Functional classification of protein 3D structures from predicted local interaction sites

Ramya Parasuram, Joslynn S. Lee, Pengcheng Yin, Srinivas Somarowthu, Mary Jo Ondrechen

Research output: Contribution to journalArticlepeer-review

16 Scopus citations


A new approach to the functional classification of protein 3D structures is described with application to some examples from structural genomics. This approach is based on functional site prediction with THEMATICS and POOL. THEMATICS employs calculated electrostatic potentials of the query structure. POOL is a machine learning method that utilizes THEMATICS features and has been shown to predict accurate, precise, highly localized interaction sites. Extension to the functional classification of structural genomics proteins is now described. Predicted functionally important residues are structurally aligned with those of proteins with previously characterized biochemical functions. A 3D structure match at the predicted local functional site then serves as a more reliable predictor of biochemical function than an overall structure match. Annotation is confirmed for a structural genomics protein with the ribulose phosphate binding barrel (RPBB) fold. A putative glucoamylase from Bacteroides fragilis (PDB ID 3eu8) is shown to be in fact probably not a glucoamylase. Finally a structural genomics protein from Streptomyces coelicolor annotated as an enoyl-CoA hydratase (PDB ID 3g64) is shown to be misannotated. Its predicted active site does not match the well-characterized enoyl-CoA hydratases of similar structure but rather bears closer resemblance to those of a dehalogenase with similar fold.

Original languageEnglish (US)
Pages (from-to)1-15
Number of pages15
JournalJournal of Bioinformatics and Computational Biology
Issue numberSUPPL. 1
StatePublished - Dec 2010

Bibliographical note

Funding Information:
We are grateful to the U.S. National Science Foundation for support of this work under grant number MCB-0843603 and for a Graduate Research Fellowship and an IGERT Traineeship awarded to Joslynn Lee. We also thank Professor Patsy Babbitt and Dr. Leonel F. Murga for helpful discussions.


  • Functional annotation
  • POOL
  • structural genomics


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