Alex Kang

This study designs de novo amide ligases with a cysteine catalytic triad to selectively form amide bonds across diverse substrates, demonstrating high selectivity and applicability in synthesizing bioactive molecules.

The paper designs and validates protein readers that specifically detect disease-related modifications in Tau and Abeta in Alzheimer's disease models and human samples.

CaGE alternates guided structure prediction with sequence redesign, fading guidance each cycle, so sequences recover complex catalytic sites unaided; designed nonheme-iron and PLP-dependent enzymes were active.

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 study designs miniprotein inhibitors targeting bacterial adhesins to prevent and treat urinary tract infections.

This paper develops a computational method to design glycoproteins with controlled N-linked glycan composition by encoding glycan location and structure into amino acid sequences.

This study designs allosterically controlled enzymes using denoising diffusion models, showing that activity can be activated or repressed by designed effectors.

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

The paper designs de novo proteins that form autocatalytic isopeptide bonds, enabling rigid covalent protein assemblies through intramolecular and intermolecular crosslinking.

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

The paper designs miniproteins that inhibit Bacteroides fragilis toxin by blocking receptor binding, preventing gut pathology without antibiotics.

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

This paper designs synthetic fusogens using machine learning, showing they enable efficient cell-cell and intracellular membrane fusion with higher activity than native SNARE complexes.

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 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 highly active metallohydrolases using a new generative AI method, achieving kcat/KM values up to 53,000 M-1 s-1 without experimental optimization.

This paper designs twistless alpha-helical repeat proteins that inhibit ice recrystallization with activity comparable to native ice-binding proteins.

The paper designs high-affinity miniprotein binders targeting Flpp3, a virulence factor of Francisella tularensis, using physics-based and deep-learning methods, achieving sub-nanomolar affinities.

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 proteins with controlled dissociation rates by introducing strained excited states, enabling rapid cytokine receptor dissociation.

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

The paper designs orthogonal fluorophore-binding proteins using deep learning for multiplex imaging, demonstrating high specificity and utility in labeling and monitoring subcellular processes.

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 paper introduces RFpeptides, a deep learning framework for designing high-affinity macrocyclic peptides, achieving sub-nanomolar binding affinities and accurate structural predictions.

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

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

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