Novel Descriptors Derived from the Aggregation Propensity of Di- and Tripeptides Can Predict the Critical Aggregation Concentration of Longer Peptides

Self-assembling amphiphilic
peptides have recently received special
attention in medicine. Nonetheless, testing the myriad of combinations
generated from at least 20 coded and several hundreds of noncoded
amino acids to obtain candidate sequences for each application, if
possible, is time-consuming and expensive. Therefore, rapid and accurate
approaches are needed to select candidates from countless combinations.
In the current study, we examined three conventional descriptor sets
along with a novel descriptor set derived from the simulated aggregation
propensity of di- and tripeptides to model the critical aggregation
concentration (CAC) of amphiphilic peptides. In contrast to the conventional
descriptors, the radial kernel model derived from the novel descriptor
set accurately predicted the critical aggregation concentration of
the test set with a residual standard error of 0.10. The importance
of aromatic side chains, as well as neighboring amino acids in the
self-assembly, was emphasized by analysis of the influential descriptors.
The addition of very long peptides (70–100 residues) to the
data set decreased the model accuracy and changed the influential
descriptors. The developed model can be used to predict the CAC of
self-assembling amphiphilic peptides and also to derive rules to apply
in designing novel amphiphilic peptides with desired properties.