Peptide-Based Drug Design

148 Hilpert et al.
of substituting amino acids in the hydrophobic face with less lipophilic ones
to decrease hemolytic effect without significantly affecting antimicrobial or
cytotoxic activity.
In a second paper, Frecer (77) performed QSAR analysis on 97 protegrin
derivatives of 14 amino acids in length, whose activity was already published.
In this study he calculated 14 descriptors including features such as charge,
overall lipophilicity, separate lipophilicity of polar and nonpolar faces of the
molecule, molecular surface areas for polar and nonpolar faces, total numbers
of lipophilic and aromatic residues, and numbers of hydrogen bond donors
and receivers. In addition, 10 amphipathicity measures were calculated from
these involving ratios of, for example, charge to overall lipophilicity. A genetic
function approximation (GFA) was used to generate linear equations involving
up to five descriptors to describe antibacterial and hemolytic activity. Models
were evaluated for lack-of-fit score and the best equation found. They found
only moderate predictive power with antibacterial activity due mostly to charge
and amphipathicity (ratio of charge to lipophilicity of nonpolar face). Also,
hemolytic activity was found to be due to lipophilicity of the nonpolar face for
this set of peptides with moderate correlation.
Ostberg and Kaznessis (78) examined protegrin and analogues using QSAR
descriptors such as charge, molecular weight, as well as molecular structural
properties such as volume, density, globularity, energy components, and
solvent accessible surface area (SASA). The data set in this study consisted
of 62 protegrin and analogues and the multivariate linear regression produced
moderate correlation between predicted and actual activity: antibacterial activity
was found using five descriptors, four descriptors for cytotoxicity, and four
descriptors for hemolysis.
3.2.3. QSAR of Scrambled Bactenecin-Derived Peptides
A linearvariantofthe bovine cationic peptide bactenecin, Bac2A,has been used
in studies of positional importance of amino acids. Hilpert et al. (36) examined
the effect of scrambling the amino acid sequence of Bac2A and investigated the
activity of the resulting peptides. A QSAR analysis was performed on a total of 49
peptides using 18 descriptors based largely on positions of arginines, distributions
of hydrophobic amino acids, and water-accessible surface. A binary classification
algorithm was used to create a decision tree to classify peptides that are active or
inactive, with an accuracy of 74% trained on the full set of peptides.
3.3. Limitations of Current Studies
There are several limitations of existing QSAR modeling of antibacterial
activity. The primary limitation concerns the size of the data sets. Despite the