The first of the latest two insights into how proteins fold has been uncovered by a team led by biophysicist Jeremy Smith of the University of Tennessee (UT) and Oak Ridge National Laboratory (ORNL) in the US.
Using ORNL’s Cray XT4 Jaguar supercomputer alongside computer systems in Italy and Germany, the team revealed a driving force behind protein folding involving the way its constituents interact with water.
The team’s results have just been published in the Proceedings of the National Academy of Sciences.
Proteins are the workhorses of the body, taking on a wide variety of tasks. They fight infections, turn food into energy, copy DNA and catalyse chemical reactions. For example, insulin is a protein, as are antibodies and many hormones.
A protein is a string of amino acids, and what it does is determined by the shape it takes. That shape is determined by the sequence of the amino acids. Like a piece of biological origami, the protein folds itself into the form necessary to carry out its job. Without the shape the protein would be worthless.
“Understanding the mechanism by which proteins fold up into unique three-dimensional architectures is a holy grail in molecular biology,” explained Smith, who holds the first UT-ORNL Governor’s Chair and is a member of the Biochemistry and Molecular Biology Department at UT.
“Unfortunately, if you give me the sequence of amino acid building blocks in the protein, I cannot tell you what the structure would be,” he said.
“If I had been able to do that with a computer a while ago, the work behind about a dozen Nobel prizes – those awarded for experimental work on protein structure determination – would not have been necessary,” he adeded.
Peptides
Working on a smaller chain of amino acids known as a peptide, the group showed that the folding is determined largely by how parts of the peptide interact with water. Areas that shun water are said to be hydrophobic, and the team’s results show that the way water wets these hydrophobic areas determines the ultimate shape and behaviour of the peptide.
Hydrophobic areas
In particular, the team determined that small hydrophobic areas of the peptide, up to the size of a water molecule, induce different behaviour in water than larger hydrophobic areas, and that this difference is crucial for the folding. This insight builds on the work of another team, based at the University of California-Berkeley.
“David Chandler and his colleagues at Berkeley have a theory stating that hydrophobicity is qualitatively different on different length scales,” Smith said.
“If you have small hydrophobic molecules or groups that are themselves roughly the size of a water molecule, the water doesn’t seem to be too bothered by these groups.
“But when you get hydrophobic entities as long as several water molecules, the water molecules have a problem with that. They can’t cloak themselves around the hydrophobic surface anymore, and there is a dewetting or drying effect as they are repelled from the surface.
“Our simulations have shown that Chandler’s theory works for peptides, and, moreover, that the drying effect determines which structure our peptide adopts. It’s kind of ‘dry it off then fold it up.’”
High-performance computing
Smith said his team’s achievement was made possible by high-performance computing, noting that Jaguar is currently rated the second most powerful computing system in the world.
Smith also said that his team will need increasingly powerful supercomputers for additional simulation. While the team so far has been able to simulate about a microsecond in the life of a peptide, they must eventually be able to increase that time a thousand-fold, to milliseconds, and simulate proteins that are 10 to 100 times as large as the peptides.
“The runs were a couple of microseconds, which was adequate for the peptide that was simulated,” Smith explained.
“But the team is looking forward to increased computing capacity as it moves forward. The technique used is molecular dynamics simulation, and it needs high-performance leadership supercomputing to reach the length and timescales needed to fold a complete functional protein in the computer. With the projected capability improvements in Jaguar over the next couple of years, we will soon be approaching that goal.”
Smith made it clear that the achievement would represent a watershed in the field.
“When we do eventually find out how to calculate protein structure from sequence,” he said, “then a major revolution will come upon us, as we will have the basis to move forward with understanding much of biology and medicine, and the applications will range from rationally designing drugs to fit clefts in protein structures to engineering protein shapes for useful functions in nanotechnology and bioenergy.”
Or pull them apart
The second piece of new research takes a quite different approach – protein pulling. Houston-based Rice University physicists have unveiled an innovative way of finding out how proteins get their shape based on how they unfold when pulled apart.
They believe that this experimental method could be of widespread use in the field of protein folding science, which has grown dramatically in the past decade, due in part to the discovery that misfolded proteins play a key role in diseases such as Alzheimer’s and Parkinson’s.
Rice’s new findings, which were three years in the making, are available online and appear in an upcoming issue of Physical Review Letters. The article describes a new method scientists can use to map out exactly how much free energy is required throughout the folding process.
“We believe the method can be applied to any protein,” said lead author Ching-Hwa Kiang, assistant professor of physics and astronomy. “Many people are working on this problem, and when we present our work at scientific conferences it often creates a good deal of excitement.”
If DNA is the blueprint for life, then proteins are the machines built from those blueprints. All living cells produce proteins by stringing together strands of amino acids based on the sequences of their DNA.
Proteins are created in linear chains, like strands of pearls, with each amino acid representing a bead on the strand.
However, knowing the order of the amino acids in the strand gives no clue about how a protein functions. That’s because every protein folds into a 3D shape within about one second of being made, and it is this shape that dictates the protein’s function.
By studying how much free energy it takes for a protein to fold into its final shape, scientists hope to learn more about how amino acid sequences affect protein function and how folding goes awry, as with some diseases.
At the halfway point between its folded and unfolded state, a protein is like a rollercoaster balanced at the crest of the highest hill on the track. Like the rollercoaster, the protein requires a certain amount of energy to make it over the hill and wind its course to a final resting place – its folded state. If it lacks the energy to clear the hill, it will slide back into a partially folded or misfolded state.
Energy states
Kiang and graduate student Nolan Harris’s new approach to probing these energy states yields something akin to a map of the rollercoaster’s path. For example, theirs is the only experimental method that can reveal the slope and height of the energy barrier that the protein must overcome.
“Other experimental methods give researchers a pretty clear picture of the energy states at the beginning and the end – the two equilibrium states,” Kiang said. “Our approach helps fill in what happens in between, when the system is between folded and unfolded.”
Kiang and Harris’s experiments were conducted on one piece of a protein named Titin. The Titin piece, dubbed I27, contains 89 amino acids.
Harris suspended thousands of intact, folded I27s in a dilute saline solution and let the solution sit long enough for the proteins to become stuck to the bottom of the sample dish. The needle from an atomic force microscope (AFM) was repeatedly dipped into the solution.
The tip of the AFM operates much like a phonograph needle. The AFM needle is on the end of a cantilever arm that bobs up and down over the sample. The tip of the AFM needle is just a few atoms wide. Bobbing down, it randomly grabbed I27s that were pulled into their string-like, unfolded shape as the needle rose.
Harris measured the force exerted on the cantilever arm each time an I27 was unfolded. To get the energy maps, he wrote software incorporating a statistical mechanics equation called the ‘Jarzynski equality’.
The equation related the non-equilibrium energy from the unfolding events to the equilibrium profiles along the trajectory from the folded to the unfolded state.
Kiang said the software, and the use of the Jarzynski equality, makes the new method unique and useful.
“Christopher Jarzynski only discovered this relationship ten years ago,” Kiang said. “It’s a very powerful technique.”
