Advancing capability in high performance protein structure and function prediction through optimisation of IntFOLD

生物学的主要挑战之一是理解蛋白质是如何折叠成不同形状的，这些形状是由它们的氨基酸组成部分的序列所指定的。如果我们知道蛋白质是如何折叠的，那么我们就能理解它们是做什么的，以及它们是如何作为所有生命系统中基本的分子机器一起工作的。我们的研究旨在提高我们理解蛋白质结构及其功能的能力。这些信息可用于帮助我们解决各种紧迫问题，例如，确保未来的粮食供应，生产新的药物和能源，以及确保人、植物和动物更健康。蛋白质是每一个活细胞最重要的组成部分，它们有数千种不同的形状和大小。基因包含了制造许多不同蛋白质分子的密码。我们有非常高效的机器来分析基因和收集基因序列密码。我们已经收集了数千种生物的基因序列，从细菌到植物和动物，但还有更多的东西需要研究。可用遗传信息的数量正在以前所未有的速度增长，我们正在大步解码这些信息，以了解编码蛋白质的作用。我们可以做几种不同类型的实验来找出蛋白质的形状或结构。不幸的是，做一个实验来找出一种蛋白质的结构可能需要很多年，而且可能非常昂贵。这意味着我们现在有很大的知识缺口，缺少关于蛋白质的样子以及它们如何协同工作的信息。为了充分利用我们正在收集的遗传信息，我们需要能够弥补这些知识上的空白，完成这个谜题。幸运的是，我们已经开发了计算机软件系统，称为IntFOLD，来模拟蛋白质的结构，这比物理实验快很多倍，也便宜很多倍。IntFOLD软件利用我们现有的蛋白质序列和结构知识来帮助填补新序列的缺失信息。通过从我们已知的知识中学习，软件可以预测新蛋白质的形状。然后，我们可以建立分子的虚拟模型，看看所有原子在三维空间中的可能位置。这样我们就能更好地理解分子是如何结合在一起形成生物机器的。这个变革性的项目是关于IntFOLD软件的主要增强，使其更加有用，并将其推广给英国和世界各地的更多生物学家。该软件已经被全世界成千上万的研究人员使用了数十万次。IntFOLD产生的模型帮助了对所有生命领域的分子机制、疾病和蛋白质进化的新研究。我们现在需要改进我们的IntFOLD软件，使模型更加精确，这将进一步提高它们的实用性。我们还需要包括更多关于蛋白质如何组装的预测，这将提高我们对蛋白质功能的理解。为了实现这一步骤的改变，我们需要聘请一名专门的博士后研究员来协助开发新的IntFOLD，并向全世界的研究人员提供它的可用性。计算机的速度和容量对于跟上需求的增长至关重要，因此我们也要求资金来保持我们的硬件更新。

One of the major challenges in biology is to understand how proteins fold up into the different shapes that are specified by their sequences of amino acid building blocks. If we know how proteins fold then we can understand what they do and how they work together as the fundamental molecular machines in all living systems. Our research aims to improve our ability to understand protein structures and how they function. This information can be used to help us tackle a wide range of urgent problems, such as, securing future food supplies, producing new medicines and sources of energy, and ensuring healthier people, plants and animals. Proteins are the most important components of every single living cell and they come in thousands of different shapes and sizes. Genes contain the code for making the many different protein molecules. We have very efficient machines for analysing genes and collecting genetic sequence code. We have already collected the genetic sequences for thousands of living things, from bacteria to plants and animals, but there are still many more to investigate. The amount of available genetic information is increasing at an ever faster rate and we are making strides to decode this information to understand what the encoded proteins do.There are several different types of experiments that we can do to find out the shapes or structures of proteins. Unfortunately, doing an experiment to find out the structure of just one protein can take many years and it can be very expensive. This means that we now have large knowledge gaps with missing information about what proteins look like and how they work together. In order to make full use of the genetic information that we are collecting, we need to be able to close these gaps in our knowledge and complete the puzzle.Fortunately, we have developed our computer software system, called IntFOLD, to model the structures of proteins, which is many times faster and cheaper than physical experiments. The IntFOLD software makes use of our existing knowledge of protein sequences and structures to help fill in the missing information about new sequences. By learning from what we already know, the software can make predictions about the shapes of the new proteins. We can then build virtual models of the molecules and see where all of the atoms are likely to be in three dimensions. We can then better understand how the molecules combine together to form biological machines.This transformative project is about the major enhancement of our IntFOLD software, making it even more useful and promoting it to more biologists in the UK and around the world. The software has already been used hundreds of thousands of times by thousands of researchers worldwide. The models produced by IntFOLD have helped new research into molecular mechanisms, diseases and the evolution of proteins across all kingdoms of life. We now need to improve our IntFOLD software to make the models more precise, which will improve their usefulness further. We also need to include more predictions about how proteins assemble, which will improve our understanding of their functions. To effect this step change, we will need to employ a dedicated post doctoral researcher to assist in the development of the new IntFOLD, as well as to provide its availability to researchers worldwide. Computer speed and capacity is of the essence to keep up with the growth in demand, so we are also requesting funding to keep our hardware up to date.