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

基本信息

  • 批准号:
    BB/T003545/1
  • 负责人:
  • 金额:
    $ 50.45万
  • 依托单位:
  • 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

项目摘要

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

期刊论文数量(10)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
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:
  • 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:
  • 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
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Florian Hollfelder其他文献

Marmoset and human trophoblast stem cells differ in signaling requirements and recapitulate divergent modes of trophoblast invasion
  • DOI:
    10.1016/j.stem.2024.09.004
  • 发表时间:
    2024-10-03
  • 期刊:
  • 影响因子:
  • 作者:
    Dylan Siriwardena;Clara Munger;Christopher Penfold;Timo N. Kohler;Antonia Weberling;Madeleine Linneberg-Agerholm;Erin Slatery;Anna L. Ellermann;Sophie Bergmann;Stephen J. Clark;Thomas M. Rawlings;Joshua M. Brickman;Wolf Reik;Jan J. Brosens;Magdalena Zernicka-Goetz;Erika Sasaki;Rüdiger Behr;Florian Hollfelder;Thorsten E. Boroviak
  • 通讯作者:
    Thorsten E. Boroviak
Expanding the repertoire of imine reductases by mining divergent biosynthetic pathways for promiscuous reactivity
通过挖掘具有混杂反应性的不同生物合成途径来扩大亚胺还原酶的种类
  • DOI:
    10.1016/j.checat.2024.101160
  • 发表时间:
    2024-12-19
  • 期刊:
  • 影响因子:
    11.600
  • 作者:
    Godwin A. Aleku;Florian Hollfelder
  • 通讯作者:
    Florian Hollfelder
Enzymes under the nanoscope
纳米显微镜下的酶
  • DOI:
    10.1038/456045a
  • 发表时间:
    2008-11-05
  • 期刊:
  • 影响因子:
    48.500
  • 作者:
    Anthony J. Kirby;Florian Hollfelder
  • 通讯作者:
    Florian Hollfelder
Enzymes under the nanoscope
纳米显微镜下的酶
  • DOI:
    10.1038/456045a
  • 发表时间:
    2008-11-05
  • 期刊:
  • 影响因子:
    48.500
  • 作者:
    Anthony J. Kirby;Florian Hollfelder
  • 通讯作者:
    Florian Hollfelder

Florian Hollfelder的其他文献

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{{ truncateString('Florian Hollfelder', 18)}}的其他基金

Novel Plastizymes: discovery and improvement of plastic-degrading enzymes by integrated cycles of computational and experimental approaches
新型塑料酶:通过计算和实验方法的综合循环发现和改进塑料降解酶
  • 批准号:
    BB/X00306X/1
  • 财政年份:
    2023
  • 资助金额:
    $ 50.45万
  • 项目类别:
    Research Grant
Ultrahigh throughput total transcriptomics
超高通量全转录组学
  • 批准号:
    EP/Y032756/1
  • 财政年份:
    2023
  • 资助金额:
    $ 50.45万
  • 项目类别:
    Research Grant
Biocatalysis by plastic-degrading enzymes for bioremediation and recycling
塑料降解酶的生物催化用于生物修复和回收
  • 批准号:
    EP/X03464X/1
  • 财政年份:
    2022
  • 资助金额:
    $ 50.45万
  • 项目类别:
    Research Grant
Mapping the overlapping fitness landscapes of a superfamily of promiscuous enzymes: strategies for directed evolution?
绘制混杂酶超家族的重叠适应度景观:定向进化策略?
  • 批准号:
    BB/W000504/1
  • 财政年份:
    2022
  • 资助金额:
    $ 50.45万
  • 项目类别:
    Research Grant
CAZyme evolution and discovery: Ultrahigh throughput screening of carbohydrate-active enzymes in modular assays modular based on coupled reactions
CAZyme 的演变和发现:基于耦合反应的模块化测定中碳水化合物活性酶的超高通量筛选
  • 批准号:
    BB/W006391/1
  • 财政年份:
    2022
  • 资助金额:
    $ 50.45万
  • 项目类别:
    Research Grant
Towards Novel Glycoside Hydrolases
迈向新型糖苷水解酶
  • 批准号:
    BB/L002469/1
  • 财政年份:
    2014
  • 资助金额:
    $ 50.45万
  • 项目类别:
    Research Grant
New detection modes for droplet microfluidics
液滴微流控的新检测模式
  • 批准号:
    BB/K013629/1
  • 财政年份:
    2013
  • 资助金额:
    $ 50.45万
  • 项目类别:
    Research Grant
Exploring the Potential of Networked Directed Evolution Based on Novel LacI/effector Pairs
探索基于新型 LacI/效应器对的网络化定向进化的潜力
  • 批准号:
    BB/J008214/1
  • 财政年份:
    2012
  • 资助金额:
    $ 50.45万
  • 项目类别:
    Research Grant
Catalytic promiscuity in a protein superfamily
蛋白质超家族中的催化混杂
  • 批准号:
    BB/I004327/1
  • 财政年份:
    2011
  • 资助金额:
    $ 50.45万
  • 项目类别:
    Research Grant
Bronsted Analysis of Catalytic Promicuity in Enzyme Models and Model Enzymes
酶模型和模型酶中催化相似性的布朗斯台德分析
  • 批准号:
    EP/E019390/1
  • 财政年份:
    2007
  • 资助金额:
    $ 50.45万
  • 项目类别:
    Research Grant

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基于Safe screening的多任务稀疏学习理论与算法的研究
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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
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    2020
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    $ 50.45万
  • 项目类别:
    Research Grant
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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/T002735/1
  • 财政年份:
    2020
  • 资助金额:
    $ 50.45万
  • 项目类别:
    Research Grant
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