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; 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的数量，生命科学家现在面临着表征编码蛋白的生物学作用的挑战，以提高我们对细胞生理的理解。大多数基因旨在编码在生物体中具有有用功能的蛋白质。蛋白质本质上是较简单的分子字符串，称为氨基酸，一旦细胞中的蛋白质制造机械（核糖体）形成蛋白质，这些细胞就可以自组装成复杂的3-D结构。正是这种独特的结构决定了蛋白质的精确化学功能（即细胞中的作用以及它的作用）。通过向结晶蛋白发射X射线，科学家可以确定其结构，但是此过程可能需要数月甚至几年。由于本机结构未知的数十万蛋白质，科学家想找到一个聪明的捷径来制定蛋白质的结构也就不足为奇了。像许多其他科学家一样，我们一直在试图“破解蛋白质结构的代码”，即制定控制蛋白质如何找到其独特结构的规则，然后试图与这些规则进行编程，以允许科学家快速预测“他们感兴趣的蛋白质结构可能是什么可能是什么。PSIPREDWORKER可能是在UCL上维护的蛋白质的构建良好的概念。多年来，它通过提供这些服务来帮助成千上万的科学家进行工作，现在我们不仅希望升级和维护这些现有的服务器，而且还希望实施新方法，这些方法甚至可以通过计算机模拟来推导最困难的蛋白质的结构。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）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个稳定的结构，PSIPRED仍然可以帮助科学家推断其蛋白质的功能可能是什么。使用计算机算法在大规模上生成此类信息可以帮助扩大我们在细胞水平上蛋白质生物学作用的知识基础，而这种理解将是技术和药品开发的关键垫脚石，以靶向患病基因及其产物以及来自病毒或病毒植物等病理生物的蛋白质。以类似的方式，关于某些细菌基因功能的知识可以通过修饰基因来使它们产生新型化学化合物，甚至通过产生可以使用有毒化学物质作为食物的友好型细菌来帮助对工业废物进行排毒的知识。