Exploiting Differentiable Programming Models For Protein Structure Prediction And Modelling

利用可微分编程模型进行蛋白质结构预测和建模

• 批准号: BB/W008556/1
• 负责人: David Jones
• 金额: $ 51.79万
• 依托单位: University College London
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2022
• 资助国家: 英国
• 起止时间: 2022 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=BB%2FW008556%2F1
• 关键词: Exploiting; Differentiable; Programming; Models; Protein

Proteins are molecules present in every cell that carry out essential biological processes. These molecules are essentially strings of simpler chemicals, called amino acids and these strings are able to self-assemble into a unique 3-D structure as soon as the protein is made by the cell's protein-making machinery (called ribosomes). It's this unique structure that determines the function of the protein (i.e. what is does in the cell and how it does it). By shining X-rays on crystallised proteins, scientists can determine their structure by looking at how the rays reflect off the layers of atoms that make up the crystal. However, this process can take many months or even years of effort. With hundreds of thousands of proteins for which the native structure is unknown, it is not surprising that scientists are keen to find clever shortcuts to working out the structure of proteins. We, like many other scientists have been trying to decipher the so-called protein folding "code" i.e. trying to work out the rules which govern how the protein finds its unique structure and then trying to program a computer with these rules to allow scientists to quickly "predict" what the structure of their protein of interest might be.Although the shape or "fold" of a single protein is an important piece of information, it is arguably even more useful to determine which proteins interact with a given protein of interest, and the geometry these so-called protein complexes i.e. groups of proteins which have evolved to stick together in a very specific way. Good examples of such complexes are found in many areas of biology and medicine. For example, a number of different protein complexes play a crucial role in controlling how blood clots. In general, protein-protein complexes underlie our whole understanding of how cells and organisms operate as "systems" - which is a field known as "systems biology". Unfortunately, experimentally studying the structure of a protein complex is even more difficult than studying the structure of a single protein, and so scientists have an urgent need for better computational tools to allow them to predict which proteins could interact and the likely overall shape of the complex that they form.In this project, we propose to exploit some recent breakthroughs in computing and artificial intelligence to allow us to deduce which parts of proteins are likely to interact and the structures of the complexes that they form when they do. In a nutshell we start by looking for pairs of amino acids that appear to change in synchrony when we look at the different versions of the proteins found in different organisms i.e. we look for cases where a change in one amino acid always seem to occur when we see another amino acid changing. These linked changes are called "correlated mutations" and when we find them, we can be reasonably sure that the two amino acids have evolved to be close together in 3-D space in the final folded form of the protein. If we find enough correlated mutations, we can even go as far as predicting the complete structure of the protein and we hope as far as predicting the structure of a protein-protein complex in a similar way. To do this we will use a new type of computer software called "differentiable programming". This means that our computer programs are treated like mathematical formulae which can be improved by applying basic rules of calculus. In this way, the accuracy of our methods can be automatically improved as more data is obtained to optimize the algorithms.

蛋白质是存在于每个细胞中的分子，执行基本的生物过程。这些分子基本上是由更简单的化学物质组成的字符串，称为氨基酸，一旦蛋白质被细胞的蛋白质制造机器（称为核糖体）制造，这些字符串就能够自组装成独特的3-D结构。正是这种独特的结构决定了蛋白质的功能（即在细胞中做什么以及如何做）。通过将X射线照射在结晶蛋白质上，科学家可以通过观察射线如何反射构成晶体的原子层来确定它们的结构。然而，这个过程可能需要数月甚至数年的努力。由于有数十万种蛋白质的天然结构是未知的，因此科学家热衷于找到解决蛋白质结构的聪明捷径也就不足为奇了。我们和许多其他科学家一样，一直在试图破译所谓的蛋白质折叠“密码”，即试图找出蛋白质如何找到其独特结构的规则，然后试图用这些规则编写计算机程序，让科学家快速“预测”他们感兴趣的蛋白质的结构可能是什么。虽然单个蛋白质的形状或“折叠”是一个重要的信息，确定哪些蛋白质与给定的感兴趣蛋白质相互作用，以及这些所谓的蛋白质复合物（即已经进化为以非常特异的方式粘在一起的蛋白质组）的几何形状，可以说是更有用的。在生物学和医学的许多领域都可以找到这种复合物的好例子。例如，许多不同的蛋白质复合物在控制血液凝块方面起着至关重要的作用。一般来说，蛋白质-蛋白质复合物是我们对细胞和生物体如何作为“系统”运作的整个理解的基础-这是一个被称为“系统生物学”的领域。不幸的是，通过实验研究蛋白质复合物的结构比研究单个蛋白质的结构更困难，因此科学家迫切需要更好的计算工具，以使他们能够预测哪些蛋白质可以相互作用以及它们形成的复合物的可能整体形状。我们打算利用计算机和人工智能领域最近的一些突破，来推断蛋白质的哪些部分可能相互作用，以及它们相互作用时形成的复合物的结构。简而言之，当我们观察在不同生物体中发现的蛋白质的不同版本时，我们首先寻找似乎同步变化的氨基酸对，即我们寻找当我们看到另一个氨基酸变化时，一个氨基酸的变化似乎总是发生的情况。这些相关的变化被称为“相关突变”，当我们发现它们时，我们可以合理地确定这两个氨基酸在蛋白质的最终折叠形式中已经进化到在三维空间中靠近在一起。如果我们找到足够多的相关突变，我们甚至可以预测蛋白质的完整结构，我们希望以类似的方式预测蛋白质-蛋白质复合物的结构。为了做到这一点，我们将使用一种新型的计算机软件，称为“可微编程”。这意味着我们的计算机程序被视为数学公式，可以通过应用微积分的基本规则来改进。通过这种方式，我们的方法的准确性可以自动提高更多的数据来优化算法。

项目成果

Machine learning methods for predicting protein structure from single sequences.

• DOI: 10.1016/j.sbi.2023.102627
• 发表时间: 2023-06
• 期刊: Current opinion in structural biology
• 影响因子: 6.8
• 作者: S. M. Kandathil;Andy M. Lau;David T. Jones

Merizo: a rapid and accurate domain segmentation method using invariant point attention

Merizo：一种使用不变点注意力的快速准确的域分割方法

• DOI: 10.1101/2023.02.19.529114
• 发表时间: 2023
• 作者: Lau A

Merizo: a rapid and accurate protein domain segmentation method using invariant point attention.

• DOI: 10.1038/s41467-023-43934-4
• 发表时间: 2023-12-19
• 期刊: NATURE COMMUNICATIONS
• 影响因子: 16.6
• 作者: Lau, Andy M.;Kandathil, Shaun M.;Jones, David T.
• 通讯作者: Jones, David T.