DockEM: an enhanced method for atomic-scale protein-ligand docking refinement leveraging low-to-medium resolution cryo-EM density maps

被引:0
作者
Zou, Jing [1 ]
Zhang, Wenyi [2 ]
Hu, Jun [3 ]
Zhou, Xiaogen [1 ]
Zhang, Biao [3 ,4 ]
机构
[1] Zhejiang Univ Technol, Coll Informat Engn, 288 Liuhe Rd,Liuxia St, Hangzhou 310023, Peoples R China
[2] Westlake Lab Life Sci & Biomed, Westlake AI Therapeut Lab, Hangzhou 310024, Peoples R China
[3] Chinese Acad Med Sci, Suzhou Inst Syst Med, 100 Chongwen Rd, Suzhou 215123, Peoples R China
[4] Zhejiang Univ Technol, Coll Informat Engn, 100 Chongwen Rd, Suzhou 215123, Peoples R China
基金
国家重点研发计划; 中国国家自然科学基金;
关键词
docking; cryo-EM; protein-ligand; REMC simulation; refinement; HOST-CELL; NUCLEUS; CANCER;
D O I
10.1093/bib/bbaf091
中图分类号
Q5 [生物化学];
学科分类号
071010 ; 081704 ;
摘要
Protein-ligand docking plays a pivotal role in virtual drug screening, and recent advancements in cryo-electron microscopy (cryo-EM) technology have significantly accelerated the progress of structure-based drug discovery. However, the majority of cryo-EM density maps are of medium to low resolution (3-10 & Aring;), which presents challenges in effectively integrating cryo-EM data into molecular docking workflows. In this study, we present an updated protein-ligand docking method, DockEM, which leverages local cryo-EM density maps and physical energy refinement to precisely dock ligands into specific protein binding sites. Tested on a dataset of 121 protein-ligand compound, our results demonstrate that DockEM outperforms other advanced docking methods. The strength of DockEM lies in its ability to incorporate cryo-EM density map information, effectively leveraging the structural information of ligands embedded within these maps. This advancement enhances the use of cryo-EM density maps in virtual drug screening, offering a more reliable framework for drug discovery.
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页数:12
相关论文
共 42 条
[1]   DOCK 6: Impact of New Features and Current Docking Performance [J].
Allen, William J. ;
Balius, Trent E. ;
Mukherjee, Sudipto ;
Brozell, Scott R. ;
Moustakas, Demetri T. ;
Lang, P. Therese ;
Case, David A. ;
Kuntz, Irwin D. ;
Rizzo, Robert C. .
JOURNAL OF COMPUTATIONAL CHEMISTRY, 2015, 36 (15) :1132-1156
[2]   DockRMSD: an open-source tool for atom mapping and RMSD calculation of symmetric molecules through graph isomorphism [J].
Bell, Eric W. ;
Zhang, Yang .
JOURNAL OF CHEMINFORMATICS, 2019, 11 (1)
[3]   A dual-population multi-objective evolutionary algorithm driven by generative adversarial networks for benchmarking and protein-peptide docking [J].
Cheng H. ;
Wang G.-G. ;
Chen L. ;
Wang R. .
Computers in Biology and Medicine, 2024, 168
[4]   Allosteric Antagonist Modulation of TRPV2 by Piperlongumine Impairs Glioblastoma Progression [J].
Conde, Joao ;
Pumroy, Ruth A. ;
Baker, Charlotte ;
Rodrigues, Tiago ;
Guerreiro, Ana ;
Sousa, Barbara B. ;
Marques, Marta C. ;
de Almeida, Bernardo P. ;
Lee, Sohyon ;
Leites, Elvira P. ;
Picard, Daniel ;
Samanta, Amrita ;
Vaz, Sandra H. ;
Sieglitz, Florian ;
Langini, Maike ;
Remke, Marc ;
Roque, Rafael ;
Weiss, Tobias ;
Weller, Michael ;
Liu, Yuhang ;
Han, Seungil ;
Corzana, Francisco ;
Morais, Vanessa A. ;
Faria, Claudia C. ;
Carvalho, Tania ;
Filippakopoulos, Panagis ;
Snijder, Berend ;
Barbosa-Morais, Nuno L. ;
Moiseenkova-Bell, Vera Y. ;
Bernardes, Goncalo J. L. .
ACS CENTRAL SCIENCE, 2021, 7 (05) :868-881
[5]   Pharmacological Chaperones: Design and Development of New Therapeutic Strategies for the Treatment of Conformational Diseases [J].
Convertino, Marino ;
Das, Jhuma ;
Dokholyan, Nikolay V. .
ACS CHEMICAL BIOLOGY, 2016, 11 (06) :1471-1489
[6]   Rapid Flexible Docking Using a Stochastic Rotamer Library of Ligands [J].
Ding, Feng ;
Yin, Shuangye ;
Dokholyan, Nikolay V. .
JOURNAL OF CHEMICAL INFORMATION AND MODELING, 2010, 50 (09) :1623-1632
[7]   The integrative bioinformatics approaches to predict the xanthohumol as anti-breast cancer molecule: Targeting cancer cells signaling PI3K and AKT kinase pathway [J].
Gupta, Kartikey Kumar ;
Sharma, Kamal Kant ;
Chandra, Harish ;
Panwar, Himalaya ;
Bhardwaj, Nitin ;
Altwaijry, Najla A. ;
Alsfouk, Aisha A. ;
Dlamini, Zodwa ;
Afzal, Obaid ;
Altamimi, Abdulmalik S. A. ;
Khan, Shahanavaj ;
Mishra, Abhay Prakash .
FRONTIERS IN ONCOLOGY, 2022, 12
[8]   Highly accurate protein structure prediction with AlphaFold [J].
Jumper, John ;
Evans, Richard ;
Pritzel, Alexander ;
Green, Tim ;
Figurnov, Michael ;
Ronneberger, Olaf ;
Tunyasuvunakool, Kathryn ;
Bates, Russ ;
Zidek, Augustin ;
Potapenko, Anna ;
Bridgland, Alex ;
Meyer, Clemens ;
Kohl, Simon A. A. ;
Ballard, Andrew J. ;
Cowie, Andrew ;
Romera-Paredes, Bernardino ;
Nikolov, Stanislav ;
Jain, Rishub ;
Adler, Jonas ;
Back, Trevor ;
Petersen, Stig ;
Reiman, David ;
Clancy, Ellen ;
Zielinski, Michal ;
Steinegger, Martin ;
Pacholska, Michalina ;
Berghammer, Tamas ;
Bodenstein, Sebastian ;
Silver, David ;
Vinyals, Oriol ;
Senior, Andrew W. ;
Kavukcuoglu, Koray ;
Kohli, Pushmeet ;
Hassabis, Demis .
NATURE, 2021, 596 (7873) :583-+
[9]  
Khan S., Crit Rev Clin Lab Sci, V61
[10]   Prediction of mycoplasma hominis proteins targeting in mitochondria and cytoplasm of host cells and their implication in prostate cancer etiology [J].
Khan, Shahanavaj ;
Zakariah, Mohammed ;
Rolfo, Christian ;
Robrecht, Lembrechts ;
Palaniappan, Sellappan .
ONCOTARGET, 2017, 8 (19) :30830-30843