Prediction of multi-drug resistance transporters using a novel sequence analysis method [version 2; referees [electronic resource] : 2 approved].
- Washington, D.C. : United States. Dept. of Energy, 2015.
Oak Ridge, Tenn. : Distributed by the Office of Scientific and Technical Information, U.S. Dept. of Energy
- Physical Description:
- 28 pages : digital, PDF file
- Additional Creators:
- Pacific Northwest National Laboratory (U.S.)
United States. Department of Energy
United States. Department of Energy. Office of Scientific and Technical Information
- There are many examples of groups of proteins that have similar function, but the determinants of functional specificity may be hidden by lack of sequencesimilarity, or by large groups of similar sequences with different functions. Transporters are one such protein group in that the general function, transport, can be easily inferred from the sequence, but the substrate specificity can be impossible to predict from sequence with current methods. In this paper we describe a linguistic-based approach to identify functional patterns from groups of unaligned protein sequences and its application to predict multi-drug resistance transporters (MDRs) from bacteria. We first show that our method can recreate known patterns from PROSITE for several motifs from unaligned sequences. We then show that the method, MDRpred, can predict MDRs with greater accuracy and positive predictive value than a collection of currently available family-based models from the Pfam database. Finally, we apply MDRpred to a large collection of protein sequences from an environmental microbiome study to make novel predictions about drug resistance in a potential environmental reservoir.
- Published through SciTech Connect.
F1000Research 6 C ISSN 2046-1402 AM
McDermott, Jason; Bruillard, Paul; Overall, Christopher; Gosink, Luke; Lindemann, Stephen.
- Funding Information:
View MARC record | catkey: 23493578