Skip to main content

Advertisement

SpringerLink
Log in

High-throughput virtual screening of small-molecule inhibitors targeting immune cell checkpoints to discover new immunotherapeutics for human diseases

  • Original Article
  • Published:
Molecular Diversity Aims and scope Submit manuscript

Abstract

Immunotherapy is widely used to treat various cancers, and the drugs used are called immune checkpoint (ICP) inhibitors. Overexpression of immune cell checkpoints is reported for other human diseases such as acute infections (malaria), chronic viral infection (HIV, hepatitis B virus, TB infections), allergy, asthma, neurodegeneration, and autoimmune diseases. Some mAbs (monoclonal antibodies) are available against ICPs, but they have side effects. Small molecule seems to be safer in comparison with mAbs. Three independent small-molecule inhibitor libraries consisting of 9466 compounds were screened against seven immune cell checkpoints by applying high-throughput virtual screening approach. A total of 13 ICP inhibitors were finalized based on docking, MM-GBSA scores, and ADME properties. Six compounds were selected for MD simulation, and then, rutin hydrate (targeting all seven immune cell checkpoints), amikacin hydrate (targeting six), and 6-hydroxyluteolin (targeting three) were found to be the best immune cell checkpoint inhibitors. These three potential inhibitors have shown the potential to activate human immune cells and thus may control the spread of human lifestyle or infectious diseases. Proposed inhibitors warrant the in vitro and in vivo validation to develop it as an immunotherapeutic.

Graphical abstract

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

Similar content being viewed by others

References

  1. Pardoll DM (2012) The blockade of immune checkpoints in cancer immunotherapy. Nat Rev Cancer 12(4):252–264

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Illingworth J, Butler NS, Roetynck S, Mwacharo J, Pierce SK, Bejon P, Crompton PD, Marsh K, Ndungu FM (2013) Chronic exposure to plasmodium falciparum is associated with phenotypic evidence of B and T cell exhaustion. J Immunol 190(3):1038–1047

    Article  CAS  PubMed  Google Scholar 

  3. Gonçalves-Lopes RM, Lima NF, Carvalho KI, Scopel KK, Kallás EG, Ferreira MU (2016) Surface expression of inhibitory (CTLA-4) and stimulatory (OX40) receptors by CD4+ regulatory T cell subsets circulating in human malaria. Microbes Infect 18(10):639–648

    Article  PubMed  PubMed Central  Google Scholar 

  4. Trautmann L, Janbazian L, Chomont N, Said EA, Gimmig S, Bessette B, Boulassel MR, Delwart E, Sepulveda H, Balderas RS, Routy JP (2006) Upregulation of PD-1 expression on HIV-specific CD8+ T cells leads to reversible immune dysfunction. Nat Med 12(10):1198–1202

    Article  CAS  PubMed  Google Scholar 

  5. Chew GM, Fujita T, Webb GM, Burwitz BJ, Wu HL, Reed JS, Hammond KB, Clayton KL, Ishii N, Abdel-Mohsen M, Liegler T (2016) TIGIT marks exhausted T cells, correlates with disease progression, and serves as a target for immune restoration in HIV and SIV infection. PLoS Pathog 12(1):e1005349

    Article  PubMed  PubMed Central  Google Scholar 

  6. Pollock KM, Montamat-Sicotte DJ, Grass L, Cooke GS, Kapembwa MS, Kon OM, Sampson RD, Taylor GP, Lalvani A (2016) PD-1 expression and cytokine secretion profiles of mycobacterium tuberculosis-specific CD4+ T-cell subsets; potential correlates of containment in HIV-TB co-infection. PLoS ONE 11(1):e0146905

    Article  PubMed  PubMed Central  Google Scholar 

  7. Wykes MN, Lewin SR (2018) Immune checkpoint blockade in infectious diseases. Nat Rev Immunol 18(2):91–104

    Article  CAS  PubMed  Google Scholar 

  8. Lipson EJ, Drake CG (2011) Ipilimumab: an anti-CTLA-4 antibody for metastatic melanoma. Clin Cancer Res 17(22):6958–6962

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Vial T, Choquet-Kastylevsky G, Descotes J (2002) Adverse effects of immunotherapeutics involving the immune system. Toxicology 174(1):3–11

    Article  CAS  PubMed  Google Scholar 

  10. The_American_Cancer_Society_medical_and_editorial_content_team, Monoclonal antibodies and their side effects. American Cancer Society, 2019

  11. Sansom DM (2000) CD28, CTLA-4 and their ligands: Who does what and to whom? Immunology 101(2):169

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Olsson C, Michaëlsson E, Parra E, Pettersson U, Lando PA, Dohlsten M (1998) Biased dependency of CD80 versus CD86 in the induction of transcription factors regulating the human IL-2 promoter. Int Immunol 10(4):499–506

    Article  CAS  PubMed  Google Scholar 

  13. van der Merwe PA, Bodian DL, Daenke S, Linsley P, Davis SJ (1997) CD80 (B7–1) binds both CD28 and CTLA-4 with a low affinity and very fast kinetics. J Exp Med 185(3):393–404

    Article  PubMed  PubMed Central  Google Scholar 

  14. Linsley PS, Greene JL, Brady W, Bajorath J, Ledbetter JA, Peach R (1994) Human B7–1 (CD80) and B7–2 (CD86) bind with similar avidities but distinct kinetics to CD28 and CTLA-4 receptors. Immunity 1(9):793–801

    Article  CAS  PubMed  Google Scholar 

  15. Keir ME, Butte MJ, Freeman GJ, Sharpe AH (2008) PD-1 and its ligands in tolerance and immunity. Annu Rev Immunol 26:677–704

    Article  CAS  PubMed  Google Scholar 

  16. Liang SC, Latchman YE, Buhlmann JE, Tomczak MF, Horwitz BH, Freeman GJ, Sharpe AH (2003) Regulation of PD-1, PD-L1, and PD-L2 expression during normal and autoimmune responses. Eur J Immunol 33(10):2706–2716

    Article  CAS  PubMed  Google Scholar 

  17. Almahmoud S, Zhong HA (2019) Molecular modeling studies on the binding mode of the PD-1/PD-L1 complex inhibitors. Int J Mol Sci 20(18):4654

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Nishimura H, Nose M, Hiai H, Minato N, Honjo T (1999) Development of lupus-like autoimmune diseases by disruption of the PD-1 gene encoding an ITIM motif-carrying immunoreceptor. Immunity 11(2):141–151

    Article  CAS  PubMed  Google Scholar 

  19. Raedler LA (2015) Keytruda (pembrolizumab): first PD-1 inhibitor approved for previously treated unresectable or metastatic melanoma. Am Health Drug Benefits 8(1):96

    PubMed  PubMed Central  Google Scholar 

  20. Monney L, Sabatos CA, Gaglia JL, Ryu A, Waldner H, Chernova T, Manning S, Greenfield EA, Coyle AJ, Sobel RA, Freeman GJ (2002) Th1-specific cell surface protein Tim-3 regulates macrophage activation and severity of an autoimmune disease. Nature 415(6871):536–541

    Article  CAS  PubMed  Google Scholar 

  21. Wolf Y, Anderson AC, Kuchroo VK (2020) TIM3 comes of age as an inhibitory receptor. Nat Rev Immunol 20(3):173–185

    Article  CAS  PubMed  Google Scholar 

  22. Murtaza A, Laken H, Correia JDS, McNeeley P, Altobell L, Zhang J, Vancutsem P, Wilcoxen K, Jenkins D (2016) Discovery of TSR-022, a novel, potent anti-human TIM-3 therapeutic antibody. Eur J Cancer 1(69):S102

    Article  Google Scholar 

  23. Klein C, Schaefer W, Regula JT, Dumontet C, Brinkmann U, Bacac M, Umaña P (2019) Engineering therapeutic bispecific antibodies using CrossMab technology. Methods 154:21–31

    Article  CAS  PubMed  Google Scholar 

  24. Yu X, Harden K, Gonzalez LC, Francesco M, Chiang E, Irving B, Tom I, Ivelja S, Refino CJ, Clark H, Eaton D (2009) The surface protein TIGIT suppresses T cell activation by promoting the generation of mature immunoregulatory dendritic cells. Nat Immunol 10(1):48–57

    Article  CAS  PubMed  Google Scholar 

  25. Dolgin E (2020) Antibody engineers seek optimal drug targeting TIGIT checkpoint. Nat Biotechnol 38(9):1007–1010

    Article  CAS  PubMed  Google Scholar 

  26. Gonzalez LC, Loyet KM, Calemine-Fenaux J, Chauhan V, Wranik B, Ouyang W, Eaton DL (2005) A coreceptor interaction between the CD28 and TNF receptor family members B and T lymphocyte attenuator and herpesvirus entry mediator. Proc Natl Acad Sci 102(4):1116–1121

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Rajalingam R (2012) Overview of the killer cell immunoglobulin-like receptor system. Immunogenetics, 391–414

  28. Campbell KS, Purdy AK (2011) Structure/function of human killer cell immunoglobulin-like receptors: lessons from polymorphisms, evolution, crystal structures and mutations. Immunology 132(3):315–325

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Núñez F, Taura J, Camacho J, López-Cano M, Fernández-Dueñas V, Castro N, Castro J, Ciruela F (2018) PBF509, an adenosine A2A receptor antagonist with efficacy in rodent models of movement disorders. Front Pharmacol, 1200

  30. Furukawa, F. (2018) The Nobel prize in physiology or medicine 2018 was awarded to cancer therapy by inhibition of negative immune regulation. Trends Immunother 2(1)

  31. Zang X (2018) 2018 Nobel prize in medicine awarded to cancer immunotherapy: Immune checkpoint blockade–a personal account. Genes Dis 5(4):302

    Article  PubMed  PubMed Central  Google Scholar 

  32. Larkin J, Chiarion-Sileni V, Gonzalez R, Grob JJ, Cowey CL, Lao CD, Schadendorf D, Dummer R, Smylie M, Rutkowski P, Ferrucci PF (2015) Combined nivolumab and ipilimumab or monotherapy in untreated melanoma. N Engl J Med 373(1):23–34

    Article  PubMed  PubMed Central  Google Scholar 

  33. Sznol M, Kluger HM, Callahan MK, Postow MA, Gordon RA, Segal NH, Rizvi NA, Lesokhin AM, Atkins MB, Kirkwood JM, Burke MM (2014) Survival, response duration, and activity by BRAF mutation (MT) status of nivolumab (NIVO, anti-PD-1, BMS-936558, ONO-4538) and ipilimumab (IPI) concurrent therapy in advanced melanoma (MEL)

  34. 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):235–242

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Gandhi AK, Kim WM, Sun ZYJ, Huang YH, Bonsor DA, Sundberg EJ, Kondo Y, Wagner G, Kuchroo VK, Petsko G, Blumberg RS (2018) High resolution X-ray and NMR structural study of human T-cell immunoglobulin and mucin domain containing protein-3. Sci Rep 8(1):1–13

    Article  CAS  Google Scholar 

  36. Stengel KF, Harden-Bowles K, Yu X, Rouge L, Yin J, Comps-Agrar L, Wiesmann C, Bazan JF, Eaton DL, Grogan JL (2012) Structure of TIGIT immunoreceptor bound to poliovirus receptor reveals a cell–cell adhesion and signaling mechanism that requires cis-trans receptor clustering. Proc Natl Acad Sci 109(14):5399–5404

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Moradi S, Berry R, Pymm P, Hitchen C, Beckham SA, Wilce MC, Walpole NG, Clements CS, Reid HH, Perugini MA, Brooks AG (2015) The structure of the atypical killer cell immunoglobulin-like receptor, KIR2DL4. J Biol Chem 290(16):10460–10471

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Compaan DM, Gonzalez LC, Tom I, Loyet KM, Eaton D, Hymowitz SG (2005) Attenuating lymphocyte activity: the crystal structure of the BTLA-HVEM complex. J Biol Chem 280(47):39553–39561

    Article  CAS  PubMed  Google Scholar 

  39. Ramagopal UA, Liu W, Garrett-Thomson SC, Bonanno JB, Yan Q, Srinivasan M, Wong SC, Bell A, Mankikar S, Rangan VS, Deshpande S (2017) Structural basis for cancer immunotherapy by the first-in-class checkpoint inhibitor ipilimumab. Proc Natl Acad Sci 114(21):E4223–E4232

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Lin DYW, Tanaka Y, Iwasaki M, Gittis AG, Su HP, Mikami B, Okazaki T, Honjo T, Minato N, Garboczi DN (2008) The PD-1/PD-L1 complex resembles the antigen-binding Fv domains of antibodies and T cell receptors. Proc Natl Acad Sci 105(8):3011–3016

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Doré AS, Robertson N, Errey JC, Ng I, Hollenstein K, Tehan B, Hurrell E, Bennett K, Congreve M, Magnani F, Tate CG (2011) Structure of the adenosine A2A receptor in complex with ZM241385 and the xanthines XAC and caffeine. Structure 19(9):1283–1293

    Article  PubMed  PubMed Central  Google Scholar 

  42. Madhavi Sastry G, 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

    Article  CAS  PubMed  Google Scholar 

  43. Zeng X, Zhang P, He W, Qin C, Chen S, Tao L, Wang Y, Tan Y, Gao D, Wang B, Chen Z (2018) NPASS: natural product activity and species source database for natural product research, discovery and tool development. Nucleic Acids Res 46(D1):D1217–D1222

    Article  CAS  PubMed  Google Scholar 

  44. Selleckchem, FDA-approved drug library. https://www.selleckchem.com/screening/fda-approved-drug-library.html

  45. Selleckchem, FDA-approved & passed phase I drug library. https://www.selleckchem.com/screening/fda-approved-passed-phase-i-drug-library.html

  46. Shelley JC, Cholleti A, Frye LL, Greenwood JR, Timlin MR, Uchimaya M (2007) Epik: a software program for pK a prediction and protonation state generation for drug-like molecules. J Comput Aided Mol Des 21(12):681–691

    Article  CAS  PubMed  Google Scholar 

  47. Halgren TA (2009) Identifying and characterizing binding sites and assessing druggability. J Chem Inf Model 49(2):377–389

    Article  CAS  PubMed  Google Scholar 

  48. Halgren T (2007) New method for fast and accurate binding-site identification and analysis. Chem Biol Drug Des 69(2):146–148

    Article  CAS  PubMed  Google Scholar 

  49. Glide EP (2006) Docking and scoring incorporating a model of hydrophobic enclosure for protein−ligand complexes. J Med Chem 4:6177–6196

    Google Scholar 

  50. Friesner RA, Murphy RB, Repasky MP, Frye LL, Greenwood JR, Halgren TA, Sanschagrin PC, Mainz DT (2006) Extra precision glide: docking and scoring incorporating a model of hydrophobic enclosure for protein−ligand complexes. J Med Chem 49(21):6177–6196

    Article  CAS  PubMed  Google Scholar 

  51. Jacobson MP, Pincus DL, Rapp CS, Day TJ, Honig B, Shaw DE, Friesner RA (2004) A hierarchical approach to all-atom protein loop prediction. Proteins Struct Funct Bioinform 55(2):351–367

    Article  CAS  Google Scholar 

  52. Li J, Abel R, Zhu K, Cao Y, Zhao S, Friesner RA (2011) The VSGB 2.0 model: a next generation energy model for high resolution protein structure modeling. Proteins Struct Funct Bioinform 79(10):2794–2812

    Article  CAS  Google Scholar 

  53. Fawcett T (2006) An introduction to ROC analysis. Pattern Recogn Lett 27(8):861–874

    Article  Google Scholar 

  54. Naik B, Mattaparthi VSK, Gupta N, Ojha R, Das P, Singh S, Prajapati VK, Prusty D (2021) Chemical system biology approach to identify multi-targeting FDA inhibitors for treating COVID-19 and associated health complications. J Biomol Struct Dyn 1–25

  55. WebGRO for macromolecular simulations (https://simlab.uams.edu/)

  56. Abraham MJ, Murtola T, Schulz R, Páll S, Smith JC, Hess B, Lindahl E (2015) GROMACS: high performance molecular simulations through multi-level parallelism from laptops to supercomputers. SoftwareX 1:19–25

    Article  Google Scholar 

  57. Schüttelkopf AW, Van Aalten DM (2004) PRODRG: a tool for high-throughput crystallography of protein–ligand complexes. Acta Crystallogr D Biol Crystallogr 60(8):1355–1363

    Article  PubMed  Google Scholar 

  58. Oostenbrink C, Villa A, Mark AE, Van Gunsteren WF (2004) A biomolecular force field based on the free enthalpy of hydration and solvation: the GROMOS force-field parameter sets 53A5 and 53A6. J Comput Chem 25(13):1656–1676

    Article  CAS  PubMed  Google Scholar 

  59. Ghorbani A (2017) Mechanisms of antidiabetic effects of flavonoid rutin. Biomed Pharmacother 96:305–312

    Article  CAS  PubMed  Google Scholar 

  60. Habtemariam S (2016) Rutin as a natural therapy for Alzheimer’s disease: insights into its mechanisms of action. Curr Med Chem 23(9):860–873

    Article  CAS  PubMed  Google Scholar 

  61. Rosenberg CR, Fang X, Allison KR (2020) Potentiating aminoglycoside antibiotics to reduce their toxic side effects. PLoS ONE 15(9):e0237948

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  62. Sanders WE Jr, Hartwig C, Schnieder N, Cacciatore R, Valdez H (1982) Activity of amikacin against mycobacteria in vitro and in murine tuberculosis. Tubercle 63(3):201–208

    Article  PubMed  Google Scholar 

  63. Clinical Trials.gov, Inulin for infections in the intensive care unit.

  64. Berry ED, Wells JE (2010) Escherichia coli O157: H7: recent advances in research on occurrence, transmission, and control in cattle and the production environment. Adv Food Nutr Res 60:67–117

    Article  CAS  PubMed  Google Scholar 

  65. Scholar E (2007) Neomycin. in xpharm: The Comprehensive Pharmacology Reference. https://doi.org/10.1016/B978-008055232-3.62258-5

  66. Sykes JE, Papich MG (2013) Antiprotozoal drugs. Canine Feline Infect Dis 171:97–104

    Google Scholar 

  67. Lipinski CA (2004) Lead-and drug-like compounds: the rule-of-five revolution. Drug Discov Today Technol 1(4):337–341

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgements

SS is thankful to Central University of Rajasthan for providing fellowship. VKP is thankful to the Central University of Rajasthan for providing laboratory facility.

Author information

Authors and Affiliations

  1. Department of Biochemistry, School of Life Sciences, Central University of Rajasthan, NH-8, Bandarsindri, Kishangarh, Ajmer, Rajasthan, 305817, India

    Satyendra Singh, Ketan Kumar, Mamta Panda & Vijay Kumar Prajapati

  2. Spring-Ford Area High School, Royersford, PAS, 19468, USA

    Aryan Srivastava

  3. Cellular and Molecular Neurobiology Unit, Indian Institute of Technology Jodhpur, Jodhpur, Rajasthan, 342011, India

    Amit Mishra

Authors
  1. Satyendra Singh
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ketan Kumar
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Mamta Panda
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Aryan Srivastava
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Amit Mishra
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Vijay Kumar Prajapati
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Protocol was designed by SS and VKP. Methodology was performed by SS, KK, MP, and VKP. The manuscript was written by SS, KK, AS, AM, and VKP.

Corresponding author

Correspondence to Vijay Kumar Prajapati.

Ethics declarations

Conflict of interest

Authors have declared no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary file1 (DOCX 13858 KB)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Singh, S., Kumar, K., Panda, M. et al. High-throughput virtual screening of small-molecule inhibitors targeting immune cell checkpoints to discover new immunotherapeutics for human diseases. Mol Divers 27, 729–751 (2023). https://doi.org/10.1007/s11030-022-10452-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11030-022-10452-2

Keywords

Search

Navigation