Doctoral position
Recovering the dynamics of biological macromolecules using cryo-EM data with data-driven priors
Images courtesy Bepler et al., 2019, Ingraham et al., 2023, and Mirabello et al., 2024.
Deadline: April 25, 2025
Contact: janden@kth.se
Cryogenic electron microscopy (cryo-EM) is a powerful imaging technique for reconstructing 3D models of biological macromolecules using transmission electron microscopy. Although this method is able to reach near-atomic resolution for certain molecules many important challenges remain. Foremost is the issue of noise. Due to the low electron dose required to reduce specimen damage, cryo-EM images are extremely noisy, a fact that is exacerbated for small molecules that have a low signal power to begin with. To compensate for this, a large number of images (often in the hundreds of thousands) are required to obtain an accurate 3D reconstruction of the molecule. However, these large datasets can be quite expensive to obtain and for small enough molecules the noise is too high that reconstruction is impossible using current methods.
This project proposes a new method for reconstructing 3D models of molecules from cryo-EM images using generative models. Existing models such as AlphaFold 3 and Chroma contain a wealth of valuable information about molecular structures but can also hallucinate structures in some cases. By instead using these models as prior distributions in a Bayesian reconstruction framework, we combine information from two different sources (generative models and cryo-EM data) to construct more reliable models.
The successful doctoral candidate will be part of a larger team of researchers working on different aspects of deep learning and cryo-EM imaging at KTH Department of Mathematics. Beyond this core team, the student will also be working closely with collaborators at KTH Department of Intelligent Systems and SU Department of Biochemistry and Biophysics as part of a Digital Futures Flagship Project. The student will be enrolled in the Doctoral program in Applied and Computational Mathematics specializing in mathematical statistics and will also be involved in teaching duties for various courses in the department. This position is a full-time, five-year position starting in August 2025 or at some other date to be agreed upon.
D. Kimanius et al. Exploiting prior knowledge about biological macromolecules in cryo-EM structure determination. IUCrJ 8:60–75, 2021.
J. Andén and A. Singer. Structural varaibility from noisy tomographic projections. SIAM Journal on Imaging Science, 11(2):1441–1492, 2018.
Abramson et al. Accurate structure prediction of biomolecular interactions with AlphaFold 3. Nature, 630(8016):493–500, 2024.
Ingraham et al. Illuminating protein space with a programmable generative model. Nature, 623(7989):1070–1078, 2023.
S. B. Chandra Gutha, R. Vinuesa, and H. Azizpour. Inverse problems with diffusion models: A MAP estimation perspective. Proc. IEEE/CVF Winter Conference on Applications of Computer Vision, 2025.
W. Zhou, C. Iliffe Sprague, and H. Azizpour. Energy-based flow matching for molecular docking. In NeurIPS 2024 Workshop on Machine Learning in Structural Biology.