Skip to main content
SpringerLink
Log in

Comparing Best and Quota Fragment Picker Protocols Applied to Protein Structure Prediction

  • Conference paper
  • First Online:
Hybrid Intelligent Systems (HIS 2020)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1375))

Included in the following conference series:

  • 730 Accesses

Abstract

The use of fragment insertion in the protein structure prediction problem can be considered one of the most successful strategies to add problem-dependent information. The well known Rosetta suite provides two protocols for generating fragment libraries: Best and Quota. The first aims to maximize a given score function, while the second aims add fragment diversity. This paper presents the results with four proteins to verify the impact of using more secondary structure information through the quota protocol when compared to the best protocol. The tests were performed considering the PSIPRED, SPIDER2, and MUFold predictors. Results obtained are compared in terms of RMSD, processing time, and convergence.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight 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

Institutional subscriptions

Notes

  1. 1.

    Rosetta Suite available at https://www.rosettacommons.org/.

  2. 2.

    http://bioinf.cs.ucl.ac.uk/psipred/.

  3. 3.

    https://sparks-lab.org/.

  4. 4.

    http://mufold.org/mufold-ss-angle/.

  5. 5.

    Protein Databank available at https://www.rcsb.org/.

References

  1. Anfinsen, C.B.: Principles that govern the folding of protein chains. Science 181(4096), 223–230 (1973)

    Article  Google Scholar 

  2. Brest, J., Greiner, S., Boskovic, B., Mernik, M., Zumer, V.: Self-adapting control parameters in differential evolution: a comparative study on numerical benchmark problems. IEEE Trans. Evol. Comput. 10(6), 646–657 (2006)

    Article  Google Scholar 

  3. de Lima Corrêa, L., Dorn, M.: A multi-population memetic algorithm for the 3-D protein structure prediction problem. Swarm Evol. Comput. 55, 100677 (2020)

    Article  Google Scholar 

  4. Deng, L., Zhang, L., Sun, H., Li-yan, Q.: DSM-DE: a differential evolution with dynamic speciation-based mutation for single-objective optimization. Memet. Comput. 12, 73–86 (2019)

    Article  Google Scholar 

  5. Dhingra, S., Sowdhamini, R., Cadet, F., Offmann, B.: A glance into the evolution of template-free protein structure prediction methodologies. Biochimie 175, 85–92 (2020)

    Article  Google Scholar 

  6. Dorn, M., de Souza, O.N.: Cref: a central-residue-fragment-based method for predicting approximate 3-D polypeptides structures. In: Proceedings of the 2008 ACM Symposium on Applied Computing, SAC 2008, pp. 1261–1267. Association for Computing Machinery, New York (2008)

    Google Scholar 

  7. Garza-Fabre, M., Kandathil, S.M., Handl, J., Knowles, J., Lovell, S.C.: Generating, maintaining, and exploiting diversity in a memetic algorithm for protein structure prediction. Evol. Comput. 24(4), 577–607 (2016)

    Article  Google Scholar 

  8. Gront, D., Kulp, D.W., Vernon, R.M., Strauss, C.E.M., Baker, D.: Generalized fragment picking in Rosetta: Design, protocols and applications. PloS One 6(8), 1–10 (2011)

    Article  Google Scholar 

  9. Hao, X., Zhang, G., Zhou, X.: Guiding exploration in conformational feature space with Lipschitz underestimation for ab-initio protein structure prediction. Comput. Biol. Chem. 73, 105–119 (2018)

    Article  MathSciNet  Google Scholar 

  10. Hao, X., Zhang, G., Zhou, X., Yu, X.: A novel method using abstract convex underestimation in ab-initio protein structure prediction for guiding search in conformational feature space. IEEE/ACM Trans. Comput. Biol. Bioinform. 13(5), 887–900 (2016)

    Article  Google Scholar 

  11. Silva, R.S., Parpinelli, R.S.: A self-adaptive differential evolution with fragment insertion for the protein structure prediction problem. In: Blesa Aguilera, M.J., Blum, C., Gambini Santos, H., Pinacho-Davidson, P., Godoy del Campo, J. (eds.) Hybrid Metaheuristics, pp. 136–149. Springer, Cham (2019)

    Google Scholar 

  12. Smolarczyk, T., Roterman, I., Konieczna, I., Stapor, K.: Protein secondary structure prediction: a review of progress and directions. Curr. Bioinform. 15, 90–107 (2020)

    Article  Google Scholar 

  13. Zhang, G., Ma, L., Wang, X., Zhou, X.: Secondary structure and contact guided differential evolution for protein structure prediction. IEEE/ACM Trans. Comput. Biol. Bioinform. 17(3), 1068–1081 (2020)

    Article  Google Scholar 

  14. Zhang, G., Yu, X., Zhou, X., Hao, X.: A population-based conformational optimal algorithm using replica-exchange in ab-initio protein structure prediction. In: 2016 Chinese Control and Decision Conference (CCDC), pp. 701–706 (2016)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. UDESC Graduate Program in Applied Computing, Santa Catarina State University, Joinville, Brazil

    Nilcimar Neitzel Will & Rafael Stubs Parpinelli

Authors
  1. Nilcimar Neitzel Will
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Rafael Stubs Parpinelli
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Rafael Stubs Parpinelli .

Editor information

Editors and Affiliations

  1. Scientific Network for Innovation and Research Excellence, Machine Intelligence Research Labs (MIR Labs), Auburn, WA, USA

    Ajith Abraham

  2. Institute for Information Systems, University of Applied Sciences and Arts Northwestern Switzerland, Olten, Solothurn, Switzerland

    Thomas Hanne

  3. Division of Graduate Studies, Tijuana Institute of Technology, Tijuana, Mexico

    Oscar Castillo

  4. Scientific Network for Innovation and Research Excellence, Machine Intelligence Research Labs (MIR Labs), Auburn, AL, USA

    Niketa Gandhi

  5. Universidade Federal da Bahia, Salvador, Bahia, Brazil

    Tatiane Nogueira Rios

  6. Computer Science and Information Engineering, National University of Kaohsiung, Kaohsiung, Taiwan

    Tzung-Pei Hong

Rights and permissions

Reprints and permissions

Copyright information

© 2021 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Will, N.N., Parpinelli, R.S. (2021). Comparing Best and Quota Fragment Picker Protocols Applied to Protein Structure Prediction. In: Abraham, A., Hanne, T., Castillo, O., Gandhi, N., Nogueira Rios, T., Hong, TP. (eds) Hybrid Intelligent Systems. HIS 2020. Advances in Intelligent Systems and Computing, vol 1375. Springer, Cham. https://doi.org/10.1007/978-3-030-73050-5_65

Download citation

Publish with us

Policies and ethics

Search

Navigation