基于结构类似物的深度学习辅助蛋白质结构域组装。

Structural analogue-based protein structure domain assembly assisted by deep learning.

机构信息

College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, China.

出版信息

Bioinformatics. 2022 Sep 30;38(19):4513-4521. doi: 10.1093/bioinformatics/btac553.

DOI:10.1093/bioinformatics/btac553

PMID:35962986

Abstract

MOTIVATION

With the breakthrough of AlphaFold2, the protein structure prediction problem has made remarkable progress through deep learning end-to-end techniques, in which correct folds could be built for nearly all single-domain proteins. However, the full-chain modelling appears to be lower on average accuracy than that for the constituent domains and requires higher demand on computing hardware, indicating the performance of full-chain modelling still needs to be improved. In this study, we investigate whether the predicted accuracy of the full-chain model can be further improved by domain assembly assisted by deep learning.

RESULTS

In this article, we developed a structural analogue-based protein structure domain assembly method assisted by deep learning, named SADA. In SADA, a multi-domain protein structure database was constructed for the full-chain analogue detection using individual domain models. Starting from the initial model constructed from the analogue, the domain assembly simulation was performed to generate the full-chain model through a two-stage differential evolution algorithm guided by the energy function with an inter-residue distance potential predicted by deep learning. SADA was compared with the state-of-the-art domain assembly methods on 356 benchmark proteins, and the average TM-score of SADA models is 8.1% and 27.0% higher than that of DEMO and AIDA, respectively. We also assembled 293 human multi-domain proteins, where the average TM-score of the full-chain model after the assembly by SADA is 1.1% higher than that of the model by AlphaFold2. To conclude, we find that the domains often interact in the similar way in the quaternary orientations if the domains have similar tertiary structures. Furthermore, homologous templates and structural analogues are complementary for multi-domain protein full-chain modelling.

AVAILABILITY AND IMPLEMENTATION

http://zhanglab-bioinf.com/SADA.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

摘要

动机

随着 AlphaFold2 的突破，通过深度学习端到端技术，蛋白质结构预测问题取得了显著进展，几乎所有的单域蛋白都可以构建正确的折叠结构。然而，全链建模的平均精度似乎低于组成域的精度，并且需要更高的计算硬件要求，这表明全链建模的性能仍有待提高。在这项研究中，我们研究了通过深度学习辅助的结构域组装是否可以进一步提高全链模型的预测准确性。

结果

在本文中，我们开发了一种基于结构类似物的蛋白质结构域组装方法，该方法由深度学习辅助，称为 SADA。在 SADA 中，使用单个结构域模型构建了一个多域蛋白质结构数据库，用于全链类似物检测。从由类似物构建的初始模型开始，通过两阶段差分进化算法，在由深度学习预测的残基间距离势能的能量函数指导下，进行结构域组装模拟，生成全链模型。SADA 与最先进的结构域组装方法在 356 个基准蛋白上进行了比较，SADA 模型的平均 TM 评分比 DEMO 和 AIDA 分别高 8.1%和 27.0%。我们还组装了 293 个人类多域蛋白，其中通过 SADA 组装后的全链模型的平均 TM 评分比 AlphaFold2 高 1.1%。总之，我们发现如果结构域具有相似的三级结构，那么它们在四级构象中通常以相似的方式相互作用。此外，同源模板和结构类似物对多域蛋白全链建模是互补的。