Ig-VAE: Generative modeling of protein structure by direct 3D coordinate generation.
Ig-VAE: Generative modeling of protein structure by direct 3D coordinate generation.
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DOI：
10.1371/journal.pcbi.1010271
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发表时间：
2022-06
影响因子：
4.3
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中科院分区：
文献类型：
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虽然深度学习模型在蛋白质科学中的应用越来越多，但很少有人将其应用于蛋白质骨架的生成--这是活性部位和界面设计等基于结构的问题中的一项重要任务。我们提出了一种构建特定类别骨架的新方法，使用变分自动编码器直接生成免疫球蛋白的3D坐标。我们的模型是扭矩和距离感知的，学习数据集的高分辨率嵌入，并生成与现有设计工具兼容的新颖、高质量的结构。我们证明了Ig-VAE可以与Rosetta一起通过潜在空间采样来创建SARS-CoV2-RBD结合剂的计算模型。我们进一步证明了该模型的生成先验是指导计算蛋白质设计的有力工具，激发了一种新的范式，在该范式下，骨架设计被求解为生成模型潜在空间中的约束优化问题。许多基本的生化过程是由蛋白质-蛋白质相互作用(PPI)控制的，我们制造调节蛋白质相互作用的结合蛋白的能力对于治疗学的创造和细胞信号的研究至关重要。PPI设计的一个关键方面是捕捉蛋白质构象的灵活性。深度生成模型是一类能够从有限的训练样本集合中合成新数据的数学模型。在这里，我们通过开发一个深层生成模型，利用免疫球蛋白折叠创建蛋白质骨架，在计算蛋白质设计方法方面取得了进展，免疫球蛋白折叠存在于抗体等天然结合蛋白质中。虽然生殖模型在图像生成等任务中一直很强大，但使用它们来创造蛋白质仍然是一个挑战。我们用一种新的模型解决了这个问题，该模型允许直接生成新的3D分子，并表明它们具有高的化学精度。生成的结构与现有的蛋白质设计方法(如Rosetta)配合良好，提供了获得大量新型免疫球蛋白结构的途径。最后，我们提出了一个新的蛋白质设计框架，称为“生成性设计”，它展示了像我们这样的深层生成性模型如何应用于几乎任何蛋白质设计问题。
While deep learning models have seen increasing applications in protein science, few have been implemented for protein backbone generation—an important task in structure-based problems such as active site and interface design. We present a new approach to building class-specific backbones, using a variational auto-encoder to directly generate the 3D coordinates of immunoglobulins. Our model is torsion- and distance-aware, learns a high-resolution embedding of the dataset, and generates novel, high-quality structures compatible with existing design tools. We show that the Ig-VAE can be used with Rosetta to create a computational model of a SARS-CoV2-RBD binder via latent space sampling. We further demonstrate that the model’s generative prior is a powerful tool for guiding computational protein design, motivating a new paradigm under which backbone design is solved as constrained optimization problem in the latent space of a generative model. Many essential biochemical processes are governed by protein-protein interactions (PPIs), and our ability to make binding proteins that modulate PPIs is crucial to the creation of therapeutics and the study of cell-signaling. One critical aspect of PPI design is to capture protein conformational flexibility. Deep generative models are a class of mathematical models that are able to synthesize novel data from a finite set of training examples. Here, we make advances in computational protein design methodology by developing a deep generative model that creates protein backbones adopting the immunoglobulin fold, which is found in natural binding proteins such as antibodies. While generative models have been powerful in tasks such as image generation, using them to create proteins has remained a challenge. We solve this problem with a new model that allows for the direct generation of novel 3D molecules and show that they are of high chemical accuracy. Generated structures work well with existing protein design methods such as Rosetta, providing access to a large collection of novel immunoglobulin structures. Finally, we present a new protein design framework, called “generative design,” that shows how deep generative models such as ours can be applied to virtually any protein design problem.