1 EXERCISE III . 1 STRUCTURAL DATA : THE BASIS FOR MOLECULAR MODELING

Theoretical Introduction Proteins can properly and correctly work only when they fold into their native structures. The possibility of observing the three-dimensional structure of a protein, through X-ray diffraction or NMR techniques, is essential to understand how a protein works and to rationally design potential ligands, capable of inhibiting or activating a biological target. We can, in fact, assume that structural data represent the milestone for reliable molecular modeling and computer-aided drug design approaches. Nevertheless, determining the exact structure into which a protein naturally folds is a very complex and time-consuming procedure. Thus, the molecule has to be purified, crystallized, exposed to X-radiation, and the diffraction diagram must be transformed into an intelligible electron density map. Unfortunately, some proteins are not easy to crystallize and others are not able to generate clear diffraction patterns after X-ray exposure, so, different computational approaches and simulations must be applied, in order to find new potential lead compounds. Actually, there are two different methods used to design new drugs: the ab initio approach and the in silico virtual screening. The first is based on the knowledge of the three-dimensional structure of the target binding pocket and aims to design drugs sterically and physicochemically complementary to the active site properties and shape. The second one is able to screen thousand of compounds belonging to a large database of known molecules, and to computationally evaluate their binding affinity toward the target protein (Lengauer 2003). If we are very lucky and we know both the three-dimensional structure of our biological target and of the potential lead compound, or at least of the potential chemical scaffold, we can simply apply structure-based drug design procedures, trying to improve the inhibition potency of the potential new drugs. If the protein structure is known, but we do not have any clue about the structure of a possible inhibitor, or activator, we can use combinatorial chemistry techniques to generate millions of molecules, to be subsequently virtually tested, with docking and scoring simulations. Knowing the structure of the receptor and the shape and chemical nature of the binding pocket, it is also possible to model a lead compound, in order to achieve an almost perfect geometrical and chemical complementarity, thus combining random virtual screening and rational drug design processes, or to choose a complete de novo design approach. On the contrary, ligandbased design can be used when only the ligand configuration is known, but the target structure is not available. Quantitative structure–activity relationship (QSAR) investigation could represent a useful tool to improve the inhibition properties of a lead compound, computationally modifying its chemical properties. Moreover, QSAR techniques could elucidate also the chemical peculiarities of the binding pocket, driving to the prediction of a pharmacophore model. At last, when no structural information is available on either the biological target or the ligands, the unique possibility is to generate thousands of different chemical compounds through combinatorial chemistry and to experimentally screen them. Virtual screening is the computational in silico variant of highthroughput screening, and even if the accuracy of virtual predictions is usually lower than the accuracy of experimental measurements, virtual screening presents some benefits. The calculations, in fact, are cheaper and faster, and it is possible to test and model compounds not yet purchased or synthesized.