Template-based prediction of protein structure with deep learning.
Template-based prediction of protein structure with deep learning.
复制标题
基于模板的蛋白质结构预测与深度学习。
DOI：
10.1186/s12864-020-07249-8
复制
发表时间：
2020-12-29
期刊：
影响因子：
4.4
通讯作者：
Shen Y
中科院分区：
文献类型：
--
作者：
Zhang H;Shen Y
蛋白质结构的准确预测对于理解蛋白质的生物学功能至关重要。基于模板的蛋白质建模（包括蛋白质线程和同源建模）是蛋白质三级结构预测的一种常用方法。然而，准确的模板查询比对和模板选择仍然是非常具有挑战性的，特别是对于只有远距离同源物可用的蛋白质。我们提出了一种新的基于模板的建模方法，称为ThreaderAI，以改善蛋白质三级结构预测。ThreaderAI将查询序列与模板对齐的任务制定为计算机视觉中的经典像素分类问题，并自然地将深度残差神经网络应用于预测。ThreaderAI首先利用深度学习，通过整合序列图谱、预测的序列结构特征和预测的残基-残基接触来预测残基-残基比对概率矩阵，然后通过对概率矩阵应用动态规划算法来构建模板-查询比对。我们评估了我们的方法在生成准确的模板查询比对和蛋白质线程。实验结果表明，ThreaderAI优于目前流行的基于模板的建模方法HHpred，CNFpred和最新的接触辅助方法CEthreader，特别是在没有已知结构的同源蛋白质上。特别是，在用TM评分测量的比对准确性方面，ThreaderAI在SCOPe数据的折叠水平相似性上的模板查询对上分别比HHpred、CNFpred和CEthreader高出56%、13%和11%。在CASP 13的TBM硬数据上，ThreaderAI在TM分数方面分别比HHpred，CNFpred和CEthreader高出16%，9%和8%。这些结果表明，在深度学习的帮助下，ThreaderAI可以显着提高基于模板的结构预测的准确性，特别是对于远距离同源蛋白质。
Accurate prediction of protein structure is fundamentally important to understand biological function of proteins. Template-based modeling, including protein threading and homology modeling, is a popular method for protein tertiary structure prediction. However, accurate template-query alignment and template selection are still very challenging, especially for the proteins with only distant homologs available. We propose a new template-based modelling method called ThreaderAI to improve protein tertiary structure prediction. ThreaderAI formulates the task of aligning query sequence with template as the classical pixel classification problem in computer vision and naturally applies deep residual neural network in prediction. ThreaderAI first employs deep learning to predict residue-residue aligning probability matrix by integrating sequence profile, predicted sequential structural features, and predicted residue-residue contacts, and then builds template-query alignment by applying a dynamic programming algorithm on the probability matrix. We evaluated our methods both in generating accurate template-query alignment and protein threading. Experimental results show that ThreaderAI outperforms currently popular template-based modelling methods HHpred, CNFpred, and the latest contact-assisted method CEthreader, especially on the proteins that do not have close homologs with known structures. In particular, in terms of alignment accuracy measured with TM-score, ThreaderAI outperforms HHpred, CNFpred, and CEthreader by 56, 13, and 11%, respectively, on template-query pairs at the similarity of fold level from SCOPe data. And on CASP13’s TBM-hard data, ThreaderAI outperforms HHpred, CNFpred, and CEthreader by 16, 9 and 8% in terms of TM-score, respectively. These results demonstrate that with the help of deep learning, ThreaderAI can significantly improve the accuracy of template-based structure prediction, especially for distant-homology proteins.
登录
查看更多内容
影响因子：
2.9
作者：
Klausen, Michael Schantz;Jespersen, Martin Closter;Marcatili, Paolo
通讯作者：
Marcatili, Paolo
影响因子：
48
作者：
Remmert, Michael;Biegert, Andreas;Soeding, Johannes
通讯作者：
Soeding, Johannes
影响因子：
5.8
作者：
Söding, J
通讯作者：
Söding, J
影响因子：
5.8
作者：
Yang, Yuedong;Faraggi, Eshel;Zhou, Yaoqi
通讯作者：
Zhou, Yaoqi
提高蛋白质螺纹精度。
DOI：
10.1007/978-3-642-02008-7_3
发表时间：
2009
期刊：
LECTURE NOTES IN ARTIFICIAL INTELLIGENCE
影响因子：
--
作者：
Peng, Jian;Xu, Jinbo
通讯作者：
Xu, Jinbo