Tytuł artykułu
Autorzy
Wybrane pełne teksty z tego czasopisma
Identyfikatory
Warianty tytułu
Języki publikacji
Abstrakty
The transition state ensemble during the folding process of globular proteins occurs when a sufficient number of intrachain contacts are formed, mainly, but not exclusively, due to hydrophobic interactions. These contacts are related to the folding nucleus, and they contribute to the stability of the native structure, although they may disappear after the energetic barrier of transition states has been passed. A number of structure and sequence analyses, as well as protein engineering studies, have shown that the signature of the folding nucleus is surprisingly present in the native three-dimensional structure, in the form of closed loops, and also in the early folding events. These findings support the idea that the residues of the folding nucleus become buried in the very first folding events, therefore helping the formation of closed loops that act as anchor structures, speed up the process, and overcome the Levinthal paradox. We present here a review of an algorithm intended to simulate in a discrete space the early steps of the folding process. It is based on a Monte Carlo simulation where perturbations, or moves, are randomly applied to residues within a sequence. In contrast with many technically similar approaches, this model does not intend to fold the protein but to calculate the number of non-covalent neighbors of each residue, during the early steps of the folding process. Amino acids along the sequence are categorized as most interacting residues (MIRs) or least interacting residues. The MIR method can be applied under a variety of circumstances. In the cases tested thus far, MIR has successfully identified the exact residue whose mutation causes a switch in conformation. This follows with the idea that MIR identifies residues that are important in the folding process. Most MIR positions correspond to hydrophobic residues; correspondingly, MIRs have zero or very low accessible surface area. Alongside the review of the MIR method, we present a new postprocessing method called smoothed MIR (SMIR), which refines the original MIR method by exploiting the knowledge of residue hydrophobicity. We review known results and present new ones, focusing on the ability of MIR to predict structural changes, secondary structure, and the improved precision with the SMIR method.
Czasopismo
Rocznik
Tom
Strony
227--242
Opis fizyczny
Bibliogr. 47 poz., rys., tab., wykr.
Twórcy
autor
- Scientific Data Management Laboratory, School of Electrical, Computer and Energy Engineering (ECEE), Arizona State University, Tempe, AZ 85282-5706, USA
autor
- Scientific Data Management Laboratory, School of Electrical, Computer and Energy Engineering (ECEE), Arizona State University, Tempe, AZ 85282-5706, USA
autor
- Genetics Department, Agricultural University of Athens, Athens, Greece
autor
- Protein Structure Prediction group, IMPMC, Sorbonne University, UPMC, CNRS, MNHN, IRD, Paris, France
- RPBS, Paris, France
Bibliografia
- 1. Anfinsen CB. Principles that govern the folding of protein chains. Science 1973;181:223-30.
- 2. Anfinsen CB, Haber E. Studies on the reduction and re-formation of protein disulfide bonds. J Biol Chem 1961:236:1361-3.
- 3. Go N, Taketomi H. Respective roles of short-and long-range interactions in protein folding. Proc Matl Acad Sci USA 1978;75:559-63.
- 4. Miyazawa S, Jernigan RL. Residue-residue potentials with a favorable contact pair term and an unfavorable high packing density term, for simulation and threading. J Mol Biol 1996:256:623-44.
- 5. Papandreou N, Kanehisa M, Chomilier J. Folding of the human protein FKBP. Lattice Monte-Carlo simulations. C R Acad Sci III 1998;321:835-43.
- 6. Skolnick J, Kolinski A. Dynamic Monte Carlo simulations of a new lattice model of globular protein folding, structure and dynamics. J Mol Biol 1991;221:499-531.
- 7. Skotnick J, Kolinski A, Ortiz AR. Reduced protein models and their application to the protein folding problem. J Biomol Struct Dyn 1998;16:381-96.
- 8. Kolinski A, Rotkiewicz P, Skolnick J. Application of a high coordination lattice model in protein structure prediction. In: Proceedings of the Workshop on Monte Carlo approach to biopolymers and protein folding, Singapore: World Scientific, 1998:377-88.
- 9. Chomilier J, Lamarine M, Mornon JP, Torres JH, Etiopoulos, E, Papandreou N. Analysis of fragments induced by simulated lattice protein folding. C R Biol 2004;327:431-43.
- 10.Prudhomme N, Chomilier J. Prediction of the protein folding core: application to the immunoglobulin fold. Biochimie 2009:91:1465-74.
- 11. Lonquety M, Lacroix Z, Chomilier J. Evaluation of the stability of folding nucleus upon mutation. Pattern Recogn Bioinform LNCS 2008:5265:54-65.
- 12. Lonquety M, Lacroix Z, Papandreou N, Chomilier J. SPROUTS: a database for the evaluation of protein stability upon point mutation. Nucleic Acids Res 2009;37:D374-9.
- 13. Alland C, Moreews F, Boens D, Carpentier M, Chiusa S, Lonquety M, et at. RPBS: a web resource for structural bioinformatics. Nucleic Acids Res 2005;33:W44-9.
- 14. Callebaut I, Labesse G, Durand P, Poupon A, Canard L, Chomilier J, et al. Deciphering protein sequence information through hydrophobic cluster analysis (HCA): current status and perspectives. Cell Mol Life Sci 1997:53:621-45.
- 15. Acuña R, Lacroix Z, Chomilier J, Papandreou N. SMIR: a method to predict the residues involved in the core of a protein. In: Proceedings of the International Conference on Bioinformatics & Computational Biology (BIOCOMP). Las Vegas, USA, 2014.
- 16. Lipman DJ, Pearson WR. Rapid and sensitive protein similarity searches. Science 1985:227:1435-41. doi:10.1126/science.2983426. PMID 2983426
- 17. Bostock M, Ogievetsky V, Heer J. D3: data-driven documents. IEEE Trans Vis Comput Graph 2011;17:2301-9.
- 18. Murzin AG, Brenner SE, Hubbard T, Chothia C. SCOP: a structural classification of proteins database for the investigation of sequences and structures. J Mol Biol 1995:247:536-40.
- 19. Hamill S, Steward A, Clarke J. The folding of an immunoglobulin like Greek key protein is defined by a common core nucleus and regions constrained by topology. J Mol Biol 2000:297: 165-78.
- 20. Berezovsky IN, Grosberg AY, Trifonov EN. Closed loops of nearly standard size: common basic element of protein structure. FEBS Lett 2000:466:283-6.
- 21. Lamarine M, Mornon JP, Berezovsky IN, Chomilier J. Distribution of tightened end fragments of globular proteins statistically match that of topohydrophobic positions: towards an efficient punctuation of protein folding? Cell Mol Life Sci 2001:58:492-8.
- 22. Chintapalli SV, Illingworth CJ, Upton GJ, Sacquin-Mora S, Reeves PJ, Mohammedali HS, et al. Assessing the effect of dynamics on the closed-loop protein-folding hypothesis. J R Soc Interface 2013:11:20130935.
- 23. Potapov V, Cohen M, Schreiber G. Assessing computational methods for predicting protein stability upon mutation: good on average but not in the details. Protein Eng Des Sel 2009:22:553-60.
- 24. Schymkowitz J, Borg J, Stricher F, Nys R, Rousseau F, Serrano L. The FoldX web server: an online force field. Nucleic Acids Res 2005;33:W382-8.
- 25. Cheng J, Randall A, Baldi P. Prediction of protein stability changes for single-site mutations using support vector machines. Proteins 2006;62:1125-32.
- 26. Zhou H, Zhou Y. Distance-scaled, finite ideal-gas reference state improves structure-derived potentials of mean force for structure selection and stability prediction. Protein Sci 2002:11:2714-26.
- 27. Capriotti E, Fariselli P, Casadio R. l-Mutant2.0: predicting stability changes upon mutation from the protein sequence or structure. Nucleic Acids Res 2005;33:W306-10.
- 28. He Y, Yeh DC, Alexander P, Bryan PN, Orban J. Solution NMR structures of IgG binding domains with artificially evolved high levels of sequence identity but different folds. Biochemistry 2005:44:14055-61.
- 29. Alexander PA, He Y, Chen Y, Orban J, Bryan PN. The design and characterization of two proteins with 88% sequence identity but different structure and function. Proc Natl Acad Sci USA 2007:104:11963-8.
- 30. Alexander PA, He Y, Chen Y, Orban J, Bryan PN. A minimal sequence code for switching protein structure and function. Proc Natl Acad Sci USA 2009;106:21149-54.
- 31. Kabsch W, Sander C. Dictionary of protein secondary structure: pattern recognition of hydrogen-bonded and geometrical features. Biopolymers 1983:22:2577-637.
- 32. Tsodikov OV, Record, MT, Sergeev YV. A novel computer program for fast exact calculation of accessible and molecular surface areas and average surface curvature. J Comput Chem 2002:23:600-9.
- 33. Fafsca PF, Travasso RD, Parisi A, Rey A. Why do protein folding rates correlate with metrics of native topology? PLoS ONE 2012;7:e35599.
- 34. Travasso RD, Faísca PF, Rey A. The protein folding transition state: insight from kinetics and thermodynamics. J Chem Phys 2010:133:125102.
- 35. Lappalainen I, Hurley M, Clarke J. Plasticity within the obligatory folding nucleus of an immunoglobulin like domain. J Mol Biol 2008;375:547-59.
- 36. Galzitskaya OV, Ivankov DN, Finkelstein AV. Folding nuclei in proteins. FEBS Lett 2001;489:113-8.
- 37. Galzitskaya OV, Skoogarev AV, Ivankov DN, Finkelstein AV. Folding nuclei in 3D protein structures. Pac Symp Biocomput 2000:5:131-42.
- 38. Lonquety M, Chomilier J, Papandreou N, Lacroix Z. Prediction of stability upon mutation in the context of the folding nucleus. OMICS 2010:14:151-6.
- 39. Acuña R, Lacroix Z, Chomilier J. A workflow for the prediction of the effects of residue substitution on protein stability. Pattern Recogn Bioinform LNCS 2013; 7986:253-64.
- 40. Néron B, Ménager H, Maufrais C, Joly N, Maupetit J, Letart S, et al. Mobyle: a new full web bioinformatics framework. Bioinformatics 2009:25:3005-11.
- 41. Strauser E, Naveau M, Menager H, Maupetit J, Lacroix Z, Tufféry P. Semantic map for structural bioinformatics: enhanced service discovery based on high level concept ontology. Resour Discov LNCS 2010:6799:57-70.
- 42. Lacroix Z, Critchlow T, editors. Bioinformatics: managing scientific data. San Francisco: Morgan Kaufmann, 2003.
- 43. Kolinski A, Rotkiewicz P, Ilkowski B, Skolnick J. Protein folding: flexible lattice models. Prog Theor Phys Suppl 2000;138:292-300.
- 44. Ravichandran L, Papandreou-Suppappola A, Spanias A, Lacroix Z, Legendre C. Waveform mapping and time-frequency processing of DNA and protein sequences. IEEE Trans Signal Process 2011:59:4210-24.
- 45. Papandreou N, Berezovsky IN, Lopes A, Eliopoulos E, Chomilier J. Universal positions in globular proteins - from observation to simulation. Eur J Biochem 2004:271:4762-8.
- 46. Miyazawa S, Jernigan RL. Estimation of effective interresidue contact energies from protein crystal structures: quasi-chemical approximation. Macromolecules 1985;18:534-52.
- 47. Marsaglia G, Zaman A. The KISS generator. Technical report, Department of Statistics, Florida State University, 1993.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-c1471f9d-e16a-499c-ad22-2a3ced7336e8