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Rational design, conformational analysis and membrane-penetrating dynamics study of Bac2A-derived antimicrobial peptides against gram-positive clinical strains isolated from pyemia. (English) Zbl 1414.92198
Summary: The Bac2A (RLARIVVIRVAR\(^{-\mathrm{NH2}}\)) is a linearized counterpart of cationic cyclic peptide Bactenecin – one of the smallest naturally occurring antimicrobial peptides (AMPs), which, however, generally exhibits a low or moderate antibacterial potency against gram-positive bacteria. Here, it is found that the Bac2A and its linear derivates cannot spontaneously fold into a well-defined helical conformation in solution, thus impairing the peptide amphipathicity and antibacterial activity. Hydrocarbon stapling is rationally designed to constrain the helical conformation of these linear peptides. Atomistic dynamics simulations reveal that the membrane-penetrating course of linear and stapled peptides include four distinct phases, during which the stapled peptides can maintain in an ordered helical conformation, while linear peptides are structured from intrinsic disorder in water solution to partially helical state in membrane interior, indicating that lipid environment can help the linear peptide refolding into amphipathic helix, although the refolding process would incur a large configurational entropy penalty. The antibacterial activities of the most potent stapled peptide are determined as MIC = 7.6 and \(16\mu\)g/ml against two gram-positive Staphylococcus aureus clinical strains isolated from pyemia. The activity values are improved by 7.1-fold and 5-fold as compared to that of native Bac2A peptide with MIC = 54 and \(80\mu\)g/ml, respectively.
MSC:
92C60 Medical epidemiology
92D20 Protein sequences, DNA sequences
92C40 Biochemistry, molecular biology
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[1] Bai, Z.; Hou, S.; Zhang, S.; Li, Z.; Zhou, P., Targeting self-binding peptides as a novel strategy to regulate protein activity and function: a case study on the proto-oncogene tyrosine protein kinase c-Src, J. Chem. Inf. Model., 57, 835-845, (2017)
[2] Bird, G. H.; Crannell, W. C.; Walensky, L. D., Chemical synthesis of hydrocarbon-stapled peptides for protein interaction research and therapeutic targeting, Curr. Protoc. Chem. Biol., 3, 99-117, (2011)
[3] Chen, W.; Lin, H.; Feng, P. M.; Ding, C.; Zuo, Y. C., iNuc-PhysChem: a sequence-based predictor for identifying nucleosomes via physicochemical properties, PLoS ONE, 7, e47843, (2012)
[4] Chen, W.; Lin, H., Pseudo nucleotide composition or PseKNC: an effective formulation for analyzing genomic sequences, Mol. BioSyst., 11, 2620-2634, (2015)
[5] Cheng, X.; Xiao, X., pLoc-mPlant: predict subcellular localization of multi-location plant proteins via incorporating the optimal GO information into general PseAAC, Mol. BioSyst., 13, 1722-1727, (2017)
[6] Cheng, X.; Xiao, X., pLoc-mGneg: predict subcellular localization of Gram-negative bacterial proteins by deep gene ontology learning via general PseAAC, Genomics, 110, 231-239, (2018)
[7] Cheng, X.; Xiao, X., pLoc-mEuk: predict subcellular localization of multi-label eukaryotic proteins by extracting the key GO information into general PseAAC, Genomics, 110, 50-58, (2018)
[8] Chugunov, A.; Pyrkova, D.; Nolde, D.; Polyansky, A.; Pentkovsky, V.; Efremov, R., Lipid-II forms potential “landing terrain” for lantibiotics in simulated bacterial membrane, Sci. Rep., 3, 1678, (2013)
[9] Chou, K. C.; Zhang, C. T.; Maggiora, G. M., Solitary wave dynamics as a mechanism for explaining the internal motion during microtubule growth, Biopolymers, 34, 143-153, (1994)
[10] Chou, K. C., Prediction of protein cellular attributes using pseudo amino acid composition, Proteins, 43, 246-255, (2001)
[11] Chou, K. C., Using amphiphilic pseudo amino acid composition to predict enzyme subfamily classes, Bioinformatics, 21, 10-19, (2005)
[12] Chou, K. C., Graphic rule for drug metabolism systems, Curr. Drug Metab., 11, 369-378, (2010)
[13] Chou, K. C., Some remarks on protein attribute prediction and pseudo amino acid composition, J. Theor. Biol., 273, 236-247, (2011) · Zbl 1405.92212
[14] Chou, K. C., Impacts of bioinformatics to medicinal chemistry, Med. Chem., 11, 218-234, (2015)
[15] Chou, K. C., An unprecedented revolution in medicinal chemistry driven by the progress of biological science, Curr. Top. Med. Chem., 17, 2337-2358, (2017)
[16] Dehzangi, A.; Heffernan, R.; Sharma, A.; Lyons, J.; Paliwal, K.; Sattar, A., Gram-positive and Gram-negative protein subcellular localization by incorporating evolutionary-based descriptors into Chou’s general PseAAC, J. Theor. Biol., 364, 284-294, (2015) · Zbl 1405.92092
[17] Fjell, C. D.; Jenssen, H.; Hilpert, K.; Cheung, W. A.; Panté, N.; Hancock, R. E.; Cherkasov, A., Identification of novel antibacterial peptides by chemoinformatics and machine learning, J. Med. Chem., 52, 2006-2015, (2009)
[18] Frecer, V.; Ho, B.; Ding, J. L., De novo design of potent antimicrobial peptides, Antimicrob. Agents Chemother., 48, 3349-3357, (2004)
[19] Friedrich, C. L.; Moyles, D.; Beveridge, T. J.; Hancock, R. E., Antibacterial action of structurally diverse cationic peptides on gram-positive bacteria, Antimicrob. Agents Chemother., 44, 2086-2092, (2000)
[20] Fu, J.; Yang, H.; Wang, J., Computational design of the helical hairpin structure of membrane-active antibacterial peptides based on RSV glycoprotein epitope scaffold, Comput. Biol. Chem., 73, 200-205, (2018)
[21] Gautier, R.; Douguet, D.; Antonny, B.; Drin, G., HELIQUEST: a web server to screen sequences with specific alpha-helical properties, Bioinformatics, 24, 2101-2102, (2008)
[22] Huang, R. B.; Du, Q. S.; Wang, C. H., An in-depth analysis of the biological functional studies based on the NMR M2 channel structure of influenza A virus, Biochem. Biophys. Res. Comm., 377, 1243-1247, (2008)
[23] Hilpert, K.; Elliott, M. R.; Volkmer-Engert, R.; Henklein, P.; Donini, O.; Zhou, Q.; Winkler, D. F.; Hancock, R. E., Sequence requirements and an optimization strategy for short antimicrobial peptides, Chem. Biol., 13, 1101-1107, (2006)
[24] Hussain, W.; Khan, Y. D.; Rasool, N.; Khan, S. A., SPalmitoylC-PseAAC: a sequence-based model developed via Chou’s 5-steps rule and general PseAAC for identifying S-palmitoylation sites in proteins, Anal. Biochem., 568, 14-23, (2019)
[25] Hussain, W.; Khan, Y. D.; Rasool, N.; Khan, S. A., SPrenylC-PseAAC: a sequence-based model developed via Chou’s 5-steps rule and general PseAAC for identifying S-prenylation sites in proteins, J. Theor. Biol., 468, 1-11, (2019) · Zbl 1411.92233
[26] Li, Z.; Yan, F.; Miao, Q.; Meng, Y.; Wen, L.; Jiang, Q.; Zhou, P., Self-binding peptides: binding-upon-folding versus folding-upon-binding, J. Theor. Biol., 469, 25-34, (2019)
[27] Liu, B.; Liu, F.; Wang, X.; Chen, J.; Fang, L., Pse-in-One: a web server for generating various modes of pseudo components of DNA, RNA, and protein sequences, Nucleic Acids Res., 43, W65-W71, (2015)
[28] Liu, B.; Wu, H., Pse-in-One 2.0: an improved package of web servers for generating various modes of pseudo components of DNA, RNA, and protein sequences, Nat. Sci., 9, 67-91, (2017)
[29] Malanovic, N.; Lohner, K., Antimicrobial peptides targeting gram-positive bacteria, Pharmaceuticals, 9, E59, (2016)
[30] Malanovic, N.; Lohner, K., Gram-positive bacterial cell envelopes: the impact on the activity of antimicrobial peptides, Biochim. Biophys. Acta, 1858, 936-946, (2016)
[31] Meher, P. K.; Sahu, T. K.; Saini, V.; Rao, A. R., Predicting antimicrobial peptides with improved accuracy by incorporating the compositional, physico-chemical and structural features into Chou’s general PseAAC, Sci. Rep., 7, 42362, (2017)
[32] Migoń, D.; Neubauer, D.; Kamysz, W., Hydrocarbon stapled antimicrobial peptides, Protein J., 37, 2-12, (2018)
[33] Qiu, W. R.; Jiang, S. Y.; Xu, Z. C.; Xiao, X., iRNAm5C-PseDNC: identifying RNA 5-methylcytosine sites by incorporating physical-chemical properties into pseudo dinucleotide composition, Oncotarget, 8, 41178-41188, (2017)
[34] Qiu, W. R.; Sun, B. Q.; Xiao, X.; Xu, D., iPhos-PseEvo: identifying human phosphorylated proteins by incorporating evolutionary information into general PseAAC via grey system theory, Mol. Inf., 36, Article 1600010 pp., (2017)
[35] Romeo, D.; Skerlavaj, B.; Bolognesi, M.; Gennaro, R., Structure and bactericidal activity of an antibiotic dodecapeptide purified from bovine neutrophils, J. Biol. Chem., 263, 9573-9575, (1988)
[36] Schnell, J. R.; Chou, J. J., Structure and mechanism of the M2 proton channel of influenza A virus, Nature, 451, 591-595, (2008)
[37] Van Der Spoel, D.; Lindahl, E.; Hess, B.; Groenhof, G.; Mark, A. E.; Berendsen, H. J., GROMACS: fast, flexible, and free, J. Comput. Chem., 26, 1701-1718, (2005)
[38] Wang, J.; Pielak, R. M.; McClintock, M. A.; Chou, J. J., Solution structure and functional analysis of the influenza B proton channel, Nat. Struct. Mol. Biol., 16, 1267-1271, (2009)
[39] Wang, S. Q.; Du, Q. S., Study of drug resistance of chicken influenza A virus (H5N1) from homology-modeled 3D structures of neuraminidases, Biochem. Biophys. Res. Comm., 354, 634-640, (2007)
[40] Wu, M.; Hancock, R. E., Improved derivatives of bactenecin, a cyclic dodecameric antimicrobial cationic peptide, Antimicrob. Agents Chemother., 43, 1274-1276, (1999)
[41] Xiao, X.; Min, J. L.; Lin, W. Z.; Liu, Z.; Cheng, X., iDrug-Target: predicting the interactions between drug compounds and target proteins in cellular networking via the benchmark dataset optimization approach, J. Biomol. Struct. Dyn., 33, 2221-2233, (2015)
[42] Xiao, X.; Cheng, X.; Chen, G.; Mao, Q., pLoc_bal-mGpos: predict subcellular localization of Gram-positive bacterial proteins by quasi-balancing training dataset and PseAAC, Genomics, 26, S0888-S7543, (2018)
[43] Yang, C.; Wang, C.; Zhang, S.; Huang, J.; Zhou, P., Structural and energetic insights into the intermolecular interaction among human leukocyte antigens, clinical hypersensitive drugs and antigenic peptides, Mol. Simul., 41, 741-751, (2015)
[44] Yang, C.; Zhang, S.; He, P.; Wang, C.; Huang, J.; Zhou, P., Self-binding peptides: folding or binding, J. Chem. Inf. Model., 55, 329-342, (2015)
[45] Yang, C.; Zhang, S.; Bai, Z.; Hou, S.; Wu, D.; Huang, J.; Zhou, P., A two-step binding mechanism for the self-binding peptide recognition of target domains, Mol. Biosyst., 12, 1201-1213, (2016)
[46] Yang, R.; Zhang, G.; Zhang, F.; Li, Z.; Huang, C., Membrane permeabilization design of antimicrobial peptides based on chikungunya virus fusion domain scaffold and its antibacterial activity against gram-positive Streptococcus pneumoniae in respiratory infection, Biochimie, 146, 139-147, (2018)
[47] Ye, H., Molecular design of antimicrobial peptides based on hemagglutinin fusion domain to combat antibiotic resistance in bacterial infection, J. Pept. Sci., 24, e3068, (2018)
[48] Yu, H.; Zhou, P.; Deng, M.; Shang, Z., Indirect readout in protein-peptide recognition: a different story from classical biomolecular recognition, J. Chem. Inf. Model., 54, 2022-2032, (2014)
[49] Zhou, P.; Wang, C.; Tian, F.; Ren, Y.; Yang, C.; Huang, J., Biomacromolecular quantitative structure-activity relationship (BioQSAR): a proof-of-concept study on the modeling, prediction and interpretation of protein-protein binding affinity, J. Comput. Aided Mol. Des., 27, 67-78, (2013)
[50] Zhou, P.; Yang, C.; Ren, Y.; Wang, C.; Tian, F., What are the ideal properties for functional food peptides with antihypertensive effect? A computational peptidology approach, Food Chem., 141, 2967-2973, (2013)
[51] Zhou, P.; Zhang, S.; Wang, Y.; Yang, C.; Huang, J., Structural modeling of HLA-B1502 peptide carbamazepine T-cell receptor complex architecture: implication for the molecular mechanism of carbamazepine-induced Stevens-Johnson syndrome toxic epidermal necrolysis, J. Biomol. Struct. Dyn., 34, 1806-1817, (2016)
[52] Zhou, P.; Hou, S.; Bai, Z.; Li, Z.; Wang, H.; Chen, Z.; Meng, Y., Disrupting the intramolecular interaction between proto-oncogene c-Src SH3 domain and its self-binding peptide PPII with rationally designed peptide ligands, Artif. Cells Nanomed. Biotechnol., 46, 1122-1131, (2018)
[53] Zhou, G. P., The disposition of the LZCC protein residues in wenxiang diagram provides new insights into the protein-protein interaction mechanism, J. Theor. Biol., 284, 142-148, (2011) · Zbl 1397.92245
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