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.

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.

项目成果

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

• DOI: 10.1021/acs.analchem.2c03164
• 发表时间: 2022-12-06
• 期刊: ANALYTICAL CHEMISTRY
• 影响因子: 7.4
• 作者: Neun, Stefanie;van Vliet, Liisa;Hollfelder, Florian;Gielen, Fabrice
• 通讯作者: Gielen, Fabrice

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

化学酶照明形成：塑料原料产生太阳能燃料的可持续方法。

• DOI: 10.1021/jacs.3c05486
• 发表时间: 2023-09-20
• 期刊: JOURNAL OF THE AMERICAN CHEMICAL SOCIETY
• 影响因子: 15
• 作者: Bhattacharjee, Subhajit;Guo, Chengzhi;Lam, Erwin;Holstein, Josephin M.;Rangel Pereira, Mariana;Pichler, Christian M.;Pornrungroj, Chanon;Rahaman, Motiar;Uekert, Taylor;Hollfelder, Florian;Reisner, Erwin
• 通讯作者: Reisner, Erwin