Hydropathy scale

There are several other hydropathy scales (like the electro-negativity scales ). Such scales have been used extensively to constmct empirical force fields used in the study of protein folding with the implicit solvent model (where solvent molecules are not present at all). 

Such a characterization has been immensely useful because it allows us to realize that the core of a protein is almost always filled with these hydrophobic residues, hence the origin of the term hydrophobic core . Most of the hydrophilic amino acid residues are usually found on the surface and they interact with the water molecules to stabilize the protein. 

---

Fig. 2. Conditional probability plot for Sweet and Eisenberg s (1983) hydropathy scale. The black line is the probability ( axis) that a residue is ordered given the hydropathy score indicated on the x-axis. The dashed line is the probability of disorder. Negative values for hydropathy indicate hydrophilicity, positive values indicate hydrophobicity. The area between the two curves is divided by the total area of the graph to obtain the area ratio.

Uversky and co-workers recently used a pair of sequence attributes, specifically the Kyte-Doolittle hydropathy scale and net charge, to... 

Palliser CC, Parry DA. Quantitative comparison of the ability of hydropathy scales to recognize surface fS-strands in proteins. Proteins 2001 42 243-255. 

By using the cross-validation statistical procedure and Kyte-Doolittle hydropathy scale, the prediction results for TMH in the training data base of 63 membrane proteins common to us and to Rost et al. [9] and also to Jones et al. [33] were similar in accuracy by all three methods. When training data base is enlarged to 168 proteins, we maintain the 95% accuracy for predicted transmembrane helices and almost 80% (78.6%) of proteins are predicted with 100% correct transmembrane topology. When 168 proteins are divided in the above mentioned training set of 63 proteins and an independent test set of 105 proteins, all performance parameters for TMH prediction associated with a set of 105 proteins exhibited a decrease which was smaller in our case than for Rost et al. [9]. 

The SPLIT algorithm was optimized for predicting transmembrane a-helices by using the Kyte-Doolittle hydropathy scale to create profile of a-helix preferences. The digital version of prediction for transmembrane a-helices is designated as the TMH predictor. Predicted profile of P-strand preferences can be used to find sequence location of potential membrane-embedded or surface-attached P-strands. The score for potential membrane-attached P-strand... 

MODKD Modified Kyte- This work (Table 4) Doolittle hydropathy scale 0 711 95.7 76.8... 

Figure 3 Score profiles for cxlbjarde (Figure 3A) and for cox3 parde (Figure 3B) of cytochrome oxidase from Paracoccus denitrificans [14] are obtained by substraction of turn preferences from a-helix preferences (full line). Digital predictions, as outcome of the best training procedure for the SPLIT algorithm with Kyte-Doolittle hydropathy scale (Methods), are shown as bold horizontal bars at the score level 0.5. Observed location of TMH segments are shown as bold horizontal bars at the score level 0.2.

Two data bases of soluble proteins of known structure used to find false positive prediction results (Table I and Table II). Gaussian parameters needed for evaluation of preference functions based on the Kyte-Doolittle hydropathy scale [17] (Table III). Table with detailed prediction results for transmembrane helices in 168 integral membrane proteins (Table IV). Table with a detailed comparison of prediction results for 10 best known membrane proteins for our and three other algorithms (Table V). All these tables together with the FORTRAN 77 source code are available from the anonymous ftp server mia.os.camet.hr in the /pub/pssp directory. The anonymous login is ftp and the e-mail address is accepted as password. The list of files with short descriptions is contained in the 00index.txt file. 

Table 15.1 Hydropathy index (or hydropathy scale) of different amino acids. This scale determines how more hydrophobic a particular amino acid is compared to others. The more positive is the number, the more hydrophobic it is and vice versa. (Table 15.1 has been adapted with permission from J. Mol. Biol,. 157 (1982), 105-132. Copyright (1982) Elsevier.)...

The first point to note is that the PMF obtained by this more elaborate scheme is in general good agreement with the simple hydropathy-scale-based potential proposed earlier. Second, this PMF (from PDB) correctly reproduces the effective attractive interaction between two hydrophobic residues, such as phenylalanine, and effective repulsive interaction between two hydrophilic residues, such as lysine, as shown in Figure 15.7. 

Orientation-dependent PMF gives valuable insight into the nature of the orientation-dependent interaction between any two amino acid residues. The orientation-dependent PMF also reveals many unexpected pair interactions which defy the trend given by the hydropathy scale. An example is provided by the Arg-Arg pair interaction, which is found to be surprisingly attractive at short separahon, even though it is one of the most hydrophilic residues. 

The reason was found to be the presence of an HB that forms a bridge between the two arginine residues. Such specihc many-body effects cannot be captured in a hydropathy scale. 

Understanding hydrophobic hydration requires an estimate of the chemical potential of the non-polar solute in water. Actually, one measures the change in chemical potential as the non-polar solute is transferred from its own liquid to water. This quantity is related to the hydropathy scale discussed earlier in the context of protein folding. 

---