Matthew Penner

Latent generative search unlocks de novo design of untapped biomolecular interactions at scale

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.

Unsupervised protein language models learn patterns of enzyme function

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.

A metagenomic thermostable monomeric meganuclease with novel specificity and unique palindromic 3-prime overhangs

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

Convergent Acquisition of Glucomannan β-galactosyltransferases in Asterids and Rosids

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

Microdroplet screening rapidly profiles a biocatalyst to enable its AI-assisted engineering

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

YeastIT: Reducing mutational bias for in vivo directed evolution using a novel yeast mutator strain based on dual adenine-/cytosine-targeting and error-prone DNA repair

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