A novel and rapid approach to predict protein structure

蛋白质结构知识的重要性蛋白质是执行有机体大部分基本功能的分子机器。它们是由被称为氨基酸的较小分子链组成的。有二十种氨基酸，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，在水中可折叠成球状结构。氨基酸之间以特定的方式相互作用。了解蛋白质的形状对我们来说很重要，因为这有助于我们深入了解它的功能，并有助于实验设计。对蛋白质结构的了解可以作为系统设计药物和农用制剂等新型活性调节剂的起点。蛋白质结构预测直接找出蛋白质的结构速度慢、成本高、难度大。然而，我们现在有许多重要生物的DNA序列，包括人类，我们通常可以从DNA序列中获得蛋白质序列。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它的结构。许多成功的预测方法使用未知结构的序列和已知结构的序列之间的相似性。被称为基于模板的建模，但如果找不到这样的相似性怎么办？目前，有两种主要方法正在产生有用的预测。一种是片段折叠，它试图用其他结构的小片段组成一个结构。这是过去几年中最成功的无模板方法，成功率约为50%。它需要高性能计算(每次预测高达数年的CPU时间)。另一种方法，分子动力学，模拟蛋白质中原子之间的相互作用。虽然这种方法为最小的蛋白质提供了有用的预测，但在单个处理器上需要多年的计算时间。我们的方法我们开发了一种新的方法，称为POING，它的目的是解决这些其他方法的一些问题。我们的方法基于70年代中期引入的高度简化的模型，该模型将蛋白质表示为球和弹簧模型。每个氨基酸只由两个球代表，不到分子动力学中使用的数字的十分之一。这使得弹跳变得非常快。球之间的弹力是使用启发式方法来建模的，以表示特定的效应，这些效应在蛋白质如何折叠中是已知的重要的。我们的初步结果表明，我们的方法可以在单CPU上运行20小时的情况下产生有用的预测。这项建议我们建议开发新的模型，使其在预测结构方面更加准确。我们还将参加一个常规的蛋白质结构预测实验，在这个实验中，不同的预测方法被测试在新的蛋白质上，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，并允许其他人自由获得该软件的副本，以便在他们自己的计算机上更改和运行。所有这些工作将需要三年时间。

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.