Aggregation in protein-based biotherapeutics: Computational studies and tools to identify aggregation-prone regions

Journal of Pharmaceutical Sciences

Volumn 100, Issue 12, 2011, Pages 5081-5095

  Agrawal, Neeraj J ^a   Kumar, Sandeep ^b   Wang, Xiaoling ^b   Helk, Bernhard ^c   Singh, Satish K ^b   Trout, Bernhardt L ^a  

^a Massachusetts Institute of Technology Cambridge   (United States)

^b PFIZER GLOBAL RESEARCH AND DEVELOPMENT   (United States)

^c NOVARTIS PHARMA AG   (Switzerland)

Author keywords

Biophysical models; Biotechnology; Proteins; Simulations; Stability

Indexed keywords

AMYLIN; AMYLIN[1-37]; AMYLOID BETA PROTEIN; AMYLOID BETA PROTEIN[1-42]; AMYLOID PROTEIN; HEXAPEPTIDE; INSULIN; MONOCLONAL ANTIBODY; POLYGLUTAMINE; POLYPEPTIDE; PRION PROTEIN; UNCLASSIFIED DRUG;

AGGRESCAN; ALGORITHM; AMINO ACID SEQUENCE; AMYLPRED; BIOINFORMATICS; COMPUTER PROGRAM; DRUG POTENCY; DRUG SOLUBILITY; DRUG STABILITY; MACHINE LEARNING; MATHEMATICAL MODEL; MOLECULAR DYNAMICS; MOLECULAR MODEL; MONTE CARLO METHOD; PACKING DENSITY BASED METHOD; PAFIG; POLYACRYLAMIDE GEL ELECTROPHORESIS; PREDICTION OF AMYLOID STRUCTURE AGGREGATION; PROFILE METHOD; PROTEIN AGGREGATION; PROTEIN STRUCTURE; REVIEW; SIMPLE ALGORITHM FOR SLIDING AVERAGE; SIMULATION; SPATIAL AGGREGATION PROPENSITY; TANGO; WALTZ; ZYGGREGATOR;

EID: 80054949314     PISSN: 00223549     EISSN: 15206017     Source Type: Journal    
DOI: 10.1002/jps.22705     Document Type: Article

References (90)

1. Aggarwal S. 2010. What's fueling the biotech engine-2009-2010. Nat Biotechnol 28(11):1165-1171.
2. Cromwell MEM, Hilario E, Jacobson F. 2006. Protein aggregation and bioprocessing. AAPS J 8(3):E572-E579.
3. Manning MC, Patel K, Borchardt RT. 1989. Stability of protein pharmaceuticals. Pharm Res 6(11):903-918.
4. Rosenberg AS. 2006. Effects of protein aggregates: An immunologic perspective. AAPS J 8(3):E501-E507.
5. Kumar S, Singh SK, Wang X, Rup B, Gill D. 2011. Coupling of aggregation and immunogenicity in biotherapeutics: T- and B-cell immune epitopes may contain aggregation prone regions. Pharm Res 28:949-961.
6. Rousseau F, Schymkowitz J, Serrano L. 2006. Protein aggregation and amyloidosis: Confusion of the kinds? Curr OpinStruct Biol 16(1):118-126.
7. Voynov V, Chennamsetty N, Kayser V, Helk B, Forrer K, Zhang H, Fritsch C, Heine H, Trout BL. 2009. Dynamic fluctuations of protein-carbohydrate interactions promote protein aggregation. Plos One 4(12): e8425.
8. Philo JS, Arakawa T. 2009. Mechanisms of protein aggregation. Curr Pharm Biotechnol 10(4):348-351.
9. Manning MC, Chou DK, Murphy BM, Payne RW, Katayama DS. 2010. Stability of protein pharmaceuticals: An update. Pharm Res 27(4):544-575.
10. Roberts CJ. 2003. Kinetics of irreversible protein aggregation: Analysis of extended Lumry-Eyring models and implications for predicting protein shelf life. J Phys Chem B 107(5):1194-1207.
11. Chennamsetty N, Voynov V, Kayser V, Helk B, Trout BL. 2009. Design of therapeutic proteins with enhanced stability. Proc Natl Acad Sci U S A 106(29):11937-11942.
12. Murphy RM. 2002. Peptide aggregation in neurodegerative disease. Annu Rev Biomed Eng 4(1):155-174.
13. Chiti F, Dobson CM. 2009. Amyloid formation by globular proteins under native conditions. Nat Chem Biol 5(1):15-22.
14. Cellmer T, Bratko D, Prausnitz JM, Blanch H. 2005. Protein-folding landscapes in multichain systems. Proc Natl Acad Sci U S A 102(33):11692-11697.
15. Cellmer T, Bratko D, Prausnitz JM, Blanch HW. 2007. Protein aggregation in silico. Trends Biotechnol 25(6):254-261.
16. Clarke OJ, Parker MJ. 2009. Identification of amyloidogenic peptide sequences using a coarse-grained physicochemical model. J Comput Chem 30(4):621-630.
17. Zhang L, Lu D, Liu Z. 2008. How native proteins aggregate in solution: A dynamic Monte Carlo simulation. Biophys Chem 133(1-3):71-80.
18. Ma B, Nussinov R. 2002. Molecular dynamics simulations of alanine rich beta-sheet oligomers: Insight into amyloid formation. Protein Sci 11(10):2335-2350.
19. 10-35): Sequence effects. Proc Natl Acad Sci U S A 99(22):14126-14131.
20. Chen H-F. 2009. Aggregation mechanism investigation of the GIFQINS cross-[beta] amyloid fibril. Comput Biol Chem 33(1):41-45.
21. Ma B, Nussinov R. 2006. Simulations as analytical tools to understand protein aggregation and predict amyloid conformation. Curr Opin Chem Biol 10(5):445-452.
22. Chennamsetty N, Voynov V, Kayser V, Helk B, Trout BL. 2010. Prediction of aggregation prone regions of therapeutic proteins. J Phys Chem B 114(19):6614-6624.
23. Dill KA, Chan HS. 1997. From Levinthal to pathways to funnels. Nat Struct Biol 4(1):10-19.
24. Kumar S, Ma B, Tsai C-J, Sinha N, Nussinov R. 2000. Folding and binding cascades: Dynamic landscapes and population shifts. Protein Sci 9(1):10-19.
25. Tsai C-J, Kumar S, Ma B, Nussinov R. 1999. Folding funnels, binding funnels, and protein function. Protein Sci 8(6):1181-1190.
26. Sinha N, Nussinov R. 2001. Point mutations and sequence variability in proteins: Redistributions of preexisting populations. Proc Natl Acad Sci U S A 98(6):3139-3144.
27. Schmit JD, Ghosh K, Dill K. 2011. What drives amyloid molecules to assemble into oligomers and fibrils? Biophys J 100(2):450-458.
28. Gupta P, Hall CK, Voegler AC. 1998. Effect of denaturant and protein concentrations upon protein refolding and aggregation: A simple lattice model. Protein Sci 7(12):2642-2652.
29. Nguyen HD, Hall CK. 2004. Molecular dynamics simulations of spontaneous fibril formation by random-coil peptides. Proc Natl Acad Sci U S A 101(46):16180-16185.
30. Cellmer T, Bratko D, Prausnitz JM, Blanch H. 2005. The competition between protein folding and aggregation: Off-lattice minimalist model studies. Biotechnol Bioeng 89(1):78-87.
31. Bratko D, Blanch HW. 2001. Competition between protein folding and aggregation: A three-dimensional lattice-model simulation. J Chem Phys 114(1):561-569.
32. Bratko D, Cellmer T, Prausnitz JM, Blanch HW. 2006. Effect of single-point sequence alterations on the aggregation propensity of a model protein. J Am Chem Soc 128(5):1683-1691.
33. Tsai HH, Reches M, Tsai CJ, Gunasekaran K, Gazit E, Nussinov R. 2005. Energy landscape of amyloidogenic peptide oligomerization by parallel-tempering molecular dynamics simulation: Significant role of Asn ladder. Proc Natl Acad Sci U S A 102(23):8174-8179.
34. Cecchini M, Curcio R, Pappalardo M, Melki R, Caflisch A. 2006. A molecular dynamics approach to the structural characterization of amyloid aggregation. J Mol Biol 357(4):1306-1321.
35. Chennamsetty N, Helk B, Voynov V, Kayser V, Trout BL. 2009. Aggregation-prone motifs in human immunoglobulin G. J Mol Biol 391(2):404-413.
36. Balbach JJ, Ishii Y, Antzutkin ON, Leapman RD, Rizzo NW, Dyda F, Reed J, Tycko R. 2000. Amyloid fibril formation by Aβ16-22, a seven-residue fragment of the Alzheimer's β-amyloid peptide, and structural characterization by solid state NMR. Biochemistry 39(45):13748-13759.
37. Lührs T, Ritter C, Adrian M, Riek-Loher D, Bohrmann B, Döbeli H, Schubert D, Riek R. 2005. 3D structure of Alzheimer's amyloid-β(1-42) fibrils. Proc Natl Acad Sci U S A 102(48):17342-17347.
38. Valerio M, Colosimo A, Conti F, Giuliani A, Grottesi A, Manetti C, Zbilut JP. 2005. Early events in protein aggregation: Molecular flexibility and hydrophobicity/charge interaction in amyloid peptides as studied by molecular dynamics simulations. Proteins 58(1):110-118.
39. De Simone A, Esposito L, Pedone C, Vitagliano L. 2008. Insights into stability and toxicity of amyloid-like oligomers by replica exchange molecular dynamics analyses. Biophys J 95(4):1965-1973.
40. Zanuy D, Gunasekaran K, Lesk AM, Nussinov R. 2006. Computational study of the fibril organization of polyglutamine repeats reveals a common motif identified in β-helices. J Mol Biol 358(1):330-345.
41. Vitalis A, Wang X, Pappu RV. 2008. Atomistic simulations of the effects of polyglutamine chain length and solvent quality on conformational equilibria and spontaneous homodimerization. J Mol Biol 384(1):279-297.
42. Nelson R, Sawaya MR, Balbirnie M, Madsen AO, Riekel C, Grothe R, Eisenberg D. 2005. Structure of the cross-β spine of amyloid-like fibrils. Nature 435(7043):773-778.
43. Eakin CM, Berman AJ, Miranker AD. 2006. A native to amyloidogenic transition regulated by a backbone trigger. Nat Struct Mol Biol 13(3):202-208.
44. Wright CF, Teichmann SA, Clarke J, Dobson CM. 2005. The importance of sequence diversity in the aggregation and evolution of proteins. Nature 438(7069):878-881.
45. Baumketner A, Shea JE. 2005. Free energy landscapes for amyloidogenic tetrapeptides dimerization. Biophys J 89(3):1493-1503.
46. Tjernberg L, Hosia W, Bark N, Thyberg J, Johansson J. 2002. Charge attraction and beta propensity are necessary for amyloid fibril formation from tetrapeptides. J Biol Chem 277(45):43243-43246.
47. Wolf MG, Jongejan JA, Laman JD, de Leeuw SW. 2008. Quantitative prediction of amyloid fibril growth of short peptides from simulations: Calculating association constants to dissect side chain importance. J Am Chem Soc 130(47):15772-15773.
48. Voynov V, Chennamsetty N, Kayser V, Helk B, Trout BL. 2009. Predictive tools for stabilization of therapeutic proteins. MAbs 1(6):580-582.
49. de Groot N, Pallares I, Aviles F, Vendrell J, Ventura S. 2005. Prediction of "hot spots" of aggregation in disease-linked polypeptides. BMC Struct Biol 5(1):18.
50. Chiti F, Taddei N, Baroni F, Capanni C, Stefani M, Ramponi G, Dobson CM. 2002. Kinetic partitioning of protein folding and aggregation. Nat Struct Mol Biol 9(2):137-143.
51. Ventura S, Zurdo J, Narayanan S, Parreño M, Mangues R, Reif B, Chiti F, Giannoni E, Dobson CM, Aviles FX, Serrano L. 2004. Short amino acid stretches can mediate amyloid formation in globular proteins: The Src homology 3 (SH3) case. Proc Natl Acad Sci U S A 101(19):7258-7263.
52. Ivanova MI, Sawaya MR, Gingery M, Attinger A, Eisenberg D. 2004. An amyloid-forming segment of β2-microglobulin suggests a molecular model for the fibril. Proc Natl Acad Sci U S A 101(29):10584-10589.
53. Monsellier E, Ramazzotti M, de Laureto PP, Tartaglia GG, Taddei N, Fontana A, Vendruscolo M, Chiti F. 2007. The distribution of residues in a polypeptide sequence is a determinant of aggregation optimized by evolution. Biophys J 93(12):4382-4391.
54. Chiti F, Calamai M, Taddei N, Stefani M, Ramponi G, Dobson CM. 2002. Studies of the aggregation of mutant proteins in vitro provide insights into the genetics of amyloid diseases. Proc Natl Acad Sci U S A 99(Suppl 4):16419-16426.
55. Chiti F, Stefani M, Taddei N, Ramponi G, Dobson CM. 2003. Rationalization of the effects of mutations on peptide and protein aggregation rates. Nature 424(6950):805-808.
56. Chiti F, Webster P, Taddei N, Clark A, Stefani M, Ramponi G, Dobson CM. 1999. Designing conditions for in vitro formation of amyloid protofilaments and fibrils. Proc Natl Acad Sci U S A 96(7):3590-3594.
57. Pawar AP, Dubay KF, Zurdo J, Chiti F, Vendruscolo M, Dobson CM. 2005. Prediction of "aggregation-prone" and "aggregation-susceptible" regions in proteins associated with neurodegenerative diseases. J Mol Biol 350(2):379-392.
58. Zhang Z, Chen H, Lai L. 2007. Identification of amyloid fibril-forming segments based on structure and residue-based statistical potential. Bioinformatics 23(17):2218-2225.
59. Thompson MJ, Sievers SA, Karanicolas J, Ivanova MI, Baker D, Eisenberg D. 2006. The 3D profile method for identifying fibril-forming segments of proteins. Proc Natl Acad Sci U S A 103(11):4074-4078.
60. Tian J, Wu N, Guo J, Fan Y. 2009. Prediction of amyloid fibril-forming segments based on a support vector machine. BMC Bioinformatics 10(Suppl 1):S45.
61. Maurer-Stroh S, Debulpaep M, Kuemmerer N, de la Paz ML, Martins IC, Reumers J, Morris KL, Copland A, Serpell L, Serrano L, Schymkowitz JWH, Rousseau F. 2010. Exploring the sequence determinants of amyloid structure using position-specific scoring matrices. Nat Methods 7(3):237-242.
62. Lauer TM, Agrawal NJ, Chennamsetty N, Helk B, Trout BL. Developability index: A rapid in silico tool for the screening of antibody aggregation propensities. In preparation.
63. Conchillo-Sole O, de Groot N, Aviles F, Vendrell J, Daura X, Ventura S. 2007. AGGRESCAN: A server for the prediction and evaluation of "hot spots" of aggregation in polypeptides. BMC Bioinformatics 8(1):65.
64. Tartaglia GG, Cavalli A, Pellarin R, Caflisch A. 2005. Prediction of aggregation rate and aggregation-prone segments in polypeptide sequences. Protein Sci 14(10):2723-2734.
65. Trovato A, Chiti F, Maritan A, Seno F. 2006. Insight into the structure of amyloid fibrils from the analysis of globular proteins. PLoS Comput Biol 2(12):e170.
66. Trovato A, Seno F, Tosatto SCE. 2007. The PASTA server for protein aggregation prediction. Protein Eng Des Sel 20(10):521-523.
67. Fernandez-Escamilla A-M, Rousseau F, Schymkowitz J, Serrano L. 2004. Prediction of sequence-dependent and mutational effects on the aggregation of peptides and proteins. Nat Biotechnol 22(10):1302-1306.
68. Zibaee S, Makin OS, Goedert M, Serpell LC. 2007. A simple algorithm locates beta-strands in the amyloid fibril core of alpha-synuclein, Abeta, and tau using the amino acid sequence alone. Protein Sci 16(5):906-918.
69. Chou PY, Fasman GD. 1974. Conformational parameters for amino-acids in helical, beta-sheet, and random coil regions calculated from proteins. Biochemistry 13(2):211-222.
70. Tartaglia GG, Pawar AP, Campioni S, Dobson CM, Chiti F, Vendruscolo M. 2008. Prediction of aggregation-prone regions in structured proteins. J Mol Biol 380(2):425-436.
71. DuBay KF, Pawar AP, Chiti F, Zurdo J, Dobson CM, Vendruscolo M. 2004. Prediction of the absolute aggregation rates of amyloidogenic polypeptide chains. J Mol Biol 341(5):1317-1326.
72. Tartaglia GG, Vendruscolo M. 2008. The Zyggregator method for predicting protein aggregation propensities. Chem Soc Rev 37(7):1395-1401.
73. Stallwood Y, Michael R, Jiménez JL, Demir M, Arnell A, Zurdo J. 2010. Workshop on protein aggregation and immunogenicity. Breckenridge, Colorado.
74. Frousios KK, Iconomidou VA, Karletidi C-MK, Hamodrakas SJ. 2009. Amyloidogenic determinants are usually not buried. BMC Struct Biol 9(1):44.
75. Galzitskaya OV, Garbuzynskiy SO, Lobanov MY. 2006. Prediction of amyloidogenic and disordered regions in protein chains. PLoS Comput Biol 2(12):e177.
76. Hamodrakas SJ. 1988. A protein secondary structure prediction scheme for the IBM PC and compatibles. Comput Appl Biosci 4(4):473-477.
77. Kumar S, Wang X, Singh SK. 2010. Identification and impact of aggregation-prone regions in proteins and therapeutic monoclonal antibodies. In Aggregation of Therapeutic Proteins; Wang W, Roberts CJ, Eds. Wiley.
78. Lopez de la Paz M, Serrano L. 2004. Sequence determinants of amyloid fibril formation. Proc Natl Acad Sci U S A 101(1):87-92.
79. Voynov V, Chennamsetty N, Kayser V, Wallny HJ, Helk B, Trout BL. 2010. Design and application of antibody cysteine variants. Bioconjugate Chem 21(2):385-392.
80. Wang X, Das TK, Singh SK, Kumar S. 2009. Potential aggregation prone regions in biotherapeutics: A survey of commercial monoclonal antibodies. MAbs 1(3):1-14.
81. Wang X, Singh SK, Kumar S. 2010. Potential aggregation prone regions in monoclonal antibodies and their contributions towards antigen recognition. Pharm Res 27(8):1512-1529.
82. Chennamsetty N, Voynov V, Kayser V, Helk B, Trout BL. 2011. Prediction of protein binding regions. Proteins Struc. Func. Bioinformatics. 79(3):888-879.
83. Berman HM, Westbrook J, Feng Z, Gilliland G, Bhat TN, Weissig H, Shindyalov IN, Bourne PE. 2000. The Protein data bank. Nucleic Acids Res 28(1):235-242.
84. Saphire EO, Parren PWHI, Pantophlet R, Zwick MB, Morris GM, Rudd PM, Dwek RA, Stanfield RL, Burton DR, Wilson IA. 2001. Crystal structure of a neutralizing human IgG against HIV-1: A template for vaccine design. Science 293(5532):1155-1159.
85. Voynov V, Chennamestty N, Kayser V, Helk B, Trout B. 2009. Predictvie tools for stablization of therapeutic proteins. MAbs 1(6):1-3.
86. Marshall SA, Lazar GA, Chirino AJ, Desjarlais JR. 2003. Rational design and engineering of therapeutic proteins. Drug Discov Today 8(5):212-221.
87. Carter PJ. 2006. Potent antibody therapeutics by design. Nat Rev Immunol 6(5):343-357.
88. Hwang I, Park S. 2008. Computational design of protein therapeutics. Drug Discov Today Technol 5(2-3):e43-e48.
89. Trevino SR, Scholtz MJ, Pace NC. 2007. Amino acid contribution to protein solubility: Asp, Glu, and Ser contribute more favorably than the other hydrophilic amino acids in RNase Sa. J Mol Biol 366(2):449-460.
90. Paulson B. 2008. Forum assesses biotech QbD progress. Int Pharm Qual 2(5):1-5.