1932

Abstract

Nonnative protein aggregation is the process by which otherwise folded, monomeric proteins are converted to stable aggregates composed of protein chains that have undergone some degree of unfolding. Often, a conformational change is needed to allow certain sequences of amino acids—so-called aggregation-prone regions (APRs)—to form stable interprotein contacts such as β-sheet structures. In addition to APRs that are needed to stabilize aggregates, other factors or driving forces are also important in inducing aggregation in practice. This review focuses first on the overall process and mechanistic drivers for nonnative aggregation, followed by a more detailed summary of the factors currently thought to be important for determining which amino acid sequences most greatly stabilize nonnative protein aggregates, as well as a survey of many of the existing algorithms that are publicly available to attempt to predict APRs. Challenges with experimental validation of predicted APRs for proteins are briefly discussed.

Loading

Article metrics loading...

/content/journals/10.1146/annurev-chembioeng-060816-101404
2017-06-07
2024-12-05
Loading full text...

Full text loading...

/deliver/fulltext/chembioeng/8/1/annurev-chembioeng-060816-101404.html?itemId=/content/journals/10.1146/annurev-chembioeng-060816-101404&mimeType=html&fmt=ahah

Literature Cited

  1. Andrews JM, Roberts CJ. 1.  2007. A Lumry-Eyring nucleated polymerization model of protein aggregation kinetics: 1. Aggregation with pre-equilibrated unfolding. J. Phys. Chem. B 111:7897–913 [Google Scholar]
  2. Weiss WF IV, Young TM, Roberts CJ. 2.  2009. Principles, approaches, and challenges for predicting protein aggregation rates and shelf life. J. Pharm. Sci. 98:1246–77 [Google Scholar]
  3. Buck PM, Kumar S, Wang X, Agrawal NJ, Trout BL, Singh SK. 3.  2012. Computational methods to predict therapeutic protein aggregation. Methods Mol. Biol. 899:425–51 [Google Scholar]
  4. Roberts CJ. 4.  2014. Therapeutic protein aggregation: mechanisms, design, and control. Trends Biotechnol 32:372–80 [Google Scholar]
  5. Sahin E, Jordan JL, Spatara ML, Naranjo A, Costanzo JA. 5.  et al. 2011. Computational design and biophysical characterization of aggregation-resistant point mutations for γD crystallin illustrate a balance of conformational stability and intrinsic aggregation propensity. Biochemistry 50:628–39 [Google Scholar]
  6. O'Brien CJ, Blanco MA, Costanzo JA, Enterline M, Fernandez EJ. 6.  et al. 2016. Modulating non-native aggregation and electrostatic protein–protein interactions with computationally designed single-point mutations. Protein Eng. Design Sel. 29:231–43 [Google Scholar]
  7. Kumar S, Wang X, Singh SK. 7.  2010. Identification and impact of aggregation-prone regions in proteins and therapeutic monoclonal antibodies. Aggregation of Therapeutic Proteins W Wang, CJ Roberts 103–18 Hoboken, NJ: Wiley [Google Scholar]
  8. Chennamsetty N, Voynov V, Kayser V, Helk B, Trout BL. 8.  2010. Prediction of aggregation prone regions of therapeutic proteins. J. Phys. Chem. B 114:6614–24 [Google Scholar]
  9. Shire SJ, Shahrokh Z, Liu JUN. 9.  2004. Challenges in the development of high protein concentration formulations. J. Pharm. Sci. 93:1390–402 [Google Scholar]
  10. Rosenberg AS. 10.  2006. Effects of protein aggregates: an immunologic perspective. AAPS J 8:E501–7 [Google Scholar]
  11. Ratanji KD, Derrick JP, Dearman RJ, Kimber I. 11.  2013. Immunogenicity of therapeutic proteins: influence of aggregation. J. Immunotoxicol. 11:99–109 [Google Scholar]
  12. Chiti F, Dobson CM. 12.  2006. Protein misfolding, functional amyloid, and human disease. Annu. Rev. Biochem. 75:333–66 [Google Scholar]
  13. Berryman JT, Radford SE, Harris SA. 13.  2011. Systematic examination of polymorphism in amyloid fibrils by molecular-dynamics simulation. Biophys. J. 100:2234–42 [Google Scholar]
  14. Roberts CJ, Das TK, Sahin E. 14.  2011. Predicting solution aggregation rates for therapeutic proteins: approaches and challenges. Int. J. Pharm. 418:318–33 [Google Scholar]
  15. Roberts CJ. 15.  2003. Kinetics of irreversible protein aggregation: analysis of extended Lumry-Eyring models and implications for predicting protein shelf life. J. Phys. Chem. B 107:1194–207 [Google Scholar]
  16. Roberts CJ. 16.  2007. Non-native protein aggregation kinetics. Biotechnol. Bioeng. 98:927–38 [Google Scholar]
  17. Wang W, Roberts CJ. 17.  2013. Non-Arrhenius protein aggregation. AAPS J 15:840–51 [Google Scholar]
  18. Wu H, Kroe-Barrett R, Singh S, Robinson AS, Roberts CJ. 18.  2014. Competing aggregation pathways for monoclonal antibodies. FEBS Lett 588:936–41 [Google Scholar]
  19. Banks DD, Latypov RF, Ketchem RR, Woodard JON, Scavezze JL. 19.  et al. 2012. Native-state solubility and transfer free energy as predictive tools for selecting excipients to include in protein formulation development studies. J. Pharm. Sci. 101:2720–32 [Google Scholar]
  20. Wang W. 20.  2005. Protein aggregation and its inhibition in biopharmaceutics. Int. J. Pharm. 289:1–30 [Google Scholar]
  21. Chi EY, Krishnan S, Randolph TW, Carpenter JF. 21.  2003. Physical stability of proteins in aqueous solution: mechanism and driving forces in nonnative protein aggregation. Pharm. Res. 20:1325–36 [Google Scholar]
  22. Fink AL. 22.  1998. Protein aggregation: folding aggregates, inclusion bodies and amyloid. Folding Design 3:9–23 [Google Scholar]
  23. Dong A, Prestrelski J, Allison D, Carpenter F. 23.  1995. Infrared spectroscopic studies of lyophilization- and temperature-induced protein aggregation. J. Pharm. Sci. 84:415–24 [Google Scholar]
  24. Uversky VN, Fink AL. 24.  2004. Conformational constraints for amyloid fibrillation: the importance of being unfolded. Biochim. Biophys. Acta 1698:131–53 [Google Scholar]
  25. Wörn A, Plückthun A. 25.  2001. Stability engineering of antibody single-chain Fv fragments. J. Mol. Biol. 305:989–1010 [Google Scholar]
  26. Miller BR, Demarest SJ, Lugovskoy A, Huang F, Wu X. 26.  et al. 2010. Stability engineering of scFvs for the development of bispecific and multivalent antibodies. Protein Eng. Design Sel. 23:549–57 [Google Scholar]
  27. Perchiacca JM, Bhattacharya M, Tessier PM. 27.  2011. Mutational analysis of domain antibodies reveals aggregation hotspots within and near the complementarity determining regions. Proteins 79:2637–47 [Google Scholar]
  28. Wang N, Smith WF, Miller BR, Aivazian D, Lugovskoy AA. 28.  et al. 2009. Conserved amino acid networks involved in antibody variable domain interactions. Proteins 76:99–114 [Google Scholar]
  29. Davidson AR. 29.  2006. Multiple sequence alignment as a guideline for protein engineering strategies. Methods Mol. Biol. 340:171–81 [Google Scholar]
  30. Costanzo JA, O'Brien CJ, Tiller K, Tamargo E, Robinson AS. 30.  et al. 2014. Conformational stability as a design target to control protein aggregation. Protein Eng. Design Sel. 27:157–67 [Google Scholar]
  31. Miklos AE, Kluwe C, Der BS, Pai S, Sircar A. 31.  et al. 2012. Structure-based design of supercharged, highly thermoresistant antibodies. Chem. Biol. 19:449–55 [Google Scholar]
  32. Chennamsetty N, Voynov V, Kayser V, Helk B, Trout BL. 32.  2009. Design of therapeutic proteins with enhanced stability. PNAS 106:11937–42 [Google Scholar]
  33. Ray SS, Nowak RJ, Strokovich K, Brown RH, Walz T, Lansbury PT. 33.  2004. An intersubunit disulfide bond prevents in vitro aggregation of a superoxide dismutase-1 mutant linked to familial amytrophic lateral sclerosis. Biochemistry 43:4899–905 [Google Scholar]
  34. Melnik BS, Povarnitsyna TV, Glukhov AS, Melnik TN, Uversky VN, Sarma RH. 34.  2012. SS-stabilizing proteins rationally: intrinsic disorder-based design of stabilizing disulphide bridges in GFP. J. Biomol. Struct. Dyn. 29:815–24 [Google Scholar]
  35. Schymkowitz J, Borg J, Stricher F, Nys R, Rousseau F, Serrano L. 35.  2005. The FoldX web server: an online force field. Nucleic Acids Res 33:W382–88 [Google Scholar]
  36. Liu Y, Kuhlman B. 36.  2006. RosettaDesign server for protein design. Nucleic Acids Res 34:W235–38 [Google Scholar]
  37. Kaufmann KW, Lemmon GH, Deluca SL, Sheehan JH, Meiler J. 37.  2010. Practically useful: what the Rosetta protein modeling suite can do for you. Biochemistry 49:2987–98 [Google Scholar]
  38. Chaudhury S, Lyskov S, Gray JJ. 38.  2010. PyRosetta: a script-based interface for implementing molecular modeling algorithms using Rosetta. Bioinformatics 26:689–91 [Google Scholar]
  39. Yin S, Ding F, Dokholyan NV. 39.  2007. Eris: an automated estimator of protein stability. Nat. Methods 4:466–67 [Google Scholar]
  40. Fernandez-Escamilla A-M, Rousseau F, Schymkowitz J, Serrano L. 40.  2004. Prediction of sequence-dependent and mutational effects on the aggregation of peptides and proteins. Nat. Biotechnol. 22:1302–6 [Google Scholar]
  41. De Baets G, Van Durme J, Van Der Kant R, Schymkowitz J, Rousseau F. 41.  2015. Solubis: Optimize your protein. Bioinformatics 31:2580–82 [Google Scholar]
  42. Lawrence MS, Phillips KJ, Liu DR. 42.  2007. Supercharging proteins can impart unusual resilience. J. Am. Chem. Soc. 129:10110–12 [Google Scholar]
  43. Long WF, Labute P. 43.  2010. Calibrative approaches to protein solubility modeling of a mutant series using physicochemical descriptors. J. Comput. Aided Mol. Design 24:907–16 [Google Scholar]
  44. Blanco MA, Sahin E, Robinson AS, Roberts CJ. 44.  2013. Coarse-grained model for colloidal protein interactions, B 22, and protein cluster formation. J. Phys. Chem. B 117:16013–28 [Google Scholar]
  45. Ventura S, Zurdo J, Narayanan S, Parreño M, Mangues R. 45.  et al. 2004. Short amino acid stretches can mediate amyloid formation in globular proteins: the Src homology 3 (SH3) case. PNAS 101:7258–63 [Google Scholar]
  46. Chiti F, Taddei N, Baroni F, Capanni C, Stefani M. 46.  et al. 2002. Kinetic partitioning of protein folding and aggregation. Nat. Struct. Biol. 9:137–43 [Google Scholar]
  47. Ivanova MI, Sawaya MR, Gingery M, Attinger A, Eisenberg D. 47.  2004. An amyloid-forming segment of β2-microglobulin suggests a molecular model for the fibril. PNAS 101:10584–89 [Google Scholar]
  48. Castillo V, Graña-Montes R, Sabate R, Ventura S. 48.  2011. Prediction of the aggregation propensity of proteins from the primary sequence: aggregation properties of proteomes. Biotechnol. J. 6:674–85 [Google Scholar]
  49. Pawar AP, Dubay KF, Zurdo J, Chiti F, Vendruscolo M, Dobson CM. 49.  2005. Prediction of “aggregation-prone” and “aggregation-susceptible” regions in proteins associated with neurodegenerative diseases. J. Mol. Biol. 350:379–92 [Google Scholar]
  50. Belli M, Ramazzotti M, Chiti F. 50.  2011. Prediction of amyloid aggregation in vivo. EMBO Rep 12:657–63 [Google Scholar]
  51. Agrawal NJ, Kumar S, Wang X, Helk B, Singh SK, Trout BL. 51.  2011. Aggregation in protein-based biotherapeutics: computational studies and tools to identify aggregation-prone regions. J. Pharm. Sci. 100:5081–95 [Google Scholar]
  52. Tartaglia GG, Vendruscolo M. 52.  2008. The Zyggregator method for predicting protein aggregation propensities. Chem. Soc. Rev. 37:1395–401 [Google Scholar]
  53. Chiti F, Stefani M, Taddei N, Ramponi G, Dobson CM. 53.  2003. Rationalization of the effects of mutations on peptide and protein aggregation rates. Nature 424:805–8 [Google Scholar]
  54. DuBay KF, Pawar AP, Chiti F, Zurdo J, Dobson CM, Vendruscolo M. 54.  2004. Prediction of the absolute aggregation rates of amyloidogenic polypeptide chains. J. Mol. Biol. 341:1317–26 [Google Scholar]
  55. Sormanni P, Aprile FA, Vendruscolo M. 55.  2015. The CamSol method of rational design of protein mutants with enhanced solubility. J. Mol. Biol. 427:478–90 [Google Scholar]
  56. 56. Vendruscolo Lab. 2016. Vendruscolo Lab - Software Dep. Chem., Univ Cambridge: http://www-mvsoftware.ch.cam.ac.uk/ [Google Scholar]
  57. Tartaglia GG, Cavalli A, Pellarin R, Caflisch A. 57.  2004. The role of aromaticity, exposed surface, and dipole moment in determining protein aggregation rates. Protein Sci 13:1939–41 [Google Scholar]
  58. Tartaglia GG, Cavalli A, Pellarin R, Caflisch A. 58.  2005. Prediction of aggregation rate and aggregation-prone segments in polypeptide sequences. Protein Sci 14:2723–34 [Google Scholar]
  59. López de la Paz M, Serrano L. 59.  2004. Sequence determinants of amyloid fibril formation. PNAS 101:87–92 [Google Scholar]
  60. Rousseau F, Schymkowitz J, Serrano L. 60.  2006. Protein aggregation and amyloidosis: confusion of the kinds?. Curr. Opin. Struct. Biol. 16:1–9 [Google Scholar]
  61. Linding R, Schymkowitz J, Rousseau F, Diella F, Serrano L. 61.  2004. A comparative study of the relationship between protein structure and beta-aggregation in globular and intrinsically disordered proteins. J. Mol. Biol. 342:345–53 [Google Scholar]
  62. Thompson MJ, Sievers SA, Karanicolas J, Ivanova MI, Baker D, Eisenberg D. 62.  2006. The 3D profile method for identifying fibril-forming segments of proteins. PNAS 103:4074–78 [Google Scholar]
  63. Zhang Z, Chen H, Lai L. 63.  2007. Identification of amyloid fibril-forming segments based on structure and residue-based statistical potential. Bioinformatics 23:2218–25 [Google Scholar]
  64. Lu H, Skolnick J. 64.  2001. A distance-dependent atomic knowledge-based potential for improved protein structure selection. Proteins 44:223–32 [Google Scholar]
  65. Conchillo-Solé O, de Groot NS, Avilés FX, Vendrell J, Daura X, Ventura S. 65.  2007. AGGRESCAN: a server for the prediction and evaluation of “hot spots” of aggregation in polypeptides. BMC Bioinform 8:65 [Google Scholar]
  66. De Groot NS, Aviles FX, Vendrell J, Ventura S. 66.  2006. Mutagenesis of the central hydrophobic cluster in Aβ42 Alzheimer's peptide: Side-chain properties correlate with aggregation propensities. FEBS J 273:658–68 [Google Scholar]
  67. Sánchez de Groot N, Pallarés I, Avilés FX, Vendrell J, Ventura S. 67.  2005. Prediction of “hot spots” of aggregation in disease-linked polypeptides. BMC Struct. Biol. 5:18 [Google Scholar]
  68. Zambrano R, Jamroz M, Szczasiuk A, Pujols J, Kmiecik S, Ventura S. 68.  2015. AGGRESCAN3D (A3D): server for prediction of aggregation properties of protein structures. Nucleic Acids Res 43:W306–13 [Google Scholar]
  69. Jamroz M, Kolinski A, Kmiecik S. 69.  2013. CABS-flex: server for fast simulation of protein structure fluctuations. Nucleic Acids Res 41:W427–31 [Google Scholar]
  70. Trovato A, Chiti F, Maritan A, Seno F. 70.  2006. Insight into the structure of amyloid fibrils from the analysis of globular proteins. PLOS Comput. Biol. 2:1608–18 [Google Scholar]
  71. Trovato A, Seno F, Tosatto SCE. 71.  2007. The PASTA server for protein aggregation prediction. Protein Eng. Design Sel. 20:521–23 [Google Scholar]
  72. Walsh I, Seno F, Tosatto SCE, Trovato A. 72.  2014. PASTA 2.0: an improved server for protein aggregation prediction. Nucleic Acids Res 42:W301–7 [Google Scholar]
  73. Zibaee S, Makin OS, Goedert M, Serpell LC. 73.  2007. A simple algorithm locates β-strands in the amyloid fibril core of α-synuclein, Aβ, and tau using the amino acid sequence alone. Protein Sci 16:906–18 [Google Scholar]
  74. Chou PY, Fasman GD. 74.  1974. Prediction of protein conformation. Biochemistry 13:222–45 [Google Scholar]
  75. Tsolis AC, Papandreou NC, Iconomidou VA, Hamodrakas SJ. 75.  2013. A consensus method for the prediction of ‘aggregation-prone’ peptides in globular proteins. PLOS ONE 8:e54175 [Google Scholar]
  76. Hamodrakas SJ, Liappa C, Iconomidou VA. 76.  2007. Consensus prediction of amyloidogenic determinants in amyloid fibril-forming proteins. Int. J. Biol. Macromol. 41:295–300 [Google Scholar]
  77. Kim C, Choi J, Lee SJ, Welsh WJ, Yoon S. 77.  2009. NetCSSP: web application for predicting chameleon sequences and amyloid fibril formation. Nucleic Acids Res 37:469–73 [Google Scholar]
  78. Bryan AW Jr., Menke M, Cowen LJ, Lindquist SL, Berger B. 78.  2009. BETASCAN: probable β-amyloids identified by pairwise probabilistic analysis. PLOS Comput. Biol. 5:e1000333 [Google Scholar]
  79. Bryan AW, O'Donnell CW, Menke M, Cowen LJ, Lindquist S, Berger B. 79.  2012. STITCHER: dynamic assembly of likely amyloid and prion β-structures from secondary structure predictions. Proteins 80:410–20 [Google Scholar]
  80. Garbuzynskiy SO, Lobanov MY, Galzitskaya OV. 80.  2009. FoldAmyloid: a method of prediction of amyloidogenic regions from protein sequence. Bioinformatics 26:326–32 [Google Scholar]
  81. Yoon S, Jung H. 81.  2006. Analysis of chameleon sequences by energy decomposition on a pairwise per-residue basis. Protein J 25:361–68 [Google Scholar]
  82. Yoon S, Welsh WJ. 82.  2004. Detecting hidden sequence propensity for amyloid fibril formation. Protein Sci 13:2149–60 [Google Scholar]
  83. Yoon S, Welsh WJ. 83.  2005. Rapid assessment of contact-dependent secondary structure propensity: relevance to amyloidogenic sequences. Proteins 60:110–17 [Google Scholar]
  84. Yoon S, Welsh WJ, Jung H, Do Y. 84.  2007. CSSP2: an improved method for predicting contact-dependent secondary structure propensity. Comput. Biol. Chem. 31:373–77 [Google Scholar]
  85. Zhao C, Zhang H, Luan F, Zhang R, Liu M. 85.  et al. 2007. QSAR method for prediction of protein-peptide binding affinity: application to MHC class I molecule HLA-A*0201. J. Mol. Graph. Model. 26:246–54 [Google Scholar]
  86. Nantasenamat C, Isarankura-Na-Ayudhya C, Naenna T, Prachayasittikul V. 86.  2009. A practical overview of quantitative structure-activity relationship. EXCLI J 8:74–88 [Google Scholar]
  87. Votano JR, Parham M, Hall LM, Hall LH, Kier LB. 87.  et al. 2006. QSAR modeling of human serum protein binding with several modeling techniques utilizing structure-information representation. J. Med. Chem 49:7169–81 [Google Scholar]
  88. Tian J, Wu N, Guo J, Fan Y. 88.  2009. Prediction of amyloid fibril-forming segments based on a support vector machine. BMC Bioinform 10:Suppl. 1S45 [Google Scholar]
  89. Kawashima S, Kanehisa M. 89.  2000. AAindex: amino acid index database. Nucleic Acids Res 28:374 [Google Scholar]
  90. Kawashima S, Ogata H, Kanehisa M. 90.  1999. AAindex: amino acid index database. Nucleic Acids Res 27:368–69 [Google Scholar]
  91. Chang C-C, Lin C-J. 91.  2011. LIBSVM: a library for support vector machines. ACM Trans. Intell. Syst. Technol. 2:271–2727 http://www.csie.ntu.edu.tw/∼cjlin/libsvm [Google Scholar]
  92. Lauer TM, Agrawal NJ, Chennamsetty N, Egodage K, Helk B, Trout BL. 92.  2012. Developability Index: a rapid in silico tool for the screening of antibody aggregation propensity. J. Pharm. Sci. 101:102–15 [Google Scholar]
  93. Oliveberg M. 93.  2010. Waltz, an exciting new move in amyloid prediction. Nat. Methods 7:187–88 [Google Scholar]
  94. Maurer-Stroh S, Debulpaep M, Kuemmerer N, Lopez de la Paz M, Martins IC. 94.  et al. 2010. Exploring the sequence determinants of amyloid structure using position-specific scoring matrices. Nat. Methods 7:237–42 [Google Scholar]
  95. O'Donnell CW, Waldispühl J, Lis M, Halfmann R, Devadas S. 95.  et al. 2011. A method for probing the mutational landscape of amyloid structure. Bioinformatics 27:i34–42 [Google Scholar]
  96. Berman HM, Westbrook J, Feng Z, Gilliland G, Bhat TN. 96.  et al. 2000. The protein data bank. Nucleic Acids Res 28:235–42 [Google Scholar]
  97. Liaw C, Tung CW, Ho SY. 97.  2013. Prediction and analysis of antibody amyloidogenesis from sequences. PLOS ONE 8:e53235 [Google Scholar]
  98. Gasior P, Kotulska M. 98.  2014. FISH Amyloid—a new method for finding amyloidogenic segments in proteins based on site specific co-occurrence of amino acids. BMC Bioinform 15:54 [Google Scholar]
  99. Thangakani AM, Kumar S, Nagarajan R, Velmurugan D, Gromiha MM. 99.  2014. GAP: towards almost 100 percent prediction for β-strand-mediated aggregating peptides with distinct morphologies. Bioinformatics 30:1983–90 [Google Scholar]
  100. Thangakani AM, Kumar S, Velmurugan D, Gromiha MM. 100.  2013. Distinct position-specific sequence features of hexa-peptides that form amyloid-fibrils: application to discriminate between amyloid fibril and amorphous β-aggregate forming peptide sequences. BMC Bioinform 14:Suppl. 8S6 [Google Scholar]
  101. Família C, Dennison SR, Quintas A, Phoenix DA. 101.  2015. Prediction of peptide and protein propensity for amyloid formation. PLOS ONE 10:1–16 [Google Scholar]
  102. Ahmed AB, Znassi N, Château M-T, Kajava AV. 102.  2015. A structure-based approach to predict predisposition to amyloidosis. Alzheimer's Dement 11:681–90 [Google Scholar]
  103. Yon JM. 103.  2002. Protein folding in the post-genomic era. J. Cell. Mol. Med. 6:307–27 [Google Scholar]
  104. Gruebele M. 104.  2005. Downhill protein folding: Evolution meets physics. C. R. Biol. 328:701–12 [Google Scholar]
  105. Dill KA, Ozkan SB, Shell MS, Weikl TR. 105.  2008. The protein folding problem. Annu. Rev. Biophys. 37:289–316 [Google Scholar]
  106. Zhang Y. 106.  2008. Progress and challenges in protein structure prediction. Curr. Opin. Struct. Biol. 18:342–48 [Google Scholar]
  107. Vicatos S, Roca M, Warshel A. 107.  2009. Effective approach for calculations of absolute stability of proteins using focused dielectric constants. Proteins 77:670–84 [Google Scholar]
  108. Roca M, Messer B, Warshel A. 108.  2007. Electrostatic contributions to protein stability and folding energy. FEBS Lett 581:2065–71 [Google Scholar]
  109. Magliery TJ. 109.  2015. Protein stability: computation, sequence statistics, and new experimental methods. Curr. Opin. Struct. Biol. 33:161–68 [Google Scholar]
  110. Hammarström P, Carlsson U. 110.  2000. Is the unfolded state the Rosetta Stone of the protein folding problem?. Biochem. Biophys. Res. Commun. 276:393–98 [Google Scholar]
  111. Parthiban V, Gromiha MM, Schomburg D. 111.  2006. CUPSAT: prediction of protein stability upon point mutations. Nucleic Acids Res 34:W239–42 [Google Scholar]
  112. Zhou H, Zhou Y. 112.  2002. Distance-scaled, finite ideal-gas reference state improves structure-derived potentials of mean force for structure selection and stability prediction. Protein Sci 11:2714–26 [Google Scholar]
  113. Dehouck Y, Kwasigroch JM, Gilis D, Rooman M. 113.  2011. PoPMuSiC 2.1: a web server for the estimation of protein stability changes upon mutation and sequence optimality. BMC Bioinform 12:151 [Google Scholar]
  114. Capriotti E, Fariselli P, Casadio R. 114.  2005. I-Mutant2.0: predicting stability changes upon mutation from the protein sequence or structure. Nucleic Acids Res 33:W306–10 [Google Scholar]
  115. Ibarra-Molero B, Loladze VV, Makhatadze GI, Sanchez-Ruiz JM. 115.  1999. Thermal versus guanidine-induced unfolding of ubiquitin. An analysis in terms of the contributions from charge-charge interactions to protein stability. Biochemistry 38:8138–49 [Google Scholar]
  116. Khan S, Vihinen M. 116.  2010. Performance of protein stability predictors. Hum. Mutat. 31:675–84 [Google Scholar]
  117. Potapov V, Cohen M, Schreiber G. 117.  2009. Assessing computational methods for predicting protein stability upon mutation: good on average but not in the details. Protein Eng. Design Sel. 22:553–60 [Google Scholar]
  118. Asthagiri D, Paliwal A, Abras D, Lenhoff AM, Paulaitis ME. 118.  2005. A consistent experimental and modeling approach to light-scattering studies of protein-protein interactions in solution. Biophys. J. 88:3300–9 [Google Scholar]
  119. Paliwal A, Asthagiri D, Abras D, Lenhoff AM, Paulaitis ME. 119.  2005. Light-scattering studies of protein solutions: role of hydration in weak protein-protein interactions. Biophys. J. 89:1564–73 [Google Scholar]
  120. McGuffee SR, Elcock AH. 120.  2006. Atomically detailed simulations of concentrated protein solutions: the effects of salt, pH, point mutations, and protein concentration in simulations of 1000-molecule systems. J. Am. Chem. Soc. 128:12098–110 [Google Scholar]
  121. Valerio M, Colosimo A, Conti F, Giuliani A, Grottesi A. 121.  et al. 2005. Early events in protein aggregation: molecular flexibility and hydrophobicity/charge interaction in amyloid peptides as studied by molecular dynamics simulations. Proteins 58:110–18 [Google Scholar]
  122. Urbanc B, Cruz L, Ding F, Sammond D, Khare S. 122.  et al. 2004. Molecular dynamics simulation of amyloid beta dimer formation. Biophys. J. 87:2310–21 [Google Scholar]
  123. Redler RL, Shirvanyants D, Dagliyan O, Ding F, Kim DN. 123.  et al. 2014. Computational approaches to understanding protein aggregation in neurodegeneration. J. Mol. Cell Biol. 6:104–15 [Google Scholar]
  124. Dovidchenko NV, Galzitskaya OV. 124.  2015. Computational approaches to identification of aggregation sites and the mechanism of amyloid growth. Adv. Exp. Med. Biol 855:213–39 [Google Scholar]
  125. Emily M, Talvas A, Delamarche C. 125.  2013. MetAmyl: a METa-predictor for AMYLoid proteins. PLOS ONE 8:e79722 [Google Scholar]
  126. Das M, Gursky O. 126.  2015. Amyloid-forming properties of human apolipoproteins: sequence analyses and structural insights. Adv. Exp. Med. Biol 855:175–211 [Google Scholar]
  127. Frousios KK, Iconomidou VA, Karletidi C-M, Hamodrakas SJ. 127.  2009. Amyloidogenic determinants are usually not buried. BMC Struct. Biol. 9:44 [Google Scholar]
  128. Goldsbury C, Baxa U, Simon MN, Steven AC, Engel A. 128.  et al. 2011. Amyloid structure and assembly: insights from scanning transmission electron microscopy. J. Struct. Biol. 173:1–13 [Google Scholar]
  129. Bemporad F, Calloni G, Campioni S, Plakoutsi G, Taddei N, Chiti F. 129.  2006. Sequence and structural determinants of amyloid fibril formation. Acc. Chem. Res. 39:620–27 [Google Scholar]
  130. Tsiolaki PL, Louros NN, Hamodrakas SJ, Iconomidou VA. 130.  2015. Exploring the ‘aggregation-prone’ core of human Cystatin C: a structural study. J. Struct. Biol. 191:272–80 [Google Scholar]
  131. Li L, Von Bergen M, Mandelkow EM, Mandelkow E. 131.  2002. Structure, stability, and aggregation of paired helical filaments from tau protein and FTDP-17 mutants probed by tryptophan scanning mutagenesis. J. Biol. Chem. 277:41390–400 [Google Scholar]
  132. Zhang A, Jordan JL, Ivanova MI, Weiss WF, Roberts CJ, Fernandez EJ. 132.  2010. Molecular level insights into thermally induced α-chymotrypsinogen A amyloid aggregation mechanism and semiflexible protofibril morphology. Biochemistry 49:10553–64 [Google Scholar]
  133. Del Mar C, Greenbaum EA, Mayne L, Englander SW, Woods VL. 133.  2005. Structure and properties of α-synuclein and other amyloids determined at the amino acid level. PNAS 102:15477–82 [Google Scholar]
  134. Wu SJ, Luo J, O'Neil KT, Kang J, Lacy ER. 134.  et al. 2010. Structure-based engineering of a monoclonal antibody for improved solubility. Protein Eng. Design Sel. 23:643–51 [Google Scholar]
/content/journals/10.1146/annurev-chembioeng-060816-101404
Loading
/content/journals/10.1146/annurev-chembioeng-060816-101404
Loading

Data & Media loading...

Supplementary Data

  • Article Type: Review Article
This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error