Learning protein dynamics

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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.