Isak Johansson-Åkhe

Improving Peptide-Protein Docking with AlphaFold-Multimer using Forced Sampling

This study benchmarks AlphaFold-Multimer for peptide-protein interaction prediction and shows that forced sampling improves docking accuracy.

InterPepScore: A Deep Learning Score for Improving the FlexPepDock Refinement Protocol

The paper introduces a graph neural network called InterPepScore to enhance the Rosetta FlexPepDock refinement protocol for peptide-protein docking, improving model quality on a benchmark.

InterPepRank: Assessment of Docked Peptide Conformations by a Deep Graph Network

InterPepRank ranks peptide-protein docking decoys using a deep graph network, improving the selection of high-quality models.

InterPep2: Global Peptide-Protein Docking with Structural Templates

InterPep2 predicts peptide-protein interaction structures using structural templates, outperforming existing methods on a benchmark dataset.

Predicting protein-peptide interaction sites using distant protein complexes as structural templates

This paper introduces InterPep, a computational pipeline that predicts protein-peptide interaction sites using structural templates and machine learning, achieving 50.7% rank 1 accuracy and 69.3% top five accuracy.