PreprintCited by 12Computational Drug Discovery MethodsProtein Structure and DynamicsEnzyme Structure and Function

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VirtuousPocketome: A Computational Tool for Screening Protein-ligand Complexes to Identify Similar Binding Sites

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Preprint — published as 10.1038/s41598-024-56893-7 · Paper page

Abstract

Protein residues within binding pockets play a critical role in determining the range of ligands that can interact with a protein, influencing its structure and function. Identifying structural similarities in proteins offers valuable insights into their function and activation mechanisms, aiding in predicting protein-ligand interactions, anticipating off-target effects, and facilitating the development of therapeutic agents. Numerous computational methods assessing global or local similarity in protein cavities have emerged, but their utilization is impeded by complexity, impractical automation for amino acid pattern searches, and an inability to evaluate the dynamics of scrutinized protein-ligand systems. Here, we present a general, automatic and unbiased computational pipeline, named VirtuousPocketome , aimed at screening huge databases of proteins for similar binding pockets starting from an interested protein-ligand complex. We demonstrate the pipeline’s potential by exploring a recently-solved human bitter taste receptor, i.e. the TAS2R46, complexed with strychnine. We pinpointed 145 proteins sharing similar binding sites compared to the analysed bitter taste receptor and the enrichment analysis highlighted the related biological processes, molecular functions and cellular components. This work represents the foundation for future studies aimed at understanding the effective role of tastants outside the gustatory system: this could pave the way towards the rationalization of the diet as a supplement to standard pharmacological treatments and the design of novel tastants-inspired compounds to target other proteins involved in specific diseases or disorders. The proposed pipeline is publicly accessible, can be applied to any protein-ligand complex, and could be expanded to screen any database of protein structures.

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Figures

Figure 1 Main interactions defining the three motifs for the bitter taste receptor interacting with strychnine. (A) Representative snapshot of the bitter-ligand complex, (B) PLIP interaction analysis identifying Hydrophobic Interaction (HI) and Saltbridge Interaction (SB), (C, D, E) site views of the three motifs identified. The bitter taste receptor is represented in grey, the strychnine in blue and the interacting residues in green (hydrophobic interactions) and purple (salt bridges).
Figure 1 Main interactions defining the three motifs for the bitter taste receptor interacting with strychnine. (A) Representative snapshot of the bitter-ligand complex, (B) PLIP interaction analysis identifying Hydrophobic Interaction (HI) and Saltbridge Interaction (SB), (C, D, E) site views of the three motifs identified. The bitter taste receptor is represented in grey, the strychnine in blue and the interacting residues in green (hydrophobic interactions) and purple (salt bridges).CC BY · PubMed Central
Figure 2 (A) Number of total structures in the original human proteome database and the number of selected hits from the similarity search and the subsequent multi-step filtering. (B) Binding site view of the strychnine bound to the best hit (PDB: 3A4S) according to the docking score at the end of the multi-step filtering process. Protein is rendered in grey, strychnine in blue, residues in the original motif of the bitter taste receptor in red and matching residues in the 3A4S structure in violet.
Figure 2 (A) Number of total structures in the original human proteome database and the number of selected hits from the similarity search and the subsequent multi-step filtering. (B) Binding site view of the strychnine bound to the best hit (PDB: 3A4S) according to the docking score at the end of the multi-step filtering process. Protein is rendered in grey, strychnine in blue, residues in the original motif of the bitter taste receptor in red and matching residues in the 3A4S structure in violet.CC BY · PubMed Central
Figure 3 Bar plots representing the best 5 retrieved GO terms for each category in the third level of the GO hierarchy relative to Biological Processes (BP), Molecular Functions (MF) and Cellular Components (CC).
Figure 3 Bar plots representing the best 5 retrieved GO terms for each category in the third level of the GO hierarchy relative to Biological Processes (BP), Molecular Functions (MF) and Cellular Components (CC).CC BY · PubMed Central
Figure 4 Flow Chart of the overall workflow of VirtuousPocketome.
Figure 4 Flow Chart of the overall workflow of VirtuousPocketome.CC BY · PubMed Central

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Research works printed but not linked: 121

  1. 10.1038/40310 · DOI
  2. 2018, A benchmark driven guide to binding site comparison: An exhaustive evaluation using tailor-made data sets (ProSPECCTs), PLoS Comput Biol, 14, e1006483, 10.1371/journal.pcbi.1006483 · DOI
  3. 2016, In silico methods for drug repurposing and pharmacology, WIREs Mechanisms of Disease, 8, 186, 10.1002/wsbm.1337 · DOI
  4. Independent Interdisciplinary Consultant, Oxford, United Kingdom & Flower, D, 2020, Drug Discovery: Today and Tomorrow, Bioinformation, 16, 1, 10.6026/97320630016001 · DOI
  5. 10.1016/j.phrs.2021.106055 · DOI
  6. 2020, An up-to-date overview of computational polypharmacology in modern drug discovery, Expert Opinion on Drug Discovery, 15, 1025, 10.1080/17460441.2020.1767063 · DOI
  7. 10.1371/journal.pone.0270955 · DOI
  8. 2022, Estimating the Similarity between Protein Pockets, IJMS, 23, 12462, 10.3390/ijms232012462 · DOI
  9. 2018, A benchmark driven guide to binding site comparison: An exhaustive evaluation using tailor-made data sets (ProSPECCTs), PLoS Comput Biol, 14, e1006483, 10.1371/journal.pcbi.1006483 · DOI
  10. 10.1002/prot.10339 · DOI
  11. 10.1016/S0022-2836(03)00045-7 · DOI
  12. 2004, Identification of Protein Biochemical Function by Searching the Similar Shape and Electrostatic Potential on the Molecular Surface of Proteins, Seibutsu Butsuri, 44, 150, 10.2142/biophys.44.150 · DOI
  13. 10.1093/nar/gki524 · DOI · Paper page
  14. 10.1093/nar/gki482 · DOI
  15. 10.1093/nar/gkl788 · DOI
  16. 10.1109/ParCompTech.2013.6621397 · DOI
  17. 10.1186/1471-2105-9-543 · DOI
  18. 10.1002/prot.21858 · DOI
  19. 10.1093/nar/gkn185 · DOI
  20. 10.1016/j.jmb.2008.12.072 · DOI
  21. 10.1093/nar/gkp900 · DOI
  22. 10.1093/bioinformatics/btp220 · DOI
  23. 10.1093/bioinformatics/btq288 · DOI
  24. 10.1093/bioinformatics/btq116 · DOI
  25. 10.1186/1471-2105-11-555 · DOI
  26. 10.1021/ci900349y · DOI
  27. 10.1021/ci300184x · DOI
  28. 10.1021/ci3000776 · DOI
  29. 10.1093/nar/gks435 · DOI
  30. 10.1093/nar/gkr1130 · DOI
  31. 10.1093/nar/gks372 · DOI
  32. 10.1093/nar/gks401 · DOI
  33. 10.1093/bioinformatics/bts095 · DOI
  34. 2013, IsoCleft Finder – a web-based tool for the detection and analysis of protein binding-site geometric and chemical similarities, F1000Res, 2, 117, 10.12688/f1000research.2-117.v1 · DOI
  35. 10.1093/nar/gkt403 · DOI
  36. 10.1093/nar/gkt431 · DOI
  37. 10.1093/bioinformatics/btt024 · DOI
  38. 10.1021/ci300566n · DOI
  39. 10.1093/bioinformatics/btt664 · DOI
  40. 2014, SiteHopper - a unique tool for binding site comparison, J Cheminform, 6, P57, 10.1186/1758-2946-6-S1-P57 · DOI
  41. 10.1093/bioinformatics/btv616 · DOI
  42. 10.1021/acs.jcim.5b00333 · DOI
  43. 2015, Large-Scale Mining for Similar Protein Binding Pockets: With RAPMAD Retrieval on the Fly Becomes Real, J. Chem. Inf. Model., 55, 165, 10.1021/ci5005898 · DOI
  44. 10.1002/pro.2890 · DOI
  45. 2016, Geomfinder: a multi-feature identifier of similar three-dimensional protein patterns: a ligand-independent approach, Cheminform, 8, 19, 10.1186/s13321-016-0131-9 · DOI
  46. 2019, PatchSearch: a web server for off-target protein identification, Nucleic Acids Research, 47, W365, 10.1093/nar/gkz478 · DOI
  47. 2019, Drug ReposER: a web server for predicting similar amino acid arrangements to known drug binding interfaces for potential drug repositioning, Nucleic Acids Research, 47, W350, 10.1093/nar/gkz391 · DOI
  48. 10.1021/acs.jcim.9b00554 · DOI
  49. 10.1016/j.cub.2013.04.010 · DOI
  50. 10.1111/exd.14250 · DOI
  51. 10.1371/journal.pone.0205322 · DOI
  52. 10.1016/j.bbrc.2011.02.016 · DOI
  53. Kyriazis, G. A. , Soundarapandian, M. M. & Tyrberg, B. Sweet taste receptor signaling in beta cells mediates fructose-induced potentiation of glucose-stimulated insulin secretion. Proc. Natl. Acad. Sci. U.S.A. 109, (2012). · DOI
  54. 10.1210/en.2013-2015 · DOI
  55. 10.1371/journal.pone.0064579 · DOI
  56. 10.1096/fj.14-256305 · DOI
  57. 10.1073/pnas.1402436111 · DOI
  58. 10.1038/nm.2237 · DOI
  59. 2015, Targeting extra-oral bitter taste receptors modulates gastrointestinal motility with effects on satiation, Scientific Reports, 5, 1
  60. 10.1254/jphs.11R08CP · DOI
  61. 10.1152/ajpendo.00036.2019 · DOI
  62. 2019, Taste Receptors in Upper Airway Innate Immunity, Nutrients, 11, 2017, 10.3390/nu11092017 · DOI
  63. 2022, Extra-Oral Taste Receptors—Function, Disease, and Perspectives, Front. Nutr., 9, 881177, 10.3389/fnut.2022.881177 · DOI
  64. 10.18632/oncotarget.8549 · DOI
  65. 10.1016/S0092-8674(00)80706-0 · DOI
  66. 10.1074/jbc.REV120.009348 · DOI
  67. 2016, Comparing Class A GPCRs to bitter taste receptors, Biophysical Methods in Cell Biology, 132, 401, 10.1016/bs.mcb.2015.10.005 · DOI
  68. 10.1073/pnas.0913862107 · DOI
  69. 10.1016/j.cub.2010.04.043 · DOI
  70. 10.1093/chemse/bjp092 · DOI
  71. 10.25080/Majora-629e541a-00e · DOI
  72. 10.1126/science.abo1633 · DOI
  73. 10.1523/JNEUROSCI.3248-12.2013 · DOI
  74. 10.1016/j.ejphar.2006.04.010 · DOI
  75. DiPizio, A. , et al. Rational design of agonists for bitter taste receptor TAS2R14: from modeling to bench and back. Cellular and Molecular Life Sciences 77, (2020). · DOI
  76. 2019, Dual binding mode of “bitter sugars” to their human bitter taste receptor target, Scientific Reports, 9, 1
  77. 10.1021/acs.jcim.1c01056 · DOI
  78. Topin, J. , et al. Functional molecular switches of mammalian G protein-coupled bitter-taste receptors. Cellular and Molecular Life Sciences 6, 2020.10.23.348706 (2021). · DOI
  79. 10.1124/mol.51.5.889 · DOI
  80. 10.1038/360161a0 · DOI
  81. 1991, A point mutation in the seventh hydrophobic domain of the alpha 2 adrenergic receptor increases its affinity for a family of beta receptor antagonists, J Biol Chem, 266, 15488, 10.1016/S0021-9258(18)98642-4 · DOI
  82. 1993, Amino acid substitutions at position 312 in the seventh hydrophobic segment of the beta 2-adrenergic receptor modify ligand-binding specificity, Mol Pharmacol, 44, 111
  83. 10.1074/jbc.270.9.4185 · DOI
  84. 10.1124/mol.52.3.499 · DOI
  85. 10.1074/jbc.270.10.5019 · DOI
  86. 10.1093/chemse/bjh064 · DOI
  87. 1994, Inhibitory effect of strychnine on acetylcholine receptor activation in bovine adrenal medullary chromaffin cells, British J Pharmacology, 113, 471, 10.1111/j.1476-5381.1994.tb17013.x · DOI
  88. 10.1073/pnas.96.7.4113 · DOI
  89. 10.1039/D1NP00079A · DOI
  90. 10.1016/j.softx.2015.06.001 · DOI · Paper page
  91. 2018, pdb-tools: a swiss army knife for molecular structures, F1000Res, 7, 1961, 10.12688/f1000research.17456.1 · DOI
  92. 10.1093/NAR/GKAB294 · DOI
  93. 10.1021/ci0255984 · DOI
  94. 1973, Algorithm 457: finding all cliques of an undirected graph, Commun. ACM, 16, 575, 10.1145/362342.362367 · DOI
  95. 2006, PLANTS: Application of Ant Colony Optimization to Structure-Based Drug Design, Ant Colony Optimization and Swarm Intelligence, 4150, 247
  96. 10.1007/s11721-007-0006-9 · DOI
  97. 10.1021/acs.jcim.9b00346 · DOI
  98. 10.1023/A:1007996124545 · DOI
  99. 10.1021/ci3004094 · DOI
  100. 10.1021/acs.jcim.5b00066 · DOI
  101. 2022, Sodium or Not Sodium: Should Its Presence Affect the Accuracy of Pose Prediction in Docking GPCR Antagonists?, Pharmaceuticals, 15, 346, 10.3390/ph15030346 · DOI
  102. 10.1038/srep28288 · DOI
  103. 10.1021/jm2011589 · DOI
  104. 10.1038/nprot.2008.211 · DOI · Paper page
  105. 10.1093/NAR/GKAC194 · DOI
  106. 10.1093/nar/gkm883 · DOI
  107. 10.1093/nar/gkab1028 · DOI
  108. 10.1093/nar/28.1.27 · DOI · Paper page
  109. 2023, KEGG for taxonomy-based analysis of pathways and genomes, Nucleic Acids Research, 51, D587, 10.1093/nar/gkac963 · DOI · Paper page
  110. 2006, On the Benjamini–Hochberg method, The Annals of Statistics, 34, 1827
  111. 10.1093/nar/gkx1095 · DOI
  112. 10.1093/nar/28.1.235 · DOI · Paper page
  113. 10.1002/jcc.20945 · DOI · Paper page
  114. 10.1021/acs.jctc.1c01217 · DOI
  115. 2020, ff19SB: Amino-Acid-Specific Protein Backbone Parameters Trained against Quantum Mechanics Energy Surfaces in Solution, J. Chem. Theory Comput., 16, 528, 10.1021/acs.jctc.9b00591 · DOI
  116. 10.1002/jcc.20035 · DOI
  117. 10.1063/1.448118 · DOI
  118. 10.1063/1.447334 · DOI
  119. 10.1063/1.328693 · DOI
  120. 10.1002/(SICI)1096-987X(199709)18:12<1463::AID-JCC4>3.3.CO;2-L · DOI
  121. 10.1016/0263-7855(96)00018-5 · DOI · Paper page

Metadata

Authors
Pallante, L., Cannariato, M., Androutsos, L., Zizzi, E. A., Bompotas, A., Hada, X., Grasso, G., Kalogeras, A., Mavroudi, S., di Benedetto, G., Theofilatos, K., & Deriu, M. A.
Affiliations
  • Politecnico di Torino (IT)
  • Industrial Systems Institute (GR)
  • University of Applied Sciences and Arts of Southern Switzerland (CH)
  • University of Patras (GR)
Corresponding author
Deriu, Marco A.
Venue
Scientific Reports
Publisher
openRxiv
Published
2023-12-13
Volume
14
Issue
1
Pages
6296-6296
ISSN
2045-2322 (ISSN-L)
OpenAlex
W4392847330
Cited by
12
References
1
Citations by year
2026: 1 · 2025: 6 · 2024: 5
FWCI
2.54
Type
journal article
Language
en
Open access
Licence
cc-by
Locations
  • Scientific Reports (publishedVersion, cc-by)
  • bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • PORTO@Iris (submittedVersion, cc-by)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
Funders
  • European Commission (872181, HORIZON2020)
  • H2020 Marie Skłodowska-Curie Actions (872181)
Topics
  • Life Sciences › Pharmacology, Toxicology and Pharmaceutics › Drug Discovery › Computational Drug Discovery Methods
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Structural Biology › Enzyme Structure and Function
Keywords
protein pockets, protein-ligand complex, binding site similarity, protein structure comparison, strychnine, off-target effects
MeSH terms
Binding Sites, Humans, Ligands, Protein Binding, Proteins, Taste, Taste Buds

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