular structure of a given protein using a sequence overlay with a related protein of known structure. 

Molecular modeling itself can be simply described as the computer-assisted calculation, modulation, and visualization of realistic 3D-molecular structures and their physical-chemical properties using force fields/ molecular mechanics. 

Moreover, molecular modeling is one key method of a wide range of computer-assisted methods to analyze and predict relationships between protein sequence, 3D-molecular structure, and biological function (sequence-structure-function relationships). In molecular pharmacology these methods focus predominantly on analysis of interactions between different proteins, and between ligands (hormones, drugs) and proteins as well gaining information at the amino acid and even to atomic level. 

Data Bank (PDB) [56], Computational chemists recognized that these compilations of 3D molecular structures would prove very useful, especially as more pharmaceutically relevant compounds were deposited. The CSD was supported by subscribers, including pharmaceutical companies. On the other hand, the PDB was supported by American taxpayers. 

Ivanov J, Karabunarliev S, Mekenyan O. 3DGEN a system for exhaustive 3D molecular design proceeding from molecular topology. J Chem Inf Comput Sci 1994 34 234-43. 

The increased interest in 3D aspects of organic chemistry and quantitative structure-activity relationship (QSAR) studies has caused an increasing need for a much broader access to 3D molecular structures from experiment or calculation. 

Theorehcal methods such as quantum mechanics or molecular mechanics can produce 3D molecular models of high quality and predict a number of molecular properhes with high precision. Unfortunately, these techniques also require at least some reasonable 3D geometry of the molecule as starhng point. 

Here, an attempt to classify different strategies to generate 3D molecular models is undertaken with the aim to specify the remit of methods which will be covered under the term automatic 3D structure generators . The focus will be on methods designed for small, dmg-like molecules. The prediction of the geometry of polymers, in parhcular of biopolymers, is a task of its own and not even attempted by the approaches discussed here. 

Furet, P., Sele, A., Cohen, N. C. 3D molecular lipophilicity potential profiles a new tool in molecular modeling. J. 

In addition, the calculation of many different ID, 2D and 3D descriptors is possible using a range of commercially available software packages, such as Sybyl, Cerius2, Tsar, Molconn-Z and Hybot. Several new descriptor sets are based on quantification of 3D molecular surface properties, and these have been explored for the prediction of, e.g., Caco-2 permeability and oral absorption. It is pointed out here that a number of these new descriptors are strongly correlated to the more traditional physico-chemical properties. 

The interaction of drug molecules with biological membranes is a three-dimensional (3D) recognition that is mediated by surface properties such as shape, Van der Waals forces, electrostatics, hydrogen bonding, and hydrophobicity. Therefore, the GRID force field [5-7], which is able to calculate energetically favorable interaction sites around a molecule, was selected to produce 3D molecular interaction fields. 

A molecular field involves mapping the chemical forces between an interacting partner and a target (macro)molecule. As the information contained in 3D molecular fields is related to the interacting molecular partners, the amount of information in molecular interaction fields (MIFs) is in general superior to other mono-dimensionally or bi-dimensionally computed molecular descriptors. 

Fig. 17.1. Multivariate characterization with VolSurf descriptors. Molecular Interaction Fields (MIF shaded areas) are computed from the 3D-molecular structure. MIFs are transformed in a table of descriptors, and statistical multivariate analysis is performed.

In the following section, the calculation of the VolSurf parameters from GRID interaction energies will be explained and the physico-chemical relevance of these novel descriptors demonstrated by correlation with measured absorption/ distribution/metabolism/elimination (ADME) properties. The applications will be shown by correlating 3D molecular structures with Caco-2 cell permeabilities, thermodynamic solubilities and metabolic stabilities. Special emphasis will be placed on interpretation of the models by multivariate statistics, because a rational design to improve molecular properties is critically dependent on an understanding of how molecular features influence physico-chemical and ADME properties. 

The model interpretation is in good agreement with the known molecular factors influencing Caco-2 permeability. In addition - and this outlines the originality of the method - VolSurf allows the relevant 3D molecular properties to be quantified. Once the model is developed, as reported above, simple projection of the compound descriptors into it allows predictions to be made for new compounds. 

The VolSurf approach was used to correlate the 3D molecular descriptors by utilizing the water solubilities for as many compounds as could be found. Although over 2000 solubility values were identified, many showed contradictory results (both low and high values published). Moreover, some of the estimations had not been made by the authors and the original reference was not reported, while others were simply wrong, having not been measured under the standard conditions required. From the 2000 compounds, about 850 were carefully selected in addi-... 

Calculated molecular properties from 3D molecular fields of interaction energies are a novel approach to correlate 3D molecular structures with pharmacodynamic, pharmacokinetic and physico-chemical properties. The novel VolSurf descriptors quantitatively characterize size, shape, polarity, hydrophobicity and the balance between them. 

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