CoChat
Sign Up
Cited by 12Computational Drug Discovery MethodsProtein Structure and DynamicsEnzyme Structure and Function

The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.

VirtuousPocketome: a computational tool for screening protein–ligand complexes to identify similar binding sites

Open access
Pallante, Lorenzo, Cannariato, Marco, Androutsos, Lampros, Zizzi, Eric A., Bompotas, Agorakis, Hada, Xhesika, Grasso, Gianvito, Kalogeras, Athanasios, Mavroudi, Seferina, Di Benedetto, Giacomo, Theofilatos, Konstantinos, Deriu, Marco A.·2024·Scientific Reports
DOI 10.1038/s41598-024-56893-7PMC PMC10943019

Published version of a preprint

Open this paper in CoChat — free accountOpen PDF

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.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1038/s41598-024-56893-7

Cites

2 of 2 references verified0 citation errors · 0 not checkable

Statistics

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

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Show all 2 references

Research works printed but not linked: 119

  1. Artymiuk, P. J., Poirrette, A. R., Rice, D. W. & Willett, P. A polymerase I palm in adenylyl cyclase?. Nature 388, 33–34 (1997). · DOI
  2. Ehrt, C., Brinkjost, T. & Koch, O. A benchmark driven guide to binding site comparison: An exhaustive evaluation using tailor-made data sets (ProSPECCTs). PLoS Comput. Biol. 14, e1006483 (2018). · DOI
  3. Hodos, R. A., Kidd, B. A., Shameer, K., Readhead, B. P. & Dudley, J. T. In silico methods for drug repurposing and pharmacology. WIREs Mech. Dis. 8, 186–210 (2016). · DOI
  4. Independent Interdisciplinary Consultant & Flower, D. Drug discovery: Today and tomorrow. Bioinformation 16, 1–3 (2020). · DOI
  5. Kabir, A. & Muth, A. Polypharmacology: The science of multi-targeting molecules. Pharmacol. Res. 176, 106055 (2022). · DOI
  6. Chaudhari, R., Fong, L. W., Tan, Z., Huang, B. & Zhang, S. An up-to-date overview of computational polypharmacology in modern drug discovery. Expert Opin. Drug Discov. 15, 1025–1044 (2020). · DOI
  7. Cannariato, M., Miceli, M. & Deriu, M. A. In silico investigation of Alsin RLD conformational dynamics and phosphoinositides binding mechanism. PLoS ONE 17, e0270955 (2022). · DOI
  8. Eguida, M. & Rognan, D. Estimating the similarity between protein pockets. IJMS 23, 12462 (2022). · DOI
  9. Jambon, M., Imberty, A., Deléage, G. & Geourjon, C. A new bioinformatic approach to detect common 3D sites in protein structures: Detection of 3D sites in protein structures. Proteins 52, 137–145 (2003). · DOI
  10. Stark, A., Sunyaev, S. & Russell, R. B. A model for statistical significance of local similarities in structure. J. Mol. Biol. 326, 1307–1316 (2003). · DOI
  11. Kinoshita, K. Identification of protein biochemical function by searching the similar shape and electrostatic potential on the molecular surface of proteins. Seibutsu Butsuri 44, 150–154 (2004). · DOI
  12. Zhang, Y. TM-align: A protein structure alignment algorithm based on the TM-score. Nucleic Acids Res. 33, 2302–2309 (2005). · DOI · Paper page
  13. Shulman-Peleg, A., Nussinov, R. & Wolfson, H. J. SiteEngines: Recognition and comparison of binding sites and protein-protein interfaces. Nucleic Acids Res. 33, W337–W341 (2005). · DOI
  14. Lisewski, A. M. & Lichtarge, O. Rapid detection of similarity in protein structure and function through contact metric distances. Nucleic Acids Res. 34, e152–e152 (2006). · DOI
  15. Nagarajan, D. & Chandra, N. PocketMatch (version 2.0): A parallel algorithm for the detection of structural similarities between protein ligand binding-sites. in 2013 National Conference on Parallel Computing Technologies (PARCOMPTECH) 1–6 (IEEE, 2013). https://doi.org/10.1109/ParCompTech.2013.6621397. · DOI
  16. Yeturu, K. & Chandra, N. PocketMatch: A new algorithm to compare binding sites in protein structures. BMC Bioinform. 9, 543 (2008). · DOI
  17. Schalon, C., Surgand, J., Kellenberger, E. & Rognan, D. A simple and fuzzy method to align and compare druggable ligand-binding sites. Proteins 71, 1755–1778 (2008). · DOI
  18. Shulman-Peleg, A., Shatsky, M., Nussinov, R. & Wolfson, H. J. MultiBind and MAPPIS: Webservers for multiple alignment of protein 3D-binding sites and their interactions. Nucleic Acids Res. 36, W260–W264 (2008). · DOI
  19. Tseng, Y. Y., Dundas, J. & Liang, J. Predicting protein function and binding profile via matching of local evolutionary and geometric surface patterns. J. Mol. Biol. 387, 451–464 (2009). · DOI
  20. Tseng, Y. Y., Chen, Z. J. & Li, W.-H. f POP: Footprinting functional pockets of proteins by comparative spatial patterns. Nucleic Acids Res. 38, D288–D295 (2010). · DOI
  21. Xie, L., Xie, L. & Bourne, P. E. A unified statistical model to support local sequence order independent similarity searching for ligand-binding sites and its application to genome-based drug discovery. Bioinformatics 25, i305–i312 (2009). · DOI
  22. Das, S., Krein, M. P. & Breneman, C. M. PESDserv: A server for high-throughput comparison of protein binding site surfaces. Bioinformatics 26, 1913–1914 (2010). · DOI
  23. Standley, D. M., Yamashita, R., Kinjo, A. R., Toh, H. & Nakamura, H. SeSAW: Balancing sequence and structural information in protein functional mapping. Bioinformatics 26, 1258–1259 (2010). · DOI
  24. Moll, M., Bryant, D. H. & Kavraki, L. E. The LabelHash algorithm for substructure matching. BMC Bioinform. 11, 555 (2010). · DOI
  25. Weill, N. & Rognan, D. Alignment-free ultra-high-throughput comparison of druggable protein−ligand binding sites. J. Chem. Inf. Model. 50, 123–135 (2010). · DOI
  26. Desaphy, J., Azdimousa, K., Kellenberger, E. & Rognan, D. Comparison and druggability prediction of protein-ligand binding sites from pharmacophore-annotated cavity shapes. J. Chem. Inf. Model. 52, 2287–2299 (2012). · DOI
  27. Wood, D. J., Vlieg, J. D., Wagener, M. & Ritschel, T. Pharmacophore fingerprint-based approach to binding site subpocket similarity and its application to bioisostere replacement. J. Chem. Inf. Model. 52, 2031–2043 (2012). · DOI
  28. Konc, J. & Janezic, D. ProBiS-2012: Web server and web services for detection of structurally similar binding sites in proteins. Nucleic Acids Res. 40, W214–W221 (2012). · DOI
  29. Ito, J.-I., Tabei, Y., Shimizu, K., Tsuda, K. & Tomii, K. PoSSuM: A database of similar protein-ligand binding and putative pockets. Nucleic Acids Res. 40, D541–D548 (2012). · DOI
  30. Roy, A., Yang, J. & Zhang, Y. COFACTOR: An accurate comparative algorithm for structure-based protein function annotation. Nucleic Acids Res. 40, W471–W477 (2012). · DOI
  31. Nadzirin, N., Gardiner, E. J., Willett, P., Artymiuk, P. J. & Firdaus-Raih, M. SPRITE and ASSAM: Web servers for side chain 3D-motif searching in protein structures. Nucleic Acids Res. 40, W380–W386 (2012). · DOI
  32. Lin, Y., Yoo, S. & Sanchez, R. SiteComp: A server for ligand binding site analysis in protein structures. Bioinformatics 28, 1172–1173 (2012). · DOI
  33. Kurbatova, N., Chartier, M., Zylber, M. I. & Najmanovich, R. IsoCleft Finder: A web-based tool for the detection and analysis of protein binding-site geometric and chemical similarities. F1000Res 2, 117 (2013). · DOI
  34. Kirshner, D. A., Nilmeier, J. P. & Lightstone, F. C. Catalytic site identification: A web server to identify catalytic site structural matches throughout PDB. Nucleic Acids Res. 41, W256–W265 (2013). · DOI
  35. Nadzirin, N., Willett, P., Artymiuk, P. J. & Firdaus-Raih, M. IMAAAGINE: A webserver for searching hypothetical 3D amino acid side chain arrangements in the Protein Data Bank. Nucleic Acids Res. 41, W432–W440 (2013). · DOI
  36. Gao, M. & Skolnick, J. APoc: Large-scale identification of similar protein pockets. Bioinformatics 29, 597–604 (2013). · DOI
  37. Desaphy, J., Raimbaud, E., Ducrot, P. & Rognan, D. Encoding protein-ligand interaction patterns in fingerprints and graphs. J. Chem. Inf. Model. 53, 623–637 (2013). · DOI
  38. Caprari, S., Toti, D., Viet Hung, L., Di Stefano, M. & Polticelli, F. ASSIST: A fast versatile local structural comparison tool. Bioinformatics 30, 1022–1024 (2014). · DOI
  39. Batista, J., Hawkins, P. C., Tolbert, R. & Geballe, M. T. SiteHopper: A unique tool for binding site comparison. J. Cheminform. 6, 57 (2014). · DOI
  40. Chartier, M., Adriansen, E. & Najmanovich, R. IsoMIF Finder: Online detection of binding site molecular interaction field similarities. Bioinformatics 32, 621–623 (2016). · DOI
  41. Chartier, M. & Najmanovich, R. Detection of binding site molecular interaction field similarities. J. Chem. Inf. Model. 55, 1600–1615 (2015). · DOI
  42. Krotzky, T., Grunwald, C., Egerland, U. & Klebe, G. Large-scale mining for similar protein binding pockets: With RAPMAD retrieval on the fly becomes real. J. Chem. Inf. Model. 55, 165–179 (2015). · DOI
  43. Lee, H. S. & Im, W. G-LoSA: An efficient computational tool for local structure-centric biological studies and drug design: G-LoSA. Protein Sci. 25, 865–876 (2016). · DOI
  44. Núñez-Vivanco, G., Valdés-Jiménez, A., Besoaín, F. & Reyes-Parada, M. Geomfinder: a multi-feature identifier of similar three-dimensional protein patterns: A ligand-independent approach. J. Cheminform. 8, 19 (2016). · DOI
  45. Rey, J., Rasolohery, I., Tufféry, P., Guyon, F. & Moroy, G. PatchSearch: A web server for off-target protein identification. Nucleic Acids Res. 47, W365–W372 (2019). · DOI
  46. Ab Ghani, N. S., Ramlan, E. I. & Firdaus-Raih, M. Drug ReposER: A web server for predicting similar amino acid arrangements to known drug binding interfaces for potential drug repositioning. Nucleic Acids Res. 47, W350–W356 (2019). · DOI
  47. Simonovsky, M. & Meyers, J. DeeplyTough: Learning structural comparison of protein binding sites. J. Chem. Inf. Model. 60, 2356–2366 (2020). · DOI
  48. Breslin, P. A. S. An evolutionary perspective on food and human taste. Curr. Biol. 23, R409–R418 (2013). · DOI
  49. Ho, H. K. et al. Functionally expressed bitter taste receptor TAS2R14 in human epidermal keratinocytes serves as a chemosensory receptor. Exp. Dermatol. 30, 216–225 (2021). · DOI
  50. Shaw, L. et al. Personalized expression of bitter ‘taste’ receptors in human skin. PLoS ONE 13, e0205322 (2018). · DOI
  51. Singh, N., Vrontakis, M., Parkinson, F. & Chelikani, P. Functional bitter taste receptors are expressed in brain cells. Biochem. Biophys. Res. Commun. 406, 146–151 (2011). · DOI
  52. 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. USA. 109, 1115 (2012). · DOI
  53. Kyriazis, G. A., Smith, K. R., Tyrberg, B., Hussain, T. & Pratley, R. E. Sweet taste receptors regulate basal insulin secretion and contribute to compensatory insulin hypersecretion during the development of diabetes in male mice. Endocrinology 155, 2112–2121 (2014). · DOI
  54. Foster, S. R. et al. Expression, regulation and putative nutrient-sensing function of taste GPCRs in the heart. PLoS ONE 8, e64579 (2013). · DOI
  55. Foster, S. R. et al. Bitter taste receptor agonists elicit G-protein-dependent negative inotropy in the murine heart. FASEB J. 28, 4497–4508 (2014). · DOI
  56. Deckmann, K. et al. Bitter triggers acetylcholine release from polymodal urethral chemosensory cells and bladder reflexes. Proc. Natl. Acad. Sci. USA. 111, 8287–8292 (2014). · DOI
  57. Deshpande, D. A. et al. Bitter taste receptors on airway smooth muscle bronchodilate by localized calcium signaling and reverse obstruction. Nat. Med. 16, 1299–1304 (2010). · DOI
  58. Avau, B. et al. Targeting extra-oral bitter taste receptors modulates gastrointestinal motility with effects on satiation. Sci. Rep. 5, 1–12 (2015). · DOI
  59. Iwatsuki, K. & Uneyama, H. Sense of taste in the gastrointestinal tract. J. Pharmacol. Sci. 118, 123–128 (2012). · DOI
  60. Raka, F., Farr, S., Kelly, J., Stoianov, A. & Adeli, K. Metabolic control via nutrient-sensing mechanisms: Role of taste receptors and the gut-brain neuroendocrine axis. Am. J. Physiol. Endocrinol. Metab. 317, E559–E572 (2019). · DOI
  61. Carey, R. M. & Lee, R. J. Taste receptors in upper airway innate immunity. Nutrients 11, 2017 (2019). · DOI
  62. Behrens, M. & Lang, T. Extra-oral taste receptors: Function, disease, and perspectives. Front. Nutr. 9, 881177 (2022). · DOI
  63. Zhai, K. et al. Activation of bitter taste receptors (tas2rs) relaxes detrusor smooth muscle and suppresses overactive bladder symptoms. Oncotarget 7, 21156–21167 (2016). · DOI
  64. Chandrashekar, J. et al. T2Rs function as bitter taste receptors. Cell 100, 703–711 (2000). · DOI
  65. Sutkeviciute, I. & Vilardaga, J.-P. Structural insights into emergent signaling modes of G protein–coupled receptors. J. Biol. Chem. 295, 11626–11642 (2020). · DOI
  66. Di Pizio, A. et al. Comparing class A GPCRs to bitter taste receptors. Biophys. Methods Cell Biol. 132, 401–427 (2016). · DOI
  67. Brockhoff, A., Behrens, M., Niv, M. Y. & Meyerhof, W. Structural requirements of bitter taste receptor activation. Proc. Natl. Acad. Sci. USA 107, 11110–11115 (2010). · DOI
  68. Slack, J. P. et al. Modulation of bitter taste perception by a small molecule hTAS2R antagonist. Curr. Biol. 20, 1104–1109 (2010). · DOI
  69. Meyerhof, W. et al. The molecular receptive ranges of human TAS2R bitter taste receptors. Chem. Sens. 35, 157–170 (2009). · DOI
  70. Gowers, R. et al. MDAnalysis: A Python Package for the Rapid Analysis of Molecular Dynamics Simulations, 98–105 (2016). https://doi.org/10.25080/Majora-629e541a-00e. · DOI
  71. Xu, W. et al. Structural basis for strychnine activation of human bitter taste receptor TAS2R46. Science 377, 1298–1304 (2022). · DOI
  72. Born, S., Levit, A., Niv, M. Y., Meyerhof, W. & Behrens, M. The human bitter taste receptor TAS2R10 is tailored to accommodate numerous diverse ligands. J. Neurosci. 33, 201–213 (2013). · DOI
  73. Jensen, A. A., Gharagozloo, P., Birdsall, N. J. M. & Zlotos, D. P. Pharmacological characterisation of strychnine and brucine analogues at glycine and α7 nicotinic acetylcholine receptors. Eur. J. Pharmacol. 539, 27–33 (2006). · DOI
  74. DiPizio, A. et al. Rational design of agonists for bitter taste receptor TAS2R14: From modeling to bench and back. Cell. Mol. Life Sci. 77, 531 (2020). · DOI
  75. Fierro, F., Giorgetti, A., Carloni, P., Meyerhof, W. & Alfonso-Prieto, M. Dual binding mode of “bitter sugars” to their human bitter taste receptor target. Sci. Rep. 9, 1–16 (2019). · DOI
  76. Nicoli, A., Dunkel, A., Giorgino, T., de Graaf, C. & Di Pizio, A. Classification model for the second extracellular loop of class A GPCRs. J. Chem. Inf. Model. 62, 511–522 (2022). · DOI
  77. Topin, J. et al. Functional molecular switches of mammalian G protein-coupled bitter-taste receptors. Cell. Mol. Life Sci. 6, 348706 (2021).
  78. Kuipers, W. et al. Study of the interaction between aryloxypropanolamines and Asn386 in helix VII of the human 5-hydroxytryptamine1A receptor. Mol. Pharmacol. 51, 889–896 (1997). · DOI
  79. Oksenberg, D. et al. A single amino-acid difference confers major pharmacological variation between human and rodent 5-HT1B receptors. Nature 360, 161–163 (1992). · DOI
  80. Suryanarayana, S., Daunt, D. A., Von Zastrow, M. & Kobilka, B. K. 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, 15488–15492 (1991). · DOI
  81. Suryanarayana, S. & Kobilka, B. K. Amino acid substitutions at position 312 in the seventh hydrophobic segment of the beta 2-adrenergic receptor modify ligand-binding specificity. Mol. Pharmacol. 44, 111–114 (1993).
  82. Erb, L. et al. Site-directed mutagenesis of P2U purinoceptors: Positively charged amino acids in transmembrane helices 6 and 7 affect agonist potency and specificity. J. Biol. Chem. 270, 4185–4188 (1995). · DOI
  83. Jiang, Q. et al. A mutational analysis of residues essential for ligand recognition at the human P2Y1 receptor. Mol. Pharmacol. 52, 499–507 (1997). · DOI
  84. Kopin, A. S., McBride, E. W., Quinn, S. M., Kolakowski, L. F. & Beinborn, M. The role of the cholecystokinin-B/gastrin receptor transmembrane domains in determining affinity for subtype-selective ligands. J. Biol. Chem. 270, 5019–5023 (1995). · DOI
  85. Pronin, A. N. Identification of ligands for two human bitter T2R receptors. Chem. Sens. 29, 583–593 (2004). · DOI
  86. Kuijpers, G. A. J., Vergara, L. A., Calvo, S. & Yadid, G. Inhibitory effect of strychnine on acetylcholine receptor activation in bovine adrenal medullary chromaffin cells. Br. J. Pharmacol. 113, 471–478 (1994). · DOI
  87. García-Colunga, J. & Miledi, R. Modulation of nicotinic acetylcholine receptors by strychnine. Proc. Natl. Acad. Sci. USA 96, 4113–4118 (1999). · DOI
  88. Zlotos, D. P., Mandour, Y. M. & Jensen, A. A. Strychnine and its mono- and dimeric analogues: A pharmaco-chemical perspective. Nat. Prod. Rep. 39, 1910–1937 (2022). · DOI
  89. Abraham, M. J. et al. GROMACS: High performance molecular simulations through multi-level parallelism from laptops to supercomputers. SoftwareX 1–2, 19–25 (2015). · DOI · Paper page
  90. Rodrigues, J. P. G. L. M., Teixeira, J. M. C., Trellet, M. & Bonvin, A. M. J. J. pdb-tools: A swiss army knife for molecular structures. F1000 Res. 7, 1961 (2018). · DOI
  91. Adasme, M. F. et al. PLIP 2021: Expanding the scope of the protein–ligand interaction profiler to DNA and RNA. Nucleic Acids Res. 49, W530–W534 (2021). · DOI
  92. Spriggs, R. V., Artymiuk, P. J. & Willett, P. Searching for patterns of amino acids in 3D protein structures. J. Chem. Inf. Comput. Sci. 43, 412–421 (2003). · DOI
  93. Bron, C. & Kerbosch, J. Algorithm 457: Finding all cliques of an undirected graph. Commun. ACM 16, 575–577 (1973). · DOI
  94. Korb, O., Stützle, T. & Exner, T. E. PLANTS: Application of ant colony optimization to structure-based drug design. In Ant Colony Optimization and Swarm Intelligence Vol. 4150 (eds Dorigo, M. et al.) 247–258 (Springer, 2006). · DOI
  95. Korb, O., Stützle, T. & Exner, T. E. An ant colony optimization approach to flexible protein–ligand docking. Swarm. Intell. 1, 115–134 (2007). · DOI
  96. Çınaroğlu, S. S. & Timuçin, E. Comparative assessment of seven docking programs on a nonredundant metalloprotein subset of the PDBbind refined. J. Chem. Inf. Model. 59, 3846–3859 (2019). · DOI
  97. Eldridge, M. D., Murray, C. W., Auton, T. R., Paolini, G. V. & Mee, R. P. Empirical scoring functions: I. The development of a fast empirical scoring function to estimate the binding affinity of ligands in receptor complexes. J. Comput. Aid. Mol. Des. 11, 425–445 (1997). · DOI
  98. Sirci, F. et al. Virtual fragment screening: Discovery of histamine H3 receptor ligands using ligand-based and protein-based molecular fingerprints. J. Chem. Inf. Model. 52, 3308–3324 (2012). · DOI
  99. Kooistra, A. J., Leurs, R., De Esch, I. J. P. & De Graaf, C. Structure-based prediction of G-protein-coupled receptor ligand function: A β-adrenoceptor case study. J. Chem. Inf. Model. 55, 1045–1061 (2015). · DOI
  100. Bassani, D., Pavan, M., Sturlese, M. & Moro, S. Sodium or not sodium: Should its presence affect the accuracy of pose prediction in docking GPCR antagonists?. Pharmaceuticals 15, 346 (2022). · DOI
  101. Kooistra, A. J. et al. Function-specific virtual screening for GPCR ligands using a combined scoring method. Sci. Rep. 6, 28288 (2016). · DOI
  102. De Graaf, C. et al. Crystal structure-based virtual screening for fragment-like ligands of the human histamine H1 receptor. J. Med. Chem. 54, 8195–8206 (2011). · DOI
  103. Huang, D. W., Sherman, B. T. & Lempicki, R. A. Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat. Protoc. 4, 44–57 (2009). · DOI · Paper page
  104. Sherman, B. T. et al. DAVID: A web server for functional enrichment analysis and functional annotation of gene lists (2021 update). Nucleic Acids Res. 50, W216–W221 (2022). · DOI
  105. The Gene Ontology Consortium. The gene ontology project in 2008. Nucleic Acids Res. 36, D440–D444 (2008). · DOI
  106. Gillespie, M. et al. The reactome pathway knowledgebase 2022. Nucleic Acids Res. 50, D687–D692 (2022). · DOI
  107. Kanehisa, M. KEGG: Kyoto encyclopedia of genes and genomes. Nucleic Acids Res. 28, 27–30 (2000). · DOI · Paper page
  108. Ferreira, J. A. & Zwinderman, A. H. On the Benjamini–Hochberg method. Ann. Stat. 34, 1827–1849 (2006). · DOI
  109. NCBI Resource Coordinators et al. Database resources of the National center for biotechnology information. Nucleic Acids Res. 46, D8–D13 (2018). · DOI
  110. Berman, H. M. The protein data bank. Nucleic Acids Res. 28, 235–242 (2000). · DOI · Paper page
  111. Jo, S., Kim, T., Iyer, V. G. & Im, W. CHARMM-GUI: A web-based graphical user interface for CHARMM. J. Comput. Chem. 29, 1859–1865 (2008). · DOI · Paper page
  112. Dickson, C. J., Walker, R. C. & Gould, I. R. Lipid21: Complex lipid membrane simulations with AMBER. J. Chem. Theory Comput. 18, 1726–1736 (2022). · DOI
  113. Tian, C. et al. ff19SB: Amino-acid-specific protein backbone parameters trained against quantum mechanics energy surfaces in solution. J. Chem. Theory Comput. 16, 528–552 (2020). · DOI
  114. Wang, J., Wolf, R. M., Caldwell, J. W., Kollman, P. A. & Case, D. A. Development and testing of a general amber force field. J. Comput. Chem. 25, 1157–1174 (2004). · DOI
  115. Berendsen, H. J. C., Postma, J. P. M., van Gunsteren, W. F., DiNola, A. & Haak, J. R. Molecular dynamics with coupling to an external bath. J. Chem. Phys. 81, 3684–3690 (1984). · DOI
  116. Nosé, S. A unified formulation of the constant temperature molecular dynamics methods. J. Chem. Phys. 81, 511–519 (1984). · DOI
  117. Parrinello, M. & Rahman, A. Polymorphic transitions in single crystals: A new molecular dynamics method. J. Appl. Phys. 52, 7182–7190 (1981). · DOI
  118. Hess, B., Bekker, H., Berendsen, H. J. C. & Fraaije, J. G. E. M. LINCS: A linear constraint solver for molecular simulations. J. Comput. Chem. 18, 1463–1472 (1997). · DOI
  119. Humphrey, W., Dalke, A. & Schulten, K. VMD: Visual molecular dynamics. J. Mol. Graph. 14, 33–38 (1996). · 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.
ORCID
Venue
Scientific Reports
Publisher
Springer Science and Business Media LLC
Published
2024-03-15
Volume
14
Issue
1
Pages
6296-6296
ISSN
2045-2322 (ISSN-L)
DOI
10.1038/s41598-024-56893-7
PMC
PMC10943019
OpenAlex
W4392847330
Cited by
12
References
2
Citations by year
2026: 1 · 2025: 6 · 2024: 5
FWCI
2.54
Type
journal article
Language
en
Open access
Licence
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
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

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

How to cite

Cite CoChat for the checks on this page, and the paper for its own findings.

This page
CoChat. (2026). Citation check: VirtuousPocketome: a computational tool for screening protein–ligand complexes to identify similar binding sites. https://app.cochat.ai/papers/10.1038/s41598-024-56893-7
The paper
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. (2024). VirtuousPocketome: a computational tool for screening protein–ligand complexes to identify similar binding sites. Scientific Reports. https://doi.org/10.1038/s41598-024-56893-7