Title:	Structural prediction of viral proteins, protein-ligand interactions and molecular dynamics using Alphafold 3 and GROMACS
DNr:	NAISS 2025/22-531
Project Type:	NAISS Small Compute
Principal Investigator:	Ambjörn Kärmander <ambjorn.karmander@gu.se>
Affiliation:	Göteborgs universitet
Duration:	2025-04-07 – 2026-05-01
Classification:	30109
Keywords:

Abstract

Predicting a protein’s structure solely from its amino acid sequence has long been a major challenge in structural biology. Recent advancements in artificial intelligence now enable highly accurate predictions of complex structures. Analyzing the three-dimensional structures of viral proteins is crucial for understanding the pathogenic mechanisms of viral diseases. This project will leverage artificial intelligence to predict the structures of viral proteins where the structure is either unknown or contains experimentally observed mutagenic sites. By integrating experimental data from our lab—such as the locations of these mutagenic sites—we can enhance the confidence and accuracy of our structural models. Additionally, we aim to model the binding of antiviral compounds, glycans, or antibodies to these proteins. To further analyze structural dynamics, mutagenic sites, and ligand interactions, we will perform molecular dynamics simulations.