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Protein structure prediction defines career

Home Blog Protein structure prediction defines career

In 2001, Dong Xu, now the James C. Dowell computer science professor in the University of Missouri College of Engineering, and department chair, collaborated with then fellow researcher Ying Xu at Oak Ridge National Lab—now at the University of Georgia—to develop a set of computational tools for protein structure prediction and evaluation. Labeled PROSPECT, the young men’s software program won them an international R & D 1000 award.

Within each gene, Dong Xu explained, is a sequence of nucleotides that produces a chain of amino acids, creating proteins.

“Proteins are the major molecule in every biological system,” he said, adding that every protein is made up of, on average, 300 amino acids, and the order of their sequences causes protein to fold into unique three-dimensional structures.

“The topology of proteins is intrinsically very beautiful, not only because of this physical order, but also because of its biological diversity,” Xu said. “In humans there are 30,000 proteins, and with so many species of living organisms, there are potentially billions of proteins.”

A protein’s structure reveals its mechanism. If its sequence changes, its structure—and mechanism—changes, and such alterations may explain many diseases including cancer and HIV.

“One of the ways that protein structure research can be useful is in drug design,” said Xu. “If a small molecule can be discovered that interacts with a disease protein to make it inactive, the pathogen will die.”

“For a new protein, we use known structures, then combine them with other information for novel structure prediction. We use algorithms that search and find pieces here and there, and many models are generated,” said Xu, explaining the intensely computational process.

Though prediction quality varies, some predictions are successful. There are approximately 40,000 verified protein structures in the international protein database (PDB). Some proteins have similar structures to known proteins and are easier to predict.

Applications for such successful bioinformatic predictions are endless, and as director of MU’s Digital Biology Lab, Xu is a collaborator on the informatics end of a dozen of the University’s life science research projects, supported by DOE, NSF, USDA, NIH, the U.S. Army, the United Soybean Board, Missouri Soybean Merchandising Council, Missouri Life Science Trust Fund, Monsanto Research Fund and the National Center for Soybean Biotechnology.

Researchers on the MU and Washington University campuses are collaborating on a bio-energy project utilizing protein structure prediction that is aimed at more efficiently converting grasses into energy/alcohol.

“A microbial species—a bacteria—has the enzyme to convert grasses into alcohol with a protein, but a better protein would make it more efficient,” said Xu. “There is a lot of technology out there, but it’s not efficient enough to compete with oil. Using protein design, microbes can be engineered to more efficiently break down grasses so that they can compete as a source of energy.”

Management of protein food allergies, such as the extreme allergic reaction some people have to peanuts, is another area of research where protein structure prediction offers hope. “Recognizing the mechanism behind the allergic reaction can help scientists to re-engineer peanuts so that they do not contain the protein that causes it,” Xu said.

Xu is working to develop new bioinformatic software to predict protein structure. One such program is MUPRED, which aids in predicting secondary structures of protein helices. The software has been made available to the general community, and Xu is now developing a new program called MUFold aimed at more difficult aspects of protein prediction. MUFold was used in this year’s Critical Assessment of Techniques for Protein Structure Prediction (CASP), an international protein structure prediction contest. Xu is among a handful of invited speakers at the CASP8 meeting in December, based on the success of MUFold’s predictions.

Recently, the National Institute of Health awarded Xu and two MU colleagues—Yi Shang in computer science and Ioan Koztin in physics—a five-year, $1 million grant to research assembly and evaluation techniques in protein structure prediction.

“I’ve worked on protein for a long time. It’s just molecular research, but to me proteins are like people. They have their own personalities,” said Xu. “And they are beautiful.”

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