Jeffrey A. Ruffolo

Aug 3, 2024 | biorxiv.org | Jeffrey A. Ruffolo |Aadyot Bhatnagar |Stephen Nayfach |Joel Beazer

AbstractGenerative models for protein design trained on experimentally determined structures have proven useful for a variety of design tasks. However, such methods are limited by the quantity and diversity of structures used for training, which represent a small, biased fraction of protein space. Here, we describe proseLM, a method for protein sequence design based on adaptation of protein language models to incorporate structural and functional context.

Apr 22, 2024 | biorxiv.org | Jeffrey A. Ruffolo |Stephen Nayfach |Joseph Gallagher |Aadyot Bhatnagar

AbstractGene editing has the potential to solve fundamental challenges in agriculture, biotechnology, and human health. CRISPR-based gene editors derived from microbes, while powerful, often show significant functional tradeoffs when ported into non-native environments, such as human cells. Artificial intelligence (AI) enabled design provides a powerful alternative with potential to bypass evolutionary constraints and generate editors with optimal properties.

Feb 14, 2024 | nature.com | Jeffrey A. Ruffolo

Protein language models learn from diverse sequences spanning the evolutionary tree and have proven to be powerful tools for sequence design, variant effect prediction and structure prediction. What are the foundations of protein language models, and how are they applied in protein engineering?

Jan 15, 2024 | biorxiv.org | Jeffrey A. Ruffolo |Jeffrey J. Gray |Johns Hopkins |Michael Chungyoun

AbstractThe successful application of machine learning in therapeutic antibody design relies heavily on the ability of models to accurately represent the sequence-structure-function landscape, also known as the fitness landscape.