Mapping membrane activity in undiscovered peptide sequence space using machine learning

Proceedings of the National Academy of Sciences of the United States of America

Volumn 113, Issue 48, 2016, Pages 13588-13593

  Lee, Ernest Y ^a   Fulan, Benjamin M ^b   Wong, Gerard C L ^a   Ferguson, Andrew L ^b,c  

^a Engineering IV   (United States)

^b UNIVERSITY OF ILLINOIS AT URBANA CHAMPAIGN   (United States)

^c United States of America   (United States)

Author keywords

Antimicrobial peptides; Cell penetrating peptides; Machine learning; Membrane curvature; Membrane permeation

Indexed keywords

AMYLOID; NEUROPEPTIDE; PEPTIDE; POLYPEPTIDE ANTIBIOTIC AGENT; VIRUS FUSION PROTEIN; ANTIINFECTIVE AGENT; ANTIMICROBIAL CATIONIC PEPTIDE;

ALPHA HELIX; AMINO ACID SEQUENCE; ANTIMICROBIAL ACTIVITY; ARTICLE; CORRELATION ANALYSIS; DRUG EFFICACY; GAUSSIAN MEMBRANE CURVATURE; GEOMETRY; MEMBRANE TRANSPORT; MINIMUM INHIBITORY CONCENTRATION; NONHUMAN; PRIORITY JOURNAL; PROTEIN FUNCTION; SEQUENCE HOMOLOGY; SUPPORT VECTOR MACHINE; TAXONOMY; X RAY CRYSTALLOGRAPHY; CELL MEMBRANE; CHEMISTRY; GENETICS; MACHINE LEARNING; METABOLISM; MICROBIAL SENSITIVITY TEST; PROCEDURES; THEORETICAL MODEL; UTILIZATION; X RAY DIFFRACTION;

AMINO ACID SEQUENCE; ANTI-BACTERIAL AGENTS; ANTI-INFECTIVE AGENTS; ANTIMICROBIAL CATIONIC PEPTIDES; CELL MEMBRANE; MACHINE LEARNING; MICROBIAL SENSITIVITY TESTS; MODELS, THEORETICAL; PEPTIDES; SUPPORT VECTOR MACHINE; X-RAY DIFFRACTION;

EID: 84998865440     PISSN: 00278424     EISSN: 10916490     Source Type: Journal    
DOI: 10.1073/pnas.1609893113     Document Type: Article

References (58)

1. Zasloff M (2002) Antimicrobial peptides of multicellular organisms. Nature 415(6870):389-395.
2. Shai Y (1999) Mechanism of the binding, insertion and destabilization of phospholipid bilayer membranes by α-helical antimicrobial and cell non-selective membrane-lytic peptides. Biochim Biophys Acta 1462(1-2):55-70.
3. Brogden KA (2005) Antimicrobial peptides: Pore formers or metabolic inhibitors in bacteria? Nat Rev Microbiol 3(3):238-250.
4. Hancock REW, Lehrer R (1998) Cationic peptides: A new source of antibiotics. Trends Biotechnol 16(2):82-88.
5. Hancock REW, Sahl H-G (2006) Antimicrobial and host-defense peptides as new anti-infective therapeutic strategies. Nat Biotechnol 24(12):1551-1557.
6. Yeaman MR, Yount NY (2003) Mechanisms of antimicrobial peptide action and resistance. Pharmacol Rev 55(1):27-55.
7. Yang L, et al. (2008) Mechanism of a prototypical synthetic membrane-active antimicrobial: Efficient hole-punching via interaction with negative intrinsic curvature lipids. Proc Natl Acad Sci USA 105(52):20595-20600.
8. Matsuzaki K (1999) Why and how are peptide-lipid interactions utilized for self-defense? Magainins and tachyplesins as archetypes. Biochim Biophys Acta 1462(1-2):1-10.
9. Matsuzaki K, et al. (1998) Relationship of membrane curvature to the formation of pores by magainin 2. Biochemistry 37(34):11856-11863.
10. Huang HW (2000) Action of antimicrobial peptides: Two-state model. Biochemistry 39(29):8347-8352.
11. Schmidt NW, Wong GCL (2013) Antimicrobial peptides and induced membrane curvature: Geometry, coordination chemistry, and molecular engineering. Curr Opin Solid State Mater Sci 17(4):151-163.
12. Brötz H, Bierbaum G, Leopold K, Reynolds PE, Sahl HG (1998) The lantibiotic mersacidin inhibits peptidoglycan synthesis by targeting lipid II. Antimicrob Agents Chemother 42(1):154-160.
13. Park CB, Kim HS, Kim SC (1998) Mechanism of action of the antimicrobial peptide buforin II: Buforin II kills microorganisms by penetrating the cell membrane and inhibiting cellular functions. Biochem Biophys Res Commun 244(1):253-257.
14. Yonezawa A, Kuwahara J, Fujii N, Sugiura Y (1992) Binding of tachyplesin I to DNA revealed by footprinting analysis: Significant contribution of secondary structure to DNA binding and implication for biological action. Biochemistry 31(11):2998-3004.
15. Patrzykat A, Friedrich CL, Zhang L, Mendoza V, Hancock REW (2002) Sublethal concentrations of pleurocidin-derived antimicrobial peptides inhibit macromolecular synthesis in Escherichia coli. Antimicrob Agents Chemother 46(3):605-614.
16. Otvos L, Jr, et al. (2000) Interaction between heat shock proteins and antimicrobial peptides. Biochemistry 39(46):14150-14159.
17. Bowdish DME, Davidson DJ, Hancock REW (2005) A re-evaluation of the role of host defence peptides in mammalian immunity. Curr Protein Pept Sci 6(1):35-51.
18. Gilliet M, Lande R (2008) Antimicrobial peptides and self-DNA in autoimmune skin inflammation. Curr Opin Immunol 20(4):401-407.
19. Porto WF, Pires ÁS, Franco OL (2012) CS-AMPPred: An updated SVM model for antimicrobial activity prediction in cysteine-stabilized peptides. PLoS One 7(12):e51444.
20. Fjell CD, et al. (2009) Identification of novel antibacterial peptides by chemoinformatics and machine learning. J Med Chem 52(7):2006-2015.
21. Hilpert K, Fjell CD, Cherkasov A (2008) Short linear cationic antimicrobial peptides: Screening, optimizing, and prediction. Methods Mol Biol 494(8):127-159.
22. Fjell CD, Hiss JA, Hancock REW, Schneider G (2011) Designing antimicrobial peptides: Form follows function. Nat Rev Drug Discov 11(1):37-51.
23. Mitchell JBO (2014) Machine learning methods in chemoinformatics. Wiley Interdiscip Rev Comput Mol Sci 4(5):468-481.
24. Yee LC, Wei YC (2012) Current modeling methods used in QSAR/QSPR. Statistical Modelling of Molecular Descriptors in QSAR/QSPR, eds Dehmer M, Varmuza K, Bonchev D (Wiley-VCH, Weinheim, Germany), Vol 2, pp 1-31.
25. Lata S, Sharma BK, Raghava G (2007) Analysis and prediction of antibacterial peptides. BMC Bioinformatics 8:263.
26. Torrent M, Andreu D, Nogués VM, Boix E (2011) Connecting peptide physicochemical and antimicrobial properties by a rational prediction model. PLoS One 6(2):e16968.
27. Maccari G, et al. (2013) Antimicrobial peptides design by evolutionary multiobjective optimization. PLOS Comput Biol 9(9):e1003212.
28. Fjell CD, et al. (2008) Identification of novel host defense peptides and the absence of α-defensins in the bovine genome. Proteins 73(2):420-430.
29. Cherkasov A, et al. (2009) Use of artificial intelligence in the design of small peptide antibiotics effective against a broad spectrum of highly antibiotic-resistant superbugs. ACS Chem Biol 4(1):65-74.
30. Bi J, Bennett K, Embrechts M, Breneman C, Song M (2003) Dimensionality reduction via sparse support vector machines. J Mach Learn Res 3:1229-1243.
31. Cortes C, Vapnik V (1995) Support-vector networks. Mach Learn 20(3):273-297.
32. Pedregosa F, et al. (2011) Scikit-learn: Machine learning in Python. J Mach Learn Res 12:2825-2830.
33. Aizerman A, Braverman EM, Rozoner LI (1964) Theoretical foundations of the potential function method in pattern recognition learning. Autom Remote Control 25:821-837.
34. Boser BE, Guyon IM, Vapnik VN (1992) A Training Algorithm for Optimal Margin Classifiers (ACM, New York), pp 144-152.
35. Engel T (2006) Basic overview of chemoinformatics. J Chem Inf Model 46(6):2267-2277.
36. Schneider G, Baringhaus K-H (2008) Molecular Design (Wiley-VCH, Weinheim, Germany), pp 212-231.
37. Arora JS (2017) Introduction to Optimum Design (Academic, New York), 4th Ed, pp 771-781.
38. Shoval O, et al. (2012) Evolutionary trade-offs, Pareto optimality, and the geometry of phenotype space. Science 336(6085):1157-1160.
39. Schmidt NW, et al. (2012) Molecular basis for nanoscopic membrane curvature generation from quantum mechanical models and synthetic transporter sequences. J Am Chem Soc 134(46):19207-19216.
40. Gilks WR (July 15, 2005) Markov Chain Monte Carlo. Encyclopedia of Biostatistics, eds Armitage P, Colton T, (John Wiley & Sons, Ltd., Chichester, UK), 10.1002/0470011815.b2a14021.
41. Gilks WR, Richardson S, Spiegelhalter DJ (1995) Introducing Markov Chain Monte Carlo. Markov Chain Monte Carlo in Practice, eds Gilks WR, Richardson S, Spiegelhalter DJ (Chapman & Hall/CRC, Boca Raton, FL), pp 1-19.
42. Geyer CJ (1992) Practical Markov Chain Monte Carlo. Statist Sci 7(4):473-483.
43. McGuffin LJ, Bryson K, Jones DT (2000) The PSIPRED protein structure prediction server. Bioinformatics 16(4):404-405.
44. Zhang H, et al. (2011) Critical assessment of high-throughput standalone methods for secondary structure prediction. Brief Bioinform 12(6):672-688.
45. Montgomerie S, Sundararaj S, Gallin WJ, Wishart DS (2006) Improving the accuracy of protein secondary structure prediction using structural alignment. BMC Bioinformatics 7:301.
46. Torrie GM, Valleau JP (1977) Nonphysical sampling distributions in Monte Carlo free-energy estimation: Umbrella sampling. J Comput Phys 23(2):187-199.
47. McInturff JE, et al. (2005) Granulysin-derived peptides demonstrate antimicrobial and anti-inflammatory effects against Propionibacterium acnes. J Invest Dermatol 125(2):256-263.
48. El Karim IA, Linden GJ, Orr DF, Lundy FT (2008) Antimicrobial activity of neuropeptides against a range of micro-organisms from skin, oral, respiratory and gastrointestinal tract sites. J Neuroimmunol 200(1-2):11-16.
49. Hansen CJ, Burnell KK, Brogden KA (2006) Antimicrobial activity of Substance P and Neuropeptide Y against laboratory strains of bacteria and oral microorganisms. J Neuroimmunol 177(1-2):215-218.
50. Brogden KA, Guthmiller JM, Salzet M, Zasloff M (2005) The nervous system and innate immunity: The neuropeptide connection. Nat Immunol 6(6):558-564.
51. Maisetta G, et al. (2013) pH-dependent disruption of Escherichia coli ATCC 25922 and model membranes by the human antimicrobial peptides hepcidin 20 and 25. FEBS J 280(12):2842-2854.
52. Singh M, Mukhopadhyay K (2014) Alpha-melanocyte stimulating hormone: An emerging anti-inflammatory antimicrobial peptide. BioMed Res Int 2014(6):874610.
53. Friedman R, Pellarin R, Caflisch A (2009) Amyloid aggregation on lipid bilayers and its impact on membrane permeability. J Mol Biol 387(2):407-415.
54. Caillon L, Killian JA, Lequin O, Khemtémourian L (2013) Biophysical investigation of the membrane-disrupting mechanism of the antimicrobial and amyloid-like peptide dermaseptin S9. PLoS One 8(10):e75528.
55. Schmidt NW, Mishra A, Wang J, DeGrado WF, Wong GCL (2013) Influenza virus A M2 protein generates negative Gaussian membrane curvature necessary for budding and scission. J Am Chem Soc 135(37):13710-13719.
56. von Heijne G (1986) Towards a comparative anatomy of N-terminal topogenic protein sequences. J Mol Biol 189(1):239-242.
57. Cao D-S, Xu Q-S, Liang Y-Z (2013) Propy: A tool to generate various modes of Chou's PseAAC. Bioinformatics 29(7):960-962.
58. Pedregosa F, et al. (2011) Scikit-learn: Machine learning in python. Journal Mach Learn Res 12:2825-2830.