Molecular surface visualization

To be introduced to molecular surfaces and to different models for visualization... 

A completely new method of determining siufaces arises from the enormous developments in electron microscopy. In contrast to the above-mentioned methods where the surfaces were calculated, molecular surfaces can be determined experimentally through new technologies such as electron cryomicroscopy [188]. Here, the molecular surface is limited by the resolution of the experimental instruments. Current methods can reach resolutions down to about 10 A, which allows the visualization of protein structures and secondary structure elements [189]. The advantage of this method is that it can be apphed to derive molecular structures of maaomolecules in the native state. 

As the graphical capabilities of the computer systems became more powerful simultaneously the number of visualized structures increased. With the introduction of raster graphics (1974) and colored raster graphics displays (1979), other forms of molecular representations were possible [197]. CPK models could be represented and colored bonds or molecular surfaces could be visualized. 

The representation of molecular properties on molecular surfaces is only possible with values based on scalar fields. If vector fields, such as the electric fields of molecules, or potential directions of hydrogen bridge bonding, need to be visualized, other methods of representation must be applied. Generally, directed properties are displayed by spatially oriented cones or by field lines. 

Molecular lipophilicity potential (MLP) has been developed as a tool in 3D-QSAR, for the visualization of lipophilicity distribution on a molecular surface and as an additional field in CoMFA studies [49]. MLP can also be used to estimate conformation-dependent log P values. 

Laskowski RA (1995) SURFNET a program for visualizing molecular surfaces, cavities, and intermolecular interactions. J Mol Graphics 13 307-308... 

In order to visualize the results of MLP calculations for a given set of parameters and molecules, a LipoDyn output file for the molecular surface can be color-coded by the MLP values and displayed in VRML 2.0 format... 

The second section of this chapter deals with the question of how to transform the molecular scenario into a representation for which the eyeball technique can still be used. We shall consider ways in which the molecular scenario can be transformed into one in which human pattern recognition abilities can be successfully used. New instruments of man-machine communication in molecular science will be described and the concept of molecular surfaces will be introduced. These surfaces are envisioned as the interface between different molecules or between a molecule and its solvent. This section also deals with some visualization techniques and the mapping of patterns onto the molecular surfaces it also demonstrates that this type of molecular representation is well suited to the application of the eyeball technique. 

Human abilities for pattern recognition can be successfully applied in the field of molecular recognition only when there are procedures and tools available that allow a transformation of the molecular scenario into one which can be manipulated with visual control. This can be realized by exploitation of the concept of molecular surfaces with the aid of modern graphical workstation technologies. We now consider the relevant concepts in some detail. 

We shall not describe all the possibilities of molecular visualizations here, but restrict our discussion to molecular surfaces and the mapping of... 

The molecular surface concept is not only useful for a representation of the bulkiness and the shape of molecules. These surfaces can also be used as screens for the visualization of many properties by means of color coding techniques. Color coding is a popular means of displaying scalar information on a surface. " " Every three-dimensional scalar or vector field that may be generated on the basis of the position of atomic or molecular fragments can be visualized by color coding on a given surface. 

In the work of Zachmann et al. new approaches to the quantification of surface flexibility have been suggested. The basis data for these approaches are supplied by molecular dynamics (MD) simulations. The methods have been applied to two proteins (PTI and ubiquitin). The calculation and visualization of the local flexibility of molecular surfaces is based on the notion of the solvent accessible surface (SAS), which was introduced by Connolly. For every point on this surface a probability distribution p(r) is calculated in the direction of the surface normal, i.e., the rigid surface is replaced by a soft surface. These probability distributions are well suited for the interactive treatment of molecular entities because the former can be visualized as color coded on the molecular surface although they cannot be directly used for quantitative shape comparisons. In Section IV we show that the p values can form the basis for a fuzzy definition of vaguely defined surfaces and their quantitative comparison. 

A software that provides visualization and display of molecular surfaces, orbitals, electrostatic potentials, charge densities and spin densities (http //www.cambridgesoft.com/)... 

In the ensuing discussion of surface chemical reactions, the contribution of mlveetion to transport will not be explicitly included. Advection is readily I tea ted on a physical basis without consideration of individual molecular characteristics. In many laboratory situations, bulk solution can be considered "well-mixed and molecular transport visualized as a migration across a Mugnant layer near surfaces where advective transport is negligible. 

D and 3D autocorrelation vectors [70] represent intramolecular 2D topologies or 3D distances within particular molecules. An autocorrelation coefficient is a sum over all atom pair properties separated by a predefined number of bonds (2D) or distance (3D), while the entire vector represents a series of coefficients for all topological or Cartesian distances. Atomic properties involve hydrophobicity [71], partial atomic charges, hydrogen bonding potential and others. Again, a PCA is often used to reduce the number of variables. 3D autocorrelation vectors of properties based on distances calculated from 3D molecular surfaces [72] have also been applied to visually assess the diversity of different libraries [73]. 

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