Robert J. Pecoraro

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 the EDEN family of foundation models for AI-programmable therapeutic design, demonstrating their ability to generate functional gene insertions, antibiotic peptides, and synthetic microbiomes.

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.

The paper introduces BaseData, a new biological sequence database that expands known protein diversity by over 10x using a global biodiscovery pipeline.