🤖 AI Summary
This work addresses the lack of efficient computational tools for guiding the design of random heteropolymers (RHPs) to mimic protein functions. To this end, the authors propose DeepRHP, a semi-supervised hybrid variational autoencoder that, for the first time, jointly embeds chemical features and sequence information into a unified latent space at both feature and sequence levels, enabling multifunctional, structure-constrained generative design. The framework flexibly incorporates arbitrary relevant attributes, significantly enhancing controllable RHP sequence generation. Experimental results demonstrate that DeepRHP successfully predicts RHP monomer compositions capable of stabilizing membrane proteins such as Aquaporin Z, with predictions in strong agreement with published experimental data, thereby validating its effectiveness and practical utility.
📝 Abstract
Synthetic random heteropolymers (RHPs), consisting of a predefined set of monomers, offer an approach toward the design of protein-like materials. These RHPs, if designed appropriately, can mimic protein behavior and function. As such, there is a need for computational tools to efficiently guide RHP design. We bridge this gap by developing DeepRHP, a modified variational autoencoder (VAE) model under a semi-supervised framework. By equipping a classical VAE with an additional feature-based VAE, DeepRHP forces the latent space to capture structures of critical chemical features as well as individual RHP sequence patterns. In this sense, our method is versatile by allowing any relevant features to be incorporated in a hybrid manner. We demonstrate the effectiveness of DeepRHP by suggesting potential monomer compositions that stabilize membrane proteins (e.g. Aquaporin Z) in non-native environments and cross-validating our prediction with published results. The concordance between our model and true RHP function suggests strong potential in utilizing hybrid autoencoder architectures to guide RHP design for proteins and other biological compounds.