New methods for computation drug design

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A key aspect of the research done by the Folding@Home project has been the use of computational methods to design new drugs, mostly for Alzheimer’s disease. At University of Virginia, Michael Shirts' laboratory is developing these methods to use FAH power to fight diseases.

Generally, the small molecule works as drugs by biding to specific locations of important proteins. For example, an antibiotic works by binding to a protein of bacteria which interfere enough with the pathogen's internal workings to disable it or to kill it. By targeting only protein sites that are unique to the pathogen, drugs can have a very precise effect without risking to hurt the human molecules or desired bacteria that live in our body (like intestinal flora which contributes to digestion). The same principles can turn on or off specific parts of our own protein machinery, allowing the design of drugs that fight diseases related to the breakdown, mutation or malfunction of our own cellular machinery, like Alzheimer disease, heart diseases, diabetes and many other conditions.

It is however very hard to calculate exactly how tightly a given small protein will bind to a target protein, or even exactly where and by what mechanism it will bind. A number of computational methods are used in industry today to estimate the binding affinity of small molecules in the process of drug design, but they mostly rely on approximations that are computationally cheap and very approximate, rather than more expensive methods that have the potential to be much more accurate. With Folding@Home, researchers now have the capability to perform rigorous evaluations of these more complete methods, understand their limits, and make them more efficient and reliable.

Michael Shirts' team has been developing its own method that works mostly on well known systems such as FKBP, a protein on the immune system signaling pathway. Once the methods are well-understood, we will be moving on try to design small molecules to treat AIDS (the HIV reverse transcriptase enzyme, required for DNA to replicate) and influenza (various proteins involved in virus cell entry). Such molecules will still require significant effort to make into drugs, since drugs also have to dissolve easily, penetrate cells, and not be broken down to quickly, but being able to predict more easily which molecules interact tightly with the intended targets will be a huge step in the right direction.

Michael Shirts' team is also contributing to improve Folding@Home infrastructure by working to port new versions of the Gromacs molecular simulation platform to Folding@home and improving the interface and integration between Gromacs and Folding@home.

Source: Vijay's blog