Peptide-Based Drug Design

Cationic Antimicrobial Peptides 151
yielding a relative IC50(as a fraction of the IC50of Bac2A). These peptides
showed a wide range of activity, from inactive to much more active than
the control peptide Bac2A. We then calculated the set of QSAR descriptors
(descriptors due to Cherksov et al., indicated in Table 2) for each peptide using
molecular modeling software (85). (Because of computational time constraints
and uncertainty concerning the appropriate water or lipid environment of the
active peptide, the 3D structure of the peptides was not calculated; rather an
initial, �-helical structure was used.) Where descriptor values correlated with
another descriptor at greater than 0.95 (absolute value), one descriptor was
dropped so that the descriptor set did not cause problems in model building. A
total of 44 descriptors were identified for modeling (indicated in Table 2 with *).
We wished to determine if inductive QSAR combined with an advanced
machine learning technology such as ANNs is able to distinguish peptides with
high activity versus those without high activity, as such a capability would be
powerful for an in silico search for completely novel antibiotic peptides. (The
structure of an ANN is shown in Fig. 2.) We therefore classified each peptide
as more active (0.75 IC50of Bac2A)
Fig. 2. Structure of an artificial neural network. The network consists of three layers:
the input layer, the hidden layer, and the output layer. The input nodes take the values of
the normalized QSAR descriptors. Each node in the hidden layer takes the weighted sum
of the input nodes (represented as lines) and transforms the sum into an output value.
The output node takes the weighted sum of these hidden node values and transforms the
sum into an output value between 0 and 1.