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Differential Evolution Multi-Objective for Tertiary Protein Structure Prediction

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Applications of Evolutionary Computation (EvoApplications 2020)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 12104))

Abstract

The determination of proteins’ structure is very expensive and time-consuming, making computer-aided methods attractive. However, in computational terms, the protein structure prediction is a NP-Hard problem [17], meaning that there is no efficient algorithm that can find a solution in a viable computational time. Nonetheless, the energy terms that compose different force fields seem to be conflicting among themselves, leading to a multi-objective problem. In this sense, different works in the literature have proposed multi-objective formulations of search mechanisms. Hence, we use the Differential Evolution Multi-Objective (DEMO) algorithm with the Rosetta score3 energy function as a force field. In our work, we split the energy terms into two objectives, one with only the van der Waals values, while the second one contains the remaining bonded and non-bonded, including the secondary structure reinforcement. Moreover, we enhance the DEMO algorithm with structural knowledge provided by the Angle Probability List (APL). From this perspective, our work provides different contributions to the research area, since the DEMO algorithm was never used in this problem, neither the APL with this algorithm. Also, the multi-objective formulation using Rosetta score3 was not yet explored by related works, even though its relevance for the problem. Results obtained show that the DEMO found better structures than the single-objective differential evolution that uses the same mutation mechanism, energy function, and APL. Also, DEMO reached competitive results when comparing with state-of-art bi-objective approaches.

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Notes

  1. 1.

    www.pyrosetta.org.

  2. 2.

    http://sbcb.inf.ufrgs.br/nias.

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Acknowledgements

This work was supported by grants from MCT/CNPq [311611/2018-4], CAPES PROBRAL [88881.198766/2018-01] - Brazil, Alexander von Humboldt-Stiftung (AvH) [BRA 1190826 HFST CAPES-P] - Germany, and FAPERGS [19/2551-0001906-8, APE]. This study was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - Brasil (CAPES) - Finance Code 001.

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Authors and Affiliations

  1. Institute of Informatics, Federal University of Rio Grande do Sul, Porto Alegre, Brazil

    Pedro Henrique Narloch & Márcio Dorn

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  1. Pedro Henrique Narloch
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  2. Márcio Dorn
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Correspondence to Márcio Dorn .

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Editors and Affiliations

  1. University of Granada, Granada, Spain

    Pedro A. Castillo

  2. Université Le Havre Normandie, Le Havre, France

    Juan Luis Jiménez Laredo

  3. Universidad de Extremadura, Mérida, Spain

    Francisco Fernández de Vega

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Narloch, P.H., Dorn, M. (2020). Differential Evolution Multi-Objective for Tertiary Protein Structure Prediction. In: Castillo, P.A., Jiménez Laredo, J.L., Fernández de Vega, F. (eds) Applications of Evolutionary Computation. EvoApplications 2020. Lecture Notes in Computer Science(), vol 12104. Springer, Cham. https://doi.org/10.1007/978-3-030-43722-0_11

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  • DOI: https://doi.org/10.1007/978-3-030-43722-0_11

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