Artificial intelligence method to design and fold alpha-helical structural proteins from the primary amino acid sequence

Zhao Qin, Lingfei Wu, Hui Sun, Siyu Huo, Tengfei Ma, Eugene Lim, Pin Yu Chen, Benedetto Marelli, Markus J. Buehler

Research output: Contribution to journalArticle

1 Scopus citations


The development of rational techniques to discover new mechanically relevant proteins for use in variety of applications ranging from mechanics, agriculture to biotechnology remains an outstanding nanomechanical design problem. The key barrier is to design a sequence to fold into a predictable structure to achieve a certain material function. Focused on alpha-helical proteins (as found in skin, hair, and many other mechanically relevant protein materials), we report a Multi-scale Neighborhood-based Neural Network (MNNN) model to learn how a specific amino acid sequence folds into a protein structure. The algorithm predicts the protein structure without using a template or co-evolutional information at a maximum error of 2.1 Å. We find that the prediction accuracy is higher than other models and the prediction consumes less than six orders of magnitude time than ab initio folding methods. We demonstrate that MNNN can predict the structure of an unknown protein that agrees with experiments, and our model hence shows a great advantage in the rational design of de novo proteins.

Original languageEnglish (US)
Article number100652
JournalExtreme Mechanics Letters
StatePublished - Apr 2020
Externally publishedYes



  • Artificial intelligence
  • Computation
  • Deep neural networks
  • Folding
  • Machine learning
  • Nanomechanics
  • Protein
  • Structure prediction

ASJC Scopus subject areas

  • Bioengineering
  • Chemical Engineering (miscellaneous)
  • Engineering (miscellaneous)
  • Mechanics of Materials
  • Mechanical Engineering

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