Programmable Icosahedral Protein Nanocages: A De Novo Design Framework for Reducing Kinetic Mis-Assembly and Enhancing Experimental Assembly Fidelity
Discovered via OpenAlex
Abstract
The de novo design of symmetric protein nanocages has emerged as a major frontier in programmable biomolecular engineering due to its potential applications in nanomedicine, vaccine development, molecular encapsulation, catalysis, and synthetic cellular systems. Despite substantial progress in computational protein design, the reliable construction of large polyhedral assemblies remains limited by kinetic mis-assembly, off-pathway oligomerization, interface instability, and low experimental success rates. This study presents a computationally integrated framework for the design of programmable icosahedral protein nanocages with improved assembly fidelity. The proposed strategy combines symmetry-guided scaffold selection, Rosetta-based symmetric docking, RFdiffusion-driven interface backbone generation, ProteinMPNN sequence optimization, and AlphaFold2-Multimer computational filtering to produce experimentally tractable cage architectures. The target system consists of a two-component assembly formed from trimeric and dimeric oligomeric building blocks organized into a 60-subunit icosahedral nanocage approximately 25 nm in diameter. The framework emphasizes reduction of non-specific intermolecular interactions through interface-specific design constraints and computational pre-screening of unstable configurations. Predicted outcomes indicate that the integration of diffusion-based backbone generation with sequence-level optimization and AF2-based structural validation substantially improves the likelihood of obtaining stable self-assembling cages while reducing experimental screening burdens. The study further evaluates how this integrated workflow compares with existing approaches in symmetric protein cage engineering. Collectively, the proposed methodology provides a scalable route toward high-fidelity programmable protein nanomaterials and contributes to the broader development of de novo self-assembling biomolecular systems.