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.

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.

[1]  M. Rosales-Hernández,et al.  Exploration of human serum albumin binding sites by docking and molecular dynamics flexible ligand-protein interactions. , 2010, Biopolymers.

[2]  A. Barabasi,et al.  Drug—target network , 2007, Nature Biotechnology.

[3]  David S. Wishart,et al.  DrugBank: a knowledgebase for drugs, drug actions and drug targets , 2007, Nucleic Acids Res..

[4]  C. Schein,et al.  Using property based sequence motifs and 3D modeling to determine structure and functional regions of proteins. , 2004, Current medicinal chemistry.

[5]  Lourdes Santana,et al.  Quantitative structure-activity relationship and complex network approach to monoamine oxidase A and B inhibitors. , 2008, Journal of medicinal chemistry.

[6]  Thierry Langer,et al.  The Protein Data Bank (PDB), its related services and software tools as key components for in silico guided drug discovery. , 2008, Journal of medicinal chemistry.

[7]  Joëlle Quetin-Leclercq,et al.  NL MIND-BEST: a web server for ligands and proteins discovery--theoretic-experimental study of proteins of Giardia lamblia and new compounds active against Plasmodium falciparum. , 2011, Journal of theoretical biology.

[8]  A. Barabasi,et al.  Lethality and centrality in protein networks , 2001, Nature.

[9]  Chihae Yang,et al.  Computational Toxicology Approaches at the US Food and Drug Administration a , 2009, Alternatives to laboratory animals : ATLA.

[10]  Aliuska Duardo-Sanchez,et al.  QSAR Models for Proteins of Parasitic Organisms, Plants and Human Guests: Theory, Applications, Legal Protection, Taxes, and Regulatory Issues , 2009 .

[11]  Lourdes Santana,et al.  Proteomics, networks and connectivity indices , 2008, Proteomics.

[12]  Lourdes Santana,et al.  A model for the recognition of protein kinases based on the entropy of 3D van der Waals interactions. , 2007, Journal of proteome research.

[13]  E. Sobarzo-Sánchez,et al.  Complete structural and spectral assignment of oxoisoaporphines by HMQC and HMBC experiments , 2003 .

[14]  J. Dorado,et al.  Plasmod-PPI: A web-server predicting complex biopolymer targets in plasmodium with entropy measures of protein–protein interactions , 2010 .

[15]  Humberto González Díaz,et al.  Computational chemistry study of 3D‐structure‐function relationships for enzymes based on Markov models for protein electrostatic, HINT, and van der Waals potentials , 2009, J. Comput. Chem..

[16]  Yoanna María Alvarez-Ginarte,et al.  Applying pattern recognition methods plus quantum and physico‐chemical molecular descriptors to analyze the anabolic activity of structurally diverse steroids , 2008, J. Comput. Chem..

[17]  Grace Patlewicz,et al.  Current topics on software use in medicinal chemistry: intellectual property, taxes, and regulatory issues. , 2008, Current topics in medicinal chemistry.

[18]  Lourdes Santana,et al.  Unified QSAR approach to antimicrobials. Part 2: predicting activity against more than 90 different species in order to halt antibacterial resistance. , 2007, Bioorganic & medicinal chemistry.

[19]  L. Bero,et al.  The decision-making process of US Food and Drug Administration advisory committees on switches from prescription to over-the-counter status: a comparative case study. , 2006, Clinical therapeutics.

[20]  Ernesto Estrada.  Virtual identification of essential proteins within the protein interaction network of yeast , 2005, Proteomics.

[21]  Yoshihiro Yamanishi,et al.  Prediction of drug–target interaction networks from the integration of chemical and genomic spaces , 2008, ISMB.

[22]  Paola Gramatica,et al.  Statistically Validated QSARs, Based on Theoretical Descriptors, for Modeling Aquatic Toxicity of Organic Chemicals in Pimephales promelas (Fathead Minnow) , 2005, J. Chem. Inf. Model..

[23]  L. G. Pérez-Montoto,et al.  Predicting drugs and proteins in parasite infections with topological indices of complex networks: theoretical backgrounds, applications, and legal issues. , 2010, Current pharmaceutical design.

[24]  L. G. Pérez-Montoto,et al.  Review of MARCH-INSIDE & complex networks prediction of drugs: ADMET, anti-parasite activity, metabolizing enzymes and cardiotoxicity proteome biomarkers. , 2010, Current drug metabolism.

[25]  Lourdes Santana,et al.  A QSAR model for in silico screening of MAO-A inhibitors. Prediction, synthesis, and biological assay of novel coumarins. , 2006, Journal of medicinal chemistry.

[26]  Mahmud Tareq Hassan Khan,et al.  Ligand-based computer-aided discovery of tyrosinase inhibitors. Applications of the TOMOCOMD-CARDD method to the elucidation of new compounds. , 2010, Current pharmaceutical design.

[27]  Humberto González-Díaz,et al.  Multi-target spectral moment: QSAR for antifungal drugs vs. different fungi species. , 2009, European journal of medicinal chemistry.

[28]  C. Schein,et al.  Common physical-chemical properties correlate with similar structure of the IgE epitopes of peanut allergens. , 2005, Journal of agricultural and food chemistry.

[29]  S. Carradori,et al.  Synthesis, stereochemical identification, and selective inhibitory activity against human monoamine oxidase-B of 2-methylcyclohexylidene-(4-arylthiazol-2-yl)hydrazones. , 2008, Journal of medicinal chemistry.

[30]  Roberto Todeschini,et al.  Handbook of Molecular Descriptors , 2002 .

[31]  Cristian R. Munteanu,et al.  MIND-BEST: Web server for drugs and target discovery; design, synthesis, and assay of MAO-B inhibitors and theoretical-experimental study of G3PDH protein from Trichomonas gallinae. , 2011, Journal of proteome research.

[32]  Maykel Pérez González,et al.  Applications of 2D descriptors in drug design: a DRAGON tale. , 2008, Current topics in medicinal chemistry.

[33]  J. Dorado,et al.  Trypano-PPI: a web server for prediction of unique targets in trypanosome proteome by using electrostatic parameters of protein-protein interactions. , 2010, Journal of proteome research.

[34]  M Natália D S Cordeiro,et al.  Probing the anticancer activity of nucleoside analogues: a QSAR model approach using an internally consistent training set. , 2007, Journal of medicinal chemistry.

[35]  S. Curry,et al.  Crystal Structure Analysis of Warfarin Binding to Human Serum Albumin , 2001, The Journal of Biological Chemistry.

[36]  Julio Caballero,et al.  Linear and nonlinear modeling of antifungal activity of some heterocyclic ring derivatives using multiple linear regression and Bayesian-regularized neural networks , 2006, Journal of molecular modeling.

[37]  Humberto González-Díaz,et al.  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. , 2011, European journal of medicinal chemistry.

[38]  Ernesto Estrada,et al.  Structural contributions of substrates to their binding to P-Glycoprotein. A TOPS-MODE approach. , 2010, Current pharmaceutical design.

[39]  Julio Caballero,et al.  Linear and nonlinear QSAR study of N-hydroxy-2-[(phenylsulfonyl)amino]acetamide derivatives as matrix metalloproteinase inhibitors. , 2006, Bioorganic & medicinal chemistry.

[40]  E. Sobarzo-Sánchez,et al.  An Expedient Synthesis of Unusual Oxoisoaporphine and Anellated Quinoline Derivatives. , 2004 .

[41]  E. Sobarzo-Sánchez,et al.  Synthesis and total assignment of 1H and 13C NMR spectra of new oxoisoaporphines by long‐range heteronuclear correlations , 2005, Magnetic resonance in chemistry : MRC.

[42]  M. Vassura,et al.  Reconstruction of 3D Structures From Protein Contact Maps , 2007, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

[43]  E. Sobarzo-Sánchez,et al.  Complete 1H and 13C NMR spectral assignment of hydrogenated oxoisoaporphine derivatives , 2003 .

[44]  E. Sobarzo-Sánchez,et al.  An Expedient Synthesis of Unusual Oxoisoaporphine and Annelated Quinoline Derivatives , 2003 .

[45]  David S. Wishart,et al.  DrugBank 3.0: a comprehensive resource for ‘Omics’ research on drugs , 2010, Nucleic Acids Res..

[46]  David S. Wishart,et al.  DrugBank: a comprehensive resource for in silico drug discovery and exploration , 2005, Nucleic Acids Res..

[47]  Humberto González-Díaz,et al.  Multi-target spectral moment QSAR versus ANN for antiparasitic drugs against different parasite species. , 2010, Bioorganic & medicinal chemistry.

[48]  Falk Schreiber,et al.  Exploration of biological network centralities with CentiBiN , 2006, BMC Bioinformatics.

[49]  F. Prado-Prado,et al.  Predicting antimicrobial drugs and targets with the MARCH-INSIDE approach. , 2008, Current topics in medicinal chemistry.

[50]  Humberto González Díaz,et al.  Computational chemistry comparison of stable/nonstable protein mutants classification models based on 3D and topological indices , 2007, J. Comput. Chem..