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AGGRESCAN3D: Toward the Prediction of the Aggregation Propensities of Protein Structures

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Computational Drug Discovery and Design

Part of the book series: Methods in Molecular Biology ((MIMB,volume 1762))

Abstract

Protein aggregation is responsible for the onset and spread of many human diseases, ranging from neurodegenerative disorders to cancer and diabetes. Moreover, it is one of the major bottlenecks for the production of protein-based therapeutics such as antibodies or enzymes. AGGRESCAN3D (A3D) is a web server aimed to identify and evaluate structural aggregation prone regions, overcoming the limitations of sequence-based algorithms in the prediction of the aggregation propensity of globular proteins. A3D allows the redesign of protein solubility by predicting in silico the impact of mutations and protein conformational fluctuations on the aggregation of native polypeptides.

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References

  1. Chiti F, Dobson C (2017) Amyloid formation, protein homeostasis, and human disease: a summary of progress over the last decade. Annu Rev Biochem 86:1–42

    Article  Google Scholar 

  2. Linding R, Schymkowitz J, Rousseau F, Diella F, Serrano L (2004) A comparative study of the relationship between protein structure and b-aggregation in globular and intrinsically disordered proteins. J Mol Biol 342:345–353

    Article  CAS  PubMed  Google Scholar 

  3. Ventura S, Zurdo J, Narayanan S, Aviles FX et al (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 101:7258–7263

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Ivanova MI, Sawaya MR, Gingery M, Attinger A, Eisenberg D (2004) An amyloid-forming segment of beta2-microglobulin suggests a molecular model for the fibril. Proc Natl Acad Sci U S A 101:10584–10589

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Cheon M, Chang I, Mohanty S, Luheshi LM, Dobson CM, Vendruscolo M, Favrin G (2007) Structural reorganisation and potential toxicity of oligomeric species formed during the assembly of amyloid fibrils. PLoS Comput Biol 3:1727–1738

    Article  CAS  PubMed  Google Scholar 

  6. Monsellier E, Ramazzotti M, Taddei N, Chiti F (2008) Aggregation propensity of the human proteome. PLoS Comput Biol 4(10):e1000199

    Article  PubMed  PubMed Central  Google Scholar 

  7. Pechmann S, Levy ED, Tartaglia GG, Vendruscolo M (2009) Physicochemical principles that regulate the competition between functional and dysfunctional association of proteins. Proc Natl Acad Sci U S A 106:10159–10164

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Hartl FU, Bracher A, Hayer-Hartl M (2011) Molecular chaperones in protein folding and proteostasis. Nature 475:324–332

    Article  CAS  PubMed  Google Scholar 

  9. Kim YE, Hipp MS, Bracher A, Hayer-Hartl M, Ulrich Hartl F (2013) Molecular chaperone functions in protein folding and proteostasis. Annu Rev Biochem 82:323–355

    Article  CAS  PubMed  Google Scholar 

  10. Balchin D, Hayer-Hartl M, Hartl FU (2016) In vivo aspects of protein folding and quality control. Science 353:4354–4354

    Article  Google Scholar 

  11. Labbadia J, Morimoto RI (2015) The biology of proteostasis in aging and disease. Annu Rev Biochem 84:435–464

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Mckinnon C, Tabrizi SJ (2014) The ubiquitin-proteasome system in neurodegeneration. Antioxid Redox Signal 5:1–61

    Google Scholar 

  13. Wang G, Fersht AR (2017) Multisite aggregation of p53 and implications for drug rescue. Proc Natl Acad Sci U S A 114(13):2634–2643

    Article  Google Scholar 

  14. Mathias Jucker LCW (2013) Self-propagation of pathogenic aggregates in neurodegenerative diseases. Nature 501:45–51

    Article  PubMed  PubMed Central  Google Scholar 

  15. Johnson SM, Connelly S, Fearns C, Powers ET, Kelly JW (2012) The transthyretin amyloidoses: from delineating the molecular mechanism of aggregation linked to pathology to a regulatory agency approved drug. J Mol Biol 421:185–203

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Gallardo R, Ramakers M, De Smet F et al (2016) De novo design of a biologically active amyloid. Science 354:720–730

    Article  CAS  Google Scholar 

  17. Walsh G (2014) Biopharmaceutical benchmarks 2014. Nat Biotechnol 32:992–1000

    Article  CAS  PubMed  Google Scholar 

  18. Hamrang Z, Rattray NJW, Pluen A (2013) Proteins behaving badly: emerging technologies in profiling biopharmaceutical aggregation. Trends Biotechnol 31:448–458

    Article  CAS  PubMed  Google Scholar 

  19. 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

    Article  CAS  PubMed  Google Scholar 

  20. Wang W, Singh SK, Li N, Toler MR, King KR, Nema S (2012) Immunogenicity of protein aggregates – concerns and realities. Int J Pharm 431:1–11

    Article  CAS  PubMed  Google Scholar 

  21. Roberts CJ (2008) Protein aggregation and its impact on product quality. Curr Opin Biotechnol 23:1–7

    Google Scholar 

  22. Knowles TP, Fitzpatrick AW, Meehan S, Mott HR, Vendruscolo M, Dobson CM, Welland ME (2007) Role of intermolecular forces in defining material properties of protein nanofibrils. Science 318:1900–1903

    Article  CAS  PubMed  Google Scholar 

  23. Li D, Jones EM, Sawaya MR et al (2014) Structure-based design of functional amyloid materials. J Am Chem Soc 136:18044–18051

    Article  CAS  PubMed  Google Scholar 

  24. Hauser CAE, Maurer-Stroh S, Martins IC (2014) Amyloid-based nanosensors and nanodevices. Chem Soc Rev 43:5326–5345

    Article  CAS  PubMed  Google Scholar 

  25. Shimanovich U, Efimov I, TO M et al (2014) Protein microgels from amyloid fibril networks. ACS Nano 9:43–51

    Article  Google Scholar 

  26. Kamada A, Mittal N, Söderberg LD, Ingverud T, Ohm W, Roth SV, Lundell F, Lendel C (2017) Flow-assisted assembly of nanostructured protein microfibers. Proc Natl Acad Sci U S A 114:1232–1237

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Lee CC, Perchiacca JM, Tessier PM (2013) Toward aggregation-resistant antibodies by design. Trends Biotechnol 31:612–620

    Article  CAS  PubMed  Google Scholar 

  28. Dudgeon K, Rouet R, Kokmeijer I, Schofield P, Stolp J, Langley D, Stock D, Christ D (2012) General strategy for the generation of human antibody variable domains with increased aggregation resistance. Proc Natl Acad Sci U S A 109:10879–10884

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Perchiacca JM, Tessier PM (2012) Engineering aggregation-resistant antibodies. Annu Rev Chem Biomol Eng 3:263–286

    Article  CAS  PubMed  Google Scholar 

  30. Van der Kant R, Karow-Zwick AR, Van Durme J et al (2017) Prediction and reduction of the aggregation of monoclonal antibodies. J Mol Biol 429:1244–1261

    Article  PubMed  PubMed Central  Google Scholar 

  31. Belli M, Ramazzotti M, Chiti F (2011) Prediction of amyloid aggregation in vivo. EMBO Rep 12:657–663

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Buck PM, Kumar S, Singh SK (2013) On the role of aggregation prone regions in protein evolution, stability, and enzymatic catalysis: insights from diverse analyses. PLoS Comput Biol 9:e1003291

    Article  PubMed  PubMed Central  Google Scholar 

  33. Zambrano R, Jamroz M, Szczasiuk A, Pujols J, Kmiecik S, Ventura S (2015) AGGRESCAN3D (A3D): server for prediction of aggregation properties of protein structures. Nucleic Acids Res 43:w306–w313

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Conchillo-Solé O, de Groot NS, Avilés FX, Vendrell J, Daura X, Ventura S (2007) AGGRESCAN: a server for the prediction and evaluation of ‘hot spots’ of aggregation in polypeptides. BMC Bioinformatics 8:65

    Article  PubMed  PubMed Central  Google Scholar 

  35. Jamroz M, Kolinski A, Kmiecik S (2013) CABS-flex: server for fast simulation of protein structure fluctuations. Nucleic Acids Res 41:427–431

    Article  Google Scholar 

  36. Lee B, Richards FM (1971) The interpretation of protein structures: estimation of static accessibility. J Mol Biol 55:379–400

    Article  CAS  PubMed  Google Scholar 

  37. Schymkowitz J, Borg J, Stricher F, Nys R, Rousseau F, Serrano L (2005) The FoldX web server: an online force field. Nucleic Acids Res 33:382–388

    Article  Google Scholar 

  38. Dutta SR, Gauri SS, Ghosh T, Halder SK, DasMohapatra PK, Mondal KC, Ghosh AK (2017) Elucidation of structural and functional integration of a novel antimicrobial peptide from Antheraea mylitta. Bioorg Med Chem Lett 27:1686–1692

    Article  CAS  PubMed  Google Scholar 

  39. Polo A, Colonna G, Guariniello S, Ciliberto G, Costantini S (2016) Deducing the functional characteristics of the human selenoprotein SELK from the structural properties of its intrinsically disordered C-terminal domain. Mol Biosyst 12(3):758–772

    Article  CAS  PubMed  Google Scholar 

  40. Pulido P, Llamas E, Llorente B, Ventura S, Wright LP, Rodríguez-Concepción M (2016) Specific Hsp100 chaperones determine the fate of the first enzyme of the plastidial isoprenoid pathway for either refolding or degradation by the stromal Clp protease in Arabidopsis. PLoS Genet 12:1–19

    Article  Google Scholar 

  41. Chiti F, Dobson CM (2009) Amyloid formation by globular proteins under native conditions. Nat Chem Biol 5:15–22

    Article  CAS  PubMed  Google Scholar 

  42. Canet D, Last AM, Tito P, Sunde M, Spencer A, Archer DB, Redfield C, Robinson CV, Dobson CM (2002) Local cooperativity in the unfolding of an amyloidogenic variant of human lysozyme. Nat Struct Biol 9:308–315

    Article  CAS  PubMed  Google Scholar 

  43. Eakin CM, Berman AJ, Miranker AD (2006) A native to amyloidogenic transition regulated by a backbone trigger. Nat Struct Mol Biol 13:202–208

    Article  CAS  PubMed  Google Scholar 

  44. Davies HA, Rigden DJ, Phelan MM, Madine J (2017) Probing medin monomer structure and its amyloid nucleation using 13C-direct detection NMR in combination with structural bioinformatics. Sci Rep 7:45224

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Soler MA, de Marco A, Fortuna S (2016) Molecular dynamics simulations and docking enable to explore the biophysical factors controlling the yields of engineered nanobodies. Sci Rep 6:34869

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Pulido D, Arranz-Trullén J, Prats-Ejarque G, Velázquez D, Torrent M, Moussaoui M, Boix E (2016) Insights into the antimicrobial mechanism of action of human RNase6: structural determinants for bacterial cell agglutination and membrane permeation. Int J Mol Sci 17:552

    Article  PubMed  PubMed Central  Google Scholar 

  47. Xia X, Kumru OS, Blaber SI, Middaugh CR, Li L, David M, Sutherland MA, Tenorio CA, Blaber M (2017) Factor-1 for ‘second generation’ therapeutic application. J Pharm Sci 105:1444–1453

    Article  Google Scholar 

  48. Tartaglia GG, Pawar AP, Campioni S, Dobson CM, Chiti F, Vendruscolo M (2008) Prediction of aggregation-prone regions in structured proteins. J Mol Biol 380:425–436

    Article  CAS  PubMed  Google Scholar 

  49. Walsh I, Seno F, Tosatto SCE, Trovato A (2014) PASTA 2.0: an improved server for protein aggregation prediction. Nucleic Acids Res 42:301–307

    Article  Google Scholar 

  50. Garbuzynskiy SO, Lobanov MY, Galzitskaya OV (2009) FoldAmyloid: a method of prediction of amyloidogenic regions from protein sequence. Bioinformatics 26:326–332

    Article  PubMed  Google Scholar 

  51. Fernandez-Escamilla AM, Rousseau F, Schymkowitz J, Serrano L (2004) Prediction of sequence-dependent and mutational effects on the aggregation of peptides and proteins. Nat Biotechnol 22:1302–1306

    Article  CAS  PubMed  Google Scholar 

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Author information

Authors and Affiliations

  1. Institut de Biotecnologia i Biomedicina, Universitat Autònoma de Barcelona, Bellaterra, Spain

    Jordi Pujols, Samuel Peña-Díaz & Salvador Ventura

  2. Departament de Bioquímica i Biologia Molecular, Universitat Autònoma de Barcelona, Bellaterra, Spain

    Jordi Pujols, Samuel Peña-Díaz & Salvador Ventura

Authors
  1. Jordi Pujols
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  2. Samuel Peña-Díaz
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  3. Salvador Ventura
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Corresponding author

Correspondence to Salvador Ventura .

Editor information

Editors and Affiliations

  1. Department of Basic and Applied Sciences, Dayananda Sagar University, Bangalore, KA, India

    Mohini Gore

  2. Department of Biotechnology, Shivaji University, Kolhapur, MH, India

    Umesh B. Jagtap

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Pujols, J., Peña-Díaz, S., Ventura, S. (2018). AGGRESCAN3D: Toward the Prediction of the Aggregation Propensities of Protein Structures. In: Gore, M., Jagtap, U. (eds) Computational Drug Discovery and Design. Methods in Molecular Biology, vol 1762. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-7756-7_21

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  • DOI: https://doi.org/10.1007/978-1-4939-7756-7_21

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  • Publisher Name: Humana Press, New York, NY

  • Print ISBN: 978-1-4939-7755-0

  • Online ISBN: 978-1-4939-7756-7

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