A graph-theoretic approach for classification and structure prediction of transmembrane β-barrel proteins

BMC Genomics

Volumn 13, Issue , 2012, Pages

  Tran, Van Du T ^a   Chassignet, Philippe ^a   Sheikh, Saad ^a   Steyaert, Jean Marc ^a  

^a ÉCOLE POLYTECHNIQUE   (France)

Author keywords

[No Author keywords available]

Indexed keywords

LIPOCALIN; MEMBRANE PROTEIN;

ALGORITHM; ALPHA HELIX; ARTICLE; BETA SHEET; CONTROLLED STUDY; MEASUREMENT ACCURACY; PHYSICAL CHEMISTRY; PREDICTION; PROTEIN FOLDING; RESIDUE ANALYSIS; SCORING SYSTEM; SENSITIVITY AND SPECIFICITY; STRUCTURE ANALYSIS; BIOLOGY; CHEMICAL STRUCTURE; CHEMISTRY; CLASSIFICATION; METHODOLOGY; PROTEIN SECONDARY STRUCTURE;

COMPUTATIONAL BIOLOGY; MEMBRANE PROTEINS; MODELS, MOLECULAR; PROTEIN STRUCTURE, SECONDARY;

EID: 84883624671     PISSN: None     EISSN: 14712164     Source Type: Journal    
DOI: 10.1186/1471-2164-13-S2-S5     Document Type: Article

References (43)

1. Tamm LK, Hong H, Liang B: Folding and assembly of β-barrel membrane proteins. Biochim Biophys Acta 2004, 1666:250-263
2. Berman HM, Westbrook J, Feng Z, Gilliland G, Bhat T, Weissig H, Shindyalov IN, Bourne PE: The Protein Data Bank. Nucleic Acids Res 2000, 28:235-242
3. Arora A, Tamm LK: Biophysical approaches to membrane protein structure determination. Curr Opin Struct Biol 2001, 11:540-547
4. Casadio R, Fariselli P, Martelli PL: In silico prediction of the structure of membrane proteins: Is it feasible? Brief Bioinform 2003, 4(4):341-348
5. Taylor PD, Toseland CP, Attwood TK, Flower DR: Beta-barrel transmembrane proteins: Enhanced prediction using a Bayesian approach. Bioinformation 2006, 1(6):231-233
6. Freeman TCJ, Wimley WC: A highly accurate statistical approach for the prediction of transmembrane beta-barrels. Bioinformatics 2010, 26(16):1965-74
7. Gromiha M, Ahmad S, Suwa M: Neural network-based prediction of transmembrane β-strand segments in outer membrane proteins. J Comput Chem 2004, 25:762-767
8. Gromiha MM, Ahmad S, Suwa M: TMBETA-NET: discrimination and prediction of membrane spanning β-strands in outer membrane proteins. Nucleic Acids Res 2005, 33:W164-W167
9. Randall A, Cheng J, Sweredoski M, Baldi P: TMBpro: secondary structure, β-contact and tertiary structure prediction of transmembrane β-barrel proteins. Bioinformatics 2008, 24:513-520
10. Ou YY, Chen SA, Gromiha MM: Prediction of membrane spanning segments and topology in β-barrel membrane proteins at better accuracy. J Comput Chem 2010, 31:217-223
11. Bagos P, Liakopoulos T, Hamodrakas S: Evaluation of methods for predicting the topology of beta-barrel outer membrane proteins and a consensus prediction method. BMC Bioinformatics 2005, 6:7
12. Waldispühl J, Berger B, Clote P, Steyaert JM: Predicting transmembrane β-barrels and interstrand residue interactions from sequence. Proteins 2006, 65:61-74
13. McGuffin LJ, Bryson K, Jones DT: The PSIPRED protein structure prediction server. Bioinformatics 2000, 16:404-405
14. Jacoboni I, Martelli PL, Fariselli P, Pinto VD, Casadio R: Prediction of the transmembrane regions of β-barrel membrane proteins with a neural network-based predictor. Protein Sci 2001, 10:779-787
15. Martelli P, Fariselli P, Krogh A, Casadio R: A sequence-profile-based HMM for predicting and discriminating β-barrel membrane proteins. Bioinformatics 2002, 18(Suppl 1):S46-S53
16. Ahn C, Yoo S, Park H: Prediction for beta-barrel transmembrane protein region using HMM. KISS 2003, 30(2):802-804
17. Bigelow HR, Petrey DS, Liu J, Przybylski D, Rost B: Predicting transmembrane beta-barrels in proteomes. Nucleic Acids Res 2004, 32:2566-2577
18. Bagos PG, Liakopoulos TD, Spyropoulos IC, Hamodrakas SJ: PRED-TMBB: a web server for predicting the topology of β-barrel outer membrane proteins. Nucleic Acids Res 2004, 32:W400-W404
19. Natt NK, Kaur H, Raghava G: Prediction of transmembrane regions of β-barrel proteins using ANN-and SVM-based methods. Proteins 2004, 56:11-18
20. Tusnády GE, Dosztányi Z, Simon I: PDB_TM: selection and membrane localization of transmembrane proteins in the Protein Data Bank. Nucleic Acids Res 2005, 33:D275-D278
21. Bagos P, Liakopoulos T, Spyropoulos I, Hamodrakas S: A Hidden Markov Model method, capable of predicting and discriminating β-barrel outer membrane proteins. BMC Bioinformatics 2004, 5:29
22. Dayhoff MO, Schwartz RM, Orcutt CB: A model of evolutionary change in proteins. Atlas of Protein Sequence and Structure 1978, 5(Suppl 3):345-352
23. Koebnik R, Krämer L: Membrane assembly of circularly permuted variants of the E. coli outer membrane protein OmpA. J Mol Biol 1995, 250:617-626
24. Beta-Barrel Predictor Web Server. [http://www.lix.polytechnique.fr/Labo/ Van-Du.Tran/bbp/]
25. Tran VD, Chassignet P, Steyaert JM: Prediction of permuted supersecondary structures in beta-barrel proteins. Proceedings of the 2011 ACM Symposium on Applied Computing SAC'11, ACM Digital Library Taichung, Taiwan; 2011, 110-111
26. Tran VD, Chassignet P, Sheikh S, Steyaert JM: Energy-based classification and structure prediction of transmembrane beta-barrel proteins. Proceedings of the 2011 IEEE International Conference on Computational Advances in Bio and Medical Sciences (ICCABS), IEEE Xplore Orlando, FL, USA; 2011, 159-164
27. Marsh D: Infrared dichroism of twisted beta-sheet barrels. The structure of E. coli outer membrane proteins. J Mol Biol 2000, 297:803-808
28. Murzin AG, Lesk AM, Chothia C: Principles determining the structure of β-sheet barrels in proteins I. A theoretical analysis. J Mol Biol 1994, 236:1369-1381
29. Murzin AG, Lesk AM, Chothia C: Principles determining the structure of β-sheet barrels in proteins II. The observed structures. J Mol Biol 1994, 236:1382-1400
30. Chou KC, Carlacci L, Maggiora GM: Conformational and geometrical properties of idealized beta-barrels in proteins. J Mol Biol 1990, 213:315-326
31. Kyte J, Doolittle RF: A simple method for displaying the hydropathic character of a protein. J Mol Biol 1982, 157:105-132
32. Fano R: Transmission of Information Wiley, New York; 1961
33. Gibrat JF, Garnier J, Robson B: Further developments of protein secondary structure prediction using information theory. New parameters and consideration of residue pairs. J Mol Biol 1987, 198:425-443
34. Cormen TH, Leiserson CE, Rivest RL, Stein C: Introduction to Algorithms. 3 edition. MIT Press; 2009
35. Zhang C, Kim SH: A comprehensive analysis of the Greek key motifs in protein β-barrels and β-sandwiches. Proteins 2000, 40:409-419
36. Lewis BA, Engelman DM: Lipid bilayer thickness varies linearly with acyl chain length in fluid phosphatidylcholine vesicles. J Mol Biol 1983, 166(2):211-217
37. Rawicz W, Olbrich K, McIntosh T, Needham D, Evans E: Effect of Chain Length and Unsaturation on Elasticity of Lipid Bilayers. Biophys J 2000, 79:328-339
38. Grantham R: Amino Acid Difference Formula to Help Explain Protein Evolution. Science 1974, 185:862-864
39. Bhaskaran R, Ponnuswamy P: Amino acid scale: average flexibility index. Int J Pept Protein Res 1988, 32:242-255
40. Dunbrack RL, Cohen FE: Bayesian statistical analysis of protein side-chain rotamer preferences. Protein Sci 1997, 6(8):1661-1681
41. van Gunsteren WF, Billeter SR, Eising AA, Hünenberger PH, Krüger P, Mark AE, Scott WRP, Tironi IG: Biomolecular simulation: the GROMOS96 manual and user guide. vdf Hochschulverlag AG an der ETH Zürich and BIOMOS b.v.: Zürich, Groningen 1996
42. Li W, Godzik A: Cd-hit: a fast program for clustering and comparing large sets of protein or nucleotide sequences. Bioinformatics 2006, 22(13):1658-1659
43. Liu WM: Shear numbers of protein β-barrels: definition, refinements and statistics. J Mol Biol 1998, 275:541-545