Large scale identification and categorization of protein sequences using structured logistic regression

PLoS One. 2014 Jan 20;9(1):e85139. doi: 10.1371/journal.pone.0085139. eCollection 2014.


Background: Structured Logistic Regression (SLR) is a newly developed machine learning tool first proposed in the context of text categorization. Current availability of extensive protein sequence databases calls for an automated method to reliably classify sequences and SLR seems well-suited for this task. The classification of P-type ATPases, a large family of ATP-driven membrane pumps transporting essential cations, was selected as a test-case that would generate important biological information as well as provide a proof-of-concept for the application of SLR to a large scale bioinformatics problem.

Results: Using SLR, we have built classifiers to identify and automatically categorize P-type ATPases into one of 11 pre-defined classes. The SLR-classifiers are compared to a Hidden Markov Model approach and shown to be highly accurate and scalable. Representing the bulk of currently known sequences, we analysed 9.3 million sequences in the UniProtKB and attempted to classify a large number of P-type ATPases. To examine the distribution of pumps on organisms, we also applied SLR to 1,123 complete genomes from the Entrez genome database. Finally, we analysed the predicted membrane topology of the identified P-type ATPases.

Conclusions: Using the SLR-based classification tool we are able to run a large scale study of P-type ATPases. This study provides proof-of-concept for the application of SLR to a bioinformatics problem and the analysis of P-type ATPases pinpoints new and interesting targets for further biochemical characterization and structural analysis.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Algorithms
  • Amino Acid Sequence
  • Artificial Intelligence
  • Computational Biology / methods
  • Databases, Protein
  • Logistic Models*
  • Sequence Alignment
  • Sequence Analysis, Protein / methods*
  • Software

Grant support

BPP was supported by a post-doctoral fellowship from the Carlsberg Foundation and by the Danish Cancer Society. CW was supported by the Danish Cancer Society. GI was supported by Science Foundation Ireland INSIGHT Centre for Data Analytics, Science Foundation Ireland grant 10/IN.1/I3032 and by the Danish Cancer Society. PN was supported by a Hallas-Møller stipend from the Novo Nordisk Foundation and by the BIOMEMOS advanced research program of the European Research Council. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.