• 文献检索
  • 文档翻译
  • 深度研究
  • 学术资讯
  • Suppr Zotero 插件Zotero 插件
  • 邀请有礼
  • 套餐&价格
  • 历史记录
应用&插件
Suppr Zotero 插件Zotero 插件浏览器插件Mac 客户端Windows 客户端微信小程序
定价
高级版会员购买积分包购买API积分包
服务
文献检索文档翻译深度研究API 文档MCP 服务
关于我们
关于 Suppr公司介绍联系我们用户协议隐私条款
关注我们

Suppr 超能文献

核心技术专利:CN118964589B侵权必究
粤ICP备2023148730 号-1Suppr @ 2026

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验

基于结构的神经网络在残基水平上对蛋白质-碳水化合物相互作用的预测。

Structure-based neural network protein-carbohydrate interaction predictions at the residue level.

作者信息

Canner Samuel W, Shanker Sudhanshu, Gray Jeffrey J

机构信息

Program in Molecular Biophysics, The Johns Hopkins University, Baltimore, MD, United States.

Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, United States.

出版信息

Front Bioinform. 2023 Jun 20;3:1186531. doi: 10.3389/fbinf.2023.1186531. eCollection 2023.

DOI:10.3389/fbinf.2023.1186531
PMID:37409346
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10318439/
Abstract

Carbohydrates dynamically and transiently interact with proteins for cell-cell recognition, cellular differentiation, immune response, and many other cellular processes. Despite the molecular importance of these interactions, there are currently few reliable computational tools to predict potential carbohydrate-binding sites on any given protein. Here, we present two deep learning (DL) models named CArbohydrate-Protein interaction Site IdentiFier (CAPSIF) that predicts non-covalent carbohydrate-binding sites on proteins: (1) a 3D-UNet voxel-based neural network model (CAPSIF:V) and (2) an equivariant graph neural network model (CAPSIF:G). While both models outperform previous surrogate methods used for carbohydrate-binding site prediction, CAPSIF:V performs better than CAPSIF:G, achieving test Dice scores of 0.597 and 0.543 and test set Matthews correlation coefficients (MCCs) of 0.599 and 0.538, respectively. We further tested CAPSIF:V on AlphaFold2-predicted protein structures. CAPSIF:V performed equivalently on both experimentally determined structures and AlphaFold2-predicted structures. Finally, we demonstrate how CAPSIF models can be used in conjunction with local glycan-docking protocols, such as GlycanDock, to predict bound protein-carbohydrate structures.

摘要

碳水化合物与蛋白质动态且短暂地相互作用,以实现细胞间识别、细胞分化、免疫反应及许多其他细胞过程。尽管这些相互作用在分子层面具有重要意义,但目前几乎没有可靠的计算工具来预测任何给定蛋白质上潜在的碳水化合物结合位点。在此,我们提出了两种名为碳水化合物 - 蛋白质相互作用位点识别器(CAPSIF)的深度学习(DL)模型,用于预测蛋白质上的非共价碳水化合物结合位点:(1)一种基于3D - UNet体素的神经网络模型(CAPSIF:V)和(2)一种等变图神经网络模型(CAPSIF:G)。虽然这两种模型都优于先前用于碳水化合物结合位点预测的替代方法,但CAPSIF:V的表现优于CAPSIF:G,其测试Dice分数分别为0.597和0.543,测试集马修斯相关系数(MCC)分别为0.599和0.538。我们进一步在AlphaFold2预测的蛋白质结构上测试了CAPSIF:V。CAPSIF:V在实验确定的结构和AlphaFold2预测的结构上表现相当。最后,我们展示了如何将CAPSIF模型与局部聚糖对接协议(如GlycanDock)结合使用,以预测结合的蛋白质 - 碳水化合物结构。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5c7c/10318439/d6a4b4c73859/fbinf-03-1186531-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5c7c/10318439/45e7bcfded56/fbinf-03-1186531-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5c7c/10318439/ac849ba20120/fbinf-03-1186531-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5c7c/10318439/07076b6543b7/fbinf-03-1186531-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5c7c/10318439/2974613b3a60/fbinf-03-1186531-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5c7c/10318439/a2f69926df42/fbinf-03-1186531-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5c7c/10318439/d6a4b4c73859/fbinf-03-1186531-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5c7c/10318439/45e7bcfded56/fbinf-03-1186531-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5c7c/10318439/ac849ba20120/fbinf-03-1186531-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5c7c/10318439/07076b6543b7/fbinf-03-1186531-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5c7c/10318439/2974613b3a60/fbinf-03-1186531-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5c7c/10318439/a2f69926df42/fbinf-03-1186531-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5c7c/10318439/d6a4b4c73859/fbinf-03-1186531-g006.jpg

相似文献

1
Structure-based neural network protein-carbohydrate interaction predictions at the residue level.基于结构的神经网络在残基水平上对蛋白质-碳水化合物相互作用的预测。
Front Bioinform. 2023 Jun 20;3:1186531. doi: 10.3389/fbinf.2023.1186531. eCollection 2023.
2
Structure-Based Neural Network Protein-Carbohydrate Interaction Predictions at the Residue Level.基于结构的神经网络在残基水平上预测蛋白质-碳水化合物相互作用
bioRxiv. 2023 Mar 15:2023.03.14.531382. doi: 10.1101/2023.03.14.531382.
3
3D-equivariant graph neural networks for protein model quality assessment.用于蛋白质模型质量评估的 3D 等变图神经网络。
Bioinformatics. 2023 Jan 1;39(1). doi: 10.1093/bioinformatics/btad030.
4
Prediction of carbohydrate binding sites on protein surfaces with 3-dimensional probability density distributions of interacting atoms.利用相互作用原子的三维概率密度分布预测蛋白质表面的碳水化合物结合位点。
PLoS One. 2012;7(7):e40846. doi: 10.1371/journal.pone.0040846. Epub 2012 Jul 25.
5
Highly accurate carbohydrate-binding site prediction with DeepGlycanSite.利用 DeepGlycanSite 进行高精度糖基结合位点预测。
Nat Commun. 2024 Jun 17;15(1):5163. doi: 10.1038/s41467-024-49516-2.
6
Sequence and structural features of carbohydrate binding in proteins and assessment of predictability using a neural network.蛋白质中碳水化合物结合的序列和结构特征以及使用神经网络评估可预测性
BMC Struct Biol. 2007 Jan 3;7:1. doi: 10.1186/1472-6807-7-1.
7
Predicting residue-specific qualities of individual protein models using residual neural networks and graph neural networks.使用残差神经网络和图神经网络预测个体蛋白质模型的残基特异性性质。
Proteins. 2022 Dec;90(12):2091-2102. doi: 10.1002/prot.26400. Epub 2022 Jul 30.
8
Combining protein sequences and structures with transformers and equivariant graph neural networks to predict protein function.将蛋白质序列和结构与变换器和等变图神经网络相结合以预测蛋白质功能。
bioRxiv. 2023 Jan 20:2023.01.17.524477. doi: 10.1101/2023.01.17.524477.
9
Combining protein sequences and structures with transformers and equivariant graph neural networks to predict protein function.将蛋白质序列和结构与转换器和等变图神经网络相结合,以预测蛋白质功能。
Bioinformatics. 2023 Jun 30;39(39 Suppl 1):i318-i325. doi: 10.1093/bioinformatics/btad208.
10
Deep learning-driven insights into super protein complexes for outer membrane protein biogenesis in bacteria.深度学习驱动的细菌外膜蛋白生物发生中超蛋白复合物的研究进展
Elife. 2022 Dec 28;11:e82885. doi: 10.7554/eLife.82885.

引用本文的文献

1
Tools for structural lectinomics: From structures to lectomes.结构凝集素组学工具:从结构到凝集素组
BBA Adv. 2025 Mar 6;7:100154. doi: 10.1016/j.bbadva.2025.100154. eCollection 2025.
2
Predictions from Deep Learning Propose Substantial Protein-Carbohydrate Interplay.深度学习预测表明蛋白质与碳水化合物之间存在大量相互作用。
bioRxiv. 2025 Mar 15:2025.03.07.641884. doi: 10.1101/2025.03.07.641884.
3
DIONYSUS: a database of protein-carbohydrate interfaces.狄俄尼索斯:一个蛋白质-碳水化合物界面数据库。

本文引用的文献

1
Fast, accurate antibody structure prediction from deep learning on massive set of natural antibodies.基于大规模天然抗体数据集的深度学习实现快速、准确的抗体结构预测。
Nat Commun. 2023 Apr 25;14(1):2389. doi: 10.1038/s41467-023-38063-x.
2
PeSTo: parameter-free geometric deep learning for accurate prediction of protein binding interfaces.PeSTo:用于准确预测蛋白质结合界面的无参几何深度学习。
Nat Commun. 2023 Apr 18;14(1):2175. doi: 10.1038/s41467-023-37701-8.
3
GlyNet: a multi-task neural network for predicting protein-glycan interactions.
Nucleic Acids Res. 2025 Jan 6;53(D1):D387-D395. doi: 10.1093/nar/gkae890.
4
Computational toolbox for the analysis of protein-glycan interactions.用于分析蛋白质-聚糖相互作用的计算工具箱。
Beilstein J Org Chem. 2024 Aug 22;20:2084-2107. doi: 10.3762/bjoc.20.180. eCollection 2024.
5
The Human Ganglioside Interactome in Live Cells Revealed Using Clickable Photoaffinity Ganglioside Probes.利用可点击光亲和性神经节苷脂探针揭示活细胞中的人类神经节苷脂相互作用组。
J Am Chem Soc. 2024 Jul 3;146(26):17801-17816. doi: 10.1021/jacs.4c03196. Epub 2024 Jun 18.
6
PeSTo-Carbs: Geometric Deep Learning for Prediction of Protein-Carbohydrate Binding Interfaces.PeSTo-Carbs:用于预测蛋白质-碳水化合物结合界面的几何深度学习
J Chem Theory Comput. 2024 Apr 23;20(8):2985-2991. doi: 10.1021/acs.jctc.3c01145. Epub 2024 Apr 11.
7
HumanLectome, an update of UniLectin for the annotation and prediction of human lectins.人源凝集素组(HumanLectome),UniLectin 的更新版本,用于注释和预测人类凝集素。
Nucleic Acids Res. 2024 Jan 5;52(D1):D1683-D1693. doi: 10.1093/nar/gkad905.
8
Editorial: Structural modeling and computational analyses of immune system molecules.社论:免疫系统分子的结构建模与计算分析
Front Immunol. 2023 Sep 5;14:1274670. doi: 10.3389/fimmu.2023.1274670. eCollection 2023.
GlyNet:一种用于预测蛋白质-聚糖相互作用的多任务神经网络。
Chem Sci. 2022 May 16;13(22):6669-6686. doi: 10.1039/d1sc05681f. eCollection 2022 Jun 7.
4
ColabFold: making protein folding accessible to all.ColabFold:让蛋白质折叠变得人人可用。
Nat Methods. 2022 Jun;19(6):679-682. doi: 10.1038/s41592-022-01488-1. Epub 2022 May 30.
5
3DLigandSite: structure-based prediction of protein-ligand binding sites.3DLigandSite:基于结构的蛋白质-配体结合位点预测。
Nucleic Acids Res. 2022 Jul 5;50(W1):W13-W20. doi: 10.1093/nar/gkac250.
6
Antibody structure prediction using interpretable deep learning.使用可解释深度学习进行抗体结构预测。
Patterns (N Y). 2021 Dec 9;3(2):100406. doi: 10.1016/j.patter.2021.100406. eCollection 2022 Feb 11.
7
LectinOracle: A Generalizable Deep Learning Model for Lectin-Glycan Binding Prediction.LectinOracle:一种可推广的用于凝集素-聚糖结合预测的深度学习模型。
Adv Sci (Weinh). 2022 Jan;9(1):e2103807. doi: 10.1002/advs.202103807. Epub 2021 Dec 4.
8
The trRosetta server for fast and accurate protein structure prediction.TrRosetta 服务器:用于快速准确的蛋白质结构预测。
Nat Protoc. 2021 Dec;16(12):5634-5651. doi: 10.1038/s41596-021-00628-9. Epub 2021 Nov 10.
9
Shotgun scanning glycomutagenesis: A simple and efficient strategy for constructing and characterizing neoglycoproteins. shotgun 扫描糖突变:构建和鉴定糖基化蛋白的简单高效策略。
Proc Natl Acad Sci U S A. 2021 Sep 28;118(39). doi: 10.1073/pnas.2107440118.
10
PUResNet: prediction of protein-ligand binding sites using deep residual neural network.PUResNet:使用深度残差神经网络预测蛋白质-配体结合位点。
J Cheminform. 2021 Sep 8;13(1):65. doi: 10.1186/s13321-021-00547-7.