Generic placeholder image

Current Topics in Medicinal Chemistry

Editor-in-Chief

ISSN (Print): 1568-0266
ISSN (Online): 1873-4294

Review Article

A New Advanced Approach: Design and Screening of Affinity Peptide Ligands Using Computer Simulation Techniques

Author(s): Zheng Wei, Meilun Chen, Xiaoling Lu, Yijie Liu, Guangnan Peng, Jie Yang, Chunhua Tang and Peng Yu*

Volume 24, Issue 8, 2024

Published on: 15 February, 2024

Page: [667 - 685] Pages: 19

DOI: 10.2174/0115680266281358240206112605

Price: $65

Abstract

Peptides acquire target affinity based on the combination of residues in their sequences and the conformation formed by their flexible folding, an ability that makes them very attractive biomaterials in therapeutic, diagnostic, and assay fields. With the development of computer technology, computer-aided design and screening of affinity peptides has become a more efficient and faster method. This review summarizes successful cases of computer-aided design and screening of affinity peptide ligands in recent years and lists the computer programs and online servers used in the process. In particular, the characteristics of different design and screening methods are summarized and categorized to help researchers choose between different methods. In addition, experimentally validated sequences are listed, and their applications are described, providing directions for the future development and application of computational peptide screening and design.

Next »
Graphical Abstract

[1]
Kim, S.J.; Park, Y.; Hong, H.J. Antibody engineering for the development of therapeutic antibodies. Mol. Cells, 2005, 20(1), 17-29.
[http://dx.doi.org/10.1016/S1016-8478(23)25245-0] [PMID: 16258237]
[2]
O'Hare, M.J. Human monoclonal antibodies as cellular and molecular probes: A review. In: Molecular and Cellular Probes; Elsevier, 1987.
[3]
Tozzi, C.; Anfossi, L.; Giraudi, G. Affinity chromatography techniques based on the immobilisation of peptides exhibiting specific binding activity. J. Chromatogr. B Analyt. Technol. Biomed. Life Sci., 2003, 797(1-2), 289-304.
[http://dx.doi.org/10.1016/S1570-0232(03)00481-1] [PMID: 14630156]
[4]
Robinson, J.A. β-hairpin peptidomimetics: Design, structures and biological activities. Acc. Chem. Res., 2008, 41(10), 1278-1288.
[http://dx.doi.org/10.1021/ar700259k] [PMID: 18412373]
[5]
Hamley, I.W. Small bioactive peptides for biomaterials design and therapeutics. Chem. Rev., 2017, 117(24), 14015-14041.
[http://dx.doi.org/10.1021/acs.chemrev.7b00522] [PMID: 29227635]
[6]
Kuo, F.Y.; Lin, W.L.; Chen, Y.C. Affinity capture using peptide-functionalized magnetic nanoparticles to target Staphylococcus aureus. Nanoscale, 2016, 8(17), 9217-9225.
[http://dx.doi.org/10.1039/C6NR00368K] [PMID: 27087258]
[7]
Lowe, C.R.; Burton, S.J.; Burton, N.P.; Alderton, W.K.; Pitts, J.M.; Thomas, J.A. Designer dyes: ‘Biomimetic’ ligands for the purification of pharmaceutical proteins by affinity chromatography. Trends Biotechnol., 1992, 10(12), 442-448.
[http://dx.doi.org/10.1016/0167-7799(92)90294-6] [PMID: 1369134]
[8]
Fang, Y.M.; Lin, D.Q.; Yao, S.J. Review on biomimetic affinity chromatography with short peptide ligands and its application to protein purification. J. Chromatogr. A, 2018, 1571, 1-15.
[http://dx.doi.org/10.1016/j.chroma.2018.07.082] [PMID: 30097342]
[9]
Smith, G.P. Filamentous fusion phage: Novel expression vectors that display cloned antigens on the virion surface. Science, 1985, 228(4705), 1315-1317.
[http://dx.doi.org/10.1126/science.4001944] [PMID: 4001944]
[10]
Roberts, R.W.; Szostak, J.W. RNA-peptide fusions for the in vitro selection of peptides and proteins. Proc. Natl. Acad. Sci., 1997, 94(23), 12297-12302.
[http://dx.doi.org/10.1073/pnas.94.23.12297] [PMID: 9356443]
[11]
Liu, R.; Li, X.; Lam, K.S. Combinatorial chemistry in drug discovery. Curr. Opin. Chem. Biol., 2017, 38, 117-126.
[http://dx.doi.org/10.1016/j.cbpa.2017.03.017] [PMID: 28494316]
[12]
Tripathi, N.M.; Bandyopadhyay, A. High throughput virtual screening (HTVS) of peptide library: Technological advancement in ligand discovery. Eur. J. Med. Chem., 2022, 243, 114766.
[http://dx.doi.org/10.1016/j.ejmech.2022.114766] [PMID: 36122548]
[13]
Audie, J.; Swanson, J. Advances in the prediction of protein-peptide binding affinities: Implications for peptide-based drug discovery. Chem. Biol. Drug Des., 2013, 81(1), 50-60.
[http://dx.doi.org/10.1111/cbdd.12076] [PMID: 23066895]
[14]
Yuriev, E.; Ramsland, P.A. Latest developments in molecular docking: 2010-2011 in review. J. Mol. Recognit., 2013, 26(5), 215-239.
[http://dx.doi.org/10.1002/jmr.2266] [PMID: 23526775]
[15]
Yang, W.; Lai, L. Computational design of ligand-binding proteins. Curr. Opin. Struct. Biol., 2017, 45, 67-73.
[http://dx.doi.org/10.1016/j.sbi.2016.11.021] [PMID: 27951448]
[16]
Vanhee, P.; van der Sloot, A.M.; Verschueren, E.; Serrano, L.; Rousseau, F.; Schymkowitz, J. Computational design of peptide ligands. Trends Biotechnol., 2011, 29(5), 231-239.
[http://dx.doi.org/10.1016/j.tibtech.2011.01.004] [PMID: 21316780]
[17]
Sammond, D.W.; Bosch, D.E.; Butterfoss, G.L.; Purbeck, C.; Machius, M.; Siderovski, D.P.; Kuhlman, B. Computational design of the sequence and structure of a protein-binding peptide. J. Am. Chem. Soc., 2011, 133(12), 4190-4192.
[http://dx.doi.org/10.1021/ja110296z] [PMID: 21388199]
[18]
D’Annessa, I.; Di Leva, F.S.; La Teana, A.; Novellino, E.; Limongelli, V.; Di Marino, D. Bioinformatics and biosimulations as toolbox for peptides and peptidomimetics design: Where are we? Front. Mol. Biosci., 2020, 7, 66.
[http://dx.doi.org/10.3389/fmolb.2020.00066] [PMID: 32432124]
[19]
Obarska-Kosinska, A.; Iacoangeli, A.; Lepore, R.; Tramontano, A. PepComposer: Computational design of peptides binding to a given protein surface. Nucleic Acids Res., 2016, 44(W1), W522-W528.
[http://dx.doi.org/10.1093/nar/gkw366] [PMID: 27131789]
[20]
Gao, M.; Cheng, K.; Yin, H. Targeting protein-protein interfaces using macrocyclic peptides. Biopolymers, 2015, 104(4), 310-316.
[http://dx.doi.org/10.1002/bip.22625] [PMID: 25664609]
[21]
Wolfe, M.; Webb, S.; Chushak, Y.; Krabacher, R.; Liu, Y.; Swami, N.; Harbaugh, S.; Chávez, J. A high-throughput pipeline for design and selection of peptides targeting the SARS-Cov-2 Spike protein. Sci. Rep., 2021, 11(1), 21768.
[http://dx.doi.org/10.1038/s41598-021-01225-2] [PMID: 34741099]
[22]
Scognamiglio, P.; Di Natale, C.; Perretta, G.; Marasco, D. From peptides to small molecules: An intriguing but intricated way to new drugs. Curr. Med. Chem., 2013, 20(31), 3803-3817.
[http://dx.doi.org/10.2174/09298673113209990184] [PMID: 23895692]
[23]
Mustafa, G.; Mahrosh, H.S.; Attique, S.A.; Arif, R.; Farah, M.A.; Al-Anazi, K.M.; Ali, S. Identification of plant peptides as novel inhibitors of orthohepevirus A (HEV) capsid protein by virtual screening. Molecules, 2023, 28(6), 2675.
[http://dx.doi.org/10.3390/molecules28062675] [PMID: 36985647]
[24]
Qian, J.; Zheng, L.; Su, G.; Huang, M.; Luo, D.; Zhao, M. Identification and screening of potential bioactive peptides with sleep-enhancing effects in bovine milk casein hydrolysate. J. Agric. Food Chem., 2021, 69(38), 11246-11258.
[http://dx.doi.org/10.1021/acs.jafc.1c03937] [PMID: 34543014]
[25]
Liang, T.; Chen, J.; Rui, Y.; Hexi, L. The designation, synthesis, and affinity determination of affinity peptide for anthrax protective antigen. Chem. Biol. Drug Des., 2023, 102(4), 669-675.
[http://dx.doi.org/10.1111/cbdd.14280] [PMID: 37286890]
[26]
Shinde, S.D.; Rao, K.B.; Behera, S.K.; Arya, N.; Sahu, B. Epithelial cell adhesion molecule (EpCAM) binding short peptides derived from antibody MOC-31; De-novo design, synthesis and their in-vitro evaluation. Biochem. Biophys. Res. Commun., 2022, 600, 1-5.
[http://dx.doi.org/10.1016/j.bbrc.2022.01.120] [PMID: 35182969]
[27]
Forli, S.; Huey, R.; Pique, M.E.; Sanner, M.F.; Goodsell, D.S.; Olson, A.J. Computational protein-ligand docking and virtual drug screening with the AutoDock suite. Nat. Protoc., 2016, 11(5), 905-919.
[http://dx.doi.org/10.1038/nprot.2016.051] [PMID: 27077332]
[28]
Eberhardt, J.; Santos-Martins, D.; Tillack, A.F.; Forli, S. AutoDock Vina 1.2.0: New docking methods, expanded force field, and python bindings. J. Chem. Inf. Model., 2021, 61(8), 3891-3898.
[http://dx.doi.org/10.1021/acs.jcim.1c00203] [PMID: 34278794]
[29]
Jones, G. Development and validation of a genetic algorithm for flexible docking11Edited by F. E. Cohen. J. Mol. Biol., 1997, 267(3), 727-748.
[30]
Jain, A.N. Surflex-Dock 2.1: Robust performance from ligand energetic modeling, ring flexibility, and knowledge-based search. J. Comput. Aided Mol. Des., 2007, 21(5), 281-306.
[http://dx.doi.org/10.1007/s10822-007-9114-2] [PMID: 17387436]
[31]
Hu, C.; Guo, T.; Zou, Y.; Gao, J.; Gao, Y.; Niu, M.; Xia, Y.; Shen, X.; Li, J. Discovery of dual S-RBD/NRP1-targeting peptides: Structure-based virtual screening, synthesis, biological evaluation, and molecular dynamics simulation studies. J. Enzyme Inhib. Med. Chem., 2023, 38(1), 2212327.
[http://dx.doi.org/10.1080/14756366.2023.2212327] [PMID: 37194732]
[32]
(a) Singh, S.; Banavath, N.H.; Godara, P.; Naik, B.; Srivastava, V.; Prusty, D. Identification of antiviral peptide inhibitors for receptor binding domain of SARS-CoV-2 omicron and its sub-variants: An in-silico approach. 3 Biotech, 2022, 12(9), 198.
[http://dx.doi.org/10.1007/s13205-022-03258-4];
(b) Reese, H.R.; Xiao, X.; Shanahan, C.C.; Chu, W.; Van Den Driessche, G.A.; Fourches, D.; Carbonell, R.G.; Hall, C.K.; Menegatti, S. Novel peptide ligands for antibody purification provide superior clearance of host cell protein impurities. J. Chromatogr. A, 2020, 1625, 461237.
[http://dx.doi.org/10.1016/j.chroma.2020.461237] [PMID: 32709313]
[33]
Patnaik, S.K.; Ayyamperumal, S.; Jade, D.; Palathoti, N.; Akey, K.S.; Jupudi, S.; Harrison, M.A.; Ponnambalam, S.; Mj, N.; Mjn, C. Virtual high throughput screening of natural peptides against ErbB1 and ErbB2 to identify potential inhibitors for cancer chemotherapy. J. Biomol. Struct. Dyn., 2023, 1-24.
[http://dx.doi.org/10.1080/07391102.2023.2226744] [PMID: 37387589]
[34]
Unni, P.A.; Ali, A.M.M.T.; Rout, M.; Thabitha, A.; Vino, S.; Lulu, S.S. Designing of an epitope-based peptide vaccine against walking pneumonia: An immunoinformatics approach. Mol. Biol. Rep., 2019, 46(1), 511-527.
[http://dx.doi.org/10.1007/s11033-018-4505-0] [PMID: 30465133]
[35]
Amarasinghe, K.N.; De Maria, L.; Tyrchan, C.; Eriksson, L.A.; Sadowski, J.; Petrović, D. Virtual screening expands the non-natural amino acid palette for peptide optimization. J. Chem. Inf. Model., 2022, 62(12), 2999-3007.
[http://dx.doi.org/10.1021/acs.jcim.2c00193] [PMID: 35699524]
[36]
Verschueren, E.; Vanhee, P.; Rousseau, F.; Schymkowitz, J.; Serrano, L. Protein-peptide complex prediction through fragment interaction patterns. Structure, 2013, 21(5), 789-797.
[http://dx.doi.org/10.1016/j.str.2013.02.023] [PMID: 23583037]
[37]
Antes, I. DynaDock: A new molecular dynamics-based algorithm for protein-peptide docking including receptor flexibility. Proteins, 2010, 78(5), 1084-1104.
[http://dx.doi.org/10.1002/prot.22629] [PMID: 20017216]
[38]
Xu, X.; Yan, C.; Zou, X. MDockPeP: An ab-initio protein-peptide docking server. J. Comput. Chem., 2018, 39(28), 2409-2413.
[http://dx.doi.org/10.1002/jcc.25555] [PMID: 30368849]
[39]
Rentzsch, R.; Renard, B.Y. Docking small peptides remains a great challenge: An assessment using AutoDock Vina. Brief. Bioinform., 2015, 16(6), 1045-1056.
[http://dx.doi.org/10.1093/bib/bbv008] [PMID: 25900849]
[40]
Weng, G.; Gao, J.; Wang, Z.; Wang, E.; Hu, X.; Yao, X.; Cao, D.; Hou, T. Comprehensive evaluation of fourteen docking programs on protein-peptide complexes. J. Chem. Theory Comput., 2020, 16(6), 3959-3969.
[http://dx.doi.org/10.1021/acs.jctc.9b01208] [PMID: 32324992]
[41]
Morris, G.M.; Huey, R.; Lindstrom, W.; Sanner, M.F.; Belew, R.K.; Goodsell, D.S.; Olson, A.J. AutoDock4 and AutoDockTools4: Automated docking with selective receptor flexibility. J. Comput. Chem., 2009, 30(16), 2785-2791.
[http://dx.doi.org/10.1002/jcc.21256] [PMID: 19399780]
[42]
Huey, R.; Morris, G.M.; Olson, A.J.; Goodsell, D.S. A semiempirical free energy force field with charge-based desolvation. J. Comput. Chem., 2007, 28(6), 1145-1152.
[http://dx.doi.org/10.1002/jcc.20634] [PMID: 17274016]
[43]
Zhang, Y.; Sanner, M.F. AutoDock CrankPep : Combining folding and docking to predict protein-peptide complexes. Bioinformatics, 2019, 35(24), 5121-5127.
[http://dx.doi.org/10.1093/bioinformatics/btz459] [PMID: 31161213]
[44]
Zhou, Y.; Zou, Y.; Yang, M.; Mei, S.; Liu, X.; Han, H.; Zhang, C.D.; Niu, M.M. Highly potent, selective, biostable, and cell-permeable cyclic D -Peptide for dual-targeting therapy of lung cancer. J. Am. Chem. Soc., 2022, 144(16), 7117-7128.
[http://dx.doi.org/10.1021/jacs.1c12075] [PMID: 35417174]
[45]
Pérez, S.; Meyer, C.; Imberty, A. Practical tools for molecular modeling of complex carbohydrates and their interactions with proteins. In: Modelling of Biomolecular Structures and Mechanisms; Springer Netherlands: Dordrecht, 1995.
[http://dx.doi.org/10.1007/978-94-011-0497-5_33]
[46]
Sato, H.; Shewchuk, L.M.; Tang, J. Prediction of multiple binding modes of the CDK2 inhibitors, anilinopyrazoles, using the automated docking programs GOLD, FlexX, and LigandFit: An evaluation of performance. J. Chem. Inf. Model., 2006, 46(6), 2552-2562.
[http://dx.doi.org/10.1021/ci600186b] [PMID: 17125195]
[47]
Yu, Q.; Wang, F.; Hu, X.; Xing, G.; Deng, R.; Guo, J.; Cheng, A.; Wang, J.; Hao, J.; Zhao, D.; Teng, M.; Zhang, G. Comparison of two docking methods for peptide-protein interactions. J. Sci. Food Agric., 2018, 98(10), 3722-3727.
[http://dx.doi.org/10.1002/jsfa.8880] [PMID: 29315602]
[48]
Wu, H.; Liu, Y.; Guo, M.; Xie, J.; Jiang, X. A virtual screening method for inhibitory peptides of Angiotensin I-converting enzyme. J. Food Sci., 2014, 79(9), C1635-C1642.
[http://dx.doi.org/10.1111/1750-3841.12559] [PMID: 25154376]
[49]
Han, J.; Tang, S.; Li, Y.; Bao, W.; Wan, H.; Lu, C.; Zhou, J.; Li, Y.; Cheong, L.; Su, X. in silico analysis and in vivo tests of the tuna dark muscle hydrolysate anti-oxidation effect. RSC Advances, 2018, 8(25), 14109-14119.
[http://dx.doi.org/10.1039/C8RA00889B] [PMID: 35539313]
[50]
Trellet, M.; Melquiond, A.S.J.; Bonvin, A.M.J.J. 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]
[51]
Kurcinski, M.; Jamroz, M.; Blaszczyk, M.; Kolinski, A.; Kmiecik, S. CABS-dock web server for the flexible docking of peptides to proteins without prior knowledge of the binding site. Nucleic Acids Res., 2015, 43(W1), W419-W424.
[http://dx.doi.org/10.1093/nar/gkv456] [PMID: 25943545]
[52]
Zhou, P.; Jin, B.; Li, H.; Huang, S.Y. HPEPDOCK: A web server for blind peptide-protein docking based on a hierarchical algorithm. Nucleic Acids Res., 2018, 46(W1), W443-W450.
[http://dx.doi.org/10.1093/nar/gky357] [PMID: 29746661]
[53]
Yan, C.; Xu, X.; Zou, X. Fully blind docking at the atomic level for protein-peptide complex structure prediction. Structure, 2016, 24(10), 1842-1853.
[http://dx.doi.org/10.1016/j.str.2016.07.021] [PMID: 27642160]
[54]
Porter, K.A.; Xia, B.; Beglov, D.; Bohnuud, T.; Alam, N.; Schueler-Furman, O.; Kozakov, D. ClusPro PeptiDock: Efficient global docking of peptide recognition motifs using FFT. Bioinformatics, 2017, 33(20), 3299-3301.
[http://dx.doi.org/10.1093/bioinformatics/btx216] [PMID: 28430871]
[55]
Alam, N.; Goldstein, O.; Xia, B.; Porter, K.A.; Kozakov, D.; Schueler-Furman, O. High-resolution global peptide-protein docking using fragments-based PIPER-FlexPepDock. PLOS Comput. Biol., 2017, 13(12), e1005905.
[http://dx.doi.org/10.1371/journal.pcbi.1005905] [PMID: 29281622]
[56]
Tzakos, A.G.; Fuchs, P.; van Nuland, N.A.J.; Troganis, A.; Tselios, T.; Deraos, S.; Matsoukas, J.; Gerothanassis, I.P.; Bonvin, A.M.J.J. NMR and molecular dynamics studies of an autoimmune myelin basic protein peptide and its antagonist. Eur. J. Biochem., 2004, 271(16), 3399-3413.
[http://dx.doi.org/10.1111/j.1432-1033.2004.04274.x] [PMID: 15291817]
[57]
Yang, S.Y. Pharmacophore modeling and applications in drug discovery: Challenges and recent advances. Drug Discov. Today, 2010, 15(11-12), 444-450.
[http://dx.doi.org/10.1016/j.drudis.2010.03.013] [PMID: 20362693]
[58]
Meyer, C.; Schepmann, D.; Yanagisawa, S.; Yamaguchi, J.; Dal Col, V.; Laurini, E.; Itami, K.; Pricl, S.; Wünsch, B. Pd-catalyzed direct C-H bond functionalization of spirocyclic σ1 ligands: Generation of a pharmacophore model and analysis of the reverse binding mode by docking into a 3D homology model of the σ1 receptor. J. Med. Chem., 2012, 55(18), 8047-8065.
[http://dx.doi.org/10.1021/jm300894h] [PMID: 22913577]
[59]
Singh, S.; Chauhan, P.; Sharma, V.; Rao, A.; Kumbhar, B.V.; Prajapati, V.K. Identification of multi-targeting natural antiviral peptides to impede SARS-CoV-2 infection. Struct. Chem., 2022, 1-16.
[PMID: 36570051]
[60]
He, M.; Wang, Y.; Huang, S.; Zhao, N.; Cheng, M.; Zhang, X. Computational exploration of natural peptides targeting ACE2. J. Biomol. Struct. Dyn., 2022, 40(17), 8018-8029.
[http://dx.doi.org/10.1080/07391102.2021.1905555] [PMID: 33826484]
[61]
Guan, K.; Li, H.; Liu, D.; Liu, M.; He, C. Identification and antioxidative mechanism of novel mitochondria-targeted MFG-E8 polypeptides in virtual screening and in vitro study. J. Dairy Sci., 2023, 106(3), 1562-1575.
[http://dx.doi.org/10.3168/jds.2022-22745] [PMID: 36710194]
[62]
Chen, H.H.; Li, W.; Wang, Y.; Xu, B.; Hu, X.; Li, X.B.; Liu, J.Y.; Zhang, C.; Zhang, C.Y.; Xing, X.H. Mining and validation of novel hemp seed-derived DPP-IV-inhibiting peptides using a combination of multi-omics and molecular docking. J. Agric. Food Chem., 2023, 71(23), 9164-9174.
[http://dx.doi.org/10.1021/acs.jafc.3c00535] [PMID: 37058363]
[63]
Kiriwan, D.; Seetaha, S.; Jiwacharoenchai, N.; Tabtimmai, L.; Sousa, S.F.; Songtawee, N.; Choowongkomon, K. Identification of tripeptides against tyrosine kinase domain of EGFR for lung cancer cell inhibition by in silico and in vitro studies. Chem. Biol. Drug Des., 2022, 99(3), 456-469.
[http://dx.doi.org/10.1111/cbdd.14010] [PMID: 34923743]
[64]
Ansar, S.; Vetrivel, U. Structure-based design of small molecule and peptide inhibitors for selective targeting of ROCK1: An integrative computational approach. J. Biomol. Struct. Dyn., 2022, 40(16), 7450-7468.
[http://dx.doi.org/10.1080/07391102.2021.1898470] [PMID: 33715594]
[65]
Hu, M.; Wang, F.; Li, N.; Xing, G.; Sun, X.; Zhang, Y.; Cao, S.; Cui, N.; Zhang, G. An antigen display system of GEM nanoparticles based on affinity peptide ligands. Int. J. Biol. Macromol., 2021, 193(Pt A), 574-584.
[http://dx.doi.org/10.1016/j.ijbiomac.2021.10.135] [PMID: 34699894]
[66]
Cantarutti, C.; Vargas, M.C.; Dongmo Foumthuim, C.J.; Dumoulin, M.; La Manna, S.; Marasco, D.; Santambrogio, C.; Grandori, R.; Scoles, G.; Soler, M.A.; Corazza, A.; Fortuna, S. Insights on peptide topology in the computational design of protein ligands: The example of lysozyme binding peptides. Phys. Chem. Chem. Phys., 2021, 23(40), 23158-23172.
[http://dx.doi.org/10.1039/D1CP02536H] [PMID: 34617942]
[67]
Saragih, M.; Stephanie, F.; Alkaff, A.H.; Tambunan, U.S.F. Identification of novel peptides targeting DNA methyltransferase 1 (DNMT-1) for breast cancer treatment. Rev. Bras. Farmacogn., 2020, 30(5), 641-651.
[http://dx.doi.org/10.1007/s43450-020-00086-6]
[68]
Poli, G.; Dimmito, M.P.; Mollica, A.; Zengin, G.; Benyhe, S.; Zador, F.; Stefanucci, A. Discovery of novel µ-opioid receptor inverse agonist from a combinatorial library of tetrapeptides through structure-based virtual screening. Molecules, 2019, 24(21), 3872.
[http://dx.doi.org/10.3390/molecules24213872] [PMID: 31717871]
[69]
Yin, S.; Mei, S.; Li, Z.; Xu, Z.; Wu, Y.; Chen, X.; Liu, D.; Niu, M.M.; Li, J. Non-covalent cyclic peptides simultaneously targeting Mpro and NRP1 are highly effective against Omicron BA.2.75. Front. Pharmacol., 2022, 13, 1037993.
[http://dx.doi.org/10.3389/fphar.2022.1037993] [PMID: 36408220]
[70]
Yan, F.; Liu, G.; Chen, T.; Fu, X.; Niu, M.M. Structure-based virtual screening and biological evaluation of peptide inhibitors for polo-box domain. Molecules, 2019, 25(1), 107.
[http://dx.doi.org/10.3390/molecules25010107] [PMID: 31892137]
[71]
Chan, H.T.H.; Moesser, M.A.; Walters, R.K.; Malla, T.R.; Twidale, R.M.; John, T.; Deeks, H.M.; Johnston-Wood, T.; Mikhailov, V.; Sessions, R.B.; Dawson, W.; Salah, E.; Lukacik, P.; Strain-Damerell, C.; Owen, C.D.; Nakajima, T.; Świderek, K.; Lodola, A.; Moliner, V.; Glowacki, D.R.; Spencer, J.; Walsh, M.A.; Schofield, C.J.; Genovese, L.; Shoemark, D.K.; Mulholland, A.J.; Duarte, F.; Morris, G.M. Discovery of SARS-CoV-2 M pro peptide inhibitors from modelling substrate and ligand binding. Chem. Sci., 2021, 12(41), 13686-13703.
[http://dx.doi.org/10.1039/D1SC03628A] [PMID: 34760153]
[72]
Yu, M.; Zhao, H.; Miao, Y.; Luo, S.Z.; Xue, S. Virtual evolution of HVEM segment for checkpoint inhibitor discovery. Int. J. Mol. Sci., 2021, 22(12), 6638.
[http://dx.doi.org/10.3390/ijms22126638] [PMID: 34205742]
[73]
Alizadeh, A.A.; Dastmalchi, S. Designing novel teduglutide analogues with improved binding affinity: An in silico peptide engineering approach. Curr. Computeraided Drug Des., 2021, 17(2), 225-234.
[http://dx.doi.org/10.2174/1573409916666200217091456] [PMID: 32065094]
[74]
Zhang, D.; He, D.; Pan, X.; Xu, Y.; Liu, L. Structural analysis and rational design of orthogonal stacking system in an E. coli DegP PDZ1-peptide complex. Chem. Pap., 2019, 73(10), 2469-2476.
[http://dx.doi.org/10.1007/s11696-019-00797-8]
[75]
Sakib, M.M.H.; Nishat, A.A.; Islam, M.T.; Raihan Uddin, M.A.; Iqbal, M.S.; Bin Hossen, F.F.; Ahmed, M.I.; Bashir, M.S.; Hossain, T.; Tohura, U.S.; Saif, S.I.; Jui, N.R.; Alam, M.; Islam, M.A.; Hasan, M.M.; Sufian, M.A.; Ali, M.A.; Islam, R.; Hossain, M.A.; Halim, M.A. Computational screening of 645 antiviral peptides against the receptor-binding domain of the spike protein in SARS-CoV-2. Comput. Biol. Med., 2021, 136, 104759.
[http://dx.doi.org/10.1016/j.compbiomed.2021.104759] [PMID: 34403938]
[76]
Behzadipour, Y.; Gholampour, M.; Pirhadi, S.; Seradj, H.; Khoshneviszadeh, M.; Hemmati, S. Viral 3CLpro as a target for antiviral intervention using milk-derived bioactive peptides. Int. J. Pept. Res. Ther., 2021, 27(4), 2703-2716.
[http://dx.doi.org/10.1007/s10989-021-10284-y] [PMID: 34548852]
[77]
Daroit, D.J.; Brandelli, A. in vivo bioactivities of food protein-derived peptides - A current review. Curr. Opin. Food Sci., 2021, 39, 120-129.
[http://dx.doi.org/10.1016/j.cofs.2021.01.002]
[78]
Yu, Z.; Fan, Y.; Zhao, W.; Ding, L.; Li, J.; Liu, J. Novel angiotensin-converting enzyme inhibitory peptides derived from oncorhynchus mykiss nebulin: Virtual screening and in silico molecular docking study. J. Food Sci., 2018, 83(9), 2375-2383.
[http://dx.doi.org/10.1111/1750-3841.14299] [PMID: 30101981]
[79]
Zhao, W.; Zhang, D.; Yu, Z.; Ding, L.; Liu, J. Aminopeptidase N inhibitory peptides derived from hen eggs: Virtual screening, inhibitory activity, and action mechanisms. Food Biosci., 2020, 37, 100703.
[http://dx.doi.org/10.1016/j.fbio.2020.100703]
[80]
Li, Y.; Zhang, F.; Gong, J.; Peng, C. Two novel dipeptidyl peptidase-IV (DPP-IV) inhibitory peptides identified from truffle (Tuber sinense) by peptidomics, in silico, and molecular docking analysis. J. Food Compos. Anal., 2023, 121, 105384.
[http://dx.doi.org/10.1016/j.jfca.2023.105384]
[81]
Liu, Y.; Lu, X.; Chen, M.; Wei, Z.; Peng, G.; Yang, J.; Tang, C.; Yu, P. Advances in screening, synthesis, modification, and biomedical applications of peptides and peptide aptamers. Biofactors, 2023, 2001.
[http://dx.doi.org/10.1002/biof.2001] [PMID: 37646383]
[82]
Chen, X. Design and identification of a novel antiviral affinity peptide against fowl adenovirus serotype 4 (FAdV-4) by targeting fiber2 protein. Viruses, 2023, 15(4), 821.
[83]
Xu, Q. Virtual screening-based peptides targeting spike protein to inhibit Porcine Epidemic Diarrhea Virus (PEDV) infection. Viruses, 2023, 15(2)
[84]
Knaff, P.M.; Müller, P.; Kersten, C.; Wettstein, L.; Münch, J.; Landfester, K.; Mailänder, V. Structure-based design of high-affinity and selective peptidomimetic hepsin inhibitors. Biomacromolecules, 2022, 23(6), 2236-2242.
[http://dx.doi.org/10.1021/acs.biomac.1c01011] [PMID: 35593713]
[85]
Li, C.W.; Osman, R.; Menconi, F.; Concepcion, E.; Tomer, Y. Cepharanthine blocks TSH receptor peptide presentation by HLA-DR3: Therapeutic implications to Graves’ disease. J. Autoimmun., 2020, 108, 102402.
[http://dx.doi.org/10.1016/j.jaut.2020.102402] [PMID: 31980336]
[86]
Hao, J.; Wang, F.; Xing, G.; Liu, Y.; Deng, R.; Zhang, H.; Cheng, A.; Zhang, G. Design and preliminary application of affinity peptide based on the structure of the porcine circovirus type II Capsid (PCV2 Cap). PeerJ, 2019, 7, e8132.
[http://dx.doi.org/10.7717/peerj.8132] [PMID: 31824765]
[87]
Wang, Y.; Guo, H.; Feng, Z.; Wang, S.; Wang, Y.; He, Q.; Li, G.; Lin, W.; Xie, X.Q.; Lin, Z. PD-1-targeted discovery of peptide inhibitors by virtual screening, molecular dynamics simulation, and surface plasmon resonance. Molecules, 2019, 24(20), 3784.
[http://dx.doi.org/10.3390/molecules24203784] [PMID: 31640203]
[88]
Martins, G.G.; de Jesus Holanda, R.; Alfonso, J.; Gómez Garay, A.F.; dos Santos, A.P.A.; de Lima, A.M.; Francisco, A.F.; Garcia Teles, C.B.; Zanchi, F.B.; Soares, A.M. Identification of a peptide derived from a Bothrops moojeni metalloprotease with in vitro inhibitory action on the Plasmodium falciparum purine nucleoside phosphorylase enzyme (PfPNP). Biochimie, 2019, 162, 97-106.
[http://dx.doi.org/10.1016/j.biochi.2019.04.009] [PMID: 30978375]
[89]
Mercurio, F.A.; Di Natale, C.; Pirone, L.; Marasco, D.; Calce, E.; Vincenzi, M.; Pedone, E.M.; De Luca, S.; Leone, M. Design and analysis of EphA2-SAM peptide ligands: A multi-disciplinary screening approach. Bioorg. Chem., 2019, 84, 434-443.
[http://dx.doi.org/10.1016/j.bioorg.2018.12.009] [PMID: 30576907]
[90]
Zalevsky, A.; Zlobin, A.; Gedzun, V.; Reshetnikov, R.; Lovat, M.; Malyshev, A.; Doronin, I.; Babkin, G.; Golovin, A. PeptoGrid-rescoring function for autodock vina to identify new bioactive molecules from short peptide libraries. Molecules, 2019, 24(2), 277.
[http://dx.doi.org/10.3390/molecules24020277] [PMID: 30642123]
[91]
Xiang, S.W.; Shao, J.; He, J.; Wu, X.Y.; Xu, X.H.; Zhao, W.H. A membrane-targeted peptide inhibiting PtxA of phosphotransferase system blocks <b><i>streptococcus mutans</i></b>. Caries Res., 2019, 53(2), 176-193.
[http://dx.doi.org/10.1159/000489607] [PMID: 30107375]
[92]
Zhang, Q.; Han, Z.; Tao, J.; Zhao, M.; Zhang, W.; Li, P.; Tang, L.; Gu, Y. An innovative peptide with high affinity to GPC3 for hepatocellular carcinoma diagnosis. Biomater. Sci., 2019, 7(1), 159-167.
[http://dx.doi.org/10.1039/C8BM01016A] [PMID: 30417190]
[93]
Feng, L.; Tu, M.; Qiao, M.; Fan, F.; Chen, H.; Song, W.; Du, M. Thrombin inhibitory peptides derived from Mytilus edulis proteins: Identification, molecular docking and in silico prediction of toxicity. Eur. Food Res. Technol., 2018, 244(2), 207-217.
[http://dx.doi.org/10.1007/s00217-017-2946-7]
[94]
Chenna, A. An efficient computational protocol for template-based design of peptides that inhibit interactions involving SARS-CoV-2 proteins. Prot.-Struc. Func. Bioinform., 2023, 91(9), 1222-1234.
[http://dx.doi.org/10.1002/prot.26511]
[95]
Tubiana, J.; Adriana-Lifshits, L.; Nissan, M.; Gabay, M.; Sher, I.; Sova, M.; Wolfson, H.J.; Gal, M. Funneling modulatory peptide design with generative models: Discovery and characterization of disruptors of calcineurin protein-protein interactions. PLOS Comput. Biol., 2023, 19(2), e1010874.
[http://dx.doi.org/10.1371/journal.pcbi.1010874] [PMID: 36730443]
[96]
Abhinand, C.S. SARS-CoV-2 variants infectivity prediction and therapeutic peptide design using computational approaches. J. Biomol. Struct. Dyn., 2022.
[PMID: 36572420]
[97]
Mukherjee, R.P.; Yow, G.Y.; Sarakbi, S.; Menegatti, S.; Gurgel, P.V.; Carbonell, R.G.; Bobay, B.G. Integrated in silico and experimental discovery of trimeric peptide ligands targeting Butyrylcholinesterase. Comput. Biol. Chem., 2023, 102, 107797.
[http://dx.doi.org/10.1016/j.compbiolchem.2022.107797] [PMID: 36463785]
[98]
Yu, Z.; Wang, Y.; Zhao, W.; Li, J.; Shuian, D.; Liu, J. Identification of Oncorhynchus mykiss nebulin-derived peptides as bitter taste receptor TAS2R14 blockers by in silico screening and molecular docking. Food Chem., 2022, 368, 130839.
[http://dx.doi.org/10.1016/j.foodchem.2021.130839] [PMID: 34419799]
[99]
Yu, M.; Ghamsari, L.; Rotolo, J.A.; Kappel, B.J.; Mason, J.M. Combined computational and intracellular peptide library screening: Towards a potent and selective Fra1 inhibitor. RSC Chem. Biol., 2021, 2(2), 656-668.
[http://dx.doi.org/10.1039/D1CB00012H] [PMID: 34458807]
[100]
Ma, T.; Fu, Q.; Mei, Q.; Tu, Z.; Zhang, L. Extraction optimization and screening of angiotensin-converting enzyme inhibitory peptides from Channa striatus through bioaffinity ultrafiltration coupled with LC-Orbitrap-MS/MS and molecular docking. Food Chem., 2021, 354, 129589.
[http://dx.doi.org/10.1016/j.foodchem.2021.129589] [PMID: 33773481]
[101]
Weber, F.; Casalini, T.; Valentino, G.; Brülisauer, L.; Andreas, N.; Koeberle, A.; Kamradt, T.; Contini, A.; Luciani, P. Targeting transdifferentiated hepatic stellate cells and monitoring the hepatic fibrogenic process by means of IGF2R-specific peptides designed in silico. J. Mater. Chem. B Mater. Biol. Med., 2021, 9(8), 2092-2106.
[http://dx.doi.org/10.1039/D0TB02372H] [PMID: 33595041]
[102]
Wang, F.; Yu, Q.; Hu, M.; Xing, G.; Zhao, D.; Zhang, G. Purification of classical swine fever virus E2 subunit vaccines based on high affinity peptide ligand. Protein Pept. Lett., 2021, 28(5), 554-562.
[http://dx.doi.org/10.2174/0929866527666201103152100] [PMID: 33143607]
[103]
Rasafar, N.; Barzegar, A.; Mehdizadeh Aghdam, E. Structure-based designing efficient peptides based on p53 binding site residues to disrupt p53-MDM2/X interaction. Sci. Rep., 2020, 10(1), 11449.
[http://dx.doi.org/10.1038/s41598-020-67510-8] [PMID: 32651397]
[104]
Shastry, D.G.; Irudayanathan, F.J.; Williams, A.; Koffas, M.; Linhardt, R.J.; Nangia, S.; Karande, P. Rational identification and characterisation of peptide ligands for targeting polysialic acid. Sci. Rep., 2020, 10(1), 7697.
[http://dx.doi.org/10.1038/s41598-020-64088-z] [PMID: 32376914]
[105]
Nguyen, A.T.V.; Trinh, T.T.T.; Hoang, V.T.; Dao, T.D.; Tuong, H.T.; Kim, H.S.; Park, H.; Yeo, S.J. Peptide aptamer of complementarity-determining region to detect avian influenza virus. J. Biomed. Nanotechnol., 2019, 15(6), 1185-1200.
[http://dx.doi.org/10.1166/jbn.2019.2772] [PMID: 31072427]
[106]
Mascini, M.; Dikici, E.; Robles Mañueco, M.; Perez-Erviti, J.A.; Deo, S.K.; Compagnone, D.; Wang, J.; Pingarrón, J.M.; Daunert, S. Computationally designed peptides for zika virus detection: An incremental construction approach. Biomolecules, 2019, 9(9), 498.
[http://dx.doi.org/10.3390/biom9090498] [PMID: 31533374]
[107]
He, Y.; Zhou, L.; Deng, L.; Feng, Z.; Cao, Z.; Yin, Y. An electrochemical impedimetric sensing platform based on a peptide aptamer identified by high-throughput molecular docking for sensitive l-arginine detection. Bioelectrochemistry, 2021, 137, 107634.
[http://dx.doi.org/10.1016/j.bioelechem.2020.107634] [PMID: 32882443]
[108]
Li, R.J. Identification, in silico screening, and molecular docking of novel ACE inhibitory peptides isolated from the edible symbiot Boletus griseus-Hypomyces chrysospermus. Lebensm. Wiss. Technol., 2022, 169.
[109]
Chang, L.W. Revolutionizing peptide-based drug discovery: Advances in the post-AlphaFold era. Wiley Interdiscip. Rev. Comput. Mol. Sci., 2023, 14(1)

Rights & Permissions Print Cite
© 2024 Bentham Science Publishers | Privacy Policy