Ab-Initio Membrane Protein Amphipathic Helix Structure Prediction Using Deep Neural Networks
Ab-Initio Membrane Protein Amphipathic Helix Structure Prediction Using Deep Neural Networks
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DOI：
10.1109/tcbb.2020.3029274
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发表时间：
2022-03-01
影响因子：
4.5
通讯作者：
Shen, Hong-Bin
中科院分区：
文献类型：
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作者：
Feng, Shi-Hao;Xia, Chun-Qiu;Shen, Hong-Bin
两亲性螺旋（Amphipathic helix，AH）具有极性和非极性残基分离的特点，通过与脂相和可溶相的相互作用，在许多膜相关的生物过程中发挥重要作用。虽然AH结构已经被发现了很长一段时间，但由于训练数据量有限，很少有基于从头算机器学习的预测模型被报道。在本研究中，我们报告了一种新的基于深度学习的预测模型，该模型由残差神经网络和非均匀阈值决策算法组成。它构建在121个膜蛋白上，总共51640个残留样品，这些样品是从最新的膜蛋白结构数据库中筛选出来的。通过一个严格的10倍嵌套交叉验证实验，我们证明了我们的模型可以实现有希望的预测，并超过目前在这一领域的最先进的方法。这为准确预测AHs提供了新的途径。通过对输入残差的贡献和实例分析，进一步揭示了模型的可解释性和推广性。
Amphipathic helix (AH)features the segregation of polar and nonpolar residues and plays important roles in many membrane-associated biological processes through interacting with both the lipid and the soluble phases. Although the AH structure has been discovered for a long time, few ab initio machine learning-based prediction models have been reported, due to the limited amount of training data. In this study, we report a new deep learning-based prediction model, which is composed of a residual neural network and the uneven-thresholds decision algorithm. It is constructed on 121 membrane proteins, in total 51640 residue samples, which are curated from an up-to-date membrane protein structure database. Through a rigid 10-fold nested cross-validation experiment, we demonstrate that our model can achieve promising predictions and exceed current state-of-the-art approaches in this field. This presents a new avenue for accurately predicting AHs. Analysis on the contribution of the input residues and some cases further reveals the high interpretability and the generalization of our model.