Knowledge-Guided Docking of WW Domain Proteins and Flexible Ligands

  • Haiyun Lu
  • Hao Li
  • Shamima Banu Bte Sm Rashid
  • Wee Kheng Leow
  • Yih-Cherng Liou
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5780)

Abstract

Studies of interactions between protein domains and ligands are important in many aspects such as cellular signaling. We present a knowledge-guided approach for docking protein domains and flexible ligands. The approach is applied to the WW domain, a small protein module mediating signaling complexes which have been implicated in diseases such as muscular dystrophy and Liddle’s syndrome. The first stage of the approach employs a substring search for two binding grooves of WW domains and possible binding motifs of peptide ligands based on known features. The second stage aligns the ligand’s peptide backbone to the two binding grooves using a quasi-Newton constrained optimization algorithm. The backbone-aligned ligands produced serve as good starting points to the third stage which uses any flexible docking algorithm to perform the docking. The experimental results demonstrate that the backbone alignment method in the second stage performs better than conventional rigid superposition given two binding constraints. It is also shown that using the backbone-aligned ligands as initial configurations improves the flexible docking in the third stage. The presented approach can also be applied to other protein domains that involve binding of flexible ligand to two or more binding sites.

Keywords

Root Mean Square Deviation Protein Docking Flexible Ligand Docking Algorithm Average Root Mean Square Deviation 
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 2009

Authors and Affiliations

  • Haiyun Lu
    • 1
  • Hao Li
    • 1
  • Shamima Banu Bte Sm Rashid
    • 1
  • Wee Kheng Leow
    • 1
  • Yih-Cherng Liou
    • 2
  1. 1.Dept. of Computer Science, School of ComputingNational University of SingaporeSingapore
  2. 2.Dept. of Biological Sciences, Faculty of ScienceNational University of SingaporeSingapore

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