W. Boomsma

Deep learning assisted single particle tracking for automated correlation between diffusion and function

This paper introduces DeepSPT, a deep learning framework that automates the analysis of sub-cellular diffusion to infer functional information, achieving high accuracy in identifying endosomal structures during viral infections.

Lysine deserts prevent adventitious ubiquitylation of ubiquitin-proteasome components

The paper identifies lysine deserts in UPS proteins and shows that introducing lysines increases ubiquitylation, with functional consequences for protein stability and function.

Rapid protein stability prediction using deep learning representations

This paper introduces RaSP, a deep learning method for rapid protein stability prediction, showing it matches biophysics-based methods and reveals population-level patterns in stability changes.

Monte Carlo Sampling of Protein Folding by Combining an All-Atom Physics-Based Model with a Native State Bias

The paper introduces a hybrid protein folding model, ProfasiGo, combining all-atom physics-based and structure-based potentials, and applies it to study folding free energy landscapes of four proteins.