论文标题
通过全息卷积神经网络学习蛋白质微环境的形状
Learning the shape of protein micro-environments with a holographic convolutional neural network
论文作者
论文摘要
蛋白质从免疫识别到大脑活动的生物学中起着核心作用。尽管机器学习的重大进展提高了我们从序列预测蛋白质结构的能力，但从结构中确定蛋白质功能仍然是一个主要挑战。在这里，我们引入了用于蛋白质的全息卷积神经网络（H-CNN），这是一种具有物理动机的机器学习方法，用于模拟蛋白质结构中的氨基酸偏好。 H-CNN反映了蛋白质结构中的物理相互作用，并概括了存储在进化数据中的功能信息。 H-CNN准确地预测了突变对蛋白质功能的影响，包括稳定性和蛋白质复合物的结合。我们针对蛋白质结构功能图的可解释的计算模型可以指导具有所需功能的新型蛋白质的设计。
Proteins play a central role in biology from immune recognition to brain activity. While major advances in machine learning have improved our ability to predict protein structure from sequence, determining protein function from structure remains a major challenge. Here, we introduce Holographic Convolutional Neural Network (H-CNN) for proteins, which is a physically motivated machine learning approach to model amino acid preferences in protein structures. H-CNN reflects physical interactions in a protein structure and recapitulates the functional information stored in evolutionary data. H-CNN accurately predicts the impact of mutations on protein function, including stability and binding of protein complexes. Our interpretable computational model for protein structure-function maps could guide design of novel proteins with desired function.
