Molecular modeling study of natural products as potential bioactive compounds against SARS-CoV-2

被引:7
作者
Ribeiro, Rayssa [1 ]
Botelho, Fernanda D. [2 ]
Pinto, Amanda M. V. [2 ]
La Torre, Antonia M. A. [2 ]
Almeida, Joyce S. F. D. [2 ]
LaPlante, Steven R. [3 ]
Franca, Tanos C. C. [2 ,3 ,4 ]
Veiga-Junior, Valdir F. [1 ]
dos Santos, Marcelo C. [2 ]
机构
[1] Mil Inst Engn, Dept Chem Engn, Rio De Janeiro, RJ, Brazil
[2] Mil Inst Engn, Lab Mol Modeling Appl Chem & Biol Def LMCBD, Rio De Janeiro, RJ, Brazil
[3] INRS, Ctr Armand Frappier Sante Biotechnol 531, Blvd Prairies, Laval, PQ, Canada
[4] Univ Hradec Kralove, Fac Sci, Dept Chem, Hradec Kralove, Czech Republic
关键词
SARS-CoV-2; Natural products; Molecular dynamics; Spike protein; HIGH-THROUGHPUT; FORCE-FIELD; DRUG DESIGN; SIMULATION; DYNAMICS;
D O I
10.1007/s00894-023-05586-5
中图分类号
Q5 [生物化学]; Q7 [分子生物学];
学科分类号
071010 ; 081704 ;
摘要
ContextThe severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the causative agent of the COVID-19 infection and responsible for millions of victims worldwide, remains a significant threat to public health. Even after the development of vaccines, research interest in the emergence of new variants is still prominent. Currently, the focus is on the search for effective and safe drugs, given the limitations and side effects observed for the synthetic drugs administered so far. In this sense, bioactive natural products that are widely used in the pharmaceutical industry due to their effectiveness and low toxicity have emerged as potential options in the search for safe drugs against COVID-19. Following this line, we screened 10 bioactive compounds derived from cholesterol for molecules capable of interacting with the receptor-binding domain (RBD) of the spike protein from SARS-CoV-2 (SC2Spike), responsible for the virus's invasion of human cells. Rounds of docking followed by molecular dynamics simulations and binding energy calculations enabled the selection of three compounds worth being experimentally evaluated against SARS-CoV-2.MethodsThe 3D structures of the cholesterol derivatives were prepared and optimized using the Spartan 08 software with the semi-empirical method PM3. They were then exported to the Molegro Virtual Docking (MVD (R)) software, where they were docked onto the RBD of a 3D structure of the SC2Spike protein that was imported from the Protein Data Bank (PDB). The best poses obtained from MVD (R) were subjected to rounds of molecular dynamics simulations using the GROMACS software, with the OPLS/AA force field. Frames from the MD simulation trajectories were used to calculate the ligand's free binding energies using the molecular mechanics - Poisson-Boltzmann surface area (MM-PBSA) method. All results were analyzed using the xmgrace and Visual Molecular Dynamics (VMD) software.
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页数:9
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共 45 条
  • [41] Optimization of parameters for semiempirical methods IV: extension of MNDO, AM1, and PM3 to more main group elements
    Stewart, JJP
    [J]. JOURNAL OF MOLECULAR MODELING, 2004, 10 (02) : 155 - 164
  • [42] MolDock: A new technique for high-accuracy molecular docking
    Thomsen, Rene
    Christensen, Mikael H.
    [J]. JOURNAL OF MEDICINAL CHEMISTRY, 2006, 49 (11) : 3315 - 3321
  • [43] COMPUTER-SIMULATION OF MOLECULAR-DYNAMICS - METHODOLOGY, APPLICATIONS, AND PERSPECTIVES IN CHEMISTRY
    VANGUNSTEREN, WF
    BERENDSEN, HJC
    [J]. ANGEWANDTE CHEMIE-INTERNATIONAL EDITION, 1990, 29 (09) : 992 - 1023
  • [44] Structural basis for the recognition of SARS-CoV-2 by full-length human ACE2
    Yan, Renhong
    Zhang, Yuanyuan
    Li, Yaning
    Xia, Lu
    Guo, Yingying
    Zhou, Qiang
    [J]. SCIENCE, 2020, 367 (6485) : 1444 - +
  • [45] In-Hospital Use of Statins Is Associated with a Reduced Risk of Mortality among Individuals with COVID-19
    Zhang, Xiao-Jin
    Qin, Juan-Juan
    Cheng, Xu
    Shen, Lijun
    Zhao, Yan-Ci
    Yuan, Yufeng
    Lei, Fang
    Chen, Ming-Ming
    Yang, Huilin
    Bai, Liangjie
    Song, Xiaohui
    Lin, Lijin
    Xia, Meng
    Zhou, Feng
    Zhou, Jianghua
    She, Zhi-Gang
    Zhu, Lihua
    Ma, Xinliang
    Xu, Qingbo
    Ye, Ping
    Chen, Guohua
    Liu, Liming
    Mao, Weiming
    Yan, Youqin
    Xiao, Bing
    Lu, Zhigang
    Peng, Gang
    Liu, Mingyu
    Yang, Jun
    Yang, Luyu
    Zhang, Changjiang
    Lu, Haofeng
    Xia, Xigang
    Wang, Daihong
    Liao, Xiaofeng
    Wei, Xiang
    Zhang, Bing-Hong
    Zhang, Xin
    Yang, Juan
    Zhao, Guang-Nian
    Zhang, Peng
    Liu, Peter P.
    Loomba, Rohit
    Ji, Yan-Xiao
    Xia, Jiahong
    Wang, Yibin
    Cai, Jingjing
    Guo, Jiao
    Li, Hongliang
    [J]. CELL METABOLISM, 2020, 32 (02) : 176 - +