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International Conference on Swarm Intelligence

ICSI 2016: Advances in Swarm Intelligence pp 66–73Cite as

Scatter Search for Homology Modeling

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 9712))

Abstract

Homology modeling is an effective technique in protein structure prediction (PSP). However this technique suffers from poor initial target-template alignments. To improve homology based PSP, we propose a scatter search (SS) metaheuristic algorithm. SS is an evolutionary approach that is based on a population of candidate solutions. These candidates undergo evolutionary operations that combine search intensification and diversification over a number of iterations. The metaheuristic optimizes the initial poor alignments and uses fitness functions. We assess our algorithm on a number of proteins whose structures are present in the Protein Data Bank and which have been used in previous literature. Results obtained by our SS algorithm are compared with other approaches. The 3D models predicted by our algorithm show improved root mean standard deviations with respect to the native structures.

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Author information

Authors and Affiliations

  1. Department of Computer Science and Mathematics, Lebanese American University, Beirut, Lebanon

    Mouses Stamboulian & Nashat Mansour

Authors
  1. Mouses Stamboulian
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  2. Nashat Mansour
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Corresponding author

Correspondence to Nashat Mansour .

Editor information

Editors and Affiliations

  1. Peking University, Beijing, China

    Ying Tan

  2. Xi'an Jiaotong-Liverpool University, Suzhou, China

    Yuhui Shi

  3. Shenzhen University, Shenzhen, China

    Ben Niu

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© 2016 Springer International Publishing Switzerland

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Stamboulian, M., Mansour, N. (2016). Scatter Search for Homology Modeling. In: Tan, Y., Shi, Y., Niu, B. (eds) Advances in Swarm Intelligence. ICSI 2016. Lecture Notes in Computer Science(), vol 9712. Springer, Cham. https://doi.org/10.1007/978-3-319-41000-5_7

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  • DOI: https://doi.org/10.1007/978-3-319-41000-5_7

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-40999-3

  • Online ISBN: 978-3-319-41000-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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