Prediction of Cell Penetrating Peptides by Support Vector Machines

PLoS Computational Biology

Volumn 7, Issue 7, 2011, Pages

  Sanders, William S ^a   Johnston, C Ian ^a   Bridges, Susan M ^a,c   Burgess, Shane C ^a,b   Willeford, Kenneth O ^a  

^a MISSISSIPPI STATE UNIVERSITY   (United States)

^b MISSISSIPPI STATE UNIVERSITY   (United States)

^c MISSISSIPPI STATE UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

CELLS; CLASSIFICATION (OF INFORMATION); CYTOLOGY; DRUG DELIVERY; FORECASTING; SUPPORT VECTOR MACHINES;

BIOCHEMICAL PROPERTIES; CELL-PENETRATING PEPTIDE; CELLULAR COMPONENTS; MACHINE LEARNING METHODS; PORE FORMING; POSITIVE EXAMPLES; RAPID SCREENING; SUPPORT VECTORS MACHINE; TRAINING DATASET; TRAINING SETS;

PEPTIDES;

CELL PENETRATING PEPTIDE; PEPTIDE; FLUORESCEIN ISOTHIOCYANATE;

ACCURACY; ANIMAL CELL; ARTICLE; CELL MEMBRANE PERMEABILITY; CHICKEN; CLASSIFIER; CONTROLLED STUDY; EMBRYO; FALSE POSITIVE RESULT; INTERMETHOD COMPARISON; NONHUMAN; PEPTIDE SYNTHESIS; PREDICTION; QUAIL; RECEIVER OPERATING CHARACTERISTIC; SUPPORT VECTOR MACHINE; VALIDATION STUDY; ALGORITHM; AMINO ACID SEQUENCE; ANIMAL; ARTIFICIAL INTELLIGENCE; BIOLOGY; CELL LINE; CHEMISTRY; CLASSIFICATION; FLUORESCENCE MICROSCOPY; HUMAN; METABOLISM; METHODOLOGY; MOLECULAR GENETICS; PROTEIN DATABASE; REPRODUCIBILITY; SEQUENCE ANALYSIS; TRANSPORT AT THE CELLULAR LEVEL;

ALGORITHMS; AMINO ACID SEQUENCE; ANIMALS; ARTIFICIAL INTELLIGENCE; BIOLOGICAL TRANSPORT; CELL LINE; CELL-PENETRATING PEPTIDES; CHICKENS; COMPUTATIONAL BIOLOGY; DATABASES, PROTEIN; FLUORESCEIN-5-ISOTHIOCYANATE; HUMANS; MICROSCOPY, FLUORESCENCE; MOLECULAR SEQUENCE DATA; QUAIL; REPRODUCIBILITY OF RESULTS; SEQUENCE ANALYSIS, PROTEIN;

EID: 81055126634     PISSN: 1553734X     EISSN: 15537358     Source Type: Journal    
DOI: 10.1371/journal.pcbi.1002101     Document Type: Article

References (26)

1. Kilk K, (2004) Cell-penetrating peptides and bioactive cargoes. Strategies and mechanisms Stockholm Stockholm University 66.
2. Richard JP, Melikov K, Vives E, Ramos C, Verbeure B, et al.(2003) Cell-penetrating peptides. A reevaluation of the mechanism of cellular uptake. J Biol Chem 278: 585-590.
3. Schwartz JJ, Zhang S, (2000) Peptide-mediated cellular delivery. Curr Opin Mol Ther 2: 162-167.
4. Vives E, (2005) Present and future of cell-penetrating peptide mediated delivery systems: "is the Trojan horse too wild to go only to Troy?". J Control Release 109: 77-85.
5. Sandberg M, Eriksson L, Jonsson J, Sjostrom M, Wold S, (1998) New chemical descriptors relevant for the design of biologically active peptides. A multivariate characterization of 87 amino acids. J Med Chem 41: 2481-2491.
6. Hallbrink M, Kilk K, Elmquist A, Lundberg P, Lindgren M, et al.(2005) Prediction of Cell-Penetrating Peptides. Int J Pept Res Ther 11: 249-259.
7. Hansen M, Kilk K, Langel U, (2008) Predicting cell-penetrating peptides. Adv Drug Deliv Rev 60: 572-579.
8. Dobchev DA, Mager I, Tulp I, Karelson G, Tamm T, et al.(2010) Prediction of Cell-Penetrating Peptides Using Artificial Neural Networks. Current Computer Aided Drug Des 6: 79-89.
9. Provost F, (2000) Machine Learning from Imbalanced Data Sets 101. AAAI Workshop on Learning from Imbalaced Data Sets pp. 1-3.
10. Kersey PJ, Duarte J, Williams A, Karavidopoulou Y, Birney E, et al.(2004) The International Protein Index: an integrated database for proteomics experiments. Proteomics 4: 1985-1988.
11. Anaspec I, (2010) Cell Permeable Peptides (CPP)/ Drug Delivery Peptides. In: Anaspec I, editors. Anaspec's Catalog Listing of Cell Permeable Peptides (CPP).
12. Lopez F, Torres M, Batista B, Perez J, Moreno-Vega J, (2006) Solving feature subset selection problem by a Parallel Scatter Search. Eur J Oper Res 169: 477-489.
13. Platt J, (1999) Fast Training of Support Vector Machines using Sequential Minimal Optimization. In: Scholkopf B, Burges C, Smola A, editors. Advances in Kernel Methods: Support Vector Learning Cambridge, MA MIT Press pp. 185-208.
14. Ustun B, Melssen W, Buydens L, (2005) Facilitating the application of Support Vector Regression by using a universal Pearson VII function based kernel. Chemometrics and Intelligent Laboratory Systems 81: 29-40.
15. Soomets U, Lindgren M, Gallet X, Hallbrink M, Elmquist A, et al.(2000) Deletion analogues of transportan. Biochim Biophys Acta 1467: 165-176.
16. Liu H, Li J, Wong L, (2002) A Comparative Study on Feature Selection and Classification Methods Using Gene Expression Profiles and Proteomic Patterns. Genome Informatics 13: 51-60.
17. Hall M, Frank E, Holmes G, Pfahringer B, Reutemann P, et al.(2009) The WEKA Data Mining Software: An Update. SIGKDD Explorations 11: 10-18.
18. Frankel AD, Pabo CO, (1988) Cellular uptake of the tat protein from human immunodeficiency virus. Cell 55: 1189-1193.
19. Derossi D, Joliot AH, Chassaing G, Prochiantz A, (1994) The third helix of the Antennapedia homeodomain translocates through biological membranes. J Biol Chem 269: 10444-10450.
20. Morris MC, Depollier J, Mery J, Heitz F, Divita G, (2001) A peptide carrier for the delivery of biologically active proteins into mammalian cells. Nat Biotechnol 19: 1173-1176.
21. Schumacher D, Tischer BK, Teifke JP, Wink K, Osterrieder N, (2002) Generation of a permanent cell line that supports efficient growth of Marek's disease virus (MDV) by constitutive expression of MDV glycoprotein E. J Gen Virol 83: 1987-1992.
22. Sanders WS, Bridges SM, McCarthy FM, Nanduri B, Burgess SC, (2007) Prediction of peptides observable by mass spectrometry applied at the experimental set level. BMC Bioinformatics 8 (Suppl 7): S23.
23. Kawashima S, Ogata H, Kanehisa M, (1999) AAindex: Amino Acid Index Database. Nucleic Acids Res 27: 368-369.
24. Gulaboski R, Scholz F, (2003) Lipophilicity of Peptide Anions: An Experimental Data Set for Lipophilicity Calculations. J Phys Chem B 107: 5650-5657.
25. Mitaku S, Hirokawa T, Tsuji T, (2002) Amphiphilicity index of polar amino acids as an aid in the characterization of amino acid preference at membrane-water interfaces. Bioinformatics 18: 608-616.
26. Sen TZ, Jernigan RL, Garnier J, Kloczkowski A, (2005) GOR V server for protein secondary structure prediction. Bioinformatics 21: 2787-2788.