Biomedical subjects
Publications and source records attributed to J M Blaney.
Color-coded computer graphics representations of the electrostatic potentials of trypsin, trypsin-inhibitor, prealbumin and its thyroxine complex, fragments of double-helical DNA, and a netropsin--DNA complex illustrate the electrostatic and topographic complementarity in macromolecule-ligand interactions. This approach is powerful in revealing intermolecular specificity and shows promise of having predictive value in drug design.
A quantitative structure-activity relationship (QSAR) has been formulated for the inhibition of purified E. coli dihydrofolate reductase by 23 5-(substituted benzyl)-2,4-diaminopyrimidines: log 1/C = 1.14MR'3,4,5 + 5.73; r = 0.887; s = 0.285. In this expression, MR'3,4,5 refers to the sum of MR values for X in the 3, 4 and 5 positions of the phenyl moiety. MR' signifies that the effective value of MR is limited to 0.79. Comparison of the QSAR for E. coli enzyme inhibition with that previously obtained for bovine enzyme offers the first general explanation for the great selectivity of the important antibacterial agent trimethoprim. Such QSSR promise to be of value in devising more selective drugs.
The inhibitory effect for a set of 23 5-(X-benzyl)-2,4-diaminopyrimidines acting on bovine liver dihydrofolate reductase (DHFR) had led to the following quantitative structure-activity relationship (QSAR): log 1/C = 0.62pi3 + 0.33epsilon sigma + 4.99, where r = 0.931 and s = 0.146. C in this expression is the molar concentration of inhibitor producing 50% inhibition, pi3 is the hydrophobic parameter for substituents on the 3 position of the phenyl moiety, and epsilon sigma is the the sum of the Hammett sigma constants for the 3, 4, and 5 substituents of the phenyl ring.
Dual representation of peptide and non-peptide structures in a chemical database as atomic-level molecular graphs and sequence strings permits chemical substructure and similarity searches as well as sequence-based substring and regular expression searches. CHUCKLES interconverts monomer-based sequences with SMILES, which represent atomic-level molecular graphs. Forward-translation maps peptide or other sequences into SMILES. Back-translation extracts monomer sequences from SMILES. This approach permits a generalized representation of monomers allowing user specification of any monomer. CHUCKLES allows mixing of atoms with user-defined monomer names; that is, monomer representation is consistent with SMILES notation. In addition, oligomer branching and cyclization are handled.
Screening mixtures of synthetic oligomers or fixed templates (e.g., rings) with varying substituents is increasingly the focus of drug discovery programs. CHORTLES is designed and implemented to facilitate representation, storage, and searching of oligomeric and template-based mixtures of any size. Building upon the CHUCKLES method of representing oligomers as both monomer-based sequences and all-atom structures, CHORTLES compactly represents a mixture without explicitly enumerating individual molecules. This method lends itself to a hierarchy relating mixtures to submixtures and individual compounds, as one finds when deconvoluting mixtures in drug lead discovery programs. In addition, we describe two methods of searching mixtures at the monomer level. We also present a simple pictorial representation for describing all components in a mixture, which becomes essential as the list of monomer names is expanded beyond common names (e.g., amino acids).