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Current Pharmaceutical Design

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ISSN (Print): 1381-6128
ISSN (Online): 1873-4286

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Review Article

Recent Advances and Computational Approaches in Peptide Drug Discovery

Author(s): Neha S. Maurya, Sandeep Kushwaha and Ashutosh Mani*

Volume 25, Issue 31, 2019

Page: [3358 - 3366] Pages: 9

DOI: 10.2174/1381612825666190911161106

Price: $65

Abstract

Background: Drug design and development is a vast field that requires huge investment along with a long duration for providing approval to suitable drug candidates. With the advancement in the field of genomics, the information about druggable targets is being updated at a fast rate which is helpful in finding a cure for various diseases.

Methods: There are certain biochemicals as well as physiological advantages of using peptide-based therapeutics. Additionally, the limitations of peptide-based drugs can be overcome by modulating the properties of peptide molecules through various biomolecular engineering techniques. Recent advances in computational approaches have been helpful in studying the effect of peptide drugs on the biomolecular targets. Receptor – ligand-based molecular docking studies have made it easy to screen compatible inhibitors against a target.Furthermore, there are simulation tools available to evaluate stability of complexes at the molecular level. Machine learning methods have added a new edge by enabling accurate prediction of therapeutic peptides.

Results: Peptide-based drugs are expected to take over many popular drugs in the near future due to their biosafety, lower off-target binding chances and multifunctional properties.

Conclusion: This article summarises the latest developments in the field of peptide-based therapeutics related to their usage, tools, and databases.

Keywords: Peptide drugs, therapeutics, druggable-targets, Gonadotropin-releasing hormone, immunoglobulins, drug discovery.

« Previous Next »
[1]
Diller DJ, Swanson J, Bayden AS, Jarosinski M, Audie J. Rational, computer-enabled peptide drug design: principles, methods, applications and future directions. Future Med Chem 2015; 7(16): 2173-93.
[http://dx.doi.org/10.4155/fmc.15.142] [PMID: 26510691]
[2]
Lau JL, Dunn MK. Therapeutic peptides: historical perspectives, current development trends, and future directions. Bioorg Med Chem 2018; 26(10): 2700-7.
[http://dx.doi.org/10.1016/j.bmc.2017.06.052] [PMID: 28720325]
[3]
Sharma S, Singh R, Rana S. Bioactive peptides: a review. Int J Bioautomation 2011; 15(4): 223-50.
[4]
Tsomaia N. Peptide therapeutics: targeting the undruggable space. Eur J Med Chem 2015; 94: 459-70.
[http://dx.doi.org/10.1016/j.ejmech.2015.01.014] [PMID: 25591543]
[5]
Hollenstein K, de Graaf C, Bortolato A, Wang MW, Marshall FH, Stevens RC. Insights into the structure of class B GPCRs. Trends Pharmacol Sci 2014; 35(1): 12-22.
[http://dx.doi.org/10.1016/j.tips.2013.11.001] [PMID: 24359917]
[6]
Agoulnik AI, Agoulnik IU, Hu X, Marugan J. Synthetic non-peptide low molecular weight agonists of the relaxin receptor 1. Br J Pharmacol 2017; 174(10): 977-89.
[http://dx.doi.org/10.1111/bph.13656] [PMID: 27771940]
[7]
Dunn MK. Peptide Therapeutics Update 2018.http://www.peptidetherapeutics.org/wp-content/uploads/2018/11/PTS2018-Dunn-Therapeutics-Update.pdf
[8]
Bray BL. Large-scale manufacture of peptide therapeutics by chemical synthesis. Nat Rev Drug Discov 2003; 2(7): 587-93.
[http://dx.doi.org/10.1038/nrd1133] [PMID: 12815383]
[9]
Thayer AM. Making peptides at large scale chemical & engineering news. American Chemical Society 2011; pp. 21-5.
[10]
Behrendt R, White P, Offer J. Advances in Fmoc solid-phase peptide synthesis. J Pept Sci 2016; 22(1): 4-27.
[http://dx.doi.org/10.1002/psc.2836]
[11]
Hannon JP, Nunn C, Stolz B, et al. Drug design at peptide receptors: somatostatin receptor ligands. J Mol Neurosci 2002; 18(1-2): 15-27.
[http://dx.doi.org/10.1385/JMN:18:1-2:15] [PMID: 11931345]
[12]
Peng SB, Zhang X, Paul D, et al. Identification of LY2510924, a novel cyclic peptide CXCR4 antagonist that exhibits antitumor activities in solid tumor and breast cancer metastatic models. Mol Cancer Ther 2015; 14(2): 480-90.
[http://dx.doi.org/10.1158/1535-7163.MCT-14-0850] [PMID: 25504752]
[13]
Clemmensen C, Müller TD, Woods SC, Berthoud HR, Seeley RJ, Tschöp MH. Gut-brain cross-talk in metabolic control. Cell 2017; 168(5): 758-74.
[http://dx.doi.org/10.1016/j.cell.2017.01.025] [PMID: 28235194]
[14]
Finan B, Ma T, Ottaway N, et al. Unimolecular dual incretins maximize metabolic benefits in rodents, monkeys, and humans. Sci Transl Med 2019; 209(5): 151-209.
[http://dx.doi.org/10.1126/scitranslmed.3007218]
[15]
Fosgerau K, Hoffmann T. Peptide therapeutics: current status and future directions. Drug Discov Today 2015; 20(1): 122-8.
[http://dx.doi.org/10.1016/j.drudis.2014.10.003] [PMID: 25450771]
[16]
Uhlig T, Kyprianou T, Martinelli FG, et al. The emergence of peptides in the pharmaceutical business: from exploration to exploitation. EuPA Open Proteom 2014; 4: 58-69.
[http://dx.doi.org/10.1016/j.euprot.2014.05.003]
[17]
Pichereau C, Allary C. Therapeutic peptides under the spotlight. Eur Biopharm 2005; (05): 88-91.
[18]
Kaspar AA, Reichert JM. Future directions for peptide therapeutics development. Drug Discov Today 2013; 18(17-18): 807-17.
[http://dx.doi.org/10.1016/j.drudis.2013.05.011] [PMID: 23726889]
[19]
Global Peptide Therapeutics Market 2019-2023.. http://www.technavio.com
[20]
Bhatt DL, Topol EJ. Scientific and therapeutic advances in antiplatelet therapy. Nat Rev Drug Discov 2003; 2(1): 15-28.
[http://dx.doi.org/10.1038/nrd985] [PMID: 12509756]
[21]
Gan ZR, Gould RJ, Jacobs JW, Friedman PA, Polokoff MA. Echistatin. A potent platelet aggregation inhibitor from the venom of the viper, Echis carinatus. J Biol Chem 1988; 263(36): 19827-32.
[PMID: 3198653]
[22]
Scarborough RM, Rose JW, Hsu MA, et al. Barbourin. A GPIIb-IIIa-specific integrin antagonist from the venom of Sistrurus m. barbouri. J Biol Chem 1991; 266(15): 9359-62.
[PMID: 2033037]
[23]
Koh CY, Kini RM. From snake venom toxins to therapeutics--cardiovascular examples. Toxicon 2012; 59(4): 497-506.
[http://dx.doi.org/10.1016/j.toxicon.2011.03.017] [PMID: 21447352]
[24]
Crane J. Medical education in the future: lessons from the past Royal Victoria Hospital, September 2013. Ulster Med J 2015; 84(2): 124-8.
[PMID: 26170491]
[25]
Haycraft JB. On the action of a secretion obtained from the medicinal leech on the coagulation of the blood. Proc R Soc Lond 1884; 36: 478-87.
[26]
Markwardt F. Hirudin as alternative anticoagulant- a historical review. Thromb Haemost 1955; 42: 537-8.
[27]
Markwardt F. Die antagonistische wirkung des hirudins gegen thrombin in vivo. Naturwissenschaften 1956; 43: 111-2.
[http://dx.doi.org/10.1007/BF00600885]
[28]
Markwardt F. Hirudin as an inhibitor of thrombin. Methods Enzymol 1970; 19: 924-32.
[http://dx.doi.org/10.1016/0076-6879(70)19082-3]
[29]
Jain S, Zain J. Romidepsin in the treatment of cutaneous T-cell lymphoma. J Blood Med 2011; 2: 37-47.
[PMID: 22287862]
[30]
Pennington MW, Czerwinski A, Norton RS. Peptide therapeutics from venom: current status and potential. Bioorg Med Chem 2018; 26(10): 2738-58.
[http://dx.doi.org/10.1016/j.bmc.2017.09.029] [PMID: 28988749]
[31]
Busby RW, Bryant AP, Bartolini WP, et al. Linaclotide, through activation of guanylate cyclase C, acts locally in the gastrointestinal tract to elicit enhanced intestinal secretion and transit. Eur J Pharmacol 2010; 649(1-3): 328-35.
[http://dx.doi.org/10.1016/j.ejphar.2010.09.019] [PMID: 20863829]
[32]
Bechara C, Sagan S. Cell-penetrating peptides: 20 years later, where do we stand? FEBS Lett 2013; 587(12): 1693-702.
[http://dx.doi.org/10.1016/j.febslet.2013.04.031] [PMID: 23669356]
[33]
Passioura T, Katoh T, Goto Y, Suga H. Selection-based discovery of druglike macrocyclic peptides. Annu Rev Biochem 2014; 83: 727-52.
[http://dx.doi.org/10.1146/annurev-biochem-060713-035456] [PMID: 24580641]
[34]
Walensky LD, Bird GH. Hydrocarbon-stapled peptides: principles, practice, and progress. J Med Chem 2014; 57(15): 6275-88.
[http://dx.doi.org/10.1021/jm4011675] [PMID: 24601557]
[35]
Rentzsch R, Renard BY. Docking small peptides remains a great challenge: an assessment using AutoDock Vina. Brief Bioinform 2015; 16(6): 1045-56.
[http://dx.doi.org/10.1093/bib/bbv008] [PMID: 25900849]
[36]
Pike DH, Nanda V. Empirical estimation of local dielectric constants: toward atomistic design of collagen mimetic peptides. Biopolymers 2015; 104(4): 360-70.
[http://dx.doi.org/10.1002/bip.22644] [PMID: 25784456]
[37]
Audie J, Swanson J. Recent work in the development and application of protein-peptide docking. Future Med Chem 2012; 4(12): 1619-44.
[http://dx.doi.org/10.4155/fmc.12.99] [PMID: 22917249]
[38]
Das R. Four small puzzles that Rosetta doesn’t solve. PLoS One 2011; 6(5)e20044
[http://dx.doi.org/10.1371/journal.pone.0020044] [PMID: 21625446]
[39]
Kapoor P, Singh H, Gautam A, Chaudhary K, Kumar R, Raghava GP. TumorHoPe: a database of tumor homing peptides. PLoS One 2012; 7(4)e35187
[http://dx.doi.org/10.1371/journal.pone.0035187] [PMID: 22523575]
[40]
TumorHoPe – Tumor homing peptide database.. http://crdd.osdd.net/raghava/tumorhope
[41]
Volpe DA. Drug-permeability and transporter assays in Caco-2 and MDCK cell lines. Future Med Chem 2011; 3(16): 2063-77.
[http://dx.doi.org/10.4155/fmc.11.149] [PMID: 22098353]
[42]
Brainpeps.. http://brainpeps.ugent.be
[43]
Hansen MR, Villar HO, Feyfant E. Development of an informatics platform for therapeutic protein and peptide analytics. J Chem Inf Model 2013; 53(10): 2774-9.
[http://dx.doi.org/10.1021/ci400333x] [PMID: 24099460]
[44]
Altoris - SARvision.. www.chemapps.com/sarvision-biologics
[45]
Thévenet P, Shen Y, Maupetit J, Guyon F, Derreumaux P, Tufféry P. PEP-FOLD: an updated de novo structure prediction server for both linear and disulfide bonded cyclic peptides. Nucleic Acids Res 2012; 40(Web Server issue): W288-93.
[http://dx.doi.org/10.1093/nar/gks419] [PMID: 22581768]
[46]
Server PEP-FOLD.. http://bioserv.rpbs.univ-paris-diderot.fr/services/PEP-FOLD
[47]
Vanhee P, Reumers J, Stricher F, et al. PepX: a structural database of non-redundant protein-peptide complexes. Nucleic Acids Res 2010; 38(Database issue): D545-51.
[http://dx.doi.org/10.1093/nar/gkp893] [PMID: 19880386]
[48]
Pep X. www.switchlab.org/bioinformatics/pepx
[49]
Das AA, Sharma OP, Kumar MS, Krishna R, Mathur PP. PepBind: a comprehensive database and computational tool for analysis of protein-peptide interactions. Genomics Proteomics Bioinformatics 2013; 11(4): 241-6.
[http://dx.doi.org/10.1016/j.gpb.2013.03.002] [PMID: 23896518]
[50]
PepBind.. http://pepbind.bicpu.edu.in
[51]
London N, Movshovitz-Attias D, Schueler-Furman O. The structural basis of peptide-protein binding strategies. Structure 2010; 18(2): 188-99.
[http://dx.doi.org/10.1016/j.str.2009.11.012] [PMID: 20159464]
[52]
London N, Raveh B, Cohen E, Fathi G, Schueler-Furman O. osetta FlexPepDock web server--high resolution modeling of peptide- protein interactions. Nucleic Acids Res 2011; 39(Web Server issue): W249-53.
[http://dx.doi.org/10.1093/nar/gkr431] [PMID: 21622962]
[53]
FlexPepDock.. http://flexpepdock.furmanlab.cs.huji.ac.il
[54]
Raveh B, London N, Zimmerman L, Schueler-Furman O. Rosetta FlexPepDock ab-initio: simultaneous folding, docking and refinement of peptides onto their receptors. PLoS One 2011; 6(4) e18934
[http://dx.doi.org/10.1371/journal.pone.0018934] [PMID: 21572516]
[55]
Li H, Lu L, Chen R, Quan L, Xia X, Lü Q. PaFlexPepDock: parallel ab-initio docking of peptides onto their receptors with full flexibility based on Rosetta. PLoS One 2014; 9(5) e94769
[http://dx.doi.org/10.1371/journal.pone.0094769] [PMID: 24801496]
[56]
Trellet M, Melquiond AS, Bonvin AM. A unified conformational selection and induced fit approach to protein-peptide docking. PLoS One 2013; 8(3) e58769
[http://dx.doi.org/10.1371/journal.pone.0058769] [PMID: 23516555]
[57]
HADDOCK. http://haddocking.org
[58]
Lata S, Sharma BK, Raghava GPS. Analysis and prediction of antibacterial peptides. BMC Bioinformatics 2007; 8: 263.
[http://dx.doi.org/10.1186/1471-2105-8-263] [PMID: 17645800]
[59]
Waghu FH, Barai RS, Gurung P, Idicula-Thomas S. CAMPR3: a database on sequences, structures and signatures of antimicrobial peptides. Nucleic Acids Res 2016; 44(D1): D1094-7.
[http://dx.doi.org/10.1093/nar/gkv1051] [PMID: 26467475]
[60]
Wang G, Li X, Wang Z. APD3: the antimicrobial peptide database as a tool for research and education. Nucleic Acids Res 2016; 44(D1): D1087-93.
[http://dx.doi.org/10.1093/nar/gkv1278] [PMID: 26602694]
[61]
Bhadra P, Yan J, Li J, Fong S, Siu SWI. AmPEP: sequence-based prediction of antimicrobial peptides using distribution patterns of amino acid properties and random forest. Sci Rep 2018; 8(1): 1697.
[http://dx.doi.org/10.1038/s41598-018-19752-w] [PMID: 29374199]
[62]
Gogoladze G, Grigolava M, Vishnepolsky B, et al. DBAASP: database of antimicrobial activity and structure of peptides. FEMS Microbiol Lett 2014; 357(1): 63-8.
[http://dx.doi.org/10.1111/1574-6968.12489] [PMID: 24888447]
[63]
Veltri D, Kamath U, Shehu A. Deep learning improves antimicrobial peptide recognition. Bioinformatics 2018; 34(16): 2740-7.
[http://dx.doi.org/10.1093/bioinformatics/bty179] [PMID: 29590297]
[64]
Tyagi A, Kapoor P, Kumar R, Chaudhary K, Gautam A, Raghava GP. In silico models for designing and discovering novel anticancer peptides. Sci Rep 2013; 3(10): 2984.
[http://dx.doi.org/10.1038/srep02984] [PMID: 24136089]
[65]
Manavalan B, Basith S, Shin TH, Choi S, Kim MO, Lee G. MLACP: machine-learning-based prediction of anticancer peptides. Oncotarget 2017; 8(44): 77121-36.
[http://dx.doi.org/10.18632/oncotarget.20365] [PMID: 29100375]
[66]
Wei L, Zhou C, Chen H, Song J, Su R. ACPred-FL: a sequence-based predictor using effective feature representation to improve the prediction of anti-cancer peptides. Bioinformatics 2018; 34(23): 4007-16.
[http://dx.doi.org/10.1093/bioinformatics/bty451] [PMID: 29868903]
[67]
Vijayakumar S, Lakshmi P. ACPP: a web server for prediction and design of anti-cancer peptides. Int J Pept Res Ther 2015; 21: 99-106.
[http://dx.doi.org/10.1007/s10989-014-9435-7]
[68]
Chen W, Ding H, Feng P, Lin H, Chou KC. iACP: a sequence-based tool for identifying anticancer peptides. Oncotarget 2016; 7(13): 16895-909.
[http://dx.doi.org/10.18632/oncotarget.7815] [PMID: 26942877]
[69]
Mathur D, Mehta A, Firmal P, et al. TopicalPdb: a database of topically delivered peptides. PLoS One 2018; 13(2) e0190134
[http://dx.doi.org/10.1371/journal.pone.0190134] [PMID: 29432422]
[70]
http://crdd.osdd.net/raghava/biotherapi/pdrug.html
[71]
Ribarič S. Peptides as potential therapeutics for Alzheimer’s disease. Molecules 2018; 23(2): 283.
[http://dx.doi.org/10.3390/molecules23020283] [PMID: 29385735]
[72]
Tajimi T, Wakui N, Yanagisawa K, Yoshikawa Y, Ohue M, Akiyama Y. Computational prediction of plasma protein binding of cyclic peptides from small molecule experimental data using sparse modeling techniques. BMC Bioinformatics 2018; 19(Suppl. 19): 527.
[http://dx.doi.org/10.1186/s12859-018-2529-z] [PMID: 30598072]
[73]
Manavalan B, Shin TH, Kim MO, Lee G. AIPpred: Sequence-based prediction of anti-inflammatory peptides using random forest. Front Pharmacol 2018; 9: 276.
[http://dx.doi.org/10.3389/fphar.2018.00276] [PMID: 29636690]
[74]
Feng S, Zou L, Ni Q, et al. Modulation, bioinformatic screening, and assessment of small molecular peptides targeting the vascular endothelial growth factor receptor. Cell Biochem Biophys 2014; 70(3): 1913-21.
[http://dx.doi.org/10.1007/s12013-014-0151-x] [PMID: 25069724]
[75]
Tal-Gan Y, Hurevich M, Klein S, et al. Backbone cyclic peptide inhibitors of protein kinase B (PKB/Akt). J Med Chem 2011; 54(14): 5154-64.
[http://dx.doi.org/10.1021/jm2003969] [PMID: 21650457]

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