Skip to main content
SpringerLink
Log in
Book cover

Prediction of Protein Secondary Structure pp 83–113Cite as

One-Dimensional Structural Properties of Proteins in the Coarse-Grained CABS Model

  • Protocol
  • First Online:

Part of the book series: Methods in Molecular Biology ((MIMB,volume 1484))

Abstract

Despite the significant increase in computational power, molecular modeling of protein structure using classical all-atom approaches remains inefficient, at least for most of the protein targets in the focus of biomedical research. Perhaps the most successful strategy to overcome the inefficiency problem is multiscale modeling to merge all-atom and coarse-grained models. This chapter describes a well-established CABS coarse-grained protein model. The CABS (C-Alpha, C-Beta, and Side chains) model assumes a 2–4 united-atom representation of amino acids, knowledge-based force field (derived from the statistical regularities seen in known protein sequences and structures) and efficient Monte Carlo sampling schemes (MC dynamics, MC replica-exchange, and combinations). A particular emphasis is given to the unique design of the CABS force-field, which is largely defined using one-dimensional structural properties of proteins, including protein secondary structure. This chapter also presents CABS-based modeling methods, including multiscale tools for de novo structure prediction, modeling of protein dynamics and prediction of protein–peptide complexes. CABS-based tools are freely available at http://biocomp.chem.uw.edu.pl/tools

The original version of this chapter was revised. The erratum to this chapter is available at: DOI 10.1007/978-1-4939-6406-2_21

An erratum to this chapter can be found at http://dx.doi.org/10.1007/978-1-4939-6406-2_21

This is a preview of subscription content, log in via an institution.

Protocol
USD   49.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   109.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   139.00
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD   169.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

Springer Nature is developing a new tool to find and evaluate Protocols. Learn more

References

  1. Lindorff-Larsen K, Piana S, Dror RO, Shaw DE (2011) How fast-folding proteins fold. Science 334:517–520

    Article  CAS  PubMed  Google Scholar 

  2. Kmiecik S, Wabik J, Kolinski M, Kouza M, Kolinski A (2014) Coarse-grained modeling of protein dynamics. In: Computational methods to study the structure and dynamics of biomolecules and biomolecular processes, vol 1, Springer, Heidelberg, Berlin, pp 55–79

    Google Scholar 

  3. Kmiecik S, Gront D, Kolinski M, Wieteska L, Dawid AE, Kolinski A (2016) Coarse-grained protein models and their applications. Chem Rev. doi: 10.1021/acs.chemrev.6b00163

    Google Scholar 

  4. Kmiecik S, Jamroz M, Kolinski A (2011) Multiscale approach to protein folding dynamics. In: Multiscale approaches to protein modeling. Springer, New York, pp 281–293

    Google Scholar 

  5. Levitt M, Warshel A (1975) Computer simulation of protein folding. Nature 253:694–698

    Article  CAS  PubMed  Google Scholar 

  6. Rohl CA, Strauss CEM, Misura KMS, Baker D (2004) Protein structure prediction using Rosetta. In: Methods Enzymol, vol 383, Academic Press, pp 66–93

    Google Scholar 

  7. Mao B, Tejero R, Baker D, Montelione GT (2014) Protein NMR structures refined with Rosetta have higher accuracy relative to corresponding X-ray crystal structures. J Am Chem Soc 136:1893–1906

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Roy A, Kucukural A, Zhang Y (2010) I-TASSER: a unified platform for automated protein structure and function prediction. Nat Protoc 5:725–738

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Yang J, Yan R, Roy A, Xu D, Poisson J, Zhang Y (2015) The I-TASSER suite: protein structure and function prediction. Nat Methods 12:7–8

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Kolinski A (2004) Protein modeling and structure prediction with a reduced representation. Acta Biochim Pol 51:349–371

    CAS  PubMed  Google Scholar 

  11. Kmiecik S, Kolinski A (2007) Characterization of protein-folding pathways by reduced-space modeling. Proc Natl Acad Sci U S A 104:12330–12335

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Liwo A, Khalili M, Scheraga HA (2005) Ab initio simulations of protein-folding pathways by molecular dynamics with the united-residue model of polypeptide chains. Proc Natl Acad Sci U S A 102:2362–2367

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Liwo A, He Y, Scheraga HA (2011) Coarse-grained force field: general folding theory. Phys Chem Chem Phys 13:16890–16901

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Blaszczyk M, Jamroz M, Kmiecik S, Kolinski A (2013) CABS-fold: server for the de novo and consensus-based prediction of protein structure. Nucleic Acids Res 41:W406–W411

    Article  PubMed  PubMed Central  Google Scholar 

  15. Kurcinski M, Jamroz M, Blaszczyk M, Kolinski A, Kmiecik S (2015) CABS-dock web server for the flexible docking of peptides to proteins without prior knowledge of the binding site. Nucleic Acids Res 43:W419–W424

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Blaszczyk M, Kurcinski M, Kouza M, Wieteska L, Debinski A, Kolinski A, Kmiecik S (2016) Modeling of protein–peptide interactions using the CABS-dock web server for binding site search and flexible docking. Methods 93:72–83

    Google Scholar 

  17. Jamroz M, Kolinski A, Kmiecik S (2014) Protocols for efficient simulations of long-time protein dynamics using coarse-grained CABS model. Methods Mol Biol 1137:235–250

    Article  CAS  PubMed  Google Scholar 

  18. Kar P, Feig M (2014) Recent advances in transferable coarse-grained modeling of proteins. Adv Protein Chem Struct Biol 96:143–180

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Kolinski A, Bujnicki JM (2005) Generalized protein structure prediction based on combination of fold-recognition with de novo folding and evaluation of models. Proteins 61(Suppl 7):84–90

    Article  CAS  PubMed  Google Scholar 

  20. Skolnick J, Zhang Y, Arakaki AK, Kolinski A, Boniecki M, Szilagyi A, Kihara D (2003) TOUCHSTONE: a unified approach to protein structure prediction. Proteins 53(Suppl 6):469–479

    Article  CAS  PubMed  Google Scholar 

  21. Debe DA, Danzer JF, Goddard WA, Poleksic A (2006) STRUCTFAST: protein sequence remote homology detection and alignment using novel dynamic programming and profile-profile scoring. Proteins 64:960–967

    Article  CAS  PubMed  Google Scholar 

  22. Boniecki M, Rotkiewicz P, Skolnick J, Kolinski A (2003) Protein fragment reconstruction using various modeling techniques. J Comput Aided Mol Des 17:725–738

    Article  CAS  PubMed  Google Scholar 

  23. Jamroz M, Kolinski A (2010) Modeling of loops in proteins: a multi-method approach. BMC Struct Biol 10:5

    Article  PubMed  PubMed Central  Google Scholar 

  24. Kmiecik S, Jamroz M, Kolinski M (2014) Structure prediction of the second extracellular loop in G-protein-coupled receptors. Biophys J 106:2408–2416

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Sali A, Blundell TL (1993) Comparative protein modelling by satisfaction of spatial restraints. J Mol Biol 234:779–815

    Article  CAS  PubMed  Google Scholar 

  26. Latek D, Kolinski A (2011) CABS-NMR—de novo tool for rapid global fold determination from chemical shifts, residual dipolar couplings and sparse methyl-methyl NOEs. J Comput Chem 32:536–544

    Article  CAS  PubMed  Google Scholar 

  27. Kurcinski M, Kolinski A, Kmiecik S (2014) Mechanism of folding and binding of an intrinsically disordered protein as revealed by ab initio simulations. J Chem Theor Comput 10:2224–2231

    Article  CAS  Google Scholar 

  28. Steczkiewicz K, Zimmermann MT, Kurcinski M, Lewis BA, Dobbs D, Kloczkowski A, Jernigan RL, Kolinski A, Ginalski K (2011) Human telomerase model shows the role of the TEN domain in advancing the double helix for the next polymerization step. Proc Natl Acad Sci U S A 108:9443–9448

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Kurcinski M, Kolinski A (2007) Hierarchical modeling of protein interactions. J Mol Model 13:691–698

    Article  CAS  PubMed  Google Scholar 

  30. Kmiecik S, Gront D, Kouza M, Kolinski A (2012) From coarse-grained to atomic-level characterization of protein dynamics: transition state for the folding of B domain of protein A. J Phys Chem B 116:7026–7032

    Article  CAS  PubMed  Google Scholar 

  31. Kmiecik S, Kolinski A (2011) Simulation of chaperonin effect on protein folding: a shift from nucleation-condensation to framework mechanism. J Am Chem Soc 133:10283–10289

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Kmiecik S, Kolinski A (2008) Folding pathway of the b1 domain of protein G explored by multiscale modeling. Biophys J 94:726–736

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Kmiecik S, Kurcinski M, Rutkowska A, Gront D, Kolinski A (2006) Denatured proteins and early folding intermediates simulated in a reduced conformational space. Acta Biochim Pol 53:131–144

    CAS  PubMed  Google Scholar 

  34. Wabik J, Kmiecik S, Gront D, Kouza M, Kolinski A (2013) Combining coarse-grained protein models with replica-exchange all-atom molecular dynamics. Int J Mol Sci 14:9893–9905

    Article  PubMed  PubMed Central  Google Scholar 

  35. Jamroz M, Orozco M, Kolinski A, Kmiecik S (2013) Consistent view of protein fluctuations from all-atom molecular dynamics and coarse-grained dynamics with knowledge-based force-field. J Chem Theor Comput 9:119–125

    Article  CAS  Google Scholar 

  36. Jamroz M, Kolinski A, Kmiecik S (2013) CABS-flex: server for fast simulation of protein structure fluctuations. Nucleic Acids Res 41:W427–W431

    Article  PubMed  PubMed Central  Google Scholar 

  37. Jamroz M, Kolinski A, Kmiecik S (2014) CABS-flex predictions of protein flexibility compared with NMR ensembles. Bioinformatics 30:2150–2154

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. 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 

  39. McGuffin LJ, Bryson K, Jones DT (2000) The PSIPRED protein structure prediction server. Bioinformatics 16:404–405

    Article  CAS  PubMed  Google Scholar 

  40. Xu D, Zhang Y (2011) Improving the physical realism and structural accuracy of protein models by a two-step atomic-level energy minimization. Biophys J 101:2525–2534

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Eswar N, Webb B, Marti-Renom MA, Madhusudhan MS, Eramian D, Shen M-Y, Pieper U, Sali A (2007) Comparative protein structure modeling using MODELLER. Curr Protoc Protein Sci 2:1–31

    Google Scholar 

  42. Claessens M, Van Cutsem E, Lasters I, Wodak S (1989) Modelling the polypeptide backbone with “spare parts” from known protein structures. Protein Eng 2:335–345

    Article  CAS  PubMed  Google Scholar 

  43. Gront D, Kmiecik S, Kolinski A (2007) Backbone building from quadrilaterals: a fast and accurate algorithm for protein backbone reconstruction from alpha carbon coordinates. J Comput Chem 28:1593–1597

    Article  CAS  PubMed  Google Scholar 

  44. Wang Q, Canutescu AA, Dunbrack RL Jr (2008) SCWRL and MolIDE: computer programs for side-chain conformation prediction and homology modeling. Nat Protoc 3:1832–1847

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Kmiecik S, Gront D, Kolinski A (2007) Towards the high-resolution protein structure prediction. Fast refinement of reduced models with all-atom force field. BMC Struct Biol 7:43

    Article  PubMed  PubMed Central  Google Scholar 

  46. Gront D, Kmiecik S, Blaszczyk M, Ekonomiuk D, Kolinski A (2012) Optimization of protein models. Wiley Interdiscipl Rev-Comput Mol Sci 2:479–493

    Article  CAS  Google Scholar 

  47. Kim H, Kihara D (2015) Protein structure prediction using residue- and fragment-environment potentials in CASP11. Proteins. doi:10.1002/prot.24920

    Google Scholar 

  48. Zimmermann MT, Leelananda SP, Kloczkowski A, Jernigan RL (2012) Combining statistical potentials with dynamics-based entropies improves selection from protein decoys and docking poses. J Phys Chem B 116:6725–6731

    Article  CAS  PubMed  Google Scholar 

  49. Faraggi E, Kloczkowski A (2014) A global machine learning based scoring function for protein structure prediction. Proteins 82:752–759

    Article  CAS  PubMed  Google Scholar 

  50. Jamroz M, Kolinski A (2013) ClusCo: clustering and comparison of protein models. BMC Bioinformatics 14:62

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Gront D, Kolinski A (2005) HCPM—program for hierarchical clustering of protein models. Bioinformatics 21:3179–3180

    Article  CAS  PubMed  Google Scholar 

  52. Theobald DL, Steindel PA (2012) Optimal simultaneous superpositioning of multiple structures with missing data. Bioinformatics 28:1972–1979

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Kolinski A, Betancourt MR, Kihara D, Rotkiewicz P, Skolnick J (2001) Generalized comparative modeling (GENECOMP): a combination of sequence comparison, threading, and lattice modeling for protein structure prediction and refinement. Proteins 44:133–149

    Article  CAS  PubMed  Google Scholar 

  54. Schwede T, Diemand A, Guex N, Peitsch MC (2000) Protein structure computing in the genomic era. Res Microbiol 151:107–112

    Article  CAS  PubMed  Google Scholar 

  55. Raveh B, London N, Schueler-Furman O (2010) Sub-angstrom modeling of complexes between flexible peptides and globular proteins. Proteins 78:2029–2040

    CAS  PubMed  Google Scholar 

Download references

Acknowledgments

Funding for this work was provided by the National Science Center grant [MAESTRO 2014/14/A/ST6/00088] and by the Foundation for Polish Science TEAM project (TEAM/2011-7/6) cofinanced by the EU European Regional Development Fund operated within the Innovative Economy Operational Program.

Author information

Authors and Affiliations

  1. Faculty of Chemistry, University of Warsaw, Pasteura 1, Warszawa, 02-093, Poland

    Sebastian Kmiecik & Andrzej Kolinski

Authors
  1. Sebastian Kmiecik
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Andrzej Kolinski
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Andrzej Kolinski .

Editor information

Editors and Affiliations

  1. Institute for Glycomics and School of Information and Communication Technology, Griffith University, Southport, Queensland, Australia

    Yaoqi Zhou

  2. Battelle Center for Mathematical Medicine, Nationwide Children’s Hospital, Columbus, Ohio, USA

    Andrzej Kloczkowski

  3. Department of Biochemistry and Molecular Biology, Indiana University School of Medicine, Indianapolis, Indiana, USA

    Eshel Faraggi

  4. Institute for Glycomics and School of Information and Communication Technology, Griffith University, Southport, Queensland, Australia

    Yuedong Yang

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer Science+Business Media New York

About this protocol

Cite this protocol

Kmiecik, S., Kolinski, A. (2017). One-Dimensional Structural Properties of Proteins in the Coarse-Grained CABS Model. In: Zhou, Y., Kloczkowski, A., Faraggi, E., Yang, Y. (eds) Prediction of Protein Secondary Structure. Methods in Molecular Biology, vol 1484. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-6406-2_8

Download citation

  • DOI: https://doi.org/10.1007/978-1-4939-6406-2_8

  • Published:

  • Publisher Name: Humana Press, New York, NY

  • Print ISBN: 978-1-4939-6404-8

  • Online ISBN: 978-1-4939-6406-2

  • eBook Packages: Springer Protocols

Publish with us

Policies and ethics

Search

Navigation