Computational chemistry: A faster way to untangle intermolecular interactions

Jan 30, 2013
The MDM2 protein (gray) binds the p53 peptide (orange and magenta). Each end of p53 undergoes conformational changes during the simulations, yielding subpopulations with different binding energies. Credit: 2013 A*STAR Bioinformatics Institute

A powerful computational technique used by the pharmaceutical industry to expedite new drug development has just received a performance boost. Chandra Verma and his co-workers at the A*STAR Bioinformatics Institute in Singapore have developed a method for extracting greater information from the simulations that are used to predict how candidate drug molecules will interact with biomolecular targets. The technique could enable drug makers to create highly effective medicines for a broader range of individuals.

Pharmaceutical researchers currently process using a technique known as 'MM-PBSA' free-energy calculations. These calculations predict how tightly a will bind to its inside the body. In general, the more tightly a drug binds to its target, the more effective it will be. "MM-PBSA can be used to rapidly assess the relative binding propensities of a series of molecules to a protein to distil out a select few candidates that can then be tested experimentally," Verma explains. By cutting down on experimental work, companies can save time and money.

Since the MM-PBSA method was first developed in the late 1990s, computer processing power has increased significantly. Researchers can now run simulations that map drug–protein interactions over much longer timeframes. As simulation times have lengthened, the complexity of has become an increasingly important consideration. Proteins are inherently flexible structures with multiple possible conformations, each of which interacts differently with the drug molecule over time. Taking these differences into account provides more reliable results.

Verma and his co-workers' method for analyzing and reporting the results of MM-PBSA calculations, which they named MM-PBSA_segmentation, separately captures the free energies of binding for multiple protein conformations. MM-PBSA_segmentation is based on an algorithm that can extract the binding behavior of individual protein subpopulations from the overall free-energy calculations. For example, using their method on the well-characterized interaction between two proteins called p53 and MDM2, the researchers identified six distinct subpopulations of p53 conformations (see image). They also established each subpopulation's relative size and hence overall importance.

MM-PBSA_segmentation, which is freely available from the team, expands the range of protein conformations accessible for analysis, Verma says. This ability to examine multiple protein conformations could be particularly important for cancer drug development, for example, where protein targets can become mutated and so change their conformation. As these changes can differ among individuals, Verma and his team are currently investigating whether they can use their technique to develop drugs targeted to individual patients.

Explore further: The anti-inflammatory factory

More information: Zhou, W., Motakis, E., Fuentes, G. & Verma, C. S. Macrostate identification from biomolecular simulations through time series analysis. Journal of Chemical Information and Modeling 52, 2319–2324 (2012). dx.doi.org/10.1021/ci300341v

add to favorites email to friend print save as pdf

Related Stories

Measuring protein movements with nanosecond resolution

Mar 15, 2010

Researchers at the Department of Chemistry at the Technische Universität München (TUM, Germany) have developed a method that allows the observation of local movements in proteins on a time scale of nanoseconds to microseconds. ...

Intrinsic changes in protein shape influence drug binding

Aug 19, 2009

Computational biologists at the University of Pittsburgh School of Medicine have shown that proteins have an intrinsic ability to change shape, and this is required for their biological activity. This shape-changing also ...

New computational technique can predict drug side effects

Dec 11, 2007

Early identification of adverse effects of drugs before they are tested in humans is crucial in developing new therapeutics, as unexpected effects account for a third of all drug failures during the development process.

Recommended for you

The anti-inflammatory factory

Apr 22, 2014

Russian scientists, in collaboration with their colleagues from Pittsburgh University, have discovered how lipid mediators are produced. The relevant paper was published in Nature Chemistry. Lipid mediators are molecules that p ...

Breakthrough points to new drugs from nature

Apr 16, 2014

Researchers at Griffith University's Eskitis Institute have developed a new technique for discovering natural compounds which could form the basis of novel therapeutic drugs.

User comments : 0

More news stories

Mantis shrimp stronger than airplanes

(Phys.org) —Inspired by the fist-like club of a mantis shrimp, a team of researchers led by University of California, Riverside, in collaboration with University of Southern California and Purdue University, ...

Male-biased tweeting

Today women take an active part in public life. Without a doubt, they also converse with other women. In fact, they even talk to each other about other things besides men. As banal as it sounds, this is far ...

High-calorie and low-nutrient foods in kids' TV

Fruits and vegetables are often displayed in the popular Swedish children's TV show Bolibompa, but there are also plenty of high-sugar foods. A new study from the University of Gothenburg explores how food is portrayed in ...