Inverse design of viral infectivity-enhancing peptide fibrils from continuous protein-vector embeddings

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

Cell-instructive surface gradients of photo-responsive amyloid-like fibrils

Gradients of bioactive molecules play a crucial role in various biological processes like vascularization, tissue regeneration or cell migration. In order to study these complex biological systems, it is necessary to control the concentration of bioactive molecules on their substrates. Here, we created a photochemical strategy to generate gradients using amyloid-like fibrils as scaffolds functionalized with a model epitope, i.e. the integrin-binding peptide RGD, to modulate cell adhesion. The self-assembling -sheet forming peptide (CKFKFQF) was connected to the RGD epitope via a photo-sensitive nitrobenzyl linker and assembled into photo-responsive nanofibrils. The fibrils were spray-coated on glass substrates and macroscopic gradients were generated by UV-light over a cm-scale. We confirmed the gradient formation using matrix assisted laser desorption ionization mass spectroscopy imaging (MALDI-MSI), which directly visualizes the molecular species on the surface. The RGD gradient was used to instruct cells. In consequence, A549 adapted their adhesion properties in dependence of the RGD-epitope densit

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

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

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

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

Photoinduced Amyloid Fibril Degradation for Controlled Cell Patterning

Amyloid-like fibrils are a special class of self-assembling peptides that have emerged as a promising nanomaterial with rich bioactivity for applications such as cell adhesion and growth. Unlike the extracellular matrix, the intrinsically stable amyloid-like fibrils do not respond nor adapt to stimuli of their natural environment. Here, we designed a self-assembling motif (CKFKFQF), in which a photosensitive o-nitrobenzyl linker (PCL) was inserted. This peptide (CKFK-PCL-FQF) assembled into amyloid-like fibrils comparable to the unsubstituted CKFKFQF and revealed a strong response to UV-light. After UV irradiation, the secondary structure of the fibrils, fibril morphology and bioactivity were lost. Thus, coating surfaces with the pre-formed fibrils and exposing them to UV-light through a photomask generated well-defined areas with patterns of intact and destroyed fibrillar morphology. The unexposed, fibril-coated surface areas retained their ability to support cell adhesion in culture, in contrast to the light-exposed regions, where the cell-supportive fibril morphology was destroyed. Consequently, the photoresponsive peptide nanofibrils provide a facile and efficient way of cell patterning, exemplarily demonstrated for A549 cells. This study introduces photoresponsive amyloid-like fibrils as adaptive functional materials to precisely arrange cells on surface