The overall aim of the research is to develop a computational platform based on HINT paradigm for manipulating, predicting and analyzing biomacromolecular-ligand structure. A second synergistic goal is to apply the above methodology to design novel and potent anti-cancer agents. The crucial role of the microtubule in cell division has identified tubulin as an interesting target for the development of therapeutics for cancer. Pyrrole-containing molecules derived from nature have proven to be particularly useful as lead compounds for drug development. We have designed and developed a series of substituted pyrroles that inhibit growth and promote death of breast tumor cells at nM and μM concentrations in human breast tumor cell lines. In another project, stilbene analogs were designed and developed as microtubule depolymerizing agents that showed anti-leukemic activity. A molecular modeling study was carried out to accurately represent the complex structure and the binding mode of a new class of tubulin inhibitors that bind at the αβ-tubulin colchicine site. These studies coupled with HINT interaction analyses were able to describe the complex structure and the binding modes of inhibitors. Qualitative analyses of the results showed general agreement with the experimental in vitro biological activity for these derivatives. Consequently, we have been designing new analogs that can be synthesized and tested; we believe that these molecules will be highly selective against cancer cells with minimal toxicity to the host tissue. Another goal of our research is to develop computational tools for drug design. The development and implementation of a novel cavity detection algorithm is also reported and discussed. The algorithm named VICE (Vectorial Identification of Cavity Extents) utilizes HINT toolkit functions to identify and delineate a binding pocket in a protein. The program is based on geometric criteria and applies simple integer grid maps to delineate binding sites. The algorithm was extensively tested on a diverse set of proteins and detects binding pockets of different shapes and sizes. The study also implemented the computational titration algorithm to understand the complexity of ligand binding and protonation state in the active site of HIV-1 protease. The Computational titration algorithm is a powerful tool for understanding ligand binding in a complex biochemical environment and allows generating hypothesis on the best model for binding.
| Identifer | oai:union.ndltd.org:vcu.edu/oai:scholarscompass.vcu.edu:etd-2865 |
| Date | 15 July 2009 |
| Creators | Tripathi, Ashutosh |
| Publisher | VCU Scholars Compass |
| Source Sets | Virginia Commonwealth University |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | Theses and Dissertations |
| Rights | © The Author |
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