ProtWave-VAE: Integrating Autoregressive Sampling with Latent-Based Inference for Data-Driven Protein Design.
ProtWave-VAE: Integrating Autoregressive Sampling with Latent-Based Inference for Data-Driven Protein Design.
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ProtWave-VAE：将自回归采样与基于潜在的推理相结合，实现数据驱动的蛋白质设计。
DOI：
10.1021/acssynbio.3c00261
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发表时间：
2023
影响因子：
4.7
通讯作者：
Ferguson,AndrewL
中科院分区：
文献类型：
--
作者：
Praljak,Nikša;Lian,Xinran;Ranganathan,Rama;Ferguson,AndrewL
深度生成模型（DGMs）在蛋白质的理解和数据驱动设计方面取得了巨大成功。变分自编码器（VAE）是一种流行的DGM方法，它可以学习蛋白质序列的多序列比对（MSA）中氨基酸突变的相关模式，并将这些信息提取到低维潜在空间中，以揭示系统发育和功能关系，并指导生成蛋白质设计。自回归（AR）模型是另一种流行的DGM方法，其通常缺乏低维潜在嵌入，但不需要将训练序列对齐到MSA中，并且能够设计可变长度蛋白质。在这项工作中，我们提出ProtWave-VAE作为一种新的和轻量级的DGM，采用信息最大化VAE与扩张卷积编码器和自回归WaveNet解码器。这种架构融合了VAE和AR范式的优势，能够在未对齐的序列数据上进行训练，并从可解释的低维学习潜在空间中进行可变长度序列的条件生成设计。我们评估了该模型的能力，推断模式和设计规则内的同源蛋白质家族序列和设计新的合成蛋白质在四个不同的蛋白质家族。我们表明，我们的模型可以推断潜在空间内有意义的功能和系统发育嵌入，并在半监督下游适应度预测任务中进行高度准确的预测。在面包酵母中Sho 1跨膜β-受体的C-末端SH 3结构域的应用中，我们将ProtWave-VAE设计的序列进行实验基因合成和β-受体传感功能的选择-seq测定，以显示该模型能够合成蛋白质设计，条件C-末端多样化，并将β-受体传感功能工程化为SH 3旁系同源物。
Deep generative models (DGMs) have shown great success in the understanding and data-driven design of proteins. Variational autoencoders (VAEs) are a popular DGM approach that can learn the correlated patterns of amino acid mutations within a multiple sequence alignment (MSA) of protein sequences and distill this information into a low-dimensional latent space to expose phylogenetic and functional relationships and guide generative protein design. Autoregressive (AR) models are another popular DGM approach that typically lacks a low-dimensional latent embedding but does not require training sequences to be aligned into an MSA and enable the design of variable length proteins. In this work, we propose ProtWave-VAE as a novel and lightweight DGM, employing an information maximizing VAE with a dilated convolution encoder and an autoregressive WaveNet decoder. This architecture blends the strengths of the VAE and AR paradigms in enabling training over unaligned sequence data and the conditional generative design of variable length sequences from an interpretable, low-dimensional learned latent space. We evaluated the model’s ability to infer patterns and design rules within alignment-free homologous protein family sequences and to design novel synthetic proteins in four diverse protein families. We show that our model can infer meaningful functional and phylogenetic embeddings within latent spaces and make highly accurate predictions within semisupervised downstream fitness prediction tasks. In an application to the C-terminal SH3 domain in the Sho1 transmembrane osmosensing receptor in baker’s yeast, we subject ProtWave-VAE-designed sequences to experimental gene synthesis and select-seq assays for the osmosensing function to show that the model enables synthetic protein design, conditional C-terminus diversification, and engineering of the osmosensing function into SH3 paralogues.