SUPR
Protein engineering with Generative AI for optimization of thermal stability and enzymatic activity
Dnr:

NAISS 2025/6-265

Type:

NAISS Medium Storage

Principal Investigator:

Aleksej Zelezniak

Affiliation:

Chalmers tekniska högskola

Start Date:

2025-06-30

End Date:

2026-07-01

Primary Classification:

10203: Bioinformatics (Computational Biology) (Applications at 10610)

Secondary Classification:

10601: Structural Biology

Tertiary Classification:

10610: Bioinformatics and Computational Biology (Methods development to be 10203)

Webpage:

Allocation

Abstract

Proteins fold into 3D structures that determine their function and underlie cellular processes. Recent advances in structure prediction have enabled models with experimental-level accuracy, opening the door to incorporating structure directly into machine learning pipelines for protein engineering. In this project, we will refine and extend our ML workflows to integrate structural features alongside sequence data to predict and engineer physicochemical properties such as thermal resistance and enzymatic activity. This shift marks a move from predictive modeling to generative design, requiring high-speed access to large protein datasets, embeddings, and model checkpoints. The work is part of a broader international effort to develop next-generation protein engineering tools with applications in biotechnology and medicine.