Prediction of multi-drug resistance transporters using a novel sequence analysis method

McDermott, Jason E.; Bruillard, Paul J.; Overall, Christopher C.; Gosink, Luke J.; Lindemann, Stephen R.

doi:10.12688/f1000research.6200.2

Title: Prediction of multi-drug resistance transporters using a novel sequence analysis method

Journal Article · Mon Mar 09 00:00:00 EDT 2015 · F1000Research

DOI:https://doi.org/10.12688/f1000research.6200.2· OSTI ID:1214792

^[1]; Bruillard, Paul J. ^[2]; Overall, Christopher C. ^[2]; Gosink, Luke J. ^[2]; Lindemann, Stephen R. ^[2]

Pacific Northwest National Lab. (PNNL), Richland, WA (United States); Oregon Health & Science Univ., Portland, OR (United States)
Pacific Northwest National Lab. (PNNL), Richland, WA (United States)

There are many examples of groups of proteins that have similar function, but the determinants of functional specificity may be hidden by lack of sequence similarity, 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.

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Research Organization:: Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)

Sponsoring Organization:: USDOE

Grant/Contract Number:: AC05-76RL01830

OSTI ID:: 1214792

Alternate ID(s):: OSTI ID: 1813736

Report Number(s):: PNNL-SA-108738

Journal Information:: F1000Research, Vol. 6, Issue C; ISSN 2046-1402

Publisher:: F1000ResearchCopyright Statement

Country of Publication:: United States

Language:: English

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Prediction of bacterial E₃ ubiquitin ligase effectors using reduced amino acid peptide fingerprinting McDermott, Jason; Cort, John; Nakayasu, Ernesto PeerJ Preprints https://doi.org/10.7287/peerj.preprints.27292v1	journal	October 2018

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Related Subjects

59 BASIC BIOLOGICAL SCIENCES
60 APPLIED LIFE SCIENCES
machine learning
antibiotic resistance
microbiome
bacteria
protein function