Using Multiobjective Optimization and Energy Minimization to Design an Isoform-Selective Ligand of the 14-3-3 Protein

  • Hernando Sanchez-Faddeev
  • Michael T. M. Emmerich
  • Fons J. Verbeek
  • Andrew H. Henry
  • Simon Grimshaw
  • Herman P. Spaink
  • Herman W. van Vlijmen
  • Andreas Bender
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7610)


Computer simulation techniques are being used extensively in the pharmaceutical field to model protein-ligand and protein-protein interactions; however, few procedures have been established yet for the design of ligands from scratch (‘de novo’). To improve upon the current state, in this work the problem of finding a peptide ligand was formulated as a bi-objective optimization problem and a state-of-the-art algorithm for evolutionary multiobjective optimization, namely SMS-EMOA, has been employed for exploring the search space. This algorithm is tailored to this problem class and used to produce a Pareto front in high-dimensional space, here consisting of 2322 or about 1030 possible solutions. From the knee point of the Pareto front we were able to select a ligand with preferential binding to the gamma versus the epsilon isoform of the Danio rerio (zebrafish) 14-3-3 protein. Despite the high-dimensional space the optimization algorithm is able to identify a 22-mer peptide ligand with a predicted difference in binding energy of 291 kcal/mol between the isoforms, showing that multiobjective optimization can be successfully employed in selective ligand design.


protein design ligand design de novo assembly SMS-EMOA multiobjective optimization 14-3-3 Pareto front multiobjective selection hypervolume indicator 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Hernando Sanchez-Faddeev
    • 1
  • Michael T. M. Emmerich
    • 1
  • Fons J. Verbeek
    • 1
  • Andrew H. Henry
    • 2
  • Simon Grimshaw
    • 2
  • Herman P. Spaink
    • 3
  • Herman W. van Vlijmen
    • 4
  • Andreas Bender
    • 4
  1. 1.Leiden Institute of Advanced Computer ScienceLeiden UniversityLeidenThe Netherlands
  2. 2.Chemical Computing GroupSt John’s Innovation CentreCambridgeUnited Kingdom
  3. 3.Institute of BiologyLeiden UniversityLeidenThe Netherlands
  4. 4.Medicinal Chemistry Division, Leiden / Amsterdam Center for Drug ResearchLeiden UniversityLeidenThe Netherlands

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