Frederick Marty Ytreberg

PepXPro curates and generates standardized protein-peptide datasets for benchmarking computational methods, providing a reproducible framework for dataset creation and evaluation.

The paper uses AlphaFold and molecular dynamics to model Saccharomyces killer toxins, revealing structural homology and functional insights into their mechanisms.

This paper trains a neural network to correct FoldX's free energy predictions for protein folding and binding, significantly improving accuracy for single and double mutations and epistasis.

The paper surveys killer yeast prevalence in attine ant fungus gardens, identifies a new killer toxin Ksino, and highlights its antifungal activity and ecological significance.

The paper evaluates predictive models for {Phi}X174 bacteriophage viability using biophysical and molecular modeling approaches, finding that large-effect mutations are highly informative for predicting phenotypes.

The paper introduces an automated bioinformatics pipeline called HitList to identify novel antifungal targets in fungi by analyzing genomic and proteomic data, discovering eight new potential targets.

This study evaluates the MD+FoldX method's ability to predict SARS-CoV-2 antibody escape mutations using a large deep mutational scanning dataset, showing moderate correlation with experimental data.

This study identifies six critical amino acids in NUP153 essential for HIV capsid interaction using computational and experimental methods.