Department of Applied Physics, Stanford University, Stanford, California, USA.
Department of Computer Science, Stanford University, Stanford, California, USA.
Proteins. 2021 May;89(5):493-501. doi: 10.1002/prot.26033. Epub 2020 Dec 31.
Predicting the structure of multi-protein complexes is a grand challenge in biochemistry, with major implications for basic science and drug discovery. Computational structure prediction methods generally leverage predefined structural features to distinguish accurate structural models from less accurate ones. This raises the question of whether it is possible to learn characteristics of accurate models directly from atomic coordinates of protein complexes, with no prior assumptions. Here we introduce a machine learning method that learns directly from the 3D positions of all atoms to identify accurate models of protein complexes, without using any precomputed physics-inspired or statistical terms. Our neural network architecture combines multiple ingredients that together enable end-to-end learning from molecular structures containing tens of thousands of atoms: a point-based representation of atoms, equivariance with respect to rotation and translation, local convolutions, and hierarchical subsampling operations. When used in combination with previously developed scoring functions, our network substantially improves the identification of accurate structural models among a large set of possible models. Our network can also be used to predict the accuracy of a given structural model in absolute terms. The architecture we present is readily applicable to other tasks involving learning on 3D structures of large atomic systems.
预测多蛋白复合物的结构是生物化学领域的一个重大挑战，对基础科学和药物发现都有重大影响。计算结构预测方法通常利用预定义的结构特征来区分准确的结构模型和不太准确的结构模型。这就提出了一个问题，即是否可以直接从蛋白质复合物的原子坐标中学习准确模型的特征，而无需任何先验假设。在这里，我们介绍了一种机器学习方法，该方法可以直接从所有原子的 3D 位置学习来识别蛋白质复合物的准确模型，而无需使用任何预先计算的基于物理或统计的术语。我们的神经网络架构结合了多种成分，这些成分共同实现了从包含数万个原子的分子结构进行端到端学习的能力：基于点的原子表示、对旋转和平移的等变性、局部卷积和层次化的子采样操作。当与以前开发的评分函数结合使用时，我们的网络大大提高了在一组可能的模型中识别准确结构模型的能力。我们的网络还可以用于以绝对术语预测给定结构模型的准确性。我们提出的架构可以很容易地应用于其他涉及学习大型原子系统 3D 结构的任务。
