利用解释变分自动编码器生成蛋白质三级结构
Generating Tertiary Protein Structures via an Interpretative Variational Autoencoder
摘要：
许多跨学科的科学探索都建立在对将形式与功能联系在一起的动态系统的机械处理之上。一个非常明显的例子是在分子生物学中，其中一个重要的目标是确定蛋白质分子与活细胞中的分子伴侣相互作用的功能相关形式/结构。这一目标通常是在随机优化的保护伞下追求的，其算法优化了评分函数。研究一再表明，目前的评分功能，虽然稳步改善，与分子活性弱相关。受生成式深度学习的最新发展势头的启发，本文提出并评估了一种生成蛋白质功能相关三维结构的替代方法。虽然典型的深度生成模型难以处理高度结构化的数据，但本文提出的工作通过图形生成模型规避了这一挑战。对几种深层结构的综合评价显示了生成模型在直接揭示采样新三级结构的潜在空间方面的前景，以及在突出承载结构意义的轴/因素和打开通常与深层模型相关的黑盒子方面的前景。这里介绍的工作是第一步，解释性的，深入的生成模型成为可行的和信息丰富的补充方法，蛋白质结构预测。
英文摘要：
Much scientific enquiry across disciplines is founded upon a mechanistic treatment of dynamic systems that ties form to function. A highly visible instance of this is in molecular biology, where an important goal is to determine functionally-relevant forms/structures that a protein molecule employs to interact with molecular partners in the living cell. This goal is typically pursued under the umbrella of stochastic optimization with algorithms that optimize a scoring function. Research repeatedly shows that current scoring function, though steadily improving, correlate weakly with molecular activity. Inspired by recent momentum in generative deep learning, this paper proposes and evaluates an alternative approach to generating functionally-relevant three-dimensional structures of a protein. Though typically deep generative models struggle with highly-structured data, the work presented here circumvents this challenge via graph-generative models. A comprehensive evaluation of several deep architectures shows the promise of generative models in directly revealing the latent space for sampling novel tertiary structures, as well as in highlighting axes/factors that carry structural meaning and open the black box often associated with deep models. The work presented here is a first step towards interpretative, deep generative models becoming viable and informative complementary approaches to protein structure prediction.
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