Delivering accurate structural bioinformatics to the yeast community with the HHprY database

随着从基因组测序发展而来的新技术的发展，人们对细胞的理解有所增加。实验由机器人进行，以产生大量的结果。因此，我们现在对活细胞的了解是如此详细，以至于我们可以很容易地想象到，在未来，整个有机体都可以在分子水平上被理解。最有可能成为这种生物的候选者是面包师(或酿酒者)酵母，它是许多革命性实验的先锋细胞类型，包括第一个对其基因组进行测序的实验。由于酵母和人类在分子水平上的惊人相似程度，在酵母中的突破性发现通常揭示了人类细胞中类似事件的许多信息。蛋白质是在细胞内进行活动的主要参与者。因此，了解任何生物体的一种方法是对其蛋白质的功能进行分类。在某些情况下，可以研究纯蛋白质，但这对每一种蛋白质都太具有挑战性，因此需要另一种方法来对蛋白质进行分类。利用基因组序列，我们可以非常容易地确定由基因编码的蛋白质的序列。然后，我们可以依次查看每种蛋白质的序列，以找出它是否与我们已经知道其功能的蛋白质相似。序列相似的蛋白质，即使一个在酵母中，另一个在人类中，那么就被认为是属于一个蛋白质家族。随着家族的扩大，一种新的现象出现在一起：我们经常发现蛋白质共享的微妙模式。这些模式非常有用，因为我们经常可以使用这些模式来找到更多的序列，这些序列的亲缘关系稍微远一点，但仍然在家族中。这种方法已经普遍应用于所有新的基因组，它有助于识别许多蛋白质的功能。但它远不是所有人都成功的。对于酵母蛋白来说，这是一个前景问题。我们通常开始研究蛋白质家族的地方是人类。然而，对于其他动物，特别是脊椎动物，有很多已测序的基因组。所以我们发现的模式对脊椎动物成员有很强的偏见，有时酵母家族成员表现出的相似性太模糊而不被注意到。第二个问题是，使用家庭寻找新成员的整个方法现在已经过时了。一种新的方法是计算出一种新的蛋白质，在其他密切相关的物种中，它紧密联系的家族中有哪些蛋白质，并利用这个家族来寻找共享序列的模式。然后，不是使用该模式来查找另一个序列，而是仅将该模式与其他模式进行比较。因为每个模式包含的信息比一个序列包含的信息多得多，所以这看到了更多微妙的相似之处，所以它最终确定了更多我们以前看不到的重复关系。我们怀疑，比较模式将增加目前对酵母蛋白和其他已知生物(包括人类)中蛋白质之间关系的了解。在130种蛋白质的样本中(占酵母总蛋白质的2%)，我们发现了至少部分蛋白质的20多种新关系--每6种蛋白质中就有一条新信息。对于以前没有家族关系的蛋白质，这一比例上升到三分之一。发现这些新的关系是朝着完成这种模式生物图谱迈出的相当大的一步。我们现在将对整个酵母基因组进行分析，并创建一个供酵母研究人员自由访问的网络资源。以前还没有人做过全基因组的模式分析。这些模式将由计算机制作和比较，研究团队只需最少的投入。该项目的一个主要部分将通过将我们的结果链接到酵母使用的最著名的网络资源来提高人们对我们结果的认识

The understanding of cells has increased with new technology that has developed from genome sequencing. Experiments are run by robots to produce huge sets of results. As a result, our understanding of living cells is now so detailed that we can easily imagine a future where an entire organism is understood at the molecular level. The most likely candidate to be this organism is baker's (or brewer's) yeast, which was the pioneer cell type for many revolutionary experiments, including the first to have its genome sequenced. Because of the surprising degree of similarity at the molecular level between yeast and man, ground-breaking discoveries in yeast often reveal much about equivalent events in human cells.Proteins are the major players that do things inside cells. So one way to understand any organism is to classify what its proteins do. In some cases pure proteins can be studied, but this is too challenging to do for every protein, and so another way to classify proteins is needed. Using genome sequences, we can very easily determine the sequence of the proteins coded by the genes. We can then look at the sequence of each protein in turn to find out if it is similar to a protein whose function we already know. Proteins whose sequences are similar, even if one is in yeast and another in human, are then said to be in a single protein family. As the families get bigger a new phenomenon occurs from looking at all the sequences together: we often find subtle patterns that the proteins share. The patterns are very useful, because often we can use the patterns to find even more sequences, slightly more distantly related but still in the family.This approach is the one that has been applied universally to all new genomes and it helps identify what many of the proteins are doing. But it is far from universally successful. For yeast proteins there is a problem of perspective. The place where we typically start looking at a protein family is in humans. However, there are very many sequenced genomes for other animals, particularly vertebrates. So the patterns we find are very strongly biased to the vertebrate members, and sometimes the similarity shown by the yeast family member is too vague to be noticed. A second problem is that the whole approach of using a family to find a new member is that it has now been rendered out of date. A new approach is to work out for a new protein what proteins are in its close-knit family among other closely related species, and to use this family to find the pattern of shared sequence. Then, instead of using the pattern to find another sequence, the pattern is compared only to other patterns. Because each pattern holds within it much more information than one sequence can, this see far more subtle similarities, so it ends up identifying more ditant relationships that we could not see before.We suspected that comparing patterns would increase what is currently known about the relationships between yeast proteins and proteins in other well understood organisms, including humans. In a sample of 130 proteins (2% of yeast's total) we found over 20 new relationships for at least part of the protein - one new piece of information for every six proteins. This ratio rose to one in three for proteins where no family relationship had been known previously. Finding these new relationships is a considerable step towards the complete mapping of this model organism.We will now carry out our analysis for the whole yeast genome and create a web resource for yeast researchers to freely access. No genome-wide analysis of patterns has been done before. The patterns will be made and compared by computers, with minimal input from the research team. A major part of the project will raising awareness of our results by linking them to the most prominent web resource used by yeast