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发表于 2014-9-5 23:24:16
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A discussion of recent FAH work on protein aggregation-related diseases
September 5, 2014 by Jingcheng Wu ·
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/diseas ... r/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.
大意：
汇报下FAH最近对蛋白质聚集相关疾病的研究成果
蛋白质聚集相关疾病
刚合成的蛋白质为了发挥作用，必须先折叠成特定的三维结构（即自然态）。但是有时蛋白质会出现误折，在某个中间态就停止了折叠，然后就会出现蛋白质聚集，并损伤周围的组织。
蛋白质误折与很多疾病有关，比如半数85岁以上老人都患有的Alzheimer（老年痴呆症），ALS（最近很火的冰桶挑战），疯牛病。这些疾病各有特点。但他们共同的特点就是当前仍然“无法治愈”。所以我们需要尽快研究它。
为何FAH适合研究蛋白质聚集相关疾病？
在我们和疾病开战初期，我们必须知己知彼，对病因进行深入研究。蛋白质折叠是一个非常动态和复杂的过程。无数的折叠事件会同时发生。此外，蛋白质折叠对组成原子非常敏感。比如，把关键蛋白的5个原子改成10个，就可以让人患上Alzheimer症。因此，在原子级别，对整个误折过程进行模拟进行仔细观察至关重要。而这需要极大的计算量，于是FAH闪亮登场了。
研究设计
我们对之前的16个模型蛋白质（这些蛋白质各不相同，代表了绝大多数的蛋白质）进行了分析。除了FAH数据，我们还加入了ANTON超级计算机的数据，我们修改了MSM算法对数据进行了处理，同时我们还加入了最新的‘S-总成’算法。
‘S-总成’算法本来是研究玻璃形状的，在玻璃冷却过程中，由于内部化学性质和外部环境的不同，它最终会形成不同的内部晶型。这和蛋白质的折叠有很多相似之处，相对于其他方法而言，‘S-总成’算法更加适用于我们此次的研究。
此次研究的主要发现
我们发现了很多有趣的不活跃中间态。他们形成很缓慢（小分子要10-100微秒，大分子需要几毫秒），很长寿（最少0.5毫秒）。这玻璃很类似，所以我们管这些亚稳态叫‘玻璃态’。
在我们分析的16个蛋白质中有7个，它们的‘玻璃态’既包含全β薄膜结构，也包含与自然态不太一样的β薄膜。β薄膜是氨基酸在相互作用结合形成蛋白质链的过程中局部形成的一种扭曲、褶皱的薄膜。这些富含β薄膜的玻璃态与形成有害蛋白质聚集的玻璃态非常类似。但是由于数据量太少，我们暂时无法归纳总结出造成蛋白质聚集成因的理论。
将来还能做啥？
因为玻璃态相对稳定，持续时间也长。所以我们将来有希望能在实验室里实际观测到它们。特别是，此次研究发现了蛋白质误折的一个重要成因——这对未来相当疾病的研究至关重要。 |
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