Accelerating and enhancing the PSIPRED Workbench with deep learning

通过深度学习加速和增强 PSIPRED Workbench

• 批准号: BB/T019409/1
• 负责人: David Jones
• 金额: $ 77.79万
• 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: 2021
• 资助国家: 英国
• 起止时间: 2021 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=BB%2FT019409%2F1
• 关键词: Accelerating; enhancing; PSIPRED; Workbench; deep

With the growing number of completely sequenced genomes, life scientists now face the challenge of characterizing the biological role of the encoded proteins as to advance our understanding of cell physiology. Most genes are designed to code for proteins which have useful functions in an organism. Proteins are essentially strings of simpler molecules, called amino acids and these strings can self-assemble into a complex 3-D structure as soon as the protein is formed by the protein-making machinery (ribosomes) in the cell. It is this unique structure which determines the precise chemical function of the protein (i.e. what is does in the cell and how it does it). By firing X-rays at crystallised proteins, scientists can determine their structure, but this process can take many months or even years. With hundreds of thousands of proteins for which the native structure is unknown, it is not surprising that scientists want to find a clever shortcut to working out the structure of proteins. We, like many other scientists have been trying to "crack the code" of protein structure i.e. working 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.The PSIPRED Workbench is a collection of Web servers maintained at UCL which does just this i.e. it allows biologists to predict the structure of their protein structure given just its amino acid sequence. Over the years it has helped many thousands of scientists with their work by providing these services and we now wish not only to upgrade and maintain these existing servers but also to implement new methods which allow the structures of even the most difficult proteins to be deduced by computer simulations.More recently, however, PSIPRED has been given a wider range of features to cover other important problems in biology. For example, using PSIPRED, a scientist can predict which proteins do not fold into stable shapes (called disordered proteins) or which chemical substances are likely to bind to a protein. Even where a protein does not appear to fold into a single stable structure, PSIPRED can still help scientists deduce what the function of his or her protein is likely to be. Generating such information on a large scale using computer algorithms can help expand our knowledge base of the biological role of proteins at a cellular level, and such understanding will be a key stepping stone in the development of techniques and pharmaceuticals to target diseased genes and their products as well as proteins from pathological organisms such as bacteria or viruses. In a similar way, knowledge on the function of certain bacterial genes can, for example, help develop new industrial processes by modifying the genes to make them produce novel chemical compounds, or even helping to detoxify industrial waste by producing friendly bacteria that can use the poisonous chemicals as food.

随着越来越多的基因组完全测序，生命科学家现在面临的挑战是确定编码蛋白质的生物学作用，以促进我们对细胞生理学的理解。大多数基因都被设计成编码蛋白质，这些蛋白质在生物体中具有有用的功能。蛋白质本质上是一串更简单的分子，称为氨基酸，一旦蛋白质由细胞中的蛋白质制造机制(核糖体)形成，这些分子串就可以自我组装成复杂的三维结构。正是这种独特的结构决定了蛋白质的确切化学功能(即它在细胞中做什么以及它是如何做的)。通过向结晶蛋白质发射X射线，科学家可以确定它们的结构，但这个过程可能需要几个月甚至几年的时间。由于有数十万种蛋白质的天然结构尚不清楚，科学家们想要找到一种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，我们一直在努力破解蛋白质结构的密码，即找出控制蛋白质如何找到其独特结构的规则，然后尝试用这些规则对计算机进行编程，使科学家能够快速地“预测”他们感兴趣的蛋白质的结构。PSIPRED工作台是在伦敦大学学院维护的一组网络服务器，它就是这样做的，即它允许生物学家预测他们的蛋白质结构的结构，只给出它的氨基酸序列。多年来，PSIPRED通过提供这些服务帮助了数千名科学家的工作，我们现在不仅希望升级和维护这些现有的服务器，而且希望实施新的方法，即使是最困难的蛋白质的结构也可以通过计算机模拟来推断。然而，最近，PSIPRED被赋予了更广泛的功能，以涵盖生物学中的其他重要问题。例如，使用PSIPRED，科学家可以预测哪些蛋白质不会折叠成稳定的形状(称为无序蛋白质)，或者哪些化学物质可能与蛋白质结合。即使在蛋白质似乎没有折叠成单一稳定结构的情况下，PSIPRED仍然可以帮助科学家推断他或她的蛋白质可能具有的功能。使用计算机算法大规模生成此类信息有助于扩大我们在细胞水平上对蛋白质生物学作用的知识基础，这种理解将是开发针对患病基因及其产品以及来自细菌或病毒等病理性生物的蛋白质的技术和药物的关键踏脚石。以类似的方式，对某些细菌基因的功能的了解可以帮助开发新的工业流程，方法是修改基因，使其产生新的化合物，甚至通过生产友好的细菌来帮助工业废物解毒，这些细菌可以将有毒化学物质用作食物。

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.