Supervision: Michaël Defferrard,Laurène Donati

Project type: Master thesis

Finished

Single-particle cryo-electron microscopy (cryo-EM) is a Nobel-prized technology that aims to characterize the 3D structure of proteins at the atomic level. The electron microscope first images numerous (~100k) replicates of a protein, positioned at various orientations. Algorithms then reconstruct a high-resolution 3D structure from the acquired images (cf illustration below).

The main challenge in cryo-EM reconstruction, compared to traditional tomographic set-ups, is that the angles at which the images were taken are unknown. Another challenge is that the images are extremely noisy and blurred. The sheer amount of images per protein (~100k), as well as the number of imaged proteins (~4k), should however enable a data-driven approach to overcome those challenges.

Project goal. Design a neural network to estimate the angular relation between images of a protein. The developed neural network will be trained and tested on simulated and real data.

Prerequisites. Highly motivated, ambitious, and independent student. Experience with Python programming. Experience with (Deep) Machine Learning (with any framework) is desirable. No experience in biology is required. Experience in imaging is a plus.

What to expect. The student will interact on a weekly basis with the supervisors and receive ample directions and advices. The student will produce midterm and final presentations. The student will be evaluated on his work during the semester, a report, the final presentation, and the produced code. This project can lead to a publication.

Supervisors & Contact