Designing Multifunctional Biomaterials via Protein Self-Assembly

Aleksei Solomonov, Anna Kozell, Ulyana Shimanovich

Research output: Contribution to journalReview articlepeer-review


Protein self-assembly is a fundamental biological process where proteins spontaneously organize into complex and functional structures without external direction. This process is crucial for the formation of various biological functionalities. However, when protein self-assembly fails, it can trigger the development of multiple disorders, thus making understanding this phenomenon extremely important. Up until recently, protein self-assembly has been solely linked either to biological function or malfunction; however, in the past decade or two it has also been found to hold promising potential as an alternative route for fabricating materials for biomedical applications. It is therefore necessary and timely to summarize the key aspects of protein self-assembly: how the protein structure and self-assembly conditions (chemical environments, kinetics, and the physicochemical characteristics of protein complexes) can be utilized to design biomaterials. This minireview focuses on the basic concepts of forming supramolecular structures, and the existing routes for modifications. We then compare the applicability of different approaches, including compartmentalization and self-assembly monitoring. Finally, based on the cutting-edge progress made during the last years, we summarize the current knowledge about tailoring a final function by introducing changes in self-assembly and link it to biomaterials' performance.This Minireview sums up cutting-edge concepts regarding the formation of protein-based supramolecular structures, compartmentalization, and self-assembly monitoring; it compares the routes of their modifications and applications in multifunctional biomaterial design. We summarize the current knowledge about machine learning/artificial intelligence applications for protein structure prediction/obtainment and link it to biomaterial performance.+image
Original languageEnglish
Article numbere202318365
Number of pages18
JournalAngewandte Chemie - International Edition
Issue number14
Early online date11 Jan 2024
StatePublished - 2 Apr 2024


  • Artificial Intelligence
  • Biomaterials
  • Machine Learning
  • Proteins
  • Self-Assembly

All Science Journal Classification (ASJC) codes

  • General Chemistry
  • Catalysis


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