.: Marcos Gestal :.     -----


Datos de la Publicación:

Humberto González-Díaz, Cristian Robert Munteanu, Lucian Postelnicu, Francisco Prado-Prado, Marcos Gestal, Alejandro Pazos
Título: LIBP-Pred: Web Server for Lipid Binding Proteins using Structural Network Parameters; PDB Mining of Human Cancer Biomarkers and Drug Targets in Parasites and Bacteria
Revista: Molecular BioSystems
ISSN: 1742-206X
Editorial: RSC Publishing
Fecha Publicación: Enero 2012
Factor de Impacto: 3.350
PubMed ID: 22234525
doi: 10.1039/C2MB05432A
Categorías WoS: Biochemistry & Molecular Biology - Cuartil: Q2 - Tercil: T2 - Posición 106 de 290
Citas ISI: 11
Citas Scopus: 11
Citas Google Scholar: 9


Lipid-Binding proteins (LIBPs) or Fatty-Acid Binding Proteins (FABPs) play an important role in many diseases such as different types of cancer, kidney injury, atherosclerosis, diabetes, intestinal ischemia and parasitic infections. Thus, the computational methods that can predict LIBPs based on 3D structure parameters became a goal of major importance for drug-target discovery, vaccine design and biomarker selection. In addition, the Protein Data Bank (PDB) contains 3,000+ protein 3D structures with unknown function. This list, as well as new experimental outcomes in proteomics research, is a very interesting source to discover relevant proteins, including LIBPs. However, to the best of our knowledge, there are no general models to predict new LIBPs based on 3D structure. We developed a new Quantitative Structure Activity Relationship (QSAR) models based on 3D electrostatic parameters of 1801 different proteins, including 801 LIBPs. We calculated these electrostatic parameters with the software MARCH-INSIDE and they correspond to the total protein or to specific protein regions named core, inner, middle, and surface. We used these parameters as inputs to develop both Linear Discriminant Analysis (LDA) and non-linear Artificial Neural Network (ANN) classifiers to discriminate 3D structure of LIBPs from other proteins. Later, we developed a Receiver Operating Characteristic (ROC) curve analysis to demonstrate that the present model has significant differences with respect to a random classifier. We implemented this predictor in the web server named LIBP-Pred, freely available to the public at http://miaja.tic.udc.es/Bio-AIMS/LIBPpred.php, along with other important web servers of the Bio-AIMS portal. It is possible an automatic retrieval of protein structures from PDB or uploading your custom protein structural models from your disk created with LOMETS server. We demonstrated the PDB mining option performing a predictive study of 2000+ proteins with unknown function. Interesting results regarding the discovery of new Cancer Biomarkers in humans or Drug Targets in parasites have been discussed here in this sense.