Peptide-Based Drug Design

Cationic Antimicrobial Peptides 149
use of z values to reduce the number the variables used for analysis of lactoferricin
derivatives, the number of peptides (the number of equations) is still
small compared to the number of variables. While techniques such as PLS or
PCA can be used to produce models based on this amount of data, other powerful
modeling techniques will not work. In addition to having more confidence in
parameter estimation, having much more data than number of variables also
allows more definitive determination of model performance using techniques
such as cross-validation whereby a representative fraction of the data is set aside
for testing after models have been built on the remaining data.
Another limitation of published QSAR studies into antibacterial activity of
peptides is due to the types of models used. Both PCA and PLS produce models
that are linear with respect to the QSAR descriptors. Often the choice to use
simpler linear models is made deliberately (e.g., as stated [76,77]) sincethe
resulting models allow straightforward interpretation for predicting improvements
in peptide activity. However, more complex models such as ANNs are
capable of modeling nonlinear relationships as well, where descriptors interact
with one another in a nonadditive manner. The main disadvantages of using
more complex models such as ANNs are the cryptic nature of the models
produced (contributions of individual descriptors to activity are not clear) and the
requirement for larger amounts of data, since the number of parameters used to
determine the model grows more quickly than the number of QSAR descriptors
on which it is based.
3.4. Inductive QSAR Descriptors
3.4.1. Introduction
In addition to the modeling techniques, QSAR descriptors used to date in
modeling antibacterial peptides have relied on sets of peptides with a high degree
of similarity. The QSAR descriptors for the lactoferricin studies have measured
differences in activity between peptides that differ in only a few amino acids,
for example. However, a set of QSAR descriptors has been developed recently
that allows for full 3D-sensitive properties of peptides: the inductive QSAR
descriptors (reviewed in ref. 80), which we feel may dramatically improve
the prediction of activity of antimicrobial peptides by allowing models to be
constructed that are valid for a large range of peptides rather than in narrow
range of nearly identical peptides.
Previously, inductive QSAR descriptors have been successfully applied to
a number of molecular modeling studies, including quantification of antibacterial
activity of organic compounds (89), calculation of partial charges in small
molecules and proteins (81), and in comparative docking analysis as well as
in in silico lead discovery (82). Inductive QSAR descriptors have been used