The most popular and universally predictive protein simulation models employ all-atom molecular dynamics (MD), but they come at extreme computational cost. The development of a universal, computationally efficient coarse-grained (CG) model with similar prediction performance has been a long-standing challenge. By combining recent deep learning methods with a large and diverse training set of all-atom protein simulations, we here develop a bottom-up CG force field with chemical transferability, which can be used for extrapolative molecular dynamics on new sequences not used during model parametrization. We demonstrate that the model successfully predicts folded structures, intermediates, metastable folded and unfolded basins, and the fluctuations of intrinsically disordered proteins while it is several orders of magnitude faster than an all-atom model. This showcases the feasibility of a universal and computationally efficient machine-learned CG model for proteins.

翻译：摘要：最流行且具有普遍预测性的蛋白质模拟模型采用全原子分子动力学，但其计算成本极高。开发一种具有相似预测性能且计算高效的通用粗粒化模型一直是一个长期挑战。通过将最新的深度学习方法与大而多样的全原子蛋白质模拟训练集相结合，我们在此开发了一种具有化学可迁移性的自下而上粗粒化力场，该力场可用于模型参数化过程中未使用的新序列的外推分子动力学。我们证明，该模型成功预测了折叠结构、中间体、亚稳态折叠与未折叠盆地以及 intrinsically disordered proteins 的波动，同时其速度比全原子模型快数个数量级。这展示了用于蛋白质的通用且计算高效的机器学习粗粒化模型的可行性。