Skip to content
2000
Volume 20, Issue 2
  • ISSN: 1574-8936
  • E-ISSN: 2212-392X

Abstract

Peptidomics is the study of total peptides that describe the functions, structures, and interactions of peptides within living organisms. It comprises bioactive peptides derived naturally or synthetically designed that exhibit various therapeutic properties against microbial infections, cancer progression, inflammation, . With the current state of the art, Bioinformatics tools and techniques help analyse large peptidomics data and predict peptide structure and functions. It also aids in designing peptides with enhanced stability and efficacy. Peptidomics studies are gaining importance in therapeutics as they offer increased target specificity with the least side effects. The molecular size and flexibility of peptides make them a potential drug candidate for designing protein-protein interaction inhibitors. These features increased their drug potency with the considerable increase in the number of peptide drugs available in the market for various health commodities. The present review extensively analyses the peptidomics field, focusing on different bioactive peptides and therapeutics, such as anticancer peptide drugs. Further, the review provides comprehensive information on tools available for peptide research. The importance of personalised peptide medicines in disease therapy is discussed along with the case study. Further, the major limitations of peptide drugs and the different strategies to overcome those limitations are reviewed.

Loading

Article metrics loading...

/content/journals/cbio/10.2174/0115748936351054241010091822
2024-10-11
2025-05-06
Loading full text...

Full text loading...

References

  1. HellingerR. SigurdssonA. WuW. RomanovaE.V. LiL. SweedlerJ.V. SüssmuthR.D. GruberC.W. Peptidomics.Nat. Rev. Methods Primers2023312510.1038/s43586‑023‑00205‑237250919
    [Google Scholar]
  2. DallasD.C. GuerreroA. ParkerE.A. RobinsonR.C. GanJ. GermanJ.B. BarileD. LebrillaC.B. Current peptidomics: Applications, purification, identification, quantification, and functional analysis.Proteomics2015155-61026103810.1002/pmic.20140031025429922
    [Google Scholar]
  3. MielkeM.M. FowlerN.R. Alzheimer disease blood biomarkers: Considerations for population-level use.Nat. Rev. Neurol.202420849550410.1038/s41582‑024‑00989‑138862788
    [Google Scholar]
  4. PeiZ.F. ZhuL. NairS.K. Core-dependent post-translational modifications guide the biosynthesis of a new class of hypermodified peptides.Nat. Commun.2023141773410.1038/s41467‑023‑43604‑538007494
    [Google Scholar]
  5. HayesM. Bioactive peptides in preventative healthcare: An overview of bioactivities and suggested methods to assess potential applications.Curr. Pharm. Des.202127111332134110.2174/138161282766621012515504833550961
    [Google Scholar]
  6. AkbarianM. KhaniA. EghbalpourS. UverskyV.N. Bioactive peptides: Synthesis, sources, applications, and proposed mechanisms of action.Int. J. Mol. Sci.2022233144510.3390/ijms2303144535163367
    [Google Scholar]
  7. LiG. ShuliZ. LinlinL. YinghuZ. NanS. HaibinW. HongyuX. Bioinformatics and computer simulation approaches to the discovery and analysis of bioactive peptides.Curr. Pharm. Biotechnol.202223131541155510.2174/138920102366622010616101634994325
    [Google Scholar]
  8. WenQ. ZhangL. ZhaoF. ChenY. SuY. ZhangX. ChenP. ZhengT. Production technology and functionality of bioactive peptides.Curr. Pharm. Des.202329965267410.2174/138161282966623020112135336725828
    [Google Scholar]
  9. HermannJ. SchurgersL. JankowskiV. Identification and characterization of post-translational modifications: Clinical implications.Mol. Aspects Med.20228610106610.1016/j.mam.2022.10106635033366
    [Google Scholar]
  10. MartiniS. TagliazucchiD. Bioactive peptides in human health and disease.Int. J. Mol. Sci.2023246583710.3390/ijms2406583736982911
    [Google Scholar]
  11. WalterM.H. AbeleH. PlappertC.F. The role of oxytocin and the effect of stress during childbirth: Neurobiological basics and implications for mother and child.Front. Endocrinol. (Lausanne)20211274223610.3389/fendo.2021.74223634777247
    [Google Scholar]
  12. RaoS. PenaC. ShurmurS. NugentK. Atrial natriuretic peptide: Structure, function, and physiological effects: A narrative review.Curr. Cardiol. Rev.2021176e05112119100310.2174/1573403X1766621020210221033530911
    [Google Scholar]
  13. BurbachJ.P.H. What are neuropeptides?Methods Mol. Biol.201178913610.1007/978‑1‑61779‑310‑3_121922398
    [Google Scholar]
  14. Duarte-MataD.I. Salinas-CarmonaM.C. Antimicrobial peptides´ immune modulation role in intracellular bacterial infection.Front. Immunol.202314111957410.3389/fimmu.2023.111957437056758
    [Google Scholar]
  15. BesharatiM. LacknerM. Bioactive peptides: A review.EuroBiotech Journal20237417618810.2478/ebtj‑2023‑0013
    [Google Scholar]
  16. AuadaA.V.V. FallaM.V.A. LebrunI. Bioactive peptides (cryptides) obtained by Bothrops jararaca serine peptidases action on myoglobin.Toxicon202424710783510.1016/j.toxicon.2024.10783538942240
    [Google Scholar]
  17. ParisiM.G. OzónB. Vera GonzálezS.M. García-PardoJ. ObregónW.D. Plant protease inhibitors as emerging antimicrobial peptide agents: A comprehensive review.Pharmaceutics202416558210.3390/pharmaceutics1605058238794245
    [Google Scholar]
  18. SwordT.T. AbbasG.S.K. BaileyC.B. Cell-free protein synthesis for nonribosomal peptide synthetic biology.Front. Nat. Prod.20243135336210.3389/fntpr.2024.1353362
    [Google Scholar]
  19. IacovelliR. BovenbergR.A.L. DriessenA.J.M. Nonribosomal peptide synthetases and their biotechnological potential in Penicillium rubens.J. Ind. Microbiol. Biotechnol.2021487-8kuab04510.1093/jimb/kuab04534279620
    [Google Scholar]
  20. ChauhanV. KanwarS. Chapter 4 - Bioactive peptides: Synthesis, functions and biotechnological applications. In: Biotechnological Production of Bioactive Compounds107.(137) Elsevier 2020; p.10.1016/B978‑0‑444‑64323‑0.00004‑7
    [Google Scholar]
  21. AlzaydiA. BarbhuiyaR.I. RoutrayW. ElsayedA. SinghA. Bioactive peptides: Synthesis, applications, and associated challenges.Food Bioeng.20232327329010.1002/fbe2.12057
    [Google Scholar]
  22. KaurJ. KumarV. SharmaK. KaurS. GatY. GoyalA. TanwarB. Opioid peptides: An Overview of functional significance.Int. J. Pept. Res. Ther.2020261334110.1007/s10989‑019‑09813‑7
    [Google Scholar]
  23. SarmadiB.H. IsmailA. Antioxidative peptides from food proteins: A review.Peptides201031101949195610.1016/j.peptides.2010.06.02020600423
    [Google Scholar]
  24. ShivannaS.K. NatarajB.H. Revisiting therapeutic and toxicological fingerprints of milk-derived bioactive peptides: An overview.Food Biosci.20203810077110.1016/j.fbio.2020.100771
    [Google Scholar]
  25. YangH. ZhangQ. ZhangB. ZhaoY. WangN. Potential active marine peptides as anti-aging drugs or drug candidates.Mar. Drugs202321314410.3390/md2103014436976193
    [Google Scholar]
  26. ArabuliL. LoveckaP. JezekR. ViktorovaJ. MacekT. JunkovaP. GakhokidzeR. SharifianjaziF. EsmaeilkhanianA. SalahshourP. PoursafaP. SabouriP. AChE inhibitory effect, anti oxidant and anti-inflammatory properties of cyclen and L-Dopa related compounds: Targeting in neurodegenerative disease.J. Mol. Struct.2023128713566510.1016/j.molstruc.2023.135665
    [Google Scholar]
  27. ChenC.H. LuT.K. Development and challenges of antimicrobial peptides for therapeutic applications.Antibiotics (Basel)2020912410.3390/antibiotics901002431941022
    [Google Scholar]
  28. Midura-NowaczekK. MarkowskaA. Antimicrobial peptides and their analogs: Searching for new potential therapeutics.Perspect. Medicin. Chem.20146PMC.S1321510.4137/PMC.S1321525374459
    [Google Scholar]
  29. FontanotA. EllingerI. UngerW.W.J. HaysJ.P. A comprehensive review of recent research into the effects of antimicrobial peptides on biofilms — January 2020 to September 2023.Antibiotics (Basel)202413434310.3390/antibiotics1304034338667019
    [Google Scholar]
  30. AntonyP. VijayanR. Bioactive peptides as potential nutraceuticals for diabetes therapy: A comprehensive review.Int. J. Mol. Sci.20212216905910.3390/ijms2216905934445765
    [Google Scholar]
  31. SuryaningtyasI.T. JeJ.Y. Bioactive peptides from food proteins as potential anti-obesity agents: Mechanisms of action and future perspectives.Trends Food Sci. Technol.202313814115210.1016/j.tifs.2023.06.015
    [Google Scholar]
  32. LiuW. ChenX. LiH. ZhangJ. AnJ. LiuX. Anti-inflammatory function of plant-derived bioactive peptides: A review.Foods20221115236110.3390/foods1115236135954128
    [Google Scholar]
  33. LepantoM.S. RosaL. PaesanoR. ValentiP. CutoneA. Lactoferrin in aseptic and septic inflammation.Molecules2019247132310.3390/molecules2407132330987256
    [Google Scholar]
  34. SaravananP. RP. BalachanderN. KK.R.S. SS. SR. Anti-inflammatory and wound healing properties of lactic acid bacteria and its peptides.Folia Microbiol. (Praha)202368333735310.1007/s12223‑022‑01030‑y36780113
    [Google Scholar]
  35. SharmaA. GoelA. Pathogenesis of rheumatoid arthritis and its treatment with anti-inflammatory natural products.Mol. Biol. Rep.20235054687470610.1007/s11033‑023‑08406‑437022525
    [Google Scholar]
  36. XuF. YangF. QiuY. WangC. ZouQ. WangL. LiX. JinM. LiuK. ZhangS. ZhangY. LiB. The alleviative effect of C-phycocyanin peptides against TNBS-induced inflammatory bowel disease in zebrafish via the MAPK/Nrf2 signaling pathways.Fish Shellfish Immunol.202414510935110.1016/j.fsi.2023.10935138171429
    [Google Scholar]
  37. ZhouY. WangD. YanW. Treatment effects of natural products on inflammatory bowel disease in vivo and their mechanisms: Based on animal experiments.Nutrients2023154103110.3390/nu1504103136839389
    [Google Scholar]
  38. Diniz-SousaR. CaldeiraC.A.S. PereiraS.S. Da SilvaS.L. FernandesP.A. TeixeiraL.M.C. ZulianiJ.P. SoaresA.M. Therapeutic applications of snake venoms: An invaluable potential of new drug candidates.Int. J. Biol. Macromol.202323812435710.1016/j.ijbiomac.2023.12435737028634
    [Google Scholar]
  39. OliveiraA.L. ViegasM.F. da SilvaS.L. SoaresA.M. RamosM.J. FernandesP.A. The chemistry of snake venom and its medicinal potential.Nat. Rev. Chem.20226745146910.1038/s41570‑022‑00393‑7
    [Google Scholar]
  40. ZhangY. WangC. ZhangW. LiX. Bioactive peptides for anticancer therapies.Biomater. Transl.20234151710.12336/biomatertransl.2023.01.00337206303
    [Google Scholar]
  41. ChalamaiahM. YuW. WuJ. Immunomodulatory and anticancer protein hydrolysates (peptides) from food proteins: A review.Food Chem.201824520522210.1016/j.foodchem.2017.10.08729287362
    [Google Scholar]
  42. GhalyG. TallimaH. DabbishE. Badr ElDinN. Abd El-RahmanM.K. IbrahimM.A.A. ShoeibT. Anti-cancer peptides: Status and future prospects.Molecules2023283114810.3390/molecules2803114836770815
    [Google Scholar]
  43. BrownJ.S. AmendS.R. AustinR.H. GatenbyR.A. HammarlundE.U. PientaK.J. Updating the definition of cancer.Mol. Cancer Res.202321111142114710.1158/1541‑7786.MCR‑23‑041137409952
    [Google Scholar]
  44. KumarV.B. OzguneyB. VlachouA. ChenY. GazitE. TamamisP. Peptide self-assembled nanocarriers for cancer drug delivery.J. Phys. Chem. B202312791857187110.1021/acs.jpcb.2c0675136812392
    [Google Scholar]
  45. SchubertM. BergmannR. FörsterC. SihverW. VonhoffS. KlussmannS. BethgeL. WaltherM. SchlesingerJ. PietzschJ. SteinbachJ. PietzschH.J. Novel tumor pretargeting system based on complementary 1-configured oligonucleotides.Bioconjug. Chem.20172841176118810.1021/acs.bioconjchem.7b0004528222590
    [Google Scholar]
  46. TurturroF. Denileukin diftitox: A biotherapeutic paradigm shift in the treatment of lymphoid-derived disorders.Expert Rev. Anticancer Ther.200771111710.1586/14737140.7.1.1117187516
    [Google Scholar]
  47. ForyśU. NahshonyA. ElishmereniM. Mathematical model of hormone sensitive prostate cancer treatment using leuprolide: A small step towards personalization.PLoS One2022172e026364810.1371/journal.pone.026364835167616
    [Google Scholar]
  48. PlourdeP.V. JehaS. HijiyaN. KellerF.G. SilvermanL.B. RheingoldS.R. DreyerZ.E. DahlG.V. MercedesT. LaiC. CornT. Safety profile of asparaginase Erwinia chrysanthemi in a large compassionate‐use trial.Pediatr. Blood Cancer20146171232123810.1002/pbc.2493824436152
    [Google Scholar]
  49. GonzalezM.E. Eluvathingal MuttikkalT.J. RehmP.K. Sialadenitis following low dose I-131 diagnostic thyroid scan with Thyrogen® (recombinant human thyroid stimulating hormone - thyrotropin alfa).J. Radiol. Case Rep.201596444910.3941/jrcr.v9i6.222026622936
    [Google Scholar]
  50. KayeJ.A. The clinical development of recombinant human interleukin 11 (NEUMEGA rhIL-11 growth factor).Stem Cells199614Suppl 125626010.1002/stem.553014073311012229
    [Google Scholar]
  51. LiuD. SeybothB. MathewS. GilheeneyS.W. ChouA.J. DrillE. KobosR. Retrospective evaluation of palifermin use in nonhematopoietic stem cell transplant pediatric patients.J. Pediatr. Hematol. Oncol.2017394e177e18210.1097/MPH.000000000000079128234746
    [Google Scholar]
  52. RotteA. BhandaruM. ZhouY. McElweeK.J. Immunotherapy of melanoma: Present options and future promises.Cancer Metastasis Rev.201534111512810.1007/s10555‑014‑9542‑025589384
    [Google Scholar]
  53. GrahamM. Pegaspargase: A review of clinical studies.Adv. Drug Deliv. Rev.200355101293130210.1016/S0169‑409X(03)00110‑814499708
    [Google Scholar]
  54. NewsomeS.D. KieseierB.C. ArnoldD.L. ShangS. LiuS. HungS. SabatellaG. Subgroup and sensitivity analyses of annualized relapse rate over 2 years in the ADVANCE trial of peginterferon beta-1a in patients with relapsing remitting multiple sclerosis.J. Neurol.201626391778178710.1007/s00415‑016‑8182‑427314959
    [Google Scholar]
  55. NebijaD. Kopelent-FrankH. UrbanE. NoeC.R. LachmannB. Comparison of two-dimensional gel electrophoresis patterns and MALDI-TOF MS analysis of therapeutic recombinant monoclonal antibodies trastuzumab and rituximab.J. Pharm. Biomed. Anal.201156468469110.1016/j.jpba.2011.07.00621813259
    [Google Scholar]
  56. SzakácsZ. LalA. KristensenJ. FarkasN. RitterZ. KissS. AlizadehH. BalikóA. 90Y-ibritumomab tiuxetan in b-cell non-hodgkin lymphomas: real-world data from the united arab emirates.Adv. Radiat. Oncol.20227510088210.1016/j.adro.2021.10088236148378
    [Google Scholar]
  57. TanC.R.C. Abdul-MajeedS. CaelB. BartaS.K. Clinical pharmacokinetics and pharmacodynamics of bortezomib.Clin. Pharmacokinet.201958215716810.1007/s40262‑018‑0679‑929802543
    [Google Scholar]
  58. NoguchiS. KimH.J. JesenaA. ParmarV. SatoN. WangH.C. LokejaroenlarbS. IsidroJ. KimK.S. ItohY. ShinE. Phase 3, open-label, randomized study comparing 3-monthly with monthly goserelin in pre-menopausal women with estrogen receptor-positive advanced breast cancer.Breast Cancer201623577177910.1007/s12282‑015‑0637‑426350351
    [Google Scholar]
  59. JayaweeraS.P.E. Wanigasinghe KanakanamgeS.P. RajalingamD. SilvaG.N. Carfilzomib: A promising proteasome inhibitor for the treatment of relapsed and refractory multiple myeloma.Front. Oncol.20211174079610.3389/fonc.2021.74079634858819
    [Google Scholar]
  60. CornfordP. JeffersonK. ColeO. GilbodyJ. Effects of initiating or switching to a six-monthly triptorelin formulation on prostate cancer patient–healthcare interactions and hospital resource use: A real-world, retrospective, non-interventional study.Oncol. Ther.20186217318710.1007/s40487‑018‑0087‑132700031
    [Google Scholar]
  61. ZengerlingF. JakobJ.J. SchmidtS. MeerpohlJ.J. BlümleA. SchmuckerC. MayerB. KunathF. Degarelix for treating advanced hormone-sensitive prostate cancer.Cochrane Libr.202120218CD01254810.1002/14651858.CD012548.pub234350976
    [Google Scholar]
  62. RyanP. PhanA. AdelmanD. IwasakiM. Neuroendocrine tumors and lanreotide depot: Clinical considerations and nurse and patient preferences.Clin. J. Oncol. Nurs.2016206E139E14610.1188/16.CJON.E139‑E14627857269
    [Google Scholar]
  63. JohnsonD.B. PengC. AbramsonR.G. YeF. ZhaoS. WolchokJ.D. SosmanJ.A. CarvajalR.D. AriyanC.E. Clinical activity of ipilimumab in acral melanoma: A retrospective review.Oncologist201520664865210.1634/theoncologist.2014‑046825964307
    [Google Scholar]
  64. JakubczykA. KaraśM. Rybczyńska-TkaczykK. ZielińskaE. ZielińskiD. Current trends of bioactive peptides — New sources and therapeutic effect.Foods20209784610.3390/foods907084632610520
    [Google Scholar]
  65. WangG. VaismanI.I. van HoekM.L. Machine learning prediction of antimicrobial peptides.Computational Peptide ScienceHumanaNew York SimonsonT. 202213710.1007/978‑1‑0716‑1855‑4_1
    [Google Scholar]
  66. de OliveiraE.C.L. SantanaK. JosinoL. Lima e LimaA.H. de Souza de Sales JúniorC. Predicting cell-penetrating peptides using machine learning algorithms and navigating in their chemical space.Sci. Rep.2021111762810.1038/s41598‑021‑87134‑w33828175
    [Google Scholar]
  67. MulliganV.K. The emerging role of computational design in peptide macrocycle drug discovery.Expert Opin. Drug Discov.202015783385210.1080/17460441.2020.175111732345066
    [Google Scholar]
  68. BinetteV. MousseauN. TufferyP. A generalized attraction–repulsion potential and revisited fragment library improves PEP-FOLD peptide structure prediction.J. Chem. Theory Comput.20221842720273610.1021/acs.jctc.1c0129335298162
    [Google Scholar]
  69. TyagiA. TuknaitA. AnandP. GuptaS. SharmaM. MathurD. JoshiA. SinghS. GautamA. RaghavaG.P.S. CancerPPD: A database of anticancer peptides and proteins.Nucleic Acids Res.201543D1D837D84310.1093/nar/gku89225270878
    [Google Scholar]
  70. KapoorP. SinghH. GautamA. ChaudharyK. KumarR. RaghavaG.P.S. TumorHoPe: A database of tumor homing peptides.PLoS One201274e3518710.1371/journal.pone.003518722523575
    [Google Scholar]
  71. BhallaS. VermaR. KaurH. KumarR. UsmaniS.S. SharmaS. RaghavaG.P.S. CancerPDF: A repository of cancer-associated peptidome found in human biofluids.Sci. Rep.201771151110.1038/s41598‑017‑01633‑328473704
    [Google Scholar]
  72. KumarR. ChaudharyK. SharmaM. NagpalG. ChauhanJ.S. SinghS. GautamA. RaghavaG.P.S. AHTPDB: A comprehensive platform for analysis and presentation of antihypertensive peptides.Nucleic Acids Res.201543D1D956D96210.1093/nar/gku114125392419
    [Google Scholar]
  73. SahaS. RaghavaG.P.S. AlgPred: Prediction of allergenic proteins and mapping of IgE epitopes.Nucleic Acids Res.200634Web Server issueW202W20910.1093/nar/gkl34316844994
    [Google Scholar]
  74. McSparronH. BlytheM.J. ZygouriC. DoytchinovaI.A. FlowerD.R. JenPep: A novel computational information resource for immunobiology and vaccinology.J. Chem. Inf. Comput. Sci.20034341276128710.1021/ci030461e12870921
    [Google Scholar]
  75. SahaS. BhasinM. RaghavaG.P.S. Bcipep: A database of B-cell epitopes.BMC Genomics2005617910.1186/1471‑2164‑6‑7915921533
    [Google Scholar]
  76. RecheP.A. ZhangH. GluttingJ.P. ReinherzE.L. EPIMHC: A curated database of MHC-binding peptides for customized computational vaccinology.Bioinformatics20052192140214110.1093/bioinformatics/bti26915657103
    [Google Scholar]
  77. QureshiA. ThakurN. KumarM. HIPdb: A database of experimentally validated HIV inhibiting peptides.PLoS One201381e5490810.1371/journal.pone.005490823359817
    [Google Scholar]
  78. RashidM. SinglaD. SharmaA. KumarM. RaghavaG.P.S. Hmrbase: A database of hormones and their receptors.BMC Genomics200910130710.1186/1471‑2164‑10‑30719589147
    [Google Scholar]
  79. VitaR. MahajanS. OvertonJ.A. DhandaS.K. MartiniS. CantrellJ.R. WheelerD.K. SetteA. PetersB. The immune epitope database (IEDB): 2018 update.Nucleic Acids Res.201947D1D339D34310.1093/nar/gky100630357391
    [Google Scholar]
  80. Di LucaM. MaccariG. MaisettaG. BatoniG. BaAMPs: The database of biofilm-active antimicrobial peptides.Biofouling201531219319910.1080/08927014.2015.102134025760404
    [Google Scholar]
  81. PirtskhalavaM. GabrielianA. CruzP. GriggsH.L. SquiresR.B. HurtD.E. GrigolavaM. ChubinidzeM. GogoladzeG. VishnepolskyB. AlekseyevV. RosenthalA. TartakovskyM. DBAASP v.2: An enhanced database of structure and antimicrobial/cytotoxic activity of natural and synthetic peptides.Nucleic Acids Res.20164413650310.1093/nar/gkw24327060142
    [Google Scholar]
  82. WhitmoreL. WallaceB.A. The Peptaibol Database: A database for sequences and structures of naturally occurring peptaibols.Nucleic Acids Res.200432Database issue593D59410.1093/nar/gkh07714681489
    [Google Scholar]
  83. NielsenS.D.H. LiangN. RathishH. KimB.J. LueangsakulthaiJ. KohJ. QuY. SchulzH.J. DallasD.C. Bioactive milk peptides: An updated comprehensive overview and database.Crit. Rev. Food Sci. Nutr.202312010.1080/10408398.2023.224039637504497
    [Google Scholar]
  84. QureshiA. ThakurN. TandonH. KumarM. AVPdb: A database of experimentally validated antiviral peptides targeting medically important viruses.Nucleic Acids Res.201442D1D1147D115310.1093/nar/gkt119124285301
    [Google Scholar]
  85. ReyJ. DeschavanneP. TufferyP. BactPepDB: A database of predicted peptides from a exhaustive survey of complete prokaryote genomes.Database (Oxford)201420140bau10610.1093/database/bau10625377257
    [Google Scholar]
  86. MinkiewiczP. IwaniakA. DarewiczM. BIOPEP-UWM database of bioactive peptides: Current opportunities.Int. J. Mol. Sci.20192023597810.3390/ijms2023597831783634
    [Google Scholar]
  87. WangJ. YinT. XiaoX. HeD. XueZ. JiangX. WangY. StraPep: A structure database of bioactive peptides.Database (Oxford)20182018bay03810.1093/database/bay03829688386
    [Google Scholar]
  88. AgrawalP. BhallaS. UsmaniS.S. SinghS. ChaudharyK. RaghavaG.P.S. GautamA. CPPsite 2.0: A repository of experimentally validated cell-penetrating peptides.Nucleic Acids Res.201644D1D1098D110310.1093/nar/gkv126626586798
    [Google Scholar]
  89. ZamyatninA.A. BorchikovA.S. VladimirovM.G. VoroninaO.L. The EROP-Moscow oligopeptide database.Nucleic Acids Res.200634Database issueD261D26610.1093/nar/gkj00816381860
    [Google Scholar]
  90. LataS. BhasinM. RaghavaG.P.S. MHCBN 4.0: A database of MHC/TAP binding peptides and T-cell epitopes.BMC Res. Notes2009216110.1186/1756‑0500‑2‑6119379493
    [Google Scholar]
  91. GuptaS. KapoorP. ChaudharyK. GautamA. KumarR. RaghavaG.P.S. In silico approach for predicting toxicity of peptides and proteins.PLoS One201389e7395710.1371/journal.pone.007395724058508
    [Google Scholar]
  92. KimY. BarkS. HookV. BandeiraN. NeuroPedia: Neuropeptide database and spectral library.Bioinformatics201127192772277310.1093/bioinformatics/btr44521821666
    [Google Scholar]
  93. WangY. WangM. YinS. JangR. WangJ. XueZ. XuT. NeuroPep: A comprehensive resource of neuropeptides.Database (Oxford)201520150bav03810.1093/database/bav03825931458
    [Google Scholar]
  94. MehtaD. AnandP. KumarV. JoshiA. MathurD. SinghS. TuknaitA. ChaudharyK. GautamS.K. GautamA. VarshneyG.C. RaghavaG.P.S. ParaPep: A web resource for experimentally validated antiparasitic peptide sequences and their structures.Database (Oxford)201420140bau05110.1093/database/bau05124923818
    [Google Scholar]
  95. van WijkK.J. LeppertT. SunZ. KearlyA. LiM. MendozaL. GuzchenkoI. DebleyE. SauermannG. RoutrayP. MalhotraS. NelsonA. SunQ. DeutschE.W. Detection of the Arabidopsis proteome and its post-translational modifications and the nature of the unobserved (dark) proteome in peptideatlas.J. Proteome Res.202423118521410.1021/acs.jproteome.3c0053638104260
    [Google Scholar]
  96. DasD. JaiswalM. KhanF.N. AhamadS. KumarS. PlantPepDB: A manually curated plant peptide database.Sci. Rep.2020101219410.1038/s41598‑020‑59165‑232042035
    [Google Scholar]
  97. WynendaeleE. BronselaerA. NielandtJ. D’HondtM. StalmansS. BrackeN. VerbekeF. Van De WieleC. De TréG. De SpiegeleerB. Quorumpeps database: Chemical space, microbial origin and functionality of quorum sensing peptides.Nucleic Acids Res.201341D1D655D65910.1093/nar/gks113723180797
    [Google Scholar]
  98. SchulerM.M. NastkeM-D. StevanovićS. SYFPEITHI: Database for searching and T-cell epitope prediction.Immunoinformatics: Predicting Immunogenicity In Silico. FlowerD.R. Totowa, NJHumana Press2007759310.1007/978‑1‑60327‑118‑9_5
    [Google Scholar]
  99. SinghS. ChaudharyK. DhandaS.K. BhallaS. UsmaniS.S. GautamA. TuknaitA. AgrawalP. MathurD. RaghavaG.P.S. SATPdb: A database of structurally annotated therapeutic peptides.Nucleic Acids Res.201644D1D1119D112610.1093/nar/gkv111426527728
    [Google Scholar]
  100. JainS. GuptaS. PatiyalS. RaghavaG.P.S. THPdb2: Compilation of FDA approved therapeutic peptides and proteins.Drug Discov. Today202429710404710.1016/j.drudis.2024.10404738830503
    [Google Scholar]
  101. KumarV. PatiyalS. KumarR. SahaiS. KaurD. LathwalA. RaghavaG.P.S. B3Pdb: An archive of blood–brain barrier-penetrating peptides.Brain Struct. Funct.202122682489249510.1007/s00429‑021‑02341‑534269889
    [Google Scholar]
  102. GautamA. ChaudharyK. SinghS. JoshiA. AnandP. TuknaitA. MathurD. VarshneyG.C. RaghavaG.P.S. Hemolytik: A database of experimentally determined hemolytic and non-hemolytic peptides.Nucleic Acids Res.201442D1D444D44910.1093/nar/gkt100824174543
    [Google Scholar]
  103. TyagiA. KapoorP. KumarR. ChaudharyK. GautamA. RaghavaG.P.S. In silico models for designing and discovering novel anticancer peptides.Sci. Rep.20133298410.1038/srep02984
    [Google Scholar]
  104. LataS. MishraN.K. RaghavaG.P.S. AntiBP2: Improved version of antibacterial peptide prediction.BMC Bioinformatics201011Suppl 1S1910.1186/1471‑2105‑11‑S1‑S1920122190
    [Google Scholar]
  105. JosephS. KarnikS. NilaweP. JayaramanV.K. Idicula-ThomasS. ClassAMP: A prediction tool for classification of antimicrobial peptides.IEEE/ACM Trans. Comput. Biol. Bioinformatics2012951535153810.1109/TCBB.2012.8922732690
    [Google Scholar]
  106. GautamA. ChaudharyK. KumarR. SharmaA. KapoorP. TyagiA. RaghavaG.P.S. In silico approaches for designing highly effective cell penetrating peptides.J. Transl. Med.20131117410.1186/1479‑5876‑11‑7423517638
    [Google Scholar]
  107. GarbuzynskiyS.O. LobanovM.Y. GalzitskayaO.V. FoldAmyloid: A method of prediction of amyloidogenic regions from protein sequence.Bioinformatics201026332633210.1093/bioinformatics/btp69120019059
    [Google Scholar]
  108. ChaudharyK. KumarR. SinghS. TuknaitA. GautamA. MathurD. AnandP. VarshneyG.C. RaghavaG.P.S. A web server and mobile app for computing hemolytic potency of peptides.Sci. Rep.2016612284310.1038/srep2284326953092
    [Google Scholar]
  109. SharmaA. SinglaD. RashidM. RaghavaG.P.S. Designing of peptides with desired half-life in intestine-like environment.BMC Bioinformatics201415128210.1186/1471‑2105‑15‑28225141912
    [Google Scholar]
  110. DhandaS.K. VirP. RaghavaG.P.S. Designing of interferon-gamma inducing MHC class-II binders.Biol. Direct2013813010.1186/1745‑6150‑8‑3024304645
    [Google Scholar]
  111. DhandaS.K. GuptaS. VirP. RaghavaG.P.S. Prediction of IL4 inducing peptides.Clin. Dev. Immunol.2013201311910.1155/2013/26395224489573
    [Google Scholar]
  112. SinghH. RaghavaG.P.S. ProPred: Prediction of HLA-DR binding sites.Bioinformatics200117121236123710.1093/bioinformatics/17.12.123611751237
    [Google Scholar]
  113. Almagro ArmenterosJ.J. TsirigosK.D. SønderbyC.K. PetersenT.N. WintherO. BrunakS. von HeijneG. NielsenH. SignalP 5.0 improves signal peptide predictions using deep neural networks.Nat. Biotechnol.201937442042310.1038/s41587‑019‑0036‑z30778233
    [Google Scholar]
  114. SharmaA. KapoorP. GautamA. ChaudharyK. KumarR. ChauhanJ.S. TyagiA. RaghavaG.P.S. Computational approach for designing tumor homing peptides.Sci. Rep.201331160710.1038/srep0160723558316
    [Google Scholar]
  115. SinghH. SinghS. Singh RaghavaG.P. Peptide secondary structure prediction using evolutionary information.bioRxiv201910.1101/558791
    [Google Scholar]
  116. ThevenetP. ShenY. MaupetitJ. PEP-FOLD: An updated de novo structure prediction server for both linear and disulfide bonded cyclic peptides.Nucleic Acids Res.201240W1W288W29310.1093/nar/gks419
    [Google Scholar]
  117. SinghS. SinghH. TuknaitA. PEPstrMOD: Structure prediction of peptides containing natural, non-natural and modified residues.Biol. Direct.2015107310.1186/s13062‑015‑0103‑426690490
    [Google Scholar]
  118. BatemanA. MartinM-J. OrchardS. MagraneM. AhmadS. AlpiE. Bowler-BarnettE.H. BrittoR. Bye-A-JeeH. CukuraA. DennyP. DoganT. EbenezerT.G. FanJ. GarmiriP. da Costa GonzalesL.J. Hatton-EllisE. HusseinA. IgnatchenkoA. InsanaG. IshtiaqR. JoshiV. JyothiD. KandasaamyS. LockA. LucianiA. LugaricM. LuoJ. LussiY. MacDougallA. MadeiraF. MahmoudyM. MishraA. MoulangK. NightingaleA. PundirS. QiG. RajS. RaposoP. RiceD.L. SaidiR. SantosR. SperettaE. StephensonJ. TotooP. TurnerE. TyagiN. VasudevP. WarnerK. WatkinsX. ZaruR. ZellnerH. BridgeA.J. AimoL. Argoud-PuyG. AuchinclossA.H. AxelsenK.B. BansalP. BaratinD. Batista NetoT.M. BlatterM-C. BollemanJ.T. BoutetE. BreuzaL. GilB.C. Casals-CasasC. EchioukhK.C. CoudertE. CucheB. de CastroE. EstreicherA. FamigliettiM.L. FeuermannM. GasteigerE. GaudetP. GehantS. GerritsenV. GosA. GruazN. HuloC. Hyka-NouspikelN. JungoF. KerhornouA. Le MercierP. LieberherrD. MassonP. MorgatA. MuthukrishnanV. PaesanoS. PedruzziI. PilboutS. PourcelL. PouxS. PozzatoM. PruessM. RedaschiN. RivoireC. SigristC.J.A. SonessonK. SundaramS. WuC.H. ArighiC.N. ArminskiL. ChenC. ChenY. HuangH. LaihoK. McGarveyP. NataleD.A. RossK. VinayakaC.R. WangQ. WangY. ZhangJ. UniProt: The universal protein knowledgebase in 2023.Nucleic Acids Res.202351D1D523D53110.1093/nar/gkac105236408920
    [Google Scholar]
  119. GasteigerE. HooglandC. GattikerA. Protein identification and analysis tools on the ExPASy server.The Proteomics Protocols Handbook WalkerJ.M. Totowa, NJHumana Press200557160710.1385/1‑59259‑890‑0:571
    [Google Scholar]
  120. AliN. ShamoonA. YadavN. SharmaT. Peptide combination generator: A tool for generating peptide combinations.ACS Omega20205115781578310.1021/acsomega.9b0384832226857
    [Google Scholar]
  121. MishraS.K. JebaP.J. GeorrgeJ.J. An emerging trends of bioinformatics and big data analytics in healthcare.Digital Transformation in Healthcare 5.0. MalviyaR. SundramS. DhanarajR.K. KadryS. Berlin, BostonWalter De Gruyter202415918810.1515/9783111398549‑007
    [Google Scholar]
  122. MishraS.K. GeorrgeJ.J. Chapter 9 - Tools and platform for allergenicity prediction. In: Reverse VaccinologyAcademic Press202416517810.1016/B978‑0‑443‑13395‑4.00003‑4
    [Google Scholar]
  123. MishraS.K. PandyaM. BhattT. GeorrgeJ.J. Chapter 11 - Reverse vaccinology 2.0: Computational resources for B-cell epitope prediction. In: Reverse VaccinologyAcademic Press202420321610.1016/B978‑0‑443‑13395‑4.00001‑0
    [Google Scholar]
  124. VinjodaP. MishraS.K. SharmaK. GeorrgeJ.J. Chapter 26 - In silico identification of novel drug target and its natural product inhibitors for herpes simplex virus. In: Nanotechnology and In Silico Tools.Elsevier202437738310.1016/B978‑0‑443‑15457‑7.00007‑1
    [Google Scholar]
  125. VaghasiaV.V. SharmaK. MishraS.K. GeorrgeJ.J. In silico identification of natural product inhibitor for multidrug resistance proteins from selected gram-positive bacteria. In: Nanotechnology and In Silico Tools.Elsevier202430931710.1016/B978‑0‑443‑15457‑7.00015‑0
    [Google Scholar]
  126. BojkoM. WęgrzynK. SikorskaE. CiuraP. BattinC. SteinbergerP. Magiera-MularzK. DubinG. KuleszaA. SieradzanA.K. SpodziejaM. Rodziewicz-MotowidłoS. Peptide-based inhibitors targeting the PD-1/PD-L1 axis: Potential immunotherapeutics for cancer.Transl. Oncol.20244210189210.1016/j.tranon.2024.10189238359715
    [Google Scholar]
  127. HanY. KrálP. Computational design of ACE2-based peptide inhibitors of SARS-CoV-2.ACS Nano20201445143514710.1021/acsnano.0c0285732286790
    [Google Scholar]
  128. JitonnomJ. MeeluaW. Tue-nguenP. SaparpakornP. HannongbuaS. ChotpatiwetchkulW. 3D-QSAR and molecular docking studies of peptide-hybrids as dengue virus NS2B/NS3 protease inhibitors.Chem. Biol. Interact.202439611104010.1016/j.cbi.2024.11104038735453
    [Google Scholar]
  129. HanafiahA. Abd AzizS.N.A. Md NesranZ.N. WezenX.C. AhmadM.F. Molecular investigation of antimicrobial peptides against Helicobacter pylori proteins using a peptide-protein docking approach.Heliyon2024106e2812810.1016/j.heliyon.2024.e2812838533069
    [Google Scholar]
  130. MishraS.K. PriyaP. RaiG.P. HaqueR. ShankerA. Coevolution based immunoinformatics approach considering variability of epitopes to combat different strains: A case study using spike protein of SARS-CoV-2.Comput. Biol. Med.202316310723310.1016/j.compbiomed.2023.10723337422941
    [Google Scholar]
  131. TomasellaC. FlorisM. GuccioneS. PappalardoM. BasileL. Peptidomimetics in silico.Mol. Inform.2021403200008710.1002/minf.20200008732954671
    [Google Scholar]
  132. FlorisM. MasciocchiJ. FantonM. MoroS. Swimming into peptidomimetic chemical space using pepMMsMIMIC.Nucleic Acids Res.201139Web Server issueW261W26910.1093/nar/gkr28721622954
    [Google Scholar]
  133. DrewK. RenfrewP.D. CravenT.W. ButterfossG.L. ChouF.C. LyskovS. BullockB.N. WatkinsA. LabonteJ.W. PacellaM. KilambiK.P. Leaver-FayA. KuhlmanB. GrayJ.J. BradleyP. KirshenbaumK. AroraP.S. DasR. BonneauR. Adding diverse noncanonical backbones to rosetta: Enabling peptidomimetic design.PLoS One201387e6705110.1371/journal.pone.006705123869206
    [Google Scholar]
  134. BohacekR. McmartinC. GlunzP. RichD.H. Growmol, a de novo computer program, and its application to thermolysin and pepsin: Results of the design and synthesis of a novel inhibitor.Rational Drug DesignSpringerNew York TruhlarD.G. HoweW.J. HopfingerA.J. BlaneyJ. DammkoehlerR.A. 199910311410.1007/978‑1‑4612‑1480‑9_9
    [Google Scholar]
  135. LuoP. CanzianiG. Cunto-AmestyG. Kieber-EmmonsT. A molecular basis for functional peptide mimicry of a carbohydrate antigen.J. Biol. Chem.200027521161461615410.1074/jbc.M90912119910748116
    [Google Scholar]
  136. Stefanicka-WojtasD. KurpasD. Personalised medicine — Implementation to the healthcare system in europe (focus group discussions).J. Pers. Med.202313338010.3390/jpm1303038036983562
    [Google Scholar]
  137. NCI drug dictionary.Available from: https://www.cancer.gov/publications/dictionaries/cancer-drug(Accessed on: 16 July 2024)
  138. ZhuY.J. LiX. ChenT.T. WangJ.X. ZhouY.X. MuX.L. DuY. WangJ.L. TangJ. LiuJ.Y. Personalised neoantigen‐based therapy in colorectal cancer.Clin. Transl. Med.20231311e146110.1002/ctm2.146137921274
    [Google Scholar]
  139. SuekaneS. UedaK. NishiharaK. SasadaT. YamashitaT. KogaN. YutaniS. ShichijoS. ItohK. IgawaT. NoguchiM. Personalized peptide vaccination as second‐line treatment for metastatic upper tract urothelial carcinoma.Cancer Sci.2017108122430243710.1111/cas.1340428940789
    [Google Scholar]
  140. StephensA.J. Burgess-BrownN.A. JiangS. Beyond just peptide antigens: The complex world of peptide-based cancer vaccines.Front. Immunol.20211269679110.3389/fimmu.2021.69679134276688
    [Google Scholar]
  141. BlassE. OttP.A. Advances in the development of personalized neoantigen-based therapeutic cancer vaccines.Nat. Rev. Clin. Oncol.202118421522910.1038/s41571‑020‑00460‑233473220
    [Google Scholar]
  142. HeB. HuangZ. HuangC. NiceE.C. Clinical applications of plasma proteomics and peptidomics: Towards precision medicine.Proteomics Clin. Appl.2022166210009710.1002/prca.20210009735490333
    [Google Scholar]
  143. OlssonB. LautnerR. AndreassonU. ÖhrfeltA. PorteliusE. BjerkeM. HölttäM. RosénC. OlssonC. StrobelG. WuE. DakinK. PetzoldM. BlennowK. ZetterbergH. CSF and blood biomarkers for the diagnosis of Alzheimer’s disease: A systematic review and meta-analysis.Lancet Neurol.201615767368410.1016/S1474‑4422(16)00070‑327068280
    [Google Scholar]
  144. de Castro BrásL.E. LindseyM.L. Using peptidomics to identify extracellular matrix-derived peptides as novel therapeutics for cardiac disease.Fibrosis in Disease WillisM.S. YatesC.C. SchislerJ.C. ChamHumana Press201934936510.1007/978‑3‑319‑98143‑7_13
    [Google Scholar]
  145. BansalN. GuptaA. SankhwarS.N. MahdiA.A. Low and high grade bladder cancer appraisal via serum-based proteomics approach.Clin. Chim. Acta20144369710310.1016/j.cca.2014.05.01224875752
    [Google Scholar]
  146. SharmaK. SharmaK.K. SharmaA. JainR. Peptide-based drug discovery: Current status and recent advances.Drug Discov. Today202328210346410.1016/j.drudis.2022.10346436481586
    [Google Scholar]
  147. BatraR. LoefflerT.D. ChanH. SrinivasanS. CuiH. KorendovychI.V. NandaV. PalmerL.C. SolomonL.A. FryH.C. SankaranarayananS.K.R.S. Machine learning overcomes human bias in the discovery of self-assembling peptides.Nat. Chem.202214121427143510.1038/s41557‑022‑01055‑336316409
    [Google Scholar]
  148. BasithS. ManavalanB. Hwan ShinT. LeeG. Machine intelligence in peptide therapeutics: A next‐generation tool for rapid disease screening.Med. Res. Rev.20204041276131410.1002/med.2165831922268
    [Google Scholar]
  149. MuttenthalerM. KingG.F. AdamsD.J. AlewoodP.F. Trends in peptide drug discovery.Nat. Rev. Drug Discov.202120430932510.1038/s41573‑020‑00135‑833536635
    [Google Scholar]
  150. LaiX. TangJ. ElSayedM.E.H. Recent advances in proteolytic stability for peptide, protein, and antibody drug discovery.Expert Opin. Drug Discov.202116121467148210.1080/17460441.2021.194283734187273
    [Google Scholar]
  151. VermaS. GoandU.K. HusainA. KatekarR.A. GargR. GayenJ.R. Challenges of peptide and protein drug delivery by oral route: Current strategies to improve the bioavailability.Drug Dev. Res.202182792794410.1002/ddr.2183233988872
    [Google Scholar]
  152. WangL. WangN. ZhangW. ChengX. YanZ. ShaoG. WangX. WangR. FuC. Therapeutic peptides: Current applications and future directions.Signal Transduct. Target. Ther.2022714810.1038/s41392‑022‑00904‑435165272
    [Google Scholar]
  153. LeeJ.M. HammarénH.M. SavitskiM.M. BaekS.H. Control of protein stability by post-translational modifications.Nat. Commun.202314120110.1038/s41467‑023‑35795‑836639369
    [Google Scholar]
  154. OlivaR. ChinoM. PaneK. PistorioV. De SantisA. PizzoE. D’ErricoG. PavoneV. LombardiA. Del VecchioP. NotomistaE. NastriF. PetracconeL. Exploring the role of unnatural amino acids in antimicrobial peptides.Sci. Rep.201881888810.1038/s41598‑018‑27231‑529892005
    [Google Scholar]
/content/journals/cbio/10.2174/0115748936351054241010091822
Loading
/content/journals/cbio/10.2174/0115748936351054241010091822
Loading

Data & Media loading...

This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error
Please enter a valid_number test