Hydrophobic potential

The representation of molecules by molecular surface properties was introduced in Section 2.10. Different properties such as the electrostatic potential, hydrogen bonding potential, or hydrophobicity potential can be mapped to this surface and seiwe for shape analysis [44] or the calculation of surface autocorrelation vectors (refer to Section 8.4.2). 

Several simulations have been undertaken to study this interesting peptide in detail. In an early simulation, a mean field approximation was used representing the bilayer by a hydrophobic potential [85]. The starting structure was a surface-bound... 

Mean-field hydrophobic interactions will be briefly presented as well. Finally, implementation of the mean-field electrostatic and hydrophobic potentials to an effective MD approach will be also presented. 

A wide variety of energy functions have been used as part of the various GA-based protein structure prediction protocols. These range from the hydrophobic potential in the simple HP lattice model [19] to energy models such as CHARMM, based on full fledged, detailed molecular mechanics [9]. Apparently, the ease by which various energy functions can be incorporated within the framework of GAs as fitness functions encouraged researchers to modify the energy function in very creative ways to include terms that are not used with the traditional methods for protein structure prediction. 

F. W. Lichtenthaler and S. Immel, Sucrose, Sucralose and Fructose Correlations between Hydrophobicity Potential Profiles and AH-B-X Assignments, in Sweet Taste Chemoreception (G. G. Birch, M. A. Kanters, and M. Mathlouti, eds.). Elsevier, Amsterdam, 1993. 

Fig. (9). Hydrophobic potentials on the two faces of the gomesin P-sheet [115]. The orientation of the peptide backbone is indicated on the right side at (A) and (B) images which were obtained by 180° rotation. Hydrophobicity increases from blue to brown while green is a colour halfway for intermediate potentials. These images were kindly provided by Dr Fran9oise Vovelle (CNRS, Orleans, France).

Projection of the hydrophobic potential onto the van der Waals surface of the molecules shows that the diphenyl group represents a huge hydrophobic moiety that might act like an anchor for the molecules in the lipid bilayer (Figure 7.3a). In this case, the phenone moiety, which has not been varied in this compound series, is supposed to interact with the protein. This possible change of the binding mode is also reflected by a different HiH coefficient of the dose-response curve (Figure 7.3b). 

Molecular similarity The degree of similarity between molecules, although quantitatively measurable, very much depends on what molecular features are used to establish the degree of similarity. One of the many comparators is the electron density of a pair of molecules. Other comparators include electrostatic potentials, reactivity indices, hydrophobicity potentials, molecular geometry such as distances and angles between key atoms, solvent accessible surface area, etc. It is an open question as to how much or what part(s) of the molecular structure is to be compared. The Tanimoto coefficient which compares dissimilarity to similarity is often used in molecular diversity analysis. 

Choosing autocorrelation of the electrostatic (ESP), the hydrogen bonding (HBP), and the hydrophobicity potential (HYP) for the representation of the molecules in this dataset results in the Kohonen maps illustrated in Plate 2 (see plate section). Neurons with hits are coloured in magenta, whereas neurons containing only non-hits are in red. 

Hydrophobic interactions are rather typical for organic colloidal particles (e.g., the particles of latex) dissolved in water. They produce a net attractive contribution. The hydrophobic potential for two equal spherical colloidal particles is expressed as [13] ... 

For a 3D-QSAR autocorrelation matrix, the distances are calculated from the 3D structures of the molecules. Both points on a CoMFA-like lattice and points on the molecular surface have been used for these distance calcula-tions.2 7 208 Similarly, the 3D autocorrelation properties are based on properties at these points (e.g., electrostatic or hydrophobic potential). Wagener et al. used a point density of 10 points/A on the van der Waals surface for the property calculation for the autocorrelation matrix, they considered distances from 1 to 13 A and a distance interval of 1 A to produce an autocorrelation vector of length 12.2° These 12 properties were then analyzed by principal components analysis, a Kohonen map, and a feed-forward multilayer neural... 

An essay on the structural representation of sucrose has been published. Following an historical account of the establishment of the constitutional formula and confoimational features of sucrose, the present possibilities for graphics displays of its molecular geometry, contact surfaces, and hydrophobicity potential are given. ... 

Another way to consider protein hydration is to extend the concept of the solvent accessible surface (cf. O section Solvent-Accessible Surface ) applying continuum dielectrics models. For example, combination of the electrostatic and an appropriate hydrophobic potential, accounting for hydrophilic, and hydrophobic interactions, respectively, maybe used as a solvation function, which considerably increases the reliability of protein structure predictions (Lin et al. 2007). 

Lin, M. S., Fawzi, N. L., Head-Gordon, T. (2007). Hydrophobic potential of mean force as a solvation function for protein structure prediction. Structure, 15, 111,... 

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