MOZART, a QSAR Multi-Target Web-Based Tool to Predict Multiple Drug-Enzyme Interactions.

Developing models able to predict interactions between drugs and enzymes is a primary goal in computational biology since these models may be used for predicting both new active drugs and the interactions between known drugs on untested targets. With the compilation of a large dataset of drug-enzyme pairs (62,524), we recognized a unique opportunity to attempt to build a novel multi-target machine learning (MTML) quantitative structure-activity relationship (QSAR) model for probing interactions among different drugs and enzyme targets. To this end, this paper presents an MTML-QSAR model based on using the features of topological drugs together with the artificial neural network (ANN) multi-layer perceptron (MLP). Validation of the final best model found was carried out by internal cross-validation statistics and other relevant diagnostic statistical parameters. The overall accuracy of the derived model was found to be higher than 96%. Finally, to maximize the diffusion of this model, a public and accessible tool has been developed to allow users to perform their own predictions. The developed web-based tool is public accessible and can be downloaded as free open-source software.

Computational Modeling on Binding Interactions of Cyclodextrin s with the Human Multidrug Resistance P-glycoprotein Toward Efficient Drug-delivery System Applications.

BACKGROUND: Herein, molecular docking approaches and DFT ab initio simulations were combined for the first time, to study the key interactions of cyclodextrins (CDs: α-CD, ß-CD, and γ-CD) family with potential pharmacological relevance and the multidrug resistance P-gp protein toward efficient drug-delivery applications. The treatment of neurological disorders and cancer therapy where the multiple drug-resistance phenomenon mediated by the P-gp protein constitutes the fundamental cause of unsuccessful therapies. OBJECTIVES: To understand more about the CD docking mechanism and the P-gp. METHODS: In order to achieve the main goal, the computational docking process was used. The observed docking-mechanism of the CDs on the P-gp was fundamentally based on hybrid backbone/side-chain hydrophobic interactions,and also hybrid electrostatic/side-chain interactions of the CD-ligands' OHmotifs with acceptor and donor characteristics, which might theoretically cause local perturbations in the TMD/P-gp inter-residues network, influencing ligand extrusion through the blood-brain barrier. P-gp residues were conformationally favored. Despite the structural differences, all the cyclodextrins exhibit very close Gibbs free binding energy values (or affinity) by the P-gp binding site (transmembrane domains - TMDs). RESULT: The obtained theoretical docking-mechanism of the CDs on the P-gp was fundamentally based on hybrid backbone/side-chain hydrophobic interactions, and also hybrid electrostatic/side-chain interactions of the OH-motifs of the CD-ligands with acceptor and donor properties which theoretically could induce allosteric local-perturbations in the TMDs-inter-residues network of P-gp modulating to the CD-ligand extrusion from the blood-brain-barrier (or cancer cells). CONCLUSION: Finally, these theoretical results open new horizons for evaluating new nanotherapeutic drugs with potential pharmacological relevance for efficient drug-delivery applications and precision nanomedicine.

Computational Biology: A New Frontier in Applied Biology.

All living things are related to one another [...].

New Mechanistic Insights on Carbon Nanotubes' Nanotoxicity Using Isolated Submitochondrial Particles, Molecular Docking, and Nano-QSTR Approaches.

Single-walled carbon nanotubes can induce mitochondrial F0F1-ATPase nanotoxicity through inhibition. To completely characterize the mechanistic effect triggering the toxicity, we have developed a new approach based on the combination of experimental and computational study, since the use of only one or few techniques may not fully describe the phenomena. To this end, the in vitro inhibition responses in submitochondrial particles (SMP) was combined with docking, elastic network models, fractal surface analysis, and Nano-QSTR models. In vitro studies suggest that inhibition responses in SMP of F0F1-ATPase enzyme were strongly dependent on the concentration assay (from 3 to 5 µg/mL) for both pristine and COOH single-walled carbon nanotubes types (SWCNT). Besides, both SWCNTs show an interaction inhibition pattern mimicking the oligomycin A (the specific mitochondria F0F1-ATPase inhibitor blocking the c-ring F0 subunit). Performed docking studies denote the best crystallography binding pose obtained for the docking complexes based on the free energy of binding (FEB) fit well with the in vitro evidence from the thermodynamics point of view, following an affinity order such as: FEB (oligomycin A/F0-ATPase complex) = -9.8 kcal/mol > FEB (SWCNT-COOH/F0-ATPase complex) = -6.8 kcal/mol ~ FEB (SWCNT-pristine complex) = -5.9 kcal/mol, with predominance of van der Waals hydrophobic nano-interactions with key F0-ATPase binding site residues (Phe 55 and Phe 64). Elastic network models and fractal surface analysis were performed to study conformational perturbations induced by SWCNT. Our results suggest that interaction may be triggering abnormal allosteric responses and signals propagation in the inter-residue network, which could affect the substrate recognition ligand geometrical specificity of the F0F1-ATPase enzyme in order (SWCNT-pristine > SWCNT-COOH). In addition, Nano-QSTR models have been developed to predict toxicity induced by both SWCNTs, using results of in vitro and docking studies. Results show that this method may be used for the fast prediction of the nanotoxicity induced by SWCNT, avoiding time- and money-consuming techniques. Overall, the obtained results may open new avenues toward to the better understanding and prediction of new nanotoxicity mechanisms, rational drug design-based nanotechnology, and potential biomedical application in precision nanomedicine.

Targeting Beta-Blocker Drug-Drug Interactions with Fibrinogen Blood Plasma Protein: A Computational and Experimental Study.

In this work, one of the most prevalent polypharmacology drug-drug interaction events that occurs between two widely used beta-blocker drugs-i.e., acebutolol and propranolol-with the most abundant blood plasma fibrinogen protein was evaluated. Towards that end, molecular docking and Density Functional Theory (DFT) calculations were used as complementary tools. A fibrinogen crystallographic validation for the three best ranked binding-sites shows 100% of conformationally favored residues with total absence of restricted flexibility. From those three sites, results on both the binding-site druggability and ligand transport analysis-based free energy trajectories pointed out the most preferred biophysical environment site for drug-drug interactions. Furthermore, the total affinity for the stabilization of the drug-drug complexes was mostly influenced by steric energy contributions, based mainly on multiple hydrophobic contacts with critical residues (THR22: P and SER50: Q) in such best-ranked site. Additionally, the DFT calculations revealed that the beta-blocker drug-drug complexes have a spontaneous thermodynamic stabilization following the same affinity order obtained in the docking simulations, without covalent-bond formation between both interacting beta-blockers in the best-ranked site. Lastly, experimental ultrasound density and velocity measurements were performed and allowed us to validate and corroborate the computational obtained results.

Developing a Multi-target Model to Predict the Activity of Monoamine Oxidase A and B Drugs.

INTRODUCTION: Monoamine oxidase inhibitors (MAOIs) are compounds largely used in the treatment of Parkinson's disease (PD), Alzheimer's disease and other neuropsychiatric disorders since they are closely related to the MAO enzymes activity. The two isoforms of the MAO enzymes, MAO-A and MAO-B, are responsible for the degradation of monoamine neurotransmitters and due to this, relevant efforts have been devoted to finding new compounds with more selectivity and less side effects. One of the most used approaches is based on the use of computational approaches since they are time and money-saving and may allow us to find a more relevant structure-activity relationship. OBJECTIVE: In this manuscript, we will review the most relevant computational approaches aimed at the prediction and development of new MAO inhibitors. Subsequently, we will also introduce a new multitask model aimed at predicting MAO-A and MAO-B inhibitors. METHODS: The QSAR multi-task model herein developed was based on the use of the linear discriminant analysis. This model was developed gathering 5,759 compounds from the public dataset Chembl. The molecular descriptors used was calculated using the Dragon software. Classical statistical tests were performed to check the validity and robustness of the model. RESULTS: The herein proposed model is able to correctly classify all the 5,759 compounds. All the statistical performed tests indicated that this model is robust and reproducible. CONCLUSION: MAOIs are compounds of large interest since they are largely used in the treatment of very serious illness. These inhibitors may lose efficacy and produce severe side effects. Due to this, the development of selective MAO-A or MAO-B inhibitors is crucial for the treatment of these diseases and their effects. The herein proposed multi-target QSAR model may be a relevant tool in the development of new and more selective MAO inhibitors.

Alignment-Free Method to Predict Enzyme Classes and Subclasses.

The Enzyme Classification (EC) number is a numerical classification scheme for enzymes, established using the chemical reactions they catalyze. This classification is based on the recommendation of the Nomenclature Committee of the International Union of Biochemistry and Molecular Biology. Six enzyme classes were recognised in the first Enzyme Classification and Nomenclature List, reported by the International Union of Biochemistry in 1961. However, a new enzyme group was recently added as the six existing EC classes could not describe enzymes involved in the movement of ions or molecules across membranes. Such enzymes are now classified in the new EC class of translocases (EC 7). Several computational methods have been developed in order to predict the EC number. However, due to this new change, all such methods are now outdated and need updating. In this work, we developed a new multi-task quantitative structure-activity relationship (QSAR) method aimed at predicting all 7 EC classes and subclasses. In so doing, we developed an alignment-free model based on artificial neural networks that proved to be very successful.

PTML Model of Enzyme Subclasses for Mining the Proteome of Biofuel Producing Microorganisms.

Predicting enzyme function and enzyme subclasses is always a key objective in fields such as biotechnology, biochemistry, medicinal chemistry, physiology, and so on. The Protein Data Bank (PDB) is the largest information archive of biological macromolecular structures, with more than 150 000 entries for proteins, nucleic acids, and complex assemblies. Among these entries, there are more than 4000 proteins whose functions remain unknown because no detectable homology to proteins whose functions are known has been found. The problem is that our ability to isolate proteins and identify their sequences far exceeds our ability to assign them a defined function. As a result, there is a growing interest in this topic, and several methods have been developed to identify protein function based on these innovative approaches. In this work, we have applied perturbation theory to an original data set consisting of 19 187 enzymes representing all 59 subclasses present in the protein data bank. In addition, we developed a series of artificial neural network models able to predict enzyme-enzyme pairs of query-template sequences with accuracy, specificity, and sensitivity greater than 90% in both training and validation series. As a likely application of this methodology and to further validate our approach, we used our novel model to predict a set of enzymes belonging to the yeast Pichia stipites. This yeast has been widely studied because it is commonly present in nature and produces a high ethanol yield by converting lignocellulosic biomass into bioethanol through the xylose reductase enzyme. Using this premise, we tested our model on 222 enzymes including xylose reductase, that is, the enzyme responsible for the conversion of biomass into bioethanol.

Computational MitoTarget Scanning Based on Topological Vacancies of Single-Walled Carbon Nanotubes with the Human Mitochondrial Voltage-Dependent Anion Channel (hVDAC1).

We present an in silico approach for modeling the noncovalent interactions between the human mitochondrial voltage-dependent anion channel (hVDAC1) and a family of single-walled carbon nanotubes (SWCNTs) with a defined pattern of topological vacancies ( v = 1-16), obtained by removing atoms from the SWCNT surface. The general results showed more stable docking interaction complexes (SWCNT-hVDAC1), with more negative Gibbs free energy of binding affinity values, and a strong dependence on the vacancy number ( R2 = 0.93) and vacancy formation energy ( R2 = 0.96). In addition, for most of the SWCNT vacancies that were analyzed, the interatomic distances for the interactions of the SWCNT-hVDAC1 complex with the functional catalytic residues (i.e., Pro7, Gln199, Gln182, Phe181, Val20, Asp19, Lys15, Gly14, Asp12, Ala11, and Arg18) that form the hVDAC1 active site (i.e., the voltage-sensing N-terminal α-helix segment) were very similar to or shorter than the interatomic distances of these residues for ATP-hVDAC1 interactions. In particular, the hVDAC1 residues that can be phosphorylated like Tyr10, Tyr198, and Se16 were significantly perturbed by the interactions with SWCNT with at least nine vacancies. In addition, the SWCNT vacancy family members can affect the flexibility properties of the hVDAC1 N-terminal α-helix segment inducing different patterns of local perturbations in inter-residue communication. Finally, vacancy quantitative structure-binding relationships (V-QSBRs) were unveiled for setting up a robust model that can predict the strength of docking interactions between SWCNTs with a specific topological vacancy and hVDAC1. The developed V-QSBR model classified properly all of the SWCNTs with a different number of SWCNT vacancies with exceptional sensitivity and specificity (both equal to 100%), indicating a strong potential to unequivocally predict the influence of SWCNT vacancies on the mitochondrial channel interactions.

MitoTarget Modeling Using ANN-Classification Models Based on Fractal SEM Nano-Descriptors: Carbon Nanotubes as Mitochondrial F0F1-ATPase Inhibitors.

Recently, it has been suggested that the mitochondrial oligomycin A-sensitive F0-ATPase subunit is an uncoupling channel linked to apoptotic cell death, and as such, the toxicological inhibition of mitochondrial F0-ATP hydrolase can be an interesting mitotoxicity-based therapy under pathological conditions. In addition, carbon nanotubes (CNTs) have been shown to offer higher selectivity like mitotoxic-targeting nanoparticles. In this work, linear and nonlinear classification algorithms on structure-toxicity relationships with artificial neural network (ANN) models were set up using the fractal dimensions calculated from CNTs as a source of supramolecular chemical information. The potential ability of CNT-family members to induce mitochondrial toxicity-based inhibition of the mitochondrial H+-F0F1-ATPase from in vitro assays was predicted. The attained experimental data suggest that CNTs have a strong ability to inhibit the F0-ATPase active-binding site following the order oxidized-CNT (CNT-COOH > CNT-OH) > pristine-CNT and mimicking the oligomycin A mitotoxicity behavior. Meanwhile, the performance of the ANN models was found to be improved by including different nonlinear combinations of the calculated fractal scanning electron microscopy (SEM) nanodescriptors, leading to models with excellent internal accuracy and predictivity on external data to classify correctly CNT-mitotoxic and nonmitotoxic with specificity (Sp > 98.9%) and sensitivity (Sn > 99.0%) from ANN models compared with linear approaches (LNN) with Sp ≈ Sn > 95.5%. Finally, the present study can contribute toward the rational design of carbon nanomaterials and opens new opportunities toward mitochondrial nanotoxicology-based in silico models.

Looking for New Inhibitors for the Epidermal Growth Factor Receptor.

Epidermal Growth Factor Receptor (EGFR) is still the main target of the Head and Neck Squamous Cell Cancer (HNSCC) because its overexpression has been detected in more than 90% of this type of cancer. This overexpression is usually linked with more aggressive disease, increased resistance to chemotherapy and radiotherapy, increased metastasis, inhibition of apoptosis, promotion of neoplastic angiogenesis, and, finally, poor prognosis and decreased survival. Due to this reason, the main target in the search of new drugs and inhibitors candidates is to downturn this overexpression. Quantitative Structure-Activity Relationship (QSAR) is one of the most widely used approaches while looking for new and more active inhibitors drugs. In this contest, a lot of authors used this technique, combined with others, to find new drugs or enhance the activity of well-known inhibitors. In this paper, on one hand, we will review the most important QSAR approaches developed in the last fifteen years, spacing from classical 1D approaches until more sophisticated 3D; the first paper is dated 2003 while the last one is from 2017. On the other hand, we will present a completely new QSAR approach aimed at the prediction of new EGFR inhibitors drugs. The model presented here has been developed over a dataset consisting of more than 1000 compounds using various molecular descriptors calculated with the DRAGON 7.0© software.

Cetuximab and the Head and Neck Squamous Cell Cancer.

The Head and Neck Squamous Cell Cancer (HNSCC) is the most common type of head and neck cancer (more than 90%), and all over the world more than a half million people have been developing this cancer in the last years. This type of cancer is usually marked by a poor prognosis with a really significant morbidity and mortality. Cetuximab received early favor as an exciting and promising new therapy with relatively mild side effect, and due to this, received authorization in 2004 from the European Medicines Agency (EMA) and in 2006 from the Food and Drug Association (FDA) for the treatment of patients with squamous cell cancer of the head and neck in combination with radiation therapy for locally advanced disease. In this work we will review the application and the efficacy of the Cetuximab in the treatment of the HNSCC.

Probing the toxicity of nanoparticles: a unified in silico machine learning model based on perturbation theory.

Nanoparticles (NPs) are part of our daily life, having a wide range of applications in engineering, physics, chemistry, and biomedicine. However, there are serious concerns regarding the harmful effects that NPs can cause to the different biological systems and their ecosystems. Toxicity testing is an essential step for assessing the potential risks of the NPs, but the experimental assays are often very expensive and usually too slow to flag the number of NPs that may cause adverse effects. In silico models centered on quantitative structure-activity/toxicity relationships (QSAR/QSTR) are alternative tools that have become valuable supports to risk assessment, rationalizing the search for safer NPs. In this work, we develop a unified QSTR-perturbation model based on artificial neural networks, aimed at simultaneously predicting general toxicity profiles of NPs under diverse experimental conditions. The model is derived from 54,371 NP-NP pair cases generated by applying the perturbation theory to a set of 260 unique NPs, and showed an accuracy higher than 97% in both training and validation sets. Physicochemical interpretation of the different descriptors in the model are additionally provided. The QSTR-perturbation model is then employed to predict the toxic effects of several NPs not included in the original dataset. The theoretical results obtained for this independent set are strongly consistent with the experimental evidence found in the literature, suggesting that the present QSTR-perturbation model can be viewed as a promising and reliable computational tool for probing the toxicity of NPs.

Molecular Dynamics Simulation Study of the Selectivity of a Silica Polymer for Ibuprofen.

In the past few years, the sol-gel polycondensation technique has been increasingly employed with great success as an alternative approach to the preparation of molecularly imprinted materials (MIMs). The main aim of this study was to study, through a series of molecular dynamics (MD) simulations, the selectivity of an imprinted silica xerogel towards a new template-the (±)-2-(P-Isobutylphenyl) propionic acid (Ibuprofen, IBU). We have previously demonstrated the affinity of this silica xerogel toward a similar molecule. In the present study, we simulated the imprinting process occurring in a sol-gel mixture using the Optimized Potentials for Liquid Simulations-All Atom (OPLS-AA) force field, in order to evaluate the selectivity of this xerogel for a template molecule. In addition, for the first time, we have developed and verified a new parameterisation for the Ibuprofen(®) based on the OPLS-AA framework. To evaluate the selectivity of the polymer, we have employed both the radial distribution functions, interaction energies and cluster analyses.

Measurement artifacts identified in the UV-vis spectroscopic study of adduct formation within the context of molecular imprinting of naproxen.

The ultraviolet-visible spectroscopy has been assessed as a technique for the evaluation of the strength of template-precursor adduct in the development of molecular imprints of the non-steroidal anti-inflammatory drug naproxen (NAP). The commonly employed approach relies on the collection of UV spectra of drug+precursor mixtures at different proportions, the spectra being recorded against blanks containing the same concentration of the precursor. The observation of either blue or red band-shifts and abatement of a major band are routinely attributed to template-precursor adduct formation. Following the described methodology, the precursors 1-(triethoxysilylpropyl)-3-(trimethoxysilylpropyl)-4,5-dihydroimidazolium iodide (AO-DHI(+)) and 4-(2-(trimethoxysilyl)ethyl)pyridine (PETMOS) provoked a blue-shift and band abatement effect on the NAP spectrum. Molecular dynamics simulations indicated a reasonable affinity between NAP and these precursors (coordination numbers 0.33 for AO-DHI(+) and 0.18 for PETMOS), hence showing that NAP-precursor complexation is in fact effective. However, time dependent density functional theory (TD-DFT) calculations of the spectra of both free and precursor-complexed NAP were identical, thus providing no theoretical basis for the complexation-induced effects observed. We realized that the intense spectral bands of AO-DHI(+) and PETMOS (at around 265 nm) superimpose partially with the NAP bands, and the apparent "blue-shifting" in the NAP spectra when mixed with AO-DHI+ and PETMOS was in this case a spurious effect of the intense background subtraction. Therefore, extreme care must be taken when interpreting other spectroscopic results obtained in a similar fashion.

Molecularly imprinted sol-gel materials for medical applications.

The present review deals with the sol-gel imprinting of both drug and non-drug templates of medical relevance, namely neurotransmitters, biomarkers, hormones, proteins and cells. Nearly a hundred recent works, either developmental or applied in a medical-related context, were critically analyzed. It may be concluded that, although research is still at an early stage, the potential of these sol-gel materials was well demonstrated in a few applications of critical interest for medicinal/biomedical science. The vast room left for expansion and improvement envisages a continuously growing interest by researchers in the future, eventually resulting in important medical applications able to enter the professional and consumer medical markets.

Molecular dynamics simulations of complex mixtures aimed at the preparation of naproxen-imprinted xerogels.

The main objective of this study was to simulate for the first time a complex sol-gel system aimed at preparing the (S)-naproxen-imprinted xerogel with an explicit representation of all the ionic species at pH 9. For this purpose, a series of molecular dynamics (MD) simulations of different mixtures, including species never studied before using the OPLS-AA force field, were prepared. A new parametrization for these species was developed and found to be acceptable. Three different systems were simulated, representing two types of pregelification models: the first one represented the initial mixture after complete hydrolysis and condensation to cyclic trimers (model A); the second one corresponded to the same mixture after the evaporation process (model B); and the last one was a simpler initial mixture without an explicit representation of all of the imprinting-mixture constituents (model C). The comparison of systems A and C mainly served the purpose of evaluating whether an explicit representation of all of the components (model A) was needed or if a less computationally demanding system in which the alkaline forms of the silicate species were ignored (model C) would be sufficient. The results confirmed our hypothesis that an explicit representation of all of the imprinting-mixture constituents is essential to study the molecular imprinting process because a poor representation of the ionic species present in the mixture may lead to erroneous conclusions or lost information. In general, the radial distribution function (RDF) analysis and interaction energies demonstrated a high affinity of the template molecule, 2-(6-methoxynaphthalen-2-yl)propanoate (NAP(-), the conjugate base of (S)-naproxen), for the gel backbone, especially targeting the units containing the dihydroimidazolium moiety used as a functional group. Model B, representing a nearly gelled sol where the density of silicates and solvent polarity were much higher relative to the other models, allowed for much faster simulations. That gave us the chance to observe the templating effect through a comparative analysis and observation of the trajectories from simulations with the template- versus non-template-containing mixtures. Overall, a strong coherence between the imprinting-relevant interactions, aggregation, or the silicate network texturing effects taken out of the simulations and the experimentally high imprinting performance and porosity features of the corresponding gels was achieved.

Patents of bio-active compounds based on computer-aided drug discovery techniques.

In recent times, there has been an increased use of Computer-Aided Drug Discovery (CADD) techniques in Medicinal Chemistry as auxiliary tools in drug discovery. Whilst the ultimate goal of Medicinal Chemistry research is for the discovery of new drug candidates, a secondary yet important outcome that results is in the creation of new computational tools. This process is often accompanied by a lack of understanding of the legal aspects related to software and model use, that is, the copyright protection of new medicinal chemistry software and software-mediated discovered products. In the center of picture, which lies in the frontiers of legal, chemistry, and biosciences, we found computational modeling-based drug discovery patents. This article aims to review prominent cases of patents of bio-active organic compounds that involved/protect also computational techniques. We put special emphasis on patents based on Quantitative Structure-Activity Relationships (QSAR) models but we include other techniques too. An overview of relevant international issues on drug patenting is also presented.