Zhang Peidong, Xia Chunqiu, Shen Hong-Bin
Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, and Key Laboratory of System Control and Information Processing, Ministry of Education of China, 200240 Shanghai, China.
Brief Bioinform. 2023 Mar 19;24(2). doi: 10.1093/bib/bbac614.
Great improvement has been brought to protein tertiary structure prediction through deep learning. It is important but very challenging to accurately rank and score decoy structures predicted by different models. CASP14 results show that existing quality assessment (QA) approaches lag behind the development of protein structure prediction methods, where almost all existing QA models degrade in accuracy when the target is a decoy of high quality. How to give an accurate assessment to high-accuracy decoys is particularly useful with the available of accurate structure prediction methods. Here we propose a fast and effective single-model QA method, QATEN, which can evaluate decoys only by their topological characteristics and atomic types. Our model uses graph neural networks and attention mechanisms to evaluate global and amino acid level scores, and uses specific loss functions to constrain the network to focus more on high-precision decoys and protein domains. On the CASP14 evaluation decoys, QATEN performs better than other QA models under all correlation coefficients when targeting average LDDT. QATEN shows promising performance when considering only high-accuracy decoys. Compared to the embedded evaluation modules of predicted ${C}_{\alpha^{-}} RMSD$ (pRMSD) in RosettaFold and predicted LDDT (pLDDT) in AlphaFold2, QATEN is complementary and capable of achieving better evaluation on some decoy structures generated by AlphaFold2 and RosettaFold. These results suggest that the new QATEN approach can be used as a reliable independent assessment algorithm for high-accuracy protein structure decoys.
深度学习给蛋白质三级结构预测带来了巨大进步。准确地对不同模型预测的诱饵结构进行排名和评分既重要又极具挑战性。CASP14的结果表明，现有的质量评估（QA）方法落后于蛋白质结构预测方法的发展，几乎所有现有的QA模型在目标是高质量诱饵时，其准确性都会下降。随着准确的结构预测方法的出现，如何对高精度诱饵进行准确评估尤为有用。在此，我们提出了一种快速有效的单模型QA方法QATEN，它仅通过诱饵的拓扑特征和原子类型来评估诱饵。我们的模型使用图神经网络和注意力机制来评估全局和氨基酸水平的分数，并使用特定的损失函数来约束网络，使其更多地关注高精度诱饵和蛋白质结构域。在CASP14评估诱饵上，当以平均LDDT为目标时，QATEN在所有相关系数下的表现都优于其他QA模型。当仅考虑高精度诱饵时，QATEN表现出良好的性能。与RosettaFold中预测的${C}_{\alpha^{-}} RMSD$（pRMSD）和AlphaFold2中预测的LDDT（pLDDT）的嵌入式评估模块相比，QATEN具有互补性，能够对AlphaFold2和RosettaFold生成的一些诱饵结构实现更好的评估。这些结果表明，新的QATEN方法可作为一种可靠的独立评估算法，用于高精度蛋白质结构诱饵。
