Matthew Penner

Reward-guided inference-time search over the Proteína-Complexa sequence-structure codesign model yields validated binders across 127 targets, kinases and a free carbohydrate, with codesigned sequences outperforming ProteinMPNN redesign.

This paper introduces a framework using protein language models to discover novel enzymes with specific functions by clustering sequence embeddings and recursively exploring functional reservoirs.

The paper discovers a novel thermostable monomeric meganuclease from metagenomic data with unique DNA recognition and cleavage properties.

This study identifies a convergent evolution of MBGT genes in asterids and rosids, showing different evolutionary pathways for similar biochemical functions.

The paper combines microfluidic screening with machine learning to rapidly engineer biocatalysts, achieving 23-fold improvement in catalytic efficiency through sequence-function mapping.

The paper develops a fluorogenic assay for NAD(P)H detection to enable high-throughput screening of biocatalysts in microdroplets, demonstrating improved sensitivity and applicability for glycosyl hydrolases and IREDs.

YeastIT enables continuous in vivo protein evolution by reducing mutational bias and improving mutation rates compared to existing methods.