Brian Coventry

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

This study designs de novo cysteine proteases with high activity and structural novelty, achieving 19% success rate and up to 3 x 10^7 rate enhancement over background reactions.

This paper designs de novo metalloproteases using RoseTTAFold Diffusion 2, achieving high activity and specificity for cleaving various substrates.

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 designs proteins to reprogram T cell signaling pathways, improving CAR-T and TCR-T performance in solid tumor models.

This paper introduces a generative AI framework for designing transmembrane β-barrel nanopores with customizable properties for ion sensing, DNA translocation, and transport.

This study designs genetically encoded miniproteins to modulate FAK kinase activity, showing they can inhibit or activate FAK in vitro and in cells, and adapt them to target other kinases.

This study designs proteins to control the phase, form, and function of metal-carbonate materials, demonstrating their use as electrocatalysts for alkaline water splitting.

This study identifies germline-encoded features as the primary driver of immunodominance in antibody responses to SARS-CoV-2, using structural modeling and mutational analysis.

This paper designs a chemically induced dimerization system using a de novo macrocycle and protein homodimer to control cellular processes.

This paper designs a de novo Tie2 binder that activates the Angiopoietin-Tie2 pathway without integrin engagement, showing improved therapeutic potential in a mouse model of ARDS.

This paper designs a protein family that binds and senses small molecules using deep learning and physics-based methods, achieving nanomolar sensitivity for cortisol.

This paper demonstrates that bioPROTACs can reprogram CAR T-cells by targeting DNMT3A, offering a tunable alternative to gene editing for enhancing T-cell function.

Conditioning RFdiffusion on edge beta-strand pairing yields specific binders to polar targets, with in silico success above 9.2% versus 0.98% for hotspot-only conditioning.

RFdiffusion2 scaffolds unindexed atom-level catalytic active sites via flow matching without inverse rotamer enumeration, solving all 41 in silico benchmark cases and yielding active de novo enzymes across four reactions.

This paper designs synthetic insulin receptor agonists that modulate receptor conformation and signaling, showing improved potency and activity in disease mutants.

The paper designs novel cytokines by fusing computationally generated receptor-binding domains, identifying several that activate signaling pathways and have specific biological effects.

This paper designs a synthetic ligand, H2F, that induces HER2-FGFR proximity to reprogram fibroblasts into myogenic cells and promote myogenic regeneration.

This paper introduces computational methods to design pH-sensitive binders that dissociate under acidic conditions, demonstrating their application in targeting various receptors and proteins.

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

This paper designs potent small protein inhibitors of C. difficile toxin B using deep learning and Rosetta, showing high affinity and stability for potential clinical use.

An all-atom diffusion model using a 14-atom residue representation and sparse U-Net architecture accelerates biomolecular design 10-fold, enabling experimentally validated DNA-binding proteins and multi-turnover cysteine hydrolases.

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

The paper develops RFdiffusion to generate protein binders for IDPs/IDRs, achieving nanomolar affinity and functional applications in amylin and IL2RG.

The paper designs proteins that bind intrinsically disordered regions with high affinity and specificity across diverse targets.

This study designs miniproteins that inhibit MERS-CoV by targeting the spike protein, showing broad neutralization and protective efficacy in mice.

This paper introduces Transposon-Display, a method to identify and optimize AI-designed protein binders for intracellular use, enabling controlled protein manipulation in human cells.

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.

This study designs protein minibinders that act as agonists for TLR3, showing nanomolar binding and biological activity in cell lines.