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Calpain pp 121-147 | Cite as

CalCleaveMKL: a Tool for Calpain Cleavage Prediction

  • David A. duVerleEmail author
  • Hiroshi Mamitsuka
Protocol
Part of the Methods in Molecular Biology book series (MIMB, volume 1915)

Abstract

Calpain, an intracellular Ca2+-dependent cysteine protease, is known to play a role in a wide range of metabolic pathways through limited proteolysis of its substrates. However, only a limited number of these substrates are currently known, with the exact mechanism of substrate recognition and cleavage by calpain still largely unknown.

Current sequencing technologies have made it possible to compile large amounts of cleavage data and brought greater understanding of the underlying protein interactions. However, the practical impossibility of exhaustively retrieving substrate sequences through experimentation alone has created the need for efficient computational prediction methods. Such methods must be able to quickly mark substrate candidates and putative cleavage sites for further analysis. While many methods exist for both calpain and other types of proteolytic actions, the expected reliability of these methods depends heavily on the type and complexity of proteolytic action, as well as the availability of well-labeled experimental datasets, which both vary greatly across enzyme families.

This chapter introduces CalCleaveMKL: a tool for calpain cleavage prediction based on multiple kernel learning, an extension to the classic support vector machine framework that is able to train complex models based on rich, heterogeneous feature sets, leading to significantly improved prediction quality. Along with its improved accuracy, the method used by CalCleaveMKL provided numerous insights on the respective importance of sequence-related features, such as solvent accessibility and secondary structure. It notably demonstrated there existed significant specificity differences across calpain subtypes, despite previous assumption to the contrary.

An online implementation of this prediction tool is available at http://calpain.org.

Key words

Calpain Cleavage prediction Support vector machines Multiple kernel learning 

Notes

Acknowledgements

The authors would like to thank Prof. H. Sorimachi of the Department of Advanced Science for Biomolecules at Tokyo Metropolitan Institute of Medical Science, for providing many insights on the biological aspects of calpain proteolysis and providing us with the crystallography view of calpain–calpastatin docking illustrating this chapter.

References

  1. 1.
    Akbani R, Kwek S, Japkowicz N (2004) Applying support vector machines to imbalanced datasets. In: Machine Learning: ECML 2004, pp 39–50Google Scholar
  2. 2.
    Backes C, Kuentzer J, Lenhof H, Comtesse N, Meese E (2005) Grabcas: a bioinformatics tool for score-based prediction of caspase-and granzyme b-cleavage sites in protein sequences. Nucleic Acids Res 33(Suppl 2):W208PubMedPubMedCentralGoogle Scholar
  3. 3.
    Banik N, Chou C, Deibler G, Krutzch H, Hogan E (1994) Peptide bond specificity of calpain: proteolysis of human myelin basic protein. J Neurosci Res 37(4):489–496PubMedGoogle Scholar
  4. 4.
    Barkan D, Hostetter D, Mahrus S, Pieper U, Wells J, Craik C, Sali A (2010) Prediction of protease substrates using sequence and structure features. Bioinformatics 26(14):1714–1722PubMedPubMedCentralGoogle Scholar
  5. 5.
    Barrett A, Rawlings N, Woessner J (1998) Handbook of proteolytic enzymes. Academic, New YorkGoogle Scholar
  6. 6.
    Bartoli M, Richard I (2005) Calpains in muscle wasting. Int J Biochem Cell Biol 37(10):2115–2133PubMedGoogle Scholar
  7. 7.
    Bertipaglia L, Carafoli E (2007) Calpains and human disease. Subcell Biochem 45:29–53PubMedGoogle Scholar
  8. 8.
    Cai Y, Chou K (1998) Artificial neural network model for predicting HIV protease cleavage sites in protein. Adv Eng Softw 29(2):119–128Google Scholar
  9. 9.
    Cai Y, Lin S, Chou K (2003) Support vector machines for prediction of protein signal sequences and their cleavage sites. Peptides 24(1):159–161PubMedGoogle Scholar
  10. 10.
    Carillo S, Pariat M, Steff A, Jariel-Encontre I, Poulat F, Berta P, Piechaczyk M (1996) PEST motifs are not required for rapid calpain-mediated proteolysis of c-fos protein. Biochem J 313(Pt 1):245PubMedPubMedCentralGoogle Scholar
  11. 11.
    Chapelle O, Vapnik V, Bousquet O, Mukherjee S (2002) Choosing multiple parameters for support vector machines. Mach Learn 46(1):131–159Google Scholar
  12. 12.
    Cheng J, Randall A, Sweredoski M, Baldi P (2005) SCRATCH: a protein structure and structural feature prediction server. Nucleic Acids Res 33(Web Server Issue):W72Google Scholar
  13. 13.
    Chou K (1996) Review: prediction of HIV protease cleavage sites in proteins. Anal Biochem 233(1):1–14PubMedGoogle Scholar
  14. 14.
    Craik C, Largman C, Fletcher T, Roczniak S, Barr P, Fletterick R, Rutter W (1985) Redesigning trypsin: alteration of substrate specificity. Science 228(4697):291PubMedGoogle Scholar
  15. 15.
    Croall D, Ersfeld K (2007) The calpains: modular designs and functional diversity. Genome Biol 8(6):218PubMedPubMedCentralGoogle Scholar
  16. 16.
    Croall D, Chacko S, Wang Z (1996) Cleavage of caldesmon and calponin by calpain: substrate recognition is not dependent on calmodulin binding domains. Biochim Biophys Acta Protein Struct Mol Enzymol 1298(2):276Google Scholar
  17. 17.
    Cuerrier D, Moldoveanu T, Davies P (2005) Determination of peptide substrate specificity for μ-calpain by a peptide library-based approach. J Biol Chem 280(49):40632PubMedPubMedCentralGoogle Scholar
  18. 18.
    Demon D, Van Damme P, Berghe T, Vandekerckhove J, Declercq W, Gevaert K, Vandenabeele P (2009) Caspase substrates: easily caught in deep waters? Trends Biotechnol 27:680–688PubMedGoogle Scholar
  19. 19.
    duVerle D, Takigawa I, Ono Y, Sorimachi H, Mamitsuka H (2010) Campdb: a resource for calpain and modulatory proteolysis. In: Genome informatics. International Conference on Genome Informatics, vol 22, p 202Google Scholar
  20. 20.
    duVerle D, Ono Y, Sorimachi H, Mamitsuka H (2011) Calpain cleavage prediction using multiple kernel learning. PLoS One 6(5):e19035. http://dx.doi.org/10.1371/journal.pone.0019035PubMedPubMedCentralGoogle Scholar
  21. 21.
    duVerle DA, Mamitsuka H (2011) A review of statistical methods for prediction of proteolytic cleavage. Brief Bioinform 13(3):337–349PubMedGoogle Scholar
  22. 22.
    Friedrich P, Bozóky Z (2005) Digestive versus regulatory proteases: on calpain action in vivo. Biol Chem 386(7):609PubMedGoogle Scholar
  23. 23.
    Goll D, Thompson V, Taylor R, Zalewska T (1992) Is calpain activity regulated by membranes and autolysis or by calcium and calpastatin? BioEssays 14(8):549–556PubMedGoogle Scholar
  24. 24.
    Goll D, Thompson V, Li H, Wei W, Cong J (2003) The calpain system. Physiol Rev 83:731–801PubMedPubMedCentralGoogle Scholar
  25. 25.
    Hand D, Till R (2001) A simple generalisation of the area under the ROC curve for multiple class classification problems. Mach Learn 45(2):171–186Google Scholar
  26. 26.
    Hanna R, Campbell R, Davies P (2008) Calcium-bound structure of calpain and its mechanism of inhibition by calpastatin. Nature 456(7220):409–412PubMedGoogle Scholar
  27. 27.
    Harris F, Biswas S, Singh J, Dennison S, Phoenix D (2006) Calpains and their multiple roles in diabetes mellitus. Ann N Y Acad Sci 1084:452PubMedGoogle Scholar
  28. 28.
    Horikawa Y, Oda N, Cox N, Li X, Orho-Melander M, Hara M, Hinokio Y, Lindner T, Mashima H, Schwarz P et al (2000) Genetic variation in the gene encoding calpain-10 is associated with type 2 diabetes mellitus. Nat Genet 26(2):163–175PubMedPubMedCentralGoogle Scholar
  29. 29.
    Jones D (1999) Protein secondary structure prediction based on position-specific scoring matrices1. J Mol Biol 292(2):195–202PubMedGoogle Scholar
  30. 30.
    Kawasaki H, Emori Y, Suzuki K (1993) Calpastatin has two distinct sites for interaction with calpain-effect of calpastatin fragments on the binding of calpain to membranes. Arch Biochem Biophys 305(2):467–472PubMedGoogle Scholar
  31. 31.
    Kelly J, Cuerrier D, Graham L, Campbell R, Davies P (2009) Profiling of calpain activity with a series of FRET-based substrates. Biochim Biophys Acta Proteins Proteomics 1794(10):1505–1509Google Scholar
  32. 32.
    Kikuchi H, Imajoh-Ohmi S, Kanegasaki S (1993) Novel antibodies specific for proteolyzed forms of protein kinase C: production of anti-peptide antibodies available for in situ analysis of intracellular limited proteolysis. Biochim Biophys Acta Protein Struct Mol Enzymol 1162(1–2):171–176Google Scholar
  33. 33.
    Kimura Y, Saya H, Nakao M (2000) Calpain-dependent proteolysis of NF2 protein: involvement in schwannomas and meningiomas. Neuropathology 20(3):153–160PubMedGoogle Scholar
  34. 34.
    Lanckriet G, De Bie T, Cristianini N, Jordan M, Noble W (2004) A statistical framework for genomic data fusion. Bioinformatics 20(16):2626–2635PubMedGoogle Scholar
  35. 35.
    Liu J, Liu M, Wang K (2008) Calpain in the CNS: from synaptic function to neurotoxicity. Sci. STKE 1(14)PubMedGoogle Scholar
  36. 36.
    Moldoveanu T, Hosfield C, Lim D, Jia Z, Davies P (2003) Calpain silencing by a reversible intrinsic mechanism. Nat Struct Mol Biol 10(5):371–378Google Scholar
  37. 37.
    Moldoveanu T, Gehring K, Green D (2008) Concerted multi-pronged attack by calpastatin to occlude the catalytic cleft of heterodimeric calpains. Nature 456(7220):404–408PubMedPubMedCentralGoogle Scholar
  38. 38.
    Molinari M, Anagli J, Carafoli E (1995) PEST sequences do not influence substrate susceptibility to calpain proteolysis. J Biol Chem 270(5):2032PubMedGoogle Scholar
  39. 39.
    Ono Y, Shimada H, Sorimachi H, Richard I, Saido T, Beckmann J, Ishiura S, Suzuki K (1998) Functional defects of a muscle-specific calpain, p94, caused by mutations associated with limb-girdle muscular dystrophy type 2A. J Biol Chem 273(27):17073PubMedGoogle Scholar
  40. 40.
    Ono Y, Kakinuma K, Torii F, Irie A, Nakagawa K, Labeit S, Abe K, Suzuki K, Sorimachi H (2004) Possible regulation of the conventional calpain system by skeletal muscle-specific calpain, p94/calpain 3. J Biol Chem 279(4):2761PubMedGoogle Scholar
  41. 41.
    Qian N, Sejnowski T (1988) Predicting the secondary structure of globular proteins using neural network models. J Mol Biol 202(4):865–884PubMedGoogle Scholar
  42. 42.
    Richard I, Broux O, Allamand V, Fougerousse F, Chiannilkulchai N, Bourg N, Brenguier L, Devaud C, Pasturaud P, Roudaut C et al (1995) Mutations in the proteolytic enzyme calpain 3 cause limb-girdle muscular dystrophy type 2A. Cell 81(1):27–40PubMedGoogle Scholar
  43. 43.
    Rögnvaldsson T, You L (2004) Why neural networks should not be used for HIV-1 protease cleavage site prediction. Bioinformatics 20(11):1702–1709PubMedGoogle Scholar
  44. 44.
    Rolius R, Antoniou C, Nazarova L, Kim S, Cobb G, Gala P, Rajaram P, Li Q, Fung L (2010) Inhibition of calpain but not caspase activity by spectrin fragments. Cell Mol Biol Lett 15(3):395–405PubMedPubMedCentralGoogle Scholar
  45. 45.
    Saido T, Suzuki H, Yamazaki H, Tanoue K, Suzuki K (1993) In situ capture of mu-calpain activation in platelets. J Biol Chem 268(10):7422PubMedGoogle Scholar
  46. 46.
    Saido T, Yokota M, Nagao S, Yamaura I, Tani E, Tsuchiya T, Suzuki K, Kawashima S (1993) Spatial resolution of fodrin proteolysis in postischemic brain. J Biol Chem 268(33):25239PubMedGoogle Scholar
  47. 47.
    Saido T, Sorimachi H, Suzuki K (1994) Calpain: new perspectives in molecular diversity and physiological-pathological involvement. FASEB J 8(11):814PubMedGoogle Scholar
  48. 48.
    Sakai K, Akanuma H, Imahori K, Kawashima S (1987) A unique specificity of a calcium activated neutral protease indicated in histone hydrolysis. J Biochem 101(4):911PubMedGoogle Scholar
  49. 49.
    Sasaki T, Kikuchi T, Yumoto N, Yoshimura N, Murachi T (1984) Comparative specificity and kinetic studies on porcine calpain I and calpain II with naturally occurring peptides and synthetic fluorogenic substrates. J Biol Chem 259(20):12489PubMedPubMedCentralGoogle Scholar
  50. 50.
    Shen H, Chou K (2008) Hivcleave: a web-server for predicting human immunodeficiency virus protease cleavage sites in proteins. Anal Biochem 375(2):388–390PubMedGoogle Scholar
  51. 51.
    Song J, Tan H, Shen H, Mahmood K, Boyd S, Webb G, Akutsu T, Whisstock J (2010) Cascleave: towards more accurate prediction of caspase substrate cleavage sites. Bioinformatics 26(6):752PubMedGoogle Scholar
  52. 52.
    Sonnenburg S, Rätsch G, Schäfer C, Schölkopf B (2006) Large scale multiple kernel learning. J Mach Learn Res 7:1565Google Scholar
  53. 53.
    Sonnhammer EL, Eddy SR, Birney E, Bateman A, Durbin R (1998) Pfam: multiple sequence alignments and HMM-profiles of protein domains. Nucleic Acids Res 26(1):320–322PubMedPubMedCentralGoogle Scholar
  54. 54.
    Sorimachi H, Ishiura S, Suzuki K (1997) Structure and physiological function of calpains. Biochem J 328(Pt 3):721PubMedPubMedCentralGoogle Scholar
  55. 55.
    Stabach P, Cianci C, Glantz S, Zhang Z, Morrow J (1997) Site-directed mutagenesis of α II spectrin at codon 1175 modulates its μ-calpain susceptibility. Biochemistry 36(1):57–65PubMedGoogle Scholar
  56. 56.
    Suzuki K, Hata S, Kawabata Y, Sorimachi H (2004) Structure, activation, and biology of calpain. Diabetes 53(Suppl 1):S12PubMedGoogle Scholar
  57. 57.
    Thompson T, Chou K, Zheng C (1995) Neural network prediction of the HIV-1 protease cleavage sites. J Theor Biol 177(4):369–379PubMedGoogle Scholar
  58. 58.
    Tompa P, Buzder-Lantos P, Tantos A, Farkas A, Szilágyi A, Bánóczi Z, Hudecz F, Friedrich P (2004) On the sequential determinants of calpain cleavage. J Biol Chem 279(20):20775PubMedPubMedCentralGoogle Scholar
  59. 59.
    Von Heijne G (1986) A new method for predicting signal sequence cleavage sites. Nucleic Acids Res 14(11):4683Google Scholar
  60. 60.
    Wang K (2000) Calpain and caspase: can you tell the difference? Trends Neurosci 23(1):20–26PubMedGoogle Scholar
  61. 61.
    Wang N, Chen W, Linsel-Nitschke P, Martinez L, Agerholm-Larsen B, Silver D, Tall A (2003) A PEST sequence in ABCA1 regulates degradation by calpain protease and stabilization of ABCA1 by apoA-I. J Clin Investig 111(1):99–107PubMedGoogle Scholar
  62. 62.
    Wee L, Tan T, Ranganathan S (2006) Svm-based prediction of caspase substrate cleavage sites. BMC Bioinf 7(Suppl 5):S14Google Scholar
  63. 63.
    Wee L, Tan T, Ranganathan S (2007) Casvm: web server for SVM-based prediction of caspase substrates cleavage sites. Bioinformatics 23(23):3241PubMedGoogle Scholar
  64. 64.
    Wells A, Huttenlocher A, Lauffenburger D (2005) Calpain proteases in cell adhesion and motility. Int Rev Cytol 245:1–16PubMedPubMedCentralGoogle Scholar
  65. 65.
    Yang Z (2005) Prediction of caspase cleavage sites using Bayesian bio-basis function neural networks. Bioinformatics 21(9):1831PubMedGoogle Scholar
  66. 66.
    Yang Z, Chou K (2004) Bio-support vector machines for computational proteomics. Bioinformatics 20(5):735PubMedGoogle Scholar
  67. 67.
    Zhang Z, Biesiadecki B, Jin J (2006) Selective deletion of the NH2-terminal variable region of cardiac troponin T in ischemia reperfusion by myofibril-associated μ-calpain cleavage. Biochemistry 45(38):11681–11694PubMedPubMedCentralGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Graduate School of Frontier ScienceThe University of TokyoKashiwaJapan
  2. 2.Artificial Intelligence Research CenterAISTKoto-kuJapan
  3. 3.Bioinformatics CenterKyoto UniversityUjiJapan
  4. 4.Department of Computer ScienceAalto UniversityEspooFinland

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