Current Computer - Aided Drug Design - Volume 5, Issue 4, 2009

In this study we describe a method to identify important genes that appear to be involved in the cytotoxic mechanisms of key molecular fragments (biophores) contained within the structures of anticancer compounds. The anticancer biophores were mined by the MULTICASE program by analyzing 60 datasets containing 3271 compounds tested against the NCI-60 human cancer cell lines. For each identified fragment, statistically relevant genes were found by relating the activity profiles of the molecules containing the fragment and the gene expression profiles of the different cell lines. Microarray gene expression data of 13111 genes was used in conjunction with the LeFE algorithm to accomplish this task. We have demonstrated that regression analysis can then predict the cytotoxic activity of a compound in cell lines outside of those included in the regression model, even if it belongs to a different cancer type provided that the expression levels of identified genes are known for the cell lines. It is hoped that identifying key genes within the context of specific substructures responsible for the cytotoxic activity of anticancer agents could offer a better handle for designing specialized drugs targeting specific tumors based on their genetic profile.

Rational Drug Design has become a well-established discipline in pharmaceutical research. It uses computational chemistry with the aim to discover or study drugs and their related biologically active molecules. The purpose is to reduce the number of targets for a good drug that have to be subjected to expensive and time-consuming synthesis. The advanced methods developed in this field united with the increased potency of the new computer generation are the tools for the scientist to explore the conformational variability and properties of a large number of potentially active molecules and their interaction with each other or with their biological target (i.e. enzyme or receptor). Among these methodologies, Molecular Dynamics (MD) is one of the most useful tools in this process now routinely used to simulate complex dynamic processes that occur in biological systems such as molecular recognition in drug-receptor complexes. This paper reviews the current status of Molecular Dynamics methods, and some of its most recent and interesting applications in the field of Drug Design and Discovery.

From virtual screening to understanding the binding mode of novel ligands, docking methods are being increasingly used at multiple points in the drug discovery pipeline. It is now well established that the amount (and quality) of information provided to the docking programs greatly influences their accuracy. Ultimately, the docking programs should consider all the factors involved in the ligand/macromolecule binding process. In fact, developers have been moving towards the modeling of the dynamics involved in solvated protein/ligand complexes to improve the binding mode prediction accuracy. The problem of modeling “reality” can be broken down into several factors including the consideration of both ligand and receptor flexibility and the consideration of bulk and bridging water molecules. Additional factors such as directional metal coordination, covalent binding and charge or proton transfers should also be considered but are often disregarded due to time constrains or lower interest from the medicinal chemistry community. Each of these problems requires a separate or combined conformational search technique. In this review, we will discuss the current status in the development of search engines focusing on 1) ligand flexibility, including cyclic portions, 2) receptor flexibility, 3) bridging water molecules and finally 4) the inclusion of metal coordination geometry in docking.

Calmodulin plays a role in several life processes, its flexibility allows binding of a number of different ligands from small molecules to amphiphilic peptide helices and proteins. Through the diversity of its functions, it is quite difficult to find new drugs, which bind to calmodulin as a target. We present available structural information on the protein, obtained by X-ray diffraction, nuclear magnetic resonance spectroscopy and molecular modeling and try to derive some conclusions on structure-activity relationships.

Recent structural and genomic studies have clearly shown that many proteins contain long regions that do not adopt any globular structures under native conditions. These regions are termed intrinsically disordered or unstructured, and the proteins with intrinsically disordered regions are called intrinsically disordered proteins (IDPs). Current studies estimate that one third of eukaryotic proteins contain stretches of at least 30 contiguous disordered residues, the predictions are even higher in cancer-associated and signaling proteins (80% and 67%, respectively). IDPs play crucial roles in many aspects of molecular and cell biology and numerous IDPs are associated with human diseases such as cancer, cardiovascular disease, amyloidoses, neurodegenerative diseases, diabetes and others. IDPs such as tumor suppressor P53, BRCA1, Parkinson's protein α-synuclein, Alzheimer disease protein tau and many other diseaseassociated hub proteins represent attractive targets for drugs modulating protein-protein interactions. The structures and dynamics of the disordered proteins are the basis for the novel drug discovery. IDPs are lack of stable tertiary and /or secondary structure under physiological conditions in vitro. It is difficult to obtain accurate experimental measurements for the structures and dynamics of the disordered proteins. Molecular dynamics simulations provide available powerful tools to calculate the structures and their related dynamics of IDPs. In this paper, we focus on structural and dynamics insights of disease-associated disordered proteins by molecular dynamics simulations.