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

Abstract

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 structure–activity relationships because their activities typically have multi-scale and multi-parameter dependencies. Here, we used a small library of 163 peptides as a training set 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 sequence 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 sequence space of short functional self-assembling peptides exemplified for therapeutic viral gene delivery.