Dataset Information

Evolutionary Analysis and Classification of OATs, OCTs, OCTNs, and Other SLC22 Transporters: Structure-Function Implications and Analysis of Sequence Motifs.

ABSTRACT: The SLC22 family includes organic anion transporters (OATs), organic cation transporters (OCTs) and organic carnitine and zwitterion transporters (OCTNs). These are often referred to as drug transporters even though they interact with many endogenous metabolites and signaling molecules (Nigam, S.K., Nature Reviews Drug Discovery, 14:29-44, 2015). Phylogenetic analysis of SLC22 supports the view that these transporters may have evolved over 450 million years ago. Many OAT members were found to appear after a major expansion of the SLC22 family in mammals, suggesting a physiological and/or toxicological role during the mammalian radiation. Putative SLC22 orthologs exist in worms, sea urchins, flies, and ciona. At least six groups of SLC22 exist. OATs and OCTs form two Major clades of SLC22, within which (apart from Oat and Oct subclades), there are also clear Oat-like, Octn, and Oct-related subclades, as well as a distantly related group we term "Oat-related" (which may have different functions). Based on available data, it is arguable whether SLC22A18, which is related to bacterial drug-proton antiporters, should be assigned to SLC22. Disease-causing mutations, single nucleotide polymorphisms (SNPs) and other functionally analyzed mutations in OAT1, OAT3, URAT1, OCT1, OCT2, OCTN1, and OCTN2 map to the first extracellular domain, the large central intracellular domain, and transmembrane domains 9 and 10. These regions are highly conserved within subclades, but not between subclades, and may be necessary for SLC22 transporter function and functional diversification. Our results not only link function to evolutionarily conserved motifs but indicate the need for a revised sub-classification of SLC22.

SUBMITTER: Zhu C

PROVIDER: S-EPMC4633038 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature

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Publications

Evolutionary Analysis and Classification of OATs, OCTs, OCTNs, and Other SLC22 Transporters: Structure-Function Implications and Analysis of Sequence Motifs.

Zhu Christopher C Nigam Kabir B KB Date Rishabh C RC Bush Kevin T KT Springer Stevan A SA Saier Milton H MH Wu Wei W Nigam Sanjay K SK

PloS one 20151104 11

The SLC22 family includes organic anion transporters (OATs), organic cation transporters (OCTs) and organic carnitine and zwitterion transporters (OCTNs). These are often referred to as drug transporters even though they interact with many endogenous metabolites and signaling molecules (Nigam, S.K., Nature Reviews Drug Discovery, 14:29-44, 2015). Phylogenetic analysis of SLC22 supports the view that these transporters may have evolved over 450 million years ago. Many OAT members were found to ap ...[more]

PMID: 26536134

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Project description:BackgroundIn protein sequence classification, identification of the sequence motifs or n-grams that can precisely discriminate between classes is a more interesting scientific question than the classification itself. A number of classification methods aim at accurate classification but fail to explain which sequence features indeed contribute to the accuracy. We hypothesize that sequences in lower denominations (n-grams) can be used to explore the sequence landscape and to identify class-specific motifs that discriminate between classes during classification. Discriminative n-grams are short peptide sequences that are highly frequent in one class but are either minimally present or absent in other classes. In this study, we present a new substitution-based scoring function for identifying discriminative n-grams that are highly specific to a class.ResultsWe present a scoring function based on discriminative n-grams that can effectively discriminate between classes. The scoring function, initially, harvests the entire set of 4- to 8-grams from the protein sequences of different classes in the dataset. Similar n-grams of the same size are combined to form new n-grams, where the similarity is defined by positive amino acid substitution scores in the BLOSUM62 matrix. Substitution has resulted in a large increase in the number of discriminatory n-grams harvested. Due to the unbalanced nature of the dataset, the frequencies of the n-grams are normalized using a dampening factor, which gives more weightage to the n-grams that appear in fewer classes and vice-versa. After the n-grams are normalized, the scoring function identifies discriminative 4- to 8-grams for each class that are frequent enough to be above a selection threshold. By mapping these discriminative n-grams back to the protein sequences, we obtained contiguous n-grams that represent short class-specific motifs in protein sequences. Our method fared well compared to an existing motif finding method known as Wordspy. We have validated our enriched set of class-specific motifs against the functionally important motifs obtained from the NLSdb, Prosite and ELM databases. We demonstrate that this method is very generic; thus can be widely applied to detect class-specific motifs in many protein sequence classification tasks.ConclusionThe proposed scoring function and methodology is able to identify class-specific motifs using discriminative n-grams derived from the protein sequences. The implementation of amino acid substitution scores for similarity detection, and the dampening factor to normalize the unbalanced datasets have significant effect on the performance of the scoring function. Our multipronged validation tests demonstrate that this method can detect class-specific motifs from a wide variety of protein sequence classes with a potential application to detecting proteome-specific motifs of different organisms.

Dataset Information

Evolutionary Analysis and Classification of OATs, OCTs, OCTNs, and Other SLC22 Transporters: Structure-Function Implications and Analysis of Sequence Motifs.

Publications

Evolutionary Analysis and Classification of OATs, OCTs, OCTNs, and Other SLC22 Transporters: Structure-Function Implications and Analysis of Sequence Motifs.

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