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

Abstract

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 3000+ 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 structures. We developed 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 MARCH-INSIDE software and they correspond to the entire protein or to specific protein regions named core, inner, middle, and surface. We used these parameters as inputs to develop a simple Linear Discriminant Analysis (LDA) classifier to discriminate 3D structure of LIBPs from other proteins. We implemented this predictor in the web server named LIBP-Pred, freely available at , along with other important web servers of the Bio-AIMS portal. The users can carry out an automatic retrieval of protein structures from PDB or upload their custom protein structural models from their 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.

Bio-AIMS Collection of Chemoinformatics Web Tools based on Molecular Graph Information and Artificial Intelligence Models

“…This tool proposed for the first time a model to predict new LIBPs based on protein 3D structures. The QSAR model was built on 3D electrostatic parameters of 1801 different proteins, including 801 LIBPs, calculated with the MARCH-INSIDE software [91]. The model is a linear classifier that can predict with an accuracy of 89.11% if a new protein can bind to lipids.…”

Prediction of Nucleotide Binding Peptides Using Star Graph Topological Indices

“…The basic search for the best classification model that can predict a function for peptides uses the Linear Discriminant Analysis (LDA)7 and the non‐linear Artificial Neural Networks (ANNs)8. Several prediction models for protein biological properties based on graph/complex network molecular descriptors have been published by our group regarding transport proteins9, lipid‐binding proteins10, cancer‐related proteins11, lectin proteins12, cell‐death proteins13, enzyme regulatory protein14, antioxidant proteins15 or ATCUN DNA‐cleavage proteins16.…”

Markov mean properties for cell death-related protein classification

“…The new molecular descriptors are encoding physicochemical amino acid properties in a similar way with the previous molecular descriptors. One example is the descriptors based on electrostatic potential that have been used to predict enzyme class (Munteanu et al, 2008), DNA-cleavage protein activity (Munteanu et al, 2009), protein-protein interactions in parasites (Rodriguez-Soca et al, 2010a andRodriguez-Soca et al, 2010b), drug-protein interactions (Gonzalez-Diaz et al, 2011) or lipid-binding proteins (Gonzalez-Diaz et al, 2012). The classifier represents a Quantitative-Structure-Activity-Relationship (QSAR) (Archer, 1978) between the protein 3D structure and the biological activity.…”