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

Area of Science:

• Biotechnology
• Molecular Biology
• Materials Science

Background:

• Gene therapy using retroviral vectors shows promise for treating diseases.
• Additives are needed to enhance retroviral vector infectivity.
• Amyloid-like peptide nanofibers (PNFs) can enhance gene transfer, but their mechanism is unclear.

Purpose of the Study:

• To elucidate the structure-property-activity relationship of peptides enhancing retroviral gene transfer.
• To identify key material properties responsible for enhanced transduction.

Main Methods:

• Utilized data-mining to analyze structure-function relationships of transduction-enhancing peptides.
• Investigated the role of aggregate size, structure, and surface properties.
• Validated findings by designing new active peptide sequences.

Main Results:

• Microscale (µm-sized) β-sheet rich aggregates, not amyloid fibrils, enhance retroviral infectivity.
• Key properties include a hydrophobic surface pattern and positive surface charge.
• Amphiphilic sequence patterns and identified properties are broadly applicable.

Conclusions:

• Data-mining reveals critical material properties for effective retroviral transduction enhancers.
• Microscale amyloid aggregates are key for enhancing gene transfer.
• Insights into amyloid bioactivity can guide the development of new gene therapy tools.