Periodic Reporting for period 1 - B-FHAB (Hacking Photosynthesis: Biosensors for Herbicides and Beyond)
Reporting period: 2022-07-01 to 2024-06-30
The project ‘Hacking Photosynthesis: Biosensor for Herbicides and Beyond’ (B-FHAB) aims to develop a novel biosensor platform for detecting herbicides in water, addressing significant challenges in environmental monitoring. With the rising global population and increase in agrochemical activities, herbicide contamination in water sources has become a pressing issue. Traditional detection methods, such as liquid chromatography and mass spectrometry, are costly and complex. B-FHAB seeks to create an accessible biosensor that detects herbicides rapidly and with ease, thereby enhancing water monitoring frequency, safety and public health.
The proposed biosensor employs engineered photosynthetic proteins as biological recognition elements. These proteins serve a dual purpose: binding herbicides and transducing these binding events into an electrochemical signal. These signals are connected to an electrode via a redox polymer that also serves as a protein-immobilization matrix, preserving their function during prolonged and desiccated storage, and during continuous use. Herbicides inhibit the photoproteins' activities in a concentration-dependent manner, enabling the quantification of herbicide levels in a sample by monitoring changes in the electrochemical signal. Several challenges hinder the implementation of this accessible biosensing platform, including detection of herbicides below the EU's maximum residue limits, differentiating between various herbicide compounds, expanding the range of detectable compounds to cover the broad range of applied herbicides, and increasing the number of herbicide residues that can be detected on a single chip.
To overcome challenges in developing a herbicide biosensor, the project focuses on four primary objectives:
Objective 1 seeks to enhance sensitivity and selectivity of the biosensor through mutagenesis at the herbicide binding site of the photoprotein. This involves combining favorable point mutations into double or triple mutants, screening them in silico, constructing them in a wet-lab, and testing them using electrochemical means.
Objective 2 seeks to shift protein-herbicide binding from competitive to allosteric inhibition by taking mutagenesis out of the natural substrate-binding pocket and into a distally gating herbicide binding site, obviating inherent structural limitations in the current sensing modality. This will be accomplished via expansive computationally guided mutagenesis of residues at the periphery of the herbicide binding pocket.
Objective 3 aims to expand the biosensor application by docking large libraries of compounds to the photoprotein to discover novel biosensor targets that are outside of the range of our typical triazine-type herbicide pool.
Objective 4 seeks to demonstrate multi-compound sensing on a single test strip, by carefully engineering spectral and electronic triggers that activate herbicide specific detection.
The scientific impact of the B-FHAB project includes the validation of computational methods for guiding complex protein mutagenesis, thereby saving time and resources in experimental workflows. Economically, the project aims to provide cost-effective solutions for herbicide monitoring, driving growth in the environmental monitoring sector and creating new opportunities. Strategically, the project aligns with global environmental protection efforts and regulatory frameworks, contributing to sustainable development goals, particularly in clean water and responsible consumption.
The proposed biosensor employs engineered photosynthetic proteins as biological recognition elements. These proteins serve a dual purpose: binding herbicides and transducing these binding events into an electrochemical signal. These signals are connected to an electrode via a redox polymer that also serves as a protein-immobilization matrix, preserving their function during prolonged and desiccated storage, and during continuous use. Herbicides inhibit the photoproteins' activities in a concentration-dependent manner, enabling the quantification of herbicide levels in a sample by monitoring changes in the electrochemical signal. Several challenges hinder the implementation of this accessible biosensing platform, including detection of herbicides below the EU's maximum residue limits, differentiating between various herbicide compounds, expanding the range of detectable compounds to cover the broad range of applied herbicides, and increasing the number of herbicide residues that can be detected on a single chip.
To overcome challenges in developing a herbicide biosensor, the project focuses on four primary objectives:
Objective 1 seeks to enhance sensitivity and selectivity of the biosensor through mutagenesis at the herbicide binding site of the photoprotein. This involves combining favorable point mutations into double or triple mutants, screening them in silico, constructing them in a wet-lab, and testing them using electrochemical means.
Objective 2 seeks to shift protein-herbicide binding from competitive to allosteric inhibition by taking mutagenesis out of the natural substrate-binding pocket and into a distally gating herbicide binding site, obviating inherent structural limitations in the current sensing modality. This will be accomplished via expansive computationally guided mutagenesis of residues at the periphery of the herbicide binding pocket.
Objective 3 aims to expand the biosensor application by docking large libraries of compounds to the photoprotein to discover novel biosensor targets that are outside of the range of our typical triazine-type herbicide pool.
Objective 4 seeks to demonstrate multi-compound sensing on a single test strip, by carefully engineering spectral and electronic triggers that activate herbicide specific detection.
The scientific impact of the B-FHAB project includes the validation of computational methods for guiding complex protein mutagenesis, thereby saving time and resources in experimental workflows. Economically, the project aims to provide cost-effective solutions for herbicide monitoring, driving growth in the environmental monitoring sector and creating new opportunities. Strategically, the project aligns with global environmental protection efforts and regulatory frameworks, contributing to sustainable development goals, particularly in clean water and responsible consumption.
Work Package 1: Multiresidue Mutations focused on combining point mutations to improve herbicide binding affinity, specifically targeting atrazine with mutations AL186Q and LL193V. The team established and optimized protocols for bacterial reaction center expression, isolation, and purification, leading to an 18x sensitivity increase in atrazine binding affinity compared to the wild type. However, binding affinity for other herbicides indicated the need for more comprehensive computational and experimental screening.
Work Package 2: Distal Binding Site Engineering aimed to create non-competitive inhibition mechanisms by targeting distal binding sites through in silico mutagenesis. While the feasibility of distal binding site engineering at AL186 was demonstrated, overall low binding energies at other distal sites redirected efforts to WP6 due to limited success.
Work Package 3: Improving Computer-Aided Down-Selection of Bacterial RCs integrated advanced protein structure prediction tools like Rosetta Design and AlphaFold, enhancing receptor and ligand structure accuracy through improved docking simulations. High-throughput scripts for batch docking simulations and site-saturated mutagenesis were developed, leading to the discovery of new high-potential point mutations and successful incorporation into WP1.
Work Package 4: Biosensor Discovery Mutants initially aimed to screen large docking compound libraries but shifted focus to improving docking simulation accuracy due to high false-positive rates. Expanding the library of dockable compounds, including herbicide derivatives, improved docking accuracy and identified potential biosensor substrates for experimental validation.
Work Package 5: Multi-Residue Sensing involved coupling herbicide-specific mutants to distinct light-harvesting complexes for multi-residue sensing and constructing a spectro-electrochemical apparatus for wavelength-dependent excitation of bacterial reaction centers. While successful conjugation of bRCs with nanoparticles was achieved, functional increases in excitation were limited, hypothesized to stem from quenching effects due to nanoparticle proximity.
Work Package 6: Enhancing Biosensor Sensitivity focused on optimizing drop-cast formation, pH levels, buffer selection, and sample preparation to enhance sensitivity, improving detection of Terbutryn to meet EU Maximum Residue Limits (0.4 pM). Despite successful proof-of-concept for low-level detection of Terbutryn, challenges remained with atrazine detection. Efforts included generating further mutants and exploring alternative oxidase enzyme pathways for signal enhancement.
In summary, the B-FHAB project made significant strides in developing a biosensor platform for herbicide detection. Key achievements include enhanced binding affinities through targeted mutagenesis, improved computational docking simulations, and demonstrated multi-residue sensing capabilities. Despite challenges, the project successfully optimized sensor sensitivity for specific herbicides and identified pathways for future improvements.
Work Package 2: Distal Binding Site Engineering aimed to create non-competitive inhibition mechanisms by targeting distal binding sites through in silico mutagenesis. While the feasibility of distal binding site engineering at AL186 was demonstrated, overall low binding energies at other distal sites redirected efforts to WP6 due to limited success.
Work Package 3: Improving Computer-Aided Down-Selection of Bacterial RCs integrated advanced protein structure prediction tools like Rosetta Design and AlphaFold, enhancing receptor and ligand structure accuracy through improved docking simulations. High-throughput scripts for batch docking simulations and site-saturated mutagenesis were developed, leading to the discovery of new high-potential point mutations and successful incorporation into WP1.
Work Package 4: Biosensor Discovery Mutants initially aimed to screen large docking compound libraries but shifted focus to improving docking simulation accuracy due to high false-positive rates. Expanding the library of dockable compounds, including herbicide derivatives, improved docking accuracy and identified potential biosensor substrates for experimental validation.
Work Package 5: Multi-Residue Sensing involved coupling herbicide-specific mutants to distinct light-harvesting complexes for multi-residue sensing and constructing a spectro-electrochemical apparatus for wavelength-dependent excitation of bacterial reaction centers. While successful conjugation of bRCs with nanoparticles was achieved, functional increases in excitation were limited, hypothesized to stem from quenching effects due to nanoparticle proximity.
Work Package 6: Enhancing Biosensor Sensitivity focused on optimizing drop-cast formation, pH levels, buffer selection, and sample preparation to enhance sensitivity, improving detection of Terbutryn to meet EU Maximum Residue Limits (0.4 pM). Despite successful proof-of-concept for low-level detection of Terbutryn, challenges remained with atrazine detection. Efforts included generating further mutants and exploring alternative oxidase enzyme pathways for signal enhancement.
In summary, the B-FHAB project made significant strides in developing a biosensor platform for herbicide detection. Key achievements include enhanced binding affinities through targeted mutagenesis, improved computational docking simulations, and demonstrated multi-residue sensing capabilities. Despite challenges, the project successfully optimized sensor sensitivity for specific herbicides and identified pathways for future improvements.
Scientific Impact. The project successfully enhanced biosensor sensitivity to herbicides through in silico docking simulations. This innovative approach allowed for the screening of more complex double mutants, improving the feasibility of the biosensor platform for commercial applications. Additionally, meticulous optimization of drop-cast formulation and application significantly enhanced the biosensor's sensitivity. This optimization enabled the detection of Terbutryn at levels below the EU Maximum Residue Limits (MRLs), achieving a detection limit of 0.2 pM.
Career Development. The project facilitated the transition of the ER to a job in the biosensor development industry, highlighting the practical applications and relevance of the research. The outcomes were disseminated through three high-impact journal articles and a local public outreach article, contributing to scientific knowledge and public awareness of biosensor technology.
Monitoring and Evaluation Strategy. The project's progress was closely monitored through a combination of experimental validations and computational screenings. Baseline measurements and benchmarks were established using both in silico docking simulations and wet-lab experiments. Continuous adjustments and optimizations were made to address challenges and improve outcomes, achieving the desired sensitivity and selectivity of the biosensors.
Adaptations and Changes. While the project encountered challenges in achieving certain objectives, such as distal binding site engineering, it successfully shifted focus to alternative strategies like optimizing the transduction pathway and exploring novel methods to regenerate the hydroquinone substrate.
MSCA Green Charter Implementation. In alignment with the MSCA Green Charter, the project implemented several measures to reduce its CO2 footprint and material consumption. These measures included optimizing freezer temperatures to reduce power consumption, reusing laboratory plastics, purchasing refurbished equipment, sharing lab equipment, and participating in waste sorting and proper disposal practices.
Supervision and Researcher Development. Regular and structured supervision ensured thorough training and skill development. Weekly training sessions focused on electrochemical methodologies and biosensor fabrication. Monthly video conferences with secondment supervisors enhanced training in photosynthetic protein expression and computational modeling. Bi-monthly career development meetings focused on grant writing and career planning. This comprehensive supervision strategy advanced the researcher's skills in electrochemical methodologies and herbicide monitoring, preparing them for future independent research roles.
Career Development. The project facilitated the transition of the ER to a job in the biosensor development industry, highlighting the practical applications and relevance of the research. The outcomes were disseminated through three high-impact journal articles and a local public outreach article, contributing to scientific knowledge and public awareness of biosensor technology.
Monitoring and Evaluation Strategy. The project's progress was closely monitored through a combination of experimental validations and computational screenings. Baseline measurements and benchmarks were established using both in silico docking simulations and wet-lab experiments. Continuous adjustments and optimizations were made to address challenges and improve outcomes, achieving the desired sensitivity and selectivity of the biosensors.
Adaptations and Changes. While the project encountered challenges in achieving certain objectives, such as distal binding site engineering, it successfully shifted focus to alternative strategies like optimizing the transduction pathway and exploring novel methods to regenerate the hydroquinone substrate.
MSCA Green Charter Implementation. In alignment with the MSCA Green Charter, the project implemented several measures to reduce its CO2 footprint and material consumption. These measures included optimizing freezer temperatures to reduce power consumption, reusing laboratory plastics, purchasing refurbished equipment, sharing lab equipment, and participating in waste sorting and proper disposal practices.
Supervision and Researcher Development. Regular and structured supervision ensured thorough training and skill development. Weekly training sessions focused on electrochemical methodologies and biosensor fabrication. Monthly video conferences with secondment supervisors enhanced training in photosynthetic protein expression and computational modeling. Bi-monthly career development meetings focused on grant writing and career planning. This comprehensive supervision strategy advanced the researcher's skills in electrochemical methodologies and herbicide monitoring, preparing them for future independent research roles.