Skip to main content
SpringerLink
Log in

Multi-subpopulation Algorithm with Ensemble Mutation Strategies for Protein Structure Prediction

  • Conference paper
  • First Online:
Bio-inspired Computing: Theories and Applications (BIC-TA 2019)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1159))

  • 946 Accesses

Abstract

One of the most challenging problems in protein structure prediction (PSP) is the ability of the sampling low energy conformations. In this paper, a multi-subpopulation algorithm with ensemble mutation strategies (MSEMS) is proposed, based on the framework of differential evolution (DE). In proposed MSEMS, multiple subpopulations with different mutation strategy pools are applied to balance the exploitation and exploration capability. One of the subpopulations is utilized to reward the one with the best performance in the other three subpopulations. Meanwhile, a distance-based fitness score is designed to alleviate inaccuracy of low-resolution energy model. The experimental results indicate that MSEMS has the potential to improve the accuracy of protein structure prediction.

Supported by the National Nature Science Foundation of China (No. 61773346).

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 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
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

References

  1. Zhou, X., Hu, J., Zhang, C., Zhang, G., Zhang, Y.: Assembling multi-domain protein structures through analogous global structural alignments. Proc. Nat. Acad. Sci. U.S.A. 116(32), 15930–15938 (2019)

    Article  Google Scholar 

  2. Gntert, P.: Automated NMR protein structure calculation with CYANA. Prog. Nucl. Magn. Reson. Spectrosc. 43(3–4), 105–125 (2004)

    Google Scholar 

  3. 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. (2018)

    Google Scholar 

  4. Ben-David, M., Noivirt-Brik, O., Paz, A., Prilusky, J., Sussman, J.L., Levy, Y.: Assessment of CASP8 structure predictions for template free targets. Proteins: Struct. Funct. Bioinform. 77(S9), 50–65 (2009)

    Google Scholar 

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

    Article  Google Scholar 

  6. 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), 1 (2016)

    Article  Google Scholar 

  7. Gelin, B.R., Mccammon, J.A., Karplus, M.: Dynamics of folded proteins. Nature 267(5612), 585–590 (1977)

    Article  Google Scholar 

  8. Ovchinnikov, S., Park, H., David, E.K., Dimaio, F., Baker, D.: Protein structure prediction using Rosetta in CASP12. Proteins: Struct. Funct. Bioinform. 86(10), 113–121 (2018)

    Article  Google Scholar 

  9. Lee, J., Lee, J., Sasaki, T.N., Sasai, M., Seok, C., Lee, J.: De novo protein structure prediction by dynamic fragment assembly and conformational space annealing. Proteins: Struct. Funct. Bioinform. 79(8), 2403–2417 (2011)

    Google Scholar 

  10. Yuan, B., Li, B., Chen, H., Yao, X.: A new evolutionary algorithm with structure mutation for the maximum balanced biclique problem. IEEE Trans. Cybern. 45(5), 1054–1067 (2015)

    Article  Google Scholar 

  11. Lee, K.B., Kim, J.H.: Multiobjective particle swarm optimization with preference-based sort and its application to path following footstep optimization for humanoid robots. IEEE Trans. Evol. Comput. 17(6), 755–766 (2013)

    Article  Google Scholar 

  12. Zhou, X., Zhang, G., Hao, X., Yu, L., Xu, D.: Differential evolution with multi-stage strategies for global optimization. In: IEEE Congress on Evolutionary Computation, pp. 2550–2557. IEEE, Vancouver (2016)

    Google Scholar 

  13. Storn, R., Price, K.: Differential evolution-a simple and efficient heuristic for global optimization over continuous spaces. J. Global Optim. 11(4), 341–359 (1997)

    Article  MathSciNet  Google Scholar 

  14. Das, S., Mullick, S.S., Suganthan, P.N.: Recent advances in differential evolution-an updated survey. Swarm Evol. Comput. 27, 1–30 (2016)

    Article  Google Scholar 

  15. Glotić, A., Glotić, A., Kitak, P., Pihler, J., Tičar, I.: Parallel self-adaptive differential evolution algorithm for solving short-term hydro scheduling problem. IEEE Trans. Power Syst. 29(5), 2347–2358 (2014)

    Article  Google Scholar 

  16. Sharma, S., Rangaiah, G.P.: An improved multi-objective differential evolution with a termination criterion for optimizing chemical processes. Comput. Chem. Eng. 56, 155–173 (2013)

    Article  Google Scholar 

  17. Sudha, S., Baskar, S., Amali, S.M.J., Krishnaswamy, S.: Protein structure prediction using diversity controlled self-adaptive differential evolution with local search. Soft. Comput. 19(6), 1635–1646 (2014). https://doi.org/10.1007/s00500-014-1353-2

    Article  Google Scholar 

  18. De Melo, V.V., Carosio, G.L.: Investigating multi-view differential evolution for solving constrained engineering design problems. Expert Syst. Appl. 40(9), 3370–3377 (2013)

    Article  Google Scholar 

  19. Epitropakis, M.G., Tasoulis, D.K., Pavlidis, N.G., Plagianakos, V.P., Vrahatis, M.N.: Enhancing differential evolution utilizing proximity-based mutation operators. IEEE Trans. Evol. Comput. 15(1), 99–119 (2011)

    Article  Google Scholar 

  20. Zhou, X., Zhang, G.: Differential evolution with underestimation-based multimutation strategy. IEEE Trans. Cybern. 49(4), 1353–1364 (2019)

    Article  Google Scholar 

  21. Gong, W., Cai, Z.: Differential evolution with ranking-based mutation operators. IEEE Trans. Cybern. 43(6), 2066–2081 (2013)

    Article  Google Scholar 

  22. Zhou, X., Zhang, G.: Abstract convex underestimation assisted multistage differential evolution. IEEE Trans. Cybern. 47(9), 2730–2741 (2017)

    Article  Google Scholar 

  23. Qin, A.K., Huang, V.L., Suganthan, P.N.: Differential evolution algorithm with strategy adaptation for global numerical optimization. IEEE Trans. Evol. Comput. 13(2), 398–417 (2009)

    Article  Google Scholar 

  24. Gong, W., Cai, Z., Ling, C.X., Li, H.: Enhanced differential evolution with adaptive strategies for numerical optimization. IEEE Trans. Syst. Man Cybern. Part B (Cybern.) 41(2), 397–413 (2010)

    Google Scholar 

  25. Wang, Y., Cai, Z., Zhang, Q.: Differential evolution with composite trial vector generation strategies and control parameters. IEEE Trans. Evol. Comput. 15(1), 55–66 (2011)

    Article  Google Scholar 

  26. Zhou, X., Zhang, G., Hao, X., Yu, L.: A novel differential evolution algorithm using local abstract convex underestimate strategy for global optimization. Comput. Oper. Res. 75, 132–149 (2016)

    Article  MathSciNet  Google Scholar 

  27. Zhang, G., Zhou, X., Yu, X., Hao, X., Yu, L.: Enhancing protein conformational space sampling using distance profile-guided differential evolution. IEEE/ACM Trans. Comput. Biol. Bioinform. 14(6), 1288–1301 (2017)

    Article  Google Scholar 

  28. Liwo, A., Arłukowicz, P., Czaplewski, C., Ołdziej, S., Pillardy, J., Scheraga, H.A.: A method for optimizing potential-energy functions by a hierarchical design of the potential-energy landscape: application to the UNRES force field. Proc. Nat. Acad. Sci. 99(4), 1937–1942 (2002)

    Article  Google Scholar 

  29. Bowers, P.M., Strauss, C.E.M., Baker, D.: De novo protein structure determination using sparse NMR data. J. Biomol. NMR 18(4), 311–318 (2000). https://doi.org/10.1023/A:1026744431105

    Article  Google Scholar 

  30. Xu, D., Zhang, Y.: Toward optimal fragment generations for ab initio protein structure assembly. Proteins: Struct. Funct. Bioinform. 81(2), 229–239 (2013)

    Google Scholar 

  31. Custdio, F.L., Barbosa, H.J.C., Dardenne, L.E.: Full-atom ab initio protein structure prediction with a Genetic Algorithm using a similarity-based surrogate model. In: IEEE Congress on Evolutionary Computation, pp. 1–8. IEEE (2010)

    Google Scholar 

  32. Xu, J.: Rapid protein side-chain packing via tree decomposition. In: Miyano, S., Mesirov, J., Kasif, S., Istrail, S., Pevzner, P.A., Waterman, M. (eds.) RECOMB 2005. LNCS, vol. 3500, pp. 423–439. Springer, Heidelberg (2005). https://doi.org/10.1007/11415770_32

    Chapter  Google Scholar 

  33. Saleh, S., Olson, B., Shehu, A.: A population-based evolutionary search approach to the multiple minima problem in de novo protein structure prediction. BMC Struct. Biol.: Struct. Funct. Bioinform. 13(S1), 1–19 (2013). https://doi.org/10.1186/1472-6807-13-S1-S4

    Article  Google Scholar 

  34. Rohl, C.A., Strauss, C.E.M., Misura, K.M.S., Baker, D.: Protein structure prediction using Rosetta. Methods Enzymol. 383(383), 66 (2003)

    Google Scholar 

  35. Simons, K.T., Bonneau, R., Ruczinski, I., Baker, D.: Ab initio protein structure prediction of CASP III targets using ROSETTA. Proteins: Struct. Funct. Bioinform. 37(S3), 171–176 (1999)

    Article  Google Scholar 

  36. Metropolis, N., Rosenbluth, A.W., Rosenbluth, M.N., Teller, A.H., Teller, E.: Equation of state calculations by fast computing machines. J. Chem. Phys. 21(6), 1087–1092 (1953)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. College of Information Engineering, Zhejiang University of Technology, Hangzhou, 310023, China

    Chunxiang Peng & Guijun Zhang

  2. Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, 48109, USA

    Xiaogen Zhou

Authors
  1. Chunxiang Peng
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Xiaogen Zhou
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Guijun Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Guijun Zhang .

Editor information

Editors and Affiliations

  1. Huazhong University of Science and Technology, Wuhan, China

    Linqiang Pan

  2. Zhengzhou University, Zhengzhou, China

    Jing Liang

  3. Zhongyuan University of Technology, Zhengzhou, China

    Boyang Qu

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Peng, C., Zhou, X., Zhang, G. (2020). Multi-subpopulation Algorithm with Ensemble Mutation Strategies for Protein Structure Prediction. In: Pan, L., Liang, J., Qu, B. (eds) Bio-inspired Computing: Theories and Applications. BIC-TA 2019. Communications in Computer and Information Science, vol 1159. Springer, Singapore. https://doi.org/10.1007/978-981-15-3425-6_21

Download citation

  • DOI: https://doi.org/10.1007/978-981-15-3425-6_21

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-15-3424-9

  • Online ISBN: 978-981-15-3425-6

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation