Determining Protein Conformational Ensembles by Combining Machine Learning and SAXS

Sammanfattning: In structural biology, immense effort has been put into discovering functionally relevant atomic resolution protein structures. Still, most experimental, computational and machine learning-based methods alone struggle to capture all the functionally relevant states of many proteins without very involved and system-specific techniques. In this thesis, I propose a new broadly applicable method for determining an ensemble of functionally relevant protein structures. The method consists of (1) generating multiple protein structures from AlphaFold2 by stochastic subsampling of the multiple sequence alignment (MSA) depth, (2) screening these structures using small-angle X-ray scattering (SAXS) data and a structure validation scoring tool, (3) simulating the screened conformers using short molecular dynamics (MD) simulations and (4) refining the ensemble of simulated structures by reweighting it against SAXS data using a bayesian maximum entropy (BME) approach. I apply the method to the T-cell intracellular antigen-1 (TIA-1) protein and find that the generated ensemble is in good agreement with the SAXS data it is fitted to, in contrast to the original set of conformations from AF2. Additionally, the predicted radius of gyration is much more consistent with the experimental value than what is predicted from a 450 ns long MD simulation starting from a single structure. Finally, I cross-validate my findings against small-angle neutron scattering (SANS) data and find that the method-generated ensemble, although not in a perfect way, fits some of the SANS data much better than the ensemble from the long MD simulation. Since the method is fairly automatic, I argue that it could be used by non-experts in MD simulations and also in combination with more advanced methods for more accurate results. I also propose generalisations of the method by tuning it to different biological systems, by using other AI-based methods or a different type of experimental data.