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

利用 HHprY 数据库向酵母界提供准确的结构生物信息学

• 批准号: BB/M011801/1
• 负责人: Timothy Levine
• 金额: $ 8.87万
• 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: 2015
• 资助国家: 英国
• 起止时间: 2015 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=BB%2FM011801%2F1
• 关键词: Delivering; accurate; structural; bioinformatics; yeast

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

随着基因组测序技术的发展，人们对细胞的了解也在增加。实验由机器人运行，以产生大量的结果。因此，我们现在对活细胞的理解是如此详细，以至于我们可以很容易地想象未来整个生物体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酵母（或啤酒酵母），它是许多革命性实验的先驱细胞类型，包括第一个对其基因组测序。由于酵母和人类在分子水平上惊人的相似性，在酵母中的突破性发现往往揭示了人类细胞中类似的事件。蛋白质是细胞内的主要参与者。因此，了解任何生物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，但这对于每种蛋白质来说都太具有挑战性了，因此需要另一种方法来分类蛋白质。利用基因组序列，我们可以很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个蛋白质的序列，以确定它是否与我们已知功能的蛋白质相似。序列相似的蛋白质，即使一个在酵母中，另一个在人类中，也被称为单一蛋白质家族。随着家族的扩大，将所有序列放在一起观察会出现一个新的现象：我们经常会发现蛋白质共有的微妙模式。这些模式非常有用，因为我们通常可以利用这些模式找到更多的序列，这些序列的亲缘关系稍远，但仍在家族中。这种方法已被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。但它远非普遍成功。对于酵母蛋白质，存在一个前景问题。我们通常开始研究蛋白质家族的地方是人类。然而，其他动物，特别是脊椎动物的基因组序列非常多。因此，我们发现的模式非常强烈地偏向于脊椎动物成员，有时酵母家族成员显示的相似性太模糊而无法注意到。第二个问题是，使用族来寻找新成员的整个方法现在已经过时了。一种新的方法是找出一种新的蛋白质在其他密切相关的物种中属于其紧密联系的家族，并利用这个家族找到共享序列的模式。然后，不是使用该模式来寻找另一个序列，而是仅将该模式与其他模式进行比较。因为每一个模式都比一个序列包含更多的信息，所以它看到了更多微妙的相似性，所以它最终确定了更多我们以前看不到的ditant关系。我们怀疑比较模式将增加目前已知的酵母蛋白质和其他已知生物（包括人类）中蛋白质之间的关系。在130种蛋白质（占酵母总数的2%）的样本中，我们发现了至少部分蛋白质的20多个新关系-每六个蛋白质就有一个新信息。对于以前不知道家族关系的蛋白质，这一比例上升到三分之一。发现这些新的关系是朝着完整绘制这种模式生物的方向迈出的重要一步。我们现在将对整个酵母基因组进行分析，并为酵母研究人员创建一个免费访问的网络资源。以前没有进行过全基因组模式分析。这些图案将由计算机制作和比较，研究小组的投入最少。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突出的网络资源来提高人们对我们结果的认识

项目成果

Fat storage-inducing transmembrane (FIT or FITM) proteins are related to lipid phosphatase/phosphotransferase enzymes.

• DOI: 10.15698/mic2018.02.614
• 发表时间: 2017-12-28
• 期刊: Microbial cell (Graz, Austria)
• 作者: Hayes M;Choudhary V;Ojha N;Shin JJ;Han GS;Carman GM;Loewen CJ;Prinz WA;Levine T
• 通讯作者: Levine T

Identification of seipin-linked factors that act as determinants of a lipid droplet subpopulation.

• DOI: 10.1083/jcb.201704122
• 发表时间: 2018-01-02
• 期刊: The Journal of cell biology
• 作者: Eisenberg-Bord M;Mari M;Weill U;Rosenfeld-Gur E;Moldavski O;Castro IG;Soni KG;Harpaz N;Levine TP;Futerman AH;Reggiori F;Bankaitis VA;Schuldiner M;Bohnert M
• 通讯作者: Bohnert M

Using HHsearch to tackle proteins of unknown function: A pilot study with PH domains.

• DOI: 10.1111/tra.12432
• 发表时间: 2016-11
• 期刊: Traffic (Copenhagen, Denmark)
• 作者: Fidler DR;Murphy SE;Courtis K;Antonoudiou P;El-Tohamy R;Ient J;Levine TP
• 通讯作者: Levine TP

Systematic analysis of membrane contact sites in Saccharomyces cerevisiae uncovers modulators of cellular lipid distribution.

• DOI: 10.7554/elife.74602
• 发表时间: 2022-11-10
• 期刊: eLife
• 影响因子: 7.7
• 作者: Castro IG;Shortill SP;Dziurdzik SK;Cadou A;Ganesan S;Valenti R;David Y;Davey M;Mattes C;Thomas FB;Avraham RE;Meyer H;Fadel A;Fenech EJ;Ernst R;Zaremberg V;Levine TP;Stefan C;Conibear E;Schuldiner M
• 通讯作者: Schuldiner M

FFAT motif phosphorylation controls formation and lipid transfer function of inter-organelle contacts.

• DOI: 10.15252/embj.2019104369
• 发表时间: 2020-12-01
• 期刊: The EMBO journal
• 作者: Di Mattia T;Martinet A;Ikhlef S;McEwen AG;Nominé Y;Wendling C;Poussin-Courmontagne P;Voilquin L;Eberling P;Ruffenach F;Cavarelli J;Slee J;Levine TP;Drin G;Tomasetto C;Alpy F
• 通讯作者: Alpy F