College of Computer Science and Electronic Engineering, Hunan University, Changsha, 410012, Hunan, China.
MindRank AI ltd., Hangzhou, 311113, Zhejiang, China.
Brief Bioinform. 2022 Mar 10;23(2). doi: 10.1093/bib/bbab558.
Spatial structures of proteins are closely related to protein functions. Integrating protein structures improves the performance of protein-protein interaction (PPI) prediction. However, the limited quantity of known protein structures restricts the application of structure-based prediction methods. Utilizing the predicted protein structure information is a promising method to improve the performance of sequence-based prediction methods. We propose a novel end-to-end framework, TAGPPI, to predict PPIs using protein sequence alone. TAGPPI extracts multi-dimensional features by employing 1D convolution operation on protein sequences and graph learning method on contact maps constructed from AlphaFold. A contact map contains abundant spatial structure information, which is difficult to obtain from 1D sequence data directly. We further demonstrate that the spatial information learned from contact maps improves the ability of TAGPPI in PPI prediction tasks. We compare the performance of TAGPPI with those of nine state-of-the-art sequence-based methods, and TAGPPI outperforms such methods in all metrics. To the best of our knowledge, this is the first method to use the predicted protein topology structure graph for sequence-based PPI prediction. More importantly, our proposed architecture could be extended to other prediction tasks related to proteins.
蛋白质的空间结构与蛋白质的功能密切相关。整合蛋白质结构可以提高蛋白质-蛋白质相互作用（PPI）预测的性能。然而，已知蛋白质结构的数量有限，限制了基于结构的预测方法的应用。利用预测的蛋白质结构信息是提高基于序列的预测方法性能的一种很有前途的方法。我们提出了一种新的端到端框架 TAGPPI，仅使用蛋白质序列来预测 PPI。TAGPPI 通过对蛋白质序列进行 1D 卷积操作和从 AlphaFold 构建的接触图上的图学习方法来提取多维特征。接触图包含丰富的空间结构信息，这些信息很难直接从 1D 序列数据中获得。我们进一步证明，从接触图中学习到的空间信息提高了 TAGPPI 在 PPI 预测任务中的能力。我们将 TAGPPI 的性能与九种最先进的基于序列的方法进行了比较，TAGPPI 在所有指标上都优于这些方法。据我们所知，这是第一个使用预测的蛋白质拓扑结构图进行基于序列的 PPI 预测的方法。更重要的是，我们提出的架构可以扩展到其他与蛋白质相关的预测任务。
