Amyloidogenic determinants are usually not buried
Tóm tắt
Amyloidoses are a group of usually fatal diseases, probably caused by protein misfolding and subsequent aggregation into amyloid fibrillar deposits. The mechanisms involved in amyloid fibril formation are largely unknown and are the subject of current, intensive research. In an attempt to identify possible amyloidogenic regions in proteins for further experimental investigation, we have developed and present here a publicly available online tool that utilizes five different and independently published methods, to form a consensus prediction of amyloidogenic regions in proteins, using only protein primary structure data. It appears that the consensus prediction tool is slightly more objective than individual prediction methods alone and suggests several previously not identified amino acid stretches as potential amyloidogenic determinants, which (although several of them may be overpredictions) require further experimental studies. The tool is available at:
http://biophysics.biol.uoa.gr/AMYLPRED
. Utilizing molecular graphics programs, like O and PyMOL, as well as the algorithm DSSP, it was found that nearly all experimentally verified amyloidogenic determinants (short peptide stretches favouring aggregation and subsequent amyloid formation), and several predicted, with the aid of the tool AMYLPRED, but not experimentally verified amyloidogenic determinants, are located on the surface of the relevant amyloidogenic proteins. This finding may be important in efforts directed towards inhibiting amyloid fibril formation. The most significant result of this work is the observation that virtually all, to date, experimentally determined amyloidogenic determinants and the majority of predicted, but not yet experimentally verified short amyloidogenic stretches, lie 'exposed' on the surface of the relevant amyloidogenic proteins, and also several of them have the ability to act as conformational 'switches'. Experiments, focused on these fragments, should be performed to test this idea.
Tài liệu tham khảo
Chiti F, Dobson CM: Protein misfolding, functional amyloid, and human disease. Annu Rev Biochem 2006, 75: 333–366.
Uversky VN, Fink AL: Conformational constraints for amyloid fibrillation: the importance of being unfolded. Biochim Biophys Acta 2004, 1698: 131–153.
Dobson CM: Protein misfolding, evolution and disease. Trends in Biochem Sci 1999, 24: 329–332.
Harrison RS, Sharpe DC, Singh Y, Fairlie DP: Amyloid peptides and proteins in review. Rev Physiol Biochem Pharmacol 2007, 159: 1–77.
Fowler DM, Koulov AV, Balch WE, Kelly JW: Functional amyloid-from bacteria to humans. Trends Biochem Sci 2007, 32: 217–224.
Otzen D, Nielsen PH: We find them here, we find them there: Functional bacterial amyloid. Cell Mol Life Sci 2008, 65: 910–927.
Fändrich M: On the structural definitions of amyloid fibrils and other polypeptide aggregates Cell. Cell Mol Life Sci 2007, 64: 2066–2078.
Maji SK, Schubert D, Rivier C, Lee S, Rivier JE, Riek R: Amyloid as a depot for the formulation of long-acting drugs. PloS Biology 2008, 6: 240–252.
Iconomidou VA, Vriend G, Hamodrakas SJ: Amyloids protect the silkmoth oocyte and embryo. FEBS Letters 2000, 479: 141–145.
Iconomidou VA, Hamodrakas SJ: Natural protective amyloids. Curr Prot Pept Sci 2008, 9: 291–309.
López de la Paz M, Serrano L: Sequence determinants of amyloid fibril formation. Proc Natl Acad Sci 2004, 101: 87–92.
Esteras-Chopo A, Serrano L, López de la Paz M: The amyloid stretch hypothesis: recruiting proteins toward the dark side. Proc Natl Acad Sci 2005, 102: 1639–1648.
Fernandez-Escamilla AM, Rousseaux F, Schymkowitz J, Serrano L: Prediction of sequence-dependent and mutational effects on the aggregation of peptides and proteins. Nature Biotechnology 2004, 22: 1302–1306.
Yoon S, Welsh WJ: Detecting hidden sequence propensity for amyloid fibril formation. Protein Science 2004, 13: 2149–2160.
Tartaglia GG, Cavalli A, Pellarin A, Cafliesch A: Prediction of aggregation rate and aggregation-prone segments in polypeptide sequences. Protein Science 2005, 14: 2723–2734.
Pawar AP, DuBay KF, Zurdo J, Chiti F, Vendruscolo M, Dobson CM: Prediction of "aggregation-prone" and "aggregation-susceptible" regions in protein associated with neurodegenerative diseases. Mol Biol 2005, 350: 379–392.
Galzitskaya OV, Garbuzynskiy SG, Lobanov MV: Prediction of amyloidogenic and disordered regions in protein chains. PloS Comput Biol 2006, 2: 1639–1648.
Galzitskaya OV, Garbuzynskiy SO, Lobanov MY: A search for amyloidogenic regions in protein chains. Molecular Biology 2006, 40: 821–828.
Thompson MJ, Sievers SA, Karanicolas J, Ivanova MI, Baker D, Eisenberg D: The 3D profile method for identifying fibril-forming segments of proteins. Proc Natl Acad Sci 2006, 103: 4074–4078.
Trovato A, Chiti F, Maritan A, Seno F: Insight into the structure of amyloid fibrils from the analysis of globular proteins. PloS Comp Biol 2006, 2: 1608–1618.
Conchillo-Solé O, de Groot NS, Aviles FX, Vendrell J, Daura X, Ventura S: AGGRESCAN: a server for the prediction and evaluation of "hot spots" of aggregation in polypeptides. BMC Bioinformatics 2007, 8: 65–81.
Zhang Z, Chen H, Lai L: Identification of amyloid fibril-forming segments based on structure and residue-based statistical potential. Bioinformatics 2007, 23: 2218–2225.
Zibaee S, Makin OS, Goedert M, Serpell LC: A simple algorithm locates β-strands in the amyloid fibril core of α-synuclein, Áβ, and tau using the amino acide sequence alone. Protein Science 2007, 16: 906–918.
Tartaglia GG, Pawar AP, Campioni S, Dobson CM, Chiti F, Vendruscolo M: Prediction of aggregation-prone regions in structured proteins. J Mol Biol 2008, 380: 425–436.
Hamodrakas SJ, Liappa C, Iconomidou VA: Consensus prediction of amyloidogenic determinants in amyloid-forming proteins. Int J Biol Macromol 2007, 41: 295–300.
Hamodrakas SJ: A protein secondary structure prediction scheme for the IBM PC and compatibles. Comput Appl Biosci 1988, 4: 473–477.
Chou PY, Fasman GD: Conformational parameters for amino acids in α-helical, β-sheet, and random coil regions calculated from proteins. Biochemistry 1974, 13: 211–222.
Chou PY, Fasman GD: Prediction of protein conformation. Biochemistry 1974, 13: 222–245.
Nelson R, Sawaya MR, Balbirnie M, Madsen AØ, Riekel C, Grothe R, Eisenberg D: Structure of the cross-β spine of amyloid-like fibrils. Nature 2005, 435: 773–778.
Pawlicki S, Le Béchec A, Delamarche C: AMYPdb: A database dedicated to amyloid precursor proteins. BMC Bioinformatics 2008, 9: 273–284.
Sawaya MR, Sambashivan S, Nelson R, Ivanova MI, Sievers SA, Apostol MI, Thompson MJ, Balbirnie M, Wiltzius JJW, McFarlane HT, Madsen AØ, Riekel C, Eisenberg D: Atomic structures of amyloid cross-β spines reveal varied steric zippers. Nature 2007, 447: 453–457.
Baldi P, Brunak S, Chauvin Y, Andersen CAF, and Nielsen H: Assessing the accuracy of prediction algorithms for classification: an overview. Bioinformatics 2000, 16: 412–424.
Jones TA, Zou JY, Cowan SW, Kjeldgaard M: Improved methods for building protein models in electron density maps and the location of errors in these models. Acta Crystallogr 1991, A47: 110–119.
Delano WL: The PyMOL molecular graphics system. In DeLano Scientific LLC. 400, Oyster Point Blvd., Suite 213, South San Francisco, CA 94080–1918 USA; 2005.
Kabsch W, Sander C: Dictionary of protein secondary structure: Pattern recognition of hydrogen-bonded and geometrical features. Biopolymers 1983, 22: 2577–2637.
Guo JT, Jaromczyk JW, Xu Y: Analysis of chameleon sequences and their implications in biological processes. Proteins: Structure, Function and Bioinformatics 2007, 67: 548–558.
The UniProt Consortium: The Universal Protein Resource (UniProt). Nucleic Acids Res 2007, 35: D193-D197.
Berman HM, Westbrook J, Feng Z, Gilliland G, Bhat TN, Weissig H, Shindyalov IN, Bourne PE: The Protein Data Bank. Nucleic Acids Research 2000, 28: 235–242.