ArticleF1000Research2015
Prediction of multi-drug resistance transporters using a novel sequence analysis method.
Article in F1000Research, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- The mobilome landscape of biocide-resistance in Brazilian ESKAPE isolates.Brazilian journal of microbiology : [publication of the Brazilian Society for Microbiology] · 2024Article
- Snekmer: a scalable pipeline for protein sequence fingerprinting based on amino acid recoding.Bioinformatics advances · 2023Article
- Prediction of bacterial E3 ubiquitin ligase effectors using reduced amino acid peptide fingerprinting.PeerJ · 2019Article
- Prediction of multi-drug resistance transporters using a novel sequence analysis method.F1000Research · 2015Article
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
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 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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