Current Computer - Aided Drug Design - Volume 4, Issue 2, 2008

Modern drug discovery has contributed much to the progress of medicine and well being of societies during the past century. Generally, a disease relevant macromolecule is studied first in vitro, in cells and in whole organisms, and evaluated as a potential drug target for a specific therapeutic intervention. Medicinal chemistry projects then commence by the search and identification of a binding partner for the single macromolecular target. This one-drug one-target design strategy is what has been in use for several decades and is widely pursued both in the academia and in the pharmaceutical industry. However, many debilitating disorders such as cancers, cardiovascular diseases, dementias, depression, to name few, basically result from multiple molecular abnormalities, not from a single defect. Moreover, systems biology has revealed that human cells and tissues are composed of complex, networked systems with redundant, convergent and divergent signaling pathways. And hence, it is increasingly being recognized that a balanced modulation of the several but relevant and inter connected targets can provide a superior therapeutic and side effect profile of drugs compared to the more conventional one-drug one-target one-disease practice. Although the currently available drugs are inherently multiple acting, the design of multi-target selective drugs is just a recent trend and is beginning to be appreciated by the scientific community. The success of this promising drug-design paradigm will depend on advances in the identification of the correct and relevant multiple targets and their binding partners. This manuscript reviews the emerging concepts of attacking multiple targets through a deliberate design of agents which could bind with a selected number of several proteins relevant in a given disease. The current knowledge and tools for the rational design of a multitarget selective ligand is reviewed and the challenges, limitations and outlook of such novel ligand design strategy is presented.

Since the advent of QSAR (quantitative structure activity relationship) modeling quantitative representations of molecular structures are encoded in terms of information-preserving descriptor values. Nowadays, a nearly infinite variety of potential descriptors is available and descriptor selection is no longer a task which can be done manually. There is an increasing need for automation in order to reduce the dimensionality of the descriptor space. Classical feature selection (FS) and dimensionality reduction (DR) methods like principal component analysis, which relies on the selection of those descriptors that contribute most to the variance of a data set, often fail in providing the best classification result. More sophisticated methods like genetic algorithms, self-organizing-maps and stepwise linear discriminant analysis have proven to be useful techniques in the process of selecting descriptors with a significant discriminative power. The topic FS and DR becomes even more important when predictive models are approached which should describe the QSAR of highly promiscuous target proteins. The ABC-transporter family, the cardiac hERG-potassium channel, and the hepatic cytochrom-P450-family are classical representatives of such poly-specific proteins. In this case the interaction pattern is a rather complex one and thus the selection of the most predictive descriptors needs advanced methods. This review surveys FS and DR methods that have recently been successfully applied to classify ligands of poly-specific target proteins.

The various contributions to binding energies for cytochrome P450 enzyme-substrate interactions are discussed in the light of intermolecular forces of attraction in biological systems. These energies include: electrostatic, van der Waals, hydrogen bond, π-π stacking and desolvation processes. These individual components can be used to estimate the binding energies of P450 substrates, and the example of camphor in CYP101 is employed to demonstrate the particular merits of these approaches. The various methods of calculating desolvation energies are demonstrated for camphor binding to CYP101, together with estimation of the hydrogen bond energy associated with this process as they are thought to be the major contributions. The binding of warfarin to CYP2C9 is also discussed and evaluated in the light of the estimations for camphor binding to CYP101, thus indicating a degree of comparison between examples of bacterial and human P450-substrate interactions. The various force fields (Amber, Tripos and AutoDock) employed in energy calculations are also compared, together with typical values for the several individual components to the overall binding energy.

The olfactory system has sophisticated molecular mechanisms for recognizing and discriminating an enormous number of odorants. The detection of odorants in mammals is mediated by several hundreds of olfactory receptors (ORs), which comprise the largest superfamily of G-protein-coupled receptors (GPCRs) in the genome. Because GPCRs are major targets for therapeutic application, ample experimental data and computer modeling studies are available on some GPCRs. However, even though ORs represent approximately one half of all GPCRs, few structural and functional studies have been carried out for ORs. Here, we review recent studies on mechanisms underlying the molecular recognition of a large number of odorants by ORs. A combination of computational and experimental approaches has revealed the odorant- binding site of ORs. Point mutations in the odorant binding site based on the mode of odorant binding resulted in predicted changes in ligand specificity and antagonist activity, demonstrating the validity of the binding site model and indicating that it may be applied to the design of useful ligands for ORs. Understanding the molecular basis for the discriminative power of the olfactory system will also provide insight into how to design agonists or antagonists of GPCRs.

QSAR modeling, a powerful method for the computer - aided drug design, demands appropriate choice of molecular structure description. At present thousands descriptors of molecular structure are suggested in QSAR and QSPR approaches. The selection of a subset of the most relevant molecular descriptors, used as variables, is important step in model development. In this short review recently reported algorithms for variable subset selection procedure are considered. The scoring functions and some other useful guidelines are discussed.

Target-based drug design uses available 3D structural information of receptor molecules to either dock putative ligand molecules to receptor binding sites or to de-novo design new ligands. In many cases accurate prediction of putative binding geometries requires the appropriate inclusion of conformational flexibility of both the ligand as well as the receptor structure. The problem of appropriate treatment of conformational flexibility during docking is also tightly connected to the improvement of scoring a docked ligand-receptor complex. Highly accurate scoring of a ligand placement is only possible if the complex geometry has been predicted with high precision. Considerable progress has already been achieved in modeling the conformational flexibility of small organic ligands during docking. Although of similar importance, receptor flexibility has not been tackled satisfactorily despite the steady increase in computational power. Especially during virtual screening of large drug-like compound libraries the target structure often is still kept rigid. However, many protein structures undergo local structural changes (side chain or loop motions) as well as global changes in the backbone geometry upon complex formation. In recent years, several promising approaches to efficiently tackle receptor flexibility have been introduced ranging from conformational ensemble methods to explicit inclusion of the most relevant receptor degrees of freedom. Possible applications and future directions on improving flexible docking approaches will be discussed.