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Cited by 34Immune Response and InflammationComputational Drug Discovery Methodsvaccines and immunoinformatics approaches

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Recent Advances in Studying Toll-like Receptors with the Use of Computational Methods

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Bzówka, Maria, Bagrowska, Weronika, Góra, Artur·2023·Journal of Chemical Information and Modeling
DOI 10.1021/acs.jcim.3c00419PMC PMC10302489

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Abstract

Toll-like receptors (TLRs) are transmembrane proteins that recognize various molecular patterns and activate signaling that triggers the immune response. In this review, our goal is to summarize how, in recent years, various computational solutions have contributed to a better understanding of TLRs, regarding both their function and mechanism of action. We update the recent information about small-molecule modulators and expanded the topic toward next-generation vaccine design, as well as studies of the dynamic nature of TLRs. Also, we underline problems that remain unsolved.

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Figure 1 Structural organization and potential Toll-like receptors (TLRs) mechanism of action. (A) The general structure of the TLRs’ monomers. (B) Differences in the TLRs’ LRR domains between the cell membrane and intracellular membrane TLRs. (C) Various orientations (symmetric and asymmetric) of the TIR domain subunits in the TLRs’ TIR dimer. (D) Potential mechanisms of the TLRs activation. The upper panel shows the mechanisms of the cell membrane TLRs activation, while the lower panel presents the mechanisms of the intracellular membrane TLRs containing a Z-loop. (L) indicates the ligand, while the scissors symbol indicates the proteolytic cleavage of the Z-loop. (E) Binding of the adaptor proteins, MyD88 and TRIF, to the respective TLRs’ TIR dimer.
Figure 1 Structural organization and potential Toll-like receptors (TLRs) mechanism of action. (A) The general structure of the TLRs’ monomers. (B) Differences in the TLRs’ LRR domains between the cell membrane and intracellular membrane TLRs. (C) Various orientations (symmetric and asymmetric) of the TIR domain subunits in the TLRs’ TIR dimer. (D) Potential mechanisms of the TLRs activation. The upper panel shows the mechanisms of the cell membrane TLRs activation, while the lower panel presents the mechanisms of the intracellular membrane TLRs containing a Z-loop. (L) indicates the ligand, while the scissors symbol indicates the proteolytic cleavage of the Z-loop. (E) Binding of the adaptor proteins, MyD88 and TRIF, to the respective TLRs’ TIR dimer.CC BY · PubMed Central
Figure 2 A general protocol for small-molecule modulators design targeting the LRR domain of TLRs. The subunits of the LRR domain are colored gray, indicating both TLRs located in the cell membrane and TLRs located in the intracellular compartments. (L) indicates the location of the ligand binding site, while (M) points out the designed modulator and (C) the selected candidate(s).
Figure 2 A general protocol for small-molecule modulators design targeting the LRR domain of TLRs. The subunits of the LRR domain are colored gray, indicating both TLRs located in the cell membrane and TLRs located in the intracellular compartments. (L) indicates the location of the ligand binding site, while (M) points out the designed modulator and (C) the selected candidate(s).CC BY · PubMed Central
Figure 3 Examples of scaffolds of small-molecule modulators targeting the LRR domain of TLRs. The left panel shows the approximate location of small-molecule modulators (M) with respect to the LRR subunits of the TLR dimers described in this review. Agonists are presented on the middle panel, while antagonists are on the right panel. TLR4 was shown with the associated myeloid differentiation factor 2 (MD2). Please note that one of the agonists’ scaffolds is shown for more than one TLRs. This indicates the possibility of targeting both surface- and endosomal-located TLRs by a given modulator.
Figure 3 Examples of scaffolds of small-molecule modulators targeting the LRR domain of TLRs. The left panel shows the approximate location of small-molecule modulators (M) with respect to the LRR subunits of the TLR dimers described in this review. Agonists are presented on the middle panel, while antagonists are on the right panel. TLR4 was shown with the associated myeloid differentiation factor 2 (MD2). Please note that one of the agonists’ scaffolds is shown for more than one TLRs. This indicates the possibility of targeting both surface- and endosomal-located TLRs by a given modulator.CC BY · PubMed Central
Figure 4 General protocol for next-generation multiepitope vaccine design. The ability of binding different epitopes (shown as dark green and pink shapes, respectively) to LRR subunits of the TLRs located both in the cell membrane (light green) and in the intracellular membrane (light blue) has been shown.
Figure 4 General protocol for next-generation multiepitope vaccine design. The ability of binding different epitopes (shown as dark green and pink shapes, respectively) to LRR subunits of the TLRs located both in the cell membrane (light green) and in the intracellular membrane (light blue) has been shown.CC BY · PubMed Central
Figure 5 Examples of potential dynamical changes of TLRs observed in cited studies. (A and B) Structural transitions that the particular domains of TLR4 may exhibit (based on Patra et al.116 and Matamoros-Recio et al.117 works). TLR4 was shown with the associated myeloid differentiation factor 2 (MD2) and with the bound lipopolysaccharide LPS (C) Structural rearrangements of TLR3 domains and membrane (based on Patra et al.118 study). TLR3 was shown bound with dsRNA. (D) Differences in the structural organization of the transmembrane helix (TM) and cytoplasmic juxtamembrane (JM) regions that may occur in TLRs (based on Kornilov et al.34 work).
Figure 5 Examples of potential dynamical changes of TLRs observed in cited studies. (A and B) Structural transitions that the particular domains of TLR4 may exhibit (based on Patra et al.116 and Matamoros-Recio et al.117 works). TLR4 was shown with the associated myeloid differentiation factor 2 (MD2) and with the bound lipopolysaccharide LPS (C) Structural rearrangements of TLR3 domains and membrane (based on Patra et al.118 study). TLR3 was shown bound with dsRNA. (D) Differences in the structural organization of the transmembrane helix (TM) and cytoplasmic juxtamembrane (JM) regions that may occur in TLRs (based on Kornilov et al.34 work).CC BY · PubMed Central
Figure 6 Areas in TLR research that still require further development. (A) Experimental verification of the predicted structures. (B) Studying the orientation of the subunits of the TIR domain dimers of TLRs. (C) Designing small-molecule modulators (M) targeting the TIR domain of TLRs. (D) Studying the proteolytic cleavage of the Z-loop in TLR7–9. (E) Analyzing potential changes in the subunits dynamics in TLRs. (F) Analyzing the conformational changes and structural rearrangements in both TLR receptors and bilayer membrane. (G) Studying the whole process of ligand recognition through the signaling cascade to the immune response.
Figure 6 Areas in TLR research that still require further development. (A) Experimental verification of the predicted structures. (B) Studying the orientation of the subunits of the TIR domain dimers of TLRs. (C) Designing small-molecule modulators (M) targeting the TIR domain of TLRs. (D) Studying the proteolytic cleavage of the Z-loop in TLR7–9. (E) Analyzing potential changes in the subunits dynamics in TLRs. (F) Analyzing the conformational changes and structural rearrangements in both TLR receptors and bilayer membrane. (G) Studying the whole process of ligand recognition through the signaling cascade to the immune response.CC BY · PubMed Central

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Citations

Cites

Research works printed but not linked: 115

  1. 10.1016/j.immuni.2011.05.006 · DOI · Paper page
  2. 10.1016/j.intimp.2018.03.002 · DOI
  3. 10.3390/v3060920 · DOI
  4. 10.3389/fimmu.2018.02379 · DOI
  5. 10.1038/s41392-021-00687-0 · DOI
  6. 10.1016/j.cell.2010.01.022 · DOI · Paper page
  7. 10.1155/2021/9914854 · DOI
  8. 10.1038/ni.1863 · DOI · Paper page
  9. 10.1016/S1471-4906(03)00242-4 · DOI
  10. 10.1186/1471-2164-8-124 · DOI
  11. 10.1002/pro.4043 · DOI
  12. 10.1146/annurev.cellbio.21.122303.115827 · DOI
  13. 10.1111/j.1582-4934.2010.01127.x · DOI
  14. 10.1002/prot.25179 · DOI
  15. 10.3389/fphys.2011.00041 · DOI
  16. 10.3389/fimmu.2014.00461 · DOI
  17. 10.1038/nri2079 · DOI · Paper page
  18. 10.4161/cc.21572 · DOI
  19. 10.1186/s42269-019-0227-2 · DOI
  20. 10.1038/nrd3203 · DOI
  21. 10.1002/med.21553 · DOI
  22. 10.1126/science.abj8754 · DOI · Paper page
  23. 10.1038/s41541-021-00391-8 · DOI
  24. 10.1016/j.tips.2016.10.007 · DOI
  25. 10.3390/ijms17091508 · DOI
  26. 10.3390/molecules21080994 · DOI
  27. 10.1038/35040600 · DOI
  28. 10.1126/science.1115253 · DOI
  29. 10.1073/pnas.0505077102 · DOI
  30. 10.1016/j.febslet.2014.08.031 · DOI
  31. 10.1093/nar/28.1.235 · DOI · Paper page
  32. 10.1038/s41594-020-00542-w · DOI
  33. 10.1038/s42003-021-02532-0 · DOI
  34. 10.1038/s41467-023-37042-6 · DOI
  35. 10.1021/acs.accounts.9b00631 · DOI
  36. 10.1016/j.bbagen.2017.07.011 · DOI
  37. 10.1074/jbc.M110.116046 · DOI
  38. 10.1074/jbc.M111.227934 · DOI
  39. 10.1111/febs.14124 · DOI
  40. 10.1016/j.cell.2007.09.008 · DOI
  41. 10.1039/C8CC06618C · DOI
  42. 10.1016/j.ejmech.2019.02.067 · DOI
  43. 10.1016/j.bcp.2018.04.018 · DOI
  44. 10.1002/cmdc.201300445 · DOI
  45. 10.1002/anie.201204910 · DOI
  46. 10.1016/j.bcp.2020.113957 · DOI
  47. 10.1002/cmdc.202000060 · DOI
  48. 10.1021/jacs.8b09223 · DOI
  49. 10.1021/acs.jmedchem.8b01583 · DOI
  50. 10.1080/07391102.2018.1474804 · DOI
  51. 10.1021/acs.jmedchem.7b01803 · DOI
  52. 10.1038/s41598-018-37421-w · DOI
  53. 10.1021/acschemneuro.9b00225 · DOI
  54. 10.1038/mp.2014.177 · DOI
  55. 10.1016/j.bbi.2009.08.004 · DOI
  56. 10.1111/bph.13394 · DOI
  57. 10.1021/acs.jmedchem.5b00426 · DOI
  58. 10.1021/acs.jcim.7b00717 · DOI
  59. 10.1096/fj.201900173RRR · DOI
  60. 10.3390/biomedicines10092326 · DOI
  61. 10.1016/j.tet.2014.07.026 · DOI
  62. 10.1016/j.phrs.2016.09.015 · DOI
  63. 10.1021/acs.jmedchem.1c00300 · DOI
  64. 10.1080/07391102.2018.1559098 · DOI
  65. 10.1016/j.ejmech.2019.06.084 · DOI
  66. 10.1371/journal.pone.0260565 · DOI
  67. 10.3390/pharmaceutics13020142 · DOI
  68. 10.1038/s41573-021-00163-y · DOI
  69. 10.1093/nar/gkaa1100 · DOI · Paper page
  70. 10.1186/1471-2105-8-4 · DOI
  71. 10.1385/1-59259-890-0:571 · DOI
  72. 10.1186/1471-2105-8-424 · DOI
  73. 10.1111/imm.12889 · DOI
  74. 10.1093/nar/gky1006 · DOI
  75. 10.1093/nar/gkx346 · DOI
  76. 10.1002/jmr.893 · DOI
  77. 10.1186/1471-2105-14-S6-S4 · DOI
  78. 10.1093/nar/gkl343 · DOI
  79. 10.1093/bib/bbaa294 · DOI
  80. 10.1371/journal.pone.0073957 · DOI
  81. 10.1093/bib/bbac174 · DOI
  82. 10.1093/bioinformatics/11.6.681 · DOI
  83. 10.1038/nmeth.3213 · DOI · Paper page
  84. 10.1016/j.bpj.2011.10.024 · DOI
  85. 10.1093/nar/gkt458 · DOI
  86. 10.1038/nprot.2016.169 · DOI
  87. 10.1371/journal.pone.0009862 · DOI
  88. 10.1016/j.imu.2020.100438 · DOI
  89. 10.1039/D1RA06532G · DOI
  90. 10.34172/bi.2022.23769 · DOI
  91. 10.2147/AABC.S366431 · DOI
  92. 10.2147/IDR.S175114 · DOI
  93. 10.1038/s41598-018-34254-5 · DOI
  94. 10.1016/j.vaccine.2018.03.042 · DOI
  95. 10.3389/fimmu.2022.956776 · DOI
  96. 10.1016/j.ejps.2020.105279 · DOI
  97. 10.1038/s41598-019-39299-8 · DOI
  98. 10.1038/s41598-017-09199-w · DOI
  99. 10.1016/j.meegid.2018.03.007 · DOI
  100. 10.1080/14760584.2019.1711057 · DOI
  101. 10.1007/s10989-021-10283-z · DOI
  102. 10.1016/j.vaccine.2021.09.025 · DOI
  103. 10.1016/j.intimp.2022.109238 · DOI
  104. 10.3389/fmicb.2022.947852 · DOI
  105. 10.3390/v13112302 · DOI
  106. 10.3390/pharmaceutics14020423 · DOI
  107. 10.1038/srep43807 · DOI
  108. 10.1038/s41598-019-39984-8 · DOI
  109. 10.1074/jbc.M510442200 · DOI
  110. 10.1021/acs.jcim.1c00818 · DOI
  111. 10.1002/prot.25467 · DOI
  112. 10.1080/07391102.2020.1786457 · DOI
  113. 10.3389/fimmu.2018.00489 · DOI
  114. 10.1002/chem.202102995 · DOI
  115. 10.3390/ijms21082857 · DOI

Metadata

Authors
Bzówka, M., Bagrowska, W., & Góra, A.
Affiliations
Silesian University of Technology (PL)
Corresponding author
Bzówka, Maria
ORCID
Venue
Journal of Chemical Information and Modeling
Publisher
American Chemical Society (ACS)
Published
2023-06-07
Volume
63
Issue
12
Pages
3669-3687
ISSN
  • 1549-9596 (ISSN-L)
  • 1549-960X
DOI
10.1021/acs.jcim.3c00419
PMC
PMC10302489
OpenAlex
W4379600654
Cited by
34
References
4
Citations by year
2026: 13 · 2025: 11 · 2024: 9 · 2023: 1
FWCI
0.95
Type
journal article
Language
en
Open access
Licence
Locations
  • Journal of Chemical Information and Modeling (publishedVersion, cc-by)
  • ChemRxiv (acceptedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
Funders
Ministerstwo Edukacji i Nauki (DI2018 014148 / 0141/DIA/2019/48)
Topics
  • Life Sciences › Immunology and Microbiology › Immunology › Immune Response and Inflammation
  • Life Sciences › Pharmacology, Toxicology and Pharmaceutics › Drug Discovery › Computational Drug Discovery Methods
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › vaccines and immunoinformatics approaches
Keywords
Toll-like receptors, computational methods, Toll-like receptor signaling, pattern recognition, small molecule modulators, vaccine design, innate immunity
MeSH terms
Signal Transduction, Toll-Like Receptors

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