QSAR Study of Anthranilic Acid Sulfonamides as Inhibitors of Methionine Aminopeptidase-2 using different chemometrics tools

Document Type : Original Article


1 Department of Pathology, School of Medicine, Kermanshah University of Medical Sciences, Kermanshah, Iran

2 Department of Master in Public Health, Faculty of Pharmacy, School of Medicine, Shiraz University of Medical Sciences, Shiraz, Iran

3 Department of Computer Engineering, Shiraz Branch, Islamic Azad University, Shiraz, Iran.

4 Department of Medicinal Chemistry, School of Pharmacy, Shiraz University of Medical Sciences, Shiraz, Iran


Quantitative structure activity relationships (QSAR) studies, as one of the most important areas in chemometrics, play a fundamental role in predicting the biological activity of new compounds and identifying ligand-receptor interactions. Quantitative relationships between molecular structure and methionine aminopeptidase-2 inhibitory activity of a series of anthranilic acid sulfonamides derivatives were discovered by different chemometrics tools including factor analysis based multiple linear regressions (FA-MLR), principale component regression analysis (PCRA) and genetic algorithm-partial least squares GA-PLS. The FA-MLR describes the effect of geometrical and quantum indices on enzyme inhibition activity of the studied molecules. The quality of PCRA equation is better than those derived from FA-MLR. GA-PLS analysis indicated that the topological (IC4 and MPC06), constitutional (nf) and geometrical (G (N..S)) parameters were the most significant parameters on methionine aminopeptidase-2 inhibitory activity. A comparison between the different statistical methods employed revealed that GA-PLS represented superior results and it could explain and predict 85% and 77% of variances in the pIC50 data, respectively.


1.    Schmidi H. Multivariate prediction for QSAR. Chemom Intell Lab Syst. 1997:3;125-34.
2.    Hansch C, Kurup A, Garg R, Gao H. Chem-bioinformatics and QSAR: a review of QSAR lacking positive hydrophobic terms. Chem Rev. 2001 Mar;101(3):619-72. doi: 10.1021/cr0000067. PMID: 11712499.
3.    Wold S, Trygg J,  Berglund A, Antti H. Some recent developments in PLS modeling. Chemom Intell Lab Syst. 2001;53:131-50.
4.    Hemmateenejad B, Miri R, Akhond M, Shamsipur M. QSAR study of the calcium channel antagonist activity of some recently synthesized dihydropyridine derivatives. An application of genetic algorithm for variable selection in MLR and PLS methods, Chemom Intell Lab Syst. 2002;64:1-99.
5.    Hemmateenejad B, Miri R, Akhond M, Shamsipur M. Quantitative structure-activity relationship study of recently synthesized 1,4-dihydropyridine calcium channel antagonists. Application of the Hansch analysis method. Arch Pharm (Weinheim). 2002 Dec;335(10):472-80. doi: 10.1002/ardp.200290001. PMID: 12506395.
6.    Hansch C, Fujita T, Analysis ρ-σ-π. A method for the correlation of biological activity and chemical structure, J Am Chem Soc. 1964;86:1616-26.
7.    Wan J, Zhang L, Yang G, Zhan CG. Quantitative structure-activity relationship for cyclic imide derivatives of protoporphyrinogen oxidase inhibitors: a study of quantum chemical descriptors from density functional theory. J Chem Inf Comput Sci. 2004 Nov-Dec;44(6):2099-105. doi: 10.1021/ci049793p. PMID: 15554680.
8.    Hansch C, Hoekman D, Gao H. Comparative QSAR: Toward a Deeper Understanding of Chemicobiological Interactions. Chem Rev. 1996 May 9;96(3):1045-1076. doi: 10.1021/cr9400976. PMID: 11848780.
9.    R. Todeschini, V. Consonni, Handbook of Molecular Descriptors. Wiley-VCH, Weinheim, 2000.
10.    Horvath D, Mao B. Neighborhood behavior. Fuzzy molecular descriptors and their influence on the relationship between structural similarity and property similarity. QSAR Comb Sci. 2003;22:498-509.
11.    Putta S, Eksterowicz J, Lemmen C, Stanton R. A novel subshape molecular descriptor. J Chem Inf Comput Sci. 2003 Sep-Oct;43(5):1623-35. doi: 10.1021/ci0256384. PMID: 14502497.
12.    Gupta S, Singh M, Madan AK. Superpendentic index: a novel topological descriptor for predicting biological activity. J Chem Inf Comput Sci. 1999 Mar-Apr;39(2):272-7. doi: 10.1021/ci980073q. Erratum in: J Chem Inf Comput Sci 1999 Nov-Dec;39(6):1230. PMID: 10192943.
13.    Consonni V, Todeschini R, Pavan M, Gramatica P. Structure/response correlations and similarity/diversity analysis by GETAWAY descriptors. 2. Application of the novel 3D molecular descriptors to QSAR/QSPR studies. J Chem Inf Comput Sci. 2002 May-Jun;42(3):693-705. doi: 10.1021/ci0155053. PMID: 12086531.
14.    Dragon, version 2.1, Milano Chemometrics and QSPR Group., Milano, Italy, 2002.
15.    Frisch MJ, Trucks GW, Schlegel HB, Scuseria GE, Robb MA, Cheeseman JR, Zakrzewski VG, Montgomery Jr JA, Stratmann RE, Burant JC, Dapprich S. Gaussian 98, revision a. 7, Gaussian. Inc., Pittsburgh, PA. 1998;12.
16.    Kendall RL, Yamada R, Bradshaw RA. Cotranslational amino-terminal processing. Methods Enzymol. 1990;185:398-407. doi: 10.1016/0076-6879(90)85035-m. PMID: 2143260.
17.    Bradshaw RA, Brickey WW, Walker KW. N-terminal processing: the methionine aminopeptidase and N alpha-acetyl transferase families. Trends Biochem Sci. 1998 Jul;23(7):263-7. doi: 10.1016/s0968-0004(98)01227-4. PMID: 9697417.
18.    Lowther WT, Matthews BW. Metalloaminopeptidases: common functional themes in disparate structural surroundings. Chem Rev. 2002 Dec;102(12):4581-608. doi: 10.1021/cr0101757. PMID: 12475202. 
19.    Tso JY, Hermodson MA, Zalkin H. Glutamine phosphoribosylpyrophosphate amidotransferase from cloned Escherichia coli purF. NH2-terminal amino acid sequence, identification of the glutamine site, and trace metal analysis. J Biol Chem. 1982 Apr 10;257(7):3532-6. PMID: 7037784.
20.    Boissel JP, Kasper TJ, Shah SC, Malone JI, Bunn HF. Amino-terminal processing of proteins: hemoglobin South Florida, a variant with retention of initiator methionine and N alpha-acetylation. Proc Natl Acad Sci U S A. 1985 Dec;82(24):8448-52. doi: 10.1073/pnas.82.24.8448. PMID: 3866233; PMCID: PMC390933.
21.    Chen S, Vetro JA, Chang YH. The specificity in vivo of two distinct methionine aminopeptidases in Saccharomyces cerevisiae. Arch Biochem Biophys. 2002 Feb 1;398(1):87-93. doi: 10.1006/abbi.2001.2675. Erratum in: Arch Biochem Biophys. 2003 Sep 1;417(1):128. PMID: 11811952.
22.    Frottin F, Martinez A, Peynot P, Mitra S, Holz RC, Giglione C, Meinnel T. The proteomics of N-terminal methionine cleavage. Mol Cell Proteomics. 2006 Dec;5(12):2336-49. doi: 10.1074/mcp.M600225-MCP200. Epub 2006 Sep 8. PMID: 16963780.
23.    Emami L, Sabet R, Khabnadideh S, Faghih Z, Thayori P. Quinazoline analogues as cytotoxic agents; QSAR, docking, and in silico studies. Res Pharm Sci. 2021 Aug 19;16(5):528-546. doi: 10.4103/1735-5362.323919. PMID: 34522200; PMCID: PMC8407157..
24.    Griffith EC, Su Z, Turk BE, Chen S, Chang YH, Wu Z, Biemann K, Liu JO. Methionine aminopeptidase (type 2) is the common target for angiogenesis inhibitors AGM-1470 and ovalicin. Chem Biol. 1997 Jun;4(6):461-71. doi: 10.1016/s1074-5521(97)90198-8. PMID: 9224570.
25.    Sin N, Meng L, Wang MQ, Wen JJ, Bornmann WG, Crews CM. The anti-angiogenic agent fumagillin covalently binds and inhibits the methionine aminopeptidase, MetAP-2. Proc Natl Acad Sci U S A. 1997 Jun 10;94(12):6099-103. doi: 10.1073/pnas.94.12.6099. PMID: 9177176; PMCID: PMC21008.
26.    Ingber D, Fujita T, Kishimoto S, Sudo K, Kanamaru T, Brem H, Folkman J. Synthetic analogues of fumagillin that inhibit angiogenesis and suppress tumour growth. Nature. 1990 Dec 6;348(6301):555-7. doi: 10.1038/348555a0. PMID: 1701033.
27.    Folkman J. What is the evidence that tumors are angiogenesis dependent? J Natl Cancer Inst. 1990 Jan 3;82(1):4-6. doi: 10.1093/jnci/82.1.4. PMID: 1688381.
28.    Folkman J, Watson K, Ingber D, Hanahan D. Induction of angiogenesis during the transition from hyperplasia to neoplasia. Nature. 1989 May 4;339(6219):58-61. doi: 10.1038/339058a0. PMID: 2469964.
29.    Wang J, Sheppard GS, Lou P, Kawai M, BaMaung N, Erickson SA, et al. Tumor suppression by a rationally designed reversible inhibitor of methionine aminopeptidase-2. Cancer Res. 2003 Nov 15;63(22):7861-9. PMID: 14633714.
30.    Bernier SG, Lazarus DD, Clark E, Doyle B, Labenski MT, Thompson CD, et al. A methionine aminopeptidase-2 inhibitor, PPI-2458, for the treatment of rheumatoid arthritis. Proc Natl Acad Sci U S A. 2004 Jul 20;101(29):10768-73. doi: 10.1073/pnas.0404105101. Epub 2004 Jul 12. PMID: 15249666; PMCID: PMC490009.
31.    Chun E, Han CK, Yoon JH, Sim TB, Kim YK, Lee KY. Novel inhibitors targeted to methionine aminopeptidase 2 (MetAP2) strongly inhibit the growth of cancers in xenografted nude model. Int J Cancer. 2005 Mar 10;114(1):124-30. doi: 10.1002/ijc.20687. PMID: 15523682.
32.    Towbin H, Bair KW, DeCaprio JA, Eck MJ, Kim S, Kinder FR, et al. Proteomics-based target identification: bengamides as a new class of methionine aminopeptidase inhibitors. J Biol Chem. 2003 Dec 26;278(52):52964-71. doi: 10.1074/jbc.M309039200. Epub 2003 Oct 8. PMID: 14534293.
33.    Kawai M, BaMaung NY, Fidanze SD, Erickson SA, Tedrow JS, Sanders WJ, et al. Development of sulfonamide compounds as potent methionine aminopeptidase type II inhibitors with antiproliferative properties. Bioorg Med Chem Lett. 2006 Jul 1;16(13):3574-7. doi: 10.1016/j.bmcl.2006.03.085. Epub 2006 May 2. PMID: 16632353.
34.    Sheppard GS, Wang J, Kawai M, Fidanze SD, BaMaung NY, Erickson SA, et al. Discovery and optimization of anthranilic acid sulfonamides as inhibitors of methionine aminopeptidase-2: a structural basis for the reduction of albumin binding. J Med Chem. 2006 Jun 29;49(13):3832-49. doi: 10.1021/jm0601001. PMID: 16789740.
35.    Thanikaivelan P, Subramanian V, Rao JR, Nair BU. Application of quantum chemical descriptor in quantitative structure activity and structure property relationship. Chem Phys Lett. 2000;323:59-70.
36.    Franke R, Gruska A. Chemometrics Methods in molecular design, in: H. van Waterbeemd, (Ed.), Methods and Principles in Medicinal Chemistry, VCH, Weinheim, 1995, Vol. 2, pp. 113–119.
37.    Kubinyi H. The quantitative analysis of structure-activity relationships, in: M.E. Wolff, (Ed.), Burger’s Medicinal Chemistry and Drug Discovery, 5th Ed.; Wiley, New York, 1995, Vol. 1, pp. 506-509.
38.    Leardi R. Application of Genetic Algorithm-PLS for Feature Selection in Spectral Data Sets, J. Chemomtr. 14 (2000) 643-655.
39.    Leardi R, Gonzalez AL. Genetic Algorithm Applied to Feature Selection in PLS Regression: How and When to Use Them. Chemom Intell Lab Syst. 1998;41:195-207.
40.    Fassihi A, Sabet R. QSAR study of p56(lck) protein tyrosine kinase inhibitory activity of flavonoid derivatives using MLR and GA-PLS. Int J Mol Sci. 2008 Sep;9(9):1876-1892. doi: 10.3390/ijms9091876. Epub 2008 Sep 22. PMID: 19325836; PMCID: PMC2635749.
41.    Leardi R. Genetic Algorithms in Chemometrics and Chemistry: A Review, J. Chemometrics 15 (2001) 559-569.
42.    Hemmateenejad B. Optimal QSAR Analysis of the Carcinogenic Activity of Drugs by Correlation Ranking and Genetic Algorithm-Based. J Chemometrics. 2004;18:475-485. 
43.    Cho SJ, Hermsmeier MA. Genetic Algorithm guided Selection: variable selection and subset selection. J Chem Inf Comput Sci. 2002 Jul-Aug;42(4):927-36. doi: 10.1021/ci010247v. PMID: 12132894.
44.    Ahmad S, Gromiha MM. Design and training of a neural network for predicting the solvent accessibility of proteins. J Comput Chem. 2003 Aug;24(11):1313-20. doi: 10.1002/jcc.10298. PMID: 12827672.
45.    Faghih Z, Emami L, Zomoridian K, Sabet R, Bargebid R, Mansourian A, et al Aryloxy Alkyl Theophylline Derivatives as Antifungal Agents: Design, Synthesis, Biological Evaluation and Computational Studies. ChemistrySelect. 2022;7.
46.    Deeb O, Hemmateenejad B, Jaber A, Garduno-Juarez R, Miri R. Effects of the Electronic and Physicochemical Parameters on the Carcinogenesis Activity of Some Sulfa Drug Using QSAR Analysis Based on Genetic-MLR & Genetic-PLS.Chemosphere. 2007;67:2122-2130.
47.    Salehi F, Emami L, Rezaie Z, Khabnadideh S, Tajik B, Sabet R. Fluconazole-Like Compounds as Potential Antifungal Agents: QSAR, Molecular Docking, and Molecular Dynamics Simulation. J Chem. 2022 Mar;2022:1-16