Ligand design by a combinatorial approach based on modeling and experiment: application to HLA-​DR4

Combinatorial synthesis and large scale screening methods are being used increasingly in drug discovery, particularly for finding novel lead compds. Although these "random" methods sample larger areas of chem. space than traditional synthetic approaches, only a relatively small percentage of all possible compds. are practically accessible. It is therefore helpful to select regions of chem. space that have greater likelihood of yielding useful leads. When three-​dimensional structural data are available for the target mol. this can be achieved by applying structure-​based computational design methods to focus the combinatorial library. This is advantageous over the std. usage of computational methods to design a small no. of specific novel ligands, because here computation is employed as part of the combinatorial design process and so is required only to det. a propensity for binding of certain chem. moieties in regions of the target mol. This paper describes the application of the Multiple Copy Simultaneous Search (MCSS) method, an active site mapping and de novo structure-​based design tool, to design a focused combinatorial library for the class II MHC protein HLA-​DR4. Methods for the synthesizing and screening the computationally designed library are presented; evidence is provided to show that binding was achieved. Although the structure of the protein-​ligand complex could not be detd., exptl. results including cross-​exclusion of a known HLA-​DR4 peptide ligand (HA) by a compd. from the library. Computational model building suggest that at least one of the ligands designed and identified by the methods described binds in a mode similar to that of native peptides.