Julia Koehler Leman

Large protein databases reveal structural complementarity and functional locality

This work analyzes protein structure databases to reveal functional clustering and creates a unified low-dimensional representation for exploring sequence-structure-function relationships.

TM-Vec: template modeling vectors for fast homology detection and alignment

TM-Vec predicts structural similarity from sequence pairs to detect remote homologs, outperforming sequence alignment methods and matching structure-based alignment performance.

Sequence-structure-function relationships in the microbial protein universe

The paper predicts structures and annotates functions for diverse microbial protein sequences, identifying novel folds and highlighting the saturation of structural space.

Ensuring scientific reproducibility in bio-macromolecular modeling via extensive, automated benchmarks

The paper presents a framework for ensuring reproducibility in bio-macromolecular modeling through automated benchmarks and a test server, demonstrating its utility for Rosetta and other tools.

Structure-Based Function Prediction using Graph Convolutional Networks

This paper introduces a Graph Convolutional Network for predicting protein function and identifying important residues, showing improved accuracy over sequence-based methods.