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

通过优化 IntFOLD 提高高性能蛋白质结构和功能预测的能力

基本信息

批准号：
BB/T018496/1
负责人：
Liam McGuffin
金额：
$ 93.26万
依托单位：
University of Reading
依托单位国家：
英国
项目类别：
Research Grant
财政年份：
2020
资助国家：
英国
起止时间：
2020 至无数据
项目状态：
未结题

来源：
https://gtr.ukri.org/projects?ref=BB%2FT018496%2F1
关键词：
Advancing capability performance protein structure

项目摘要

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.

生物学的主要挑战之一是了解蛋白质如何折叠成由其氨基酸构建块序列指定的不同形状。如果我们知道蛋白质如何折叠，那么我们就能了解它们的作用以及它们如何作为所有生命系统中的基本分子机器一起工作。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、生产新药物和能源以及确保人类、植物和动物更健康。蛋白质是每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。基因包含制造许多不同蛋白质分子的代码。我们拥有非常高效的机器来分析基因和收集基因序列代码。我们已经收集了从细菌到植物和动物的数千种生物的基因序列，但仍有更多的东西需要研究。可用的遗传信息量正在以越来越快的速度增加，我们正在努力解码这些信息，以了解编码的蛋白质的作用。我们可以进行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，进行一项实验来找出一种蛋白质的结构可能需要很多年的时间，而且费用可能非常昂贵。这意味着我们现在存在巨大的知识差距，缺少有关蛋白质的外观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，我们需要能够弥合知识中的这些差距并完成难题。幸运的是，我们开发了名为 IntFOLD 的计算机软件系统，用于对蛋白质结构进行建模，这比物理实验要快很多倍，而且成本也低很多倍。 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有关新序列的缺失信息。通过学习我们已知的知识，该软件可以预测新蛋白质的形状。然后，我们可以构建分子的虚拟模型，并查看所有原子在三维空间中可能位于的位置。然后我们可以更好地了解分子如何组合在一起形成生物机器。这个变革性项目是关于我们的 IntFOLD 软件的重大增强，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。该软件已被全球数千名研究人员使用了数十万次。 IntFOLD 生成的模型有助于对所有生命领域的分子机制、疾病和蛋白质进化进行新的研究。我们现在需要改进 IntFOLD 软件，使模型更加精确，从而进一步提高其实用性。我们还需要对蛋白质如何组装进行更多预测，这将增进我们对其功能的理解。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其可用性。计算机速度和容量对于满足需求增长至关重要，因此我们还请求资金来保持我们的硬件最新。