title: 2D MI-DRAGON: a new predictor for protein-ligands interactions and theoretic-experimental studies of US FDA drug-target network, oxoisoaporphine inhibitors for MAO-A and human parasite proteins.
abstract: There are many pairs of possible Drug-Proteins Interactions that may take place or not (DPIs/nDPIs) between drugs with high affinity/non-affinity for different proteins. This fact makes expensive in terms of time and resources, for instance, the determination of all possible ligands-protein interactions for a single drug. In this sense, we can use Quantitative Structure-Activity Relationships (QSAR) models to carry out rational DPIs prediction. Unfortunately, almost all QSAR models predict activity against only one target. To solve this problem we can develop multi-target QSAR (mt-QSAR) models. In this work, we introduce the technique 2D MI-DRAGON a new predictor for DPIs based on two different well-known software. We use the software MARCH-INSIDE (MI) to calculate 3D structural parameters for targets and the software DRAGON was used to calculated 2D molecular descriptors all drugs showing known DPIs present in the Drug Bank (US FDA benchmark dataset). Both classes of parameters were used as input of different Artificial Neural Network (ANN) algorithms to seek an accurate non-linear mt-QSAR predictor. The best ANN model found is a Multi-Layer Perceptron (MLP) with profile MLP 21:21-31-1:1. This MLP classifies correctly 303 out of 339 DPIs (Sensitivity = 89.38%) and 480 out of 510 nDPIs (Specificity = 94.12%), corresponding to training Accuracy = 92.23%. The validation of the model was carried out by means of external predicting series with Sensitivity = 92.18% (625/678 DPIs; Specificity = 90.12% (730/780 nDPIs) and Accuracy = 91.06%. 2D MI-DRAGON offers a good opportunity for fast-track calculation of all possible DPIs of one drug enabling us to re-construct large drug-target or DPIs Complex Networks (CNs). For instance, we reconstructed the CN of the US FDA benchmark dataset with 855 nodes 519 drugs+336 targets). We predicted CN with similar topology (observed and predicted values of average distance are equal to 6.7 vs. 6.6). These CNs can be used to explore large DPIs databases in order to discover both new drugs and/or targets. Finally, we illustrated in one theoretic-experimental study the practical use of 2D MI-DRAGON. We reported the prediction, synthesis, and pharmacological assay of 10 different oxoisoaporphines with MAO-A inhibitory activity. The more active compound OXO5 presented IC(50) = 0.00083 μM, notably better than the control drug Clorgyline.
authors: Department of Organic Chemistry, Faculty of Pharmacy, University of Santiago de Compostela 15782, Spain. francisco.prado@usc.e
authors: Francisco
authors: F
authors: Prado-Prado
authors: Xerardo
authors: X
authors: García-Mera
authors: Manuel
authors: M
authors: Escobar
authors: Eduardo
authors: E
authors: Sobarzo-Sánchez
authors: Matilde
authors: M
authors: Yañez
authors: Pablo
authors: P
authors: Riera-Fernandez
authors: Humberto
authors: H
authors: González-Díaz
mesh_terms: D000981:Antiprotozoal Agents / Q000737:chemistry / Q000494:pharmacology; D001060:Aporphines / Q000737:chemistry* / Q000494:pharmacology*; D016208:Databases, Factual; D006801:Humans; D008024:Ligands; D016778:Malaria, Falciparum / Q000188:drug therapy; D008390:Markov Chains; D008954:Models, Biological; D008995:Monoamine Oxidase / Q000378:metabolism; D008996:Monoamine Oxidase Inhibitors / Q000737:chemistry* / Q000494:pharmacology*; D010963:Plasmodium falciparum / Q000187:drug effects; D015800:Protozoan Proteins / Q000037:antagonists & inhibitors* / Q000378:metabolism; D021281:Quantitative Structure-Activity Relationship; D012984:Software; D014481:United States
chemical_list: D000981:Antiprotozoal Agents; D001060:Aporphines; D008024:Ligands; D008996:Monoamine Oxidase Inhibitors; D015800:Protozoan Proteins; C081279:aporphine; D008995:Monoamine Oxidase