Department of Computer Science, George Mason University, 4400 University Drive, Fairfax, VA 22030, USA; Center for Advancing Human-Machine Partnerships, George Mason University, 4400 University Drive, Fairfax, VA 22030, USA.
Department of Computer Science, Emory University, 201 Dowman Dr, Atlanta, GA 30322, USA; Center for Advancing Human-Machine Partnerships, George Mason University, 4400 University Drive, Fairfax, VA 22030, USA.
Curr Opin Struct Biol. 2021 Apr;67:170-177. doi: 10.1016/j.sbi.2020.11.012. Epub 2020 Dec 15.
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 characterizing macromolecular structure and dynamics is central to a detailed, molecular-level understanding of biological processes in the living cell. The current computational paradigm utilizes optimization as the generative process for modeling both structure and structural dynamics. Computational biology researchers are now attempting to wield generative models employing deep neural networks as an alternative computational paradigm. In this review, we summarize such efforts. We highlight progress and shortcomings. More importantly, we expose challenges that macromolecular structure poses to deep generative models and take this opportunity to introduce the structural biology community to several recent advances in the deep learning community that promise a way forward.
许多跨学科的科学研究都是基于对将形式与功能联系起来的动态系统的机械处理。在分子生物学中，这一点表现得尤为明显，其中，描述大分子结构和动力学是深入了解活细胞中生物过程的分子水平的关键。当前的计算范例利用优化作为建模结构和结构动力学的生成过程。计算生物学研究人员现在正试图使用生成模型，将深度神经网络作为替代计算范例。在这篇综述中，我们总结了这些努力。我们强调了进展和不足之处。更重要的是，我们揭示了大分子结构对深度生成模型带来的挑战，并借此机会向结构生物学界介绍深度学习领域的一些最新进展，这些进展有望为我们指明前进的道路。
