Large contact surface interactions between proteins detected by time series analysis methods: Case study on C-phycocyanins

Proteins: Structure, Function and Genetics

Volumn 51, Issue 2, 2003, Pages 299-310

  Giuliani, Alessandro ^a   Benigni, Romualdo ^a   Colafranceschi, Mauro ^a,b   Chandrashekar, Indu ^c   Cowsik, Sudha M ^c  

^a ISTITUTO SUPERIORE DI SANITÀ   (Italy)

^b SAPIENZA UNIVERSITY OF ROME   (Italy)

^c JAWAHARLAL NEHRU UNIVERSITY   (India)

Author keywords

Bioinformatics; Principal component analysis; Protein protein interaction; Recurrence quantification analysis; Singular value decomposition

Indexed keywords

C PHYCOCYANIN; PROTEIN; UNCLASSIFIED DRUG;

AMINO ACID SEQUENCE; ARTICLE; BIOINFORMATICS; CONTROLLED STUDY; CORRELATION ANALYSIS; HYDROPHOBICITY; MOLECULAR MODEL; NONHUMAN; NONLINEAR SYSTEM; PREDICTION; PRINCIPAL COMPONENT ANALYSIS; PRIORITY JOURNAL; PROTEIN PROTEIN INTERACTION; PROTEIN STRUCTURE; STRUCTURAL HOMOLOGY; TIME SERIES ANALYSIS;

DATABASES, PROTEIN; HYDROPHOBICITY; PHYCOCYANIN; PHYLOGENY; PROTEIN BINDING; PROTEIN CONFORMATION; PROTEINS; STATISTICS;

OTHER SEQUENCES;

EID: 0037402099     PISSN: 08873585     EISSN: None     Source Type: Journal    
DOI: 10.1002/prot.10366     Document Type: Article

References (29)

1. Schirmer T, Bode W, Huber R. Refined three-dimensional structure of two cyanobacterial C-phycocyanins at 2.1 and 2.5 Angstrom resolution. J Mol Biol 1987;196:677-695.
2. Glazer AN. Phycobilisomes: structure and dynamics. Annu Rev Microbiol 1982;36:173-198.
3. Adir N, Dobrovetsky Y, Lerner N. Structure of C-phycocyanin from the thermophilic cyanobacterium synechococcus vulcanus at 2.5 Angstroms: structural implications for thermal stability in phycobilisome assembly. J Mol Biol 2001;313:71-81.
4. Mandell AJ, Selz KA, Shlesinger MF. Predicting peptide-receptor, peptide-protein and chaperone-protein binding using patterns in amino acid hydrophobic free energy sequences. J Phys Chem B 2000;104:3953-3959.
5. Mandell AJ, Owens MJ, Selz KA, Morgan WN, Shlesinger MF, Nemeroff CB. Mode matches in hydrophobic free energy eigenfunctions predict peptide-protein interactions. Biopolymers 1998;46: 89-101.
6. Dobson CM, Karplus M. The fundamentals of protein folding: bringing together theory and experiment. Curr Opin Struct Biol 1999;9:92-101.
7. Giuliani A, Benigni R, Zbilut JP, Webber CL Jr, Sirabella P, Colosimo A. Nonlinear signal analysis methods in the elucidation of protein sequence structure relationships. Chem Rev 2002;102: 1471-1491.
8. Giuliani A, Tomasi M. Recurrence quantification analysis reveals interaction patterns in paramyxoviridae envelope glycoproteins. Proteins 2002;46:171-176.
9. Mandell AJ, Selz KA, Owens MJ, Shlesinger MF, Gutman DA, Arcuragi V. Hydrophobic mode-targeted, algorithmically designed peptide ligands as modulators of protein thermodynamic structure and function. In: Raffa RB, editor. Drug-receptor thermodynamics: introduction and applications. New York: Wiley & Sons; 2001. p 655-700.
10. Giuliani A, Colafranceschi M, Webber CL Jr, Zbilut JP. A complexity score derived from principal component analysis of nonlinear order measures. Physica A 2001;301:567-588.
11. Schneider G, Wrede P. Artificial neural networks for computerbased molecular design. Progr Biophys Mol Biol 1998;70:175-222.
12. Broomhead DS, King GP. Extracting qualitative dynamics from experimental data. Physica D 1986;20:217-236.
13. Eckmann JP, Kamporst SO, Ruelle D. Recurrence plots of dynamical systems. Europhys Lett 1987;4:973-977.
14. Webber CL Jr, Zbilut JP. Dynamical assessment of physiological systems and states using recurrence plot strategies. J Appl Physiol 1994;76:965-973.
15. Manetti C, Ceruso MA, Giuliani A, Webber CL Jr, Zbilut JP. Recurrence quantification analysis as a tool for characterization of molecular dynamics simulations. Phys Rev E 1999;59:992-998.
16. Rustici M, Caravati C, Petretto E, Branca M, Marchettini M. Transition scenarios during the evolution of the BelousovZhabotinsky reaction in an unstirred batch reactor. J Phys Chem A 1999;103:6564-6570.
17. Lebart L, Morineau A, Warwick KM. Multivariate descriptive statistical analysis. New York: Wiley; 1984.
18. Murray KB, Gorse D, Thornton JM. Wavelet transforms for the characterization and detection of repeating motifs. J Mol Biol 2002;316:341-363.
19. Harman H. Modern factor analysis. Chicago: Chicago University Press; 1976.
20. Kaspar F, Schuster KG. Easily calculable measure for the complexity of spatio-temporal patterns. Phys Rev A 1987;36:842-847.
21. Wang W, Hecht MH. Rationally designed mutations convert de novo amyloid-like fibrils into monomeric beta-sheet proteins. Proc Natl Acad Sci USA 2002;99:2760-2765.
22. Baltzer L, Nilsson H, Nilsson J. De novo design of proteins - What are the rules? Chem Rev 2001;101:3153-3163.
23. Weiss O, Jimenez-Montano MA, Herzel H. Information content of protein sequences. J Theor Biol 2000;206:379-386.
24. Zimmerman JM, Eliezer N, Simha R. The characterization of amino acid sequences in proteins by statistical methods. J Theor Biol 1968;21:170-201.
25. Topliss JG, Edwards RP. Chance factors in studies of quantitative structure-activity relationships. J Med Chem 1979;22:1238-1244.
26. Kikuchi H, Wako H, Yura K, Go M, Mimuro M. Significance of a two-domain structure in subunits of phycobiliproteins revealed by the normal mode analysis. Biophys J 2000;79:1587-1600.
27. Dunker AK, Brown CJ, Lawson JD, Iakoucheva LM, Obradovic Z. Intrinsic disorder and protein function. Biochemistry 2002;41: 6573-6582.
28. Goh CS, Bogan AA, Joachimiak M, Walther D, Cohen FE. Co-evolution of proteins with their interaction partners. J Mol Biol 2000;299:283-293.
29. Goh CS, Cohen FE. Co-evolutionary analysis reveals insights into protein-protein interactions. J Mol Biol 2002;324:177-192.