Alex Kang

This study designs novel serine hydrolases with high catalytic efficiency using computational methods, achieving structures close to design models and distinct from natural enzymes.

This study uses target-conditioned diffusion to design high-affinity TNFR superfamily antagonists and agonists with switchable specificity.

This paper designs stable transmembrane beta-barrel nanopores with tailored sizes and shapes using de novo protein design, achieving conductances ranging from 110 pS to 430 pS.

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

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

The paper designs peptide traps that bind amyloidogenic segments in {beta}-strand and {beta}-hairpin conformations, effectively blocking fibril assembly for A{beta}42.

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

This paper introduces standardized protein building blocks and geometric design principles to create expandable protein nanomaterials, validated through X-ray crystallography and electron microscopy.

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

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

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

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

The paper designs closed repeat proteins with central pockets using deep learning and validates them in E. coli, showing structural consistency and potential for small molecule binding.

This paper designs reconfigurable asymmetric protein assemblies using implicit negative design principles, enabling stable subunit exchange and diverse complex formations.

This study successfully designs transmembrane beta-barrels from first principles, revealing structural constraints and enabling custom pore design for biotechnology applications.