DeepRHP：一种用于设计随机异聚合物作为蛋白质模拟物的混合变分自编码器
DeepRHP: A Hybrid Variational Autoencoder for Designing Random Heteropolymers as Protein Mimics
AI总结：
提出混合变分自编码器DeepRHP，在半监督框架下结合特征VAE与经典VAE，通过潜在空间捕获关键化学特征与序列模式，指导随机异聚合物设计，实验验证其稳定膜蛋白的有效性。
摘要：
由预定义单体组成的合成随机异聚合物（RHP）为设计类蛋白质材料提供了一种方法。如果设计得当，这些RHP可以模拟蛋白质的行为和功能。因此，需要计算工具来有效指导RHP设计。我们通过开发DeepRHP（一种在半监督框架下改进的变分自编码器（VAE）模型）来弥补这一差距。通过为经典VAE配备额外的基于特征的VAE，DeepRHP迫使潜在空间捕获关键化学特征的结构以及单个RHP序列模式。从这个意义上说，我们的方法是通用的，允许以混合方式纳入任何相关特征。我们通过提出在非原生环境中稳定膜蛋白（例如水通道蛋白Z）的潜在单体组成，并将我们的预测与已发表的结果进行交叉验证，证明了DeepRHP的有效性。我们的模型与真实RHP功能之间的一致性表明，利用混合自编码器架构来指导蛋白质和其他生物化合物的RHP设计具有巨大潜力。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
