Lucy J. Colwell

The paper maps the genome packaging landscape of AAV2 capsid protein using high-throughput synthesis and machine learning, revealing navigable subspaces of functionally viable sequences.

This paper introduces AlphaProtein Novo, a machine-learning pipeline for de novo enzyme design that outperforms natural sequence mining in creating enzymes for new-to-nature chemistry and substrates.

This paper refutes claims that AF-Cluster fails to use local evolutionary couplings in its MSA clusters and shows they play an important role in predictions.

This study combines machine learning and high-throughput screening to design improved nuclease enzymes, finding a more effective variant than standard directed evolution methods.

ProtEx improves protein function prediction by using retrieved exemplars, achieving state-of-the-art results for EC numbers, GO terms, and Pfam families.

This study uses ML to design diverse VHH antibodies that neutralize SARS-CoV-1 and SARS-CoV-2 variants, including Delta and Omicron BA.1.

ProteInfer uses deep convolutional neural networks to predict protein functions from amino acid sequences, offering efficient and accurate predictions with a lightweight software interface.

This paper introduces SLIP, a framework for creating synthetic protein fitness landscapes to benchmark ML-guided protein design pipelines.

This paper uses deep learning to improve protein function annotation by training on Pfam data, achieving better accuracy and efficiency than BLASTp.

The paper uses data augmentation with single protein-ligand models to improve drug-target interaction predictions for unseen proteins, achieving AUC > 0.9 on test sets with no experimental data.

The paper critiques protein family classification models by identifying sufficient input subsets to understand their decision-making criteria.