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Expanding the Landscape of Amyloid Sequences with CARs-DB: A Database of Polar Amyloidogenic Peptides from Disordered Proteins

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Computational Drug Discovery and Design

Part of the book series: Methods in Molecular Biology ((MIMB,volume 2714))

Abstract

Several databases collecting amyloidogenic regions have been released to provide information on protein sequences able to form amyloid fibrils. However, most of these resources are built with data from experiments that detect highly hydrophobic stretches located within transiently exposed protein segments. We recently demonstrated that cryptic amyloidogenic regions (CARs) of polar nature have the potential to form amyloid fibrils in vitro. Given the underrepresentation of these types of sequences in current amyloid databases, we developed CARs-DB, the first repository that collects thousands of predicted CARs from intrinsically disordered regions. This protocol chapter describes how to use CARs-DB to search for sequences of interest that might be connected to disease or functional protein–protein interactions. In addition, we provide study cases to illustrate the database’s features to users. The CARs-DB is readily accessible at http://carsdb.ppmclab.com/.

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References

  1. David DC (2012) Aging and the aggregating proteome. Front Genet 3:247. https://doi.org/10.3389/fgene.2012.00247

    Article  PubMed  PubMed Central  Google Scholar 

  2. Alberti S, Hyman AA (2021) Biomolecular condensates at the nexus of cellular stress, protein aggregation disease and ageing. Nat Rev Mol Cell Biol 22(3):196–213. https://doi.org/10.1038/s41580-020-00326-6

    Article  CAS  PubMed  Google Scholar 

  3. Sunde M, Serpell LC, Bartlam M, Fraser PE, Pepys MB, Blake CC (1997) Common core structure of amyloid fibrils by synchrotron X-ray diffraction. J Mol Biol 273(3):729–739. https://doi.org/10.1006/jmbi.1997.1348

    Article  CAS  PubMed  Google Scholar 

  4. Chiti F, Dobson CM (2006) Protein misfolding, functional amyloid, and human disease. Annu Rev Biochem 75:333–366. https://doi.org/10.1146/annurev.biochem.75.101304.123901

    Article  CAS  PubMed  Google Scholar 

  5. Eisenberg D, Jucker M (2012) The amyloid state of proteins in human diseases. Cell 148(6):1188–1203. https://doi.org/10.1016/j.cell.2012.02.022

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Castillo V, Ventura S (2009) Amyloidogenic regions and interaction surfaces overlap in globular proteins related to conformational diseases. PLoS Comput Biol 5(8):e1000476. https://doi.org/10.1371/journal.pcbi.1000476

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Avni A, Swasthi HM, Majumdar A, Mukhopadhyay S (2019) Intrinsically disordered proteins in the formation of functional amyloids from bacteria to humans. Prog Mol Biol Transl Sci 166:109–143. https://doi.org/10.1016/bs.pmbts.2019.05.005

    Article  CAS  PubMed  Google Scholar 

  8. Otzen D, Riek R (2019) Functional amyloids. Cold Spring Harb Perspect Biol 11(12). https://doi.org/10.1101/cshperspect.a033860

  9. Chapman MR, Robinson LS, Pinkner JS et al (2002) Role of Escherichia coli curli operons in directing amyloid fiber formation. Science 295(5556):851–855. https://doi.org/10.1126/science.1067484

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Antonets KS, Belousov MV, Sulatskaya AI et al (2020) Accumulation of storage proteins in plant seeds is mediated by amyloid formation. PLoS Biol 18(7):e3000564. https://doi.org/10.1371/journal.pbio.3000564

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Fowler DM, Koulov AV, Alory-Jost C, Marks MS, Balch WE, Kelly JW (2006) Functional amyloid formation within mammalian tissue. PLoS Biol 4(1):e6. https://doi.org/10.1371/journal.pbio.0040006

    Article  CAS  PubMed  Google Scholar 

  12. Santos J, Pujols J, Pallares I, Iglesias V, Ventura S (2020) Computational prediction of protein aggregation: advances in proteomics, conformation-specific algorithms and biotechnological applications. Comput Struct Biotechnol J 18:1403–1413. https://doi.org/10.1016/j.csbj.2020.05.026

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Invernizzi G, Papaleo E, Sabate R, Ventura S (2012) Protein aggregation: mechanisms and functional consequences. Int J Biochem Cell Biol 44(9):1541–1554. https://doi.org/10.1016/j.biocel.2012.05.023

    Article  CAS  PubMed  Google Scholar 

  14. Chiti F, Dobson CM (2017) Protein misfolding, amyloid formation, and human disease: a summary of progress over the last decade. Annu Rev Biochem 86:27–68. https://doi.org/10.1146/annurev-biochem-061516-045115

    Article  CAS  PubMed  Google Scholar 

  15. Dill KA, MacCallum JL (2012) The protein-folding problem, 50 years on. Science 338(6110):1042–1046. https://doi.org/10.1126/science.1219021

    Article  CAS  PubMed  Google Scholar 

  16. Pinheiro F, Santos J, Ventura S (2021) AlphaFold and the amyloid landscape. J Mol Biol 433(20):167059. https://doi.org/10.1016/j.jmb.2021.167059

    Article  CAS  PubMed  Google Scholar 

  17. Medina-Carmona E, Betancor-Fernandez I, Santos J et al (2019) Insight into the specificity and severity of pathogenic mechanisms associated with missense mutations through experimental and structural perturbation analyses. Hum Mol Genet 28(1):1–15. https://doi.org/10.1093/hmg/ddy323

    Article  CAS  PubMed  Google Scholar 

  18. Visentin C, Navarro S, Grasso G, Regonesi ME, Deriu MA, Tortora P, Ventura S (2018) Protein environment: a crucial triggering factor in Josephin domain aggregation: the role of 2,2,2-trifluoroethanol. Int J Mol Sci 19(8). https://doi.org/10.3390/ijms19082151

  19. Conchillo-Sole O, de Groot NS, Aviles 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. https://doi.org/10.1186/1471-2105-8-65

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. 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(10):1302–1306. https://doi.org/10.1038/nbt1012

    Article  CAS  PubMed  Google Scholar 

  21. Maurer-Stroh S, Debulpaep M, Kuemmerer N et al (2010) Exploring the sequence determinants of amyloid structure using position-specific scoring matrices. Nat Methods 7(3):237–242. https://doi.org/10.1038/nmeth.1432

    Article  CAS  PubMed  Google Scholar 

  22. Louros N, Orlando G, De Vleeschouwer M, Rousseau F, Schymkowitz J (2020) Structure-based machine-guided mapping of amyloid sequence space reveals uncharted sequence clusters with higher solubilities. Nat Commun 11(1):3314. https://doi.org/10.1038/s41467-020-17207-3

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Diaz-Caballero M, Navarro S, Fuentes I, Teixidor F, Ventura S (2018) Minimalist prion-inspired polar self-assembling peptides. ACS Nano 12(6):5394–5407. https://doi.org/10.1021/acsnano.8b00417

    Article  CAS  PubMed  Google Scholar 

  24. Batlle C, Calvo I, Iglesias V, Lynch CJ, Gil-Garcia M, Serrano M, Ventura S (2021) MED15 prion-like domain forms a coiled-coil responsible for its amyloid conversion and propagation. Commun Biol 4(1):414. https://doi.org/10.1038/s42003-021-01930-8

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Santos J, Pallares I, Iglesias V, Ventura S (2021) Cryptic amyloidogenic regions in intrinsically disordered proteins: function and disease association. Comput Struct Biotechnol J 19:4192–4206. https://doi.org/10.1016/j.csbj.2021.07.019

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Pintado-Grima C, Barcenas O, Manglano-Artunedo Z, Vilaca R, Macedo-Ribeiro S, Pallares I, Santos J, Ventura S (2022) CARs-DB: a database of cryptic amyloidogenic regions in intrinsically disordered proteins. Front Mol Biosci 9:882160. https://doi.org/10.3389/fmolb.2022.882160

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Quaglia F, Meszaros B, Salladini E et al (2021) DisProt in 2022: improved quality and accessibility of protein intrinsic disorder annotation. Nucleic Acids Res 50(D1):D480–D487. https://doi.org/10.1093/nar/gkab1082

    Article  CAS  PubMed Central  Google Scholar 

  28. Wang W, Navarro S, Azizyan RA, Bano-Polo M, Esperante SA, Kajava AV, Ventura S (2019) Prion soft amyloid core driven self-assembly of globular proteins into bioactive nanofibrils. Nanoscale 11(26):12680–12694. https://doi.org/10.1039/c9nr01755k

    Article  CAS  PubMed  Google Scholar 

  29. Fernandez MR, Pallares I, Iglesias V, Santos J, Ventura S (2019) Formation of cross-beta supersecondary structure by soft-amyloid cores: strategies for their prediction and characterization. Methods Mol Biol 1958:237–261. https://doi.org/10.1007/978-1-4939-9161-7_12

    Article  CAS  PubMed  Google Scholar 

  30. Batlle C, de Groot NS, Iglesias V, Navarro S, Ventura S (2017) Characterization of soft amyloid cores in human prion-like proteins. Sci Rep 7(1):12134. https://doi.org/10.1038/s41598-017-09714-z

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Fernandez MR, Batlle C, Gil-Garcia M, Ventura S (2017) Amyloid cores in prion domains: key regulators for prion conformational conversion. Prion 11(1):31–39. https://doi.org/10.1080/19336896.2017.1282020

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Diaz-Caballero M, Fernandez MR, Navarro S, Ventura S (2018) Prion-based nanomaterials and their emerging applications. Prion 12(5–6):266–272. https://doi.org/10.1080/19336896.2018.1521235

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Diaz-Caballero M, Navarro S, Ventura S (2021) Functionalized prion-inspired amyloids for biosensor applications. Biomacromolecules 22(7):2822–2833. https://doi.org/10.1021/acs.biomac.1c00222

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Peccati F, Diaz-Caballero M, Navarro S, Rodriguez-Santiago L, Ventura S, Sodupe M (2020) Atomistic fibrillar architectures of polar prion-inspired heptapeptides. Chem Sci 11(48):13143–13151. https://doi.org/10.1039/d0sc05638c

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. UniProt C (2023) UniProt: the Universal Protein Knowledgebase in 2023. Nucleic Acids Res 51(D1):D523–D531. https://doi.org/10.1093/nar/gkac1052

    Article  CAS  Google Scholar 

  36. Schad E, Ficho E, Pancsa R, Simon I, Dosztanyi Z, Meszaros B (2018) DIBS: a repository of disordered binding sites mediating interactions with ordered proteins. Bioinformatics 34(3):535–537. https://doi.org/10.1093/bioinformatics/btx640

    Article  CAS  PubMed  Google Scholar 

  37. Varadi M, De Baets G, Vranken WF, Tompa P, Pancsa R (2018) AmyPro: a database of proteins with validated amyloidogenic regions. Nucleic Acids Res 46(D1):D387–D392. https://doi.org/10.1093/nar/gkx950

    Article  CAS  PubMed  Google Scholar 

  38. Ghosh A, Stewart D, Matlashewski G (2004) Regulation of human p53 activity and cell localization by alternative splicing. Mol Cell Biol 24(18):7987–7997. https://doi.org/10.1128/MCB.24.18.7987-7997.2004

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Vaseva AV, Marchenko ND, Ji K, Tsirka SE, Holzmann S, Moll UM (2012) p53 opens the mitochondrial permeability transition pore to trigger necrosis. Cell 149(7):1536–1548. https://doi.org/10.1016/j.cell.2012.05.014

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Harbour JW, Luo RX, Dei Santi A, Postigo AA, Dean DC (1999) Cdk phosphorylation triggers sequential intramolecular interactions that progressively block Rb functions as cells move through G1. Cell 98(6):859–869. https://doi.org/10.1016/s0092-8674(00)81519-6

    Article  CAS  PubMed  Google Scholar 

  41. Mayans O, van der Ven PF, Wilm M, Mues A, Young P, Furst DO, Wilmanns M, Gautel M (1998) Structural basis for activation of the titin kinase domain during myofibrillogenesis. Nature 395(6705):863–869. https://doi.org/10.1038/27603

    Article  CAS  PubMed  Google Scholar 

  42. Munch J, Rucker E, Standker L et al (2007) Semen-derived amyloid fibrils drastically enhance HIV infection. Cell 131(6):1059–1071. https://doi.org/10.1016/j.cell.2007.10.014

    Article  CAS  PubMed  Google Scholar 

  43. Nanga RP, Brender JR, Vivekanandan S, Popovych N, Ramamoorthy A (2009) NMR structure in a membrane environment reveals putative amyloidogenic regions of the SEVI precursor peptide PAP(248-286). J Am Chem Soc 131(49):17972–17979. https://doi.org/10.1021/ja908170s

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Pintado C, Santos J, Iglesias V, Ventura S (2021) SolupHred: a server to predict the pH-dependent aggregation of intrinsically disordered proteins. Bioinformatics 37(11):1602–1603. https://doi.org/10.1093/bioinformatics/btaa909

    Article  CAS  PubMed  Google Scholar 

  45. Iglesias V, Pintado-Grima C, Santos J, Fornt M, Ventura S (2022) Prediction of the effect of pH on the aggregation and conditional folding of intrinsically disordered proteins with SolupHred and DispHred. Methods Mol Biol 2449:197–211. https://doi.org/10.1007/978-1-0716-2095-3_8

    Article  PubMed  Google Scholar 

  46. Wasmer C, Lange A, Van Melckebeke H, Siemer AB, Riek R, Meier BH (2008) Amyloid fibrils of the HET-s(218-289) prion form a beta solenoid with a triangular hydrophobic core. Science 319(5869):1523–1526. https://doi.org/10.1126/science.1151839

    Article  CAS  PubMed  Google Scholar 

  47. Greenwald J, Buhtz C, Ritter C et al (2010) The mechanism of prion inhibition by HET-S. Mol Cell 38(6):889–899. https://doi.org/10.1016/j.molcel.2010.05.019

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Varadi M, Anyango S, Deshpande M et al (2022) AlphaFold Protein Structure Database: massively expanding the structural coverage of protein-sequence space with high-accuracy models. Nucleic Acids Res 50(D1):D439–D444. https://doi.org/10.1093/nar/gkab1061

    Article  CAS  PubMed  Google Scholar 

  49. Jumper J, Evans R, Pritzel A et al (2021) Highly accurate protein structure prediction with AlphaFold. Nature 596(7873):583–589. https://doi.org/10.1038/s41586-021-03819-2

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Radko S, Koleva M, James KM, Jung R, Mymryk JS, Pelka P (2014) Adenovirus E1A targets the DREF nuclear factor to regulate virus gene expression, DNA replication, and growth. J Virol 88(22):13469–13481. https://doi.org/10.1128/JVI.02538-14

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Ait-Si-Ali S, Ramirez S, Barre FX et al (1998) Histone acetyltransferase activity of CBP is controlled by cycle-dependent kinases and oncoprotein E1A. Nature 396(6707):184–186. https://doi.org/10.1038/24190

    Article  CAS  PubMed  Google Scholar 

  52. Chakravarti D, Ogryzko V, Kao HY, Nash A, Chen H, Nakatani Y, Evans RM (1999) A viral mechanism for inhibition of p300 and PCAF acetyltransferase activity. Cell 96(3):393–403. https://doi.org/10.1016/s0092-8674(00)80552-8

    Article  CAS  PubMed  Google Scholar 

  53. Ferreon JC, Martinez-Yamout MA, Dyson HJ, Wright PE (2009) Structural basis for subversion of cellular control mechanisms by the adenoviral E1A oncoprotein. Proc Natl Acad Sci U S A 106(32):13260–13265. https://doi.org/10.1073/pnas.0906770106

    Article  PubMed  PubMed Central  Google Scholar 

  54. Sabate R, Rousseau F, Schymkowitz J, Ventura S (2015) What makes a protein sequence a prion? PLoS Comput Biol 11(1):e1004013. https://doi.org/10.1371/journal.pcbi.1004013

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Beerten J, Schymkowitz J, Rousseau F (2012) Aggregation prone regions and gatekeeping residues in protein sequences. Curr Top Med Chem 12(22):2470–2478. https://doi.org/10.2174/1568026611212220003

    Article  CAS  PubMed  Google Scholar 

  56. Dosztanyi Z (2018) Prediction of protein disorder based on IUPred. Protein Sci 27(1):331–340. https://doi.org/10.1002/pro.3334

    Article  CAS  PubMed  Google Scholar 

  57. Pintado-Grima C, Santos J, Iglesias V, Manglano-Artuñedo Z, Pallarés I, Ventura S (2023) Exploring cryptic amyloidogenic regions in prion-like proteins from plants. Front Plant Sci 13:1060410. https://doi.org/10.3389/fpls.2022.1060410 (PMID: 36726678)

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Funding

SV was supported by the Spanish Ministry of Science and Innovation (PID 2019-105017RB-I00), by ICREA, ICREA Academia 2020, and PhasAGE (European Union’s Horizon 2020 research and innovation programme under GA 952334). CP-G was supported by the Secretariat of Universities and Research of the Catalan Government and the European Social Fund (2021 FI_B 00087).

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Authors and Affiliations

  1. Institut de Biotecnologia i de Biomedicina and Departament de Bioquímica i Biologia Molecular, Universitat Autònoma de Barcelona, Barcelona, Spain

    Carlos Pintado-Grima, Oriol Bárcenas & Salvador Ventura

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  1. Carlos Pintado-Grima
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  2. Oriol Bárcenas
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  3. Salvador Ventura
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Correspondence to Salvador Ventura .

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Editors and Affiliations

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

    Mohini Gore

  2. Department of Botany, Rajaram College Kolhapur, Kolhapur, Maharashtra, India

    Umesh B. Jagtap

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Pintado-Grima, C., Bárcenas, O., Ventura, S. (2024). Expanding the Landscape of Amyloid Sequences with CARs-DB: A Database of Polar Amyloidogenic Peptides from Disordered Proteins. In: Gore, M., Jagtap, U.B. (eds) Computational Drug Discovery and Design. Methods in Molecular Biology, vol 2714. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-3441-7_10

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  • DOI: https://doi.org/10.1007/978-1-0716-3441-7_10

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