Shunzhi Wang

This paper introduces Pinal, a foundation model that designs proteins from natural language descriptions, achieving functional alignment and experimental success in four design tasks.

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 GEMINI, a tool that records cellular activity histories with high temporal resolution, and demonstrates its application in tracking transcriptional changes and inflammation dynamics in vivo.

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

The paper designs EndoTags to trigger endocytosis and degradation of target proteins, enabling tissue-specific targeting and therapeutic applications.

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