A QSAR Model for in Silico Screening of MAO-A Inhibitors. Prediction, Synthesis, and Biological Assay of Novel Coumarins

Structure-based design and classifications of small molecules regulating the circadian rhythm period

Circadian rhythm is an important mechanism that controls behavior and biochemical events based on 24 h rhythmicity. Ample evidence indicates disturbance of this mechanism is associated with different diseases such as cancer, mood disorders, and familial delayed phase sleep disorder. Therefore, drug discovery studies have been initiated using high throughput screening. Recently the crystal structures of core clock proteins (CLOCK/BMAL1, Cryptochromes (CRY), Periods), responsible for generating circadian rhythm, have been solved. Availability of structures makes amenable core clock proteins to design molecules regulating their activity by using in silico approaches. In addition to that, the implementation of classification features of molecules based on their toxicity and activity will improve the accuracy of the drug discovery process. Here, we identified 171 molecules that target functional domains of a core clock protein, CRY1, using structure-based drug design methods. We experimentally determined that 115 molecules were nontoxic, and 21 molecules significantly lengthened the period of circadian rhythm in U2OS cells. We then performed a machine learning study to classify these molecules for identifying features that make them toxic and lengthen the circadian period. Decision tree classifiers (DTC) identified 13 molecular descriptors, which predict the toxicity of molecules with a mean accuracy of 79.53% using tenfold cross-validation. Gradient boosting classifiers (XGBC) identified 10 molecular descriptors that predict and increase in the circadian period length with a mean accuracy of 86.56% with tenfold cross-validation. Our results suggested that these features can be used in QSAR studies to design novel nontoxic molecules that exhibit period lengthening activity.

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.

Quantitative Structure-Activity Relationship (QSAR) Studies for the Inhibition of MAOs

Monoamine oxidases are the crucial drug targets for the treatment of neurodegenerative disorders like depression, Parkinson's disease, and Alzheimer's disease. The enzymes catalyze the oxidative deamination of several monoamine containing neurotransmitters, i.e. serotonin (5-HT), melatonin, epinephrine, norepinephrine, phenylethylamine, benzylamine, dopamine, tyramine, etc. The oxidative reaction of monoamine oxidases results in the production of hydrogen peroxide that leads to the neurodegeneration process. Therefore, the inhibition of monoamine oxidases has shown a profound effect against neurodegenerative diseases. At present, the design and development of newer lead molecules for the inhibition of monoamine oxidases are under intensive research in the field of medicinal chemistry. Recently, the advancement in QSAR methodologies has shown considerable interest in the development of monoamine oxidase inhibitors. The present review describes the development of QSAR methodologies, and their role in the design of newer monoamine oxidase inhibitors. It will assist the medicinal chemist in the identification of selective and potent monoamine oxidase inhibitors from various chemical scaffolds.

A novel hybrid method named electron conformational genetic algorithm as a 4D QSAR investigation to calculate the biological activity of the tetrahydrodibenzazosines

To understand the structure-activity correlation of a group of tetrahydrodibenzazocines as inhibitors of 17β-hydroxysteroid dehydrogenase type 3, we have performed a combined genetic algorithm (GA) and four-dimensional quantitative structure-activity relationship (4D-QSAR) modeling study. The computed electronic and geometry structure descriptors were regulated as a matrix and named as electron-conformational matrix of contiguity (ECMC). A chemical property-based pharmacophore model was developed for series of tetrahydrodibenzazocines by EMRE software package. GA was employed to choose an optimal combination of parameters. A model has been developed for estimating anticancer activity quantitatively. All QSAR models were established with 40 compounds (training set), then they were considered for selective capability with additional nine compounds (test set). A statistically valid 4D-QSAR ( R training 2 = 0.856 , R test 2 = 0.851 and q² = 0.650) with good external set prediction was obtained.

New frontiers in the transition-metal-free synthesis of heterocycles from alkynoates: an overview and current status

This review highlights the successful utilization of transition-metal-free approaches for the modular assembly of various heterocycles from alkynoates.

Multioutput Perturbation-Theory Machine Learning (PTML) Model of ChEMBL Data for Antiretroviral Compounds

Retroviral infections, such as HIV, are, until now, diseases with no cure. Medicine and pharmaceutical chemistry need and consider it a huge goal to define target proteins of new antiretroviral compounds. ChEMBL manages Big Data features with a complex data set, which is hard to organize. This makes information difficult to analyze due to a big number of characteristics described in order to predict new drug candidates for retroviral infections. For this reason, we propose to develop a new predictive model combining perturbation theory (PT) bases and machine learning (ML) modeling to create a new tool that can take advantage of all the available information. The PTML model proposed in this work for the ChEMBL data set preclinical experimental assays for antiretroviral compounds consists of a linear equation with four variables. The PT operators used are founded on multicondition moving averages, combining different features and simplifying the difficulty to manage all data. More than 140 000 preclinical assays for 56 105 compounds with different characteristics or experimental conditions have been carried out and can be found in ChEMBL database, covering combinations with 359 biological activity parameters (c₀), 55 protein accessions (c₁), 83 cell lines (c₂), 64 organisms of assay (c₃), and 773 subtypes or strains. We have included 150 148 preclinical experimental assays for HIV virus, 1188 for HTLV virus, 84 for simian immunodeficiency virus, 370 for murine leukemia virus, 119 for Rous sarcoma virus, 1581 for MMTV, etc. We also included 5277 assays for hepatitis B virus. The developed PTML model reached considerable values in sensibility (73.05% for training and 73.10% for validation), specificity (86.61% for training and 87.17% for validation), and accuracy (75.84% for training and 75.98% for validation). We also compared alternative PTML models with different PT operators such as covariance, moments, and exponential terms. Finally, we made a comparison between literature ML models with our PTML model and also artificial neural network (ANN) nonlinear models. We conclude that this PTML model is the first one to consider multiple characteristics of preclinical experimental antiretroviral assays combined, generating a simple, useful, and adaptable instrument, which could reduce time and costs in antiretroviral drugs research.

Identification of novel monoamine oxidase selective inhibitors employing a hierarchical ligand-based virtual screening strategy

Aim: Due to the pivotal role in the oxidative deamination of monoamine neurotransmitters, two distinct monoamine oxidase (MAO) subtypes, MAO-A and MAO-B, present a significant pharmacological interest. Here, we reported a hierarchical and time-efficient ligand-based virtual screening strategy to identify potent selective and reversible MAO inhibitors. Result: A total of 130 compounds were assessed in dose–response biochemical assay against MAOs. Among them, 70 compounds were active with inhibition higher than 70%, involving 25 compounds with IC50 values less than 1 μM. Conclusion: Our research demonstrated the validity of Biologically Relevant Spectrum (BRS-3D) in predicting subtype-selective ligands and afforded a novel highly efficient way to develop selective inhibitors in the early stage of drug discovery.

Synthesis of 4-styrylcoumarins via FeCl3-promoted cascade reactions of propargylamines with β-keto esters

A versatile and highly regioselective FeCl₃-promoted tandem cyclization reaction of in situ generated alkynyl o-quinone methides (o-AQMs) with β-keto esters has been developed.

Perturbation Theory/Machine Learning Model of ChEMBL Data for Dopamine Targets: Docking, Synthesis, and Assay of New l-Prolyl-l-leucyl-glycinamide Peptidomimetics

Predicting drug-protein interactions (DPIs) for target proteins involved in dopamine pathways is a very important goal in medicinal chemistry. We can tackle this problem using Molecular Docking or Machine Learning (ML) models for one specific protein. Unfortunately, these models fail to account for large and complex big data sets of preclinical assays reported in public databases. This includes multiple conditions of assays, such as different experimental parameters, biological assays, target proteins, cell lines, organism of the target, or organism of assay. On the other hand, perturbation theory (PT) models allow us to predict the properties of a query compound or molecular system in experimental assays with multiple boundary conditions based on a previously known case of reference. In this work, we report the first PTML (PT + ML) study of a large ChEMBL data set of preclinical assays of compounds targeting dopamine pathway proteins. The best PTML model found predicts 50000 cases with accuracy of 70-91% in training and external validation series. We also compared the linear PTML model with alternative PTML models trained with multiple nonlinear methods (artificial neural network (ANN), Random Forest, Deep Learning, etc.). Some of the nonlinear methods outperform the linear model but at the cost of a notable increment of the complexity of the model. We illustrated the practical use of the new model with a proof-of-concept theoretical-experimental study. We reported for the first time the organic synthesis, chemical characterization, and pharmacological assay of a new series of l-prolyl-l-leucyl-glycinamide (PLG) peptidomimetic compounds. In addition, we performed a molecular docking study for some of these compounds with the software Vina AutoDock. The work ends with a PTML model predictive study of the outcomes of the new compounds in a large number of assays. Therefore, this study offers a new computational methodology for predicting the outcome for any compound in new assays. This PTML method focuses on the prediction with a simple linear model of multiple pharmacological parameters (IC₅₀, EC₅₀, K_i, etc.) for compounds in assays involving different cell lines used, organisms of the protein target, or organism of assay for proteins in the dopamine pathway.

PTML Combinatorial Model of ChEMBL Compounds Assays for Multiple Types of Cancer

Determining the target proteins of new anticancer compounds is a very important task in Medicinal Chemistry. In this sense, chemists carry out preclinical assays with a high number of combinations of experimental conditions (c _j). In fact, ChEMBL database contains outcomes of 65 534 different anticancer activity preclinical assays for 35 565 different chemical compounds (1.84 assays per compound). These assays cover different combinations of c _j formed from >70 different biological activity parameters ( c₀), >300 different drug targets ( c₁), >230 cell lines ( c₂), and 5 organisms of assay ( c₃) or organisms of the target ( c₄). It include a total of 45 833 assays in leukemia, 6227 assays in breast cancer, 2499 assays in ovarian cancer, 3499 in colon cancer, 3159 in lung cancer, 2750 in prostate cancer, 601 in melanoma, etc. This is a very complex data set with multiple Big Data features. This data is hard to be rationalized by researchers to extract useful relationships and predict new compounds. In this context, we propose to combine perturbation theory (PT) ideas and machine learning (ML) modeling to solve this combinatorial-like problem. In this work, we report a PTML (PT + ML) model for ChEMBL data set of preclinical assays of anticancer compounds. This is a simple linear model with only three variables. The model presented values of area under receiver operating curve = AUROC = 0.872, specificity = Sp(%) = 90.2, sensitivity = Sn(%) = 70.6, and overall accuracy = Ac(%) = 87.7 in training series. The model also have Sp(%) = 90.1, Sn(%) = 71.4, and Ac(%) = 87.8 in external validation series. The model use PT operators based on multicondition moving averages to capture all the complexity of the data set. We also compared the model with nonlinear artificial neural network (ANN) models obtaining similar results. This confirms the hypothesis of a linear relationship between the PT operators and the classification as anticancer compounds in different combinations of assay conditions. Last, we compared the model with other PTML models reported in the literature concluding that this is the only one PTML model able to predict activity against multiple types of cancer. This model is a simple but versatile tool for the prediction of the targets of anticancer compounds taking into consideration multiple combinations of experimental conditions in preclinical assays.

3D-QSAR and in-silico Studies of Natural Products and Related Derivatives as Monoamine Oxidase Inhibitors

BACKGROUND

The computational development of human monoamine oxidase (MAO) inhibitors led to advancement in drug design and the treatment of many neurodegenerative diseases and neuropsychiatric disorders. The computational development of human monoamine oxidase (MAO) inhibitors led to advancement in drug design and the treatment of many neurodegenerative diseases and neuropsychiatric disorders. Different natural heterocyclic structures are reported to display selective MAO inhibitory activity by preclinical and in-silico modeling.

OBJECTIVE

Currently, the major interest is devoted to the study of natural based therapeutic agents from the different categories. Therefore, we presenting the review to critically discuss and outline the recent advances in our knowledge on the importance of natural and natural based ligand-MAO insilico methods for novel MAO inhibitors.

DISCUSSION

Several natural and related synthetic heterocyclic compounds such as coumarins, β- carboline, piperine, naphthoquinone, morpholine, caffeine, amphetamine moreover flavonoids, chalcones, xanthones, curcumin are discussed for their MAO inhibitory profile along with molecular docking and quantitative structure-activity relationship studies.

CONCLUSION

It is clear that, by this computational drug design approach, more particular, reversible and potent compounds can be proposed as MAO inhibitors by exact changes on the fundamental framework.

Computational approaches to chemical hazard assessment

Computational prediction of toxicity has reached new heights as a result of decades of growth in the magnitude and diversity of biological data. Public packages for statistics and machine learning make model creation faster. New theory in machine learning and cheminformatics enables integration of chemical structure, toxicogenomics, simulated and physical data in the prediction of chemical health hazards, and other toxicological information. Our earlier publications have characterized a toxicological dataset of unprecedented scale resulting from the European REACH legislation (Registration Evaluation Authorisation and Restriction of Chemicals). These publications dove into potential use cases for regulatory data and some models for exploiting this data. This article analyzes the options for the identification and categorization of chemicals, moves on to the derivation of descriptive features for chemicals, discusses different kinds of targets modeled in computational toxicology, and ends with a high-level perspective of the algorithms used to create computational toxicology models.

Coumarin: A Natural, Privileged and Versatile Scaffold for Bioactive Compounds

Many naturally occurring substances, traditionally used in popular medicines around the world, contain the coumarin moiety. Coumarin represents a privileged scaffold for medicinal chemists, because of its peculiar physicochemical features, and the versatile and easy synthetic transformation into a large variety of functionalized coumarins. As a consequence, a huge number of coumarin derivatives have been designed, synthesized, and tested to address many pharmacological targets in a selective way, e.g., selective enzyme inhibitors, and more recently, a number of selected targets (multitarget ligands) involved in multifactorial diseases, such as Alzheimer's and Parkinson's diseases. In this review an overview of the most recent synthetic pathways leading to mono- and polyfunctionalized coumarins will be presented, along with the main biological pathways of their biosynthesis and metabolic transformations. The many existing and recent reviews in the field prompted us to make some drastic selections, and therefore, the review is focused on monoamine oxidase, cholinesterase, and aromatase inhibitors, and on multitarget coumarins acting on selected targets of neurodegenerative diseases.

Characterization of acetylcholinesterase from elm left beetle, Xanthogaleruca luteola and QSAR of temephos derivatives against its activity

Insect acetylcholinesterase (AChE) is the principal target for organophosphate (OP) and carbamate (CB) insecticides. In this research, an AChE from third instar larvae of elm left beetle, Xanthogaleruca luteola was purified by affinity chromatography. The enzyme was purified 75.29-fold with a total yield of 8.51%. As shown on denaturing SDS-PAGE, the molecular mass of purified AChE was 70kDa. The enzyme demonstrated maximum activity at pH7 and 35°C. Furthermore, a series of temephos (Tem) derivatives with the general structure of P(O)XP(O) (1-44) were prepared, synthesized and characterized by ³¹P, ¹³C, ¹H NMR and FT-IR spectral techniques. The toxicity of 36 new Tem derivatives was screened on the third instar larvae and the compound compound 1,2 cyclohexane-N,N'-bis(N,N'-piperidine phosphoramidate) exhibited the highest insecticidal potential. The method of kinetic analysis is applied in order to obtain the maximum velocity (V_max), the Michaelis constant (K_m) and the parameters characterizing the inhibition type for inhibitors with >75% mortality in preliminary bioassay. The inhibition mechanism was mixed and inhibitory constant (K_i) was calculated as 4.70μM^-1min^-1 for this compound. Quantitative structure-activity relationship (QSAR) equations of these compounds indicated that the electron orbital energy has major effect on insecticidal properties.

Visible-light-mediated radical cascade reaction: synthesis of 3-bromocoumarins from alkynoates

The development of a visible-light mediated bromo radical addition/spirocyclization/ester migration cascade reaction to generate 3-bromocoumarins from alkynoates is reported.

Key Targets for Multi-Target Ligands Designed to Combat Neurodegeneration

HIGHLIGHTS Compounds that interact with multiple targets but minimally with the cytochrome P450 system (CYP) address the many factors leading to neurodegeneration.Acetyl- and Butyryl-cholineEsterases (AChE, BChE) and Monoamine Oxidases A/B (MAO A, MAO B) are targets for Multi-Target Designed Ligands (MTDL).ASS234 is an irreversible inhibitor of MAO A >MAO B and has micromolar potency against the cholinesterases.ASS234 is a poor CYP substrate in human liver, yielding the depropargylated metabolite.SMe1EC2, a stobadine derivative, showed high radical scavenging property, in vitro and in vivo giving protection in head trauma and diabetic damage of endothelium.Control of mitochondrial function and morphology by manipulating fission and fusion is emerging as a target area for therapeutic strategies to decrease the pathological outcome of neurodegenerative diseases. Growing evidence supports the view that neurodegenerative diseases have multiple and common mechanisms in their aetiologies. These multifactorial aspects have changed the broadly common assumption that selective drugs are superior to "dirty drugs" for use in therapy. This drives the research in studies of novel compounds that might have multiple action mechanisms. In neurodegeneration, loss of neuronal signaling is a major cause of the symptoms, so preservation of neurotransmitters by inhibiting the breakdown enzymes is a first approach. Acetylcholinesterase (AChE) inhibitors are the drugs preferentially used in AD and that one of these, rivastigmine, is licensed also for PD. Several studies have shown that monoamine oxidase (MAO) B, located mainly in glial cells, increases with age and is elevated in Alzheimer (AD) and Parkinson's Disease's (PD). Deprenyl, a MAO B inhibitor, significantly delays the initiation of levodopa treatment in PD patients. These indications underline that AChE and MAO are considered a necessary part of multi-target designed ligands (MTDL). However, both of these targets are simply symptomatic treatment so if new drugs are to prevent degeneration rather than compensate for loss of neurotransmitters, then oxidative stress and mitochondrial events must also be targeted. MAO inhibitors can protect neurons from apoptosis by mechanisms unrelated to enzyme inhibition. Understanding the involvement of MAO and other proteins in the induction and regulation of the apoptosis in mitochondria will aid progress toward strategies to prevent the loss of neurons. In general, the oxidative stress observed both in PD and AD indicate that antioxidant properties are a desirable part of MTDL molecules. After two or more properties are incorporated into one molecule, the passage from a lead compound to a therapeutic tool is strictly linked to its pharmacokinetic and toxicity. In this context the interaction of any new molecules with cytochrome P450 and other xenobiotic metabolic processes is a crucial point. The present review covers the biochemistry of enzymes targeted in the design of drugs against neurodegeneration and the cytochrome P450-dependent metabolism of MTDLs.

Investigation of binding mechanism of novel 8-substituted coumarin derivatives with human serum albumin and α-1-glycoprotein

Coumarin molecules have biological activities possessing lipid-controlling activity, anti-hepatitis C activity, anti-diabetic, anti-Parkinson activity, and anti-cancer activity. Here, we have presented an inclusive study on the interaction of 8-substituted-7-hydroxy coumarin derivatives (Umb-1/Umb-2) with α-1-glycoprotein (AGP) and human serum albumin (HSA) which are the major carrier proteins in the human blood plasma. Binding constants obtained from fluorescence emission data were found to be KUmb-1=3.1 ± .01 × 10(4) M(-1), KUmb-2 = 7 ± .01 × 10(4) M(-1), which corresponds to -6.1 and -6.5 kcal/mol of free energy for Umb-1 and Umb-2, respectively, suggesting that these derivatives bind strongly to HSA. Also these molecules bind to AGP with binding constants of KUmb-1-AGP=3.1 ± .01 × 10(3) M(-1) and KUmb-2-AGP = 4.6 ± .01 × 10(3) M(-1). Further, the distance, r between the donor (HSA) and acceptor (Umb-1/Umb-2) was calculated based on the Forster's theory of non-radiation energy transfer and the values were observed to be 1.14 and 1.29 nm in Umb-1-HSA and Umb-2-HSA system, respectively. The protein secondary structure of HSA was partially unfolded upon binding of Umb-1 and Umb-2. Furthermore, site displacement experiments with lidocaine, phenylbutazone (IIA), and ibuprofen (IIIA) proves that Umb derivatives significantly bind to subdomain IIIA of HSA which is further supported by docking studies. Furthermore, Umb-1 binds to LYS402 with one hydrogen bond distance of 2.8 Å and Umb-2 binds to GLU354 with one hydrogen bond at a distance of 2.0 Å. Moreover, these molecules are stabilized by hydrophobic interactions and hydrogen bond between the hydroxyl groups of carbon-3 of coumarin derivatives.

Computational modeling in nanomedicine: prediction of multiple antibacterial profiles of nanoparticles using a quantitative structure-activity relationship perturbation model

AIMS

We introduce the first quantitative structure-activity relationship (QSAR) perturbation model for probing multiple antibacterial profiles of nanoparticles (NPs) under diverse experimental conditions.

MATERIALS & METHODS

The dataset is based on 300 nanoparticles containing dissimilar chemical compositions, sizes, shapes and surface coatings. In general terms, the NPs were tested against different bacteria, by considering several measures of antibacterial activity and diverse assay times. The QSAR perturbation model was created from 69,231 nanoparticle-nanoparticle (NP-NP) pairs, which were randomly generated using a recently reported perturbation theory approach.

RESULTS

The model displayed an accuracy rate of approximately 98% for classifying NPs as active or inactive, and a new copper-silver nanoalloy was correctly predicted by this model with consensus accuracy of 77.73%.

CONCLUSION

Our QSAR perturbation model can be used as an efficacious tool for the virtual screening of antibacterial nanomaterials.

Structure- and ligand-based virtual screening identifies new scaffolds for inhibitors of the oncoprotein MDM2

A major challenge in the field of ligand discovery is to identify chemically useful fragments that can be developed into inhibitors of specific protein-protein interactions. Low molecular weight fragments (with molecular weight less than 250 Da) are likely to bind weakly to a protein’s surface. Here we use a new virtual screening procedure which uses a combination of similarity searching and docking to identify chemically tractable scaffolds that bind to the p53-interaction site of MDM2. The binding has been verified using capillary electrophoresis which has proven to be an excellent screening method for such small, weakly binding ligands.

Synthesis and evaluation of 6-methylcoumarin derivatives as potent and selective monoamine oxidase B inhibitors

Compound 5n was a potent and selective inhibitor of hMAO-B.

Trifluoroacetic acid-promoted Michael addition-cyclization reactions of vinylogous carbamates

A simple and efficient methodology has been developed for the synthesis of pyrrolobenzoxazine and 3-arylamino coumarin derivatives promoted by trifluoroacetic acid. The initial step in the current protocol involves a Michael addition of the 1,4-benzoxazinone derivatives, a novel class of vinylogous carbamates to the Michael acceptors and subsequent cyclization.

Exploring the adenylation domain repertoire of nonribosomal peptide synthetases using an ensemble of sequence-search methods

The introduction of two-dimension (2D) graphs and their numerical characterization for comparative analyses of DNA/RNA and protein sequences without the need of sequence alignments is an active yet recent research topic in bioinformatics. Here, we used a 2D artificial representation (four-color maps) with a simple numerical characterization through topological indices (TIs) to aid the discovering of remote homologous of Adenylation domains (A-domains) from the Nonribosomal Peptide Synthetases (NRPS) class in the proteome of the cyanobacteria Microcystis aeruginosa. Cyanobacteria are a rich source of structurally diverse oligopeptides that are predominantly synthesized by NPRS. Several A-domains share amino acid identities lower than 20 % being a possible source of remote homologous. Therefore, A-domains cannot be easily retrieved by BLASTp searches using a single template. To cope with the sequence diversity of the A-domains we have combined homology-search methods with an alignment-free tool that uses protein four-color-maps. TI2BioP (Topological Indices toBioPolymers) version 2.0, available at http://ti2biop.sourceforge.net/ allowed the calculation of simple TIs from the protein sequences (four-color maps). Such TIs were used as input predictors for the statistical estimations required to build the alignment-free models. We concluded that the use of graphical/numerical approaches in cooperation with other sequence search methods, like multi-templates BLASTp and profile HMM, can give the most complete exploration of the repertoire of highly diverse protein families.

Computational methods for the discovery of mood disorder therapies

INTRODUCTION

Despite the significant progress, research is still needed to reveal details of the complex and dynamic chemical processes operating in the central nervous system (CNS) and their relationship to psychological effects such as mood disorders. The incidence of behavioral depression is widely spread worldwide, with an estimated 14.8 million adults diagnosed yearly in the United States alone. The efficacy of current antidepressants on 50 - 60% of patients, their slow onset of action and the prevalence of adverse side effects highlight the need for developing a new generation of improved antidepressants. Computational methods have the potential to aid in the discovery of mood modulators.

AREAS COVERED

This review contains three main sections: historical evolution of marketed antidepressants, physicochemical and structural properties of antidepressant compounds reported in the ChEMBL database and recent efforts in the design and discovery of antidepressants using computational methods. The authors provide details of the computational methods employed, from chemoinformatic analyses to molecular modeling.

EXPERT OPINION

While there have been numerous and important findings in depression research, the high cost and time spent on research into new therapies for brain disorders is a risky undertaking. Computational methodologies can be employed to speed up the discovery of new antidepressants and to detect new sources of chemical compounds with potential antidepressant activity. Compound collections containing compounds already approved in the pharmaceutical and food industries that cover the property space and complement the structural space of CNS drugs represent a promising starting point for the discovery of new antidepressant agents.

Synthesis and study of a series of 3-arylcoumarins as potent and selective monoamine oxidase B inhibitors

New series of 6-substituted-3-arylcoumarins displaying several alkyl, hydroxyl, halogen, and alkoxy groups in the two benzene rings have been designed, synthesized, and evaluated in vitro as human monoamine oxidase A and B (hMAO-A and hMAO-B) inhibitors. Most of the studied compounds showed a high affinity and selectivity to the hMAO-B isoenzyme, with IC(50) values on nanomolar and picomolar range. Ten of the 22 described compounds displayed higher MAO-B inhibitory activity and selectivity than selegiline. Coumarin 7 is the most active compound of this series, being 64 times more active than selegiline and also showing the highest hMAO-B specificity. In addition, docking experiments were carried out on hMAO-A and h-MAO-B structures. This study provided new information about the enzyme-inhibitor interaction and the potential therapeutic application of this 3-arylcoumarin scaffold.

MISS-Prot: web server for self/non-self discrimination of protein residue networks in parasites; theory and experiments in Fasciola peptides and Anisakis allergens

Infections caused by human parasites (HPs) affect the poorest 500 million people worldwide but chemotherapy has become expensive, toxic, and/or less effective due to drug resistance. On the other hand, many 3D structures in Protein Data Bank (PDB) remain without function annotation. We need theoretical models to quickly predict biologically relevant Parasite Self Proteins (PSP), which are expressed differentially in a given parasite and are dissimilar to proteins expressed in other parasites and have a high probability to become new vaccines (unique sequence) or drug targets (unique 3D structure). We present herein a model for PSPs in eight different HPs (Ascaris, Entamoeba, Fasciola, Giardia, Leishmania, Plasmodium, Trypanosoma, and Toxoplasma) with 90% accuracy for 15 341 training and validation cases. The model combines protein residue networks, Markov Chain Models (MCM) and Artificial Neural Networks (ANN). The input parameters are the spectral moments of the Markov transition matrix for electrostatic interactions associated with the protein residue complex network calculated with the MARCH-INSIDE software. We implemented this model in a new web-server called MISS-Prot (MARCH-INSIDE Scores for Self-Proteins). MISS-Prot was programmed using PHP/HTML/Python and MARCH-INSIDE routines and is freely available at: . This server is easy to use by non-experts in Bioinformatics who can carry out automatic online upload and prediction with 3D structures deposited at PDB (mode 1). We can also study outcomes of Peptide Mass Fingerprinting (PMFs) and MS/MS for query proteins with unknown 3D structures (mode 2). We illustrated the use of MISS-Prot in experimental and/or theoretical studies of peptides from Fasciola hepatica cathepsin proteases or present on 10 Anisakis simplex allergens (Ani s 1 to Ani s 10). In doing so, we combined electrophoresis (1DE), MALDI-TOF Mass Spectroscopy, and MASCOT to seek sequences, Molecular Mechanics + Molecular Dynamics (MM/MD) to generate 3D structures and MISS-Prot to predict PSP scores. MISS-Prot also allows the prediction of PSP proteins in 16 additional species including parasite hosts, fungi pathogens, disease transmission vectors, and biotechnologically relevant organisms.