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|dc.title||Knowledge-guided docking of WW domain proteins and flexible ligands|
|dc.contributor.author||Banu Bte Sm Rashid, S.|
|dc.identifier.citation||Lu, H.,Li, H.,Banu Bte Sm Rashid, S.,Leow, W.K.,Liou, Y.-C. (2009). Knowledge-guided docking of WW domain proteins and flexible ligands. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 5780 LNBI : 175-186. ScholarBank@NUS Repository. <a href="https://doi.org/10.1007/978-3-642-04031-3_16" target="_blank">https://doi.org/10.1007/978-3-642-04031-3_16</a>|
|dc.description.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. © 2009 Springer Berlin Heidelberg.|
|dc.description.sourcetitle||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|
|Appears in Collections:||Staff Publications|
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