Current Computer - Aided Drug Design - Volume 1, Issue 3, 2005

Solvent accessibility plays an important role to the structure and function of biological macromolecules. Generally amino acid residues located in the surface of a protein serve as active sites and/or interact with other molecules and ligands. In this review, we briefly define the concept of solvent accessibility with computational procedure. The methods available for calculating solvent accessibility of molecules from their three-dimensional structures and predicting the accessibility from amino acid sequences have been described. The salient features of the solvent accessibility of amino acid residues/nucleotides/sugars in biomolecular structures and its importance for the stability of biomacromolecules have been explored. Further, the application of solvent accessibility for identifying active site residues, binding sites in DNA binding proteins and functionally important residues in membrane proteins will be discussed. The information about the accessible surface area of the basic units of biomacromolecules will be very helpful for de novo protein and drug design.

It is essential, in order to minimize expensive drug failures, to determine potential toxicity problems as early as possible. In view of the large libraries of compounds now being handled by combinatorial chemistry and high-throughput screening, identification of drug toxicity is advisable even before synthesis. Thus, the use of predictive toxicology is called for. A great number of in silico approaches to toxicity prediction have been described in the literature, but one of the most ambitious goals of QSAR applications to toxicology is modeling of chemical carcinogenicity, which has severe consequences on the quality of life and has led to enormous investments in time, financial resources, and animal lives necessary to test the chemicals adequately. This review attempts to summarize present knowledge related to the computational prediction of carcinogenicity. Several computational protocols are described, ranging from knowledge-based approaches and statistically-based systems to simple and fast procedures based on only the 2-D graphing of the investigated structures. Comparative tests of the ability of these systems to predict carcinogenicity show that improvement is still needed. The consensus approach is recommended, whereby the results from several prediction systems are pooled.

In the past two decades, there has been growing interest in finding novel and non-toxic antioxidants to meet the requirements in chemical, food and pharmaceutical industries. To accelerate the antioxidant discovery process, various theoretical methods have been employed to investigate the structure-activity relationships of antioxidants. Accordingly, some rational-design strategies for antioxidants have been proposed and applied in practice. This review summarizes the current knowledge on this topic, which will be helpful to direct the practice in related fields.

The amount of resolved X-ray structures of protein-ligand complexes have exploded during the last decade. This has initiated much improvement of docking methods by an advanced knowledge about the key interactions in the complexes, nevertheless, it still remains a challenge even to reproduce known experimental results by ligand docking. A number of docking methods for predicting binding modes of small molecules have been developed, methods which are also thought to help to quantify energetics of different molecular interactions. Ligand docking is mainly used by the pharma industry for identifying possible compounds for development in the drug discovery process, usually in the very early hit identification phase, but also at later stages of lead optimisation. The quality of different docking methods has been thoroughly investigated, however, the relationship between methods, scoring functions and target proteins on one hand, and docking performance on the other hand still seems poorly understood. Scoring functions are especially important since minimisation algorithms rely on these functions. Therefore, an accurate scoring function is absolutely crucial to obtain correct results, i.e. correct binding modes but also correct ranking of docked ligands. The accuracy of scoring functions is target dependent, which implies that it is important to study the scope and limitations of these functions. In this report, we discuss some of the available docking methods and scoring functions applied to relevant targets for the pharma industry.

Quantitative structure-activity relationships (QSAR) are often employed to establish a correlation between structural features of potential drug candidates and their binding affinity towards a macromolecular target. In 3D-QSAR, the structures of the involved molecules are represented by three-dimensional entities, allowing to quantify electrostatic forces, hydrogen bonds and hydrophobic interactions at the atomic level. Models based on 3D-QSAR typically represent a binding site surrogate with physico-chemical properties mapped onto its surface or a grid surrounding the ligand molecules, superimposed in 3D space. Unfortunately such a single construct interacts with all ligands simultaneously, thus disabling the simulation of induced fit (receptor-to-ligand adaptation) - a fundamental shortcoming of the technology. As this entity represents all but a receptor surrogate, the bioactive conformation, orientation and protonation state of the ligand molecules might be guessed at best. Multidimensional QSAR represents a subtle extension of 3D-QSAR attempting to overcome both shortcomings. In this account, we review different concepts and demonstrate their use to predict binding affinities of chemically diverse sets of ligand molecules binding to G-protein coupled and nuclear receptors. By employing multi-dimensional QSAR on partially diverse and large data sets, predicitive r2 of 0.837 (neurokinin-1), 0.859 (bradykinin B2 receptor) and 0.907 (estrogen receptor) were for example obtained using the Raptor and Quasar software.