Megamolecule Self-Assembly Networks: A Combined Computational and Experimental Design Strategy

Jiangbo Wu, Zhaoyi Gu, Justin A. Modica, Sijia Chen, Milan Mrksich*, Gregory A. Voth*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

This work describes the use of computational strategies to design megamolecule building blocks for the self-assembly of lattice networks. The megamolecules are prepared by attaching four Cutinase-SnapTag fusion proteins (CS fusions) to a four-armed linker, followed by functionalizing each fusion with a terpyridine linker. This functionality is designed to participate in a metal-mediated self-assembly process to give networks. This article describes a simulation-guided strategy for the design of megamolecules to optimize the peptide linker in the fusion protein to give conformations that are best suited for self-assembly and therefore streamlines the typically time-consuming and labor-intensive experimental process. We designed 11 candidate megamolecules and identified the most promising linker, (EAAAK)2, along with the optimal experimental conditions through a combination of all-atom molecular dynamics, enhanced sampling, and larger-scale coarse-grained molecular dynamics simulations. Our simulation findings were validated and found to be consistent with the experimental results. Significantly, this study offers valuable insight into the self-assembly of megamolecule networks and provides a novel and general strategy for large biomolecular material designs by using systematic bottom-up coarse-grained simulations.

Original languageEnglish (US)
Pages (from-to)30553-30564
Number of pages12
JournalJournal of the American Chemical Society
Volume146
Issue number44
DOIs
StatePublished - Nov 6 2024

Funding

This research was supported by the Army Research Office (W911NF1810200). This work also used the IMSERC facility and Keck facility at Northwestern University and the Biophysics Core at University of Illinois Chicago. The computational resources for this research were provided by the University of Chicago Research Computing Center (RCC) Midway supercomputer and the NIH-funded Beagle3 HPC cluster (Award Number S10OD028655).

ASJC Scopus subject areas

  • Catalysis
  • General Chemistry
  • Biochemistry
  • Colloid and Surface Chemistry

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