Andrew J. Borst

This study characterizes polyclonal antibody responses to RSV and hMPV fusion proteins, revealing shared and distinct antigenic features that may inform vaccine design.

This paper designs protein assemblies that switch between structural states using engineered interfaces, demonstrating temperature-responsive transitions from capsids to 2D arrays.

This study identifies two antibody classes that neutralize pneumovirus by targeting a conserved epitope, revealing distinct mechanisms of action through structural and functional analyses.

This paper presents a modular strategy to design programmable, highly porous protein crystals with tunable architectures and composition, enabling applications in catalysis and biomedicine.

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 study shows that computationally designed proteins can template and confine hydroxyapatite mineralization, providing a framework for programmable protein-guided mineralization.

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

The paper redesigns TALE proteins to assemble linear protein-DNA complexes on double-stranded DNA templates, enabling controlled length and application in antigen display.

This paper designs de novo protein nanoparticles with integrated functional motifs, demonstrating their ability to form structured assemblies and elicit immune responses in mice.

This paper designs antibodies de novo with atomic precision using RFdiffusion and yeast display, validated by cryo-EM and biophysical methods.

This paper designs Ca2+ channels with specific selectivity filter geometries using a bottom-up approach, showing higher Ca2+ conductance and structural accuracy.

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

This study identifies human antibodies that broadly inhibit influenza neuraminidase by mimicking its substrate through structural convergence and water-mediated interactions.

This paper presents a computational strategy to design bifaceted protein nanomaterials with two distinct sides, enabling anisotropic functionalization and controlled structural variation.

The paper introduces a modular bond-centric design approach for protein assemblies, demonstrating the successful creation of multiple polyhedral cages, 2D arrays, and 3D lattices with high success rates.

This study shows that structural flexibility in computationally designed protein assemblies leads to multiple architectures, expanding their functional possibilities.

This paper introduces WRAPs, designed proteins that solubilize membrane proteins without detergents, enabling structural studies and vaccine development.

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

This paper identifies an unknown contaminant in protein samples using Cryo-EM and AI tools, leading to improved purification protocols.

This paper designs allosterically switchable protein assemblies using rigid-body coupling of effector-switchable modules, demonstrating cooperative ligand binding and structural changes similar to natural systems.

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

This paper designs 3D protein crystals with specified lattice architectures using computational methods, achieving atomic accuracy and demonstrating successful experimental validation.

Fine-tuning RoseTTAFold as a diffusion denoiser gives RFdiffusion, a general protein backbone generator; a designed influenza haemagglutinin binder matched its model by cryo-EM.

This paper introduces a top-down reinforcement learning approach to design protein nanomaterials, resulting in more compact and robust icosahedral assemblies than previously observed.