A high-quality homology model for the human dopamine transporter validated for drug design purposes.

The human dopamine transporter (hDAT) plays many vital functions within the central nervous system and is thus targeted by many pharmaceutical agents. Dopamine-related therapies are in current development for individuals with dopamine-related disorders including depression, Parkinson's disease, and psychostimulant addictions such as cocaine abuse. Yet, most efforts to develop new dopamine therapies are within costly structure-activity relationship studies. Through structure-based drug design techniques, the binding site of hDAT can be utilized to develop novel selective and potent dopamine therapies at reduced costs. However, no structural models of hDAT specifically validated for rational drug design purposes currently exist. Here, using the Drosophila dopamine transporter as a template, a homology model for the hDAT was developed and validated. The model was able to reproduce experimental binding modes with great accuracy, was able to rank inhibitors in the correct order of increasing potency with an R2 value of 0.81 for the test set, and it also outperformed other published hDAT models. Thus, the model can be used reliably in structure-based drug design projects.

A comprehensive ligand based mapping of the σ2 receptor binding pocket.

The sigma (σ) receptor system consists of at least two major receptor subtypes: σ1 and σ2. Several potential therapeutic applications would benefit from structural knowledge of the σ2 receptor but gaining this knowledge has been hampered by the difficulties associated with its isolation and, thus, characterization. Here, a ligand based approach has been adopted using the program PHASE® and a group of 41 potent and structurally diverse σ2 ligands to develop several pharmacophore models for different families of σ2 ligands. These pharmacophores were analyzed to identify the different binding modes to the receptor and were combined together to construct a comprehensive pharmacophore that was used to develop a structural model for the σ2 binding pocket. A total of six binding modes were identified and could be classified as neutral or charged modes. The results presented here also indicate the significance of hydrophobic interactions to σ2 binding and the requirement of hydrogen bonding interactions to increase the affinity for this receptor subtype. This work adds breadth to our knowledge of this receptor's binding site, and should contribute significantly to the development of novel selective σ2 ligands.

Computational strategies for the development of novel small molecule rheumatoid arthritis therapies.

Rheumatoid arthritis is a chronic autoimmune disorder that causes joint disfigurement and destruction leading to reduced quality of life. Effective drug therapies include the Disease Modifying Anti-Rheumatic Drugs which can help impede the progression of the disease but are not always effective. It is, therefore important to identify novel and effective therapies to combat this debilitating disorder. Several bioinformatics tools and computational approaches can be utilized to identify novel and effective therapies for rheumatoid arthritis and these are presented here.