Supramolecular structure predictions validated from sparse experimental data
Lead Research Organisation:
University of St Andrews
Department Name: Chemistry
Abstract
Unravelling the complex structures encountered in macromolecular assemblies from biology to advanced materials is paramount to functional understanding. For biomolecules (such as proteins and DNA) high-resolution structure determination techniques (such as crystallography and cryo-electron microscopy) have been indispensable for structure-function studies. However, the emergence of powerful deep learning based high-accuracy structure prediction tools has sent shock waves through the structural biology community and heralds a new era for structural studies where the routine generation of laborious experimental high-resolution structures could be replaced with computational predictions. These predictions can form the basis to design structure-function studies upon experimental validation and refinement.
The (bio)physical tool called electron paramagnetic resonance (EPR) spectroscopy is ideally suited to complement predicted structures. EPR detects the magnetism arising from the "spin", a quantum mechanical property of unpaired electrons. Electrons are contained in all matter and are commonly paired, quenching their magnetism. However, unpaired electrons such as free radicals underpin many important biological processes like photosynthesis, ageing, and respiration. Using EPR, distances in-between such spins can be determined on the nanometre (one billionth of a metre) scale. Over the past 20 years, these distance measurements have developed into an important and powerful method for investigating the nanoworld of complex (bio)molecules. Molecular biology and chemistry allow labelling specific sites in biomolecules by selectively introducing spins that can then be used as molecular "beacons". Introducing two such beacons allows measurement of the distance between them. With this approach structures of proteins and other macromolecules are successfully mapped, validated and refined.
In this project, deep learning-based structure prediction and modelling tools will be combined with state-of-the-art EPR techniques (including orthogonal copper(II)-SLIM labelling for low-concentration RIDME and unbiased deep learning-based data processing), to validate and refine the structural model of a protein evading experimental high-resolution structure determination. Based on purely computational, high-accuracy structure prediction it is possible to generate informative EPR constructs of the protein where the molecular beacons will report on features critical for structure and structural transitions during function. The distances between different beacons will be used to feed back into the structural model for validation and refinement. Interaction with binding partners during function leads to structural changes which alter distance and relative orientation of beacons. Determination of these alterations with EPR will show the potential of this approach and demonstrate its opportunities for wide-reaching impact.
Artificial intelligence is increasingly affecting many aspects of our everyday lives. Similarly, deep learning revolutionises the way structural studies are performed. This project showcases the benefits of the marriage between deep learning-based structure prediction and structural refinement and validation using EPR. The approach and workflows established here are fully transferable, widening the application scope of EPR for structure-function studies, especially regarding challenging systems currently beyond reach (owing to their size, complexity, flexibility, membrane environment or achievable amount or concentration). Here, the approach is applied to a bacterial surface protein of unknown structure implicated in rheumatic heart disease, and proposed experiments have the potential to uncover the structural mechanism of the host-pathogen interaction. Only with a greater knowledge of the biological nanoworld will it be possible to pinpoint the molecular causes of diseases, and aid in developing prevention and treatment strategies.
The (bio)physical tool called electron paramagnetic resonance (EPR) spectroscopy is ideally suited to complement predicted structures. EPR detects the magnetism arising from the "spin", a quantum mechanical property of unpaired electrons. Electrons are contained in all matter and are commonly paired, quenching their magnetism. However, unpaired electrons such as free radicals underpin many important biological processes like photosynthesis, ageing, and respiration. Using EPR, distances in-between such spins can be determined on the nanometre (one billionth of a metre) scale. Over the past 20 years, these distance measurements have developed into an important and powerful method for investigating the nanoworld of complex (bio)molecules. Molecular biology and chemistry allow labelling specific sites in biomolecules by selectively introducing spins that can then be used as molecular "beacons". Introducing two such beacons allows measurement of the distance between them. With this approach structures of proteins and other macromolecules are successfully mapped, validated and refined.
In this project, deep learning-based structure prediction and modelling tools will be combined with state-of-the-art EPR techniques (including orthogonal copper(II)-SLIM labelling for low-concentration RIDME and unbiased deep learning-based data processing), to validate and refine the structural model of a protein evading experimental high-resolution structure determination. Based on purely computational, high-accuracy structure prediction it is possible to generate informative EPR constructs of the protein where the molecular beacons will report on features critical for structure and structural transitions during function. The distances between different beacons will be used to feed back into the structural model for validation and refinement. Interaction with binding partners during function leads to structural changes which alter distance and relative orientation of beacons. Determination of these alterations with EPR will show the potential of this approach and demonstrate its opportunities for wide-reaching impact.
Artificial intelligence is increasingly affecting many aspects of our everyday lives. Similarly, deep learning revolutionises the way structural studies are performed. This project showcases the benefits of the marriage between deep learning-based structure prediction and structural refinement and validation using EPR. The approach and workflows established here are fully transferable, widening the application scope of EPR for structure-function studies, especially regarding challenging systems currently beyond reach (owing to their size, complexity, flexibility, membrane environment or achievable amount or concentration). Here, the approach is applied to a bacterial surface protein of unknown structure implicated in rheumatic heart disease, and proposed experiments have the potential to uncover the structural mechanism of the host-pathogen interaction. Only with a greater knowledge of the biological nanoworld will it be possible to pinpoint the molecular causes of diseases, and aid in developing prevention and treatment strategies.
Publications
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Ackermann K
(2024)
Pulse Dipolar Electron Paramagnetic Resonance Spectroscopy Distance Measurements at Low Nanomolar Concentrations: The Cu II -Trityl Case
in The Journal of Physical Chemistry Letters
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Grüschow S
(2024)
CRISPR antiphage defence mediated by the cyclic nucleotide-binding membrane protein Csx23.
in Nucleic acids research
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Vitali V
(2024)
Spectroscopically Orthogonal Labelling to Disentangle Site-Specific Nitroxide Label Distributions.
in Applied magnetic resonance
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Wort JL
(2023)
Enhanced sensitivity for pulse dipolar EPR spectroscopy using variable-time RIDME.
in Journal of magnetic resonance (San Diego, Calif. : 1997)
Title | CRISPR antiphage defence mediated by the cyclic nucleotide-binding membrane protein Csx23 (EPR dataset) |
Description | Dataset underpinning the publication of the same title https://doi.org/10.1101/2023.11.24.568546 |
Type Of Material | Database/Collection of data |
Year Produced | 2023 |
Provided To Others? | Yes |
Impact | This data demonstrates that the antiphage defence protein Csx23 undergoes a large conformational change in the membrane upon effector binding. |
URL | https://research-portal.st-andrews.ac.uk/en/datasets/crispr-antiphage-defence-mediated-by-the-cyclic... |
Title | Pulse Dipolar Electron Paramagnetic Resonance Spectroscopy Distance Measurements at Low Nanomolar Concentrations: the Cu(II)-Trityl Case |
Description | Data demstrating the sensitivity of pulse dipolar EPR experiments using copper(II) and trityl labels and the RIDME method. |
Type Of Material | Database/Collection of data |
Year Produced | 2023 |
Provided To Others? | Yes |
Impact | n/a |
URL | https://research-portal.st-andrews.ac.uk/en/datasets/pulse-dipolar-electron-paramagnetic-resonance-s... |
Title | Spectroscopically orthogonal labelling to disentangle site-specific nitroxide label distributions (dataset) |
Description | This dataset demonstrates that the conformer distributions of common nitroxide spin-labels used for EPR spectroscopy do show differences depending on labelling site. |
Type Of Material | Database/Collection of data |
Year Produced | 2023 |
Provided To Others? | Yes |
Impact | n/a |
URL | https://risweb.st-andrews.ac.uk/portal/en/datasets/spectroscopically-orthogonal-labelling-to-disenta... |
Description | Orientation selective pulse dipolar EPR experiments |
Organisation | Goethe University Frankfurt |
Country | Germany |
Sector | Academic/University |
PI Contribution | We designed the protein constructs used to demonstrate the power of orientation-selective pulse dipolar EPR (in particular the RIDME method), produced the spin-labelled samples, and modelled the data. |
Collaborator Contribution | Our partners in Frankfurt provided access to their high-field instruments and invaluable expertise in high field EPR. |
Impact | The first outputs are in preparation. |
Start Year | 2022 |
Description | Pulse dipolar EPR at nanomolar concentrations |
Organisation | University of Bonn |
Country | Germany |
Sector | Academic/University |
PI Contribution | We contribute our expertise in submicromolar pulse EPR experiments (relaxation-based and double resonance methods) and designed the study and models systems to demonstrate the capability on. |
Collaborator Contribution | Our partners in Bonn designed the trityl-based spin-label SLIM and synthesised it. They provided this very precious material in-kind and also provided their expertise in submicromolar pulse EPR experiments based on double quantum coherence. |
Impact | We have just published a manuscript https://doi.org/10.1021/acs.jpclett.3c03311 |
Start Year | 2023 |
Description | Top of the bench |
Form Of Engagement Activity | Participation in an activity, workshop or similar |
Part Of Official Scheme? | No |
Geographic Reach | Regional |
Primary Audience | Schools |
Results and Impact | Top of the Bench competition of local schools (heats and final) - 11 schools participating in quizzes and practical challenges from all fields of chemistry also incorporating recent research results. The winning team goes on to the national competition. |
Year(s) Of Engagement Activity | 2023,2024 |