3D-equivariant graph neural networks for protein model quality assessment.
3D-equivariant graph neural networks for protein model quality assessment.
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DOI：
10.1093/bioinformatics/btad030
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发表时间：
2023-01-01
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蛋白质三级结构预测模型的质量评估（QA）在蛋白质三级结构模型的排序和使用中起着重要的作用。随着最近深度学习端到端蛋白质结构预测技术的发展，为大多数蛋白质生成高度可信的三级结构，探索相应的QA策略来评估和选择它们预测的结构模型非常重要，因为这些模型比传统三级结构预测方法预测的模型具有更好的质量和不同的属性。我们开发了EnQA，一种新的基于图的3D等变神经网络方法，该方法与3D对象的旋转和平移等变，通过利用从最先进的三级结构预测方法AlphaFold2获得的结构特征来估计蛋白质结构模型的准确性。我们在传统的模型数据集（例如，蛋白质结构预测技术的关键评估数据集）和仅由AlphaFold2预测的高质量结构模型的新数据集上训练和测试该方法，这些模型用于最近发布的实验结构的蛋白质。我们的方法在传统蛋白质结构预测方法和最新的端到端深度学习方法AlphaFold2预测的蛋白质结构模型上实现了最先进的性能。它的性能甚至比AlphaFold2本身提供的模型QA分数更好。结果表明，三维等变图神经网络是一种很有前途的蛋白质结构模型的评价方法。将AlphaFold2特征与其他互补序列和结构特征相结合对于改善蛋白质模型QA非常重要。源代码可在https://github.com/BioinfoMachineLearning/EnQA上获得。 补充数据可在Bioinformatics在线获得。
Quality assessment (QA) of predicted protein tertiary structure models plays an important role in ranking and using them. With the recent development of deep learning end-to-end protein structure prediction techniques for generating highly confident tertiary structures for most proteins, it is important to explore corresponding QA strategies to evaluate and select the structural models predicted by them since these models have better quality and different properties than the models predicted by traditional tertiary structure prediction methods. We develop EnQA, a novel graph-based 3D-equivariant neural network method that is equivariant to rotation and translation of 3D objects to estimate the accuracy of protein structural models by leveraging the structural features acquired from the state-of-the-art tertiary structure prediction method—AlphaFold2. We train and test the method on both traditional model datasets (e.g. the datasets of the Critical Assessment of Techniques for Protein Structure Prediction) and a new dataset of high-quality structural models predicted only by AlphaFold2 for the proteins whose experimental structures were released recently. Our approach achieves state-of-the-art performance on protein structural models predicted by both traditional protein structure prediction methods and the latest end-to-end deep learning method—AlphaFold2. It performs even better than the model QA scores provided by AlphaFold2 itself. The results illustrate that the 3D-equivariant graph neural network is a promising approach to the evaluation of protein structural models. Integrating AlphaFold2 features with other complementary sequence and structural features is important for improving protein model QA. The source code is available at https://github.com/BioinfoMachineLearning/EnQA. Supplementary data are available at Bioinformatics online.
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