No abstract

Epigenetic therapy is an important focus of research for drug development in the treatment of cancer. Valproic acid (VPA) is an HDAC inhibitor that has been evaluated in clinical studies. Despite its success in treating cancer, the mechanism of inhibition of VPA in HDAC is unknown. To this end, we have used docking and molecular dynamic simulations to investigate VPA binding to HDAC, employing both native and rebuilt 3-D structures. The results showed that VPA, via its carboxyl group, coordinates the Zn atom and other local residues (H141-142 and Y360) located at the catalytic site (CS) of HDAC. This causes electrostatic and hydrogen bonding interactions while having little interaction with the hydrophobic side chains, resulting in a low affinity. However, after several docking studies on different native HDAC 3-D structures and after using several snapshots from MD simulations, it became apparent that VPA bound with highest affinity at a site located at the acetyl-releasing channel, termed the hydrophobic active site channel (HASC). The affinity of VPA for HASC was due to its highly hydrophobic properties that allow VPA to take part in van der Waals interactions with Y18, I19, Y20, V25, R37, A38, V41, H42, I135 and W137, while VPA's carboxylate group has several hydrogen bonding interactions with the backbones of S138, I19, N136 and W137. MD simulations showed that the HASC door continuously opened and closed, which affected the affinity of VPA to the HASC, but the affinity toward the HASC was consistently higher than that obtained for the CS, suggesting that the HASC could be involved in the mechanism of inhibition.

Alzheimer's disease (AD) is a neurodegenerative disease characterized by a low acetylcholine (ACh) concentration in the hippocampus and cortex. ACh is a neurotransmitter hydrolyzed by acetylcholinesterase (AChE). Therefore, it is not surprising that AChE inhibitors (AChEIs) have shown better results in the treatment of AD than any other strategy. To improve the effects of AD, many researchers have focused on designing and testing new AChEIs. One of the principal strategies has been the use of computational methods (structural bioinformatics or in silico methods). In this review, we summarize the in silico methods used to enhance the understanding of AChE, particularly at the binding site, to design new AChEIs. Several computational methods have been used, such as docking approaches, molecular dynamics studies, quantum mechanical studies, electronic properties, hindrance effects, partition coefficients (Log P) and molecular electrostatic potentials surfaces, among other physicochemical methods that exhibit quantitative structure-activity relationships.

In the past, anti-cancer drugs were identified and developed without focusing on a particular macromolecular target. Currently, the fields of molecular biochemistry, molecular biology, genetics and pharmacology, among other disciplines, have grown considerably in their ability to identify biological targets. These disciplines are now searching for specific targets to treat cancer. These targets exist in different cellular compartments (membrane, cytoplasm, nucleus) as proteins, glycoproteins, nucleic acids, etc. Computational tools have recently been used to explore such targets and to corroborate previously obtained experimental data. These methods have also been used to design new drugs with the aim of decreasing illness and the economic resources needed to discover drug candidates. Some of these computational methods include quantum mechanics (ab initio and density functional theories) and molecular mechanics (docking, molecular dynamics, and protein folding). Docking and molecular dynamics are the most commonly used computational tools for elucidating cancer targets. Using these tools, one can identify the recognition processes between ligands and targets at the atomic level. In addition, one can identify the affinity and conformational changes of these molecular complexes. In conclusion, we propose that the use of such tools is necessary in order to identify new anti-cancer drugs.