Deniz Akpinaroglu

The paper uses deep learning to design novel protein kinase sequences with multiple mutations, measures their activity, and identifies key sequence determinants of function.

The paper presents a fine-tuned Alphafold2 model that better captures geometric diversity in de novo designed proteins compared to standard methods.

This paper designs dynamic proteins with switch-like behavior using deep learning, validated by structure solving and simulations showing controllable conformational changes.

This paper introduces Frame2seq, a structure-conditioned masked language model for protein sequence design that generates novel, stable proteins with low sequence identity to native sequences.