A novel and rapid approach to predict protein structure

预测蛋白质结构的新颖且快速的方法

• 批准号: BB/G003912/1
• 负责人: Michael Sternberg
• 金额: $ 41.06万
• 依托单位: Imperial College London
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2008
• 资助国家: 英国
• 起止时间: 2008 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=BB%2FG003912%2F1
• 关键词: novel; rapid; approach; predict; protein

IMPORTANCE OF KNOWLEDGE ABOUT PROTEIN STRUCTURE Proteins are molecular machines which carry out most of the basic functions of an organism. They are made of chains of smaller molecules called amino acids. There are twenty types of amino acid, and the precise sequence of amino acids determines the shape and function of the protein. A protein is a large molecule, and in water it folds into a globular structure. The amino acids interact with each other in specific ways. It is important for us to know the shape of a protein as this provides insight into its function and can help in the design of experiments. Knowledge of the structure of a protein can be the starting point for the systematic design of novel regulators of activity such as drugs and agricultural agents. PROTEIN STRUCTURE PREDICTION It is slow, expensive and difficult to find out the structure of a protein directly. However, we now have the DNA sequences for many important organisms, including humans, and we generally can get protein sequences from DNA sequences. We know that the structure of a protein depends entirely on the sequence of its amino acids. Thus we can try to predict the structure of a protein from its sequence. Many successful prediction methods use similarities between the sequence for an unknown structure and the sequence for a known structure - . known as template-based modelling, But what if no such similarity can be found? There are two main methods that are yielding useful predictions today. One, fragment folding, tries to make a structure out of little fragments of other structures. This has been the most successful of the template-free methods in the last few years and has about 50% success rate. It requires high performance computing (up to years of cpu time per prediction). Another method, molecular dynamics, simulates the interactions between the atoms in the protein. Although this approach has provided useful predictions for the very smallest of proteins, it requires a computation time of many years on a single processor. OUR APPROACH We have developed with a new method, called poing, which aims to solve some of the problems with these other methods. We base our approach on a highly simplified model, introduced in the mid 70s, representing the protein as a ball-and-spring model. Each amino acid is represented by just two balls, less than a tenth the number that is used in molecular dynamics. This makes poing very fast. The springs between the balls are modelled using heuristics to represent specific effects which are known to be important in how a protein folds. Our preliminary results show that our approach can yield useful predictions with a run time of 20 hours on a single cpu. THIS PROPOSAL We propose to develop the new model to make it more accurate at predicting structures. We will also take part in a regular protein structure prediction experiment, where different prediction methods are tested on new proteins, and then compared with each other. We will also make our software available to the community via a public web server and by allowing others freely to obtain copies of it to change and run on their own computers. All this work will take three years.

蛋白质是执行生物体大部分基本功能的分子机器。它们是由被称为氨基酸的小分子链组成的。氨基酸有20种，氨基酸的精确序列决定了蛋白质的形状和功能。蛋白质是一种大分子，在水中它会折叠成球状结构。氨基酸以特定的方式相互作用。对我们来说，了解蛋白质的形状是很重要的，因为这可以让我们了解它的功能，并有助于实验的设计。对蛋白质结构的了解可以作为系统设计新的活性调节剂（如药物和农业制剂）的起点。蛋白质结构预测直接发现蛋白质的结构是缓慢、昂贵和困难的。然而，我们现在有了包括人类在内的许多重要生物的DNA序列，而且我们通常可以从DNA序列中得到蛋白质序列。我们知道蛋白质的结构完全取决于它的氨基酸序列。因此，我们可以尝试从蛋白质的序列来预测其结构。许多成功的预测方法利用未知结构序列和已知结构序列之间的相似性。即基于模板的建模，但如果找不到这种相似性怎么办？目前有两种主要的预测方法可以产生有用的预测结果。一种是片段折叠，试图用其他结构的小片段组成一个结构。这是近年来最成功的无模板方法，成功率约为50%。它需要高性能计算（每次预测需要长达数年的cpu时间）。另一种方法是分子动力学，它模拟蛋白质中原子之间的相互作用。尽管这种方法为最小的蛋白质提供了有用的预测，但它需要在单个处理器上花费多年的计算时间。我们的方法我们开发了一种叫做poing的新方法，它旨在解决其他方法存在的一些问题。我们的方法基于一个高度简化的模型，该模型于70年代中期引入，将蛋白质表示为球-弹簧模型。每个氨基酸只有两个球，不到分子动力学中所用球数的十分之一。这使得乒乓球非常快。球之间的弹簧使用启发式建模，以表示已知对蛋白质折叠方式很重要的特定效应。我们的初步结果表明，我们的方法可以在单个cpu上运行20小时的情况下产生有用的预测。我们建议发展新模型，使其在预测结构时更加准确。我们还会定期参加蛋白质结构预测实验，在新的蛋白质上测试不同的预测方法，然后相互比较。我们还将通过公共网络服务器向社区提供我们的软件，并允许其他人自由地获得它的副本，以便在他们自己的计算机上进行修改和运行。所有这些工作将花费三年时间。

项目成果

Protein folding requires crowd control in a simulated cell.

• DOI: 10.1016/j.jmb.2010.01.074
• 发表时间: 2010-04-16
• 期刊: Journal of molecular biology
• 影响因子: 5.6
• 作者: Jefferys BR;Kelley LA;Sternberg MJ
• 通讯作者: Sternberg MJ

XperimentR: painless annotation of a biological experiment for the laboratory scientist.

• DOI: 10.1186/1471-2105-14-8
• 发表时间: 2013-01-16
• 期刊: BMC bioinformatics
• 影响因子: 3
• 作者: Tomlinson CD;Barton GR;Woodbridge M;Butcher SA
• 通讯作者: Butcher SA