SENSE - Screening of ENvironmental SEquences to discover novel protein functions using informatics target selection and high-throughput validation

随着物种的分化和新菌株的出现，它们的蛋白质通过其序列中的突变来进化，从而改变功能特性。非常廉价和强大的技术使来自许多不同细菌群落的基因组测序成为可能，例如不同的土壤、海洋、人体部位。来自这些细菌的蛋白质(在基因组中编码)使其能够适应不同的环境，例如极端温度。虽然我们拥有关于蛋白质序列的广泛信息-UniProtKB包含1亿个序列(但&lt；0.5%是实验性的)-来自元基因组的新序列数据要大十倍，为寻找具有新功能的蛋白质提供了宝贵的宝库。然而，仅从序列预测蛋白质功能是具有挑战性的，这就是为什么我们将结合更细粒度的预测和高通量的实验测试。处理如此庞大的数据是具有挑战性的，但我们的项目受益于MGnify元基因组分析平台已经产生的结果。我们将引入新的策略对这些数据进行分类，并将重点放在包含更大功能多样性的生物群上。为了发现功能与以往观察到的蛋白质有很大不同的蛋白质，我们将相关蛋白质分类为进化家族，然后再细分为功能家族(称为FunFams)。RF和CO已经有了这样做的方法，但它们需要调整以处理大量的元基因组数据。通过比对FunFam中的序列，你可以找到在整个进化过程中高度保守的残基位置，这表明它们对功能是重要的。不同FunFam之间以不同方式保存的残基位置特别有趣，因为这些位置会发生变化，以实现不同的功能。大量的元基因组序列数据将有助于发现这些关键的功能决定因素(FDs)，因为保守模式将变得更加清晰。我们将开发新的工具来表征这些FDs的化学特征，并记录FunFam之间的属性差异，以在元基因组中发现新的FunFam，很可能具有新的功能。实验测试的结果将提供进一步的见解，例如，是否可以将特异性、效率归因于FDs，使我们的搜索更有可能成功预测功能。将研究两类典型的生物分子：(1)α/β水解酶-用于制造药物和洗涤剂的蛋白质；(2)细菌素-在抗生素新发现和食品保鲜中有价值应用的小抗菌肽。这些更复杂，因为它们是作为基因组上的一组基因(因此也是蛋白质)的一部分产生的，参与处理细菌素并使细菌对自己的细菌素免疫。我们将采用基于FD的方法来分析多个蛋白质之间的关键序列差异，以确定新的细菌素功能。与之前对酶超家族和细菌素的分析不同，我们将通过可以史无前例地验证预测的新型实验平台来测试我们对功能新颖性的预测。我们将开发一种微流控技术，在一个下午的微小液滴中筛选出100万种蛋白质的功能，并利用它对预测的基因邻域进行功能扫描(在随机化之后)，例如发现具有更好稳定性、特异性和进化性的突变。我们还将测试通过基于阵列的基因组装获得的基因比目前可能获得的基因便宜50倍的预测。因此，我们将以前所未有的规模从元基因组群落中实验性地探索蛋白质序列空间。我们将提供强大的新计算和实验技术，在对工业和人类健康重要但适用于许多蛋白质家族和次生代谢物基因簇的生物分子上进行测试。

As species diverge and new strains emerge, their proteins evolve through mutations in their sequences that alter functional properties. Very cheap and robust technologies have enabled the sequencing of genomes from many diverse bacterial communities e.g. different soils, oceans, human body sites. Proteins (encoded in the genomes) from these bacteria have enabled adaptation to different environments e.g. extremes of temperature. Although, we possess extensive information about protein sequences- UniProtKB contains >100 million sequences (but < 0.5% are experimentally characterised) - the new sequence data from metagenomes is ten-fold larger, providing a valuable treasure trove to hunt for proteins with novel functionality. Yet, it is challenging to predict protein function from sequence alone, which is why we will combine finer-grained prediction with high-throughput experimental testing. Handling this vast data is challenging but our project benefits from outputs already produced by the MGnify metagenomics analysis platform. We will introduce new strategies to classify this data and focus additional analyses on biomes containing greater functional diversity.To unearth proteins whose functions are very different from any observed previously, we will classify related proteins into evolutionary families and then sub-classify into functional families (called FunFams). RF and CO already have methods for doing this, but they need to be adapted to handle the vast metagenomic data. By aligning sequences in a FunFam, you can find residue positions highly conserved throughout evolution, indicating they are important for function. Residue positions conserved in different ways between different FunFams are particularly interesting as these are sites that change to enable different functions. The massive metagenomic sequence data will facilitate easy discovery of these key functional determinants (FDs) as conservation patterns will be much clearer.We will develop new tools to characterise chemical features of these FDs and score differences in properties of FDs between FunFams to find new FunFams in metagenomes, very likely to have novel functions. The outcomes of experimental tests will give further insights e.g. on whether specificity, efficiency can be ascribed to FDs, making our searches more likely to predict function successfully. Two exemplar classes of biomolecules will be investigated: (1) alpha/beta hydrolases- proteins used for making drugs and laundry detergents; (2) bacteriocins- small antibacterial peptides with valuable applications in novel antibiotic discovery and food preservation. These are more complicated as they are produced as part of a cluster of genes (and hence proteins) on the genome, involved in processing the bacteriocin and rendering the bacteria immune to their own bacteriocin. We will adapt our FD-based methods to analyse key sequence differences across multiple proteins to identify novel bacteriocin functionality.Unlike previous analyses of enzyme superfamilies and bacteriocins, we will test our predictions of functional novelty through novel experimental platforms that can verify the predictions on an unprecedented scale. We will exploit a microfluidic technology that screens the function of >1 million proteins in one afternoon in minute droplets and use it for functionally scanning the gene neighbourhood of predictions (after randomisation) e.g. for discovering mutants with better stability, specificity and evolvability. We will also test predictions for genes derived 50-fold cheaper than currently possible via array-based gene assembly. We will thus be experimentally exploring protein sequence space from metagenome communities at an unprecedented scale. We will deliver powerful new computational and experimental technologies, tested on biomolecules important for industry and human health but applicable to many protein families and secondary metabolite gene clusters.