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Development of human lactate dehydrogenase a inhibitors: high-throughput screening, molecular dynamics simulation and enzyme activity assay

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Abstract

Lactate dehydrogenase A (LDHA) is highly expressed in many tumor cells and promotes the conversion of pyruvate to lactic acid in the glucose pathway, providing energy and synthetic precursors for rapid proliferation of tumor cells. Therefore, inhibition of LDHA has become a widely concerned tumor treatment strategy. However, the research and development of highly efficient and low toxic LDHA small molecule inhibitors still faces challenges. To discover potential inhibitors against LDHA, virtual screening based on molecular docking techniques was performed from Specs database of more than 260,000 compounds and Chemdiv-smart database of more than 1,000 compounds. Through molecular dynamics (MD) simulation studies, we identified 12 potential LDHA inhibitors, all of which can stably bind to human LDHA protein and form multiple interactions with its active central residues. In order to verify the inhibitory activities of these compounds, we established an enzyme activity assay system and measured their inhibitory effects on recombinant human LDHA. The results showed that Compound 6 could inhibit the catalytic effect of LDHA on pyruvate in a dose-dependent manner with an EC50 value of 14.54 ± 0.83 µM. Further in vitro experiments showed that Compound 6 could significantly inhibit the proliferation of various tumor cell lines such as pancreatic cancer cells and lung cancer cells, reduce intracellular lactic acid content and increase intracellular reactive oxygen species (ROS) level. In summary, through virtual screening and in vitro validation, we found that Compound 6 is a small molecule inhibitor for LDHA, providing a good lead compound for the research and development of LDHA related targeted anti-tumor drugs.

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References

  1. Zhang S-L, He Y, Tam KY (2018) Targeting cancer metabolism to develop human lactate dehydrogenase (hLDH)5 inhibitors. Drug Discovery Today 23:1407–1415

    Article  CAS  PubMed  Google Scholar 

  2. Augoff K, Hryniewicz-Jankowska A, Tabola R (2015) Lactate dehydrogenase 5: an old friend and a new hope in the war on cancer. Cancer Lett 358:1–7

    Article  CAS  PubMed  Google Scholar 

  3. Rani R, Kumar V (2016) Recent update on human lactate dehydrogenase enzyme 5 (hLDH5) inhibitors: a Promising Approach for Cancer Chemotherapy. J Med Chem 59:487–496

    Article  CAS  PubMed  Google Scholar 

  4. Vander Heiden MG, Cantley LC, Thompson CB (2009) Understanding the Warburg effect: the metabolic requirements of cell proliferation. Sci (New York N Y) 324:1029–1033

    Article  CAS  Google Scholar 

  5. Buonfiglio R, Ferraro M, Falchi F, Cavalli A, Masetti M, Recanatini M (2013) Collecting and assessing human lactate dehydrogenase-A conformations for structure-based virtual screening. J Chem Inf Model 53:2792–2797

    Article  CAS  PubMed  Google Scholar 

  6. Kohlmann A, Zech SG, Li F, Zhou T, Squillace RM, Commodore L, Greenfield MT, Lu X, Miller DP, Huang W-S (2013) Fragment growing and linking lead to novel nanomolar lactate dehydrogenase inhibitors. J Med Chem 56:1023–1040

    Article  CAS  PubMed  Google Scholar 

  7. Kolappan S, Shen DL, Mosi R, Sun J, McEachern EJ, Vocadlo DJ, Craig L (2015) Structures of lactate dehydrogenase A (LDHA) in apo, ternary and inhibitor-bound forms. Acta Crystallogr Sect D: Biol Crystallogr 71:185–195

    Article  CAS  Google Scholar 

  8. Boudreau A, Purkey HE, Hitz A, Robarge K, Peterson D, Labadie S, Kwong M, Hong R, Gao M, Del Nagro C, Pusapati R, Ma S, Salphati L, Pang J, Zhou A, Lai T, Li Y, Chen Z, Wei B, Yen I, Sideris S, McCleland M, Firestein R, Corson L, Vanderbilt A, Williams S, Daemen A, Belvin M, Eigenbrot C, Jackson PK, Malek S, Hatzivassiliou G, Sampath D, Evangelista M, O’Brien T (2016) Metabolic plasticity underpins innate and acquired resistance to LDHA inhibition. Nat Chem Biol 12:779–786

    Article  CAS  PubMed  Google Scholar 

  9. Read J, Winter V, Eszes C, Sessions R, Brady R (2001) Structural basis for altered activity of M-and H‐isozyme forms of human lactate dehydrogenase, proteins: structure, function, and Bioinformatics, 43 175–185

  10. Shi Y, Pinto BM (2014) Human lactate dehydrogenase a inhibitors: a molecular dynamics investigation. PLoS ONE 9:e86365

    Article  PubMed  PubMed Central  Google Scholar 

  11. Boudreau A, Purkey HE, Hitz A, Robarge K, Peterson D, Labadie S, Kwong M, Hong R, Gao M (2016) Del Nagro, metabolic plasticity underpins innate and acquired resistance to LDHA inhibition. Nat Chem Biol 12:779–786

    Article  CAS  PubMed  Google Scholar 

  12. Zhou Y, Tao P, Wang M, Xu P, Lu W, Lei P, You Q (2019) Development of novel human lactate dehydrogenase A inhibitors: high-throughput screening, synthesis, and biological evaluations. Eur J Med Chem 177:105–115

    Article  CAS  PubMed  Google Scholar 

  13. Xiang S, Huang D, He Q, Li J, Tam KY, Zhang SL, He Y (2020) Development of dual inhibitors targeting pyruvate dehydrogenase kinases and human lactate dehydrogenase A: high-throughput virtual screening, synthesis and biological validation. Eur J Med Chem 203:112579

    Article  CAS  PubMed  Google Scholar 

  14. Rong Y, Wu W, Ni X, Kuang T, Jin D, Wang D, Lou W (2013) Lactate dehydrogenase A is overexpressed in pancreatic cancer and promotes the growth of pancreatic cancer cells. Tumour Biology: J Int Soc Oncodevelopmental Biology Med 34:1523–1530

    Article  CAS  Google Scholar 

  15. Hou XM, Yuan SQ, Zhao D, Liu XJ, Wu XA (2019) LDH-A promotes malignant behavior via activation of epithelial-to-mesenchymal transition in lung adenocarcinoma. Biosci Rep, 39

  16. Zhou Y, Niu W, Luo Y, Li H, Xie Y, Wang H, Liu Y, Fan S, Li Z, Xiong W, Li X, Ren C, Tan M, Li G, Zhou M (2019) p53/Lactate dehydrogenase a axis negatively regulates aerobic glycolysis and tumor progression in breast cancer expressing wild-type p53. Cancer Sci 110:939–949

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Koukourakis MI, Giatromanolaki A, Sivridis E, Gatter KC, Harris AL (2006) Lactate dehydrogenase 5 expression in operable colorectal cancer: strong association with survival and activated vascular endothelial growth factor pathway–a report of the Tumour Angiogenesis Research Group. J Clin Oncology: Official J Am Soc Clin Oncol 24:4301–4308

    Article  CAS  Google Scholar 

  18. Mohajertehran F, Ayatollahi H, Jafarian AH, Khazaeni K, Soukhtanloo M, Shakeri MT, Mohtasham N (2019) Overexpression of Lactate dehydrogenase in the saliva and tissues of patients with Head and Neck squamous cell carcinoma. Rep Biochem Mol Biology 7:142–149

    Google Scholar 

  19. Zhuang L, Scolyer RA, Murali R, McCarthy SW, Zhang XD, Thompson JF, Hersey P (2010) Lactate dehydrogenase 5 expression in melanoma increases with disease progression and is associated with expression of Bcl-XL and Mcl-1, but not Bcl-2 proteins. Mod Pathology: Official J United States Can Acad Pathol Inc 23:45–53

    Article  CAS  Google Scholar 

  20. Zhao J, Huang X, Xu Z, Dai J, He H, Zhu Y, Wang H (2017) LDHA promotes tumor metastasis by facilitating epithelial–mesenchymal transition in renal cell carcinoma. Mol Med Rep 16:8335–8344

    Article  CAS  PubMed  Google Scholar 

  21. Fantin VR, St-Pierre J, Leder P (2006) Attenuation of LDH-A expression uncovers a link between glycolysis, mitochondrial physiology, and tumor maintenance. Cancer Cell 9:425–434

    Article  CAS  PubMed  Google Scholar 

  22. Le A, Cooper CR, Gouw AM, Dinavahi R, Maitra A, Deck LM, Royer RE, Vander Jagt DL, Semenza GL, Dang CV (2010) Inhibition of lactate dehydrogenase A induces oxidative stress and inhibits tumor progression. Proc Natl Acad Sci USA 107:2037–2042

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Thornburg JM, Nelson KK, Clem BF, Lane AN, Arumugam S, Simmons A, Eaton JW, Telang S, Chesney J (2008) Targeting aspartate aminotransferase in breast cancer. Breast cancer Research: BCR 10:R84

    Article  PubMed  PubMed Central  Google Scholar 

  24. Granchi C, Roy S, Giacomelli C, Macchia M, Tuccinardi T, Martinelli A, Lanza M, Betti L, Giannaccini G, Lucacchini A, Funel N, León LG, Giovannetti E, Peters GJ, Palchaudhuri R, Calvaresi EC, Hergenrother PJ, Minutolo F (2011) Discovery of N-hydroxyindole-based inhibitors of human lactate dehydrogenase isoform A (LDH-A) as starvation agents against cancer cells. J Med Chem 54:1599–1612

    Article  CAS  PubMed  Google Scholar 

  25. Billiard J, Dennison JB, Briand J, Annan RS, Chai D, Colón M, Dodson CS, Gilbert SA, Greshock J, Jing J, Lu H, McSurdy-Freed JE, Orband-Miller LA, Mills GB, Quinn CJ, Schneck JL, Scott GF, Shaw AN, Waitt GM, Wooster RF, Duffy KJ (2013) Quinoline 3-sulfonamides inhibit lactate dehydrogenase A and reverse aerobic glycolysis in cancer cells. Cancer Metabolism 1:19

    Article  PubMed  PubMed Central  Google Scholar 

  26. Fradera X, Babaoglu K (2017) Overview of methods and strategies for conducting virtual small molecule screening. Curr Protocols Chem Biology 9:196–212

    Article  CAS  Google Scholar 

  27. Forli S (2015) Charting a path to success in virtual screening. Molecules 20:18732–18758

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. McInnes C (2007) Virtual screening strategies in drug discovery. Curr Opin Chem Biol 11:494–502

    Article  CAS  PubMed  Google Scholar 

  29. Truchon JF, Bayly CI (2007) Evaluating virtual screening methods: good and bad metrics for the early recognition problem. J Chem Inf Model 47:488–508

    Article  CAS  PubMed  Google Scholar 

  30. Kontoyianni M (2017) Docking and virtual screening in Drug Discovery, methods in molecular biology. (Clifton N J) 1647:255–266

    Google Scholar 

  31. Kitchen DB, Decornez H, Furr JR, Bajorath J (2004) Docking and scoring in virtual screening for drug discovery: methods and applications, Nature reviews. Drug Discovery 3:935–949

    Article  CAS  PubMed  Google Scholar 

  32. Bitencourt-Ferreira G, Veit-Acosta M, de Azevedo, Jr. WF (2019) Hydrogen bonds in protein-ligand complexes. Methods Mol Biology (Clifton N J) 2053:93–107

    Article  CAS  Google Scholar 

  33. Ding J, Gumpena R, Boily M O, Caron A, Chong O, H Cox J, Dumais V, Gaudreault S, Graff H A, King A, Knight J, Oballa R, Surendradoss J, Tang T, Wu J, Lowther T W, Powell A D (2021) Dual glycolate Oxidase/Lactate dehydrogenase A inhibitors for primary Hyperoxaluria. ACS Med Chem Lett 12:1116–1123

  34. Kamal S, Derbala HA, Alterary SS, Ben Bacha A, Alonazi M, El-Ashrey MK (2021) Eid El-Sayed, Synthesis, Biological, and Molecular Docking studies on 4, 5, 6, 7-Tetrahydrobenzo [b] thiophene derivatives and their nanoparticles targeting Colorectal Cancer. ACS Omega 6:28992–29008

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Doherty JR, Cleveland JL (2013) Targeting lactate metabolism for cancer therapeutics. J Clin Investig 123:3685–3692

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. de la Cruz-López KG, Castro-Muñoz LJ, Reyes-Hernández DO, García-Carrancá A (2019) Manzo-Merino, Lactate in the regulation of Tumor Microenvironment and therapeutic approaches. Front Oncol 9:1143

    Article  PubMed  PubMed Central  Google Scholar 

  37. Liberti MV, Locasale JW (2016) The Warburg Effect: how does it Benefit Cancer cells? Trends Biochem Sci 41:211–218

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Arra M, Swarnkar G, Ke K, Otero JE, Ying J, Duan X, Maruyama T, Rai MF, O’Keefe RJ, Mbalaviele G, Shen J, Abu-Amer Y (2020) LDHA-mediated ROS generation in chondrocytes is a potential therapeutic target for osteoarthritis. Nat Commun 11:3427

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Cournia Z, Allen B, Sherman W (2017) Relative binding Free Energy calculations in Drug Discovery: recent advances and practical considerations. J Chem Inf Model 57:2911–2937

    Article  CAS  PubMed  Google Scholar 

  40. Deng Y, Roux B (2009) Computations of standard binding free energies with molecular dynamics simulations. J Phys Chem 113:2234–2246

    Article  CAS  Google Scholar 

  41. Genheden S, Ryde U (2015) The MM/PBSA and MM/GBSA methods to estimate ligand-binding affinities. Expert Opin Drug Discov 10:449–461

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Sun H, Li Y, Tian S, Xu L, Hou T (2014) Assessing the performance of MM/PBSA and MM/GBSA methods. 4. Accuracies of MM/PBSA and MM/GBSA methodologies evaluated by various simulation protocols using PDBbind data set. Phys Chem Chem Phys 16:16719–16729

    Article  CAS  PubMed  Google Scholar 

  43. Wang E, Sun H, Wang J, Wang Z, Liu H, Zhang JZH, Hou T (2019) End-point binding Free Energy calculation with MM/PBSA and MM/GBSA: strategies and applications in Drug Design. Chem Rev 119:9478–9508

    Article  CAS  PubMed  Google Scholar 

  44. Friesner RA, Banks JL, Murphy RB, Halgren TA, Klicic JJ, Mainz DT, Repasky MP, Knoll EH, Shelley M, Perry JK, Shaw DE, Francis P, Shenkin PS (2004) Glide: a new approach for rapid, accurate docking and scoring. 1. Method and assessment of docking accuracy. J Med Chem 47:1739–1749

    Article  CAS  PubMed  Google Scholar 

  45. 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 27:221–234

    Article  PubMed  Google Scholar 

  46. Kawatkar S, Wang H, Czerminski R, Joseph-McCarthy D (2009) Virtual fragment screening: an exploration of various docking and scoring protocols for fragments using glide. J Comput Aided Mol Des 23:527–539

    Article  CAS  PubMed  Google Scholar 

  47. Case DA, Cheatham TE 3rd, Darden T, Gohlke H, Luo R, Merz KM Jr., Onufriev A, Simmerling C, Wang B, Woods RJ (2005) The Amber biomolecular simulation programs. J Comput Chem 26:1668–1688

  48. Ponder JW, Case DA (2003) Force fields for protein simulations. Adv Protein Chem 66:27–85

    Article  CAS  PubMed  Google Scholar 

  49. Tian C, Kasavajhala K, Belfon KAA, Raguette L, Huang H, Migues AN, Bickel J, Wang Y, Pincay J, Wu Q (2020) Simmerling, ff19SB: amino-acid-specific protein backbone parameters trained against Quantum Mechanics Energy Surfaces in Solution. J Chem Theory Comput 16:528–552

    Article  PubMed  Google Scholar 

  50. Mark P, Nilsson L (2001) Structure and Dynamics of the TIP3P, SPC, and SPC/E Water Models at 298 K. J Phys Chem A 105:9954–9960

    Article  CAS  Google Scholar 

  51. Frisch MJ, Trucks GW, Schlegel HB, Scuseria GE, Robb MA, Cheeseman JR, Scalmani G, Barone V, Petersson GA, Nakatsuji H, Li X, Caricato M, Marenich AV, Bloino J, Janesko BG, Gomperts R, Mennucci B, Hratchian HP, Ortiz JV, Izmaylov AF, Sonnenberg JL, Williams F, Ding F, Lipparini F, Egidi J, Goings B, Peng A, Petrone T, Henderson D, Ranasinghe VG, Zakrzewski J, Gao N, Rega G, Zheng W, Liang M, Hada M, Ehara K, Toyota R, Fukuda J, Hasegawa M, Ishida T, Nakajima Y, Honda O, Kitao H, Nakai T, Vreven K, Throssell JA, Montgomery JE Jr., Peralta F, Ogliaro MJ, Bearpark JJ, Heyd EN, Brothers KN, Kudin VN, Staroverov TA, Keith R, Kobayashi J, Normand K, Raghavachari AP, Rendell JC, Burant SS, Iyengar J, Tomasi M, Cossi JM, Millam M, Klene C, Adamo R, Cammi JW, Ochterski RL, Martin K. Morokuma, O. Farkas, J.B. Foresman, D.J. Fox, Gaussian 16 Rev. C.01, Wallingford, CT, 2016.

  52. Hohenstein EG, Chill ST, Sherrill CD (2008) Assessment of the performance of the M05-2X and M06-2X exchange-correlation functionals for noncovalent interactions in Biomolecules. J Chem Theory Comput 4:1996–2000

    Article  CAS  PubMed  Google Scholar 

  53. Walker M, Harvey AJ, Sen A, Dessent CE (2013) Performance of M06, M06-2X, and M06-HF density functionals for conformationally flexible anionic clusters: M06 functionals perform better than B3LYP for a model system with dispersion and ionic hydrogen-bonding interactions. J Phys Chem 117:12590–12600

    Article  CAS  Google Scholar 

  54. Becke AD (1993) Density-functional thermochemistry. III. The role of exact exchange. J Chem Phys 98:5648–5652

    Article  CAS  Google Scholar 

  55. Becke A.D. (1988) Density-functional exchange-energy approximation with correct asymptotic behavior. Phys Rev Gen Phys 38:3098–3100

  56. Lee C, Yang W, Parr RG (1988) Development of the Colle-Salvetti correlation-energy formula into a functional of the electron density, physical review. B Condens Matter 37:785–789

    CAS  Google Scholar 

  57. Lu T, Chen F (2012) Multiwfn: a multifunctional wavefunction analyzer. J Comput Chem 33:580–592

    Article  PubMed  Google Scholar 

  58. Roe DR, Cheatham TE (2013) 3rd, PTRAJ and CPPTRAJ: Software for Processing and Analysis of Molecular Dynamics Trajectory Data. J Chem Theory Comput 9:3084–3095

    Article  CAS  PubMed  Google Scholar 

  59. Bernstein J, Davis RE, Shimoni L, Chang N-L (1995) Patterns in Hydrogen Bonding: functionality and graph set analysis in crystals. Angewandte Chemie Int Ed Engl 34:1555–1573

    Article  CAS  Google Scholar 

  60. Lyublinskaya OG, Ivanova JS, Pugovkina NA, Kozhukharova IV, Kovaleva ZV, Shatrova AN, Aksenov ND, Zenin VV, Kaulin YA, Gamaley IA, Nikolsky NN (2017) Redox environment in stem and differentiated cells: a quantitative approach. Redox Biol 12:758–769

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (Grant Nos. 21903024, 32071262, 32171271, 32271329), the Natural Science Foundation of Hunan Procinve (Grant No. 2024JJ2042), the Science and Technology Innovation Program of Hunan Province (Grant No. 2020RC4023), the Scientific Research Program of FuRong Laboratory (Grant No. 2023SK2096), and Scientific research project of Department of Education of Hunan Province (Key Project, Grant No. 23A0084). We thank the Bioinformatics Center of Hunan Normal University.

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Yuanyuan Shu performed the formal analysis, developed the prediction method, designed and implemented the experiments, analysed the results, and wrote the paper. Jianda Yue contributed to the designed the experiments. Yaqi Li contributed to the investigation and data visualization. Jiaxu Wang, Yekui Yin and Tingting Li contributed to data collation and results analysis. Songping Liang contributed to the design of experimental strategies. Gaihua Zhang contributed to the supervision for the data analysis. Zhonghua Liu contributed to the design of experimental strategies. Ying Wang contributed to management and coordination responsibility for the research activity planning and execution, and contributed to the drafting and revision of the original manuscript.

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Correspondence to Gaihua Zhang, Zhonghua Liu or Ying Wang.

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Shu, Y., Yue, J., Li, Y. et al. Development of human lactate dehydrogenase a inhibitors: high-throughput screening, molecular dynamics simulation and enzyme activity assay. J Comput Aided Mol Des 38, 28 (2024). https://doi.org/10.1007/s10822-024-00568-y

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