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

SENSE - 使用信息学目标选择和高通量验证筛选环境序列以发现新的蛋白质功能

• 批准号: BB/T003545/1
• 负责人: Florian Hollfelder
• 金额: $ 50.45万
• 依托单位: University of Cambridge
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2020
• 资助国家: 英国
• 起止时间: 2020 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=BB%2FT003545%2F1
• 关键词: SENSE; Screening; ENvironmental; SEquences; discover

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.

随着物种的分化和新菌株的出现，它们的蛋白质通过序列突变而进化，从而改变功能特性。非常便宜和强大的技术已经能够对许多不同细菌群落的基因组进行测序，例如细菌群落。不同的土壤、海洋、人体部位。这些细菌的蛋白质（编码在基因组中）能够适应不同的环境，例如环境。极端温度。尽管我们拥有有关蛋白质序列的大量信息 - UniProtKB 包含超过 1 亿个序列（但通过实验表征的序列小于 0.5%） - 来自宏基因组的新序列数据要大十倍，为寻找具有新颖功能的蛋白质提供了宝贵的宝库。然而，仅根据序列预测蛋白质功能具有挑战性，这就是为什么我们将更细粒度的预测与高通量实验测试相结合。处理如此庞大的数据具有挑战性，但我们的项目受益于 MGnify 宏基因组分析平台已经产生的输出。我们将引入新的策略来对这些数据进行分类，并将更多分析重点放在包含更大功能多样性的生物群落上。为了发掘其功能与之前观察到的任何蛋白质有很大不同的蛋白质，我们将把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 已经有了做到这一点的方法，但它们需要进行调整以处理大量的宏基因组数据。通过在 FunFam 中比对序列，您可以找到在整个进化过程中高度保守的残基位置，表明它们对于功能很重要。不同 FunFams 之间以不同方式保存的残基位置特别有趣，因为这些位点会发生变化以实现不同的功能。海量的宏基因组序列数据将有助于轻松发现这些关键的功能决定簇（FD），因为保护模式将更加清晰。我们将开发新的工具来表征这些FD的化学特征，并对FunFams之间的FD特性差异进行评分，以寻找新的FunFams在宏基因组中，很可能具有新功能。实验测试的结果将提供进一步的见解，例如关于特异性、效率是否可以归因于 FD，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——用于制造药物和洗衣剂的蛋白质； (2)细菌素——小抗菌肽，在新型抗生素发现和食品保存方面具有重要应用。这些更复杂，因为它们是作为基因组上基因簇（以及蛋白质）的一部分产生的，参与处理细菌素并使细菌对其自身的细菌素免疫。我们将采用基于 FD 的方法来分析多种蛋白质之间的关键序列差异，以识别新的细菌素功能。与之前对酶超家族和细菌素的分析不同，我们将通过新颖的实验平台测试我们对功能新颖性的预测，该平台可以验证对规模空前。我们将利用一种微流体技术，在一个下午以微小液滴的形式筛选超过 100 万种蛋白质的功能，并用它来功能扫描预测的基因邻域（随机化后），例如发现具有更好稳定性、特异性和进化性的突变体。我们还将通过基于阵列的基因组装测试对比目前便宜 50 倍的基因进行的预测。因此，我们将以前所未有的规模实验性地探索宏基因组群落的蛋白质序列空间。我们将提供强大的新计算和实验技术，对对工业和人类健康重要但适用于许多蛋白质家族和次级代谢物基因簇的生物分子进行测试。

项目成果

Chemoenzymatic Photoreforming: A Sustainable Approach for Solar Fuel Generation from Plastic Feedstocks.

化学酶光重整：利用塑料原料生产太阳能燃料的可持续方法。

• DOI: http://dx.10.1021/jacs.3c05486
• 发表时间: 2023
• 期刊: Journal of the American Chemical Society
• 影响因子: 15
• 作者: Bhattacharjee S

High-Throughput Steady-State Enzyme Kinetics Measured in a Parallel Droplet Generation and Absorbance Detection Platform.

在并行液滴生成和吸光度检测平台中测量高通量稳态酶动力学。

• DOI: http://dx.10.17863/cam.92192
• 发表时间: 2022
• 作者: Neun S

Ultrahigh-Throughput Enzyme Engineering and Discovery in In Vitro Compartments

体外隔室中的超高通量酶工程和发现

• DOI: 10.1021/acs.chemrev.2c00910
• 发表时间: 2023-05-10
• 期刊: Chemical Reviews
• 影响因子: 62.1
• 作者: Gantz, Maximilian;Neun, Stefanie;Medcalf, Elliot J.;Vliet, Liisa D. van;Hollfelder, Florian
• 通讯作者: Hollfelder, Florian

High-Throughput Steady-State Enzyme Kinetics Measured in a Parallel Droplet Generation and Absorbance Detection Platform.

在并行液滴生成和吸光度检测平台中测量高通量稳态酶动力学。

• DOI: http://dx.10.17863/cam.91515
• 发表时间: 2022
• 作者: Neun S

High throughput steady-state enzyme kinetics measured in a parallel droplet generation and absorbance detection platform

在并行液滴生成和吸光度检测平台中测量高通量稳态酶动力学

• DOI: http://dx.10.1101/2022.07.28.500969
• 发表时间: 2022
• 作者: Neun S