P. Douglas Renfrew

CyclicCAE generates energetically favorable macrocycle conformations faster than Rosetta's GeneralizedKIC, using synthetic data and enabling design and structure prediction.

The paper introduces Masala, an open-source software suite designed to extend and improve upon Rosetta for synthetic heteropolymer design, offering modular architecture and performance improvements.

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

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

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

The paper develops and validates rotamer libraries for beta3-amino acids to support their inclusion in protein design frameworks.