Accelerated Engineering of ELP-Based Materials through Hybrid Biomimetic-De Novo Predictive Molecular Design

Timo Laakko, Antti Korkealaakso, Burcu Firatligil Yildirir, Piotr Batys, Ville Liljeström, Ari Hokkanen, Nonappa, Merja Penttilä, Anssi Laukkanen, Ali Miserez, Caj Södergård, Pezhman Mohammadi

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Efforts to engineer high-performance protein-based materials inspired by nature have mostly focused on altering naturally occurring sequences to confer the desired functionalities, whereas de novo design lags significantly behind and calls for unconventional innovative approaches. Here, using partially disordered elastin-like polypeptides (ELPs) as initial building blocks this work shows that de novo engineering of protein materials can be accelerated through hybrid biomimetic design, which this work achieves by integrating computational modeling, deep neural network, and recombinant DNA technology. This generalizable approach involves incorporating a series of de novo-designed sequences with α-helical conformation and genetically encoding them into biologically inspired intrinsically disordered repeating motifs. The new ELP variants maintain structural conformation and showed tunable supramolecular self-assembly out of thermal equilibrium with phase behavior in vitro. This work illustrates the effective translation of the predicted molecular designs in structural and functional materials. The proposed methodology can be applied to a broad range of partially disordered biomacromolecules and potentially pave the way toward the discovery of novel structural proteins.

Original languageEnglish
JournalAdvanced Materials
Publication statusE-pub ahead of print - 6 May 2024
Publication typeA1 Journal article-refereed


  • computational modeling
  • de novo design
  • machine learning
  • protein engineering
  • α-helical conformation

Publication forum classification

  • Publication forum level 3

ASJC Scopus subject areas

  • General Materials Science
  • Mechanics of Materials
  • Mechanical Engineering


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