Cameron J. Glasscock

This study designs synthetic activators for programmable gene expression by engineering coactivator-binding proteins and demonstrates their enhanced transcriptional activation capabilities.

This paper designs sequence-specific DNA binding proteins using deep learning, achieving a 100-fold improvement in success rates over previous methods.

This paper introduces NA-MPNN, a unified deep learning model for nucleic acid inverse folding, which outperforms existing methods in RNA sequence design and protein-DNA specificity prediction.

This paper introduces a generative diffusion model for de novo design of RNA and nucleoprotein complexes, validated experimentally with SHAPE-seq and electron microscopy.

This paper designs sequence-specific DNA-binding proteins computationally and validates them experimentally, achieving high specificity and affinity for target sequences.

This paper introduces LigandMPNN, a deep learning method for protein sequence design that models non-protein atomic contexts, achieving better performance than existing methods in recovering interacting residues.

DeepPBS predicts protein-DNA binding specificity using geometric deep learning, showing alignment with experimental mutagenesis data and enabling analysis of molecular dynamics simulations.

This study uses metabolic modeling to design E. coli strains that overproduce glycan precursors for glycoprotein production.

The paper introduces a new gene expression cassette called a riboregulated switchable feedback promoter (rSFP) to dynamically control gene expression in metabolic engineering, achieving higher titers in a Taxol precursor biosynthesis pathway.

This paper designs two new RNA repressors, toehold repressors and LASOs, that efficiently and specifically repress translation in response to input RNA sequences.