Modeling ensembles of transmembrane β-barrel proteins

Proteins: Structure, Function and Genetics

Volumn 71, Issue 3, 2008, Pages 1097-1112

  Waldispühl, Jérôme ^a,b   O'Donnell, Charles W ^b   Devadas, Srinivas ^b   Clote, Peter ^c   Berger, Bonnie ^a,b  

^a MASSACHUSETTS INSTITUTE OF TECHNOLOGY   (United States)

^b MASSACHUSETTS INSTITUTE OF TECHNOLOGY   (United States)

^c BOSTON COLLEGE   (United States)

Author keywords

Boltzmann partition function; Ensembles; Outer membrane proteins; Residue contact; Sampling; Stochastic contact map; Structure modeling; Structure prediction; Transmembrane barrels

Indexed keywords

AMINO ACID; MEMBRANE PROTEIN;

ACCURACY; ARTICLE; CRYSTAL STRUCTURE; MODEL; PRIORITY JOURNAL; PROTEIN FOLDING; X RAY;

ALGORITHMS; BACTERIAL OUTER MEMBRANE PROTEINS; BACTERIAL PROTEINS; COMPUTER SIMULATION; MEMBRANE PROTEINS; MODELS, MOLECULAR; PROTEIN FOLDING; PROTEIN STRUCTURE, SECONDARY; RNA, BACTERIAL; STOCHASTIC PROCESSES;

NEGIBACTERIA;

EID: 42449133669     PISSN: 08873585     EISSN: 10970134     Source Type: Journal    
DOI: 10.1002/prot.21788     Document Type: Article

References (47)

1. Wimley WC, White SH. Reversible unfolding of β-sheets in membranes: A calorimetric study. J Mol Biol 2004;342:703-711.
2. Koebnik R. Membrane assembly of the Escherichia Coli outer membrane protein ompa: exploring sequence constraints on transmembrane β-strands. J Mol Biol 1999;285:1801-1810.
3. Martelli PL, Fariselli P, Krogh A, Casadio R. A sequence-profile-based HMM for predicting and discriminating beta barrel membrane proteins. Bioinformatics 2002;S46-S53. Proceedings of ISMB'02.
4. Liu Q, Zhu Y-S, Wang B-H, Li Y-X. A HMM-based method to predict the transmembrane regions of β-barrel membrane proteins. Comput Biol Chem 2003;27:69-76.
5. Bigelow H, Petrey D, Liu J, Przybylski D, Rost B. Predicting transmembrane beta-barrels for entire proteomes. Nucleic Acids Res 2004;32:2566-2577.
6. Navjyot KN, Harpreet K, Raghava GPS. Prediction of transmembrane regions of β-barrel proteins using ANN- and SVM-based methods. Proteins: Struct Funct Bioinform 2004;56:11-18.
7. Gromiha M, Suwa M. A simple statistical method for discriminating outer membrane proteins with better accuracy. Bioinformatics 2005;21:961-968.
8. Tamm LK, Hong H, Liang B. Folding and assembly of β-barrel membrane proteins. Biochimica et Biophysica Acta-Biomemb 2004;1666:250-263.
9. Waldispühl J, Berger B, Clote P, Steyaert J-M. Predicting transmembrane β-barrels and inter-strand residue interactions from sequence. Proteins: Struct Func Bioinform 2006;65:61-74.
10. Zhang Y, Skolnick J. SPICKER: A clustering approach to identify near-native protein folds. J Comput Chem 2004;25:865-871.
11. Park B, Levitt M. Energy functions that discriminate X-ray and near native folds from well-constructed decoys. J Mol Biol 1996;258:367-392.
12. Fain B, Levitt M. A novel method for sampling alpha-helical protein backbones. J Mol Biol 2001;305:191-201.
13. Fain B, Levitt M. Funnel sculpting for in silico assembly of secondary structure elements of proteins. Proc Natl Acad Sci USA 2003;100:10700-10705.
14. Mirny L, Shakhnovich E. Protein folding theory: from lattice to all-atom models. Annu Rev Biophys Biomol Struct 2001;30:361-396.
15. Dill KA, Phillips AT, Rosen JB. Protein structure and energy landscape dependence on sequence using a continuous energy function. J Comput Biol 1997;4:227-239.
16. Miller DW, Dill KA. Ligand binding to proteins: the binding landscape model. Protein Sci 1997;6:2166-2179.
17. Amato NM, Dill KA, Song G. Using motion planning to map protein folding landscapes and analyze folding kinetics of known native structures. J Comput Biol 2003;10:239-255.
18. Hockenmaier J, Joshi AK, Dill KA. Routes are trees: The parsing perspective on protein folding. Proteins: Struct Funct Bioinform 2007;66:1-15.
19. Voelz VA, Dill KA. Exploring zipping and assembly as a protein folding principle. Proteins: Struct Funct Bioinform 2007;66:877-888.
20. Chiang D, Joshi AK, Dill KA. A grammatical theory for the conformational changes of simple helix bundles. J Comput Biol 2006; 13:27-42.
21. Alexandre V. Morozov, The Rockefeller University, personal communication, April 25, 2007.
22. Zuker M, Stiegler P. Optimal computer folding of large RNA sequences using thermodynamics and auxiliary information. Nucleic Acids Res 1981;9:133-148.
23. McCaskill JS. The equilibrium partition function and base pair binding probabilities for RNA secondary structure. Biopolymers 1990;29:1105-1119.
24. Ding Y, Lawrence CE. A statistical sampling algorithm for RNA secondary structure prediction. Nucleic Acids Res 2003;31:7280-7301.
25. Istrail I. Statistical mechanics, three-dimensionality and NP-completeness. I. Universality of intractability of the partition functions of the ising model across non-planar lattices. In: ACM Press, editor. Proceedings of the 32nd ACM symposium on the theory of computing (STOC00), 2000. pp. 87-96.
26. Bradley P, Cowen L, Menke M, King J, Berger B. Betawrap: successful prediction of parallel β-helices from primary sequence reveals an association with many microbial pathogens. Proc Nat Acad Sci, USA 2001;98:14819-14824.
27. Cowen L, Bradley P, Menke M, King J, Berger B. Predicting the beta-helix fold from protein sequence data. J Comput Biol 2002; 9:261-276.
28. Waldispühl J, Steyaert J-M. Modeling and predicting all-α transmembranaire proteins including helix-helix pairing. Theor Comput Sci (special issue on Pattern Discovery in the Post Genome) 2005; 67-92.
29. Schulz G. β-barrel membrane proteins. Curr Opin Struct Biol 2000; 10:443-447.
30. Xia T, SantaLucia J Jr, Burkard ME, Kierzek R, Schroeder SJ, Jiao X, Cox C, Turner DH. Thermodynamic parameters for an expanded nearest-neighbor model for formation of RNA duplexes with Watson-Crick base pairs. Biochemistry 1999;37:14719-14735.
31. Kernytsky A, Rost B. Static benchmarking of membrane helix prediction. Nucleic Acids Res 2003;31:3642-3644.
32. Berman H, Westbrook J, Feng Z, Gilliland G, Bhat T, Weissig H, Shindyalov I, Bourne P. The protein data bank. Nucleic Acids Res 2000;28:235-242.
33. Frishman D, Argos P. Knowledge-based protein secondary structure assignment. Proteins 1995;23:566-579.
34. Chotia C. The Nature of the Accessible and Buried Surfaces in Proteins. J Mol Biol 1975;105:1-14.
35. Jun Wang, Wei Wang. A computational approach to simplifying the protein folding alphabet. Nat Struct Biol 1999;6:1033-1038.
36. Clote P, Backofen R. Computational molecular biology: an introduction. Wiley; 2000. 279p.
37. Manfred J. Sippl. Calculation of conformational ensembles from potentials of mean force. J Mol Biol 1990;213:859-883.
38. Dill KA, Bromberg S. Molecular driving forces. New York: Garland Science, Taylor & Francis; 2003.
39. Clote P, Waldispühl J, Behzadi B, Steyaert J-M. Energy landscape of k-point mutants of an RNA molecule. Bioinformatics 2005;21: 4140-4147.
40. Grana O, Baker D, MacCallum RM, Meiler J, Punta M, Rost ML, Tress B, Valencia A. Casp6 assessment of contact prediction. Proteins 2005;61:214-224.
41. Critical assessment of techniques for protein structure prediction. http://predictioncenter.org/casp7/.
42. Punta B, Rost B. Profcon: novel prediction of long-range contacts. Bioinformatics 2005;21:2960-2968.
43. Cheng J, Baldi P. Three-stage prediction of protein β-sheets by neural networks, alignments and graph algorithms. Proc ISMB 2005, Bioinformatics 2005;21(suppl 1):i75-i84.
44. Cheng J, Baldi P. Improved residue contact prediction using support vector machines and a large feature set. BMC Bioinform 2007; 8:113-121.
45. Chan AW, Gail Hutchinson E, Harris D, Thornton JM. Identification, classifiction, and analysis of β-bulges in proteins. Protein Sci 1993;2:1574-1590.
46. Rhodes G. Crystallography made crystal clear, 2nd ed. San Diego: Academic Press; 2000.
47. Schlessinger A, Rost B. Protein flexibility and rigidity predicted from sequence. Proteins: Struct Funct Bioinform 2005;61: 115-126.