Data-mining unveils structure–property–activity correlation of viral infectivity enhancing self-assembling peptides

Kübra Kaygisiz, Lena Rauch-Wirth, Arghya Dutta, Xiaoqing Yu, Yuki Nagata, Tristan Bereau, Jan Münch, Christopher V. Synatschke () and Tanja Weil ()
Additional contact information
Kübra Kaygisiz: Max Planck Institute for Polymer Research
Lena Rauch-Wirth: Ulm University Medical Center, Meyerhofstraße 1
Arghya Dutta: Max Planck Institute for Polymer Research
Xiaoqing Yu: Max Planck Institute for Polymer Research
Yuki Nagata: Max Planck Institute for Polymer Research
Tristan Bereau: Max Planck Institute for Polymer Research
Jan Münch: Ulm University Medical Center, Meyerhofstraße 1
Christopher V. Synatschke: Max Planck Institute for Polymer Research
Tanja Weil: Max Planck Institute for Polymer Research

Nature Communications, 2023, vol. 14, issue 1, 1-17

Abstract: Abstract Gene therapy via retroviral vectors holds great promise for treating a variety of serious diseases. It requires the use of additives to boost infectivity. Amyloid-like peptide nanofibers (PNFs) were shown to efficiently enhance retroviral gene transfer. However, the underlying mode of action of these peptides remains largely unknown. Data-mining is an efficient method to systematically study structure–function relationship and unveil patterns in a database. This data-mining study elucidates the multi-scale structure–property–activity relationship of transduction enhancing peptides for retroviral gene transfer. In contrast to previous reports, we find that not the amyloid fibrils themselves, but rather µm-sized β-sheet rich aggregates enhance infectivity. Specifically, microscopic aggregation of β-sheet rich amyloid structures with a hydrophobic surface pattern and positive surface charge are identified as key material properties. We validate the reliability of the amphiphilic sequence pattern and the general applicability of the key properties by rationally creating new active sequences and identifying short amyloidal peptides from various pathogenic and functional origin. Data-mining—even for small datasets—enables the development of new efficient retroviral transduction enhancers and provides important insights into the diverse bioactivity of the functional material class of amyloids.

Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.nature.com/articles/s41467-023-40663-6 Abstract (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-40663-6

Ordering information: This journal article can be ordered from
https://www.nature.com/ncomms/

DOI: 10.1038/s41467-023-40663-6

Access Statistics for this article

Nature Communications is currently edited by Nathalie Le Bot, Enda Bergin and Fiona Gillespie

More articles in Nature Communications from Nature
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().