DTI Drug Target Insights 2022; 16: 54-68 ISSN 1177-3928 | DOI: 10.33393/dti.2022.2512 ORIGINAL RESEARCH ARTICLE Drug Target Insights - ISSN 1177-3928 - www.aboutscience.eu/dti © 2022 The Authors. This article is published by AboutScience and licensed under Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0). Commercial use is not permitted and is subject to Publisher’s permissions. Full information is available at www.aboutscience.eu Focus on Antimicrobial Resistance (AMR) Exploring the inhibitory mechanisms of indazole compounds against SAH/MTAN-mediated quorum sensing utilizing QSAR and docking Sisir Nandi1, Mohit Kumar2, Rashmi Kumari1, Aaruni Saxena3 1Department of Pharmaceutical Chemistry, Global Institute of Pharmaceutical Education and Research, Kashipur - India 2Department of Pharmacy, Vivek College of Technical Education, Bijnor - India 3Department of Cardiovascular Medicine, University of Nottingham, Nottingham - UK ABSTRACT The world is under the great threat of antimicrobial resistance (AMR) leading to premature deaths. Microorgan- isms can produce AMR via quorum sensing mechanisms utilizing S-adenosyl homocysteine/methylthioadenosine nucleosidase (SAH/MTAN) biosynthesis. But there is no specific drug developed to date to stop SAH/MTAN, which is a crucial target for the discovery of anti-quorum sensing compound. It has been shown that indazole com- pounds cause inhibition of SAH/MTAN-mediated quorum sensing, but the biochemical mechanisms have not yet been explored. Therefore, in this original research, an attempt has been made to explore essential structural features of these compounds by quantitative structure-activity relationship (QSAR) and molecular docking of indazole compounds having inhibition of SAH/MTAN-mediated quorum sensing. The validated QSAR predicted five essential descriptors and molecular docking helps to identify the active binding amino acid residues involved in ligand-receptor interactions that are responsible for producing the quorum sensing inhibitory mechanisms of indazole compounds against SAH/MTAN-mediated AMR. Keywords: Antimicrobial resistance, Indazole compounds, Molecular docking, QSAR, Quorum sensing, SAH/MTAN Received: October 26, 2022 Accepted: November 28, 2022 Published online: December 22, 2022 Corresponding author: Sisir Nandi Global Institute of Pharmaceutical Education & Research (GIPER) Jaspur Road Kashipur-244713, Uttarakhand - India sisir.iicb@gmail.com There are two types of AIs such as AI-1 and AI-2. AI-1 is N-acyl homoserine lactone (AHL) whereas AI-2 is furano- syl borate diester (4). The S-adenosyl methionine (SAM) and S-ribosylhomocysteine (SRH) are key components that can be catalyzed by S-adenosyl homocysteine/methylthio- adenosine nucleosidase (SAH/MTAN) to produce AI-1 and AI-2 signal molecules. SAH/MTAN is an important enzyme and essential for bacterial metabolism (5). Our lab recently reviewed QS biosynthetic pathway-mediated enzymes responsible for antimicrobial drug resistance. SAM is uti- lized to synthesize SAH, which is being catalyzed by MTAN to produce SRH. SAH/MTAN is responsible for the recycling of adenine and methionine necessary for bacterial DNA and protein synthesis, respectively (6). SAH/MTAN, a very essential component for creating bacterial virulence, could be an attractive target for the disruption of SAM biosyn- thesis. Schramm developed some MTAN transition state analogues such as methyl (MT), ethyl (EtT), and butyl (But)- substituted immucillin A and DADMe-immucillin A deriva- tives such as methyl (MT), ethyl (EtT), butyl (BuT), and PhT (Phenyl) substituted having structural resemblance with MTAN. MT-immucillin A and MT-DADMe-immucillin A were found to be slow-onset tight-binding inhibitors of cellular MTAN activity in Vibrio cholerae and wild-type Escherichia coli (7). Introduction Microbial invasion and its virulence can cause damage to the host cells and antimicrobial resistance (AMR) via quo- rum sensing (QS) mechanism, which is responsible for the intercellular communication among microbes. As per British government statistical record, it was recently estimated that by 2050 AMR can engulf 10 million lives each year and cause cumulative losses of US$ 100 trillion to world GDP (1). QS represents microbiome population density and uti- lizes signal molecules responsible for producing drug resis- tance (2). These signals are chemical autoinducers (AIs) (3). https://doi.org/dti.2022.2512 https://creativecommons.org/licenses/by-nc/4.0/legalcode mailto:sisir.iicb@gmail.com Nandi et al Drug Target Insights 2022; 16: 55 © 2022 The Authors. Published by AboutScience - www.aboutscience.eu Tedder et al designed and synthesized many SAH/MTAN inhibitors using 6-substituted purine and deaza purines as the core scaffolds. Some of them produced low nM inhibi- tors with broad-spectrum antimicrobial activity (8). Li et al designed 5-aminoindazole derivatives using structure-guided methods that screened several low-nanomolar inhibitors with broad-spectrum antimicrobial activity to combat SAH/ MTAN-mediated QS. X-ray crystal structure of lead com- pounds cocrystallized with SAH/MTAN obtained from E. coli and other pathogenic bacteria revealed the mode of binding of the inhibitor toward the target site. These cocrystal struc- tures could provide structural information for the design of more active congeneric compounds in the series (9). But there is hardly any QSAR utilizing theoretical molecu- lar descriptors and docking studies carried out toward these potential congeners. Therefore, an attempt has been made in the present study to explore the biochemical mechanisms of indazole compounds against SAH/MTAN utilizing QSAR and docking tools. Experimental methods Activity data The biological activity data consist of 40 indazole com- pounds (Tab. I) designed, synthesized, and tested by Li et al (9). These compounds evaluated the biological inhibitory effect of taking SAH/MTAN enzyme expressed on the full- length E. coli pfs gene having high conservation on the bac- terial species. The SAH/MTAN is the key target for the production of AHL-mediated AI-1 and SAH-mediated AI-2, which are the building blocks for the synthesis of the QS AIs (9). The enzyme inhibitory activities have been measured in terms of Ki measuring the affinity of the compound to bind the active cavity of SAH/MTAN. A negative logarithm of these Ki values (pKi) has been done for data reduction and taken as a dependent variable whereas molecular structural descrip- tor has been computed as independent variables for these compounds. TABLE I - Biological activity data N H NR2 R1 R Sl. no. Substitution points Structure pKi (µm)R R1 R2 *1. H N H S O O N H3C CH3 H N H N N H S O O N H3C CH3 ‒0.447 2. H H N H S O O N H3C CH3 N H N S O O N H3C CH3 H N 0 QSAR and docking of indazole compounds against SAH/MTAN-mediated QS56 © 2022 The Authors. Drug Target Insights - ISSN 1177-3928 - www.aboutscience.eu/dti *3. Cl N H S O O N H3C CH3 H N H N S O O N H3C CH3 Cl N H ‒1.34 4. CH3 N H S O O N H3C CH3 H N H N S O O N H3C CH3 N H CH3 ‒1.80 5. H S O O N H Cl H N H N S O O N H Cl ‒1.43 6. H H S O O N H Cl N H N S O O Cl H N ‒1.23 7. Cl S O O N H Cl H N H N S O O Cl Cl N H ‒0.71 8. CH3 S O O N H Cl H N H N S O O Cl CH3 N H ‒0.602 TABLE I - (Continued) Nandi et al Drug Target Insights 2022; 16: 57 © 2022 The Authors. Published by AboutScience - www.aboutscience.eu N H N S O O Cl Cl N H NH Sl. No. R1 Structure pKi *9. C OH3C N H N S O O Cl Cl N H NH C O CH3 ‒1.041 *10. CH H3C H2C CH3 N H N Cl NH H2C CH N H S O O Cl CH3 H3C 0.119 11. H2 C N H N Cl NH H2 C N H SO O Cl 0.301 12. H2C C H3C CH3 CH3 N H N Cl NH H2C C N H S O O Cl CH3 H3C CH3 ‒0.079 QSAR and docking of indazole compounds against SAH/MTAN-mediated QS58 © 2022 The Authors. Drug Target Insights - ISSN 1177-3928 - www.aboutscience.eu/dti 13. H2C CH3 N H N Cl NH H2C N H S O O Cl CH3 ‒0.431 14. H2C CH2 CH CH3 H3C N H N Cl NH H2C H2C N H S O O Cl CH H3C CH3 ‒0.69 15. H2C CH2 C CH3 H3C CH3 N H N Cl NH H2C H2C N H S O O Cl C CH3 CH3 H3C ‒0.812 16. H2C CH C CH3 H3C N H N Cl NH H2C N H S O O Cl CH C CH3 H3C ‒0.342 17. NH H2C N H N Cl NH H2C N H S O O Cl ‒0.255 TABLE I - (Continued) Nandi et al Drug Target Insights 2022; 16: 59 © 2022 The Authors. Published by AboutScience - www.aboutscience.eu 18. H2C CH2 N H N Cl NH H2C H2C N H S O O Cl ‒0.361 N H N R NH H2C CH CH3H3C N H S O O R1 Sl. No. R R1 Structure pKi *19. Cl F Cl N H N Cl NH H2C CH CH3H3C N H S O O F Cl 2.468 20. Cl Cl N H N Cl NH H2C CH CH3H3C N H S O O Cl 1.903 *21. Cl Cl Cl N H N Cl NH H2C CH CH3H3C N H S O O Cl Cl 2.795 22. Cl F F N H N Cl NH H2C CH CH3H3C N H S O O F F 2.091 QSAR and docking of indazole compounds against SAH/MTAN-mediated QS60 © 2022 The Authors. Drug Target Insights - ISSN 1177-3928 - www.aboutscience.eu/dti *23. Cl N N H N Cl NH H2C CH CH3H3C N H S O O N 1.966 *24. Cl N N H N Cl NH H2C CH CH3H3C N H S O O N NH3C 1.443 25. CH3 N NH3C N H N CH3 NH H2C CH CH3H3C N H S O O N NH3C 0.718 26. Cl N H3C N H N Cl NH H2C CH CH3H3C N H S O O N H3C 1.545 27. Cl N N H N Cl NH H2C CH CH3H3C N H S O O N 1.283 28. CH3 N N H N CH3 NH H2C CH CH3H3C N H S O O N 0.619 TABLE I - (Continued) Nandi et al Drug Target Insights 2022; 16: 61 © 2022 The Authors. Published by AboutScience - www.aboutscience.eu 29. Cl Cl H3C N H N Cl NH H2C CH CH3H3C N H S O O Cl H3C 2.408 *30. Cl F3C N H N Cl NH H2C CH CH3H3C N H S O OF3C 1.958 *31. CH3 F3C N H N CH3 NH H2C CH CH3H3C N H S O OF3C 1.698 *32. Cl H3C F N H N Cl NH H2C CH CH3H3C N H S O O H3C F 1.835 33. Cl F3C N H N Cl NH H2C CH H3C CH3 N H S F3C O O 1.886 34. Cl S H3C N H N Cl NH H2C CH H3C CH3 N H S O O S H3C 1.769 QSAR and docking of indazole compounds against SAH/MTAN-mediated QS62 © 2022 The Authors. Drug Target Insights - ISSN 1177-3928 - www.aboutscience.eu/dti 35. Cl H3C N H N Cl NH H2C CH H3C CH3 N H S O O H3C 0.443 36. CH3 H3C N H N CH3 NH H2C CH H3C CH3 N H S O O H3C 1 37. Cl N H N Cl NH H2C CH H3C CH3 N H S O O 0.026 38. CH3 N H N CH3 NH H2C CH H3C CH3 N H S O O 0.302 39. Cl HO N H N Cl NH H2C CH H3C CH3 N H S O O HO 1.602 40. CH3 H3C N H N CH3 NH H2C CH H3C CH3 N H S O O H3C 0.903 *Test compounds. TABLE I - (Continued) Nandi et al Drug Target Insights 2022; 16: 63 © 2022 The Authors. Published by AboutScience - www.aboutscience.eu Methods of computation Structure computation and molecular optimization The two-dimensional structures of 40 indazole com- pounds were drawn using Chemdraw 8.0 software. These structures were transformed into 3D files, which are mini- mized using the MM2 force field. The molecular energy mini- mization is carried out taking the convergence criterion and dielectric constant of 0.01 Kcal/mol and 1.0 respectively (10) utilizing the Chem3D Ultra window (11). Molecular descriptors computation All the three-dimensional mol files were incorporated into PaDEL Descriptor Computation software for the computation of structural descriptors. PaDEL is a freeware for the calcu- lation of molecular properties (12). A total number of 1,875 2D and 3D descriptors were calculated and before the model generation, this descriptor data had been reduced to 1,055 (Tab. II). Perfectly constant and highly intercorrelated descrip- tors were removed taking variance and correlation coefficient cut-off values of 0.0001 and 0.9 using the V-WSP algorithm of NanoBridges software (13). The reduced descriptor data set has been used for the development of QSAR models. TABLE II - Descriptors used in the current study ALogP, ALogp2, AMR, apol, naAromAtom, nAtom, nHeavyAtom, nH, nC, nN, nO, nS, nF, nCl, nX, ATS0m, ATS1m, ATS2m, ATS3m, ATS4m, ATS5m, ATS6m, ATS7m, ATS8m, ATS2v, ATS4v, ATS6v, ATS7v, ATS8v, ATS0e, ATS3e, ATS4e, ATS5e, ATS6e, ATS7e, ATS8e, ATS0p, ATS3p, ATS5p, ATS0s, ATS1s, ATS2s, ATS3s, ATS4s, ATS5s, ATS6s, ATS7s, ATS8s, AATS0m, AATS1m, AATS2m, AATS3m, AATS4m, AATS5m, AATS6m, AATS7m, AATS8m, AATS0v, AATS1v, AATS2v, AATS3v, AATS4v, AATS5v, AATS6v, AATS7v, AATS8v, AATS0e, AATS1e, AATS2e, AATS3e, AATS4e, AATS5e, AATS6e, AATS7e, AATS8e, AATS0p, AATS1p, AATS2p, AATS3p, AATS4p, AATS5p, AATS6p, AATS7p, AATS8p, AATS0i, AATS1i, AATS2i, AATS3i, AATS4i, AATS5i, AATS6i, AATS7i, AATS8i, AATS0s, AATS1s, AATS2s, AATS3s, AATS4s, AATS5s, AATS6s, AATS7s, AATS8s, ATSC0c, ATSC1c, ATSC2c, ATSC3c, ATSC4c, ATSC5c, ATSC6c, ATSC7c, ATSC8c, ATSC0m, ATSC1m, ATSC2m, ATSC3m, ATSC4m, ATSC5m, ATSC6m, ATSC7m, ATSC8m, ATSC0v, ATSC1v, ATSC2v, ATSC3v, ATSC4v, ATSC5v, ATSC6v, ATSC7v, ATSC8v, ATSC0e, ATSC1e, ATSC2e, ATSC3e, ATSC4e, ATSC5e, ATSC6e, ATSC7e, ATSC8e, ATSC0p, ATSC1p, ATSC2p, ATSC3p, ATSC4p, ATSC5p, ATSC6p, ATSC7p, ATSC8p, ATSC0i, ATSC1i, ATSC2i, ATSC3i, ATSC4i, ATSC5i, ATSC6i, ATSC7i, ATSC8i, ATSC0s, ATSC1s, ATSC2s, ATSC3s, ATSC4s, ATSC5s, ATSC6s, ATSC7s, ATSC8s, AATSC0m, AATSC1m, AATSC2m, AATSC3m, AATSC4m, AATSC5m, AATSC6m, AATSC7m, AATSC8m, AATSC0v, AATSC1v, AATSC2v, AATSC3v, AATSC4v, AATSC5v, AATSC6v, AATSC7v, AATSC8v, AATSC0e, AATSC2e, AATSC6e, AATSC7e, AATSC0p, AATSC2p, AATSC3p, AATSC4p, AATSC5p, AATSC6p, AATSC7p, AATSC8p, AATSC0i, AATSC1i, AATSC2i, AATSC3i, AATSC4i, AATSC5i, AATSC6i, AATSC7i, AATSC8i, AATSC0s, AATSC1s, AATSC2s, AATSC3s, AATSC4s, AATSC5s, AATSC6s, AATSC7s, AATSC8s, MATS1c, MATS2c, MATS3c, MATS4c, MATS5c, MATS6c, MATS7c, MATS8c, MATS1m, MATS2m, MATS3m, MATS4m, MATS5m, MATS6m, MATS7m, MATS8m, MATS1e, MATS2e, MATS3e, MATS4e, MATS5e, MATS6e, MATS7e, MATS8e, MATS1p, MATS2i, MATS3i, MATS5i, MATS6i, MATS7i, MATS8i, MATS1s, MATS2s, MATS3s, MATS4s, MATS5s, MATS6s, MATS7s, MATS8s, GATS1c, GATS2c, GATS3c, GATS4c, GATS5c, GATS6c, GATS7c, GATS8c, GATS1m, GATS2m, GATS3m, GATS4m, GATS5m, GATS6m, GATS7m, GATS8m, GATS1v, GATS2v, GATS3v, GATS4v, GATS5v, GATS6v, GATS7v, GATS8v, GATS1e, GATS2e, GATS3e, GATS4e , GATS5e, GATS6e, GATS7e, GATS8e, GATS1p, GATS2p, GATS3p, GATS4p, GATS5p, GATS6p, GATS7p, GATS8p, GATS1i, GATS2i, GATS3i, GATS4i, GATS5i, GATS6i, GATS7i, GATS8i, GATS1s, GATS2s, GATS3s, GATS4s, GATS5s, GATS6s, GATS7s, GATS8s, SpAbs_DzZ, SpMAD_ DzZ, SM1_DzZ, VE1_DzZ, VE3_DzZ, VR1_DzZ, VR3_DzZ, VR1_Dzm, VR2_Dzm, VR3_Dzm, SM1_Dzv, VE1_Dzv, VE3_Dzv, VR1_Dzv, VR2_Dzv, VR3_Dzv, SM1_Dze, VE1_Dze, VE3_Dze, VR1_Dze, VR3_Dze, SpAbs_Dzp, SpMAD_Dzp, SM1_Dzp, VE1_Dzp, VE3_Dzp, VR1_Dzp, VR3_Dzp, SM1_Dzi, VE1_Dzi, VE3_Dzi, VR1_Dzi, VR3_Dzi, SpAbs_Dzs, SpMAD_Dzs, SM1_Dzs, VE1_Dzs, VE3_Dzs, VR1_Dzs, VR2_Dzs, VR3_Dzs, BCUTw-1l, BCUTw-1h, BCUTc-1l, BCUTc-1h, BCUTp-1l, BCUTp-1h, nBondsS2, nBondsS3, nBondsD, nBondsD2, nBondsM bpol, SpMax2_ Bhm, SpMax3_Bhm, SpMax4_Bhm, SpMax5_Bhm, SpMax6_Bhm, SpMax7_Bhm, SpMax8_Bhm, SpMin1_Bhm, SpMin2_Bhm, SpMin3_ Bhm, SpMin4_Bhm, SpMin5_Bhm, SpMin6_Bhm, SpMin7_Bhm, SpMin8_Bhm, SpMax1_Bhv, SpMax2_Bhv, SpMax3_Bhv, SpMax4_Bhv, SpMax5_Bhv, SpMax6_Bhv, SpMax7_Bhv, SpMax8_Bhv, SpMin1_Bhv, SpMin2_Bhv, SpMin3_Bhv, SpMin4_Bhv, SpMin5_Bhv, SpMin6_Bhv, SpMin7_Bhv, SpMin8_Bhv, SpMax1_Bhe, SpMax2_Bhe, SpMax3_Bhe, SpMax4_Bhe, SpMax6_Bhe, SpMax7_Bhe, SpMax8_Bhe, SpMin1_ Bhe, SpMin2_Bhe, SpMin3_Bhe, SpMin4_Bhe, SpMin5_Bhe, SpMin6_Bhe, SpMin7_Bhe, SpMin8_Bhe, SpMax1_Bhp, SpMax2_Bhp, SpMax3_Bhp, SpMax4_Bhp, SpMax7_Bhp, SpMin1_Bhp, SpMin2_Bhp, SpMin3_Bhp, SpMin4_Bhp, SpMin7_Bhp, SpMin8_Bhp, SpMax2_ Bhi, SpMax3_Bhi, SpMax4_Bhi, SpMax5_Bhi, SpMax8_Bhi, SpMin2_Bhi, SpMin3_Bhi, SpMin4_Bhi, SpMin5_Bhi, SpMin7_Bhi, SpMax1_Bhs, SpMax2_Bhs, SpMax3_Bhs, SpMax4_Bhs, SpMax5_Bhs, SpMax6_Bhs, SpMax7_Bhs, SpMax8_Bhs, SpMin1_Bhs, SpMin2_Bhs, SpMin3_ Bhs, SpMin4_Bhs, SpMin5_Bhs, SpMin6_Bhs, SpMin7_Bhs, SpMin8_Bhs, C1SP2, C2SP2, C3SP2, C1SP3, C2SP3, C3SP3, C4SP3, SCH-3, SCH-6, SCH-7, VCH-6, VCH-7, SC-3, SC-4, SC-5, SC-6, VC-3, VC-5, SPC-4,SPC-5, SPC-6, VPC-4, VPC-5, VPC-6, SP-2, SP-3, SP-4, SP-6, SP-7, VP-0, VP-2, VP-3, VP-4, VP-5, VP-6, VP-7, AVP-0, AVP-1, AVP-2, Mare, Mi, CrippenLogP, SpMax_Dt, SpMAD_Dt, VE1_Dt, VE3_Dt, VR1_Dt, VR2_Dt, VR3_Dt, ECCEN, nHBd, nHBa, nwHBa, nHBint2, nHBint3, nHBint4, nHBint5, nHBint6, nHBint7, nHBint8, nHBint9, nHBint10, nHsOH, nHssNH, nHdsCH, nHaaCH, nHCsats, nHCsatu, nsCH3, nssCH2, naasC, naaaC, nssssC, naaN, nsssN, SHBd, SHBa, SwHBa, SHBint2, SHBint3, SHBint4, SHBint5, SHBint6, SHBint7, SHBint8, SHBint9, SHBint10, SHssNH, SHaaNH, SHaaCH, SHCsats, SssCH2, SaaCH, SsssCH, SdssC, SaasC, SaaaC, SssNH, SaaNH, SdO, SddssS, SsCl, minHBd, minHBa, minwHBa, minHBint2, minHBint3, minHBint5, minHBint6, minHBint9, minHBint10, minHaaCH, minHCsats, minHCsatu, minHother, minsCH3, minssCH2, minaaCH, minaasC, minaaaC, minssNH, minaaN, mindO, minsF, minsCl, maxHBd, maxHBa, maxwHBa, maxHBint2, maxHBint3, maxHBint5, maxHBint6, maxHBint10, maxHssNH, maxHaaCH, maxHCsats, maxsCH3, maxssCH2, maxaaCH, maxaasC, maxaaaC, maxssssC, maxssNH, maxaaN, maxdO, maxsCl, sumI, meanI, hmax, LipoaffinityIndex, DELS, MAXDN2, DELS2, ETA_Alpha, ETA_Epsilon_1, ETA_Epsilon_2, ETA_Epsilon_4, ETA_Epsilon_5, ETA_dEpsilon_B, ETA_Psi_1, ETA_Shape_P, ETA_Shape_Y, ETA_Shape_X, ETA_Beta, ETA_BetaP, ETA_Beta_s, ETA_BetaP_s, ETA_Beta_ns, ETA_BetaP_ns, ETA_dBeta, ETA_dBetaP, ETA_Beta_ns_d, ETA_BetaP_ns_d, ETA_Eta, ETA_EtaP, ETA_Eta_F, ETA_EtaP_F, ETA_Eta_L, ETA_EtaP_L, ETA_Eta_F_L, ETA_EtaP_F_L, ETA_Eta_B, ETA_Eta_B_RC, FMF, fragC, nHBAcc, nHBAcc2, nHBAcc3, HybRatio, IC0, IC1, IC2, IC3, IC4, IC5, TIC0, TIC1, TIC2, TIC3, SIC0, SIC1, SIC2, SIC3, SIC4, SIC5, CIC0, CIC1, CIC2, CIC3, CIC4, BIC1, BIC2, BIC3, BIC4, BIC5, MIC0, MIC1, MIC2, MIC3, MIC4, ZMIC0, ZMIC1, ZMIC2, QSAR and docking of indazole compounds against SAH/MTAN-mediated QS64 © 2022 The Authors. Drug Target Insights - ISSN 1177-3928 - www.aboutscience.eu/dti ZMIC3, ZMIC4, ZMIC5, Kier1, Kier2, Kier3, nAtomLC, nAtomP, nAtomLAC, MLogP, MDEC-11, MDEC-12, MDEC-13, MDEC-22, MDEC- 23, MDEC-24, MDEC-33, MDEC-34, MDEO-11, MDEN-22, MLFER_A, MLFER_BH, MLFER_S, MLFER_E, MLFER_L, MPC2, MPC3, MPC8, MPC10, piPC1, piPC3, piPC5, piPC6, piPC10, R_TpiPCTPC, PetitjeanNumber, nRing, n6Ring, nTRing, nHeteroRing, nF10HeteroRing, nRotB, RotBFrac, nRotBt, RotBtFrac, LipinskiFailures, topoRadius, topoDiameter, GGI1, GGI2, GGI3, GGI4, GGI5, GGI6, GGI7, GGI8 , GGI9, GGI10, JGI1, JGT, VE1_D, VE3_D, VR1_D VR3_D, TopoPSA, MWC3, MWC6, MWC10, SRW7, SRW9, AMW, WTPT-2, WTPT-3, WPATH, XLogP, TDB1u, TDB2u, TDB3u, TDB4u, TDB5u, TDB6u, TDB7u, TDB8u, TDB9u, TDB10u, TDB6m, TDB7m, TDB8m, TDB9m, TDB10m, TDB1v, TDB3v, TDB4v, TDB5v, TDB6v, TDB7v, TDB8v, TDB9v, TDB10v, TDB1e, TDB2e, TDB3e, TDB4e, TDB5e, TDB6e, TDB7e, TDB8e, TDB9e, TDB10e, TDB1p, TDB3p, TDB4p, TDB5p, TDB6p, TDB7p, TDB8p, TDB9p, TDB10p, TDB1i, TDB2i, TDB3i, TDB4i, TDB5i, TDB6i, TDB7i, TDB8i, TDB9i, TDB10i, TDB1s, TDB3s, TDB5s, TDB6s, TDB7s, TDB8s, TDB9s, TDB10s, TDB1r, TDB2r, TDB3r, TDB4r, TDB5r, TDB6r, TDB7r, TDB8r ,TDB9r, TDB10r, PPSA-1, PPSA-2, PPSA-3, PNSA-1, PNSA-2, PNSA-3, DPSA-1, DPSA-2, DPSA-3, FPSA-1, FPSA-2, FNSA-2, FNSA-3, WPSA-1, WPSA-2, WPSA- 3, WNSA-1, WNSA-2, WNSA-3, RPCG, RNCG, RPCS, RNCS, THSA, TPSA, RHSA, GRAV-1, GRAVH-3, GRAV-4, LOBMAX, LOBMIN, MOMI-X, MOMI-Y, MOMI-Z, MOMI-XY, MOMI-XZ, MOMI-R, geomRadius, geomDiameter, geomShape, RDF10u, RDF15u, RDF20u, RDF25u, RDF30u, RDF35u, RDF40u, RDF45u, RDF50u, RDF55u, RDF60u, RDF65u, RDF70u, RDF75u, RDF80u, RDF85u, RDF90u, RDF95u, RDF100u, RDF105u, RDF110u, RDF115u, RDF120u, RDF125u, RDF130u, RDF135u, RDF140u, RDF145u, RDF150u, RDF155u, RDF15m, RDF20m, RDF25m, RDF30m, RDF35m, RDF40m, RDF45m, RDF50m, RDF55m, RDF60m, RDF65m, RDF70m, RDF75m, RDF80m, RDF85m, RDF90m, RDF95m, RDF100m, RDF105m, RDF110m, RDF115m, RDF120m, RDF125m, RDF130m, RDF135m, RDF140m, RDF145m, RDF150m, RDF155m, RDF20v, RDF25v, RDF30v, RDF35v, RDF40v, RDF45v, RDF50v, RDF55v, RDF60v, RDF65v, RDF70v, RDF75v, RDF80v, RDF85v, RDF90v, RDF95v, RDF100v, RDF105v, RDF110v, RDF115v, RDF120v, RDF125v, RDF130v, RDF135v, RDF140v, RDF145v, RDF150v, RDF155v, RDF30e, RDF35e, RDF70e, RDF80e, RDF95e, RDF100e, RDF155e, RDF15p, RDF20p, RDF30p, RDF35p, RDF40p, RDF45p, RDF50p,RDF60p, RDF65p, RDF70p, RDF75p, RDF80p, RDF85p, RDF90p, RDF95p, RDF100p, RDF115p, RDF130p, RDF135p, RDF140p, RDF145p, RDF150p, RDF155p, RDF30i, RDF65i, RDF10s, RDF15s, RDF20s, RDF25s, RDF30s, RDF35s, RDF40s, RDF45s, RDF50s, RDF55s, RDF60s, RDF65s, RDF70s, RDF75s, RDF80s, RDF85s, RDF90s, RDF95s, RDF100s, RDF105s, RDF110s, RDF115s, RDF120s, RDF125s, RDF130s, RDF135s, RDF140s, RDF145s, RDF150s, RDF155s, L1u, L2u, L3u, P1u, P2u, E1u, E2u, E3u, Tu, Au, Vu, Du, L1m, L2m, L3m, P1m, P2m, E1m, E2m, E3m, Tm, Am, Vm, Dm, L1v, L2v, L3v, P1v, P2v, E1v, E2v, E3v, Tv, Av, Vv, Dv, P2e, E1e, E2e, E3e, De, L1p, P1p, P2p, E1p, E2p, E3p, Dp, E1i, E2i, Di, E1s, E2s. QSAR model generation followed by validation A number of QSAR models have been generated for the deliberated indazole compounds utilizing various sets of a combination of 2D and 3D descriptors using genetic algo- rithm coupled multiple linear regression (GA-MLR) meth- ods (14) based on the theory of mutation and crossover of the parents’ genes to generate the new solutions, taking the most appropriate transformations of the independent variables incorporated in the NanoBridges software (15,16). A population of 100 different random combinations of the structural descriptors is generated taking default parame- ters as set in the NanoBridges software (17). The impact of these indazole compounds’ computed descriptors on SAH/ MTAN inhibitory activities has been shown through QSAR model development by considering each parent combina- tion of descriptors for the entire data set using MLR. The entire data set was divided randomly into test and train- ing sets before QSAR modeling. The developed models were validated statistically. The validation parameters are denoted by R2 (R is the square root of multiple R-square for regression), Q2 (cross-validated r2) values for the training set, whereas external validation was carried out by calculat- ing predictive R2 (R2 pred) and the standard error of estimation (SEE) represents standard deviation measured by the error mean square, which expresses the variation of the residuals or the variation about the regression line (18). Further, the external predictability of the generated QSAR models was scrutinized by calculating modified r2 (r2 m), average modi- fied r2 (rm 2), and delta modified r2 (∆r2 m) respectively (19). The best training model is composed of 72.5% and the test set consists of 27.5% of the total data. The test compounds have been marked in Table I with an asterisk. Ligand docking All optimized ligands were docked into the receptor active cavity using molecular docking, which is a powerful structure-based drug discovery simulation for the identifica- tion of ligand-receptor complexes having minimal interaction energy. The energy of interactions between ligand and pro- tein was calculated in terms of the score, which can predict the affinity of the compound toward active binding (20,21). The crystal structure of E. coli SAH/MTAN (PDB ID: 1JYS) in complex with adenine cocrystal was selected as a receptor for in silico molecular docking studies (22). The protein was downloaded and prepared by remov- ing water molecules, and hydrogen atoms in the H-depleted target molecule were added. Grid points were generated surrounding the cocrystallized ligand bound with the active cavity of the target. This cocrystallized molecule is consid- ered a reference to make the binding site for the ligand X-ray group. A flexible docking module was incorporated in ArgusLab 4.0.1, which is a very powerful docking simula- tion freeware (23-25). In the present docking simulation, the ligand is freely rotated inside the target cavity to generate multiple 150 conformers that can produce many docked com- plex poses considering grid resolution (angle) of 0.4 degrees as the default value. The term pose usually designates the specific set of coordinates of a docked ligand. The coordi- nates of conformation will change concerning this docked pose. The ligand is docked inside the target of the active site, which is well-kept within the grid box. The best complex pose with minimal interaction energy has been taken into consideration for a better explanation of the mode of inter- action between the ligand and active amino acid residues of the receptor protein (26). TABLE II - (Continued) Nandi et al Drug Target Insights 2022; 16: 65 © 2022 The Authors. Published by AboutScience - www.aboutscience.eu Results and discussion QSAR modeling In the present study, QSAR modeling of indazole com- pounds having inhibitory activities against SAH/MTAN- mediated QS has been carried out utilizing a different combination of 2D and 3D structural descriptors. The impact of the different classes of computed descriptors on SAH/MTAN inhibitory activities of these compounds has been discussed by the development of optimal training QSAR model formu- lated by 72.5% of total data and the remaining 27.5% of total data is used as a test set marked by an asterisk. These model parameters have been expressed as R2 and Q2 (cross-validated r2) values for the training set while the external model vali- dation significance is carried out by calculating predictive R2 (R2 pred), the SEE and modified r2 (r2 m) given in Table III. It was shown that Equation [1] can produce an explained variance of 71.4% and an internal predicted variance of 55.6% of the observed data. For a predictive QSAR model, the value of R2 should be more than 0.6 (27). The external model validation parameters such as R2 pred, r 2 m, and SEE are given as 0.151, 0.112, and 0.660, which do not produce sig- nificant predictability because R2 pred and r2 m must be greater than 0.5, while the SEE values should be less than 0.5 to have a significant model (28). Therefore, outlier analyses have been carried out by testing of applicability domain of the training QSAR model, which determines its acceptance as per the Organization for Economic Cooperation and Development (OECD) incorpo- rated in NanoBridges software (29). The training molecules 14 and 18 were detected as an outlier and again best QSAR model (2) was modeled by deleting the outliers showing the best result on the SAH/MTAN inhibition. The developed QSAR model (2) can explain and predict 85.2% and 78.1% of variances of the SAH/MTAN inhibitory activity of the deliber- ated compounds. This model can also produce 68.5% exter- nal predictability and r2 m (test) and SEE values of 0.636 and 0.490 respectively. This model is quite acceptable as per sta- tistical validation. The square correlation coefficient between observed activities vs. predicted activities of the test com- pounds obtained from the correlation plot (Fig. 1) is calcu- lated as 0.751, which suggests good model predictivity. The model (2) parameters such as RDF55m, E1s, and AATSC7s have a positive impact on aromaticity toward SAH/ MTAN inhibition, whereas the decrease in value of AATS1v and ATSC3s of the deliberated indazoles may increase the enzyme inhibition to stop the QS. Indazole-SAH/MTAN docking The resultant binding affinity along with details of amino acid residues bound with studied 40 indazole compounds toward inhibition of SAH/MTAN target is shown in Table IV. When ligands of interest are docked inside the defined tar- get cavity of SAH/MTAN, both the conformational changes of ligand and receptor occur to make a number of ligand-receptor complexes. These ligand-receptor complexes represent the TABLE III - QSAR models QSAR model-1 pKi = 2.73333 (±0.84046) ‒3.13346 (±0.84654) CIC2 ‒0.96779 (±0.23832) nHBint4 +0.00324 (±0.00408) Am +1.06367 (±0.15668) C3SP2 ‒0.22577 (±0.07554) RDF135m. N = 29, R2 = 0.714, Q2 = 0.556, R2 pred = 0.151, r2 m (test) = 0.112, SEE = 0.660 Parameters Physical interpretation CIC2 Complementary Information Content index (neighborhood symmetry of 2-order) nHBint4 Count of E-State descriptors of strength for potential Hydrogen Bonds of path length 4 Am A total size index/weighted by mass C3SP2 Doubly bound carbon bound to three other carbons RDF135m Radial Distribution Function-135/weighted by mass QSAR model 2 pKi = ‒2.66387 (±2.92509) ‒0.01691 (±0.00928) AATS1v +0.12995 (±0.01611) RDF55m + 9.69053 (±2.60499) E1s ‒0.0602 (±0.01124) ATSC3s +2.93253 (±0.59661) AATSC7s N = 27, R2 = 0.852, Q2 = 0.781, R2 pred = 0.685, r2 m (test) = 0.636, SEE = 0.490 Parameters Physical interpretation AATS1v Averaged Moreau Broto autocorrelation of lag 1 weighted by vdW volume RDF55m Radial Distribution Function – 055/weighted by mass E1s 1st component accessibility directional WHIM index/weighted by I-state ATSC3s Centered Moreau Broto autocorrelation of lag 3 weighted by I-state AATSC7s Averaged and centered Moreau Broto autocorrelation of lag 7 weighted by intrinsic state QSAR and docking of indazole compounds against SAH/MTAN-mediated QS66 © 2022 The Authors. Drug Target Insights - ISSN 1177-3928 - www.aboutscience.eu/dti R² = 0.751 -2 -1,5 -1 -0,5 0 0,5 1 1,5 2 -2 -1,5 -1 -0,5 0 0,5 1 1,5 2 2,5 3 3,5 Observed activity Predicted activity Fig. 1 - Observed versus predicted activity of test molecules after Outlier Analysis (model 2). MET173 (highlighted in Tab. IV) are essential for ligand-protein interaction and help in unique conformational changing of ligand-receptor interaction complex, which is responsible for producing biological activity. If these amino acids are removed from the cavity then the binding affinity of a compound may be reduced. So these common amino acids play an important role in the suitable binding of the ligand toward the active site. For a better interpretation of the binding mode of the indazole ligands, molecules are categorized into three highly active, intermediate active, and lower active by consider- ing their biological activities (pKi) range as 1.50 to 2.79 µm, 0.44 to 1.4 µm, and <0.4 µm respectively. The highly active compounds such as 20, 22, 23, 26, 30-34, and 39 have more interactions with ALA150, PHE335, and VAL171 along with common amino acids bearing –14.477 to –11.191 kcal/mol dock score. The intermediate active compounds 24, 25, 27, 28, 35, 36, and 40 have –11.186 to –10.367 kcal/mol dock score with a lack of interactions with ALA150 except com- pound 28 which does not capture both ALA150 and PHE335. The remaining compounds are 1-18, 37, and 38 with lower active range having dock score greater than –10 kcal/mol, and these compounds lack either ALA150 and PHE335 or VAL171 amino acid interactions. From Table IV, we can say that the binding affinity of com- pounds depends on a number of residues and minimal dock score involved in the ligand-receptor interactions. The high- est active compound 21 shows the following pattern of inter- actions (Fig. 2). TABLE IV - Detailed study of indazole analogs-receptor interactions Comp. no. Amino acid residues interacting with ligand pKi (µm) Dock score 1. ASP197, PHE151, GLU172, ILE152, MET173, ALA150, ALA199, GLY78, LEU158, VAL 171 ‒0.447 ‒10.2544 2. ASP197, PHE151, GLU172, ILE152, MET173, ALA150, ALA77, GLY78, ASN153, SER196 0 ‒10.134 3. ASP197, PHE151, GLU172, ILE152, MET173, ALA150, GLY78, GLU 174, VAL171, LEU158 ‒1.34 ‒10.12 4. ASP197, PHE151, GLU172, ILE152, MET173, ALA150, GLY78, SER76, ALA77 ‒1.80 ‒9.4128 5. ASP197, PHE151, GLU172, ILE152, MET173, VAL 332, ALA150 ‒1.43 ‒9.4128 6. ASP197, PHE151, GLU172, ILE152, MET173, PRO343, PHE335, VAL332, ALA334, ALA77, GLY78 ‒1.23 ‒10.931 7. ASP197, PHE151, GLU172, ILE152, MET173, SER196, SER76, PHE335, VAL332, ALA150 ‒0.71 ‒9.49573 8. ASP197, PHE151, GLU172, ILE152, MET173, ASN153, ALA150, VAL171 ‒0.602 ‒9.46457 9. ASP197, PHE151, GLU172, ILE152, MET173, GLY78, SER76, ALA77, PHE335, ASN153, ALA150 ‒1.041 ‒10.4928 10. ASP197, PHE151, GLU172, ILE152, MET173, HIS98, PRO343, ALA150, VAL171, ALA77, VAL332, GLY78, PHE335 0.119 ‒9.88002 11. ASP197, PHE151, GLU172, ILE152, MET173, ALA150, VAL332, ALA77, GLY78, ALA334, PHE335 0.301 ‒10.631 12. ASP197, PHE151, GLU172, ILE152, MET173, ALA199, LEU158, LEU80, GLY78, VAL171, ALA150, PHE335, GLU174 ‒0.079 ‒9.89744 13. ASP197, PHE151, GLU172, ILE152, MET173, ALA150, VAL332, GLU174 ‒0.431 ‒9.78021 14. ASP197, PHE151, GLU172, ILE152, MET173, GLY78, ALA77, VAL332, PHE335, ALA150 ‒0.690 ‒10.6611 15. ASP197, PHE151, GLU172, ILE152, MET173, ALA150, VAL332, SER76 ‒0.812 ‒9.69068 16. ASP197, PHE151, GLU172, ILE152, MET173, ALA150, PHE335, VAL332, GLU174 ‒0.342 ‒10.2657 17. ASP197, PHE151, GLU172, ILE152, MET173, ALA150, PHE335, GLY78, ALA77, VAL171 ‒0.255 ‒11.4409 18. ASP197, PHE151, GLU172, ILE152, MET173, ALA150, SER76, VAL332 ‒0.361 ‒10.6145 conformational rotation of the ligand inside the cavity. The ligand-receptor complex having a minimal dock score may pro- duce maximum binding affinity toward the cavity of the target. The docking of indazole compounds having inhibition of SAH/ MTAN resulted in common amino acid interaction inside the SAH/MTAN cavity for all compounds. These common amino acid residues such as ASP197, PHE151, GLU172, ILE152, and Nandi et al Drug Target Insights 2022; 16: 67 © 2022 The Authors. Published by AboutScience - www.aboutscience.eu The amino group of sulfonyl amino linkage interacts with VAL171 and sulfonyl group interacts with GLU172, ILE152, and PHE151 by hydrogen bonding. ILE152 also interacts with isobutyl group by the same bonding. Chlorine atom at posi- tion-3 of 3,4-dichlorophenyl interacts with PHE335, TYR337, and PRO343, and VAL332 along with PRO343 interacts with 3,4-dichlorophenyl ring by hydrophobic bonding, whereas MET173 interacts with the sulfur bonding. Some amino acid residues such as ASP197, ALA150, ALA77, and GLY78 are also present at the binding site. Conclusion Parameters such as AATS1v, RDF55m, E1s, ATSC3s, and AATSC7s are crucially captured in the training QSAR model responsible for producing inhibition of SAH/MTAN. After molecular docking of indazole compounds, it was found that amino acids ASP197, PHE151, ILE152, GLU172, and MET173 Comp. no. Amino acid residues interacting with ligand pKi (µm) Dock score 19. ASP197, PHE151, GLU172, ILE152, MET173, ALA150, PHE335, PHE210, SER196, ALA334, VAL171 2.468 ‒12.8469 20. ASP197, PHE151, GLU172, ILE152, MET173, ALA150, PHE335, PHE210, SER196, ALA334, VAL171, SER76 1.903 ‒12.855 21. ASP197, PHE151, GLU172, ILE152, MET173, ALA150, PHE335, VAL171, GLY78, ALA77, ALA199, VAL332, TYR337, PRO343, LEU158 2.795 ‒14.4772 22. ASP197, PHE151, GLU172, ILE152, MET173, ALA150, PHE335, PHE210, SER196, VAL171, SER76 2.091 ‒12.2961 23. ASP197, PHE151, GLU172, ILE152, MET173, ALA150, VAL171, GLY78, ALA77, VAL332, PRO343, SER76 1.966 ‒10.3176 24. ASP197, PHE151, GLU172, ILE152, MET173, ALA150, PHE335, SER196, VAL171, GLY78, ALA77 1.443 ‒10.367 25. ASP197, PHE151, GLU172, ILE152, MET173, ALA150, PHE335, VAL171, GLY78, ALA77, VAL332, PHE207, ASN153 0.718 ‒10.367 26. ASP197, PHE151, GLU172, ILE152, MET173, ALA150, PHE335, SER196, VAL171, PHE201, SER76 1.545 ‒11.1919 27. ASP197, PHE151, GLU172, ILE152, MET173, PHE335, VAL171, GLY78, ALA77, VAL332, PRO343, SER76 1.283 ‒11.0913 28. ASP197, PHE151, GLU172, ILE152, MET173, VAL171, GLY78, ALA77 0.619 ‒11.1815 29. ASP197, PHE151, GLU172, ILE152, MET173, PHE335, VAL171, SER76 2.408 ‒11.6766 30. ASP197, PHE151, GLU172, ILE152, MET173, ALA150, PHE335, PHE210, SER196, VAL171, SER76 1.958 ‒11.6479 31. ASP197, PHE151, GLU172, ILE152, MET173, ALA150, PHE335, PHE210, LEU211, PRO343, GLY78, VAL332, ILE50 1.698 ‒11.0191 32. ASP197, PHE151, GLU172, ILE152, MET173, ALA150, PHE335, SER196, VAL171, VAL332 1.835 ‒12.8532 33. PHE151, GLU172, ILE152, MET173, ALA150, PHE335, PHE210, SER196, LEU158, VAL332, ASN153, GLY154 1.886 ‒9.56766 34. ASP197, PHE151, GLU172, ILE152, MET173, ALA150, SER196, VAL332, GLY78, SER76, ALA8, MET9, ALA77, GLU174, ILE50 1.769 ‒12.697 35. ASP197, PHE151, GLU172, ILE152, MET173, ALA150, PHE335, SER196, VAL171, VAL332, SER76, PRO343, PHE207, LEU211 0.443 ‒12.8692 36. ASP197, PHE151, GLU172, ILE152, MET173, PHE335, VAL171, VAL332, PHE210, GLY78 1 ‒12.9969 37. ASP197, PHE151, GLU172, ILE152, MET173, PHE335, VAL171, VAL332, SER76, PRO343, ALA77 0.026 ‒14.2768 38. ASP197, PHE151, GLU172, ILE152, MET173, ALA150, VAL171, VAL332, ASN153, GLY78, ALA77, PRO343, SER76 0.302 ‒14.2777 39. ASP197, PHE151, GLU172, ILE152, MET173, PHE335, VAL171 1.602 ‒11.6493 40. ASP197, PHE151, GLU172, ILE152, MET173, PHE335, PHE210, VAL332, GLY78 0.903 ‒13.6849 Fig. 2 - Best docking pose of highest active compound no. 21 do- cked in the cavity of S-adenosyl homocysteine/methylthioadeno- sine nucleosidase. QSAR and docking of indazole compounds against SAH/MTAN-mediated QS68 © 2022 The Authors. Drug Target Insights - ISSN 1177-3928 - www.aboutscience.eu/dti are common for all compounds’ modes of interaction and to produce biological activity. Apart from that, the higher active compounds capture ALA150, PHE335, and VAL171 amino acid residues, which are very crucial for the inhibition of the SAH/ MTA-mediated QS mechanism. The intermediate and lower active compounds lack any of these interactions. The binding affinity of indazole compounds depends on the number of amino acid residues involved in ligand-receptor interaction, for example, the highest active compound number 21 has 15 amino acid residues, which is more than the other 40 inda- zole compounds. 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