D. Baker

Diffusing protein binders to intrinsically disordered proteins

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

Atomic context-conditioned protein sequence design using LigandMPNN

This paper introduces LigandMPNN, a deep learning method for protein sequence design that models non-protein atomic contexts, achieving better performance than existing methods in recovering interacting residues.

Parametrically guided design of beta barrels and transmembrane nanopores using deep learning

This paper designs beta barrels and transmembrane nanopores using deep learning and parametric methods, achieving high success rates with experimental validation.

Design of intrinsically disordered region binding proteins

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

De Novo Design of Integrin α5β1 Modulating Proteins for Regenerative Medicine

The paper designs de novo proteins called NeoNectins that specifically bind integrin α5β1, enhancing cell attachment and tissue integration in biomaterials.

Design of Repeat Alpha-Beta Proteins with Capping Helices

This study designs 25 new stable alpha-beta repeat proteins using Rosetta and deep learning, finding that terminal caps improve solubility and design success.

Multispectral live-cell imaging with uncompromised spatiotemporal resolution

The paper introduces a multispectral imaging approach for live-cell imaging, enabling accurate unmixing of low signal-to-noise ratio data at video rates.

Accurate single domain scaffolding of three non-overlapping protein epitopes using deep learning

This paper designs small single domain proteins that present three distinct viral epitopes using deep learning, showing improved immunogenicity compared to single-epitope designs.

Design of High Affinity Binders to Convex Protein Target Sites

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

Computational stabilization of a non-heme iron enzyme enables efficient evolution of new function

The paper uses ProteinMPNN to stabilize Fe(II)/KG enzymes, enabling more effective directed evolution of new catalytic functions.

Essential and virulence-related protein interactions of pathogens revealed through deep learning

This study uses deep learning to predict protein-protein interactions in bacterial pathogens, identifying new complexes involving essential genes and virulence factors, with half of tested predictions validated.

Atomically accurate de novo design of antibodies with RFdiffusion

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

Proofreading and single-molecule sensitivity in T-cellreceptor signaling by condensate nucleation

This paper shows that T-cell receptor signaling uses condensate nucleation to selectively detect single foreign peptides by amplifying weak signals and setting spatial-temporal thresholds.

De novo design of diverse small molecule binders and sensors using Shape Complementary Pseudocycles

The paper designs nanomolar binders for diverse small molecules using a pseudocycle-based shape complementarity method, enabling direct conversion into sensing systems.

De novo design of allosterically switchable protein assemblies

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.

Small-molecule binding and sensing with a designed protein family

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

Local structural flexibility drives oligomorphism in computationally designed protein assemblies

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

Generalized Biomolecular Modeling and Design with RoseTTAFold All-Atom

This paper introduces RFAA and RFdiffusionAA, models that can predict and design complex biomolecular assemblies including proteins, nucleic acids, small molecules, and covalent modifications.

Improving protein expression, stability, and function with ProteinMPNN

This paper introduces ProteinMPNN, a deep learning model that improves protein expression, stability, and function by incorporating evolutionary and structural information.

Single-cell signaling analysis reveals that Major Vault Protein facilitates RasG12C inhibitor resistance

The paper uses single-cell signaling analysis to identify Major Vault Protein (MVP) as a mediator of RasG12C inhibitor resistance by facilitating Ras activation at different cellular compartments.

Cytogenetic resource enables mechanistic resolution of changing trends in human pluripotent stem cell aberrations linked to feeder-free culture

The paper analyzes genetic changes in human pluripotent stem cells under different culture conditions and identifies a link between feeder-free culture and increased chromosome 1q gains.

De novo designed Hsp70 activator dissolves intracellular condensates

The paper designs Hsp70 activators that dissolve intracellular condensates by modulating Hsp70 ATPase activity and promoting protein refolding.

De novo design of buttressed loops for sculpting protein functions

This paper designs proteins with structured loops supported by hydrogen bonds, demonstrating their stability and functionality in binding extended peptides.

Rapid and automated design of two-component protein nanomaterials using ProteinMPNN

This study benchmarks ProteinMPNN against Rosetta in designing two-component protein nanomaterials, showing comparable success with significantly reduced computation and no manual refinement.

Protein Ensemble Generation through VariationalAutoencoder Latent Space Sampling

The paper generates high-quality protein conformations using a variational autoencoder, achieving better sampling accuracy than MD simulations or AlphaFold2 for K-Ras.

Salmonella Infantis, the emerging human multidrug resistant pathogen - a One Health perspective

The paper analyzes whole genome sequences of Salmonella Infantis strains to identify genetic clusters, AMR determinants, and plasmid presence across different sources and regions.

Design of four component T=4 tetrahedral, octahedral, and icosahedral protein nanocages through programmed symmetry breaking

The paper designs T=4 protein nanocages with tetrahedral, octahedral, and icosahedral symmetries using pseudo-symmetrization of trimeric building blocks, resulting in structures with distinct subunits and interfaces.

Hierarchical design of pseudosymmetric protein nanoparticles

The paper designs large pseudosymmetric protein nanoparticles using a hierarchical computational method, achieving sizes up to 96 nm with 960 subunits.

De novo design of highly selective miniprotein inhibitors of integrins αvβ6 and αvβ8

This paper designs highly selective miniprotein inhibitors for integrins αvβ6 and αvβ8 with picomolar affinity and therapeutic potential in a mouse fibrosis model.

Directing polymorph specific calcium carbonate formation with de novo protein templates

De novo designed protein templates promote nucleation of nano-calcite and direct its growth by oriented particle attachment.