SASA-Net: A Spatial-Aware Self-Attention Mechanism for Building Protein 3D Structure Directly From Inter- Residue Distances
文献类型：期刊论文
| 作者 | Gong, Tiansu2,3; Ju, Fusong2,3; Sun, Shiwei1,2; Bu, Dongbo1,2 |
| 刊名 | IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS
 |
| 出版日期 | 2023-11-01 |
| 卷号 | 20期号:6页码:3482-3488 |
| 关键词 | Deep learning protein structure prediction |
| ISSN号 | 1545-5963 |
| DOI | 10.1109/TCBB.2023.3240456 |
| 英文摘要 | Protein functions are tightly related to the fine details of their 3D structures. To understand protein structures, computational prediction approaches are highly needed. Recently, protein structure prediction has achieved considerable progresses mainly due to the increased accuracy of inter-residue distance estimation and the application of deep learning techniques. Most of the distance-based ab initio prediction approaches adopt a two-step diagram: constructing a potential function based on the estimated inter-residue distances, and then build a 3D structure that minimizes the potential function. These approaches have proven very promising; however, they still suffer from several limitations, especially the inaccuracies incurred by the handcrafted potential function. Here, we present SASA-Net, a deep learning-based approach that directly learns protein 3D structure from the estimated inter-residue distances. Unlike the existing approach simply representing protein structures as coordinates of atoms, SASA-Net represents protein structures using pose of residues, i.e., the coordinate system of each individual residue in which all backbone atoms of this residue are fixed. The key element of SASA-Net is a spatial-aware self-attention mechanism, which is able to adjust a residue's pose according to all other residues' features and the estimated distances between residues. By iteratively applying the spatial-aware self-attention mechanism, SASA-Net continuously improves the structure and finally acquires a structure with high accuracy. Using the CATH35 proteins as representatives, we demonstrate that SASA-Net is able to accurately and efficiently build structures from the estimated inter-residue distances. The high accuracy and efficiency of SASA-Net enables an end-to-end neural network model for protein structure prediction through combining SASA-Net and an neural network for inter-residue distance prediction. |
| 资助项目 | National Key Research and Development Program of China |
| WOS研究方向 | Biochemistry & Molecular Biology ; Computer Science ; Mathematics |
| 语种 | 英语 |
| WOS记录号 | WOS:001133540000051 |
| 出版者 | IEEE COMPUTER SOC |
| 源URL | [http://119.78.100.204/handle/2XEOYT63/38860]  |
| 专题 | 中国科学院计算技术研究所期刊论文_英文 |
| 通讯作者 | Gong, Tiansu |
| 作者单位 | 1.Zhongke Big Data Acad, Zhengzhou 450046, Henan, Peoples R China 2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 3.Chinese Acad Sci, Key Lab Intelligent Informat Proc, Inst Comp Technol, Beijing 100190, Peoples R China |
| 推荐引用方式 GB/T 7714 | Gong, Tiansu,Ju, Fusong,Sun, Shiwei,et al. SASA-Net: A Spatial-Aware Self-Attention Mechanism for Building Protein 3D Structure Directly From Inter- Residue Distances[J]. IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS,2023,20(6):3482-3488. |
| APA | Gong, Tiansu,Ju, Fusong,Sun, Shiwei,&Bu, Dongbo.(2023).SASA-Net: A Spatial-Aware Self-Attention Mechanism for Building Protein 3D Structure Directly From Inter- Residue Distances.IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS,20(6),3482-3488. |
| MLA | Gong, Tiansu,et al."SASA-Net: A Spatial-Aware Self-Attention Mechanism for Building Protein 3D Structure Directly From Inter- Residue Distances".IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 20.6(2023):3482-3488. |
入库方式： OAI收割
来源：计算技术研究所
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