Skip to main content
SpringerLink
Log in

Decoding the Structural Keywords in Protein Structure Universe

  • Regular Paper
  • Published:
Journal of Computer Science and Technology Aims and scope Submit manuscript

Abstract

Although the protein sequence-structure gap continues to enlarge due to the development of high-throughput sequencing tools, the protein structure universe tends to be complete without proteins with novel structural folds deposited in the protein data bank (PDB) recently. In this work, we identify a protein structural dictionary (Frag-K) composed of a set of backbone fragments ranging from 4 to 20 residues as the structural “keywords” that can effectively distinguish between major protein folds. We firstly apply randomized spectral clustering and random forest algorithms to construct representative and sensitive protein fragment libraries from a large scale of high-quality, non-homologous protein structures available in PDB. We analyze the impacts of clustering cut-offs on the performance of the fragment libraries. Then, the Frag-K fragments are employed as structural features to classify protein structures in major protein folds defined by SCOP (Structural Classification of Proteins). Our results show that a structural dictionary with ~400 4- to 20-residue Frag-K fragments is capable of classifying major SCOP folds with high accuracy.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Schwede T. Protein modeling: What happened to the protein structure gap? Structure, 2013, 21(9): 1531-1540.

    Article  Google Scholar 

  2. Chothia C. Proteins. One thousand families for the molecular biologist. Nature, 1992, 357(6379): 543-544.

    Article  Google Scholar 

  3. Andreeva A, Howorth D, Chandonia J M, Brenner S E, Hubbard T J P, Chothia C, Murzin A G. Data growth and its impact on the SCOP database: New developments. Nucleic Acids Research, 2008, 36: D419-D425.

    Article  Google Scholar 

  4. Sillitoe I, Cuff A L, Dessailly B H, Dawson D L, Furnham N, Lee D, Lees J G, Lewis T E, Studer R A, Rentzsch R, Yeats C, Thornton J M, Orengo C A. New functional families (FunFams) in CATH to improve the mapping of conserved functional sites to 3D structures. Nucleic Acids Research, 2013, 41(D1): D490-D498.

    Article  Google Scholar 

  5. Chen D. Structural genomics: Exploring the 3D protein landscape, 2010. Biomedical Computation Review. http://biomedicalcomputationreview.org/content/structural-genomics-exploring-3d-protein-landscape, Nov. 2018.

  6. Kolinski A. Protein modeling and structure prediction with a reduced representation. Acta Biochimica Polonica, 2004, 51(2): 349-371.

    Google Scholar 

  7. Schwede T, Kopp J, Guex N, Peitsch M C. SWISS-MODEL: An automated protein homology-modeling server. Nucleic Acids Research, 2003, 31(13): 3381-3385.

    Article  Google Scholar 

  8. Zhou J F, Grigoryan G. Rapid search for tertiary fragments reveals protein sequence-structure relationships. Protein Science, 2015, 24(4): 508-524.

    Article  Google Scholar 

  9. Simons K T, Kooperberg C, Huang E, Baker D. Assembly of protein tertiary structures from fragments with similar local sequences using simulated annealing and Bayesian scoring functions. Journal of Molecular Biology, 1997, 268(1): 209-225.

    Article  Google Scholar 

  10. Li Y. Conformational sampling in template-free protein loop structure modeling: An overview. Computational and Structural Biotechnology Journal, 2013, 5: Article No. e201302003.

  11. Li Y, Rata I, Jakobsson E. Integrating multiple scoring functions to improve protein loop structure conformation space sampling. In Proc. IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, May 2010.

  12. Li Y, Rata I, Chiu S W, Jakobsson E. Improving predicted protein loop structure ranking using a Pareto-optimality consensus method. BMC Structural Biology, 2010, 10: Article No. 22.

  13. Simons K T, Ruczinski I, Kooperberg C, Fox B A, Bystroff C, Baker D. Improved recognition of native-like protein structures using a combination of sequence-dependent and sequence-independent features of proteins. Proteins: Structure, Function, and Genetics, 1999, 34(1): 82-95.

    Article  Google Scholar 

  14. Kolodny R, Koehl P, Guibas L, Levitt M. Small libraries of protein fragments model native protein structures accurately. Journal of Molecular Biology, 2002, 323(2): 297-307.

    Article  Google Scholar 

  15. Budowski-Tal I, Nov Y, Kolodny R. FragBag, an accurate representation of protein structure, retrieves structural neighbors from the entire PDB quickly and accurately. Proceedings of the National Academy of Sciences of the United States of America, 2010, 107(8): 3481-3486.

    Article  Google Scholar 

  16. Handl J, Knowles J, Vernon R, Baker D, Lovell S C. The dual role of fragments in fragment-assembly methods for de novo protein structure prediction. Proteins: Structure, Function, and Bioinformatics, 2012, 80(2): 490-504.

    Article  Google Scholar 

  17. Ji H, Yu W, Li Y. A rank revealing randomized singular value decomposition (R3SVD) algorithm for low-rank matrix approximations. arXiv:1605.08134, 2016. https://ar xiv.org/ftp/arxiv/papers/1605/1605.08134.pdf, September 2018.

  18. Elhefnawy W, Li M, Wang J, Li Y. Construction of protein backbone fragments libraries on large protein sets using a randomized spectral clustering algorithm. In Proc. the 13th International Symposium on Bioinformatics Research and Applications, May 2016, pp.108-119.

  19. Wang G L, Dunbrack R L. PISCES: A protein sequence culling server. Bioinformatics, 2003, 19(12): 1589-1591.

    Article  Google Scholar 

  20. Dong Q W, Zhou S G, Guan J H. A new taxonomybased protein fold recognition approach based on autocrosscovariance transformation. Bioinformatics, 2009, 25(20): 2655-2662.

    Article  Google Scholar 

  21. Ding C H Q, Dubchak I. Multi-class protein fold recognition using support vector machines and neural networks. Bioinformatics, 2001, 17(4): 349-358.

    Article  Google Scholar 

  22. Fox N K, Brenner S E, Chandonia J M. SCOPe: Structural classification of proteins-extended, integrating SCOP and ASTRAL data and classification of new structures. Nucleic Acids Research, 2014, 42(D1): D304-D309.

    Article  Google Scholar 

  23. von Luxburg U. A tutorial on spectral clustering. Statistics and Computing, 2007, 17(4): 395-416.

    Article  MathSciNet  Google Scholar 

  24. Shi J B, Malik J. Normalized cuts and image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2000, 22(8): 888-905.

    Article  Google Scholar 

  25. Ng A Y, Jordan M I, Weiss Y. On spectral clustering: Analysis and an algorithm. In Proc. the 14th International Conference on Neural Information Processing Systems: Natural and Synthetic, December 2001, pp.849-856.

  26. Halko N, Martinsson P G, Tropp J A. Finding structure with randomness: Probabilistic algorithms for constructing approximate matrix decompositions. SIAM Review, 2011, 53(2): 217-288.

    Article  MathSciNet  MATH  Google Scholar 

  27. Gu Y, Yu W, Li J, Liu S, Li Y. Single-pass PCA of large high-dimensional data. In Proc. the 26th International Joint Conference on Artificial Intelligence, August 2017, pp.3350-3356.

  28. Li Y, YuW. A fast implementation of singular value thresholding algorithm using recycling rank revealing randomized singular value decomposition. arXiv:1704.05528, 2017. https://arxiv.org/pdf/1704.05528.pdf, September 2018.

  29. Strobl C, Boulesteix A L, Zeileis A, Hothorn T. Bias in random forest variable importance measures: Illustrations, sources and a solution. BMC Bioinformatics, 2007, 8: Article No. 25.

  30. Chiang Y S, Gelfand T I, Kister A E, Gelfand I M. New classification of supersecondary structures of sandwich-like proteins uncovers strict patterns of strand assemblage. Proteins: Structure, Function, and Bioinformatics, 2007, 68(4): 915-921.

    Article  Google Scholar 

  31. Holmes J B, Tsai J. Some fundamental aspects of building protein structures from fragment libraries. Protein Science, 2004, 13(6): 1636-1650.

    Article  Google Scholar 

  32. Le Q, Pollastri G, Koehl P. Structural alphabets for protein structure classification: A comparison study. Journal of Molecular Biology, 2009, 387(2): 431-450.

    Article  Google Scholar 

  33. Bazzoli A, Tettamanzi A G B, Zhang Y. Computational protein design and large-scale assessment by I-TASSER structure assembly simulations. Journal of Molecular Biology, 2011, 407(5): 764-776.

    Article  Google Scholar 

  34. Elhefnawy W, Chen L, Han Y, Li Y. ICOSA: A distancedependent, orientation-specific coarse-grained contact potential for protein structure modeling. Journal of Molecular Biology, 2015, 427(15): 2562-2576.

    Article  Google Scholar 

  35. Li Y, Liu H, Rata I, Jakobsson E. Building a knowledgebased statistical potential by capturing high-order interresidue interactions and its applications in protein secondary structure assessment. Journal of Chemical Information and Modeling, 2013, 53(2): 500-508.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science, Old Dominion University, Norfolk, VA, 23452, USA

    Wessam Elhefnawy & Yaohang Li

  2. Department of Computer Science, Central South University, Changsha, 410083, China

    Min Li & Jian-Xin Wang

Authors
  1. Wessam Elhefnawy
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Min Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jian-Xin Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Yaohang Li
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yaohang Li.

Electronic supplementary material

ESM 1

(PDF 721 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Elhefnawy, W., Li, M., Wang, JX. et al. Decoding the Structural Keywords in Protein Structure Universe. J. Comput. Sci. Technol. 34, 3–15 (2019). https://doi.org/10.1007/s11390-019-1895-y

Download citation

  • Received:

  • Revised:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11390-019-1895-y

Keywords

Search

Navigation