title: Using entropy of drug and protein graphs to predict FDA drug-target network: theoretic-experimental study of MAO inhibitors and hemoglobin peptides from Fasciola hepatica.
abstract: There are many drugs described with very different affinity to a large number of receptors. In this work, we selected Drug-Target pairs (DTPs/nDTPs) of drugs with high affinity/non-affinity for different targets like proteins. Quantitative Structure-Activity Relationships (QSAR) models become a very useful tool in this context to substantially reduce time and resources consuming experiments. Unfortunately, most QSAR models predict activity against only one protein. To solve this problem, we developed here a multi-target QSAR (mt-QSAR) classifier using the MARCH-INSIDE technique to calculate structural parameters of drug and target plus one Artificial Neuronal Network (ANN) to seek the model. The best ANN model found is a Multi-Layer Perceptron (MLP) with profile MLP 32:32-15-1:1. This MLP classifies correctly 623 out of 678 DTPs (Sensitivity = 91.89%) and 2995 out of 3234 nDTPs (Specificity = 92.61%), corresponding to training Accuracy = 92.48%. The validation of the model was carried out by means of external predicting series. The model classifies correctly 313 out of 338 DTPs (Sensitivity = 92.60%) and 1411 out of 1534 nDTP (Specificity = 91.98%) in validation series, corresponding to total Accuracy = 92.09% for validation series (Predictability). This model favorably compares with other LDA and ANN models developed in this work and Machine Learning classifiers published before to address the same problem in different aspects. These mt-QSARs offer also a good opportunity to construct drug-protein Complex Networks (CNs) that can be used to explore large and complex drug-protein receptors databases. Finally, we illustrated two practical uses of this model with two different experiments. In experiment 1, we report prediction, synthesis, characterization, and MAO-A and MAO-B pharmacological assay of 10 rasagiline derivatives promising for anti-Parkinson drug design. In experiment 2, we report sampling, parasite culture, SEC and 1DE sample preparation, MALDI-TOF MS and MS/MS analysis, MASCOT search, MM/MD 3D structure modeling, and QSAR prediction for different peptides of hemoglobin found in the proteome of the human parasite Fasciola hepatica; which is promising for anti-parasite drug targets discovery.
authors.affiliation: Department of Organic Chemistry, Faculty of Pharmacy, USC 15782, Spain. fenol1@hotmail.com
authors.forename: Francisco
authors.initials: F
authors.lastname: Prado-Prado
authors.forename: Xerardo
authors.initials: X
authors.lastname: García-Mera
authors.forename: Paula
authors.initials: P
authors.lastname: Abeijón
authors.forename: Nerea
authors.initials: N
authors.lastname: Alonso
authors.forename: Olga
authors.initials: O
authors.lastname: Caamaño
authors.forename: Matilde
authors.initials: M
authors.lastname: Yáñez
authors.forename: Teresa
authors.initials: T
authors.lastname: Gárate
authors.forename: Mercedes
authors.initials: M
authors.lastname: Mezo
authors.forename: Marta
authors.initials: M
authors.lastname: González-Warleta
authors.forename: Laura
authors.initials: L
authors.lastname: Muiño
authors.forename: Florencio M
authors.initials: FM
authors.lastname: Ubeira
authors.forename: Humberto
authors.initials: H
authors.lastname: González-Díaz
mesh_terms: D000818:Animals; D001185:Artificial Intelligence; D016002:Discriminant Analysis; D019277:Entropy; D005210:Fasciola hepatica; D006454:Hemoglobins / Q000737:chemistry*; D006801:Humans; D008390:Markov Chains; D008958:Models, Molecular; D008995:Monoamine Oxidase / Q000378:metabolism*; D008996:Monoamine Oxidase Inhibitors / Q000737:chemistry / Q000378:metabolism* / Q000494:pharmacology; D010446:Peptide Fragments / Q000737:chemistry / Q000378:metabolism*; D011485:Protein Binding; D011487:Protein Conformation; D021281:Quantitative Structure-Activity Relationship; D015203:Reproducibility of Results; D014481:United States; D014486:United States Food and Drug Administration
chemical_list: D006454:Hemoglobins; D008996:Monoamine Oxidase Inhibitors; D010446:Peptide Fragments; D008995:Monoamine Oxidase