Journal of Structural and Functional Genomics

, Volume 16, Issue 3–4, pp 101–111 | Cite as

Automated protein motif generation in the structure-based protein function prediction tool ProMOL

  • Mikhail Osipovitch
  • Mitchell Lambrecht
  • Cameron Baker
  • Shariq Madha
  • Jeffrey L. Mills
  • Paul A. Craig
  • Herbert J. Bernstein


ProMOL, a plugin for the PyMOL molecular graphics system, is a structure-based protein function prediction tool. ProMOL includes a set of routines for building motif templates that are used for screening query structures for enzyme active sites. Previously, each motif template was generated manually and required supervision in the optimization of parameters for sensitivity and selectivity. We developed an algorithm and workflow for the automation of motif building and testing routines in ProMOL. The algorithm uses a set of empirically derived parameters for optimization and requires little user intervention. The automated motif generation algorithm was first tested in a performance comparison with a set of manually generated motifs based on identical active sites from the same 112 PDB entries. The two sets of motifs were equally effective in identifying alignments with homologs and in rejecting alignments with unrelated structures. A second set of 296 active site motifs were generated automatically, based on Catalytic Site Atlas entries with literature citations, as an expansion of the library of existing manually generated motif templates. The new motif templates exhibited comparable performance to the existing ones in terms of hit rates against native structures, homologs with the same EC and Pfam designations, and randomly selected unrelated structures with a different EC designation at the first EC digit, as well as in terms of RMSD values obtained from local structural alignments of motifs and query structures. This research is supported by NIH grant GM078077.


Template-based alignment ProMOL PyMOL Molecular visualization Structural bioinformatics Enzyme Catalytic site motif 



The authors thank the invaluable assistance of Frances C. Bernstein and current and former students who have worked at Dowling College and at RIT on the SBEVSL project. Funding for the project has been provided by NIH GM078077, NSF-DUE 0402408, Rochester Institute of Technology and Dowling College.


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Copyright information

© Springer Science+Business Media Dordrecht 2015

Authors and Affiliations

  1. 1.Gosnell School of Life SciencesRochester Institute of TechnologyRochesterUSA
  2. 2.School of Chemistry and Materials ScienceRochester Institute of TechnologyRochesterUSA

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