DASP3: identification of protein sequences belonging to functionally relevant groups

BMC Bioinformatics

Software

Patricia C. Babbitt¹  Jacquelyn S. Fetrow²  John H. Morris³  Thomas E. Ferrin³  Angela F. Harper⁴  Janelle B. Leuthaeuser⁵ 

[1] Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, 94158, San Francisco, CA, USA;Department of Chemistry, University of Richmond, 23173, Richmond, VA, USA;Department of Pharmaceutical Chemistry, University of California San Francisco, 94158, San Francisco, CA, USA;Department of Physics, Wake Forest University, 27106, Winston-Salem, NC, USA;Molecular Genetics and Genomics Program, Wake Forest University, 27106, Winston-Salem, NC, USA;Present address: University of Richmond, Gottwald Hall C302, 23173, Richmond, VA, USA;

关键词: Active site profiling;    Protein function annotation;    Functionally relevant clustering;    Misannotation;   

DOI  :  10.1186/s12859-016-1295-z

 received in 2016-04-22, accepted in 2016-10-20,  发布年份 2016

来源: Springer

【 摘 要 】

BackgroundDevelopment of automatable processes for clustering proteins into functionally relevant groups is a critical hurdle as an increasing number of sequences are deposited into databases. Experimental function determination is exceptionally time-consuming and can’t keep pace with the identification of protein sequences. A tool, DASP (Deacon Active Site Profiler), was previously developed to identify protein sequences with active site similarity to a query set. Development of two iterative, automatable methods for clustering proteins into functionally relevant groups exposed algorithmic limitations to DASP.ResultsThe accuracy and efficiency of DASP was significantly improved through six algorithmic enhancements implemented in two stages: DASP2 and DASP3. Validation demonstrated DASP3 provides greater score separation between true positives and false positives than earlier versions. In addition, DASP3 shows similar performance to previous versions in clustering protein structures into isofunctional groups (validated against manual curation), but DASP3 gathers and clusters protein sequences into isofunctional groups more efficiently than DASP and DASP2.ConclusionsDASP algorithmic enhancements resulted in improved efficiency and accuracy of identifying proteins that contain active site features similar to those of the query set. These enhancements provide incremental improvement in structure database searches and initial sequence database searches; however, the enhancements show significant improvement in iterative sequence searches, suggesting DASP3 is an appropriate tool for the iterative processes required for clustering proteins into isofunctional groups.

【 授权许可】

CC BY   
© The Author(s). 2016