IDENTIFICATION OF SMALL LIGAND MOLECULES AS POTENT INHIBITORS OF THE MYL9 PROTEIN: AS FUTURE CANCER LEADS.
Main Article Content
Keywords
.
Abstract
A collection of illnesses collectively known as Cancer arises when cells proliferate uncontrollably and spread to other parts of the body. Cancer can impact any tissue or organ. Myosin regulatory light chain 9 (MYL9) or Myosin light polypeptide 9, is the Myosin regulatory subunit that plays a vital role in regulating both smooth muscle and non-muscle cell contractile activity via phosphorylation, implicated in cytokinesis, receptor capping, and cell locomotion. The current study considers the MYL9 protein a prognostic marker and therapeutic target for various tumours. The current investigation aims to identify ligands that may act as inhibitors of the MYL9 protein for the treatment of multiple types of Cancer. The MYL9 protein's homology model is produced using Google ColabFold's AlphaFold. The protein's three-dimensional structure has been verified. The top ten ligands are suggested as effective inhibitors of MYL9 for Cancer treatment based on virtual screening experiments. It has been identified that the amino acid residues ILE41, ILE49, LEU54, MET73, GLU76, MET89, and LYS93 preferentially dock with ligands, yielding a good Glide Score. The proposed ligands are validated through MM-GBSA, SASA, and ADMET properties. The pharmacophore groups that strongly inhibit the MYL9 protein are proposed.
References
1. Wen, B., et al., MYL9 promotes squamous cervical cancer migration and invasion by enhancing aerobic glycolysis. Journal of International Medical Research, 2023. 51(11): p. 03000605231208582.
2. Lv, M., L. Luo, and X. Chen, The landscape of prognostic and immunological role of myosin light chain 9 (MYL9) in human tumors. Immunity, Inflammation and Disease, 2022. 10(2): p. 241-254.
3. Kruthika, B.S., et al., Expression pattern and prognostic significance of myosin light chain 9 (MYL9): a novel biomarker in glioblastoma. Journal of Clinical Pathology, 2019. 72(10): p. 677-681.
4. Feng, M., et al., Myosin light chain 9 promotes the proliferation, invasion, migration and angiogenesis of colorectal cancer cells by binding to Yes-associated protein 1 and regulating Hippo signaling. Bioengineered, 2022. 13(1): p. 96-106.
5. Deng, S., et al., MYL9 expressed in cancer-associated fibroblasts regulate the immune microenvironment of colorectal cancer and promotes tumor progression in an autocrine manner. Journal of Experimental & Clinical Cancer Research, 2023. 42(1): p. 294.
6. Sellers, J.R., Myosins: a diverse superfamily. Biochimica et Biophysica Acta (BBA)-Molecular Cell Research, 2000. 1496(1): p. 3-22.
7. Harrington, W.F. and M.E. Rodgers, Myosin. Annual review of biochemistry, 1984. 53(1): p. 35-73.
8. Sun, J., et al., Distinct roles of smooth muscle and non-muscle myosin light chain-mediated smooth muscle contraction. Frontiers in Physiology, 2020. 11: p. 593966.
9. Hirasawa, Y., et al., Methylation status of genes upregulated by demethylating agent 5-aza-2′-deoxycytidine in hepatocellular carcinoma. Oncology, 2007. 71(1-2): p. 77-85.
10. Luo, X.-G., et al., Histone methyltransferase SMYD3 promotes MRTF-A-mediated transactivation of MYL9 and migration of MCF-7 breast cancer cells. Cancer letters, 2014. 344(1): p. 129-137.
11. Zhao, S., W. Xiong, and K. Xu, MiR-663a, regulated by lncRNA GAS5, contributes to osteosarcoma development through targeting MYL9. Human & Experimental Toxicology, 2020. 39(12): p. 1607-1618.
12. Walsh, M.P., et al., Calcium-independent myosin light chain kinase of smooth muscle. Preparation by limited chymotryptic digestion of the calcium ion dependent enzyme, purification and characterization. Biochemistry, 1982. 21(8): p. 1919-1925.
13. Small, J.V. and A. SOBIESZEK, Ca‐Regulation of Mammalian Smooth Muscle Actomyosin via a Kinase‐Phosphatase‐Dependent Phosphorylation and Dephosphorylation of the 20000‐Mr Light Chain of Myosin. European Journal of Biochemistry, 1977. 76(2): p. 521-530.
14. Badineni, M., et al., Structure Elucidation and Identification of Novel Lead Molecules against Sulfur Import Protein cysA of Mycobacterium tuberculosis. Current Protein and Peptide Science, 2023. 24(7): p. 589-609.
15. Malkhed, V., et al., Study of interactions between Mycobacterium tuberculosis proteins: SigK and anti-SigK. Journal of molecular modeling, 2011. 17(5): p. 1109-1119.
16. Malkhed, V., et al., Identification of novel leads applying in silico studies for Mycobacterium multidrug resistant (MMR) protein. Journal of Biomolecular Structure and Dynamics, 2014. 32(12): p. 1889-1906.
17. Senior, A., et al., AlphaFold: Using AI for scientific discovery. DeepMind. Recuperado de: https://deepmind. com/blog/alphafold, 2018.
18. Varadi, M., et al., AlphaFold Protein Structure Database: massively expanding the structural coverage of protein-sequence space with high-accuracy models. Nucleic acids research, 2022. 50(D1): p. D439-D444.
19. Jumper, J., et al., Applying and improving AlphaFold at CASP14. Proteins: Structure, Function, and Bioinformatics, 2021. 89(12): p. 1711-1721.
20. da Silva, G.M., et al., Predicting Relative Populations of Protein Conformations without a Physics Engine Using AlphaFold 2. bioRxiv, 2023.
21. Hooft, R.W., et al., Errors in protein structures. Nature, 1996. 381(6580): p. 272-272.
22. Wiederstein, M. and M.J. Sippl, ProSA-web: interactive web service for the recognition of errors in three-dimensional structures of proteins. Nucleic acids research, 2007. 35(suppl_2): p. W407-W410.
23. Ziemba, P., et al., Using the PROSA method in offshore wind farm location problems. Energies, 2017. 10(11): p. 1755.
24. Prajapat, R., A. Marwal, and R. Gaur, Recognition of Errors in the Refinement and Validation of Three‐Dimensional Structures of AC1 Proteins of Begomovirus Strains by Using ProSA‐Web. Journal of Viruses, 2014. 2014(1): p. 752656.
25. 攀苏, Stereochemistry of polypeptide chain configurations. J. mol. Biol, 1963. 7: p. 95-99.
26. Pauling, L., R.B. Corey, and H.R. Branson, The structure of proteins: two hydrogen-bonded helical configurations of the polypeptide chain. Proceedings of the National Academy of Sciences, 1951. 37(4): p. 205-211.
27. Elfmann, C. and J. Stülke, PAE viewer: a webserver for the interactive visualization of the predicted aligned error for multimer structure predictions and crosslinks. Nucleic acids research, 2023. 51(W1): p. W404-W410.
28. Dundas, J., et al., CASTp: computed atlas of surface topography of proteins with structural and topographical mapping of functionally annotated residues. Nucleic acids research, 2006. 34(suppl_2): p. W116-W118.
29. Tian, W., et al., CASTp 3.0: computed atlas of surface topography of proteins. Nucleic acids research, 2018. 46(W1): p. W363-W367.
30. Connolly, M.L., Analytical molecular surface calculation. Applied Crystallography, 1983. 16(5): p. 548-558.
31. Connolly, M.L., Solvent-accessible surfaces of proteins and nucleic acids. Science, 1983. 221(4612): p. 709-713.
32. Halgren, T., New method for fast and accurate binding‐site identification and analysis. Chemical biology & drug design, 2007. 69(2): p. 146-148.
33. Halgren, T.A., Identifying and characterizing binding sites and assessing druggability. Journal of chemical information and modeling, 2009. 49(2): p. 377-389.
34. Lionta, E., et al., Structure-based virtual screening for drug discovery: principles, applications and recent advances. Current topics in medicinal chemistry, 2014. 14(16): p. 1923-1938.
35. Sastry, M., et al., Large-scale systematic analysis of 2D fingerprint methods and parameters to improve virtual screening enrichments. Journal of chemical information and modeling, 2010. 50(5): p. 771-784.
36. Nada, H., A. Elkamhawy, and K. Lee, Identification of 1H-purine-2, 6-dione derivative as a potential SARS-CoV-2 main protease inhibitor: molecular docking, dynamic simulations, and energy calculations. PeerJ, 2022. 10: p. e14120.
37. Shelley, J.C., et al., Epik: a software program for pK a prediction and protonation state generation for drug-like molecules. Journal of computer-aided molecular design, 2007. 21: p. 681-691.
38. Greenwood, J.R., et al., Towards the comprehensive, rapid, and accurate prediction of the favorable tautomeric states of drug-like molecules in aqueous solution. Journal of computer-aided molecular design, 2010. 24(6): p. 591-604.
39. Jorgensen, W.L., D.S. Maxwell, and J. Tirado-Rives, Development and testing of the OPLS all-atom force field on conformational energetics and properties of organic liquids. Journal of the american chemical society, 1996. 118(45): p. 11225-11236.
40. Madhavi Sastry, G., et al., Protein and ligand preparation: parameters, protocols, and influence on virtual screening enrichments. Journal of computer-aided molecular design, 2013. 27: p. 221-234.
41. Jorgensen, W.L. and J. Tirado-Rives, The OPLS [optimized potentials for liquid simulations] potential functions for proteins, energy minimizations for crystals of cyclic peptides and crambin. Journal of the American Chemical Society, 1988. 110(6): p. 1657-1666.
42. Shivakumar, D., et al., Prediction of absolute solvation free energies using molecular dynamics free energy perturbation and the OPLS force field. Journal of chemical theory and computation, 2010. 6(5): p. 1509-1519.
43. Friesner, R.A., et al., Glide: a new approach for rapid, accurate docking and scoring. 1. Method and assessment of docking accuracy. Journal of medicinal chemistry, 2004. 47(7): p. 1739-1749.
44. Erickson, J.A., et al., Lessons in molecular recognition: the effects of ligand and protein flexibility on molecular docking accuracy. Journal of medicinal chemistry, 2004. 47(1): p. 45-55.
45. Gilson, M.K. and H.-X. Zhou, Calculation of protein-ligand binding affinities. Annu. Rev. Biophys. Biomol. Struct., 2007. 36(1): p. 21-42.
46. Morris, G.M. and M. Lim-Wilby, Molecular docking. Molecular modeling of proteins, 2008: p. 365-382.
47. Harrison, R.L. Introduction to monte carlo simulation. in AIP conference proceedings. 2010.
48. Friesner, R.A., et al., Extra precision glide: Docking and scoring incorporating a model of hydrophobic enclosure for protein− ligand complexes. Journal of medicinal chemistry, 2006. 49(21): p. 6177-6196.
49. Halgren, T.A., et al., Glide: a new approach for rapid, accurate docking and scoring. 2. Enrichment factors in database screening. Journal of medicinal chemistry, 2004. 47(7): p. 1750-1759.
50. Dixon, S.L., et al., Medicinal Chemistry.
51. Kollman, P.A., et al., Calculating structures and free energies of complex molecules: combining molecular mechanics and continuum models. Accounts of chemical research, 2000. 33(12): p. 889-897.
52. Tsui, V. and D.A. Case, Theory and applications of the generalized Born solvation model in macromolecular simulations. Biopolymers: Original Research on Biomolecules, 2000. 56(4): p. 275-291.
53. Srinivasan, J., et al., Continuum solvent studies of the stability of DNA, RNA, and phosphoramidate− DNA helices. Journal of the American Chemical Society, 1998. 120(37): p. 9401-9409.
54. Hou, T., et al., Assessing the performance of the MM/PBSA and MM/GBSA methods. 1. The accuracy of binding free energy calculations based on molecular dynamics simulations. Journal of chemical information and modeling, 2011. 51(1): p. 69-82.
55. Genheden, S. and U. Ryde, The MM/PBSA and MM/GBSA methods to estimate ligand-binding affinities. Expert opinion on drug discovery, 2015. 10(5): p. 449-461.
56. Ausaf Ali, S., et al., A review of methods available to estimate solvent-accessible surface areas of soluble proteins in the folded and unfolded states. Current Protein and Peptide Science, 2014. 15(5): p. 456-476.
57. Ashraf, N., et al., Combined 3D-QSAR, molecular docking and dynamics simulations studies to model and design TTK inhibitors. Frontiers in Chemistry, 2022. 10: p. 1003816.
58. Durham, E., et al., Solvent accessible surface area approximations for rapid and accurate protein structure prediction. Journal of molecular modeling, 2009. 15: p. 1093-1108.
59. Shrake, A. and J.A. Rupley, Environment and exposure to solvent of protein atoms. Lysozyme and insulin. Journal of molecular biology, 1973. 79(2): p. 351-371.
60. Mostrag-Szlichtyng, A. and A. Worth, Review of QSAR models and software tools for predicting biokinetic properties. Luxembourg, 2010. 10: p. 94537.
61. Ntie-Kang, F., An in silico evaluation of the ADMET profile of the StreptomeDB database. Springerplus, 2013. 2: p. 1-11.
62. Zhu, Y., et al., ADME/toxicity prediction and antitumor activity of novel nitrogenous heterocyclic compounds designed by computer targeting of alkylglycerone phosphate synthase. Oncology letters, 2018. 16(2): p. 1431-1438.
63. Madhavaram, M., et al., High-throughput virtual screening, ADME analysis, and estimation of MM/GBSA binding-free energies of azoles as potential inhibitors of Mycobacterium tuberculosis H37Rv. Journal of Receptors and Signal Transduction, 2019. 39(4): p. 312-320.
64. Guengerich, F.P., Cytochrome p450 and chemical toxicology. Chemical research in toxicology, 2008. 21(1): p. 70-83.
65. Pelkonen, O., et al., Inhibition and induction of human cytochrome P450 (CYP) enzymes. Xenobiotica, 1998. 28(12): p. 1203-1253.
2. Lv, M., L. Luo, and X. Chen, The landscape of prognostic and immunological role of myosin light chain 9 (MYL9) in human tumors. Immunity, Inflammation and Disease, 2022. 10(2): p. 241-254.
3. Kruthika, B.S., et al., Expression pattern and prognostic significance of myosin light chain 9 (MYL9): a novel biomarker in glioblastoma. Journal of Clinical Pathology, 2019. 72(10): p. 677-681.
4. Feng, M., et al., Myosin light chain 9 promotes the proliferation, invasion, migration and angiogenesis of colorectal cancer cells by binding to Yes-associated protein 1 and regulating Hippo signaling. Bioengineered, 2022. 13(1): p. 96-106.
5. Deng, S., et al., MYL9 expressed in cancer-associated fibroblasts regulate the immune microenvironment of colorectal cancer and promotes tumor progression in an autocrine manner. Journal of Experimental & Clinical Cancer Research, 2023. 42(1): p. 294.
6. Sellers, J.R., Myosins: a diverse superfamily. Biochimica et Biophysica Acta (BBA)-Molecular Cell Research, 2000. 1496(1): p. 3-22.
7. Harrington, W.F. and M.E. Rodgers, Myosin. Annual review of biochemistry, 1984. 53(1): p. 35-73.
8. Sun, J., et al., Distinct roles of smooth muscle and non-muscle myosin light chain-mediated smooth muscle contraction. Frontiers in Physiology, 2020. 11: p. 593966.
9. Hirasawa, Y., et al., Methylation status of genes upregulated by demethylating agent 5-aza-2′-deoxycytidine in hepatocellular carcinoma. Oncology, 2007. 71(1-2): p. 77-85.
10. Luo, X.-G., et al., Histone methyltransferase SMYD3 promotes MRTF-A-mediated transactivation of MYL9 and migration of MCF-7 breast cancer cells. Cancer letters, 2014. 344(1): p. 129-137.
11. Zhao, S., W. Xiong, and K. Xu, MiR-663a, regulated by lncRNA GAS5, contributes to osteosarcoma development through targeting MYL9. Human & Experimental Toxicology, 2020. 39(12): p. 1607-1618.
12. Walsh, M.P., et al., Calcium-independent myosin light chain kinase of smooth muscle. Preparation by limited chymotryptic digestion of the calcium ion dependent enzyme, purification and characterization. Biochemistry, 1982. 21(8): p. 1919-1925.
13. Small, J.V. and A. SOBIESZEK, Ca‐Regulation of Mammalian Smooth Muscle Actomyosin via a Kinase‐Phosphatase‐Dependent Phosphorylation and Dephosphorylation of the 20000‐Mr Light Chain of Myosin. European Journal of Biochemistry, 1977. 76(2): p. 521-530.
14. Badineni, M., et al., Structure Elucidation and Identification of Novel Lead Molecules against Sulfur Import Protein cysA of Mycobacterium tuberculosis. Current Protein and Peptide Science, 2023. 24(7): p. 589-609.
15. Malkhed, V., et al., Study of interactions between Mycobacterium tuberculosis proteins: SigK and anti-SigK. Journal of molecular modeling, 2011. 17(5): p. 1109-1119.
16. Malkhed, V., et al., Identification of novel leads applying in silico studies for Mycobacterium multidrug resistant (MMR) protein. Journal of Biomolecular Structure and Dynamics, 2014. 32(12): p. 1889-1906.
17. Senior, A., et al., AlphaFold: Using AI for scientific discovery. DeepMind. Recuperado de: https://deepmind. com/blog/alphafold, 2018.
18. Varadi, M., et al., AlphaFold Protein Structure Database: massively expanding the structural coverage of protein-sequence space with high-accuracy models. Nucleic acids research, 2022. 50(D1): p. D439-D444.
19. Jumper, J., et al., Applying and improving AlphaFold at CASP14. Proteins: Structure, Function, and Bioinformatics, 2021. 89(12): p. 1711-1721.
20. da Silva, G.M., et al., Predicting Relative Populations of Protein Conformations without a Physics Engine Using AlphaFold 2. bioRxiv, 2023.
21. Hooft, R.W., et al., Errors in protein structures. Nature, 1996. 381(6580): p. 272-272.
22. Wiederstein, M. and M.J. Sippl, ProSA-web: interactive web service for the recognition of errors in three-dimensional structures of proteins. Nucleic acids research, 2007. 35(suppl_2): p. W407-W410.
23. Ziemba, P., et al., Using the PROSA method in offshore wind farm location problems. Energies, 2017. 10(11): p. 1755.
24. Prajapat, R., A. Marwal, and R. Gaur, Recognition of Errors in the Refinement and Validation of Three‐Dimensional Structures of AC1 Proteins of Begomovirus Strains by Using ProSA‐Web. Journal of Viruses, 2014. 2014(1): p. 752656.
25. 攀苏, Stereochemistry of polypeptide chain configurations. J. mol. Biol, 1963. 7: p. 95-99.
26. Pauling, L., R.B. Corey, and H.R. Branson, The structure of proteins: two hydrogen-bonded helical configurations of the polypeptide chain. Proceedings of the National Academy of Sciences, 1951. 37(4): p. 205-211.
27. Elfmann, C. and J. Stülke, PAE viewer: a webserver for the interactive visualization of the predicted aligned error for multimer structure predictions and crosslinks. Nucleic acids research, 2023. 51(W1): p. W404-W410.
28. Dundas, J., et al., CASTp: computed atlas of surface topography of proteins with structural and topographical mapping of functionally annotated residues. Nucleic acids research, 2006. 34(suppl_2): p. W116-W118.
29. Tian, W., et al., CASTp 3.0: computed atlas of surface topography of proteins. Nucleic acids research, 2018. 46(W1): p. W363-W367.
30. Connolly, M.L., Analytical molecular surface calculation. Applied Crystallography, 1983. 16(5): p. 548-558.
31. Connolly, M.L., Solvent-accessible surfaces of proteins and nucleic acids. Science, 1983. 221(4612): p. 709-713.
32. Halgren, T., New method for fast and accurate binding‐site identification and analysis. Chemical biology & drug design, 2007. 69(2): p. 146-148.
33. Halgren, T.A., Identifying and characterizing binding sites and assessing druggability. Journal of chemical information and modeling, 2009. 49(2): p. 377-389.
34. Lionta, E., et al., Structure-based virtual screening for drug discovery: principles, applications and recent advances. Current topics in medicinal chemistry, 2014. 14(16): p. 1923-1938.
35. Sastry, M., et al., Large-scale systematic analysis of 2D fingerprint methods and parameters to improve virtual screening enrichments. Journal of chemical information and modeling, 2010. 50(5): p. 771-784.
36. Nada, H., A. Elkamhawy, and K. Lee, Identification of 1H-purine-2, 6-dione derivative as a potential SARS-CoV-2 main protease inhibitor: molecular docking, dynamic simulations, and energy calculations. PeerJ, 2022. 10: p. e14120.
37. Shelley, J.C., et al., Epik: a software program for pK a prediction and protonation state generation for drug-like molecules. Journal of computer-aided molecular design, 2007. 21: p. 681-691.
38. Greenwood, J.R., et al., Towards the comprehensive, rapid, and accurate prediction of the favorable tautomeric states of drug-like molecules in aqueous solution. Journal of computer-aided molecular design, 2010. 24(6): p. 591-604.
39. Jorgensen, W.L., D.S. Maxwell, and J. Tirado-Rives, Development and testing of the OPLS all-atom force field on conformational energetics and properties of organic liquids. Journal of the american chemical society, 1996. 118(45): p. 11225-11236.
40. Madhavi Sastry, G., et al., Protein and ligand preparation: parameters, protocols, and influence on virtual screening enrichments. Journal of computer-aided molecular design, 2013. 27: p. 221-234.
41. Jorgensen, W.L. and J. Tirado-Rives, The OPLS [optimized potentials for liquid simulations] potential functions for proteins, energy minimizations for crystals of cyclic peptides and crambin. Journal of the American Chemical Society, 1988. 110(6): p. 1657-1666.
42. Shivakumar, D., et al., Prediction of absolute solvation free energies using molecular dynamics free energy perturbation and the OPLS force field. Journal of chemical theory and computation, 2010. 6(5): p. 1509-1519.
43. Friesner, R.A., et al., Glide: a new approach for rapid, accurate docking and scoring. 1. Method and assessment of docking accuracy. Journal of medicinal chemistry, 2004. 47(7): p. 1739-1749.
44. Erickson, J.A., et al., Lessons in molecular recognition: the effects of ligand and protein flexibility on molecular docking accuracy. Journal of medicinal chemistry, 2004. 47(1): p. 45-55.
45. Gilson, M.K. and H.-X. Zhou, Calculation of protein-ligand binding affinities. Annu. Rev. Biophys. Biomol. Struct., 2007. 36(1): p. 21-42.
46. Morris, G.M. and M. Lim-Wilby, Molecular docking. Molecular modeling of proteins, 2008: p. 365-382.
47. Harrison, R.L. Introduction to monte carlo simulation. in AIP conference proceedings. 2010.
48. Friesner, R.A., et al., Extra precision glide: Docking and scoring incorporating a model of hydrophobic enclosure for protein− ligand complexes. Journal of medicinal chemistry, 2006. 49(21): p. 6177-6196.
49. Halgren, T.A., et al., Glide: a new approach for rapid, accurate docking and scoring. 2. Enrichment factors in database screening. Journal of medicinal chemistry, 2004. 47(7): p. 1750-1759.
50. Dixon, S.L., et al., Medicinal Chemistry.
51. Kollman, P.A., et al., Calculating structures and free energies of complex molecules: combining molecular mechanics and continuum models. Accounts of chemical research, 2000. 33(12): p. 889-897.
52. Tsui, V. and D.A. Case, Theory and applications of the generalized Born solvation model in macromolecular simulations. Biopolymers: Original Research on Biomolecules, 2000. 56(4): p. 275-291.
53. Srinivasan, J., et al., Continuum solvent studies of the stability of DNA, RNA, and phosphoramidate− DNA helices. Journal of the American Chemical Society, 1998. 120(37): p. 9401-9409.
54. Hou, T., et al., Assessing the performance of the MM/PBSA and MM/GBSA methods. 1. The accuracy of binding free energy calculations based on molecular dynamics simulations. Journal of chemical information and modeling, 2011. 51(1): p. 69-82.
55. Genheden, S. and U. Ryde, The MM/PBSA and MM/GBSA methods to estimate ligand-binding affinities. Expert opinion on drug discovery, 2015. 10(5): p. 449-461.
56. Ausaf Ali, S., et al., A review of methods available to estimate solvent-accessible surface areas of soluble proteins in the folded and unfolded states. Current Protein and Peptide Science, 2014. 15(5): p. 456-476.
57. Ashraf, N., et al., Combined 3D-QSAR, molecular docking and dynamics simulations studies to model and design TTK inhibitors. Frontiers in Chemistry, 2022. 10: p. 1003816.
58. Durham, E., et al., Solvent accessible surface area approximations for rapid and accurate protein structure prediction. Journal of molecular modeling, 2009. 15: p. 1093-1108.
59. Shrake, A. and J.A. Rupley, Environment and exposure to solvent of protein atoms. Lysozyme and insulin. Journal of molecular biology, 1973. 79(2): p. 351-371.
60. Mostrag-Szlichtyng, A. and A. Worth, Review of QSAR models and software tools for predicting biokinetic properties. Luxembourg, 2010. 10: p. 94537.
61. Ntie-Kang, F., An in silico evaluation of the ADMET profile of the StreptomeDB database. Springerplus, 2013. 2: p. 1-11.
62. Zhu, Y., et al., ADME/toxicity prediction and antitumor activity of novel nitrogenous heterocyclic compounds designed by computer targeting of alkylglycerone phosphate synthase. Oncology letters, 2018. 16(2): p. 1431-1438.
63. Madhavaram, M., et al., High-throughput virtual screening, ADME analysis, and estimation of MM/GBSA binding-free energies of azoles as potential inhibitors of Mycobacterium tuberculosis H37Rv. Journal of Receptors and Signal Transduction, 2019. 39(4): p. 312-320.
64. Guengerich, F.P., Cytochrome p450 and chemical toxicology. Chemical research in toxicology, 2008. 21(1): p. 70-83.
65. Pelkonen, O., et al., Inhibition and induction of human cytochrome P450 (CYP) enzymes. Xenobiotica, 1998. 28(12): p. 1203-1253.
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Aug 28, 2022
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Priyadarshini Gangidi, Mounika Badineni, Madhavi Latha Bingi, Vani Kondaparthi, Hareesh Reddy Badepally, Kiran Kumar Mustyala, & Vasavi Malkhed. (2022). IDENTIFICATION OF SMALL LIGAND MOLECULES AS POTENT INHIBITORS OF THE MYL9 PROTEIN: AS FUTURE CANCER LEADS. Journal of Population Therapeutics and Clinical Pharmacology, 29(03), 2302-2320. https://doi.org/10.53555/y1g3r759
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Author Biographies
Priyadarshini Gangidi
Molecular Modelling Research Laboratory, Department of Chemistry, Osmania University, Hyderabad, Telangana, India –500007
Mounika Badineni
Molecular Modelling Research Laboratory, Department of Chemistry, Osmania University, Hyderabad, Telangana, India –500007
Madhavi Latha Bingi
Molecular Modelling Research Laboratory, Department of Chemistry, Osmania University, Hyderabad, Telangana, India –500007
Vani Kondaparthi
Molecular Modelling Research Laboratory, Department of Chemistry, Osmania University, Hyderabad, Telangana, India –500007
Hareesh Reddy Badepally
Molecular Modelling Research Laboratory, Department of Chemistry, Osmania University, Hyderabad, Telangana, India –500007
Kiran Kumar Mustyala
Department of Chemistry, Nizam College, Osmania University, Hyderabad, Telangana, India –500001
Vasavi Malkhed
Department of Chemistry, University College of Science, Saifabad, Osmania University, Hyderabad, Telangana, India –500004
How to Cite
Priyadarshini Gangidi, Mounika Badineni, Madhavi Latha Bingi, Vani Kondaparthi, Hareesh Reddy Badepally, Kiran Kumar Mustyala, & Vasavi Malkhed. (2022). IDENTIFICATION OF SMALL LIGAND MOLECULES AS POTENT INHIBITORS OF THE MYL9 PROTEIN: AS FUTURE CANCER LEADS. Journal of Population Therapeutics and Clinical Pharmacology, 29(03), 2302-2320. https://doi.org/10.53555/y1g3r759