利用图的深度学习加速识别蛋白质的信息简化表示
Accelerating the identification of informative reduced representations of proteins with deep learning for graphs
摘要：
随着计算机体系结构和算法的不断发展，大分子的分子动力学（MD）模拟的极限不断向前推进。MD轨迹的数量和范围（在大小和时间上）的这种爆炸导致需要自动化和可转移的方法来合理化原始数据并从中获得定量意义。最近，我们中的一些人开发了一种算法方法来识别蛋白质原子的子集，或映射，这使得它的信息描述最丰富。这种方法依赖于计算，对于一个给定的简化表示，相关的映射熵，也就是由于简化的信息损失的度量。虽然相对简单，但这种计算可能很耗时。在这里，我们描述了一种旨在加速映射熵计算的深度学习方法的实现。该方法依赖于深度图网络，它在输入格式上提供了极大的灵活性。我们表明，深度图网络是准确和非常有效的，与$10^5$的映射熵的算法计算的加速因子一样大。这种方法的应用，这需要一个很大的潜力，在研究生物分子时，用于重建其映射熵景观，达到比这更远，是该计划很容易转移到计算的任意功能的分子的结构。
英文摘要：
The limits of molecular dynamics (MD) simulations of macromolecules are steadily pushed forward by the relentless developments of computer architectures and algorithms. This explosion in the number and extent (in size and time) of MD trajectories induces the need of automated and transferable methods to rationalise the raw data and make quantitative sense out of them. Recently, an algorithmic approach was developed by some of us to identify the subset of a protein's atoms, or mapping, that enables the most informative description of it. This method relies on the computation, for a given reduced representation, of the associated mapping entropy, that is, a measure of the information loss due to the simplification. Albeit relatively straightforward, this calculation can be time consuming. Here, we describe the implementation of a deep learning approach aimed at accelerating the calculation of the mapping entropy. The method relies on deep graph networks, which provide extreme flexibility in the input format. We show that deep graph networks are accurate and remarkably efficient, with a speedup factor as large as $10^5$ with respect to the algorithmic computation of the mapping entropy. Applications of this method, which entails a great potential in the study of biomolecules when used to reconstruct its mapping entropy landscape, reach much farther than this, being the scheme easily transferable to the computation of arbitrary functions of a molecule's structure.
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