• 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

美杜莎对接 2.0：具有约束的高效准确蛋白-配体对接

MedusaDock 2.0: Efficient and Accurate Protein-Ligand Docking With Constraints.

机构信息

Department of Pharmacology , Penn State University College of Medicine , Hershey , Pennsylvania 17033 , United States.

Department of Biochemistry & Molecular Biology , Penn State University College of Medicine , Hershey , Pennsylvania 17033 , United States.

出版信息

J Chem Inf Model. 2019 Jun 24;59(6):2509-2515. doi: 10.1021/acs.jcim.8b00905. Epub 2019 Apr 17.

DOI:10.1021/acs.jcim.8b00905
PMID:30946779
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC6597311/
Abstract

Molecular docking is the key ingredient of virtual drug screening, a promising and cost-effective approach for finding new drugs. A critical limitation of this approach is the inadequate sampling efficiency of both ligand and/or receptor conformations for finding the lowest energy bound state. To circumvent this limitation, we develop a protein-ligand docking methodology capable of incorporating structural constraints, experimentally derived or theoretically predicted, to improve accuracy and efficiency. We develop a web server with a user-friendly online graphical interface as a platform for accurate and efficient protein-ligand molecule docking.

摘要

分子对接是虚拟药物筛选的关键组成部分，是一种有前途且具有成本效益的寻找新药的方法。这种方法的一个关键限制是配体和/或受体构象的采样效率不足，无法找到最低能量结合态。为了克服这一限制，我们开发了一种能够结合结构约束的蛋白质-配体对接方法，这些约束可以是实验得出或理论预测的，以提高准确性和效率。我们开发了一个带有用户友好的在线图形界面的网络服务器，作为准确和高效的蛋白质-配体分子对接的平台。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1d72/6597311/bf39b7692a7b/nihms-1022127-f0006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1d72/6597311/2298058f2da9/nihms-1022127-f0002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1d72/6597311/b11e62ff963f/nihms-1022127-f0003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1d72/6597311/52a8c5a6f382/nihms-1022127-f0004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1d72/6597311/db1e46cf6487/nihms-1022127-f0005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1d72/6597311/bf39b7692a7b/nihms-1022127-f0006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1d72/6597311/2298058f2da9/nihms-1022127-f0002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1d72/6597311/b11e62ff963f/nihms-1022127-f0003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1d72/6597311/52a8c5a6f382/nihms-1022127-f0004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1d72/6597311/db1e46cf6487/nihms-1022127-f0005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1d72/6597311/bf39b7692a7b/nihms-1022127-f0006.jpg

相似文献

1
MedusaDock 2.0: Efficient and Accurate Protein-Ligand Docking With Constraints.美杜莎对接 2.0：具有约束的高效准确蛋白-配体对接
J Chem Inf Model. 2019 Jun 24;59(6):2509-2515. doi: 10.1021/acs.jcim.8b00905. Epub 2019 Apr 17.
2
DockThor-VS: A Free Platform for Receptor-Ligand Virtual Screening.DockThor-VS：一个免费的受体配体虚拟筛选平台。
J Mol Biol. 2024 Sep 1;436(17):168548. doi: 10.1016/j.jmb.2024.168548. Epub 2024 Mar 20.
3
Lead finder: an approach to improve accuracy of protein-ligand docking, binding energy estimation, and virtual screening.铅离子寻找器：一种提高蛋白质-配体对接、结合能估计和虚拟筛选准确性的方法。
J Chem Inf Model. 2008 Dec;48(12):2371-85. doi: 10.1021/ci800166p.
4
Incorporating backbone flexibility in MedusaDock improves ligand-binding pose prediction in the CSAR2011 docking benchmark.在 MedusaDock 中加入骨架柔性可以提高 CSAR2011 对接基准中的配体结合构象预测。
J Chem Inf Model. 2013 Aug 26;53(8):1871-9. doi: 10.1021/ci300478y. Epub 2012 Dec 24.
5
Improving docking results via reranking of ensembles of ligand poses in multiple X-ray protein conformations with MM-GBSA.通过使用 MM-GBSA 对多个 X 射线蛋白质构象中的配体构象进行重新排序，从而提高对接结果。
J Chem Inf Model. 2014 Oct 27;54(10):2697-717. doi: 10.1021/ci5003735. Epub 2014 Sep 30.
6
Computational protein-ligand docking and virtual drug screening with the AutoDock suite.使用AutoDock套件进行蛋白质-配体对接计算和虚拟药物筛选。
Nat Protoc. 2016 May;11(5):905-19. doi: 10.1038/nprot.2016.051. Epub 2016 Apr 14.
7
Advances in Docking.对接技术的新进展。
Curr Med Chem. 2019;26(42):7555-7580. doi: 10.2174/0929867325666180904115000.
8
A review of protein-small molecule docking methods.蛋白质-小分子对接方法综述。
J Comput Aided Mol Des. 2002 Mar;16(3):151-66. doi: 10.1023/a:1020155510718.
9
Nonlinear scoring functions for similarity-based ligand docking and binding affinity prediction.基于相似性的配体对接和结合亲和力预测的非线性评分函数。
J Chem Inf Model. 2013 Nov 25;53(11):3097-112. doi: 10.1021/ci400510e. Epub 2013 Nov 11.
10
AutoDock and AutoDockTools for Protein-Ligand Docking: Beta-Site Amyloid Precursor Protein Cleaving Enzyme 1(BACE1) as a Case Study.用于蛋白质-配体对接的AutoDock和AutoDockTools：以β-分泌酶1（BACE1）为例的研究
Methods Mol Biol. 2017;1598:391-403. doi: 10.1007/978-1-4939-6952-4_20.

引用本文的文献

1
TCR-pMHC Binding Specificity Prediction From Structure Using Graph Neural Networks.基于结构利用图神经网络预测TCR-pMHC结合特异性
IEEE Trans Comput Biol Bioinform. 2025 Jan-Feb;22(1):171-179. doi: 10.1109/TCBBIO.2024.3504235.
2
DockEM: an enhanced method for atomic-scale protein-ligand docking refinement leveraging low-to-medium resolution cryo-EM density maps.DockEM：一种利用低至中等分辨率冷冻电镜密度图进行原子尺度蛋白质-配体对接优化的增强方法。
Brief Bioinform. 2025 Mar 4;26(2). doi: 10.1093/bib/bbaf091.
3
Computational Strategies for Assessing Adverse Outcome Pathways: Hepatic Steatosis as a Case Study.

本文引用的文献

1
Forging the Basis for Developing Protein-Ligand Interaction Scoring Functions.为开发蛋白质-配体相互作用评分函数奠定基础。
Acc Chem Res. 2017 Feb 21;50(2):302-309. doi: 10.1021/acs.accounts.6b00491. Epub 2017 Feb 9.
2
Pharmacological Chaperones: Design and Development of New Therapeutic Strategies for the Treatment of Conformational Diseases.药理伴侣分子：治疗构象疾病新治疗策略的设计与开发
ACS Chem Biol. 2016 Jun 17;11(6):1471-89. doi: 10.1021/acschembio.6b00195. Epub 2016 Apr 28.
3
Docking and Scoring with Target-Specific Pose Classifier Succeeds in Native-Like Pose Identification But Not Binding Affinity Prediction in the CSAR 2014 Benchmark Exercise.
计算策略评估不良结局途径：以肝脂肪变性为例。
Int J Mol Sci. 2024 Oct 17;25(20):11154. doi: 10.3390/ijms252011154.
4
OpenDock: a pytorch-based open-source framework for protein-ligand docking and modelling.OpenDock：一个基于 PyTorch 的开源蛋白质-配体对接和建模框架。
Bioinformatics. 2024 Nov 1;40(11). doi: 10.1093/bioinformatics/btae628.
5
Addressing docking pose selection with structure-based deep learning: Recent advances, challenges and opportunities.利用基于结构的深度学习解决对接姿势选择问题：最新进展、挑战与机遇
Comput Struct Biotechnol J. 2024 May 18;23:2141-2151. doi: 10.1016/j.csbj.2024.05.024. eCollection 2024 Dec.
6
DockOpt: A Tool for Automatic Optimization of Docking Models.DockOpt：一种用于对接模型自动优化的工具。
J Chem Inf Model. 2024 Feb 12;64(3):1004-1016. doi: 10.1021/acs.jcim.3c01406. Epub 2024 Jan 11.
7
Whole proteome mapping of compound-protein interactions.化合物-蛋白质相互作用的全蛋白质组图谱绘制。
Curr Res Chem Biol. 2022;2. doi: 10.1016/j.crchbi.2022.100035. Epub 2022 Sep 12.
8
Recent Deep Learning Applications to Structure-Based Drug Design.基于结构的药物设计的最新深度学习应用。
Methods Mol Biol. 2024;2714:215-234. doi: 10.1007/978-1-0716-3441-7_13.
9
The structure of phosphatidylinositol remodeling MBOAT7 reveals its catalytic mechanism and enables inhibitor identification.磷脂酰肌醇重塑 MBOAT7 的结构揭示了其催化机制，并能够识别抑制剂。
Nat Commun. 2023 Jun 14;14(1):3533. doi: 10.1038/s41467-023-38932-5.
10
Deciphering the Mechanistic Basis for Perfluoroalkyl-Protein Interactions.解析全氟烷基-蛋白相互作用的机制基础。
Chembiochem. 2023 Jul 3;24(13):e202300159. doi: 10.1002/cbic.202300159. Epub 2023 Jun 1.
docking 和 scoring 与目标特定的 pose 分类器相结合，成功地实现了类似天然构象的 pose 识别，但在 CSAR 2014 基准测试中不能预测结合亲和力。
J Chem Inf Model. 2016 Jun 27;56(6):1032-41. doi: 10.1021/acs.jcim.5b00751. Epub 2016 Apr 20.
4
Applications of Discrete Molecular Dynamics in biology and medicine.离散分子动力学在生物学和医学中的应用。
Curr Opin Struct Biol. 2016 Apr;37:9-13. doi: 10.1016/j.sbi.2015.11.001. Epub 2015 Nov 28.
5
Dynamic Docking of Conformationally Constrained Macrocycles: Methods and Applications.构象受限大环化合物的动态对接：方法与应用
ACS Chem Biol. 2016 Jan 15;11(1):10-24. doi: 10.1021/acschembio.5b00663. Epub 2015 Dec 4.
6
Molecular docking and structure-based drug design strategies.分子对接与基于结构的药物设计策略。
Molecules. 2015 Jul 22;20(7):13384-421. doi: 10.3390/molecules200713384.
7
DOCK 6: Impact of new features and current docking performance.DOCK 6：新特性及当前对接性能的影响
J Comput Chem. 2015 Jun 5;36(15):1132-56. doi: 10.1002/jcc.23905.
8
PDB-wide collection of binding data: current status of the PDBbind database.PDB 范围内的结合数据集合：PDBbind 数据库的当前状态。
Bioinformatics. 2015 Feb 1;31(3):405-12. doi: 10.1093/bioinformatics/btu626. Epub 2014 Oct 9.
9
Incorporating backbone flexibility in MedusaDock improves ligand-binding pose prediction in the CSAR2011 docking benchmark.在 MedusaDock 中加入骨架柔性可以提高 CSAR2011 对接基准中的配体结合构象预测。
J Chem Inf Model. 2013 Aug 26;53(8):1871-9. doi: 10.1021/ci300478y. Epub 2012 Dec 24.
10
Discrete molecular dynamics distinguishes nativelike binding poses from decoys in difficult targets.离散分子动力学可区分天然结合构象与困难靶标中的诱饵。
Biophys J. 2012 Jan 4;102(1):144-51. doi: 10.1016/j.bpj.2011.11.4008. Epub 2012 Jan 3.