bioRxivBiophysics
Stimulus-responsive self-assembly of protein-based fractals by computational design
N. Hernandez, W. Hansen, D. Zhu, M. Shea, M. Khalid, V. Manichev, M. Putnis, M. Chen, A. Dodge, L. Yang, M. Banal, T. Gustaffson, L. Feldman, S.-H. Lee, L. Wackett, W. Dai, S. Khare
Abstract
Fractal topologies, which are statistically self-similar over multiple length scales, are pervasive in nature. The recurrence of patterns at increasing length scales in fractal-shaped branched objects, e.g., trees, lungs, and sponges, results in high effective surface areas, and provides key functional advantages, e.g., for molecular trapping and exchange. Mimicking these topologies in designed protein-based assemblies will provide access to novel classes of functional biomaterials for wide ranging applications. Here, we describe a modular, multi-scale computational design method for the reversible self-assembly of proteins into tunable supramolecular fractal-like topologies in response to phosphorylation. Computationally-guided atomic-resolution modeling of fusions of symmetric, oligomeric proteins with Src homology 2 (SH2) binding domain and its phosphorylatable ligand peptide was used to design iterative branching leading to fractal-like assembly formation by enzymes of the atrazine degradation pathway. Structural characterization using various microscopy techniques and Cryo-electron tomography revealed a variety of dendritic, hyperbranched, and sponge-like topologies which are self-similar over three decades ([~]10nm-10m) of length scale, in agreement with models from multi-scale computational simulations. We demonstrate control over mesoscale topology (by linker design), formation dynamics, and functional enhancements due to dynamic multi-component assemblies constructed with three atrazine degradation pathway enzymes. The described design method should enable the construction of a variety of novel, spatiotemporally responsive catalytic biomaterials featuring fractal topologies.
- assay: protein structure
- research focus: protein design
- approach: deep learning
- approach: statistical model
Reproducibility ledger
Nobody has reported trying to reproduce this paper yet. Did you replicate an experiment or run the code? Your report saves the next lab a week.
Sign in to file a reproducibility report.
Related papers
- Top-down design of protein nanomaterials with reinforcement learning
Isaac D. Lutz, Shunzhi Wang, … David A. Baker·bioRxiv·Sep 25, 2022
- Hierarchical design of pseudosymmetric protein nanoparticles
Q. Dowling, Y.-J. Park, … N. King·bioRxiv·Jun 16, 2023
- Design and characterization of SAKe, a new building block for protein self-assembly
Andreu Mor Maldonado, Staf M. L. Wouters, … Arnout R. D. Voet·ACS NanoPeer-reviewed·Aug 7, 2026
- Computational design of bifaceted protein nanomaterials
Sanela Rankovic, Kenneth D. Carr, … Neil P. King·Nature MaterialsPeer-reviewed·Jul 31, 2025
- Accurate Computational Design of 3D Protein Crystals
Zhe Li, Shunzhi Wang, … David A. Baker·Nature MaterialsPeer-reviewed·Oct 16, 2023
- Dendrimer-like supramolecular assembly of proteins with a tunable size and valency through stepwise iterative growth