Accelerating and enhancing the PSIPRED Workbench with deep learning

随着基因组完全测序的数量不断增加，生命科学家现在面临的挑战是描述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，一旦蛋白质被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（即它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。由于有成千上万种蛋白质的天然结构是未知的，所以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，我们一直在试图“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编程计算机，使科学家能够快速“预测”他们感兴趣的蛋白质的结构可能是什么。PSIPRED工作台是伦敦大学学院维护的Web服务器的集合，它就是这样做的，即它允许生物学家预测其蛋白质结构的结构，只要它的氨基酸序列。多年来，它通过提供这些服务帮助了成千上万的科学家进行他们的工作，我们现在不仅希望升级和维护这些现有的服务器，而且希望实现新的方法，使即使是最困难的蛋白质的结构也能通过计算机模拟推断出来。然而，最近PSIPRED被赋予了更广泛的特征，以涵盖生物学中的其他重要问题。例如，利用PSIPRED，科学家可以预测哪些蛋白质不能折叠成稳定的形状（称为无序蛋白质），或者哪些化学物质可能与蛋白质结合。即使蛋白质没有折叠成单一的稳定结构，PSIPRED仍然可以帮助科学家推断出他或她的蛋白质的功能可能是什么。使用计算机算法大规模地生成此类信息可以帮助我们扩展蛋白质在细胞水平上的生物学作用的知识库，并且这种理解将是开发针对患病基因及其产物以及来自病理生物体（如细菌或病毒）的蛋白质的技术和药物的关键垫脚石。类似地，对某些细菌基因功能的了解可以，例如，通过修改基因使其产生新的化合物来帮助开发新的工业过程，甚至可以通过产生可以将有毒化学物质作为食物的友好细菌来帮助解毒工业废物。

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.