A group of protein theorists at Rice University have worked with protein experimentalists to experimentally corroborate the theory of protein folding for a large, multi-subunit protein.
The work, published as two back-to-back papers in this week's issue of the Proceedings of the National Academy of Sciences, is unusual because theorists usually do not study exactly the same thing as experimentalists, said Cecilia Clementi, the last author of the theoretical part of the study.
"Theorists usually work by themselves, and experimentalists usually work by themselves," said Clementi. "In this study, we developed methods to combine theory and experiment. When we compared the spectroscopy data, the overlap of the spectroscopic signal with the equivalent signal we found in theory is perfect."
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"Theorists usually work by themselves, and experimentalists usually work by themselves. In this study, we developed methods to combine theory and experiment. When we compared the spectroscopy data, the overlap of the spectroscopic signal with the equivalent signal we found in theory is perfect." |
The theoretical researchers decided to study the folding of the monomeric lactose repressor protein, or MLAc, because the experimental group, led by Pernilla Wittung-Stafshede and Kathleen Matthews, also of Rice, had already studied the protein extensively. MLAc is important for gene regulation and gene expression in Escheria coli.
According to Clementi, it took her research group about a year to express the monomeric protein in the laboratory.
"It was very difficult to express," said Clementi. "We had to make some mutations and to cut out some of the pieces that were unstructured. The protein has four domains, and we wanted to study the core domain, especially the DNA-binding domain."
Once the protein was expressed, Clementi and her group began working on developing a theoretical approach that would allow them to study protein folding on a computer. They used a physics technique called statistical mechanics that relies on averages to help simplify the protein molecule.
"If you put every single detail in it every single water molecule it is too close to reality, and it becomes too much for the computer to handle," Clementi explained. "Rather than putting all the atoms in, we grouped some together and made averages. Statistical mechanics helped us in making these averages."
On the experimental side, Wittung-Stafshede, Matthews and graduate student Corey Wilson prepared samples of MLAc and added urea to cause them to unfold. The team then injected water into the solution very fast, diluting the mixture and causing the proteins to fold. Using spectroscopy, they captured fluorescence and ultraviolet polarization patterns given off by the proteins as they folded.
"The novelty of this work is the direct and quantitative comparison of the time-dependent simulation data with the experimental measurements from circular dichroism and tryptophan fluorescence," said Payel Das, Clementi's graduate student who is the first author of the theoretical study. "The excellent agreement between experiment and theory illustrates that the existence of a well-defined 'folding route,' at least for large proteins, can be predicted within the framework of free-energy landscape theory. This has been a very controversial issue in the field of protein folding."
David Thirumalai, the director of the biophysics program at the University of Maryland's Institute for Physical Science and Technology said that Clementi and her colleagues' work is "highly significant" in the sense that it validates the computational approach by making a direct comparison with experiments.
"In general, simple off-lattice models of the sort used here have great advantages," said Thirumalai. "The computations can be done exhaustively for a range of conditions. As these authors show, even size may not be a serious limitation."
In the absence of supporting experiments, Clementi's results may not have been considered very significant, Thirumalai added. But with experimental validation, Clementi's predictions can be tested.
"Given that Dr. Clementi has validated the model by good comparison with experiments, she can make precise predictions that can be tested," he said. "It is this aspect that makes the present work exciting."
Predicting and understanding protein folding has important practical applications because proteins that misfold can cause diseases such as Alzheimer's, Parkinson's, diabetes, and Bovine Spongiform Encephalopathy.
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"I'm a theoretical physicist. My goal is really to understand what's going on why the protein misfolds, why it folds in a different way if I change it. If we can answer some of these questions, it can be important for applications, such as disease therapies." |
"I'm a theoretical physicist. My goal is really to understand what's going on why the protein misfolds, why it folds in a different way if I change it," said Clementi. "If we can answer some of these questions, it can be important for applications, such as disease therapies."
Aside from her work on MLAc, Clementi is also working on modeling a protein called photoactive yellow protein that changes shape with blue light."We want to understand what is the interaction with light," said Clementi.
In addition, Clementi is studying the S6 ribosomal protein, which has a high homology with the Alzheimer's peptide.
According to Clementi, every project that her group works on has an experimental counterpart.
"This is the strong point of my work," she said. "I don't want to start anything without an experimental counterpart. We want to manipulate proteins in reality."
Clementi said that theorists can help experimentalists by setting guidelines and limitations for experiments.
"For example, in the case of aggregation, misfolding, and diseases, we can predict the part of the protein that is responsible for the disease so that rather than trying all the possible mutations in the protein, we have a guideline," she said. "We have some prediction that can restrict experimental testing."
Thirumalai said that he believes Clementi and her colleagues' work will inspire others to use a similar approach for larger systems.
"Combining these calculations with experiments makes it very interesting," he said.
— Tien Shun Lee ([email protected])