Single-particle cryogenic electron microscopy (cryo-EM) has emerged as a powerful
technique to visualize the structural landscape sampled by a protein complex. However …

Deep learning using neural networks relies on a class of machine-learnable models
constructed using 'differentiable programs'. These programs can combine mathematical …

Dramatic advances in protein structure prediction have sparked debate as to whether the
problem of predicting structure from sequence is solved or not. Here, I argue that AlphaFold2 …

Dynamics and conformational sampling are essential for linking protein structure to
biological function. While challenging to probe experimentally, computer simulations are …

Protein structure determination from cryo-EM data requires reconstructing a 3D volume (or
distribution of volumes) from many noisy and randomly oriented 2D projection images. While …

Inferring the structure of 3D scenes from 2D observations is a fundamental challenge in
computer vision. Recently popularized approaches based on neural scene representations …

Cryo-electron microscopy (cryo-EM) is an imaging modality that provides unique insights
into the dynamics of proteins and other building blocks of life. The algorithmic challenge of …

While protein conformational heterogeneity plays an important role in many aspects of
biological function, including ligand binding, its impact has been difficult to quantify …

Single-particle cryo-electron microscopy (cryo-EM) is a technique that takes projection
images of biomolecules frozen at cryogenic temperatures. A major advantage of this …

Macromolecules change their shape (conformation) in the process of carrying out their
functions. The imaging by cryo-electron microscopy of rapidly-frozen, individual copies of …