Skip to main content
Log in

Investigation into in silico and in vitro approaches for inhibitors targeting MCM10 in Leishmania donovani: a comprehensive study

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

Abstract

Visceral Leishmaniasis (VL), the second neglected tropical disease caused by various Leishmania species, presents a significant public health challenge due to limited treatment options and the absence of vaccines. The agent responsible for visceral leishmaniasis, also referred to as “black fever” in India, is Leishmania donovani. This study focuses on L. donovani Minichromosome maintenance 10 (LdMcm10), a crucial protein in the DNA replication machinery, as a potential therapeutic target in Leishmania therapy using in silico and in vitro approaches. We employed bioinformatics tools, molecular docking, and molecular dynamics simulations to predict potential inhibitors against the target protein. The research revealed that the target protein lacks homologues in the host, emphasizing its potential as a drug target. Ligands from the DrugBank database were screened against LdMcm10 using PyRx software. The top three compounds, namely suramin, vapreotide, and pasireotide, exhibiting the best docking scores, underwent further investigation through molecular dynamic simulation and in vitro analysis. The observed structural dynamics suggested that LdMcm10-ligand complexes maintained consistent binding throughout the 300 ns simulation period, with minimal variations in their backbone. These findings suggest that these three compounds hold promise as potential lead compounds for developing new drugs against leishmaniasis. In vitro experiments also demonstrated a dose-dependent reduction in L. donovani viability for suramin, vapreotide, and pasireotide, with computed IC50 values providing quantitative metrics of their anti-leishmanial efficacy. The research offers a comprehensive understanding of LdMcm10 as a drug target and provides a foundation for further investigations and clinical exploration, ultimately advancing drug discovery strategies for leishmaniasis treatment.

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

Access this article

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

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

Data availability

All data generated or analysed during this study are included in this article [and its supplementary information files].

References

  1. Karunaweera ND, Ferreira MU (2018) Leishmaniasis: current challenges and prospects for elimination with special focus on the South Asian region. Parasitology 145:425–429

    Article  PubMed  Google Scholar 

  2. Sinha M, Jagadeesan R, Kumar N et al (2022) In-silico studies on Myo inositol-1-phosphate synthase of Leishmania donovani in search of anti-leishmaniasis. J Biomol Struct Dyn 40:3371–3384. https://doi.org/10.1080/07391102.2020.1847194

    Article  CAS  PubMed  Google Scholar 

  3. Ghorbani M, Farhoudi R (2018) Leishmaniasis in humans: drug or vaccine therapy? Drug Des Devel Ther 12:25–40

    Article  CAS  PubMed  Google Scholar 

  4. Singh OP, Singh B, Chakravarty J, Sundar S (2016) Current challenges in treatment options for visceral leishmaniasis in India: a public health perspective. Infect Dis Poverty 5:1–5

    Article  Google Scholar 

  5. Sindermann H, Engel KR, Fischer C, Bommer W (2004) Oral miltefosine for leishmaniasis in immunocompromised patients: compassionate use in 39 Patients with HIV infection. Clin Infect Dis 39(10):1520–1523

    Article  CAS  PubMed  Google Scholar 

  6. Brindha J, Balamurali MM, Chanda K (2021) An overview on the therapeutics of neglected infectious diseases—leishmaniasis and chagas diseases. Front Chem 9:622286

    Article  CAS  Google Scholar 

  7. Chawla B, Madhubala R (2010) Drug targets in Leishmania. J Parasit Dis 34:1–13

    Article  PubMed  PubMed Central  Google Scholar 

  8. Baxley RM, Bielinsky AK (2017) Mcm10: a dynamic scaffold at eukaryotic replication forks. Genes (Basel) 8:73

    Article  PubMed  Google Scholar 

  9. Chen J, Wu S, Wang J et al (2023) MCM10: An effective treatment target and a prognostic biomarker in patients with uterine corpus endometrial carcinoma. J Cell Mol Med 27:1708–1724. https://doi.org/10.1111/jcmm.17772

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Warren EM, Vaithiyalingam S, Haworth J et al (2008) Structural basis for DNA Binding by replication initiator Mcm10. Structure 16:1892–1901. https://doi.org/10.1016/j.str.2008.10.005

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Thu YM, Bielinsky AK (2013) Enigmatic roles of Mcm10 in DNA replication. Trends Biochem Sci 38:184–194

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Robertson PD, Chagot B, Chazin WJ, Eichman BF (2010) Solution NMR structure of the C-terminal DNA binding domain of Mcm10 reveals a conserved MCM motif. J Biol Chem 285:22942–22949. https://doi.org/10.1074/jbc.M110.131276

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Cassandri M, Smirnov A, Novelli F et al (2017) Zinc-finger proteins in health and disease. Cell Death Discov. https://doi.org/10.1038/cddiscovery.2017.71

    Article  PubMed  PubMed Central  Google Scholar 

  14. Yu W, Mackerell AD (2017) Computer-aided drug design methods. In: Sass P (ed) Methods in molecular biology. Humana Press Inc., Totowa, pp 85–106

    Google Scholar 

  15. Baek M, DiMaio F, Anishchenko I et al (2021) Accurate prediction of protein structures and interactions using a three-track neural network 1979. Science 373:871–876. https://doi.org/10.1126/science.abj8754

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Zhou X, Zheng W, Li Y et al (2022) I-TASSER-MTD: a deep-learning-based platform for multi-domain protein structure and function prediction. Nat Protoc 17:2326–2353. https://doi.org/10.1038/s41596-022-00728-0

    Article  CAS  PubMed  Google Scholar 

  17. Pieper U, Webb BM, Barkan DT et al (2011) ModBase, a database of annotated comparative protein structure models, and associated resources. Nucleic Acids Res. https://doi.org/10.1093/nar/gkq1091

    Article  PubMed  Google Scholar 

  18. Jumper J, Evans R, Pritzel A et al (2021) Highly accurate protein structure prediction with AlphaFold. Nature 596:583–589. https://doi.org/10.1038/s41586-021-03819-2

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Laskowski RA, MacArthur MW, Moss DS, Thornton JM (1993) PROCHECK: a program to check the stereochemical quality of protein structures. J Appl Crystallogr 26:283–291. https://doi.org/10.1107/s0021889892009944

    Article  CAS  Google Scholar 

  20. Krieger E, Joo K, Lee J et al (2009) Improving physical realism, stereochemistry, and side-chain accuracy in homology modeling: four approaches that performed well in CASP8. Proteins: Struct Funct Bioinform 77:114–122

    Article  CAS  Google Scholar 

  21. 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 31:455–461. https://doi.org/10.1002/jcc.21334

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Wallace AC, Laskowski RA, Thornton JM (1995) LIGPLOT: a program to generate schematic diagrams of protein-ligand interactions. Protein Eng 8(2):127–134. https://doi.org/10.1093/protein/8.2.127

    Article  CAS  PubMed  Google Scholar 

  23. Lill MA, Danielson ML (2011) Computer-aided drug design platform using PyMOL. J Comput Aided Mol Des 25:13–19. https://doi.org/10.1007/s10822-010-9395-8

    Article  CAS  PubMed  Google Scholar 

  24. Bowers KJ, Chow E, Xu H, et al (2006) Scalable algorithms for molecular dynamics simulations on commodity clusters. In: Proceedings of the 2006 ACM/IEEE Conference on Supercomputing, SC’06

  25. Hildebrand PW, Rose AS, Tiemann JKS (2019) Bringing molecular dynamics simulation data into view. Trends Biochem Sci 44:902–913

    Article  CAS  PubMed  Google Scholar 

  26. Rasheed MA, Iqbal MN, Saddick S et al (2021) Identification of lead compounds against scm (Fms10) in enterococcus faecium using computer aided drug designing. Life 11:1–15. https://doi.org/10.3390/life11020077

    Article  CAS  Google Scholar 

  27. Shivakumar D, Williams J, Wu Y et al (2010) Prediction of absolute solvation free energies using molecular dynamics free energy perturbation and the opls force field. J Chem Theory Comput 6:1509–1519. https://doi.org/10.1021/ct900587b

    Article  CAS  PubMed  Google Scholar 

  28. Li J, Abel R, Zhu K et al (2011) The VSGB 2.0 model: a next generation energy model for high resolution protein structure modeling. Proteins: Struct Funct Bioinform 79:2794–2812. https://doi.org/10.1002/prot.23106

    Article  CAS  Google Scholar 

  29. Palma J, Pierdominici-Sottile G (2023) On the uses of PCA to characterise molecular dynamics simulations of biological macromolecules: basics and tips for an effective use. ChemPhysChem. https://doi.org/10.1002/cphc.202200491

    Article  PubMed  Google Scholar 

  30. Kitao A (2022) Principal component analysis and related methods for investigating the dynamics of biological macromolecules. J (Basel) 5:298–317. https://doi.org/10.3390/j5020021

    Article  CAS  Google Scholar 

  31. Avti P, Chauhan A, Shekhar N et al (2022) Computational basis of SARS-CoV 2 main protease inhibition: an insight from molecular dynamics simulation based findings. J Biomol Struct Dyn 40:8894–8904. https://doi.org/10.1080/07391102.2021.1922310

    Article  CAS  PubMed  Google Scholar 

  32. van Meerloo J, Kaspers GJL, Cloos J (2011) Cell sensitivity assays: the MTT assay. In: Cree IA (ed) Cancer cell culture: methods and protocols. Humana Press, Totowa, pp 237–245

    Chapter  Google Scholar 

  33. Baranwal A, Chiranjivi AK, Kumar A et al (2018) Design of commercially comparable nanotherapeutic agent against human disease-causing parasite Leishmania. Sci Rep. https://doi.org/10.1038/s41598-018-27170-1

    Article  PubMed  PubMed Central  Google Scholar 

  34. Chakrabarti S, Lanczycki CJ (2007) Analysis and prediction of functionally important sites in proteins. Protein Sci 16:4–13. https://doi.org/10.1110/ps.062506407

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Rodina A, Godson GN (2006) Role of conserved amino acids in the catalytic activity of Escherichia coli primase. J Bacteriol 188:3614–3621. https://doi.org/10.1128/JB.188.10.3614-3621.2006

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Mayle R, Langston L, Molloy KR et al (2019) Mcm10 has potent strand-annealing activity and limits translocase-mediated fork regression. Proc Natl Acad Sci U S A 116:798–803. https://doi.org/10.1073/pnas.1819107116

    Article  CAS  PubMed  Google Scholar 

  37. Perez-Arnaiz P, Kaplan DL (2016) An Mcm10 mutant defective in ssDNA binding shows defects in DNA replication initiation. J Mol Biol 428:4608–4625. https://doi.org/10.1016/j.jmb.2016.10.014

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Broni E, Kwofie SK, Asiedu SO et al (2021) A molecular modeling approach to identify potential antileishmanial compounds against the cell division cycle (Cdc)-2-related kinase 12 (crk12) receptor of leishmania donovani. Biomolecules 11:1–32. https://doi.org/10.3390/biom11030458

    Article  CAS  Google Scholar 

  39. Pandey P, Prasad K, Prakash A, Kumar V (2020) Insights into the biased activity of dextromethorphan and haloperidol towards SARS-CoV-2 NSP6: in silico binding mechanistic analysis. J Mol Med 98:1659–1673. https://doi.org/10.1007/s00109-020-01980-1

    Article  CAS  PubMed  Google Scholar 

  40. Amadei A, Linssen ABM, Berendsen HJC (1993) Essential Dynamics of Proteins. Proteins: Struct Funct Bioinform 17(4):412–425

    Article  CAS  Google Scholar 

  41. Macip G, Garcia-Segura P, Mestres-Truyol J et al (2022) Haste makes waste: a critical review of docking-based virtual screening in drug repurposing for SARS-CoV-2 main protease (M-pro) inhibition. Med Res Rev 42:744–769

    Article  CAS  PubMed  Google Scholar 

  42. Omoboyowa DA, Iqbal MN, Balogun TA et al (2022) Inhibitory potential of phytochemicals from Chromolaena odorata L. against apoptosis signal-regulatory kinase 1: a computational model against colorectal cancer. Computational Toxicology 23:100235. https://doi.org/10.1016/j.comtox.2022.100235

    Article  CAS  Google Scholar 

  43. Hollingsworth SA, Dror RO (2018) Molecular dynamics simulation for all. Neuron 99:1129–1143

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Hao MH, Haq O, Muegge I (2007) Torsion angle preference and energetics of small-molecule ligands bound to proteins. J Chem Inf Model 47:2242–2252. https://doi.org/10.1021/ci700189s

    Article  CAS  PubMed  Google Scholar 

  45. Mitra S, Dash R (2018) Structural dynamics and quantum mechanical aspects of shikonin derivatives as CREBBP bromodomain inhibitors. J Mol Graph Model 83:42–52. https://doi.org/10.1016/j.jmgm.2018.04.014

    Article  CAS  PubMed  Google Scholar 

  46. De Vita S, Chini MG, Bifulco G, Lauro G (2021) Insights into the ligand binding to bromodomain-containing protein 9 (BRD9): a guide to the selection of potential binders by computational methods. Molecules. https://doi.org/10.3390/molecules26237192

    Article  PubMed  PubMed Central  Google Scholar 

  47. Kuldeep J, R K, Kaur P, et al (2021) Identification of potential anti-leishmanial agents using computational investigation and biological evaluation against trypanothione reductase. J Biomol Struct Dyn 39:960–969. https://doi.org/10.1080/07391102.2020.1721330

    Article  CAS  PubMed  Google Scholar 

  48. Zhang G, Su Z (2012) Inferences from structural comparison: flexibility, secondary structure wobble and sequence alignment optimization. BMC Bioinformatics 13:S12. https://doi.org/10.1186/1471-2105-13-S15-S12

  49. Carugo O, Pongor S (2001) A normalized root-mean-spuare distance for comparing protein three-dimensional structures. Protein Sci 10:1470–1473. https://doi.org/10.1110/ps.690101

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Prakash A, Kumar V, Meena NK, Lynn AM (2018) Elucidation of the structural stability and dynamics of heterogeneous intermediate ensembles in unfolding pathway of the N-terminal domain of TDP-43. RSC Adv 8:19835–19845. https://doi.org/10.1039/c8ra03368d

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Bagewadi ZK, Yunus Khan TM, Gangadharappa B et al (2023) Molecular dynamics and simulation analysis against superoxide dismutase (SOD) target of Micrococcus luteus with secondary metabolites from Bacillus licheniformis recognized by genome mining approach. Saudi J Biol Sci. https://doi.org/10.1016/j.sjbs.2023.103753

    Article  PubMed  PubMed Central  Google Scholar 

  52. Liao KH, Chen KB, Lee WY et al (2014) Ligand-based and structure-based investigation for Alzheimer’s disease from traditional Chinese medicine. Evid-Based Complement Altern Med. https://doi.org/10.1155/2014/364819

    Article  Google Scholar 

  53. Raj U, Kumar H, Gupta S, Varadwaj PK (2015) Novel DOT1L receptornatural inhibitors involved in mixed lineage leukemia: a virtual screening, molecular docking and dynamics simulation study. Asian Pac J Cancer Prev 16:3817–3825. https://doi.org/10.7314/APJCP.2015.16.9.3817

    Article  PubMed  Google Scholar 

  54. Hata H, Tran DP, Sobeh MM et al (2021) Binding free energy of protein/ligand complexes calculated using dissociation Parallel Cascade Selection Molecular Dynamics and Markov state model. Biophys Physicobiol 18:305–316. https://doi.org/10.2142/biophysico.bppb-v18.037

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. David CC, Jacobs DJ (2014) Principal component analysis: a method for determining the essential dynamics of proteins. Methods Mol Biol 1084:193–226. https://doi.org/10.1007/978-1-62703-658-0_11

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Ashraf N, Asari A, Yousaf N et al (2022) Combined 3D-QSAR, molecular docking and dynamics simulations studies to model and design TTK inhibitors. Front Chem. https://doi.org/10.3389/fchem.2022.1003816

    Article  PubMed  PubMed Central  Google Scholar 

  57. Yousaf N, Jabeen Y, Imran M et al (2023) Exploiting the co-crystal ligands shape, features and structure-based approaches for identification of SARS-CoV-2 Mpro inhibitors. J Biomol Struct Dyn 41:14325–14338. https://doi.org/10.1080/07391102.2023.2189478

    Article  CAS  PubMed  Google Scholar 

  58. Bouteille B, Buguet A (2012) The detection and treatment of human African trypanosomiasis. Res Rep Trop Med. https://doi.org/10.2147/rrtm.s24751

    Article  PubMed  PubMed Central  Google Scholar 

  59. Khanra S, Juin SK, Jawed JJ et al (2020) In vivo experiments demonstrate the potent antileishmanial efficacy of repurposed suramin in visceral leishmaniasis. PLoS Negl Trop Dis 14:1–20. https://doi.org/10.1371/journal.pntd.0008575

    Article  CAS  Google Scholar 

  60. Khan SM, Witola WH (2023) Past, current, and potential treatments for cryptosporidiosis in humans and farm animals: a comprehensive review. Front Cell Infect Microbiol 13:1115522

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  61. Simeoli C, Ferrigno R, De Martino MC et al (2020) The treatment with pasireotide in Cushing’s disease: effect of long-term treatment on clinical picture and metabolic profile and management of adverse events in the experience of a single center. J Endocrinol Invest 43:57–73. https://doi.org/10.1007/s40618-019-01077-8

    Article  CAS  PubMed  Google Scholar 

  62. Saha S, Srivastava R, Sarma P et al (2023) Identification of potential inhibitors of Leishmania donovani Sterol 24-C- methyltransferase: in silico and in vitro studies. Mol Simul 49:1311–1323. https://doi.org/10.1080/08927022.2023.2227288

    Article  CAS  Google Scholar 

  63. Gardner AF, Kelman Z (2019) Editorial: the DNA replication machinery as therapeutic targets. Front Mol Biosci 6:35

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  64. Adinehbeigi K, Razi Jalali MH, Shahriari A, Bahrami S (2017) In vitro antileishmanial activity of fisetin flavonoid via inhibition of glutathione biosynthesis and arginase activity in Leishmania infantum. Pathog Glob Health 111:176–185. https://doi.org/10.1080/20477724.2017.1312777

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  65. Bhattacharya D, Cheng J (2013) 3Drefine: consistent protein structure refinement by optimizing hydrogen bonding network and atomic-level energy minimization. Proteins: Struct. Funct. Bioinform. 81:119–131. https://doi.org/10.1002/prot.24167

    Article  CAS  Google Scholar 

  66. Feig M (2017) Computational protein structure refinement: almost there, yet still so far to go. Wiley Interdiscip Rev Comput Mol Sci. https://doi.org/10.1002/wcms.1307

    Article  PubMed  PubMed Central  Google Scholar 

  67. Thomsen R, Christensen MH (2006) MolDock: a new technique for high-accuracy molecular docking. J Med Chem 49:3315–3321. https://doi.org/10.1021/jm051197e

    Article  CAS  PubMed  Google Scholar 

  68. Karthick V, Nagasundaram N, Doss CGP et al (2016) Virtual screening of the inhibitors targeting at the viral protein 40 of Ebola virus. Infect Dis Poverty. https://doi.org/10.1186/s40249-016-0105-1

    Article  PubMed  PubMed Central  Google Scholar 

  69. Chandra A, Chaudhary M, Qamar I et al (2022) In silico identification and validation of natural antiviral compounds as potential inhibitors of SARS-CoV-2 methyltransferase. J Biomol Struct Dyn 40:6534–6544. https://doi.org/10.1080/07391102.2021.1886174

    Article  CAS  PubMed  Google Scholar 

  70. Pieroni M, Madeddu F, Di Martino J et al (2023) MD–ligand–receptor: a high-performance computing tool for characterizing ligand-receptor binding interactions in molecular dynamics trajectories. Int J Mol Sci. https://doi.org/10.3390/ijms241411671

    Article  PubMed  PubMed Central  Google Scholar 

  71. Hao MH, Haq O, Muegge I (2007) Torsion angle preference and energetics of small-molecule ligands bound to proteins. J Chem Inf Model 47:2242–2252

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgements

Deep Bhowmik acknowledges the financial support of ICMR (Fellowship/95/2022-ECD-II, dated 17/05/2022, respectively). Satabdi Saha recognizes the financial support from the Inspire Fellowship (IF180806).

Author information

Authors and Affiliations

Authors

Contributions

" SS, DB, AS and DK carried out the experiment. SS, DB and DK wrote the manuscript. SS, DB, AS and DK contributed to the analysis of the results. DK supervised the project and conceived the original idea."

Corresponding author

Correspondence to Diwakar Kumar.

Ethics declarations

Conflict of interest

The authors declare that no conflict of interest exists.

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 (DOC 36777 kb)

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Saha, S., Sharma, A., Bhowmik, D. et al. Investigation into in silico and in vitro approaches for inhibitors targeting MCM10 in Leishmania donovani: a comprehensive study. Mol Divers (2024). https://doi.org/10.1007/s11030-024-10876-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s11030-024-10876-y

Keywords

Navigation