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In Silico Design of Antimicrobial Peptides

  • Giuseppe Maccari
  • Mariagrazia Di Luca
  • Riccardo Nifosì
Protocol
Part of the Methods in Molecular Biology book series (MIMB, volume 1268)

Abstract

The rapid spread of drug-resistant pathogenic microbial strains has created an urgent need for the development of new anti-infective molecules, having different mechanism of action in comparison to existing drugs. Natural antimicrobial peptides (AMPs) represent a novel class of molecules with a broad spectrum of activity and a low rate in inducing bacterial resistance. In particular, linear alpha-helical cationic antimicrobial peptides are among the most widespread membrane-disruptive AMPs in nature, representing a particularly successful structural arrangement of the innate defense against microbes. However, until now, many AMPs have failed in clinical trials because of several drawbacks that strongly limit their applicability such as degradation, cytotoxicity, and high production cost. Thus, to overcome the limitations of native peptides, a rational in silico approach to AMPs design becomes a promising strategy that drastically reduce production costs and the time required for evaluation of activity and toxicity.

This chapter focuses on the strategies and methods for de novo design of potentially active AMPs. In particular, statistical-based design strategies and MD methods for modelling AMPs are elucidated.

Key words

AMPs Drug resistance QSAR Molecular dynamics De novo peptide design 

References

  1. 1.
    Di Luca M, Maccari G, Nifosì R (2014) Treatment of microbial biofilms in the post antibiotic era: prophylactic and therapeutic use of antimicrobial peptides and their design by bioinformatics tools. Pathog Dis. http://www.ncbi.nlm.nih.gov/pubmed/24515391. Accessed 14 Feb 2014
  2. 2.
    Salomone F, Cardarelli F, Signore G, Boccardi C, Beltram F (2013) In vitro efficient transfection by CM18-Tat11 hybrid peptide: a new tool for gene-delivery applications. PLoS One. doi: 10.1371/journal.pone.0070108 PubMedGoogle Scholar
  3. 3.
    Bahar A, Ren D (2013) Antimicrobial peptides. Pharmaceuticals 6:1543–1575. http://www.mdpi.com/1424-8247/6/12/1543/. Accessed 29 Nov 2013
  4. 4.
    Shai Y, Oren Z (2001) From “carpet” mechanism to de-novo designed diastereomeric cell-selective antimicrobial peptides. Peptides 22: 1629–1641. http://www.ncbi.nlm.nih.gov/pubmed/11587791. Accessed 29 Dec 2012
  5. 5.
    Fjell CD, Hancock REW, Cherkasov A (2007) AMPer: a database and an automated discovery tool for antimicrobial peptides. Bioinformatics 23:1148–1155. http://www.ncbi.nlm.nih.gov/pubmed/17341497. Accessed 10 May 2013
  6. 6.
    Rathinakumar R, Wimley WC (2008) Biomolecular engineering by combinatorial design and high-throughput screening: small, soluble peptides that permeabilize membranes. J Am Chem Soc 130:9849–9858. http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=2582735&tool=pmcentrez&rendertype=abstract. Accessed 22 May 2013
  7. 7.
    Marks JR, Placone J, Hristova K, Wimley WC (2011) Spontaneous membrane-translocating peptides by orthogonal high-throughput screening. J Am Chem Soc 133:8995–9004. http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=3118567&tool=pmcentrez&rendertype=abstract. Accessed 3 Jan 2013
  8. 8.
    Wang P, Hu L, Liu G, Jiang N, Chen X et al (2011) Prediction of antimicrobial peptides based on sequence alignment and feature selection methods. PLoS One 6: e18476. http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=3076375&tool=pmcentrez&rendertype=abstract. Accessed 15 Mar 2012
  9. 9.
    Ashburner M, Ball CA, Blake JA, Botstein D, Butler H et al (2000) Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nat Genet 25:25–29. http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=3037419&tool=pmcentrez&rendertype=abstract. Accessed 21 Jan 2014
  10. 10.
    Li W, Godzik A (2006) Cd-hit: a fast program for clustering and comparing large sets of protein or nucleotide sequences. Bioinformatics 22:1658–1659. http://www.ncbi.nlm.nih.gov/pubmed/16731699. Accessed 30 July 2012
  11. 11.
    Piotto SP, Sessa L, Concilio S, Iannelli P (2012) YADAMP: yet another database of antimicrobial peptides. Int J Antimicrob Agents 39:346–351. http://www.ncbi.nlm.nih.gov/pubmed/22325123. Accessed 23 Aug 2012
  12. 12.
    Thomas S, Karnik S, Barai RS, Jayaraman VK, Idicula-Thomas S (2010) CAMP: a useful resource for research on antimicrobial peptides. Nucleic Acids Res 38: D774–D780. http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=2808926&tool=pmcentrez&rendertype=abstract. Accessed 23 Aug 2012
  13. 13.
    Wold S, Jonsson J, Sjörström M, Sandberg M, Rännar S (1993) DNA and peptide sequences and chemical processes multivariately modelled by principal component analysis and partial least-squares projections to latent structures. Anal Chim Acta 277:239–253. http://linkinghub.elsevier.com/retrieve/pii/000326709380437P. Accessed 23 July 2012Google Scholar
  14. 14.
    Sokal RR, Thomson BA (2006) Population structure inferred by local spatial autocorrelation: an example from an Amerindian tribal population. Am J Phys Anthropol 129:121–131. http://www.ncbi.nlm.nih.gov/pubmed/16261547. Accessed 13 Feb 2014
  15. 15.
    Horne DS (1988) Prediction of protein helix content from an autocorrelation analysis of sequence hydrophobicities. Biopolymers 27:451–477. http://www.ncbi.nlm.nih.gov/pubmed/3359010. Accessed 13 Feb 2014
  16. 16.
    Feng ZP, Zhang CT (2000) Prediction of membrane protein types based on the hydrophobic index of amino acids. J Protein Chem 19:269–275. http://www.ncbi.nlm.nih.gov/pubmed/11043931. Accessed 13 Feb 2014
  17. 17.
    Jaiswal K, Naik PK (2008) Distinguishing compounds with anticancer activity by ANN using inductive QSAR descriptors. Bioinformation 2:441–451. http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=2561164%26tool=pmcentrez%26rendertype=abstract. Accessed 13 Feb 2014
  18. 18.
    Michaelson JJ, Sebat J (2012) forestSV: structural variant discovery through statistical learning. Nat Methods 9:819–821. http://www.ncbi.nlm.nih.gov/pubmed/22751202. Accessed 24 Aug 2012
  19. 19.
    Touw WG, Bayjanov JR, Overmars L, Backus L, Boekhorst J et al (2012) Data mining in the life sciences with random forest: a walk in the park or lost in the jungle? Brief Bioinform. http://www.ncbi.nlm.nih.gov/pubmed/22786785. Accessed 17 July 2012
  20. 20.
    Maccari G, Di Luca M, Nifosí R, Cardarelli F, Signore G, et al (2013) Antimicrobial peptides design by evolutionary multiobjective optimization. PLoS Comput Biol 9: e1003212. http://www.ploscompbiol.org/article/metrics/info:doi/10.1371/journal.pcbi.1003212. Accessed 23 Sept 2013
  21. 21.
    Hansen L, Lee EA, Hestir K, Williams LT, Farrelly D (2009) Controlling feature selection in random forests of decision trees using a genetic algorithm: classification of class I MHC peptides. Comb Chem High Throughput Screen 12: 514–519. http://www.ncbi.nlm.nih.gov/pubmed/19519331. Accessed 14 Feb 2014
  22. 22.
    Peng H, Long F, Ding C (2005) Feature selection based on mutual information: criteria of max-dependency, max-relevance, and min-redundancy. IEEE Trans Pattern Anal Mach Intell 27:1226–1238. http://www.ncbi.nlm.nih.gov/pubmed/16119262. Accessed 23 July 2012
  23. 23.
    Hiss JA, Bredenbeck A, Losch FO, Wrede P, Walden P et al (2007) Design of MHC I stabilizing peptides by agent-based exploration of sequence space. Protein Eng Des Sel 20:99–108. http://www.ncbi.nlm.nih.gov/pubmed/17314106. Accessed 14 Feb 2014
  24. 24.
    Fjell CD, Jenssen H, Cheung WA, Hancock REW, Cherkasov A (2011) Optimization of antibacterial peptides by genetic algorithms and cheminformatics. Chem Biol Drug Des 77:48–56. http://www.ncbi.nlm.nih.gov/pubmed/20942839. Accessed 25 May 2012
  25. 25.
    Deb K, Pratap A, Agarwal S, Meyarivan T (2002) A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans Evol Comput 6:182–197. http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=996017. Accessed 14 July 2012
  26. 26.
    Bocchinfuso G, Bobone S, Mazzuca C, Palleschi A, Stella L (2011) Fluorescence spectroscopy and molecular dynamics simulations in studies on the mechanism of membrane destabilization by antimicrobial peptides. Cell Mol Life Sci 68:2281–2301. http://www.ncbi.nlm.nih.gov/pubmed/21584808. Accessed 6 Aug 2013
  27. 27.
    Gurtovenko AA, Anwar J, Vattulainen I (2010) Defect-mediated trafficking across cell membranes: insights from in silico modeling. Chem Rev 110: 6077–6103. http://www.ncbi.nlm.nih.gov/pubmed/20690701. Accessed 7 Aug 2013
  28. 28.
    Marrink SJ, de Vries AH, Tieleman DP (2009) Lipids on the move: simulations of membrane pores, domains, stalks and curves. Biochim Biophys Acta 1788:149–168. http://www.ncbi.nlm.nih.gov/pubmed/19013128. Accessed 7 Aug 2013
  29. 29.
    Bolintineanu DS, Kaznessis YN (2011) Computational studies of protegrin antimicrobial peptides: a review. Peptides 32:188–201. http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=3013618&tool=pmcentrez&rendertype=abstract. Accessed 7 Aug 2013
  30. 30.
    Chen L, Gao L (2012) How the antimicrobial peptides kill bacteria: computational physics insights. Commun Comput Phys. http://www.global-sci.com/issue/abstract/readabs.php?vol=11&page=709&issue=3&ppage=725&year=2012. Accessed 7 Aug 2013
  31. 31.
    Leach A (2001) Molecular modelling: principles and applications, 2nd edn. Prentice Hall, NJGoogle Scholar
  32. 32.
    Ponder JW, Case DA (2003) Force fields for protein simulations. In: Daggett V (ed) Protein simulations, vol 66. Academic, New York, pp 27–85. doi: 10.1016/S0065-3233(03)66002-X CrossRefGoogle Scholar
  33. 33.
    Mackerell AD (2004) Empirical force fields for biological macromolecules: overview and issues. J Comput Chem 25:1584–1604. doi: 10.1002/jcc.20082 PubMedCrossRefGoogle Scholar
  34. 34.
    Lange OF, van der Spoel D, de Groot BL (2010) Scrutinizing molecular mechanics force fields on the submicrosecond timescale with NMR data. Biophys J 99:647–655. doi: 10.1016/j.bpj.2010.04.062 PubMedCentralPubMedCrossRefGoogle Scholar
  35. 35.
    Lindorff-Larsen K, Maragakis P, Piana S, Eastwood MP, Dror RO et al (2012) Systematic validation of protein force fields against experimental data. PLoS One 7: e32131. http://dx.plos.org/10.1371/journal.pone.0032131. Accessed 21 May 2013
  36. 36.
    Beauchamp KA, Lin Y-S, Das R, Pande VS (2012) Are protein force fields getting better? A systematic benchmark on 524 diverse NMR measurements. J Chem Theory Comp 8:1409–1414. doi: 10.1021/ct2007814 CrossRefGoogle Scholar
  37. 37.
    Cino EA, Choy W-Y, Karttunen M (2012) Comparison of secondary structure formation using 10 different force fields in microsecond molecular dynamics simulations. J Chem Theory Comp 8:2725–2740. doi: 10.1021/ct300323g CrossRefGoogle Scholar
  38. 38.
    Piggot TJ, Piñeiro Á, Khalid S (2012) Molecular dynamics simulations of phosphatidylcholine membranes: a comparative force field study. J Chem Theory Comp 8:4593–4609. doi: 10.1021/ct3003157 CrossRefGoogle Scholar
  39. 39.
    Jämbeck JPM, Lyubartsev AP (2012) An extension and further validation of an all-atomistic force field for biological membranes. J Chem Theory Comp 8:2938–2948. doi: 10.1021/ct300342n CrossRefGoogle Scholar
  40. 40.
    Shi Y, Xia Z, Zhang J, Best R, Wu C et al (2013) The polarizable atomic multipole-based AMOEBA force field for proteins. J Chem Theory Comp 9:4046–4063. doi: 10.1021/ct4003702 CrossRefGoogle Scholar
  41. 41.
    Guo H, Gresh N, Roques BP, Salahub DR (2000) J Phys Chem B 104:9746–9754CrossRefGoogle Scholar
  42. 42.
    Cieplak P, Caldwell J, Kollman P (2001) Molecular mechanical models for organic and biological systems going beyond the atom centered two body additive approximation: aqueous solution free energies of methanol and N-methyl acetamide, nucleic acid base, and amide hydrogen bonding and chloroform/. J Comput Chem 22:1048–1057. doi: 10.1002/jcc.1065 CrossRefGoogle Scholar
  43. 43.
    Tozzini V (2005) Coarse-grained models for proteins. Curr Opin Struct Biol 15:144–150. http://www.ncbi.nlm.nih.gov/pubmed/15837171. Accessed 3 June 2013
  44. 44.
    Baaden M, Marrink SJ (2013) Coarse-grain modelling of protein-protein interactions. Curr Opin Struct Biol 23:878–886. doi: 10.1016/j.sbi.2013.09.004 PubMedCrossRefGoogle Scholar
  45. 45.
    Monticelli L, Kandasamy SK, Periole X, Larson RG, Tieleman DP et al (2008) The MARTINI coarse-grained force field: extension to proteins. J Chem Theory Comp 4:819–834. doi: 10.1021/ct700324x CrossRefGoogle Scholar
  46. 46.
    Bennett WFD, Tieleman DP (2011) Water defect and pore formation in atomistic and coarse-grained lipid membranes : pushing the limits of coarse graining. J Chem Theory Comp 12:2981–2988CrossRefGoogle Scholar
  47. 47.
    Ayton GS, Noid WG, Voth GA (2007) Multiscale modeling of biomolecular systems: in serial and in parallel. Curr Opin Struct Biol 17:192–198. doi: 10.1016/j.sbi.2007.03.004 PubMedCrossRefGoogle Scholar
  48. 48.
    Torrie GM, Valleau JP (1977) Nonphysical sampling distributions in Monte Carlo free-energy estimation: Umbrella sampling. J Comput Phys 23:187–199. doi: 10.1016/0021-9991(77)90121-8 CrossRefGoogle Scholar
  49. 49.
    Roux B (1995) The calculation of the potential of mean force using computer simulations. Comput Phys Commun 91:275–282. doi: 10.1016/0010-4655(95)00053-I CrossRefGoogle Scholar
  50. 50.
    Laio A, Parrinello M (2002) Escaping free-energy minima. Proc Natl Acad Sci U S A 99:12562–12566. doi: 10.1073/pnas.202427399 PubMedCentralPubMedCrossRefGoogle Scholar
  51. 51.
    Huber T, Torda AE, Gunsteren WF (1994) Local elevation: a method for improving the searching properties of molecular dynamics simulation. J Comput Aided Mol Des 8:695–708. doi: 10.1007/BF00124016 PubMedCrossRefGoogle Scholar
  52. 52.
    Grubmüller H (1995) Predicting slow structural transitions in macromolecular systems: conformational flooding. Phys Rev E Stat Plasmas Fluids Retat Interdiscip Topics 52:2893–2906. doi: 10.1103/PhysRevE.52.2893 CrossRefGoogle Scholar
  53. 53.
    Adamson S, Kharlampidi D, Dementiev A (2008) Stabilization of resonance states by an asymptotic Coulomb potential. J Chem Phys 128:024101. doi: 10.1063/1.2821102 PubMedCrossRefGoogle Scholar
  54. 54.
    Yesylevskyy S, Marrink S-J, Mark AE (2009) Alternative mechanisms for the interaction of the cell-penetrating peptides penetratin and the TAT peptide with lipid bilayers. Biophys J 97: 40–49. http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=2711361&tool=pmcentrez&rendertype=abstract. Accessed 6 Aug 2013
  55. 55.
    Sugita Y, Yuko Y (1999) Replica-exchange molecular dynamics method for protein folding. Chem Phys Lett 314:141–151CrossRefGoogle Scholar
  56. 56.
    Sugita Y, Okamoto Y (2000) Replica-exchange multicanonical algorithm and multicanonical replica-exchange method for simulating systems with rough energy landscape. Chem Phys Lett 329:261–270CrossRefGoogle Scholar
  57. 57.
    Wang L, Friesner RA, Berne BJ (2011) Replica exchange with solute scaling: a more efficient version of replica exchange with solute tempering (REST2). J Phys Chem B 115:9431–9438. doi: 10.1021/jp204407d PubMedCentralPubMedCrossRefGoogle Scholar
  58. 58.
    Bussi G, Gervasio FL, Laio A, Parrinello M (2006) Free-energy landscape for beta hairpin folding from combined parallel tempering and metadynamics. J Am Chem Soc 128:13435–13441. doi: 10.1021/ja062463w PubMedCrossRefGoogle Scholar
  59. 59.
    Feller SE, Zhang Y, Pastor RW, Brooks BR (1995) Constant pressure molecular dynamics simulation: the Langevin piston method. J Chem Phys 103:4613. doi: 10.1063/1.470648 CrossRefGoogle Scholar
  60. 60.
    Parrinello M (1981) Polymorphic transitions in single crystals: a new molecular dynamics method. J Appl Phys 52:7182. doi: 10.1063/1.328693 CrossRefGoogle Scholar
  61. 61.
    Patra M, Karttunen M, Hyvönen MT, Falck E, Vattulainen I (2004) Lipid bilayers driven to a wrong lane in molecular dynamics simulations by subtle changes in long-range electrostatic interactions. J Phys Chem B 108:4485–4494. doi: 10.1021/jp031281a CrossRefGoogle Scholar
  62. 62.
    Langham A, Kaznessis YN (2010) Molecular simulations of antimicrobial peptides. Methods Mol Biol 618:267–285. doi: 10.1007/978-1-60761-594-1_17 PubMedCentralPubMedCrossRefGoogle Scholar
  63. 63.
    Venturoli M, Smit B (1999) Simulating the self-assembly of model membranes. Phys Chem Comm 2:45. doi: 10.1039/a906472i Google Scholar
  64. 64.
    Peter Tieleman D, Hess B, Sansom MSP (2002) Analysis and evaluation of channel models: simulations of alamethicin. Biophys J 83:2393–2407. http://linkinghub.elsevier.com/retrieve/pii/S0006349502752533. Accessed 7 Aug 2013Google Scholar
  65. 65.
    Thøgersen L, Schiøtt B, Vosegaard T, Nielsen NC, Tajkhorshid E (2008) Peptide aggregation and pore formation in a lipid bilayer: a combined coarse-grained and all atom molecular dynamics study. Biophys J 95:4337–4347. http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=2567951%26tool=pmcentrez%26rendertype=abstract. Accessed 7 Aug 2013
  66. 66.
    Gkeka P, Sarkisov L (2009) Spontaneous formation of a barrel-stave pore in a coarse-grained model of the synthetic LS3 peptide and a DPPC lipid bilayer. J Phys Chem B 113:6–8. doi: 10.1021/jp808417a PubMedCrossRefGoogle Scholar
  67. 67.
    Woo H-J, Wallqvist A (2011) Spontaneous buckling of lipid bilayer and vesicle budding induced by antimicrobial peptide magainin 2: a coarse-grained simulation study. J Phys Chem B 115:8122–8129. http://www.ncbi.nlm.nih.gov/pubmed/21651300. Accessed 7 Aug 2013
  68. 68.
    Perrin BS, Tian Y, Fu R, Grant C V, Chekmenev EY et al (2014) High-resolution structures and orientations of antimicrobial peptides piscidin 1 and piscidin 3 in fluid bilayers reveal tilting, kinking, and bilayer immersion. J Am Chem Soc. http://www.ncbi.nlm.nih.gov/pubmed/24410116. Accessed 14 Feb 2014
  69. 69.
    Parton DL, Akhmatskaya EV, Sansom MSP (2012) Multiscale simulations of the antimicrobial peptide maculatin 1.1: water permeation through disordered aggregates. J Phys Chem B 116:8485–8493. doi: 10.1021/jp212358y PubMedCrossRefGoogle Scholar
  70. 70.
    National Committee for Clinical Laboratory Standards (2000) Methods for dilution antimicrobial susceptibility tests for bacteria that grow aerobically; M7-A5, 5th edn. National Committee for Clinical Laboratory Standards, Wayne, PAGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  • Giuseppe Maccari
    • 1
  • Mariagrazia Di Luca
    • 2
  • Riccardo Nifosì
    • 2
  1. 1.Center for Nanotechnology Innovation @NESTIstituto Italiano di TecnologiaPisaItaly
  2. 2.NESTIstituto Nanoscienze-CNR and Scuola Normale SuperiorePisaItaly

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