Lance J. Stewart

This paper introduces scalable experimental and computational workflows to accelerate protein design by improving throughput, reducing costs, and enabling rapid validation of thousands of protein designs.

This paper introduces RFD2-MI, a deep learning framework for designing phosphotyrosine peptide binders, showing high specificity and affinity for phosphorylated targets.

The paper designs TrkA agonists that activate neuronal differentiation and neurite outgrowth without triggering pain-related gene expression.

This paper presents computational methods for designing GPCR-binding miniproteins and validates them through high-throughput screening, producing agonists and antagonists for multiple GPCRs.

The paper develops a method to assess solution properties of designed proteins using mass spectrometry and peptide barcoding, enabling analysis of large protein libraries.

This study develops a multivalent nanoparticle vaccine against EBOV and SUDV, showing protection in rodents from lethal infection.

The paper develops RosettaVS, an AI-accelerated virtual screening platform that identifies drug candidates with high binding affinity across two targets, achieving a 14% and 44% hit rate in under seven days.

The paper designs proteins that template mineralization of metal-oxides, showing they can guide hematite and ZnO growth under specific conditions.

This paper designs high-affinity proteins that bind to convex protein sites using geometrically matching concave scaffolds, achieving low nanomolar to picomolar affinities.

The paper designs EndoTags to trigger endocytosis and degradation of target proteins, enabling tissue-specific targeting and therapeutic applications.

The paper introduces a thermodynamically coupled biosensor to rapidly detect neutralizing antibodies against SARS-CoV-2 strains in serum samples.

The paper designs and characterizes trivalent miniprotein binders that neutralize SARS-CoV-2 variants with low pM IC50 values and provide prophylactic and therapeutic protection in mice.

This paper designs small, stable proteins that bind to diverse target proteins using only structural information, achieving nanomolar to picomolar affinities.

The paper predicts SARS-CoV-2 B-cell epitopes using computational methods and validates them with ELISA, finding two antigens with high sensitivity and specificity for serological diagnosis of COVID-19.

The paper designs picomolar SARS-CoV-2 miniprotein inhibitors using computational methods and experimental validation, showing high potency and structural similarity to computational models.

The paper designs modular biosensors using de novo protein switches that detect various analytes with high sensitivity and modularity.