Exploiting Differentiable Programming Models For Protein Structure Prediction And Modelling

蛋白质是存在于每个细胞中的分子，执行基本的生物过程。这些分子本质上是一串更简单的化学物质，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）制造出来，这些分子就能自我组装成一个独特的3-D结构。正是这种独特的结构决定了蛋白质的功能（即它在细胞中做什么以及如何做）。将x射线照射在结晶蛋白质上，科学家可以通过观察x射线如何反射组成晶体的原子层来确定它们的结构。然而，这个过程可能需要几个月甚至几年的努力。由于有成千上万种蛋白质的天然结构是未知的，所以科学家们热衷于寻找聪明的捷径来研究蛋白质的结构也就不足为奇了。像许多其他科学家一样，我们一直在试图破译所谓的蛋白质折叠“密码”，即试图找出控制蛋白质如何找到其独特结构的规则，然后试图用这些规则为计算机编程，使科学家能够快速“预测”他们感兴趣的蛋白质的结构可能是什么。尽管单个蛋白质的形状或“折叠”是一个重要的信息，但确定哪些蛋白质与特定的蛋白质相互作用以及这些所谓的蛋白质复合物的几何形状（即已经进化成以一种非常特定的方式粘在一起的蛋白质组）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体的好例子。例如，许多不同的蛋白质复合物在控制血凝块的过程中起着至关重要的作用。一般来说，蛋白质-蛋白质复合物奠定了我们对细胞和生物体如何作为“系统”运作的整体理解——这是一个被称为“系统生物学”的领域。不幸的是，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加困难，因此科学家迫切需要更好的计算工具来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的整体形状。在这个项目中，我们建议利用最近在计算和人工智能方面的一些突破，使我们能够推断蛋白质的哪些部分可能相互作用，以及它们在相互作用时形成的复合物的结构。简而言之，当我们观察不同生物体中发现的不同版本的蛋白质时，我们首先寻找似乎同步变化的氨基酸对，也就是说，我们寻找当我们看到另一个氨基酸发生变化时，一个氨基酸似乎总是发生变化的情况。这些相互关联的变化被称为“相关突变”，当我们发现它们时，我们可以合理地确定，在蛋白质的最终折叠形式中，这两个氨基酸已经进化到在三维空间中紧密相连。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，我们希望能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，我们将使用一种称为“可微编程”的新型计算机软件。这意味着我们的计算机程序就像数学公式一样，可以通过应用微积分的基本规则来改进。这样，当获得更多的数据来优化算法时，我们的方法的准确性就会自动提高。

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.