DeepPBS

Protein Backbone Strurcture Prediction based on deeplearning Accurate all-atom protein structures play an important role in various research and applications. However, in most cases, only coarse-grained models can be obtained for reasons. Precisely predict protein backbone structures based on alpha-carbon traces, the most-used coarse-grained model, is a pivotal step for precise all-atom modeling for protein structures. Results: In this study, we proposed a deep learning-based method to predict protein backbone structures from alpha-carbon traces. Our method achieved comparable performance as the best previous method with cRMSD between predicted coordinates and reference coordinates as measurement. Besides, we questioned the reliability of RMSD as a measurement of performance with opinions from analyzing our results. With further comparison on energy score and the median of errors our method showed a significant advantage, yielded state-of-the-art performance overall.