Advertisement

Journal of Computer-Aided Molecular Design

, Volume 29, Issue 10, pp 989–1006 | Cite as

Ultrafast protein structure-based virtual screening with Panther

  • Sanna P. Niinivehmas
  • Kari Salokas
  • Sakari Lätti
  • Hannu Raunio
  • Olli T. PentikäinenEmail author
Article

Abstract

Molecular docking is by far the most common method used in protein structure-based virtual screening. This paper presents Panther, a novel ultrafast multipurpose docking tool. In Panther, a simple shape-electrostatic model of the ligand-binding area of the protein is created by utilizing the protein crystal structure. The features of the possible ligands are then compared to the model by using a similarity search algorithm. On average, one ligand can be processed in a few minutes by using classical docking methods, whereas using Panther processing takes <1 s. The presented Panther protocol can be used in several applications, such as speeding up the early phases of drug discovery projects, reducing the number of failures in the clinical phase of the drug development process, and estimating the environmental toxicity of chemicals. Panther-code is available in our web pages (http://www.jyu.fi/panther) free of charge after registration.

Keywords

Molecular docking Panther Similarity search Virtual screening 

Abbreviations

AR

Androgen receptor

AUC

Area under curve

DUD

Directory of Useful Decoys

DUD-E

Directory of Useful Decoys: Enhanced

EF

Enrichment factor

ERα

Estrogen receptor alpha

GR

Glucocorticoid receptor

MMFF

Merck molecular force field

MMGBSA

Molecular Mechanics Generalized Born Area

MR

Mineralocorticoid receptor

NIB

Negative image-based

PDB

Protein Data Bank

PPARγ

Peroxisome proliferator activated receptor gamma

PR

Progesterone receptor

RMSD

Root mean square deviation

ROC

Receiver operating characteristics

RXRα

Retinoid X receptor alpha

COX2

Cyclo-oxygenase 2

PDE5

Phosphodiesterase type 5

Notes

Acknowledgments

This work was financially supported by National Doctoral Programme in Nanoscience (SPN) and Academy of Finland (HR). CSC, The Finnish IT Center for Science is acknowledged for computational resources (OTP; Project Nos. jyy2516 and jyy2585).

Supplementary material

10822_2015_9870_MOESM1_ESM.pdf (639 kb)
Supplementary material 1 (PDF 638 kb)

References

  1. 1.
    Ferrara P, Gohlke H, Price DJ, Klebe G, Brooks CL 3rd (2004) Assessing scoring functions for protein–ligand interactions. J Med Chem 47(12):3032–3047. doi: 10.1021/jm030489h CrossRefGoogle Scholar
  2. 2.
    Scior T, Bender A, Tresadern G, Medina-Franco JL, Martinez-Mayorga K, Langer T, Cuanalo-Contreras K, Agrafiotis DK (2012) Recognizing pitfalls in virtual screening: a critical review. J Chem Inf Model 52(4):867–881. doi: 10.1021/ci200528d CrossRefGoogle Scholar
  3. 3.
    Cross JB, Thompson DC, Rai BK, Baber JC, Fan KY, Hu Y, Humblet C (2009) Comparison of several molecular docking programs: pose prediction and virtual screening accuracy. J Chem Inf Model 49(6):1455–1474. doi: 10.1021/ci900056c CrossRefGoogle Scholar
  4. 4.
    Warren GL, Andrews CW, Capelli AM, Clarke B, LaLonde J, Lambert MH, Lindvall M, Nevins N, Semus SF, Senger S, Tedesco G, Wall ID, Woolven JM, Peishoff CE, Head MS (2006) A critical assessment of docking programs and scoring functions. J Med Chem 49(20):5912–5931. doi: 10.1021/jm050362n CrossRefGoogle Scholar
  5. 5.
    Virtanen SI, Pentikainen OT (2010) Efficient virtual screening using multiple protein conformations described as negative images of the ligand-binding site. J Chem Inf Model 50(6):1005–1011. doi: 10.1021/ci100121c CrossRefGoogle Scholar
  6. 6.
    Jain AN (2009) Effects of protein conformation in docking: improved pose prediction through protein pocket adaptation. J Comput Aided Mol Des 23(6):355–374. doi: 10.1007/s10822-009-9266-3 CrossRefGoogle Scholar
  7. 7.
    Chen Z, Li HL, Zhang QJ, Bao XG, Yu KQ, Luo XM, Zhu WL, Jiang HL (2009) Pharmacophore-based virtual screening versus docking-based virtual screening: a benchmark comparison against eight targets. Acta Pharmacol Sin 30(12):1694–1708. doi: 10.1038/aps.2009.159 CrossRefGoogle Scholar
  8. 8.
    Kirchmair J, Distinto S, Markt P, Schuster D, Spitzer GM, Liedl KR, Wolber G (2009) How to optimize shape-based virtual screening: choosing the right query and including chemical information. J Chem Inf Model 49(3):678–692. doi: 10.1021/ci8004226 CrossRefGoogle Scholar
  9. 9.
    McGaughey GB, Sheridan RP, Bayly CI, Culberson JC, Kreatsoulas C, Lindsley S, Maiorov V, Truchon JF, Cornell WD (2007) Comparison of topological, shape, and docking methods in virtual screening. J Chem Inf Model 47(4):1504–1519. doi: 10.1021/ci700052x CrossRefGoogle Scholar
  10. 10.
    Venkatraman V, Perez-Nueno VI, Mavridis L, Ritchie DW (2010) Comprehensive comparison of ligand-based virtual screening tools against the DUD data set reveals limitations of current 3D methods. J Chem Inf Model 50(12):2079–2093. doi: 10.1021/ci100263p CrossRefGoogle Scholar
  11. 11.
    von Korff M, Freyss J, Sander T (2009) Comparison of ligand- and structure-based virtual screening on the DUD data set. J Chem Inf Model 49(2):209–231. doi: 10.1021/Ci800303k CrossRefGoogle Scholar
  12. 12.
    Vainio MJ, Puranen JS, Johnson MS (2009) ShaEP: molecular overlay based on shape and electrostatic potential. J Chem Inf Model 49(2):492–502. doi: 10.1021/ci800315d CrossRefGoogle Scholar
  13. 13.
    Kortagere S, Krasowski MD, Ekins S (2009) The importance of discerning shape in molecular pharmacology. Trends Pharmacol Sci 30(3):138–147. doi: 10.1016/j.tips.2008.12.001 CrossRefGoogle Scholar
  14. 14.
    Markt P, Petersen RK, Flindt EN, Kristiansen K, Kirchmair J, Spitzer G, Distinto S, Schuster D, Wolber G, Laggner C, Langer T (2008) Discovery of novel PPAR ligands by a virtual screening approach based on pharmacophore modeling, 3D shape, and electrostatic similarity screening. J Med Chem 51(20):6303–6317. doi: 10.1021/jm800128k CrossRefGoogle Scholar
  15. 15.
    Nicholls A, McGaughey GB, Sheridan RP, Good AC, Warren G, Mathieu M, Muchmore SW, Brown SP, Grant JA, Haigh JA, Nevins N, Jain AN, Kelley B (2010) Molecular shape and medicinal chemistry: a perspective. J Med Chem 53(10):3862–3886. doi: 10.1021/jm900818s CrossRefGoogle Scholar
  16. 16.
    Dong F, Olsen B, Baker NA (2008) Computational methods for biomolecular electrostatics. Methods Cell Biol 84:843–870. doi: 10.1016/S0091-679X(07)84026-X CrossRefGoogle Scholar
  17. 17.
    Sheinerman FB, Norel R, Honig B (2000) Electrostatic aspects of protein–protein interactions. Curr Opin Struct Biol 10(2):153–159CrossRefGoogle Scholar
  18. 18.
    Bruning JB, Parent AA, Gil G, Zhao M, Nowak J, Pace MC, Smith CL, Afonine PV, Adams PD, Katzenellenbogen JA, Nettles KW (2010) Coupling of receptor conformation and ligand orientation determine graded activity. Nat Chem Biol 6(11):837–843. doi: 10.1038/nchembio.451 CrossRefGoogle Scholar
  19. 19.
    Barril X, Morley SD (2005) Unveiling the full potential of flexible receptor docking using multiple crystallographic structures. J Med Chem 48(13):4432–4443. doi: 10.1021/jm048972v CrossRefGoogle Scholar
  20. 20.
    Huang SY, Zou X (2007) Ensemble docking of multiple protein structures: considering protein structural variations in molecular docking. Proteins 66(2):399–421. doi: 10.1002/prot.21214 CrossRefGoogle Scholar
  21. 21.
    Corbeil CR, Therrien E, Moitessier N (2009) Modeling reality for optimal docking of small molecules to biological targets. Curr Comput Aided Drug Des 5(4):241–263CrossRefGoogle Scholar
  22. 22.
    Niinivehmas SP, Virtanen SI, Lehtonen JV, Postila PA, Pentikainen OT (2011) Comparison of virtual high-throughput screening methods for the identification of phosphodiesterase-5 inhibitors. J Chem Inf Model 51(6):1353–1363. doi: 10.1021/ci1004527 CrossRefGoogle Scholar
  23. 23.
    Tsui V, Case DA (2000) Theory and applications of the generalized Born solvation model in macromolecular simulations. Biopolymers 56(4):275–291. doi: 10.1002/1097-0282(2000)56:4<275:AID-BIP10024>3.0.CO;2-E CrossRefGoogle Scholar
  24. 24.
    Kollman PA, Massova I, Reyes C, Kuhn B, Huo S, Chong L, Lee M, Lee T, Duan Y, Wang W, Donini O, Cieplak P, Srinivasan J, Case DA, Cheatham TE 3rd (2000) Calculating structures and free energies of complex molecules: combining molecular mechanics and continuum models. Acc Chem Res 33(12):889–897CrossRefGoogle Scholar
  25. 25.
    Kleywegt GJ, Jones TA (1994) Detection, delineation, measurement and display of cavities in macromolecular structures. Acta Crystallogr D Biol Crystallogr 50(Pt 2):178–185. doi: 10.1107/S0907444993011333 CrossRefGoogle Scholar
  26. 26.
    Huang N, Shoichet BK, Irwin JJ (2006) Benchmarking sets for molecular docking. J Med Chem 49(23):6789–6801. doi: 10.1021/jm0608356 CrossRefGoogle Scholar
  27. 27.
    Mysinger MM, Carchia M, Irwin JJ, Shoichet BK (2012) Directory of useful decoys, enhanced (DUD-E): better ligands and decoys for better benchmarking. J Med Chem 55(14):6582–6594. doi: 10.1021/jm300687e CrossRefGoogle Scholar
  28. 28.
    Berman HM, Westbrook J, Feng Z, Gilliland G, Bhat TN, Weissig H, Shindyalov IN, Bourne PE (2000) The Protein Data Bank. Nucleic Acids Res 28(1):235–242CrossRefGoogle Scholar
  29. 29.
    Word JM, Lovell SC, Richardson JS, Richardson DC (1999) Asparagine and glutamine: using hydrogen atom contacts in the choice of side-chain amide orientation. J Mol Biol 285(4):1735–1747. doi: 10.1006/jmbi.1998.2401 CrossRefGoogle Scholar
  30. 30.
    He B, Gampe RT Jr, Kole AJ, Hnat AT, Stanley TB, An G, Stewart EL, Kalman RI, Minges JT, Wilson EM (2004) Structural basis for androgen receptor interdomain and coactivator interactions suggests a transition in nuclear receptor activation function dominance. Mol Cell 16(3):425–438. doi: 10.1016/j.molcel.2004.09.036 CrossRefGoogle Scholar
  31. 31.
    Pereira de Jesus-Tran K, Cote PL, Cantin L, Blanchet J, Labrie F, Breton R (2006) Comparison of crystal structures of human androgen receptor ligand-binding domain complexed with various agonists reveals molecular determinants responsible for binding affinity. Protein Sci 15(5):987–999. doi: 10.1110/ps.051905906 CrossRefGoogle Scholar
  32. 32.
    Shiau AK, Barstad D, Radek JT, Meyers MJ, Nettles KW, Katzenellenbogen BS, Katzenellenbogen JA, Agard DA, Greene GL (2002) Structural characterization of a subtype-selective ligand reveals a novel mode of estrogen receptor antagonism. Nat Struct Biol 9(5):359–364. doi: 10.1038/nsb787 Google Scholar
  33. 33.
    Shiau AK, Barstad D, Loria PM, Cheng L, Kushner PJ, Agard DA, Greene GL (1998) The structural basis of estrogen receptor/coactivator recognition and the antagonism of this interaction by tamoxifen. Cell 95(7):927–937CrossRefGoogle Scholar
  34. 34.
    Kim S, Wu JY, Birzin ET, Frisch K, Chan W, Pai LY, Yang YT, Mosley RT, Fitzgerald PM, Sharma N, Dahllund J, Thorsell AG, DiNinno F, Rohrer SP, Schaeffer JM, Hammond ML (2004) Estrogen receptor ligands. II. Discovery of benzoxathiins as potent, selective estrogen receptor alpha modulators. J Med Chem 47(9):2171–2175. doi: 10.1021/jm034243o CrossRefGoogle Scholar
  35. 35.
    Bledsoe RK, Montana VG, Stanley TB, Delves CJ, Apolito CJ, McKee DD, Consler TG, Parks DJ, Stewart EL, Willson TM, Lambert MH, Moore JT, Pearce KH, Xu HE (2002) Crystal structure of the glucocorticoid receptor ligand binding domain reveals a novel mode of receptor dimerization and coactivator recognition. Cell 110(1):93–105CrossRefGoogle Scholar
  36. 36.
    Suino-Powell K, Xu Y, Zhang C, Tao YG, Tolbert WD, Simons SS Jr, Xu HE (2008) Doubling the size of the glucocorticoid receptor ligand binding pocket by deacylcortivazol. Mol Cell Biol 28(6):1915–1923. doi: 10.1128/MCB.01541-07 CrossRefGoogle Scholar
  37. 37.
    Bledsoe RK, Madauss KP, Holt JA, Apolito CJ, Lambert MH, Pearce KH, Stanley TB, Stewart EL, Trump RP, Willson TM, Williams SP (2005) A ligand-mediated hydrogen bond network required for the activation of the mineralocorticoid receptor. J Biol Chem 280(35):31283–31293. doi: 10.1074/jbc.M504098200 CrossRefGoogle Scholar
  38. 38.
    Gampe RT Jr, Montana VG, Lambert MH, Miller AB, Bledsoe RK, Milburn MV, Kliewer SA, Willson TM, Xu HE (2000) Asymmetry in the PPARgamma/RXRalpha crystal structure reveals the molecular basis of heterodimerization among nuclear receptors. Mol Cell 5(3):545–555CrossRefGoogle Scholar
  39. 39.
    Kuhn B, Hilpert H, Benz J, Binggeli A, Grether U, Humm R, Marki HP, Meyer M, Mohr P (2006) Structure-based design of indole propionic acids as novel PPARalpha/gamma co-agonists. Bioorg Med Chem Lett 16(15):4016–4020. doi: 10.1016/j.bmcl.2006.05.007 CrossRefGoogle Scholar
  40. 40.
    Madauss KP, Deng SJ, Austin RJ, Lambert MH, McLay I, Pritchard J, Short SA, Stewart EL, Uings IJ, Williams SP (2004) Progesterone receptor ligand binding pocket flexibility: crystal structures of the norethindrone and mometasone furoate complexes. J Med Chem 47(13):3381–3387. doi: 10.1021/jm030640n CrossRefGoogle Scholar
  41. 41.
    Kallander LS, Washburn DG, Hoang TH, Frazee JS, Stoy P, Johnson L, Lu Q, Hammond M, Barton LS, Patterson JR, Azzarano LM, Nagilla R, Madauss KP, Williams SP, Stewart EL, Duraiswami C, Grygielko ET, Xu X, Laping NJ, Bray JD, Thompson SK (2010) Improving the developability profile of pyrrolidine progesterone receptor partial agonists. Bioorg Med Chem Lett 20(1):371–374. doi: 10.1016/j.bmcl.2009.10.092 CrossRefGoogle Scholar
  42. 42.
    Egea PF, Mitschler A, Moras D (2002) Molecular recognition of agonist ligands by RXRs. Mol Endocrinol 16(5):987–997. doi: 10.1210/mend.16.5.0823 CrossRefGoogle Scholar
  43. 43.
    Kurumbail RG, Stevens AM, Gierse JK, McDonald JJ, Stegeman RA, Pak JY, Gildehaus D, Miyashiro JM, Penning TD, Seibert K, Isakson PC, Stallings WC (1996) Structural basis for selective inhibition of cyclooxygenase-2 by anti-inflammatory agents. Nature 384(6610):644–648. doi: 10.1038/384644a0 CrossRefGoogle Scholar
  44. 44.
    Wang JL, Limburg D, Graneto MJ, Springer J, Hamper JR, Liao S, Pawlitz JL, Kurumbail RG, Maziasz T, Talley JJ, Kiefer JR, Carter J (2010) The novel benzopyran class of selective cyclooxygenase-2 inhibitors. Part 2: the second clinical candidate having a shorter and favorable human half-life. Bioorg Med Chem Lett 20(23):7159–7163. doi: 10.1016/j.bmcl.2010.07.054 CrossRefGoogle Scholar
  45. 45.
    Card GL, England BP, Suzuki Y, Fong D, Powell B, Lee B, Luu C, Tabrizizad M, Gillette S, Ibrahim PN, Artis DR, Bollag G, Milburn MV, Kim SH, Schlessinger J, Zhang KY (2004) Structural basis for the activity of drugs that inhibit phosphodiesterases. Structure 12(12):2233–2247. doi: 10.1016/j.str.2004.10.004 CrossRefGoogle Scholar
  46. 46.
    Sung BJ, Hwang KY, Jeon YH, Lee JI, Heo YS, Kim JH, Moon J, Yoon JM, Hyun YL, Kim E, Eum SJ, Park SY, Lee JO, Lee TG, Ro S, Cho JM (2003) Structure of the catalytic domain of human phosphodiesterase 5 with bound drug molecules. Nature 425(6953):98–102. doi: 10.1038/nature01914 CrossRefGoogle Scholar
  47. 47.
    LigPrep v 3.2 (2014) Schrödinger, LLC, New York, NYGoogle Scholar
  48. 48.
    Watts KS, Dalal P, Murphy RB, Sherman W, Friesner RA, Shelley JC (2010) ConfGen: a conformational search method for efficient generation of bioactive conformers. J Chem Inf Model 50(4):534–546. doi: 10.1021/ci100015j CrossRefGoogle Scholar
  49. 49.
    Halgren TA (1995) Potential energy functions. Curr Opin Struct Biol 5(2):205–210CrossRefGoogle Scholar
  50. 50.
    Hanley JA, McNeil BJ (1982) The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology 143(1):29–36. doi: 10.1148/radiology.143.1.7063747 CrossRefGoogle Scholar
  51. 51.
    Lehtonen JV, Still DJ, Rantanen VV, Ekholm J, Bjorkland D, Iftikhar Z, Huhtala M, Repo S, Jussila A, Jaakkola J, Pentikainen O, Nyronen T, Salminen T, Gyllenberg M, Johnson MS (2004) BODIL: a molecular modeling environment for structure-function analysis and drug design. J Comput Aided Mol Des 18(6):401–419CrossRefGoogle Scholar
  52. 52.
    Kraulis P (1991) Molscript: a program to produce both detailed and schematic plots of protein structures. J Appl Cryst 24(5):946–950. doi: 10.1107/SO021889891004399
  53. 53.
    Merritt EA, Bacon DJ (1997) Raster3D: photorealistic molecular graphics. Methods Enzymol 277:505–524CrossRefGoogle Scholar
  54. 54.
    Vaz de Lima LA, Nascimento AS (2013) MolShaCS: a free and open source tool for ligand similarity identification based on Gaussian descriptors. Eur J Med Chem 59:296–303. doi: 10.1016/j.ejmech.2012.11.013 CrossRefGoogle Scholar
  55. 55.
    Arciniega M, Lange OF (2014) Improvement of virtual screening results by docking data feature analysis. J Chem Inf Model 54(5):1401–1411. doi: 10.1021/ci500028u CrossRefGoogle Scholar
  56. 56.
    Wallach I, Lilien R (2011) Virtual decoy sets for molecular docking benchmarks. J Chem Inf Model 51(2):196–202. doi: 10.1021/ci100374f CrossRefGoogle Scholar
  57. 57.
    Schneider N, Hindle S, Lange G, Klein R, Albrecht J, Briem H, Beyer K, Claussen H, Gastreich M, Lemmen C, Rarey M (2012) Substantial improvements in large-scale redocking and screening using the novel HYDE scoring function. J Comput Aided Mol Des 26(6):701–723. doi: 10.1007/s10822-011-9531-0 CrossRefGoogle Scholar
  58. 58.
    Hawkins PC, Skillman AG, Nicholls A (2007) Comparison of shape-matching and docking as virtual screening tools. J Med Chem 50(1):74–82. doi: 10.1021/jm0603365 CrossRefGoogle Scholar
  59. 59.
    Kellenberger E, Rodrigo J, Muller P, Rognan D (2004) Comparative evaluation of eight docking tools for docking and virtual screening accuracy. Proteins 57(2):225–242. doi: 10.1002/prot.20149 CrossRefGoogle Scholar
  60. 60.
    Trott O, Olson AJ (2010) AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. J Comput Chem 31(2):455–461. doi: 10.1002/jcc.21334 Google Scholar
  61. 61.
    McGann M (2011) FRED pose prediction and virtual screening accuracy. J Chem Inf Model 51(3):578–596. doi: 10.1021/ci100436p CrossRefGoogle Scholar
  62. 62.
    Zsoldos Z, Reid D, Simon A, Sadjad SB, Johnson AP (2007) eHiTS: a new fast, exhaustive flexible ligand docking system. J Mol Graph Model 26(1):198–212. doi: 10.1016/j.jmgm.2006.06.002 CrossRefGoogle Scholar
  63. 63.
    Rarey M, Kramer B, Lengauer T, Klebe G (1996) A fast flexible docking method using an incremental construction algorithm. J Mol Biol 261(3):470–489. doi: 10.1006/jmbi.1996.0477 CrossRefGoogle Scholar
  64. 64.
    Irwin JJ (2008) Community benchmarks for virtual screening. J Comput Aided Mol Des 22(3–4):193–199. doi: 10.1007/s10822-008-9189-4 CrossRefGoogle Scholar
  65. 65.
    Coleman RG, Carchia M, Sterling T, Irwin JJ, Shoichet BK (2013) Ligand pose and orientational sampling in molecular docking. PLoS One 8(10):e75992. doi: 10.1371/journal.pone.0075992 CrossRefGoogle Scholar
  66. 66.
    Zhang X, Wong SE, Lightstone FC (2014) Toward fully automated high performance computing drug discovery: a massively parallel virtual screening pipeline for docking and molecular mechanics/generalized Born surface area rescoring to improve enrichment. J Chem Inf Model 54(1):324–337. doi: 10.1021/ci4005145 CrossRefGoogle Scholar
  67. 67.
    Corbeil CR, Moitessier N (2009) Docking ligands into flexible and solvated macromolecules. 3. Impact of input ligand conformation, protein flexibility, and water molecules on the accuracy of docking programs. J Chem Inf Model 49(4):997–1009. doi: 10.1021/ci8004176 CrossRefGoogle Scholar
  68. 68.
    Petukh M, Stefl S, Alexov E (2013) The role of protonation states in ligand-receptor recognition and binding. Curr Pharm Des 19(23):4182–4190CrossRefGoogle Scholar
  69. 69.
    Sastry GM, Adzhigirey M, Day T, Annabhimoju R, Sherman W (2013) Protein and ligand preparation: parameters, protocols, and influence on virtual screening enrichments. J Comput Aided Mol Des 27(3):221–234. doi: 10.1007/s10822-013-9644-8 CrossRefGoogle Scholar
  70. 70.
    Urbaczek S, Kolodzik A, Rarey M (2014) The valence state combination model: a generic framework for handling tautomers and protonation states. J Chem Inf Model 54(3):756–766. doi: 10.1021/ci400724v CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Sanna P. Niinivehmas
    • 1
  • Kari Salokas
    • 1
    • 2
  • Sakari Lätti
    • 1
    • 2
  • Hannu Raunio
    • 2
  • Olli T. Pentikäinen
    • 1
    Email author
  1. 1.Department of Biological and Environmental Science & Nanoscience CenterUniversity of JyvaskylaUniversity of JyvaskylaFinland
  2. 2.Faculty of Health Sciences, School of PharmacyUniversity of Eastern FinlandKuopioFinland

Personalised recommendations