LECTINPred: web Server that Uses Complex Networks of Protein Structure for Prediction of Lectins with Potential Use as Cancer Biomarkers or in Parasite Vaccine Design

C. Munteanu*, N. Pedreira, J. Dorado, A. Pazos, L.G. Perez-Montoto, F.M. Ubeira, H. Gonzalez-Diaz

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

Abstract

Lectins (Ls) play an important role in many diseases such as different types of cancer, parasitic infections and other diseases. Interestingly, the Protein Data Bank (PDB) contains +3000 protein 3D structures with unknown function. Thus, we can in principle, discover new Ls mining non-annotated structures from PDB or other sources. However, there are no general models to predict new biologically relevant Ls based on 3D chemical structures. We used the MARCH-INSIDE software to calculate the Markov-Shannon 3D electrostatic entropy parameters for the complex networks of protein structure of 2200 different protein 3D structures, including 1200Ls. We have performed a Linear Discriminant Analysis (LDA) using these parameters as inputs in order to seek a new Quantitative Structure-Activity Relationship (QSAR) model, which is able to discriminate 3D structure of Ls from other proteins. We implemented this predictor in the web server named LECTINPred, freely available at http://bio-aims.udc.es/LECTINPred.php. This web server showed the following goodness-of-fit statistics: Sensitivity=96.7% (for Ls), Specificity=87.6% (non-active proteins), and Accuracy=92.5% (for all proteins), considering altogether both the training and external prediction series. In mode 2, users can carry out an automatic retrieval of protein structures from PDB. We illustrated the use of this server, in operation mode 1, performing a data mining of PDB. We predicted Ls scores for +2000 proteins with unknown function and selected the top-scored ones as possible lectins. In operation mode 2, LECTINPred can also upload 3D structural models generated with structure-prediction tools like LOMETS or PHYRE2. The new Ls are expected to be of relevance as cancer biomarkers or useful in parasite vaccine design.

Original languageEnglish
Pages (from-to)276-285
Number of pages10
JournalMolecular Informatics
Volume33
Issue number4
DOIs
Publication statusPublished - Apr 2014

Keywords

  • Lectins
  • Cancer biomarkers
  • Parasite vaccine design
  • Complex networks
  • QSAR models
  • Web server
  • Markov chains
  • Entropy
  • ALIGNMENT-FREE PREDICTION
  • MULTI-LABEL CLASSIFIER
  • AMINO-ACID-COMPOSITION
  • SUBCELLULAR-LOCALIZATION
  • COMPUTATIONAL CHEMISTRY
  • TYROSINASE INHIBITORS
  • QSAR MODEL
  • MOLECULAR DESCRIPTORS
  • BIOLOGICAL-PROPERTIES
  • RATIONAL DESIGN

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