DESIGN OF ACTIVE ANALOGS OF A 15-RESIDUE PEPTIDE USING D-OPTIMAL DESIGN, QSAR AND A COMBINATORIAL SEARCH ALGORITHM

This report describes the rational design of novel analogues of a 15-r esidue antibacterial peptide CAMEL0. A constrained D-optimal design wa s carried out to derive a training set of 60 analogues. Partial least squares (PLS) models describing quantitative structure-activity relati onships (QSARs) were initially derived for the peptides using two publ ished and one novel parameter set. The novel 'Design parameters' were based on key structural features identified in hypothetical models of the mechanisms by which peptides interact with cell membranes. In an e xtension of the PLS method, influence statistics were used to decrease the weighting of compounds having a large effect on model predictions . A combinatorial search algorithm was developed which used PLS models as predictors to select a test set of 39 peptides with high predicted potencies. Within this set, the most potent analogue CAMEL135, which contained seven point mutations from CAMEL0, was identified. For a pan el of 24 bacteria, the mean MIC value of CAMEL135 was approximately ha lf of that for CAMEL0. For the parameter sets tested, covariance funct ions derived from Z-scales gave highest Q(2)-values for the training s et, whilst the model using the the 'Design parameters' gave least erro r when predicting the activity of the lest set. The predictive ability of a third published set of peptide parameters was found to compare f avourably with that of the parameters used in the design. Analysis of the PLS models indicates that hydrophobicity and amphipathicity are th e most important features influencing activity for this class of compo und. (C) Munksgaard 1997.