Supramolecular structure predictions validated from sparse experimental data

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.