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Current Computer-Aided Drug Design

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ISSN (Print): 1573-4099
ISSN (Online): 1875-6697

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Research Article

Designing Novel Teduglutide Analogues with Improved Binding Affinity: An In Silico Peptide Engineering Approach

Author(s): Ali A. Alizadeh and Siavoush Dastmalchi*

Volume 17, Issue 2, 2021

Published on: 17 February, 2020

Page: [225 - 234] Pages: 10

DOI: 10.2174/1573409916666200217091456

Price: $65

Abstract

Introduction: Short bowel syndrome (SBS) is a disabling condition that occurs following the loss of substantial portions of the intestine, leading to inadequate absorption of nutrients and fluids. Teduglutide is the only drug that has been FDA-approved for long-term treatment of SBS. This medicine exerts its biological effects through binding to the GLP-2 receptor.

Methods: The current study aimed to use computational mutagenesis approaches to design novel potent analogues of teduglutide. To this end, the constructed teduglutide-GLP2R 3D model was subjected to the alanine scanning mutagenesis where ARG20, PHE22, ILE23, LEU26, ILE27 and LYS30 were identified as the key amino acids involved in ligand-receptor interaction. In order to design potent teduglutide analogues, using MAESTROweb machine learning method, the residues of teduglutide were virtually mutated into all naturally occurring amino acids and the affinity improving mutations were selected for further analysis using PDBePISA methodology which interactively investigates the interactions established at the interfaces of macromolecules.

Results: The calculations resulted in D15I, D15L, D15M and N24M mutations, which can improve the binding ability of the ligand to the receptor. The final evaluation of identified mutations was performed by molecular dynamics simulations, indicating that D15I and D15M are the most reliable mutations to increase teduglutide affinity towards its receptor.

Conclusion: The findings in the current study may facilitate designing more potent teduglutide analogues leading to the development of novel treatments in short bowel syndrome.

Keywords: Teduglutide, GLP-2, molecular modeling, molecular dynamics simulations, virtual alanine scanning, peptide design.

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[1]
Buchman, A.L.; Scolapio, J.; Fryer, J. AGA technical review on short bowel syndrome and intestinal transplantation. Gastroenterology, 2003, 124(4), 1111-1134.
[http://dx.doi.org/10.1016/S0016-5085(03)70064-X] [PMID: 12671904]
[2]
Misiakos, E.P.; Macheras, A.; Kapetanakis, T.; Liakakos, T. Short bowel syndrome: current medical and surgical trends. J. Clin. Gastroenterol., 2007, 41(1), 5-18.
[http://dx.doi.org/10.1097/01.mcg.0000212617.74337.e9] [PMID: 17198059]
[3]
O'Keefe, S.J.; Buchman, A.L.; Fishbein, T.M.; Jeejeebhoy, K.N.; Jeppesen, P.B.; Shaffer, J. Short bowel syndrome and intestinal failure: consensus definitions and overview. Clin. Gastroenterol. Hepatol. : the official clinical practice journal of the American Gastroenterological Association, 2006, 4, 6-10.
[4]
McMellen, M.E.; Wakeman, D.; Longshore, S.W.; McDuffie, L.A.; Warner, B.W. Growth factors: possible roles for clinical management of the short bowel syndrome. Semin. Pediatr. Surg., 2010, 19(1), 35-43.
[http://dx.doi.org/10.1053/j.sempedsurg.2009.11.010] [PMID: 20123272]
[5]
Scolapio, J.S. Short bowel syndrome: recent clinical outcomes with growth hormone. Gastroenterology, 2006, 130(2)(Suppl. 1), S122-S126.
[http://dx.doi.org/10.1053/j.gastro.2005.12.003] [PMID: 16473059]
[6]
Seidner, D.L.; Schwartz, L.K.; Winkler, M.F.; Jeejeebhoy, K.; Boullata, J.I.; Tappenden, K.A. Increased intestinal absorption in the era of teduglutide and its impact on management strategies in patients with short bowel syndrome-associated intestinal failure. JPEN J. Parenter. Enteral Nutr., 2013, 37(2), 201-211.
[http://dx.doi.org/10.1177/0148607112472906] [PMID: 23343999]
[7]
Burness, C.B.; McCormack, P.L. Teduglutide: a review of its use in the treatment of patients with short bowel syndrome. Drugs, 2013, 73(9), 935-947.
[http://dx.doi.org/10.1007/s40265-013-0070-y] [PMID: 23729002]
[8]
Brubaker, P.L.; Crivici, A.; Izzo, A.; Ehrlich, P.; Tsai, C.H.; Drucker, D.J. Circulating and tissue forms of the intestinal growth factor, glucagon-like peptide-2. Endocrinology, 1997, 138(11), 4837-4843.
[http://dx.doi.org/10.1210/endo.138.11.5482] [PMID: 9348213]
[9]
Drucker, D.J.; Shi, Q.; Crivici, A.; Sumner-Smith, M.; Tavares, W.; Hill, M.; DeForest, L.; Cooper, S.; Brubaker, P.L. Regulation of the biological activity of glucagon-like peptide 2 in vivo by dipeptidyl peptidase IV. Nat. Biotechnol., 1997, 15(7), 673-677.
[http://dx.doi.org/10.1038/nbt0797-673] [PMID: 9219272]
[10]
Wallis, K.; Walters, J.R.; Gabe, S. Short bowel syndrome: the role of GLP-2 on improving outcome. Curr. Opin. Clin. Nutr. Metab. Care, 2009, 12(5), 526-532.
[http://dx.doi.org/10.1097/MCO.0b013e32832d23cd] [PMID: 19474717]
[11]
Guan, X. The CNS glucagon-like peptide-2 receptor in the control of energy balance and glucose homeostasis. Am. J. Physiol. Regul. Integr. Comp. Physiol., 2014, 307(6), R585-R596.
[http://dx.doi.org/10.1152/ajpregu.00096.2014] [PMID: 24990862]
[12]
Hornby, P.J.; Moore, B.A. The therapeutic potential of targeting the glucagon-like peptide-2 receptor in gastrointestinal disease. Expert Opin. Ther. Targets, 2011, 15(5), 637-646.
[http://dx.doi.org/10.1517/14728222.2011.556620] [PMID: 21314232]
[13]
Runge, S.; Thøgersen, H.; Madsen, K.; Lau, J.; Rudolph, R. Crystal structure of the ligand-bound glucagon-like peptide-1 receptor extracellular domain. J. Biol. Chem., 2008, 283(17), 11340-11347.
[http://dx.doi.org/10.1074/jbc.M708740200] [PMID: 18287102]
[14]
Macalino, S.J.; Gosu, V.; Hong, S.; Choi, S. Role of computer-aided drug design in modern drug discovery. Arch. Pharm. Res., 2015, 38(9), 1686-1701.
[http://dx.doi.org/10.1007/s12272-015-0640-5] [PMID: 26208641]
[15]
Sliwoski, G.; Kothiwale, S.; Meiler, J.; Lowe, E.W., Jr Computational methods in drug discovery. Pharmacol. Rev., 2013, 66(1), 334-395.
[http://dx.doi.org/10.1124/pr.112.007336] [PMID: 24381236]
[16]
Wang, T.; Wu, M.B.; Lin, J.P.; Yang, L.R. Quantitative structure-activity relationship: promising advances in drug discovery platforms. Expert Opin. Drug Discov., 2015, 10(12), 1283-1300.
[http://dx.doi.org/10.1517/17460441.2015.1083006] [PMID: 26358617]
[17]
Wang, X.; Chen, H.; Yang, F.; Gong, J.; Li, S.; Pei, J.; Liu, X.; Jiang, H.; Lai, L.; Li, H. iDrug: a web-accessible and interactive drug discovery and design platform. J. Cheminform., 2014, 6, 28.
[http://dx.doi.org/10.1186/1758-2946-6-28] [PMID: 24955134]
[18]
Gesto, D.S.; Cerqueira, N.M.; Ramos, M.J.; Fernandes, P.A. Discovery of new druggable sites in the anti-cholesterol target HMG-CoA reductase by computational alanine scanning mutagenesis. J. Mol. Model., 2014, 20(4), 2178.
[http://dx.doi.org/10.1007/s00894-014-2178-8] [PMID: 24671303]
[19]
Moal, I.H.; Jiménez-García, B.; Fernández-Recio, J. CCharPPI web server: computational characterization of protein-protein interactions from structure. Bioinformatics, 2015, 31(1), 123-125.
[http://dx.doi.org/10.1093/bioinformatics/btu594] [PMID: 25183488]
[20]
Ramos, R.M.; Moreira, I.S. Computational alanine scanning mutagenesis-an improved methodological approach for protein-dna complexes. J. Chem. Theory Comput., 2013, 9(9), 4243-4256.
[http://dx.doi.org/10.1021/ct400387r] [PMID: 26592413]
[21]
Sukhwal, A.; Sowdhamini, R. PPCheck: A webserver for the quantitative analysis of protein-protein interfaces and prediction of residue hotspots. Bioinform. Biol. Insights, 2015, 9, 141-151.
[http://dx.doi.org/10.4137/BBI.S25928] [PMID: 26448684]
[22]
Berman, H.M.; Westbrook, J.; Feng, Z.; Gilliland, G.; Bhat, T.N.; Weissig, H.; Shindyalov, I.N.; Bourne, P.E. The protein data bank. Nucleic Acids Res., 2000, 28(1), 235-242.
[http://dx.doi.org/10.1093/nar/28.1.235] [PMID: 10592235]
[23]
DaCambra, M.P.; Yusta, B.; Sumner-Smith, M.; Crivici, A.; Drucker, D.J.; Brubaker, P.L. Structural determinants for activity of glucagon-like peptide-2. Biochemistry, 2000, 39(30), 8888-8894.
[http://dx.doi.org/10.1021/bi000497p] [PMID: 10913301]
[24]
Arnold, K.; Bordoli, L.; Kopp, J.; Schwede, T. The SWISS-MODEL workspace: a web-based environment for protein structure homology modelling. Bioinformatics, 2006, 22(2), 195-201.
[http://dx.doi.org/10.1093/bioinformatics/bti770] [PMID: 16301204]
[25]
Kiefer, F.; Arnold, K.; Künzli, M.; Bordoli, L.; Schwede, T. The SWISS-MODEL Repository and associated resources. Nucleic Acids Res., 2009, 37(Database issue), D387-D392.
[http://dx.doi.org/10.1093/nar/gkn750] [PMID: 18931379]
[26]
Case, D.A.; Cheatham, T.E., III; Darden, T.; Gohlke, H.; Luo, R.; Merz, K.M., Jr; Onufriev, A.; Simmerling, C.; Wang, B.; Woods, R.J. The Amber biomolecular simulation programs. J. Comput. Chem., 2005, 26(16), 1668-1688.
[http://dx.doi.org/10.1002/jcc.20290] [PMID: 16200636]
[27]
Salomon-Ferrer, R.; Case, D.A.; Walker, R.C. An overview of the Amber biomolecular simulation package. Wiley Interdiscip. Rev. Comput. Mol. Sci., 2013, 3, 198-210.
[http://dx.doi.org/10.1002/wcms.1121]
[28]
Venneti, K.C.; Hewage, C.M. Conformational and molecular interaction studies of glucagon-like peptide-2 with its N-terminal extracellular receptor domain. FEBS Lett., 2011, 585(2), 346-352.
[http://dx.doi.org/10.1016/j.febslet.2010.12.011] [PMID: 21167157]
[29]
Laimer, J.; Hiebl-Flach, J.; Lengauer, D.; Lackner, P. MAESTROweb: a web server for structure-based protein stability prediction. Bioinformatics, 2016, 32(9), 1414-1416.
[http://dx.doi.org/10.1093/bioinformatics/btv769] [PMID: 26743508]
[30]
Laimer, J.; Hofer, H.; Fritz, M.; Wegenkittl, S.; Lackner, P. MAESTRO-multi agent stability prediction upon point mutations. BMC Bioinformatics, 2015, 16, 116.
[http://dx.doi.org/10.1186/s12859-015-0548-6] [PMID: 25885774]
[31]
Krissinel, E. Crystal contacts as nature’s docking solutions. J. Comput. Chem., 2010, 31(1), 133-143.
[http://dx.doi.org/10.1002/jcc.21303] [PMID: 19421996]
[32]
Krissinel, E.; Henrick, K. Inference of macromolecular assemblies from crystalline state. J. Mol. Biol., 2007, 372(3), 774-797.
[http://dx.doi.org/10.1016/j.jmb.2007.05.022] [PMID: 17681537]
[33]
Hospital, A.; Goñi, J.R.; Orozco, M.; Gelpí, J.L. Molecular dynamics simulations: advances and applications. Adv. Appl. Bioinform. Chem., 2015, 8, 37-47.
[PMID: 26604800]
[34]
Couvineau, A.; Rouyer-Fessard, C.; Laburthe, M. Presence of a N-terminal signal peptide in class II G protein-coupled receptors: crucial role for expression of the human VPAC1 receptor. Regul. Pept., 2004, 123(1-3), 181-185.
[http://dx.doi.org/10.1016/j.regpep.2004.06.025] [PMID: 15518910]
[35]
Parthier, C.; Reedtz-Runge, S.; Rudolph, R.; Stubbs, M.T. Passing the baton in class B GPCRs: peptide hormone activation via helix induction? Trends Biochem. Sci., 2009, 34(6), 303-310.
[http://dx.doi.org/10.1016/j.tibs.2009.02.004] [PMID: 19446460]
[36]
Buza, K.; Peška, L. Drug–target interaction prediction with Bipartite Local Models and hubness-aware regression. Neurocomputing, 2017, 260, 284-293.
[http://dx.doi.org/10.1016/j.neucom.2017.04.055]
[37]
Buza, K.; Peška, L. A New Approach for Drug–Target Interaction Prediction. Machine Learning and Knowledge Discovery in Databases. In: ECML PKDD 2017. Lecture Notes in Computer Science; Ceci, M., H.J.; Todorovski, L.; Vens, C.; Džeroski, S., Eds.; , 2017; 10535, pp. 322-337.
[http://dx.doi.org/10.1007/978-3-319-71246-8_20]
[38]
Peska, L.; Buza, K.; Koller, J. Drug-target interaction prediction: a bayesian ranking approach. Comput. Methods Programs Biomed., 2017, 152, 15-21.
[http://dx.doi.org/10.1016/j.cmpb.2017.09.003] [PMID: 29054256]
[39]
Abbasi, W.A.; Asif, A.; Ben-Hur, A.; Minhas, F.U.A.A. Learning protein binding affinity using privileged information. BMC Bioinformatics, 2018, 19(1), 425.
[http://dx.doi.org/10.1186/s12859-018-2448-z] [PMID: 30442086]

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