Learning protein dynamics

所有生物体都含有数以百万计的蛋白质；折叠成三维生物活性结构的生物聚合物在生命和疾病的调节中起着至关重要的作用。研究的重点是确定不同蛋白质的原子结构。然而，这些生物聚合物的灵活运动在其生物学（mal）功能中起着至关重要的作用。近年来，机器学习已经彻底改变了我们在许多科学领域解释数据的方式。例如，深度神经网络AlphaFold2可以预测蛋白质的3-微小结构，其形状在实验中尚不清楚。在我们的研究中，我们设计了一个深度神经网络，它也可以从分子模拟中学习特定蛋白质的整体结构。这个项目建立在这个突破之上J. Jumper等，基于AlphaFold的蛋白质结构预测方法，自然科学，596,2021. b[2]V.K. Ramaswamy， S.C. Musson， C.G. Willcocks， M.T. Degiacomi。基于卷积和潜在插值的蛋白质构象空间学习，物理评论11,2021。

All living organisms contain millions of proteins; biopolymers that fold into three-dimensional biologically active structures playing a vital role in the regulation of life and diseases. Research has seen a lot of focus on determining the atomic structure of different proteins. However, the flexible movement of these biopolymers plays a crucial role in their biological (mal)function. In recent years, machine learning has been revolutionizing the way we interpret data in many scientific areas. For example, the deep neural network AlphaFold2 can predict the 3-diminsonal structures of proteins, whose shape is not known experimentally [1]. In our research, we have designed a deep neural network that can also learn an ensemble of structures of specific proteins from molecular simulations [2]. This project builds upon this breakthrough.[1] J. Jumper et al., Highly accurate protein structure prediction with AlphaFold, Nature 596, 2021.[2] V.K. Ramaswamy, S.C. Musson, C.G. Willcocks, M.T. Degiacomi. Learning protein conformational space with convolutions and latent interpolations, Physical Review X 11, 2021.