Computational elucidation of membrane protein (MP) structures is challenging partially due to lack of sufficient solved structures for homology modeling. Here, we describe a high-throughput deep transfer learning method that first predicts MP contacts by learning from non-MPs and then predicts 3D structure models using the predicted contacts as distance restraints. Tested on 510 non-redundant MPs, our method has contact prediction accuracy at least 0.18 better than existing methods, predicts correct folds for 218 MPs, and generates 3D models with root-mean-square deviation (RMSD) less than 4 and 5 Å for 57 and 108 MPs, respectively. A rigorous blind test in the continuous automated model evaluation project shows that our method predicted high-resolution 3D models for two recent test MPs of 210 residues with RMSD ∼2 Å. We estimated that our method could predict correct folds for 1,345–1,871 reviewed human multi-pass MPs including a few hundred new folds, which shall facilitate the discovery of drugs targeting at MPs.

膜蛋白（MP）结构的计算阐明部分具有挑战性，部分原因是缺乏足够的可解析结构来进行同源性建模。在这里，我们描述了一种高吞吐量的深度转移学习方法，该方法首先通过从非MP进行学习来预测MP接触，然后使用预测的接触作为距离约束来预测3D结构模型。经过对510个非冗余MP的测试，我们的方法的接触预测精度至少比现有方法好0.18，可以预测218 MP的正确折叠倍数，并生成3D模型，其均方根偏差（RMSD）小于4和5Å。分别为57和108 MP。在连续自动模型评估项目中进行的严格盲测表明，我们的方法为RMSD〜2Å的210个残基的两个最近测试MP预测了高分辨率3D模型。