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Letters in Drug Design & Discovery

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ISSN (Print): 1570-1808
ISSN (Online): 1875-628X

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Research Article

Structural Insights into Mouse β3-adrenergic Receptor: A Promising Target for Obesity and Diabetes Therapeutics

Author(s): Vijayalakshmi Gangadhara, Kavishankar Gawli and Asha Abraham*

Volume 21, Issue 16, 2024

Published on: 18 April, 2024

Page: [3604 - 3621] Pages: 18

DOI: 10.2174/0115701808301580240405071948

Price: $65

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.

Keywords: Beta3 adrenergic receptor, homology modeling, Mus musculus, diabetes, AlphaFold, Phyre2.

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[1]
Sousa-Filho, C.P.B.; Faria, H.O.F.; Esposito, J.C.; Melo, A.; Ribeiro, M.O.; Otton, R. Green tea improves the metabolism of peripheral tissues in β3-adrenergic receptor-knockout mice. Pharmacol. Res., 2020, 159, 104956.
[http://dx.doi.org/10.1016/j.phrs.2020.104956] [PMID: 32480000]
[2]
Rodrigues, A.C.; Prímola-Gomes, T.N.; Peluzio, M.C.G.; Hermsdorff, H.H.M.; Natali, A.J. Aerobic exercise and lipolysis: A review of the β-adrenergic signaling pathways in adipose tissue. Sci. Sports, 2021, 36(1), 16-26.
[http://dx.doi.org/10.1016/j.scispo.2020.04.006]
[3]
Decara, J.; Rivera, P.; Arrabal, S.; Vargas, A.; Serrano, A.; Pavón, F.J.; Dieguez, C.; Nogueiras, R.; Rodríguez de Fonseca, F.; Suárez, J. 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., 2018, 222(4), e13008.
[http://dx.doi.org/10.1111/apha.13008] [PMID: 29193738]
[4]
He, L.; Li, H.; Zhang, L.; Zhang, J.; Zhang, G.; Tong, X.; Zhang, T.; Wu, Y.; Li, M.; Jin, L. Transcriptome analysis of norepinephrine-induced lipolysis in differentiated adipocytes of Bama pig. Gene, 2023, 888, 147753.
[http://dx.doi.org/10.1016/j.gene.2023.147753] [PMID: 37659599]
[5]
Valentine, J.M.; Ahmadian, M.; Keinan, O.; Abu-Odeh, M.; Zhao, P.; Zhou, X.; Keller, M.P.; Gao, H.; Yu, R.T.; Liddle, C.; Downes, M.; Zhang, J.; Lusis, A.J.; Attie, A.D.; Evans, R.M.; Rydén, M.; Saltiel, A.R. β3-Adrenergic receptor downregulation leads to adipocyte catecholamine resistance in obesity. J. Clin. Invest., 2022, 132(2), e153357.
[http://dx.doi.org/10.1172/JCI153357] [PMID: 34847077]
[6]
Cero, C.; Lea, H.J.; Zhu, K.Y.; Shamsi, F.; Tseng, Y.H.; Cypess, A.M. β3-Adrenergic receptors regulate human brown/beige adipocyte lipolysis and thermogenesis. JCI Insight, 2021, 6(11), e139160.
[http://dx.doi.org/10.1172/jci.insight.139160] [PMID: 34100382]
[7]
Lee, M.W.; Lee, M.; Oh, K.J. Adipose tissue-derived signatures for obesity and type 2 diabetes: adipokines, batokines and microRNAs. J. Clin. Med., 2019, 8(6), 854.
[http://dx.doi.org/10.3390/jcm8060854] [PMID: 31208019]
[8]
Sakamoto, Y.; Oniki, K.; Kumagae, N.; Morita, K.; Otake, K.; Ogata, Y.; Saruwatari, J. Beta-3-adrenergic receptor rs4994 polymorphism is a potential biomarker for the development of nonalcoholic fatty liver disease in overweight/obese individuals. Dis. Markers, 2019, 2019, 1-13.
[http://dx.doi.org/10.1155/2019/4065327] [PMID: 31929840]
[9]
Warner, A.; Kjellstedt, A.; Carreras, A.; Böttcher, G.; Peng, X.R.; Seale, P.; Oakes, N.; Lindén, D. 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., 2016, 311(6), E901-E910.
[http://dx.doi.org/10.1152/ajpendo.00204.2016] [PMID: 27780820]
[10]
Luo, Z.; Zhang, T.; Wang, S.; He, Y.; Ye, Q.; Cao, W. 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., 2020, 19(1), 99.
[http://dx.doi.org/10.1186/s12944-020-01290-y]
[11]
Nagiri, C.; Kobayashi, K.; Tomita, A.; Kato, M.; Kobayashi, K.; Yamashita, K.; Nishizawa, T.; Inoue, A.; Shihoya, W.; Nureki, O. Cryo-EM structure of the β3-adrenergic receptor reveals the molecular basis of subtype selectivity. Mol. Cell, 2021, 81(15), 3205-3215.e5.
[http://dx.doi.org/10.1016/j.molcel.2021.06.024] [PMID: 34314699]
[12]
Yadav, A.R.; Mohite, S.K. Homology modeling and generation of 3d-structure of protein. Res. J. Pharm. Dos. Forms Technol., 2020, 12(4), 313-320.
[http://dx.doi.org/10.5958/0975-4377.2020.00052.X]
[13]
Badini, S.; Regondi, S.; Pugliese, R. Unleashing the power of artificial intelligence in materials design. Materials, 2023, 16(17), 5927.
[http://dx.doi.org/10.3390/ma16175927] [PMID: 37687620]
[14]
Bertoline, L.M.F.; Lima, A.N.; Krieger, J.E.; Teixeira, S.K. Before and after AlphaFold2: An overview of protein structure prediction. Front. in Bioinform., 2023, 3, 1120370.
[http://dx.doi.org/10.3389/fbinf.2023.1120370] [PMID: 36926275]
[15]
Laurents, D.V. AlphaFold 2 and NMR Spectroscopy: Partners to understand protein structure, dynamics and function. Front. Mol. Biosci., 2022, 9, 906437.
[http://dx.doi.org/10.3389/fmolb.2022.906437] [PMID: 35655760]
[16]
Krogh, A.; Larsson, B.; von Heijne, G.; Sonnhammer, E.L.L. Predicting transmembrane protein topology with a hidden markov model: application to complete genomes11Edited by F. Cohen. J. Mol. Biol., 2001, 305(3), 567-580.
[http://dx.doi.org/10.1006/jmbi.2000.4315] [PMID: 11152613]
[17]
Wang, J.; Chitsaz, F.; Derbyshire, M.K.; Gonzales, N.R.; Gwadz, M.; Lu, S.; Marchler, G.H.; Song, J.S.; Thanki, N.; Yamashita, R.A.; Yang, M.; Zhang, D.; Zheng, C.; Lanczycki, C.J.; Marchler-Bauer, A. The conserved domain database in 2023. Nucleic Acids Res., 2023, 51(D1), D384-D388.
[http://dx.doi.org/10.1093/nar/gkac1096] [PMID: 36477806]
[18]
Rosenbaum, DM; Cherezov, V; Hanson, MA; Rasmussen, SG; Thian, FS; Kobilka, TS; Choi, HJ; Yao, XJ; Weis, WI; Stevens, RC; Kobilka, BK GPCR engineering yields high-resolution structural insights into β2-adrenergic receptor function. science, 2007, 318(5854), 1266-1273.
[19]
Warne, T.; Serrano-Vega, M.J.; Baker, J.G.; Moukhametzianov, R.; Edwards, P.C.; Henderson, R.; Leslie, A.G.W.; Tate, C.G.; Schertler, G.F.X. Structure of a β1-adrenergic G-protein-coupled receptor. Nature, 2008, 454(7203), 486-491.
[http://dx.doi.org/10.1038/nature07101] [PMID: 18594507]
[20]
Gonzalez, T.L.; Rae, J.M.; Colacino, J.A.; Richardson, R.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., 2019, 10, 1-16.
[http://dx.doi.org/10.1016/j.comtox.2018.11.003] [PMID: 30740556]
[21]
Tian, W.; Chen, C.; Lei, X.; Zhao, J.; Liang, J. CASTp 3.0: computed atlas of surface topography of proteins. Nucleic Acids Res., 2018, 46(W1), W363-W367.
[http://dx.doi.org/10.1093/nar/gky473] [PMID: 29860391]
[22]
Gasteiger, E.; Hoogland, C.; Gattiker, A.; Duvaud, S.E.; Wilkins, M.R.; Appel, R.D.; Bairoch, A. Protein identification and analysis tools on the ExPASy server; Humana press, 2005.
[http://dx.doi.org/10.1385/1-59259-890-0:571]
[23]
Mugunthan, S.P.; Harish, M.C. In silico structural homology modeling and functional characterization of Mycoplasma gallisepticum variable lipoprotein hemagglutin proteins. Front. Vet. Sci., 2022, 9, 943831.
[http://dx.doi.org/10.3389/fvets.2022.943831] [PMID: 35990271]
[24]
Noble, J.E. Quantification of protein concentration using UV absorbance and Coomassie dyes. In: Methods in enzymology; Academic Press, 2014; 536, pp. 17-26.
[http://dx.doi.org/10.1016/B978-0-12-420070-8.00002-7]
[25]
Bachmair, A; Finley, D; Varshavsky, A In vivo half-life of a protein is a function of its amino-terminal residue. science, 1986, 234(4773), 179-186.
[26]
Gonda, D.K.; Bachmair, A.; Wünning, I.; Tobias, J.W.; Lane, W.S.; Varshavsky, A. Universality and structure of the N-end rule. J. Biol. Chem., 1989, 264(28), 16700-16712.
[http://dx.doi.org/10.1016/S0021-9258(19)84762-2] [PMID: 2506181]
[27]
Guruprasad, K.; Reddy, B.V.B.; Pandit, M.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., 1990, 4(2), 155-161.
[http://dx.doi.org/10.1093/protein/4.2.155] [PMID: 2075190]
[28]
Gamage, D.G.; Gunaratne, A.; Periyannan, G.R.; Russell, T.G. Applicability of instability index for in vitro protein stability prediction. Protein Pept. Lett., 2019, 26(5), 339-347.
[http://dx.doi.org/10.2174/0929866526666190228144219] [PMID: 30816075]
[29]
Dar, H.A.; Ismail, S.; Waheed, Y.; Ahmad, S.; Jamil, Z.; Aziz, H.; Hetta, H.F.; Muhammad, K. Designing a multi-epitope vaccine against Mycobacteroides abscessus by pangenome-reverse vaccinology. Sci. Rep., 2021, 11(1), 11197.
[http://dx.doi.org/10.1038/s41598-021-90868-2] [PMID: 34045649]
[30]
Mbah, AN; Isokpehi, RD Identification of functional regulatory residues of the β-lactam inducible penicillin binding protein in methicillin-resistant Staphylococcus aureus. 2013, 2013
[31]
Kaur, G.; Pati, P.K. In silico physicochemical characterization and topology analysis of Respiratory burst oxidase homolog (Rboh) proteins from Arabidopsis and rice. Bioinformation, 2018, 14(3), 93-100.
[http://dx.doi.org/10.6026/97320630014093] [PMID: 29785067]
[32]
Kyte, J.; Doolittle, R.F. A simple method for displaying the hydropathic character of a protein. J. Mol. Biol., 1982, 157(1), 105-132.
[http://dx.doi.org/10.1016/0022-2836(82)90515-0] [PMID: 7108955]
[33]
Altschul, S.; Madden, T.L.; Schäffer, A.A.; Zhang, J.; Zhang, Z.; Miller, W.; Lipman, D.J. Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucleic Acids Res., 1997, 25(17), 3389-3402.
[http://dx.doi.org/10.1093/nar/25.17.3389] [PMID: 9254694]
[34]
Sievers, F.; Wilm, A.; Dineen, D.; Gibson, T.J.; Karplus, K.; Li, W.; Lopez, R.; McWilliam, H.; Remmert, M.; Söding, J.; Thompson, J.D.; Higgins, D.G. Fast, scalable generation of high‐quality protein multiple sequence alignments using Clustal Omega. Mol. Syst. Biol., 2011, 7(1), 539.
[http://dx.doi.org/10.1038/msb.2011.75] [PMID: 21988835]
[35]
Coman, O.A.; Păunescu, H.; Ghiţă, I.; Coman, L.; Bădărăru, A.; Fulga, I. Beta 3 adrenergic receptors: molecular, histological, functional and pharmacological approaches. Rom. J. Morphol. Embryol., 2009, 50(2), 169-179.
[PMID: 19434307]
[36]
Zhao, L.; He, X.; Jiang, H.; Cheng, X. Computational characterization of transducer recognition of β2 adrenergic receptor. Biochem. Biophys. Res. Commun., 2022, 592, 67-73.
[http://dx.doi.org/10.1016/j.bbrc.2022.01.012] [PMID: 35032834]
[37]
Moukhametzianov, R.; Warne, T.; Edwards, P.C.; Serrano-Vega, M.J.; Leslie, A.G.W.; Tate, C.G.; Schertler, G.F.X. Two distinct conformations of helix 6 observed in antagonist-bound structures of a β1 -adrenergic receptor. Proc. Natl. Acad. Sci. USA, 2011, 108(20), 8228-8232.
[http://dx.doi.org/10.1073/pnas.1100185108] [PMID: 21540331]
[38]
Sato, M.; Hutchinson, D.S.; Evans, B.A.; Summers, R.J. Functional domains of the mouse β3-adrenoceptor associated with differential G-protein coupling. Biochem. Soc. Trans., 2007, 35(5), 1035-1037.
[http://dx.doi.org/10.1042/BST0371035] [PMID: 17956271]
[39]
Singh, N.; Dalal, V.; Mahto, J.K.; Kumar, P. 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., 2017, 338, 11-22.
[http://dx.doi.org/10.1016/j.jhazmat.2017.04.055] [PMID: 28531656]
[40]
Cai, H.; Xu, Z.; Tang, J.; Sun, Y.; Chen, K.; Wang, H.; Zhu, W. The essential role for aromatic cluster in the β3 adrenergic receptor. Acta Pharmacol. Sin., 2012, 33(8), 1062-1068.
[http://dx.doi.org/10.1038/aps.2012.55] [PMID: 22728712]
[41]
Tewatia, P.; Agrawal, N.; Gaur, M.; Sahi, S. Insights into the conformational perturbations of novel agonists with β3-adrenergic receptor using molecular dynamics simulations. Biochimie, 2014, 101, 168-182.
[http://dx.doi.org/10.1016/j.biochi.2014.01.016] [PMID: 24508605]
[42]
Giltrow, E.; Eccles, P.D.; Hutchinson, T.H.; Sumpter, J.P.; Rand-Weaver, M. Characterisation and expression of β1-, β2- and β3-adrenergic receptors in the fathead minnow (Pimephales promelas). Gen. Comp. Endocrinol., 2011, 173(3), 483-490.
[http://dx.doi.org/10.1016/j.ygcen.2011.07.006] [PMID: 21827763]
[43]
Nahmias, C.; Blin, N.; Elalouf, J.M.; Mattei, M.G.; Strosberg, A.D.; Emorine, L.J. Molecular characterization of the mouse beta 3-adrenergic receptor: relationship with the atypical receptor of adipocytes. EMBO J., 1991, 10(12), 3721-3727.
[http://dx.doi.org/10.1002/j.1460-2075.1991.tb04940.x] [PMID: 1718744]
[44]
Skeberdis, V.A. Structure and function of β3-adrenergic receptors. Medicina, 2004, 40(5), 407-413.
[PMID: 15560541]
[45]
Strosberg, A.D. Structure and function of the β 3 -adrenergic receptor. Annu. Rev. Pharmacol. Toxicol., 1997, 37(1), 421-450.
[http://dx.doi.org/10.1146/annurev.pharmtox.37.1.421] [PMID: 9131260]
[46]
Qu, L.; Zhou, Q.; Xu, Y.; Guo, Y.; Chen, X.; Yao, D.; Han, G.W.; Liu, Z.J.; Stevens, R.C.; Zhong, G.; Wu, D.; Zhao, S. Structural basis of the diversity of adrenergic receptors. Cell Rep., 2019, 29(10), 2929-2935.e4.
[http://dx.doi.org/10.1016/j.celrep.2019.10.088] [PMID: 31801060]
[47]
De Sousa-Coelho, A.L.; Relat, J.; Hondares, E.; Pérez-Martí, A.; Ribas, F.; Villarroya, F.; Marrero, P.F.; Haro, D. FGF21 mediates the lipid metabolism response to amino acid starvation. J. Lipid Res., 2013, 54(7), 1786-1797.
[http://dx.doi.org/10.1194/jlr.M033415] [PMID: 23661803]
[48]
Hall, RA β-Adrenergic receptors and their interacting proteins. InSeminars in cell & developmental biology, 2004, 15(3), 281-288.
[49]
Qi, Y.; Hui, X.H. The single-cell revelation of thermogenic adipose tissue. Mol. Cells, 2022, 45(10), 673-684.
[http://dx.doi.org/10.14348/molcells.2022.0092] [PMID: 36254709]
[50]
Zhang, S.; Wang, L.; Zan, L. Investigation into the underlying molecular mechanisms of white adipose tissue through comparative transcriptome analysis of multiple tissues. Mol. Med. Rep., 2019, 19(2), 959-966.
[PMID: 30569103]
[51]
Crosara, K.T.B.; Moffa, E.B.; Xiao, Y.; Siqueira, W.L. Merging in-silico and in vitro salivary protein complex partners using the STRING database: A tutorial. J. Proteomics, 2018, 171, 87-94.
[http://dx.doi.org/10.1016/j.jprot.2017.08.002] [PMID: 28782718]
[52]
Ittisoponpisan, S.; Islam, S.A.; Khanna, T.; Alhuzimi, E.; David, A.; Sternberg, M.J.E. Can predicted protein 3D structures provide reliable insights into whether missense variants are disease associated? J. Mol. Biol., 2019, 431(11), 2197-2212.
[http://dx.doi.org/10.1016/j.jmb.2019.04.009] [PMID: 30995449]
[53]
Biasini, M.; Bienert, S.; Waterhouse, A.; Arnold, K.; Studer, G.; Schmidt, T.; Kiefer, F.; Cassarino, T.G.; Bertoni, M.; Bordoli, L.; Schwede, T. SWISS-MODEL: modelling protein tertiary and quaternary structure using evolutionary information. Nucleic Acids Res., 2014, 42(W1), W252-W258.
[http://dx.doi.org/10.1093/nar/gku340] [PMID: 24782522]
[54]
Waterhouse, A.; Bertoni, M.; Bienert, S.; Studer, G.; Tauriello, G.; Gumienny, R.; Heer, F.T.; de Beer, T.A.P.; Rempfer, C.; Bordoli, L.; Lepore, R.; Schwede, T. SWISS-MODEL: homology modelling of protein structures and complexes. Nucleic Acids Res., 2018, 46(W1), W296-W303.
[http://dx.doi.org/10.1093/nar/gky427] [PMID: 29788355]
[55]
Benkert, P.; Tosatto, S.C.E.; Schomburg, D. QMEAN: A comprehensive scoring function for model quality assessment. Proteins, 2008, 71(1), 261-277.
[http://dx.doi.org/10.1002/prot.21715] [PMID: 17932912]
[56]
Forrest, L.R.; Tang, C.L.; Honig, B. On the accuracy of homology modeling and sequence alignment methods applied to membrane proteins. Biophys. J., 2006, 91(2), 508-517.
[http://dx.doi.org/10.1529/biophysj.106.082313] [PMID: 16648166]
[57]
Kelley, L.A.; Mezulis, S.; Yates, C.M.; Wass, M.N.; Sternberg, M.J.E. The Phyre2 web portal for protein modeling, prediction and analysis. Nat. Protoc., 2015, 10(6), 845-858.
[http://dx.doi.org/10.1038/nprot.2015.053] [PMID: 25950237]
[58]
Marchler-Bauer, A.; Bo, Y.; Han, L.; He, J.; Lanczycki, C.J.; Lu, S.; Chitsaz, F.; Derbyshire, M.K.; Geer, R.C.; Gonzales, N.R.; Gwadz, M.; Hurwitz, D.I.; Lu, F.; Marchler, G.H.; Song, J.S.; Thanki, N.; Wang, Z.; Yamashita, R.A.; Zhang, D.; Zheng, C.; Geer, L.Y.; Bryant, S.H. CDD/SPARCLE: functional classification of proteins via subfamily domain architectures. Nucleic Acids Res., 2017, 45(D1), D200-D203.
[http://dx.doi.org/10.1093/nar/gkw1129] [PMID: 27899674]
[59]
Samudrala, R.; Xia, Y.; Huang, E.; Levitt, M. Ab initio protein structure prediction using a combined hierarchical approach. Proteins, 1999, 37(S3)(Suppl. 3), 194-198.
[http://dx.doi.org/10.1002/(SICI)1097-0134(1999)37:3+<194::AID-PROT24>3.0.CO;2-F] [PMID: 10526368]
[60]
Yang, J.; Yan, R.; Roy, A.; Xu, D.; Poisson, J.; Zhang, Y. The I-TASSER Suite: protein structure and function prediction. Nat. Methods, 2015, 12(1), 7-8.
[http://dx.doi.org/10.1038/nmeth.3213] [PMID: 25549265]
[61]
Zhang, Y.; Skolnick, J. Scoring function for automated assessment of protein structure template quality. Proteins, 2007, 68(4), 1020.
[http://dx.doi.org/10.1002/prot.21643]
[62]
Zhang, Y. I-TASSER server for protein 3D structure prediction. BMC Bioinformatics, 2008, 9(1), 40.
[http://dx.doi.org/10.1186/1471-2105-9-40] [PMID: 18215316]
[63]
Jumper, J.; Evans, R.; Pritzel, A.; Green, T.; Figurnov, M.; Ronneberger, O.; Tunyasuvunakool, K.; Bates, R.; Žídek, A.; Potapenko, A.; Bridgland, A.; Meyer, C.; Kohl, S.A.A.; Ballard, A.J.; Cowie, A.; Romera-Paredes, B.; Nikolov, S.; Jain, R.; Adler, J.; Back, T.; Petersen, S.; Reiman, D.; Clancy, E.; Zielinski, M.; Steinegger, M.; Pacholska, M.; Berghammer, T.; Bodenstein, S.; Silver, D.; Vinyals, O.; Senior, A.W.; Kavukcuoglu, K.; Kohli, P.; Hassabis, D. Highly accurate protein structure prediction with AlphaFold. Nature, 2021, 596(7873), 583-589.
[http://dx.doi.org/10.1038/s41586-021-03819-2] [PMID: 34265844]
[64]
Colovos, C.; Yeates, T.O. Verification of protein structures: Patterns of nonbonded atomic interactions. Protein Sci., 1993, 2(9), 1511-1519.
[http://dx.doi.org/10.1002/pro.5560020916] [PMID: 8401235]
[65]
Bowie, J.U.; Lüthy, R.; Eisenberg, D. A method to identify protein sequences that fold into a known three-dimensional structure. Science, 1991, 253(5016), 164-170.
[http://dx.doi.org/10.1126/science.1853201] [PMID: 1853201]
[66]
Lüthy, R.; Bowie, J.U.; Eisenberg, D. Assessment of protein models with three-dimensional profiles. Nature, 1992, 356(6364), 83-85.
[http://dx.doi.org/10.1038/356083a0] [PMID: 1538787]
[67]
Cherezov, V; Rosenbaum, DM; Hanson, MA; Rasmussen, SG; Thian, FS; Kobilka, TS; Choi, HJ; Kuhn, P; Weis, WI; Kobilka, BK; Stevens, RC High-resolution crystal structure of an engineered human β2-adrenergic G protein–coupled receptor. science, 2007, 318(5854), 1258-1265.
[68]
Hegedűs, T.; Geisler, M.; Lukács, G.L.; Farkas, B. Ins and outs of AlphaFold2 transmembrane protein structure predictions. Cell. Mol. Life Sci., 2022, 79(1), 73.
[http://dx.doi.org/10.1007/s00018-021-04112-1] [PMID: 35034173]
[69]
Ko, J.; Lee, J. Can AlphaFold2 predict protein-peptide complex structures accurately? BioRxiv, 2021.
[http://dx.doi.org/10.1101/2021.07.27.453972]
[70]
van Breugel, M.; Rosa e Silva, I.; Andreeva, A. Structural validation and assessment of AlphaFold2 predictions for centrosomal and centriolar proteins and their complexes. Commun. Biol., 2022, 5(1), 312.
[http://dx.doi.org/10.1038/s42003-022-03269-0] [PMID: 35383272]
[71]
He, X.; You, C.; Jiang, H.; Jiang, Y.; Xu, H.E.; Cheng, X. AlphaFold2 versus experimental structures: evaluation on G protein-coupled receptors. Acta Pharmacol. Sin., 2023, 44(1), 1-7.
[http://dx.doi.org/10.1038/s41401-022-00938-y] [PMID: 35778488]
[72]
Rasmussen, S.G.F.; DeVree, B.T.; Zou, Y.; Kruse, A.C.; Chung, K.Y.; Kobilka, T.S.; Thian, F.S.; Chae, P.S.; Pardon, E.; Calinski, D.; Mathiesen, J.M.; Shah, S.T.A.; Lyons, J.A.; Caffrey, M.; Gellman, S.H.; Steyaert, J.; Skiniotis, G.; Weis, W.I.; Sunahara, R.K.; Kobilka, B.K. Crystal structure of the β2 adrenergic receptor–Gs protein complex. Nature, 2011, 477(7366), 549-555.
[http://dx.doi.org/10.1038/nature10361] [PMID: 21772288]
[73]
Gangaraj, KP; Rajesh, MK Molecular characterisation and structural assessment of an RXLR effector from Phytophthora palmivora, the coconut bud rot pathogen. 2022, 100-109.
[74]
Kumari, R.; Dalal, V. Identification of potential inhibitors for LLM of Staphylococcus aureus: structure-based pharmacophore modeling, molecular dynamics, and binding free energy studies. J. Biomol. Struct. Dyn., 2022, 40(20), 9833-9847.
[http://dx.doi.org/10.1080/07391102.2021.1936179] [PMID: 34096457]
[75]
Gadhe, C.G.; Balupuri, A.; Cho, S.J. In silico characterization of binding mode of CCR8 inhibitor: homology modeling, docking and membrane based MD simulation study. J. Biomol. Struct. Dyn., 2015, 33(11), 2491-2510.
[http://dx.doi.org/10.1080/07391102.2014.1002006] [PMID: 25617117]
[76]
Bathula, S.; Sankaranarayanan, M.; Malgija, B.; Kaliappan, I.; Bhandare, R.R.; Shaik, A.B. 2-Amino thiazole derivatives as prospective aurora kinase inhibitors against breast cancer: qsar, admet prediction, molecular docking, and molecular dynamic simulation studies. ACS Omega, 2023.
[77]
Ul-Haq, Z.; Ashraf, S.; Bkhaitan, M.M. Molecular dynamics simulations reveal structural insights into inhibitor binding modes and mechanism of casein kinase II inhibitors. J. Biomol. Struct. Dyn., 2019, 37(5), 1120-1135.
[http://dx.doi.org/10.1080/07391102.2018.1450166] [PMID: 29527958]
[78]
Zahid, H. Structure and function studies of lipid binding to zinc-α₂- glycoprotein; Doctoral dissertation. UCL (University College London). 2020.
[79]
Dalal, V.; Golemi-Kotra, D.; Kumar, P. Quantum mechanics/molecular mechanics studies on the catalytic mechanism of a novel esterase (FmtA) of Staphylococcus aureus. J. Chem. Inf. Model., 2022, 62(10), 2409-2420.
[http://dx.doi.org/10.1021/acs.jcim.2c00057] [PMID: 35475370]
[80]
Dilcan, G.; Doruker, P.; Akten, E.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., 2019, 93(5), 883-899.
[http://dx.doi.org/10.1111/cbdd.13478] [PMID: 30637937]

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