predicted contacts and 3D models of 510 non-redundant membrane proteins with solved structures in PDBTM

1) The contacts are predicted by the deep learning method developed by Dr. Jinbo Xu.
Each protein has a .gcnn file, which is a text file containing a L*L matrix. Meanwhile, L is the protein sequence length, each entry in the matrix is a predicted probability of the corresponding residue pair forming a contact.
Please ignore those entries (i,j) where abs(i-j)<6 .

2) The 3D models generated by CNS software from predicted contacts and secondary structure.
Each predicted 3D model is a PDB file. For each protein, 5 models are predicted.

1) 本数据集的联系人预测采用徐金波博士所研发的深度学习方法。每个蛋白质均附带一个 .gcnn 文件，该文件为文本格式，包含一个 L*L 矩阵。其中，L 代表蛋白质序列的长度，矩阵中的每个条目均表示对应残基对形成接触的预测概率。请忽略那些绝对值小于6的条目（i,j）。

2) 该数据集包含由 CNS 软件从预测的接触和二级结构生成的 3D 模型。每个预测的 3D 模型均为 PDB 文件。对于每个蛋白质，均预测了5个模型。