About protein aggregation-related diseases
For newly synthesized proteins to become functional, they have to fold into a particular three-dimensional structure or conformation first. During the folding process, a protein goes through a sequence of intermediate states to reach the final functional conformation or the “native state.” Unfortunately, protein folding isn’t fail-proof. Sometimes, proteins misfold and become stuck in certain stable intermediate states without further proceeding to fold into their native states. Such misfolded proteins may aggregate and damage surrounding tissues.
Protein misfolding is implicated in a wide variety of diseases, including Alzheimer’s that affects about half of the population over 85 years of age (1), ALS that claimed the life of the legendary baseball player Lou Gehrig just before his 38th birthday (and leads to all of the recent ALS ice bucket challenges), Mad Cow Disease from eating contaminated beef that leads to spongy lesions in human brains. These diseases manifest different signs and symptoms based on varying factors. Such factors can be the type of misfolded protein and the location in organs that protein aggregation occurs. Some such diseases are limited to one specific organ, some spread to multiple organs; some are inherited, some are acquired; some have known causes, some happen without warnings; some mainly affect certain age groups, some span across generations.
However, these diseases share one trait – they’re currently incurable. Due to the widespread nature of protein aggregation-related diseases and generally poor prospect of treatments, the pathways by which proteins aggregate that contribute to these diseases have become intense subjects of study.
Why Folding@home is well suited to studying protein aggregation-related diseases?
Before we make concrete plans to combat the diseases, we need to know what, when and how it went wrong in the first place. Protein folding is a very dynamic and diverse process where a protein can take thousands of different paths with different conformations to reach its active native state from its initial unfolded state. Numerous folding events can also happen simultaneously. In addition, proteins can be extremely sensitive to small changes of their composing atoms. For example, changing 5 to 10 atoms in each copy of a key protein is enough to make the difference between people who develop early onset Alzheimer’s versus people who don’t get Alzheimer’s at all (2).
As a result, it’s paramount to capture the entire dynamic folding landscape at atomistic level so that we can pin point and scrutinize the misfolding process. To do so requires enormous computing power – which is where Folding@home comes in.
Design of this study
We analyzed 16 model proteins that had been used in a previous study. They vary significantly in size and folding timescales so that our sample can represent a large protein population. Besides Folding@home, we also included data from the ANTON supercomputer. We adopted the MSM(Markov State Model) approach that has been used to characterize dozens of folding processes, as well as a recently applied method called s-ensemble.
For the purpose of our study, the s-ensemble method works effectively for mainly two reasons. Firstly, s-ensemble is used to study a process similar to protein folding – glass forming (3). As a liquid is cooled from high temperature, it may form crystal in which the atoms are arranged in orderly repeating patterns, or it may form glass that lacks such order. Whether the liquid forms one versus the other depends on its chemical properties and ambient conditions. When glass forms, the system pauses at certain stable intermediate states, very much like what could happen during protein folding process. Secondly, among various methods used to analyze glassy state, the s-ensemble method is most reliable as it remains effective when alternative means fail (4).
Major findings of this study
We were able to uncover interesting inactive intermediate states and study their properties at atomistic level. Particularly, these inactive intermediate states are slow-forming (take 10-100μs for smaller proteins, many milliseconds for larger proteins) and long-lived (stable over the course of at least 500 μs). Moreover, they likely emerge from uncommon protein folding pathways. Although their existences are rare events, once they form, they don’t tend to fold into other conformations including the native state. Since such properties of these intermediate states resemble those of intermediate states found in glass, they are referred to as “glassy states” of a protein folding landscape.
In 7 of the 16 proteins we analyzed, their glassy states contain either all β sheet structures or some different β sheet from the native states. β sheet is a localized region of a protein that looks like a twisted and pleated sheet as a result of a specific bonding interaction among the amino acids that make up the protein chain. The similarities between these β-sheet-rich glassy states and the misfolded conformations of proteins that form toxic aggregates make us speculate that it’s possible for the β-sheet-rich glassy states to seed the protein aggregation process. However, there hasn’t been a unified theory on how aggregation starts, due to the sparseness of supporting experimental data (5,6).
What we can do in the future
Since the glassy states are highly stable and persist over a long time, it offers hope for experimental detection in the future. In particular, this work shows that perhaps the key essence of misfolding – so critically important for understanding protein misfolding diseases – lies even in the nature of how a single protein folds and misfolds.
References
(1) “Alzheimer’s Disease Frequently Asked Questions.” New York State Department of Health. Jan 2006. Web. 3 Sep 2014. <https://www.health.ny.gov/diseases/conditions/dementia/alzheimer/alzheimer_qaa.htm>
(2) Paparcone, R., Pires, M., Buehler, M. Mutations Alter the Geometry and Mechanical Properties of Alzheimer’s Aβ (1-40) Amyloid Fibrils. Biochemistry. 2010; 49: 8967-8977.
(3) Bryngelson, J.D., and P.G. Wolynes. 1987. Spin Glasses and the Statistical Mechanics of Protein Folding. Proc. Natl. Acad. Sci. USA. 84:7524-7528.
(4) Jack, R.L., L. O. Hedges, …, D. Chandler. 2011. Preparations and Relaxation of Very Stable Glassy States of a Simulated Liquid. Phys. Rev. Lett. 107:275702.
(5) Dobson, C. M. 2004. Principles of Protein Folding, Misfolding and Aggregation. Semin. Cell Dev. Biol. 15:3 –16.
(6)Luhrs, T., C. Ritter, …, R. Riek. 2005. 3D Structure of Alzheimer’s amyloid-β (1-42) fibrils. Proc. Natl. Acad. Sci. USA. 102:17342-17347.