Data-Mining Unveils Structure-Property-Activity Correlation of Viral Infectivity Enhancing Self-Assembling Peptides

13 February 2023, Version 1
This content is a preprint and has not undergone peer review at the time of posting.


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. 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 m-sized -sheet rich aggregates enhance infectivity. Specifically, microscopic aggregation of -sheet rich amyloid structures with a hydrophobic surface pattern and positive surface charge were 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.


amyloid peptides
retroviral transduction enhancer
gene delivery
structure–property–activity correlation

Supplementary materials

Data-Mining Unveils Structure–Property–Activity Correlation of Viral Infectivity Enhancing Self-Assembling Peptides
The Supporting Information contains Section 1 – 8 with Figures S1–S36, Table S1–S5. Section 1 provides further remarks on experimental and bioinformatic properties, Section 2 demonstrates cell-viability data, Section 3–7 contains detailed information on single and multi-parameter correlation, pattern analysis, and selected peptides from the library and from literature. Figures S31–S36 include all TEM micrographs, FT-IR spectra and LC-MS measurements, the supporting Tables summarize experimental and bioinformatic data of the peptide library.


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