Incorporating Knowledge of Secondary Structures in a L-System-Based Encoding for Protein Folding

  • Gabriela Ochoa
  • Gabi Escuela
  • Natalio Krasnogor
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3871)


An encoding scheme for protein folding on lattice models, inspired by parametric L-systems, was proposed. The encoding incorporates problem domain knowledge in the form of predesigned production rules that capture commonly known secondary structures: α-helices and β-sheets. The ability of this encoding to capture protein native conformations was tested using an evolutionary algorithm as the inference procedure for discovering L-systems. Results confirmed the suitability of the proposed representation. It appears that the occurrence of motifs and sub-structures is an important component in protein folding, and these sub-structures may be captured by a grammar-based encoding. This line of research suggests novel and compact encoding schemes for protein folding that may have practical implications in solving meaningful problems in biotechnology such as structure prediction and protein folding.


Secondary Structure Evolutionary Algorithm Encode Scheme Memetic Algorithm Inference Procedure 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Gabriela Ochoa
    • 1
  • Gabi Escuela
    • 1
  • Natalio Krasnogor
    • 2
  1. 1.Department of Computer ScienceUniversidad Simon BolivarCaracasVenezuela
  2. 2.School of Computer Science and I.T.University of NottinghamNottinghamUK

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