Pandey Akash, Liu Elaine, Graham Jacob, Chen Wei, Keten Sinan
Department of Mechanical Engineering, Northwestern University, Evanston, IL, USA.
Department of Civil and Environmental Engineering, Northwestern University, Evanston, IL, USA.
Patterns (N Y). 2023 Aug 4;4(9):100805. doi: 10.1016/j.patter.2023.100805. eCollection 2023 Sep 8.
B factors provide critical insight into protein dynamics. Predicting B factors of an atom in new proteins remains challenging as it is impacted by their neighbors in Euclidean space. Previous learning methods developed have resulted in low Pearson correlation coefficients beyond the training set due to their limited ability to capture the effect of neighboring atoms. With the advances in deep learning methods, we develop a sequence-based model that is tested on 2,442 proteins and outperforms the state-of-the-art models by 30%. We find that the model learns that the B factor of a site is prominently affected by atoms within a 12-15 Å radius, which is in excellent agreement with cutoffs from protein network models. The ablation study revealed that the B factor can largely be predicted from the primary sequence alone. Based on the abovementioned points, our model lays a foundation for predicting other properties that are correlated with the B factor.
B因子为蛋白质动力学提供了关键见解。预测新蛋白质中原子的B因子仍然具有挑战性，因为它在欧几里得空间中受到其相邻原子的影响。由于之前开发的学习方法捕捉相邻原子影响的能力有限，导致在训练集之外的皮尔逊相关系数较低。随着深度学习方法的进步，我们开发了一种基于序列的模型，该模型在2442种蛋白质上进行了测试，性能比现有最先进的模型高出30%。我们发现该模型了解到一个位点的B因子主要受半径为12 - 15埃内的原子影响，这与蛋白质网络模型的截断值非常吻合。消融研究表明，B因子在很大程度上仅可从一级序列预测。基于上述几点，我们的模型为预测与B因子相关的其他性质奠定了基础。
