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Volume 21, Issue 16
  • ISSN: 1570-1808
  • E-ISSN: 1875-628X

Abstract

Background

Investigating the structural attributes of the murine beta3-adrenergic receptor (β3-AR) is imperative for comprehending metabolic regulation, given its close resemblance to the human β3-AR. This receptor holds promise as a target for novel drug development against obesity and diabetes. Despite its potential, the absence of knowledge regarding the structure of murine β3-AR hampers a comprehensive understanding of its functionality.

Objective

Our study aimed to model the three-dimensional (3D) structure of murine β3-AR through various molecular structure prediction and simulation techniques, thus addressing the existing gap in structural information.

Methods

Employing diverse structure prediction programs, we refined the predicted structure of murine β3-AR. Primary sequence analysis offered insights into charge distribution, stability, and hydrophobic properties. The binding sites were identified in the modeled structure. Molecular Dynamics (MD) simulation provided the structural stability and dynamic behavior of the predicted β3-AR structure.

Results

The β3-AR protein exhibited specific characteristics, including a pI of 9.57, an aliphatic index of 98.35, a GRAVY score of 0.289, and the presence of conserved motifs and disulfide linkages. Utilizing the programs such as Phyre2, SWISS MODEL, I-Tasser, and AlphaFold2, we generated a 3D model of murine β3-AR. Subsequent refinement using ModRefiner revealed a structure comprising 13 helices, 2 strands, and 21 turns. The Ramachandran plot indicated favorable regions for 93.2% of residues, with minimal deviations. A 50 ns MD simulation demonstrated the consistent stability and integrity of the β3-AR protein. The top three binding pockets were identified based on varying areas and volumes. Dynamic behavior within residues Ser 252 and Arg 253 was observed, indicating flexibility in conformation. This study marks the first-ever exploration, offering initial structural insights into murine β3-AR.

Conclusion

This study underscores the critical role of computational approaches in predicting the 3D structure of β3-AR. We derived a refined model by employing diverse prediction techniques, elucidating key features. The findings emphasize the significance of this methodology in comprehending the structural foundation of β3-AR, providing valuable insights for targeted medication development against conditions such as obesity and diabetes.

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2024-12-23

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References

  1. Sousa-FilhoC.P.B. FariaH.O.F. EspositoJ.C. MeloA. RibeiroM.O. OttonR. Green tea improves the metabolism of peripheral tissues in β3-adrenergic receptor-knockout mice.Pharmacol. Res.202015910495610.1016/j.phrs.2020.104956 32480000
     [Google Scholar]
  2. RodriguesA.C. Prímola-GomesT.N. PeluzioM.C.G. HermsdorffH.H.M. NataliA.J. Aerobic exercise and lipolysis: A review of the β-adrenergic signaling pathways in adipose tissue.Sci. Sports2021361162610.1016/j.scispo.2020.04.006
     [Google Scholar]
  3. DecaraJ. RiveraP. ArrabalS. VargasA. SerranoA. PavónF.J. DieguezC. NogueirasR. Rodríguez de FonsecaF. SuárezJ. Cooperative role of the glucagon‐like peptide‐1 receptor and β3‐adrenergic‐mediated signalling on fat mass reduction through the downregulation of PKA/AKT/AMPK signalling in the adipose tissue and muscle of rats.Acta Physiol.20182224e1300810.1111/apha.13008 29193738
     [Google Scholar]
  4. HeL. LiH. ZhangL. ZhangJ. ZhangG. TongX. ZhangT. WuY. LiM. JinL. Transcriptome analysis of norepinephrine-induced lipolysis in differentiated adipocytes of Bama pig.Gene202388814775310.1016/j.gene.2023.147753 37659599
     [Google Scholar]
  5. ValentineJ.M. AhmadianM. KeinanO. Abu-OdehM. ZhaoP. ZhouX. KellerM.P. GaoH. YuR.T. LiddleC. DownesM. ZhangJ. LusisA.J. AttieA.D. EvansR.M. RydénM. SaltielA.R. β3-Adrenergic receptor downregulation leads to adipocyte catecholamine resistance in obesity.J. Clin. Invest.20221322e15335710.1172/JCI153357 34847077
     [Google Scholar]
  6. CeroC. LeaH.J. ZhuK.Y. ShamsiF. TsengY.H. CypessA.M. β3-Adrenergic receptors regulate human brown/beige adipocyte lipolysis and thermogenesis.JCI Insight2021611e13916010.1172/jci.insight.139160 34100382
     [Google Scholar]
  7. LeeM.W. LeeM. OhK.J. Adipose tissue-derived signatures for obesity and type 2 diabetes: adipokines, batokines and microRNAs.J. Clin. Med.20198685410.3390/jcm8060854 31208019
     [Google Scholar]
  8. SakamotoY. OnikiK. KumagaeN. MoritaK. OtakeK. OgataY. SaruwatariJ. Beta-3-adrenergic receptor rs4994 polymorphism is a potential biomarker for the development of nonalcoholic fatty liver disease in overweight/obese individuals.Dis. Markers2019201911310.1155/2019/4065327 31929840
     [Google Scholar]
  9. WarnerA. KjellstedtA. CarrerasA. BöttcherG. PengX.R. SealeP. OakesN. LindénD. Activation of β 3 -adrenoceptors increases in vivo free fatty acid uptake and utilization in brown but not white fat depots in high-fat-fed rats.Am. J. Physiol. Endocrinol. Metab.20163116E901E91010.1152/ajpendo.00204.2016 27780820
     [Google Scholar]
  10. LuoZ. ZhangT. WangS. HeY. YeQ. CaoW. The Trp64Arg polymorphism in β3 adrenergic receptor (ADRB3) gene is associated with adipokines and plasma lipids: a systematic review, meta-analysis, and meta-regression.Lipids Health Dis.20201919910.1186/s12944‑020‑01290‑y
     [Google Scholar]
  11. NagiriC. KobayashiK. TomitaA. KatoM. KobayashiK. YamashitaK. NishizawaT. InoueA. ShihoyaW. NurekiO. Cryo-EM structure of the β3-adrenergic receptor reveals the molecular basis of subtype selectivity.Mol. Cell2021811532053215.e510.1016/j.molcel.2021.06.024 34314699
     [Google Scholar]
  12. YadavA.R. MohiteS.K. Homology modeling and generation of 3d-structure of protein.Res. J. Pharm. Dos. Forms Technol.202012431332010.5958/0975‑4377.2020.00052.X
     [Google Scholar]
  13. BadiniS. RegondiS. PuglieseR. Unleashing the power of artificial intelligence in materials design.Materials20231617592710.3390/ma16175927 37687620
     [Google Scholar]
  14. BertolineL.M.F. LimaA.N. KriegerJ.E. TeixeiraS.K. Before and after AlphaFold2: An overview of protein structure prediction.Front. in Bioinform.20233112037010.3389/fbinf.2023.1120370 36926275
     [Google Scholar]
  15. LaurentsD.V. AlphaFold 2 and NMR Spectroscopy: Partners to understand protein structure, dynamics and function.Front. Mol. Biosci.2022990643710.3389/fmolb.2022.906437 35655760
     [Google Scholar]
  16. KroghA. LarssonB. von HeijneG. SonnhammerE.L.L. Predicting transmembrane protein topology with a hidden markov model: application to complete genomes11Edited by F.Cohen. J. Mol. Biol.2001305356758010.1006/jmbi.2000.4315 11152613
     [Google Scholar]
  17. WangJ. ChitsazF. DerbyshireM.K. GonzalesN.R. GwadzM. LuS. MarchlerG.H. SongJ.S. ThankiN. YamashitaR.A. YangM. ZhangD. ZhengC. LanczyckiC.J. Marchler-BauerA. The conserved domain database in 2023.Nucleic Acids Res.202351D1D384D38810.1093/nar/gkac1096 36477806
     [Google Scholar]
  18. RosenbaumDM CherezovV HansonMA RasmussenSG ThianFS KobilkaTS ChoiHJ YaoXJ WeisWI StevensRC KobilkaBK GPCR engineering yields high-resolution structural insights into β2-adrenergic receptor function.science2007318585412661273
     [Google Scholar]
  19. WarneT. Serrano-VegaM.J. BakerJ.G. MoukhametzianovR. EdwardsP.C. HendersonR. LeslieA.G.W. TateC.G. SchertlerG.F.X. Structure of a β1-adrenergic G-protein-coupled receptor.Nature2008454720348649110.1038/nature07101 18594507
     [Google Scholar]
  20. GonzalezT.L. RaeJ.M. ColacinoJ.A. RichardsonR.J. Homology models of mouse and rat estrogen receptor-α ligand-binding domain created by in silico mutagenesis of a human template: Molecular docking with 17β-estradiol, diethylstilbestrol, and paraben analogs.Comput. Toxicol.20191011610.1016/j.comtox.2018.11.003 30740556
     [Google Scholar]
  21. TianW. ChenC. LeiX. ZhaoJ. LiangJ. CASTp 3.0: computed atlas of surface topography of proteins.Nucleic Acids Res.201846W1W363W36710.1093/nar/gky473 29860391
     [Google Scholar]
  22. GasteigerE. HooglandC. GattikerA. DuvaudS.E. WilkinsM.R. AppelR.D. BairochA. Protein identification and analysis tools on the ExPASy server.Humana press200510.1385/1‑59259‑890‑0:571
     [Google Scholar]
  23. MugunthanS.P. HarishM.C. In silico structural homology modeling and functional characterization of Mycoplasma gallisepticum variable lipoprotein hemagglutin proteins.Front. Vet. Sci.2022994383110.3389/fvets.2022.943831 35990271
     [Google Scholar]
  24. NobleJ.E. Quantification of protein concentration using UV absorbance and Coomassie dyes. Methods in enzymologyAcademic Press2014536172610.1016/B978‑0‑12‑420070‑8.00002‑7
     [Google Scholar]
  25. BachmairA FinleyD VarshavskyA In vivo half-life of a protein is a function of its amino-terminal residue.science19862344773179186
     [Google Scholar]
  26. GondaD.K. BachmairA. WünningI. TobiasJ.W. LaneW.S. VarshavskyA. Universality and structure of the N-end rule.J. Biol. Chem.198926428167001671210.1016/S0021‑9258(19)84762‑2 2506181
     [Google Scholar]
  27. GuruprasadK. ReddyB.V.B. PanditM.W. Correlation between stability of a protein and its dipeptide composition: a novel approach for predicting in vivo stability of a protein from its primary sequence.Protein Eng. Des. Sel.19904215516110.1093/protein/4.2.155 2075190
     [Google Scholar]
  28. GamageD.G. GunaratneA. PeriyannanG.R. RussellT.G. Applicability of instability index for in vitro protein stability prediction.Protein Pept. Lett.201926533934710.2174/0929866526666190228144219 30816075
     [Google Scholar]
  29. DarH.A. IsmailS. WaheedY. AhmadS. JamilZ. AzizH. HettaH.F. MuhammadK. Designing a multi-epitope vaccine against Mycobacteroides abscessus by pangenome-reverse vaccinology.Sci. Rep.20211111119710.1038/s41598‑021‑90868‑2 34045649
     [Google Scholar]
  30. MbahAN IsokpehiRD Identification of functional regulatory residues of the β-lactam inducible penicillin binding protein in methicillin-resistant Staphylococcus aureus.20132013
     [Google Scholar]
  31. KaurG. PatiP.K. In silico physicochemical characterization and topology analysis of Respiratory burst oxidase homolog (Rboh) proteins from Arabidopsis and rice.Bioinformation20181439310010.6026/97320630014093 29785067
     [Google Scholar]
  32. KyteJ. DoolittleR.F. A simple method for displaying the hydropathic character of a protein.J. Mol. Biol.1982157110513210.1016/0022‑2836(82)90515‑0 7108955
     [Google Scholar]
  33. AltschulS. MaddenT.L. SchäfferA.A. ZhangJ. ZhangZ. MillerW. LipmanD.J. Gapped BLAST and PSI-BLAST: a new generation of protein database search programs.Nucleic Acids Res.199725173389340210.1093/nar/25.17.3389 9254694
     [Google Scholar]
  34. SieversF. WilmA. DineenD. GibsonT.J. KarplusK. LiW. LopezR. McWilliamH. RemmertM. SödingJ. ThompsonJ.D. HigginsD.G. Fast, scalable generation of high‐quality protein multiple sequence alignments using Clustal Omega.Mol. Syst. Biol.20117153910.1038/msb.2011.75 21988835
     [Google Scholar]
  35. ComanO.A. PăunescuH. GhiţăI. ComanL. BădărăruA. FulgaI. Beta 3 adrenergic receptors: molecular, histological, functional and pharmacological approaches.Rom. J. Morphol. Embryol.2009502169179 19434307
     [Google Scholar]
  36. ZhaoL. HeX. JiangH. ChengX. Computational characterization of transducer recognition of β2 adrenergic receptor.Biochem. Biophys. Res. Commun.2022592677310.1016/j.bbrc.2022.01.012 35032834
     [Google Scholar]
  37. MoukhametzianovR. WarneT. EdwardsP.C. Serrano-VegaM.J. LeslieA.G.W. TateC.G. SchertlerG.F.X. Two distinct conformations of helix 6 observed in antagonist-bound structures of a β1 -adrenergic receptor.Proc. Natl. Acad. Sci. USA2011108208228823210.1073/pnas.1100185108 21540331
     [Google Scholar]
  38. SatoM. HutchinsonD.S. EvansB.A. SummersR.J. Functional domains of the mouse β3-adrenoceptor associated with differential G-protein coupling.Biochem. Soc. Trans.20073551035103710.1042/BST0371035 17956271
     [Google Scholar]
  39. SinghN. DalalV. MahtoJ.K. KumarP. Biodegradation of phthalic acid esters (PAEs) and in silico structural characterization of mono-2-ethylhexyl phthalate (MEHP) hydrolase on the basis of close structural homolog.J. Hazard. Mater.2017338112210.1016/j.jhazmat.2017.04.055 28531656
     [Google Scholar]
  40. CaiH. XuZ. TangJ. SunY. ChenK. WangH. ZhuW. The essential role for aromatic cluster in the β3 adrenergic receptor.Acta Pharmacol. Sin.20123381062106810.1038/aps.2012.55 22728712
     [Google Scholar]
  41. TewatiaP. AgrawalN. GaurM. SahiS. Insights into the conformational perturbations of novel agonists with β3-adrenergic receptor using molecular dynamics simulations.Biochimie201410116818210.1016/j.biochi.2014.01.016 24508605
     [Google Scholar]
  42. GiltrowE. EcclesP.D. HutchinsonT.H. SumpterJ.P. Rand-WeaverM. Characterisation and expression of β1-, β2- and β3-adrenergic receptors in the fathead minnow (Pimephales promelas).Gen. Comp. Endocrinol.2011173348349010.1016/j.ygcen.2011.07.006 21827763
     [Google Scholar]
  43. NahmiasC. BlinN. ElaloufJ.M. MatteiM.G. StrosbergA.D. EmorineL.J. Molecular characterization of the mouse beta 3-adrenergic receptor: relationship with the atypical receptor of adipocytes.EMBO J.199110123721372710.1002/j.1460‑2075.1991.tb04940.x 1718744
     [Google Scholar]
  44. SkeberdisV.A. Structure and function of β3-adrenergic receptors.Medicina2004405407413 15560541
     [Google Scholar]
  45. StrosbergA.D. Structure and function of the β 3 -adrenergic receptor.Annu. Rev. Pharmacol. Toxicol.199737142145010.1146/annurev.pharmtox.37.1.421 9131260
     [Google Scholar]
  46. QuL. ZhouQ. XuY. GuoY. ChenX. YaoD. HanG.W. LiuZ.J. StevensR.C. ZhongG. WuD. ZhaoS. Structural basis of the diversity of adrenergic receptors.Cell Rep.2019291029292935.e410.1016/j.celrep.2019.10.088 31801060
     [Google Scholar]
  47. De Sousa-CoelhoA.L. RelatJ. HondaresE. Pérez-MartíA. RibasF. VillarroyaF. MarreroP.F. HaroD. FGF21 mediates the lipid metabolism response to amino acid starvation.J. Lipid Res.20135471786179710.1194/jlr.M033415 23661803
     [Google Scholar]
  48. Hall, RA β-Adrenergic receptors and their interacting proteins.InSeminars in cell & developmental biology2004153281288
     [Google Scholar]
  49. QiY. HuiX.H. The single-cell revelation of thermogenic adipose tissue.Mol. Cells2022451067368410.14348/molcells.2022.0092 36254709
     [Google Scholar]
  50. ZhangS. WangL. ZanL. Investigation into the underlying molecular mechanisms of white adipose tissue through comparative transcriptome analysis of multiple tissues.Mol. Med. Rep.2019192959966 30569103
     [Google Scholar]
  51. CrosaraK.T.B. MoffaE.B. XiaoY. SiqueiraW.L. Merging in-silico and in vitro salivary protein complex partners using the STRING database: A tutorial.J. Proteomics2018171879410.1016/j.jprot.2017.08.002 28782718
     [Google Scholar]
  52. IttisoponpisanS. IslamS.A. KhannaT. AlhuzimiE. DavidA. SternbergM.J.E. Can predicted protein 3D structures provide reliable insights into whether missense variants are disease associated?J. Mol. Biol.2019431112197221210.1016/j.jmb.2019.04.009 30995449
     [Google Scholar]
  53. BiasiniM. BienertS. WaterhouseA. ArnoldK. StuderG. SchmidtT. KieferF. CassarinoT.G. BertoniM. BordoliL. SchwedeT. SWISS-MODEL: modelling protein tertiary and quaternary structure using evolutionary information.Nucleic Acids Res.201442W1W252W25810.1093/nar/gku340 24782522
     [Google Scholar]
  54. WaterhouseA. BertoniM. BienertS. StuderG. TaurielloG. GumiennyR. HeerF.T. de BeerT.A.P. RempferC. BordoliL. LeporeR. SchwedeT. SWISS-MODEL: homology modelling of protein structures and complexes.Nucleic Acids Res.201846W1W296W30310.1093/nar/gky427 29788355
     [Google Scholar]
  55. BenkertP. TosattoS.C.E. SchomburgD. QMEAN: A comprehensive scoring function for model quality assessment.Proteins200871126127710.1002/prot.21715 17932912
     [Google Scholar]
  56. ForrestL.R. TangC.L. HonigB. On the accuracy of homology modeling and sequence alignment methods applied to membrane proteins.Biophys. J.200691250851710.1529/biophysj.106.082313 16648166
     [Google Scholar]
  57. KelleyL.A. MezulisS. YatesC.M. WassM.N. SternbergM.J.E. The Phyre2 web portal for protein modeling, prediction and analysis.Nat. Protoc.201510684585810.1038/nprot.2015.053 25950237
     [Google Scholar]
  58. Marchler-BauerA. BoY. HanL. HeJ. LanczyckiC.J. LuS. ChitsazF. DerbyshireM.K. GeerR.C. GonzalesN.R. GwadzM. HurwitzD.I. LuF. MarchlerG.H. SongJ.S. ThankiN. WangZ. YamashitaR.A. ZhangD. ZhengC. GeerL.Y. BryantS.H. CDD/SPARCLE: functional classification of proteins via subfamily domain architectures.Nucleic Acids Res.201745D1D200D20310.1093/nar/gkw1129 27899674
     [Google Scholar]
  59. SamudralaR. XiaY. HuangE. LevittM. Ab initio protein structure prediction using a combined hierarchical approach.Proteins199937S3Suppl. 319419810.1002/(SICI)1097‑0134(1999)37:3+<194::AID‑PROT24>3.0.CO;2‑F 10526368
     [Google Scholar]
  60. YangJ. YanR. RoyA. XuD. PoissonJ. ZhangY. The I-TASSER Suite: protein structure and function prediction.Nat. Methods20151217810.1038/nmeth.3213 25549265
     [Google Scholar]
  61. ZhangY. SkolnickJ. Scoring function for automated assessment of protein structure template quality.Proteins2007684102010.1002/prot.21643
     [Google Scholar]
  62. ZhangY. I-TASSER server for protein 3D structure prediction.BMC Bioinformatics2008914010.1186/1471‑2105‑9‑40 18215316
     [Google Scholar]
  63. JumperJ. EvansR. PritzelA. GreenT. FigurnovM. RonnebergerO. TunyasuvunakoolK. BatesR. ŽídekA. PotapenkoA. BridglandA. MeyerC. KohlS.A.A. BallardA.J. CowieA. Romera-ParedesB. NikolovS. JainR. AdlerJ. BackT. PetersenS. ReimanD. ClancyE. ZielinskiM. SteineggerM. PacholskaM. BerghammerT. BodensteinS. SilverD. VinyalsO. SeniorA.W. KavukcuogluK. KohliP. HassabisD. Highly accurate protein structure prediction with AlphaFold.Nature2021596787358358910.1038/s41586‑021‑03819‑2 34265844
     [Google Scholar]
  64. ColovosC. YeatesT.O. Verification of protein structures: Patterns of nonbonded atomic interactions.Protein Sci.1993291511151910.1002/pro.5560020916 8401235
     [Google Scholar]
  65. BowieJ.U. LüthyR. EisenbergD. A method to identify protein sequences that fold into a known three-dimensional structure.Science1991253501616417010.1126/science.1853201 1853201
     [Google Scholar]
  66. LüthyR. BowieJ.U. EisenbergD. Assessment of protein models with three-dimensional profiles.Nature19923566364838510.1038/356083a0 1538787
     [Google Scholar]
  67. CherezovV RosenbaumDM HansonMA RasmussenSG ThianFS KobilkaTS ChoiHJ KuhnP WeisWI KobilkaBK StevensRC High-resolution crystal structure of an engineered human β2-adrenergic G protein–coupled receptor.science2007318585412581265
     [Google Scholar]
  68. HegedűsT. GeislerM. LukácsG.L. FarkasB. Ins and outs of AlphaFold2 transmembrane protein structure predictions.Cell. Mol. Life Sci.20227917310.1007/s00018‑021‑04112‑1 35034173
     [Google Scholar]
  69. KoJ. LeeJ. Can AlphaFold2 predict protein-peptide complex structures accurately?BioRxiv202110.1101/2021.07.27.453972
     [Google Scholar]
  70. van BreugelM. Rosa e SilvaI. AndreevaA. Structural validation and assessment of AlphaFold2 predictions for centrosomal and centriolar proteins and their complexes.Commun. Biol.20225131210.1038/s42003‑022‑03269‑0 35383272
     [Google Scholar]
  71. HeX. YouC. JiangH. JiangY. XuH.E. ChengX. AlphaFold2 versus experimental structures: evaluation on G protein-coupled receptors.Acta Pharmacol. Sin.20234411710.1038/s41401‑022‑00938‑y 35778488
     [Google Scholar]
  72. RasmussenS.G.F. DeVreeB.T. ZouY. KruseA.C. ChungK.Y. KobilkaT.S. ThianF.S. ChaeP.S. PardonE. CalinskiD. MathiesenJ.M. ShahS.T.A. LyonsJ.A. CaffreyM. GellmanS.H. SteyaertJ. SkiniotisG. WeisW.I. SunaharaR.K. KobilkaB.K. Crystal structure of the β2 adrenergic receptor–Gs protein complex.Nature2011477736654955510.1038/nature10361 21772288
     [Google Scholar]
  73. GangarajKP RajeshMK Molecular characterisation and structural assessment of an RXLR effector from Phytophthora palmivora, the coconut bud rot pathogen.2022100109
     [Google Scholar]
  74. KumariR. DalalV. Identification of potential inhibitors for LLM of Staphylococcus aureus: structure-based pharmacophore modeling, molecular dynamics, and binding free energy studies.J. Biomol. Struct. Dyn.202240209833984710.1080/07391102.2021.1936179 34096457
     [Google Scholar]
  75. GadheC.G. BalupuriA. ChoS.J. In silico characterization of binding mode of CCR8 inhibitor: homology modeling, docking and membrane based MD simulation study.J. Biomol. Struct. Dyn.201533112491251010.1080/07391102.2014.1002006 25617117
     [Google Scholar]
  76. BathulaS. SankaranarayananM. MalgijaB. KaliappanI. BhandareR.R. ShaikA.B. 2-Amino thiazole derivatives as prospective aurora kinase inhibitors against breast cancer: qsar, admet prediction, molecular docking, and molecular dynamic simulation studies.ACS Omega2023
     [Google Scholar]
  77. Ul-HaqZ. AshrafS. BkhaitanM.M. Molecular dynamics simulations reveal structural insights into inhibitor binding modes and mechanism of casein kinase II inhibitors.J. Biomol. Struct. Dyn.20193751120113510.1080/07391102.2018.1450166 29527958
     [Google Scholar]
  78. ZahidH. Structure and function studies of lipid binding to zinc-α₂- glycoprotein; Doctoral dissertation. UCL (University College London).2020
     [Google Scholar]
  79. DalalV. Golemi-KotraD. KumarP. Quantum mechanics/molecular mechanics studies on the catalytic mechanism of a novel esterase (FmtA) of Staphylococcus aureus.J. Chem. Inf. Model.202262102409242010.1021/acs.jcim.2c00057 35475370
     [Google Scholar]
  80. DilcanG. DorukerP. AktenE.D. Ligand‐binding affinity of alternative conformers of human β 2 ‐adrenergic receptor in the presence of intracellular loop 3 (ICL 3) and their potential use in virtual screening studies.Chem. Biol. Drug Des.201993588389910.1111/cbdd.13478 30637937
     [Google Scholar]
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Structural Insights into Mouse β3-adrenergic Receptor: A Promising Target for Obesity and Diabetes Therapeutics
Letters in Drug Design & Discovery 21, 3604 (2024); https://doi.org/10.2174/0115701808301580240405071948
/content/journals/lddd/10.2174/0115701808301580240405071948
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  • Article Type:     Research Article
Keyword(s): AlphaFold; Beta3 adrenergic receptor; diabetes; homology modeling; Mus musculus; Phyre2

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