Intervals and the deduction of drug binding site models

Abstract

In the search for new drugs, it often occurs that the binding affinities of several compounds to a common receptor macromolecule are known experimentally, but the structure of the receptor is not known. This article describes an extraordinarily objective computer algorithm for deducing the important geometric and energetic features of the common binding site, starting only from the chemical structures of the ligands and their observed binding. The user does not have to propose a pharmacophore, guess the bioactive conformations of the ligands, or suggest ways to superimpose the active compounds. The method takes into account conformational flexibility of the ligands, stereospecific binding, diverse or unrelated chemical structures, inaccurate or qualitative binding data, and the possibility that chemically similar ligands may or may not bind to the receptor in similar orientations. The resulting model can be viewed graphically and interpreted in terms of one or more binding regions of the receptor, each preferring to be occupied by various sorts of chemical groups. The model always fits the given data completely and can predict the binding of any other ligand, regardless of chemical structure. The method is an outgrowth of distance geometry and Voronoi polyhedra site modeling but incorporates several novel features. The geometry of the ligand molecules and the site is described in terms of intervals of internal distances. Determining the site model consists of reducing the uncertainty in the interregion distance intervals, and this uncertainty is described as intervals of intervals. Similarly, the given binding affinities and their experimental uncertainties are treated as intervals in the affinity scale. The final site model specifies an entire region of interaction energy parameters that satisfy the training set rather than a single set of parameters. Predicted binding for test compounds results in an interval which, when compared to the experimental interval, may be correct, incorrect, or vague. There is a pervasive ternary logic involved in the assessment of predictions, in the search for a satisfactory model, and in judging whether a given molecule may bind in a particular orientation: true, false, or maybe. The approach is illustrated on an extremely simple artificial example and on a real data set of cocaine analogues binding to a nerve membrane receptor in vitro. © 1995 by John Wiley & Sons, Inc.