Researchers at Rice University in Houston, Texas, US, are developing software that unravels the complexities of protein folding – and, in the process, discovering new information about how these proteins misfold. The study, published in the Proceedings of the National Academy of Sciences by chemist Peter Wolynes and his team at Rice's BioScience Research Collaborative, could be of particular interest to those who probe the roots of degenerative diseases associated with the aggregation of amyloid fibers in the body. Wolynes is the Bullard-Welch Foundation Professor of Science, a professor of chemistry, and a senior scientist with the Center for Theoretical Biological Physics at Rice.
Designed to follow their groundbreaking 'principle of minimal frustration' (which describes the evolutionary path a protein takes toward stability), the researchers' molecular dynamics software predicts how strands of residues bend and twist into their functional shapes. These residues, the molecular beads that make up proteins, follow the path of least resistance as they fold into their native states.
The software, called AWSEM-MD (associative memory, water-mediated structure and energy model), simulates the ways beads in a strand may fold, based on the energies at work at the submolecular level. It then accurately predicts the final structure. Garegin Papoian, the Monroe Martin Associate Professor at the University of Maryland, and Aram Davtyan, a graduate student in his lab, designed and programmed most of the current version of AWSEM.
The current version builds upon previous development by Cecilia Clementi, the Wiess Career Development Chair and a professor of chemistry and of chemical and biomolecular engineering at Rice, and others in Wolynes's labs at the University of California at San Diego and the University of Illinois at Urbana-Champaign.
AWSEM-MD runs on two high-performance computing clusters at Rice, SUG@R (Shared University Grid at Rice) and DAVinCI (Data Analysis and Visualization Cyberinfrastructure). Two developers of the current version – Weihua Zheng, a postdoctoral researcher, and Nicholas Schafer, a graduate student – are co-authors of the new paper, the latest in a folding dynamics series focused on the software.
Software models confirm that 'self-recognition' among short residue sequences on neighboring proteins plays a role in misfolding, and it may lead to aggregation. Domains are conserved parts of protein chains. Occasionally, a domain in one chain encounters its doppelgänger in a nearby chain and becomes entangled with it via interactions similar to those in the correctly folded state. The research confirms that this domain swapping process is one cause of protein misfolding.
The result is often a dimer – a kind of protein Siamese twin – that is probably unable to perform its intended biological task and may become part of a damaging amyloid fibril. “Experimentalists had some strong laboratory evidence that dimerization is a consequence of minimal frustration, an idea proposed earlier by our group on more general grounds,” Wolynes says. “So we figured it would be nice to do a simulation to check it.”
The team observed domain swapping in their models of human cardiac titin, a muscle protein. However, they were surprised to see evidence that identical sequences in neighboring chains, as short as five to seven residues, had the unfortunate tendency to find each other and stick together. These instances of 'self-recognition' tip the balance of energies that dictate whether a protein will fold properly. As Wolynes's team later discovered, replacing just a few residues in one fragment eliminates self-recognition and lowers the incidence of domain swapping.
While the models do not directly connect to the formation of amyloid fibrils, anecdotal evidence indicates protein-folding diseases have some correlation with fevers that allow extra entropy to stabilize misfolded forms. “When you hear 'take two aspirins and call me in the morning,' your doctor is doing you a bigger favor than you know,” notes Wolynes. Ultimately, these results could provide a new explanation for how a disordered part of the chain contributes to the stability of misfolded states at high temperatures.
The discovery could open paths for researchers to design drugs that inhibit specific interactions. “Very minor changes seem to destroy this self-recognition in the computer simulation, and that's what we want the experimentalists to do: Make those changes to see if they decrease the self-recognition effect,” explains Wolynes. “Our simulations provide structural details of misfolded proteins at the molecular level that are difficult for experiments to probe,” adds Zheng. “These can generate specific hypotheses they can test.”
AWSEM is hosted on Google Code and is available to anyone who wants to use it.