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Title: Prediction of multi-drug resistance transporters using a novel sequence analysis method [version 2; referees: 2 approved]

Abstract

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.

Authors:
 [1];  [2];  [2];  [2];  [2]
  1. Pacific Northwest National Lab. (PNNL), Richland, WA (United States); Oregon Health & Science Univ., Portland, OR (United States)
  2. Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
Publication Date:
Research Org.:
Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1214792
Grant/Contract Number:
AC05-76RL01830
Resource Type:
Journal Article: Accepted Manuscript
Journal Name:
F1000Research
Additional Journal Information:
Journal Volume: 6; Journal Issue: C; Journal ID: ISSN 2046-1402
Publisher:
F1000Research
Country of Publication:
United States
Language:
English
Subject:
59 BASIC BIOLOGICAL SCIENCES; 60 APPLIED LIFE SCIENCES

Citation Formats

McDermott, Jason E., Bruillard, Paul, Overall, Christopher C., Gosink, Luke, and Lindemann, Stephen R. Prediction of multi-drug resistance transporters using a novel sequence analysis method [version 2; referees: 2 approved]. United States: N. p., 2015. Web. doi:10.12688/f1000research.6200.2.
McDermott, Jason E., Bruillard, Paul, Overall, Christopher C., Gosink, Luke, & Lindemann, Stephen R. Prediction of multi-drug resistance transporters using a novel sequence analysis method [version 2; referees: 2 approved]. United States. doi:10.12688/f1000research.6200.2.
McDermott, Jason E., Bruillard, Paul, Overall, Christopher C., Gosink, Luke, and Lindemann, Stephen R. Mon . "Prediction of multi-drug resistance transporters using a novel sequence analysis method [version 2; referees: 2 approved]". United States. doi:10.12688/f1000research.6200.2. https://www.osti.gov/servlets/purl/1214792.
@article{osti_1214792,
title = {Prediction of multi-drug resistance transporters using a novel sequence analysis method [version 2; referees: 2 approved]},
author = {McDermott, Jason E. and Bruillard, Paul and Overall, Christopher C. and Gosink, Luke and Lindemann, Stephen R.},
abstractNote = {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.},
doi = {10.12688/f1000research.6200.2},
journal = {F1000Research},
number = C,
volume = 6,
place = {United States},
year = {Mon Mar 09 00:00:00 EDT 2015},
month = {Mon Mar 09 00:00:00 EDT 2015}
}

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