Andreas H. Laustsen

This paper introduces a workflow for direct protein sequencing using AI-driven methods, validated across diverse proteins to improve accuracy and reduce ambiguity in therapeutic and scientific applications.

This study shows that mRNA-delivered consensus allergens induce neutralizing IgG responses against multiple food and pollen allergens, offering a safer and broader allergy immunotherapy approach.

This paper develops a pipeline combining yeast screening and AlphaFold3 to accelerate discovery of multi-target antibodies, finding that structural predictions improve binding specificity and solubility.

InstaNovo is a transformer model for de novo peptide sequencing that outperforms existing methods, enabling accurate identification of peptides and discovery of novel sequences and organisms.

This study evaluates AF2's ability to predict protein structural ensembles by comparing its results with MD simulations and NMR data for four test proteins.

This study compares three protein structure prediction tools on snake venom toxins with no reference templates, finding AlphaFold2 performs best, while ColabFold is computationally less intensive.

The paper introduces a simplified method for amplifying plasmid DNA libraries using rolling circle amplification and endonuclease treatment to improve transformation efficiency in E. coli and S. cerevisiae.

ALOHA is an AI-guided tool that quantifies venom-induced haemorrhage in mice using smartphone imaging, reducing analysis time and human error.

A recombinant human monoclonal antibody was optimized to neutralize whole cobra venom in mice, preventing lethality by targeting neurotoxins.