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.

随着完全测序的基因组数量不断增加，生命科学家现在面临着表征编码蛋白质的生物学作用的挑战，以增进我们对细胞生理学的理解。大多数基因被设计为编码在生物体中具有有用功能的蛋白质。蛋白质本质上是一串更简单的分子，称为氨基酸，一旦细胞中的蛋白质制造机器（核糖体）形成蛋白质，这些分子串就可以自组装成复杂的 3D 结构。正是这种独特的结构决定了蛋白质的精确化学功能（即在细胞中做什么以及如何做）。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数月甚至数年的时间。由于有数十万种蛋白质的天然结构未知，因此科学家们希望找到一种巧妙的捷径来确定蛋白质的结构也就不足为奇了。像许多其他科学家一样，我们一直在尝试“破解蛋白质结构的密码”，即制定出控制蛋白质如何发现其独特结构的规则，然后尝试使用这些规则对计算机进行编程，以使科学家能够快速“预测” PSIPRED Workbench 是伦敦大学学院 (UCL) 维护的 Web 服务器集合，它的作用就是做到这一点，即它允许生物学家仅根据氨基酸序列来预测其蛋白质结构。多年来，它通过提供这些服务帮助了成千上万的科学家开展工作，我们现在不仅希望升级和维护这些现有服务器，还希望实施新的方法，即使是最困难的蛋白质的结构也可以通过以下方法推导出来：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）或哪些化学物质可能与蛋白质结合。即使蛋白质似乎没有折叠成单一稳定结构，PSIPRED 仍然可以帮助科学家推断其蛋白质可能的功能。使用计算机算法大规模生成此类信息可以帮助扩展我们在细胞水平上蛋白质生物学作用的知识库，这种理解将成为开发针对患病基因及其产物的技术和药物的关键垫脚石以及来自细菌或病毒等病原生物的蛋白质。以类似的方式，有关某些细菌基因功能的知识可以通过修改基因以使其产生新的化合物来帮助开发新的工业流程，甚至通过产生可以利用这些细菌的友好细菌来帮助对工业废物进行解毒。有毒化学品如食物。

项目成果

Large-scale clustering of AlphaFold2 3D models shines light on the structure and function of proteins

AlphaFold2 3D 模型的大规模聚类揭示了蛋白质的结构和功能

• DOI: http://dx.10.1016/j.molcel.2023.10.039
• 发表时间: 2023
• 期刊: Molecular Cell
• 影响因子: 16
• 作者: Bordin N

Machine learning methods for predicting protein structure from single sequences.

用于从单个序列预测蛋白质结构的机器学习方法。

• DOI: http://dx.10.1016/j.sbi.2023.102627
• 发表时间: 2023
• 期刊: Current opinion in structural biology
• 影响因子: 6.8
• 作者: Kandathil SM

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: http://dx.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.

Merizo：一种使用不变点注意力的快速准确的蛋白质域分割方法。

• DOI: http://dx.10.1038/s41467-023-43934-4
• 发表时间: 2023
• 期刊: Nature communications
• 影响因子: 16.6
• 作者: Lau AM