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-like nanofibers from self-assembling peptides can promote viral gene transfer for therapeutic applications. Traditionally, new sequences are discovered either from screening large libraries or by creating derivatives of known active...

Amyloid-like nanofibers from self-assembling peptides can promote viral gene transfer for therapeutic applications. Traditionally, new sequences are discovered either from screening large libraries or by creating derivatives of known active peptides. However, the discovery of de novo peptides, which are sequence-wise not related to any known active peptides, is limited by the difficulty to rationally predict structureactivity relationships because their activities typically have multi-scale and multi-parameter dependencies. Here, we used a small library of 163 peptides to predict de novo sequences for viral infectivity enhancement using a machine learning (ML) approach based on natural language processing. Specifically, we trained an ML model using continuous vector representations of the peptides, which were previously shown to retain relevant information embedded in the sequences. We used the trained ML model to sample the sequence space of peptides with 6 amino acids to identify promising candidates. These 6-mers were then further screened for charge and aggregation propensity. The resulting 16 new 6-mers were tested and found to be active with a 25% hit rate. Strikingly, these de novo sequences are the shortest active peptides for infectivity enhancement reported so far and show no sequence relation to the training set. Moreover, by screening the chemical space, we discovered the first hydrophobic peptide fibrils with a moderately negative surface charge that can enhance infectivity. Hence, this ML strategy is a time- and cost-efficient way for expanding the chemical space of short functional self-assembling peptides exemplified for therapeutic viral gene delivery.