Determining the different conformational states of a protein and the transition path between them is a central challenge in protein biochemistry, and is key to better understanding the relationship between biomolecular structure and function. This task is typically accomplished by sampling the protein conformational space with microseconds of molecular dynamics (MD) simulations. Despite advances in both computing hardware and enhanced sampling techniques, MD will always yield a discretized representation of this space, with transition states undersampled proportionally to their associated energy barrier. We design a convolutional neural network capable of learning a continuous and physically plausible conformational space representation, given example conformations generated by experiments and simulations. We show that this network, trained with MD simulations of two distinct protein states, can correctly predict a possible transition path between them, without any example on the transition state provided. We then show that our network, having a protein-independent architecture, can be trained in a transfer learning scenario, leading to performances superior to those of a network trained from scratch.

利用卷积神经网络从蛋白结构中学习连续的构象空间表示，实现预测连接蛋白的不同构象状态的生物相互作用，并利用转移学习从一个蛋白结构中迁移特征以提高性能。

利用卷积和潜在插值强制执行物理规则学习蛋白质构象空间