基于点云卷积神经网络的蛋白质柔性预测

目的 蛋白质的柔性运动对生物体各种反应有着重要意义，基于蛋白质的空间结构预测其柔性运动是蛋白质结构-功能关系领域的重要问题。卷积神经网络（convolutional neural network,CNN）在蛋白质结构-功能关系研究中已有成功应用。方法 本研究借鉴计算机视觉研究中PointNet方法的思想，提出了一种蛋白质柔性预测的CNN模型。在该模型中，分别使用池化操作和空间变换网络来处理蛋白质原子三维点云的排列不变性和整体旋转不变性，针对蛋白质分子大小不一的特点，将大小不等的蛋白质小批量输入网络进行训练，并使用Pearson相关系数作为评价指标。此外为提升模型性能，在CNN模型的基础上，通过最大池化和平均池化串联的方法提取体系的全局特征，增强蛋白质全局信息的提取能力。利用243个非冗余蛋白质的B因子对所提出的模型进行训练和测试。结果 基于PointNet的CNN模型和改进模型对蛋白质B因子的预测值与实验值的平均Pearson相关系数分别为0.64、0.65，优于广泛应用的高斯网络模型（Gaussian network model,GNM）。尤其，对于天然无序蛋白质柔性的预测，本方法明...

Protein Flexibility Prediction Based on Point Cloud Convolutional Neural Network

Objective Protein flexibility plays important roles in various biochemical processes in the living organisms, such as enzyme catalysis, signal transduction, substance transport and storage, etc. Prediction of the intrinsic flexible motions based on the tertiary structure of proteins is helpful for our better understanding of the mechanism of protein functions, which is an important scientific problem in the research field of protein structure-function relationship. Convolutional neural network (CNN), one of the mainstream algorithms in deep learning, has been successfully applied in the study of protein structure-function relationship.Methods In the present work, based on the idea of PointNet method developed in the computer vision research, a CNN model was proposed to predict the protein flexibility. In this model, protein structures were treated as three-dimensional point clouds, where the atomic coordinates of proteins were directly inputted into the model, and the permutation invariance and global rotation invariance of the point cloud were delt with by using the pooling operations and a spatial transformation network, respectively. In addition, considering the varied sizes of different proteins, a new mini-batch optimization strategy was proposed, where the model was trained by using the mini-batches of protein structures with different sizes as input. The Pearson correlation coefficient was used as the evaluation function for the training of the model. Besides that, in order to further enhance the performance of the network, an improved model was constructed based on the PointNet-based CNN model, in which the max-pooling and the average-pooling were concatenated to better extract the global features of protein structures. Then the PointNet-based CNN model and the improved model were trained and tested by using the temperature factors (B-factors) of 243 non-redundant proteins.Results The results show that the average Pearson correlation coefficient between the predicted and the experimental temperature factors predicted by the PointNet-based model and the improved model were 0.64 and 0.65, respectively. The prediction accuracy of our models is better than that of the Gaussian network model that has been widely used in investigating protein flexibility. Especially, for the 74 relatively loose natural disordered proteins from the Disbind website, the average Pearson correlation coefficient predicted by our models were 0.62 and 0.64, respectively, which were significantly better than GNM.Conclusion Our studies provide an effective model for the effective prediction of the intrinsic flexibility encoded in protein structures.