Ligand intramolecular motions in ligand-protein interaction: ALPHA, a novel dynamic descriptor and a QSAR study with extended steroid benchmark dataset.

The role of intramolecular motions in ligand-macromolecule interactions has been explored by developing and validating ALPHA, a novel QSAR (quantitative structure-activity relationship) descriptor. It is based on the spectral exponents (alpha), which measure the degree of 1/f alpha noise of coordinate fluctuations in molecular dynamics (MD) simulations. ALPHA is the first truly 'dynamic' QSAR descriptor, i.e., it can be derived directly from an MD trajectory. The performance of ALPHA was tested in detail employing the CBG (corticosteroid binding globulin) affinity of 31 benchmark steroids, supplemented with 11 steroids as an external test set. The only fair (42-50%) correlations of ALPHA with static 3D and electronic descriptors mean that ALPHA forms an independent molecular property. Furthermore, inclusion of ALPHA in the SOMFA/ESP model improves the correlation coefficient from 0.86 to 0.91, and /delta/ave from 0.46 to 0.36 for the benchmark dataset. The predictive ability of ALPHA can be interpreted as indirect evidence of the dynamic contribution to ligand-macromolecule interactions. The physical background of ALPHA is discussed and the importance of molecular motions for biological activity is anticipated.

Evaluation of a novel electronic eigenvalue (EEVA) molecular descriptor for QSAR/QSPR studies: validation using a benchmark steroid data set.

A novel electronic eigenvalue (EEVA) descriptor of molecular structure for use in the derivation of predictive QSAR/QSPR models is described. Like other spectroscopic QSAR/QSPR descriptors, EEVA is also invariant as to the alignment of the structures concerned. Its performance was tested with respect to the CBG (corticosteroid binding globulin) affinity of 31 benchmark steroids. It appeared that the electronic structure of the steroids, i.e., the "spectra" derived from molecular orbital energies, is directly related to the CBG binding affinities. The predictive ability of EEVA is compared to other QSAR approaches, and its performance is discussed in the context of the Hammett equation. The good performance of EEVA is an indication of the essential quantum mechanical nature of QSAR. The EEVA method is a supplement to conventional 3D QSAR methods, which employ fields or surface properties derived from Coulombic and van der Waals interactions.