Review of Theoretical Models to Study Natural Products with Antiprotozoal Activity.

In nature, pathogenic parasite species with different susceptibility patterns of antiparasitic drugs abound. In this sense, natural products derived from plants are a potency for drugs with potential antiparasitic activity. Unfortunately, there are many metabolites and studying all of them would be costly in terms of money and resources. To this end, theoretical studies such as QSAR models could be useful. These, for the most part, predict the biological activity of the drugs against a single species of parasite. Consequently, foretell the probability with which a drug is active against many different species with a single QSAR model is an important achievement. This review consists of three parts: the first part is a review of metabolites found in nature that have antiparasitic activity, in particular the antiprotozoal (Leishmania and Trypanosoma); the second part includes a review of theoretical studies looking for a model that predicts the antiprotozoal activity of natural products; the third and final part concerns the study of theoretical models focused on the interaction between drug and receptor, analyzing new metabolites with antiprotozoal activity.

Review of Theoretical Prediction Models for Organic Extract Metabolites, Effect of Drying Temperature on Smooth Muscle Relaxing Activity Induced by Organic Extracts Specially Cecropia Obtusifolia Portal and Web Server Predictors of Drug-Protein Interaction.

Cecropia obtusifolia bertol is medicinal specie used in the treatment of diabetes mellitus and hypertension and it has scientific studies that support the traditional use. However, it is required to understand the influence of drying temperature on the yield and pharmacological activity. Drying rate, extraction efficiency, changes in the UV-Vis spectrum and estimating chlorophylls were stimulated with the increasing temperature. Finally, relaxant activity of vascular smooth muscle is increased by 70ºC and reducing ability by the method of CARF increases with temperature. Analytical studies are required to identify changes in the metabolic content and those that ensure the safety and efficacy for human consumption. In this sense, bioinformatic studies may be helpful. Studies such as QSAR can help us to study these metabolites derived from natural products. MIND-BETS model and NL MIND-BETS model to predict DPIs was introduced using MARCH-INSIDE (MI) software to calculate structural parameters for drugs and enzymes respectively. We firstly revised the state-of-art on the design with review of previous works with hypertension activity based on theoretical studies. A study, evaluating the effect of drying temperature of leaves of C. obtusifolia on the relaxing of vascular smooth muscle, antioxidant activity and the presence of chlorophylls, with a focus on Cecropia metabolites. Last, we carried out QSAR studies using MIND-BEST and NL MIND-BEST web servers in order to understand the essential metabolites structural requirement for binding with receptors for FDA proteins.

Prediction of multi-target networks of neuroprotective compounds with entropy indices and synthesis, assay, and theoretical study of new asymmetric 1,2-rasagiline carbamates.

In a multi-target complex network, the links (L(ij)) represent the interactions between the drug (d(i)) and the target (t(j)), characterized by different experimental measures (K(i), K(m), IC50, etc.) obtained in pharmacological assays under diverse boundary conditions (c(j)). In this work, we handle Shannon entropy measures for developing a model encompassing a multi-target network of neuroprotective/neurotoxic compounds reported in the CHEMBL database. The model predicts correctly >8300 experimental outcomes with Accuracy, Specificity, and Sensitivity above 80%-90% on training and external validation series. Indeed, the model can calculate different outcomes for >30 experimental measures in >400 different experimental protocolsin relation with >150 molecular and cellular targets on 11 different organisms (including human). Hereafter, we reported by the first time the synthesis, characterization, and experimental assays of a new series of chiral 1,2-rasagiline carbamate derivatives not reported in previous works. The experimental tests included: (1) assay in absence of neurotoxic agents; (2) in the presence of glutamate; and (3) in the presence of H2O2. Lastly, we used the new Assessing Links with Moving Averages (ALMA)-entropy model to predict possible outcomes for the new compounds in a high number of pharmacological tests not carried out experimentally.

Galvez-Markov network transferability indices: review of classic theory and new model for perturbations in metabolic reactions.

Topological Indices (TIs) are numerical parameters useful to carry out Quantitative Structure-Property Relationships (QSPR) analysis and predict the effect of perturbations in many types of Complex Networks. This work, focuses on a very powerful class of TIs called Galvez charge transfer indices. First, we review the classic concept and some applications of these indices. Next, we review the Galvez-Markov TIs of order k (GMk), a recent generalization to these TIs introduced by us. We also reviewed some previous examples of calculation of GMk values for different classes of networks, including metabolic networks. Here, we also demonstrated that Galvez- Markov TIs are useful to predict perturbations and the transferability of biochemical patterns forms metabolic networks of species to others. We report a linear QSPR-Perturbation theory model that predicts more than 300,000 perturbations in metabolic networks with 85 - 99% of good classification in training and validation series.

Model for high-throughput screening of drug immunotoxicity--study of the anti-microbial G1 over peritoneal macrophages using flow cytometry.

Quantitative Structure-Activity (mt-QSAR) techniques may become an important tool for prediction of cytotoxicity and High-throughput Screening (HTS) of drugs to rationalize drug discovery process. In this work, we train and validate by the first time mt-QSAR model using TOPS-MODE approach to calculate drug molecular descriptors and Linear Discriminant Analysis (LDA) function. This model correctly classifies 8258 out of 9000 (Accuracy = 91.76%) multiplexing assay endpoints of 7903 drugs (including both train and validation series). Each endpoint correspond to one out of 1418 assays, 36 molecular and cellular targets, 46 standard type measures, in two possible organisms (human and mouse). After that, we determined experimentally, by the first time, the values of EC50 = 21.58 µg/mL and Cytotoxicity = 23.6% for the anti-microbial/anti-parasite drug G1 over Balb/C mouse peritoneal macrophages using flow cytometry. In addition, the model predicts for G1 only 7 positive endpoints out 1251 cytotoxicity assays (0.56% of probability of cytotoxicity in multiple assays). The results obtained complement the toxicological studies of this important drug. This work adds a new tool to the existing pool of few methods useful for multi-target HTS of ChEMBL and other libraries of compounds towards drug discovery.

Entropy model for multiplex drug-target interaction endpoints of drug immunotoxicity.

Entropy measures are universal parameters useful to codify biologically-relevant information in many systems. In our previous work, (Gonzalez-Diaz, H., et al. Chem. Res. Toxicol. 2003, 16, 1318-1327), we introduced the molecular structure information indices called 3D-Markovian electronic delocalization entropies (3D-MEDNEs) to study the quantitative structure-toxicity relationships (QSTR) of drugs. In a second part, (Cruz-Monteagudo, M. et al. Chem. Res. Toxicol., 2008, 21 (3), 619-632), we extended 3D-MEDNEs to numerically encode toxicologically-relevant information present in Mass Spectra of the serum proteome. These works demonstrated that the idea behind classic drug QSTR models can be extended to solve more general problems in toxicological chemical research. For instance, there are not many reports of multi-target QSTR (mt-QSTR) models useful to predict multiplexed endpoints of drugs in a high number of cytotoxicity assays. In this work, we train and validate for the first time a QSTR model that correctly classifies 8,806 out of 9,001 (Accuracy = 91.1%) multiplexing assay endpoints of 7903 drugs (including both training and validation series). Each endpoint corresponds to one out of 1443 assays, 32 molecular and cellular targets, 46 standard type measures, in two possible organisms (human and mouse). We have also determined experimentally, for the first time, the values of EC50 = 8.21 µg/mL and Cytotoxicity = 26.25 % for the antimicrobial / antiparasitic drug G1 on Balb/C mouse thymic macrophages using flow cytometry. In addition, we have used the new model to predict G1 endpoints in 1,283 assays finding a low average probability of p(1) = 0.50% in 152 cytotoxicity assays. Last, we have used the model to predict average probability of the interaction of G1 with different proteins in macrophages. Interestingly, the Macrophage colony-stimulating factor receptor, the Macrophage colony-stimulating factor 1 receptor, the Macrophage migration inhibitory factor, Macrophage scavenger receptor types I and II, and the Macrophage-stimulating protein receptor, have also very low average predicted probabilities of p(1) = 0.092, 0.038, 0.077, 0.026, 0.2, 0.106, respectively. Both experimental and theoretical results show a moderate thymic macrophage cytotoxicity of G1. The obtained results are significant because they complement the immunotoxicology studies of this important drug.

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.

3D MI-DRAGON: new model for the reconstruction of US FDA drug- target network and theoretical-experimental studies of inhibitors of rasagiline derivatives for AChE.

The number of neurodegenerative diseases has been increasing in recent years. Many of the drug candidates to be used in the treatment of neurodegenerative diseases present specific 3D structural features. An important protein in this sense is the acetylcholinesterase (AChE), which is the target of many Alzheimer's dementia drugs. Consequently, the prediction of Drug-Protein Interactions (DPIs/nDPIs) between new drug candidates and specific 3D structure and targets is of major importance. To this end, we can use Quantitative Structure-Activity Relationships (QSAR) models to carry out a rational DPIs prediction. Unfortunately, many previous QSAR models developed to predict DPIs take into consideration only 2D structural information and codify the activity against only one target. To solve this problem we can develop some 3D multi-target QSAR (3D mt-QSAR) models. In this study, using the 3D MI-DRAGON technique, we have introduced a new predictor for DPIs based on two different well-known software. We have used the MARCH-INSIDE (MI) and DRAGON software to calculate 3D structural parameters for drugs and targets respectively. Both classes of 3D parameters were used as input to train Artificial Neuronal Network (ANN) algorithms using as benchmark dataset the complex network (CN) made up of all DPIs between US FDA approved drugs and their targets. The entire dataset was downloaded from the DrugBank database. The best 3D mt-QSAR predictor found was an ANN of Multi-Layer Perceptron-type (MLP) with profile MLP 37:37-24-1:1. This MLP classifies correctly 274 out of 321 DPIs (Sensitivity = 85.35%) and 1041 out of 1190 nDPIs (Specificity = 87.48%), corresponding to training Accuracy = 87.03%. We have validated the model with external predicting series with Sensitivity = 84.16% (542/644 DPIs; Specificity = 87.51% (2039/2330 nDPIs) and Accuracy = 86.78%. The new CNs of DPIs reconstructed from US FDA can be used to explore large DPI databases in order to discover both new drugs and/or targets. We have carried out some theoretical-experimental studies to illustrate the practical use of 3D MI-DRAGON. First, we have reported the prediction and pharmacological assay of 22 different rasagiline derivatives with possible AChE inhibitory activity. In this work, we have reviewed different computational studies on Drug- Protein models. First, we have reviewed 10 studies on DP computational models. Next, we have reviewed 2D QSAR, 3D QSAR, CoMFA, CoMSIA and Docking with different compounds to find Drug-Protein QSAR models. Last, we have developped a 3D multi-target QSAR (3D mt-QSAR) models for the prediction of the activity of new compounds against different targets or the discovery of new targets.

ANN multiplexing model of drugs effect on macrophages; theoretical and flow cytometry study on the cytotoxicity of the anti-microbial drug G1 in spleen.

Multiplexed biological assays provide multiple measurements of cellular parameters in the same test. In this work, we have trained and tested an Artificial Neural Network (ANN) model for the first time, in order to perform a multiplexing prediction of drugs effect on macrophage populations. In so doing, we have used the TOPS-MODE approach to calculate drug molecular descriptors and the software STATISTICA to seek different ANN models such as: Linear Neural Network (LNN), Radial Basis Function (RBF), Probabilistic Neural Networks (PNN) and Multi-Layer Perceptrons (MLP). The best model found was the LNN, which correctly classified 8258 out of 9000 (Accuracy = 93.0%) multiplexing assay endpoints of 7903 drugs (including both training and test series). Each endpoint corresponds to one out of 1418 assays, 36 molecular or cellular targets, 46 standard type measures, in two possible organisms (human and mouse). Secondly, we have determined experimentally, for the first time, the values of EC(50) = 11.41 µg/mL and Cytotoxicity = 27.1% for the drug G1 over Balb/C mouse spleen macrophages using flow cytometry. In addition, we have used the LNN model to predict the G1 activity in 1265 multiplexing assays not measured experimentally (including 152 cytotoxicity assay endpoints). Both experimental and theoretical results point out a low macrophage cytotoxicity of G1. This work breaks new ground for the 'in silico' multiplexing screening of large libraries of compounds. The results obtained are very significant because they complement the immunotoxicology studies of this important anti-microbial/anti-parasite drug.

Review of synthesis, assay, and prediction of ß and γ-secretase inhibitors.

Alzheimer's disease (AD) is characterize with several pathologies this disease, amyloid plaques, composed of the ß-amyloid peptide and ß-amyloid peptide are hallmark neuropathological lesions in Alzheimer's disease brain. Indeed, a wealth of evidence suggests that ß-amyloid is central to the pathophysiology of AD and is likely to play an early role in this intractable neurodegenerative disorder. AD is the most prevalent form of dementia, and current indications show that twenty-nine million people live with AD worldwide, a figure expected rise exponentially over the coming decades. Clearly, blocking disease progression or, in the best-case scenario, preventing AD altogether would be of benefit in both social and economic terms. However, current AD therapies are merely palliative and only temporarily slow cognitive decline, and treatments that address the underlying pathologic mechanisms of AD are completely lacking. While familial AD (FAD) is caused by autosomal dominant mutations in either amyloid precursor protein (APP) or the presenilin (PS1, PS2) genes. First, we revised Desing, synthesis, and Biological assay of ß and γ-secretase inhibitors. Next, we review 2D QSAR, 3D QSAR, CoMFA, CoMSIA and Docking with different compound to find out the structural requirements. Next, we revised QSAR studies using method of Artificial Neural Network (ANN) in order to understand the essential structural requirement for binding with receptor for ß and γ-secretase inhibitors.

From QSAR models of drugs to complex networks: state-of-art review and introduction of new Markov-spectral moments indices.

Quantitative Structure-Activity/Property Relationships (QSAR/QSPR) models have been largely used for different kind of problems in Medicinal Chemistry and other Biosciences as well. Nevertheless, the applications of QSAR models have been restricted to the study of small molecules in the past. In this context, many authors use molecular graphs, atoms (nodes) connected by chemical bonds (links) to represent and numerically characterize the molecular structure. On the other hand, Complex Networks are useful in solving problems in drug research and industry, developing mathematical representations of different systems. These systems move in a wide range from relatively simple graph representations of drug molecular structures (molecular graphs used in classic QSAR) to large systems. We can cite for instance, drug-target interaction networks, protein structure networks, protein interaction networks (PINs), or drug treatment in large geographical disease spreading networks. In any case, all complex networks have essentially the same components: nodes (atoms, drugs, proteins, microorganisms and/or parasites, geographical areas, drug policy legislations, etc.) and links (chemical bonds, drug-target interactions, drug-parasite treatment, drug use, etc.). Consequently, we can use the same type of numeric parameters called Topological Indices (TIs) to describe the connectivity patterns in all these kinds of Complex Networks irrespective the nature of the object they represent and use these TIs to develop QSAR/QSPR models beyond the classic frontiers of drugs small-sized molecules. The goal of this work, in first instance, is to offer a common background to all the manuscripts presented in this special issue. In so doing, we make a review of the most used software and databases, common types of QSAR/QSPR models, and complex networks involving drugs or their targets. In addition, we review both classic TIs that have been used to describe the molecular structure of drugs and/or larger complex networks. In second instance, we use for the first time a Markov chain model to generalize Spectral moments to higher order analogues coined here as the Stochastic Spectral Moments TIs of order k (πk). Lastly, we report for the first time different QSAR/QSPR models for different classes of networks found in drug research, nature, technology, and social-legal sciences using πk values. This work updates our previous reviews Gonzalez-Diaz et al. Curr Top Med Chem. 2007; 7(10): 1015-29 and Gonzalez-Diaz et al. Curr Top Med Chem. 2008; 8(18):1676-90. It has been prepared in response to the kind invitation of the editor Prof. AB Reitz in commemoration of the 10th anniversary of this journal in 2010.

LIBP-Pred: web server for lipid binding proteins using structural network parameters; PDB mining of human cancer biomarkers and drug targets in parasites and bacteria.

Lipid-Binding Proteins (LIBPs) or Fatty Acid-Binding Proteins (FABPs) play an important role in many diseases such as different types of cancer, kidney injury, atherosclerosis, diabetes, intestinal ischemia and parasitic infections. Thus, the computational methods that can predict LIBPs based on 3D structure parameters became a goal of major importance for drug-target discovery, vaccine design and biomarker selection. In addition, the Protein Data Bank (PDB) contains 3000+ protein 3D structures with unknown function. This list, as well as new experimental outcomes in proteomics research, is a very interesting source to discover relevant proteins, including LIBPs. However, to the best of our knowledge, there are no general models to predict new LIBPs based on 3D structures. We developed new Quantitative Structure-Activity Relationship (QSAR) models based on 3D electrostatic parameters of 1801 different proteins, including 801 LIBPs. We calculated these electrostatic parameters with the MARCH-INSIDE software and they correspond to the entire protein or to specific protein regions named core, inner, middle, and surface. We used these parameters as inputs to develop a simple Linear Discriminant Analysis (LDA) classifier to discriminate 3D structure of LIBPs from other proteins. We implemented this predictor in the web server named LIBP-Pred, freely available at , along with other important web servers of the Bio-AIMS portal. The users can carry out an automatic retrieval of protein structures from PDB or upload their custom protein structural models from their disk created with LOMETS server. We demonstrated the PDB mining option performing a predictive study of 2000+ proteins with unknown function. Interesting results regarding the discovery of new Cancer Biomarkers in humans or drug targets in parasites have been discussed here in this sense.

New Markov-Shannon Entropy models to assess connectivity quality in complex networks: from molecular to cellular pathway, Parasite-Host, Neural, Industry, and Legal-Social networks.

Graph and Complex Network theory is expanding its application to different levels of matter organization such as molecular, biological, technological, and social networks. A network is a set of items, usually called nodes, with connections between them, which are called links or edges. There are many different experimental and/or theoretical methods to assign node-node links depending on the type of network we want to construct. Unfortunately, the use of a method for experimental reevaluation of the entire network is very expensive in terms of time and resources; thus the development of cheaper theoretical methods is of major importance. In addition, different methods to link nodes in the same type of network are not totally accurate in such a way that they do not always coincide. In this sense, the development of computational methods useful to evaluate connectivity quality in complex networks (a posteriori of network assemble) is a goal of major interest. In this work, we report for the first time a new method to calculate numerical quality scores S(L(ij)) for network links L(ij) (connectivity) based on the Markov-Shannon Entropy indices of order k-th (θ(k)) for network nodes. The algorithm may be summarized as follows: (i) first, the θ(k)(j) values are calculated for all j-th nodes in a complex network already constructed; (ii) A Linear Discriminant Analysis (LDA) is used to seek a linear equation that discriminates connected or linked (L(ij)=1) pairs of nodes experimentally confirmed from non-linked ones (L(ij)=0); (iii) the new model is validated with external series of pairs of nodes; (iv) the equation obtained is used to re-evaluate the connectivity quality of the network, connecting/disconnecting nodes based on the quality scores calculated with the new connectivity function. This method was used to study different types of large networks. The linear models obtained produced the following results in terms of overall accuracy for network reconstruction: Metabolic networks (72.3%), Parasite-Host networks (93.3%), CoCoMac brain cortex co-activation network (89.6%), NW Spain fasciolosis spreading network (97.2%), Spanish financial law network (89.9%) and World trade network for Intelligent & Active Food Packaging (92.8%). In order to seek these models, we studied an average of 55,388 pairs of nodes in each model and a total of 332,326 pairs of nodes in all models. Finally, this method was used to solve a more complicated problem. A model was developed to score the connectivity quality in the Drug-Target network of US FDA approved drugs. In this last model the θ(k) values were calculated for three types of molecular networks representing different levels of organization: drug molecular graphs (atom-atom bonds), protein residue networks (amino acid interactions), and drug-target network (compound-protein binding). The overall accuracy of this model was 76.3%. This work opens a new door to the computational reevaluation of network connectivity quality (collation) for complex systems in molecular, biomedical, technological, and legal-social sciences as well as in world trade and industry.

Review of synthesis, biological assay and QSAR studies of ß-secretase inhibitors.

Alzheimer's disease (AD) is highly complex. While several pathologies characterize this disease, amyloid plaques, composed of the ß-amyloid peptide, are hallmark neuropathological lesions in Alzheimer's disease brain. Indeed, a wealth of evidence suggests that ß-amyloid is central to the pathophysiology of AD and is likely to play an early role in this intractable neurodegenerative disorder. The BACE-1 enzyme is essential for the generation of ß-amyloid. BACE-1 knockout mice do not produce ß-amyloid and are free from Alzheimer's associated pathologies, including neuronal loss and certain memory deficits. The fact that BACE-1 initiates the formation of ß-amyloid, and the observation that BACE-1 levels are elevated in this disease provide direct and compelling reasons to develop therapies directed at BACE-1 inhibition, thus reducing ß-amyloid and its associated toxicities. In this sense, quantitative structure-activity relationships (QSAR) could play an important role in studying these ß-secretase inhibitors. QSAR models are necessary in order to guide the ß-secretase synthesis. This work is aimed at reviewing different design and synthesis and computational studies for a very large and heterogeneous series of ß-secretase inhibitors. First, we review design, synthesis, and Biological assay of ß-secretase inhibitors. Next, we review 2D QSAR, 3D QSAR, CoMFA, CoMSIA and Docking with different compounds to find out the structural requirements. Next, we review QSAR studies using the method of Linear Discriminant Analysis (LDA) in order to understand the essential structural requirement for receptor binding for ß- secretase inhibitors.

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.

Theoretical study of GSK-3α: neural networks QSAR studies for the design of new inhibitors using 2D descriptors.

Glycogen synthase kinase-3 (GSK-3) targets encompass proteins implicated in AD and neurological disorders. The functions of GSK-3 and its implication in various human diseases have triggered an active search for potent and selective GSK-3 inhibitors. In this sense, QSAR could play an important role in studying these GSK-3 inhibitors. For this reason, we developed QSAR models for GSK-3α, linear discriminant analysis (LDA), and artificial neural networks (ANNs) from nearly 50,000 cases with more than 700 different GSK-3α inhibitors obtained from ChEMBL database server; in total we used more than 20,000 different molecules to develop the QSAR models. The model correctly classified 237 out of 275 active compounds (86.2%) and 14,870 out of 15,970 non-active compounds (93.2%) in the training series. The overall training performance was 93.0%. Validation of the model was carried out using an external predicting series. In these series, the model classified correctly 458 out of 549 (83.4%) compounds and 29,637 out of 31,927 non-active compounds (83.4%). The overall predictability performance was 92.7%. In this study, we propose three types of non-linear ANN as alternative to already existing models, such as LDA. Linear neural network: LNN: 236:236-1-1:1 which had an overall training performance of 96% proved to be the best model. In addition, we did a study of the different fragments of the molecules of the database to see which fragments had more influence in the activity. This can help design new inhibitors of GSK-3α. This study reports the attempts to calculate, within a unified framework probabilities of GSK-3α inhibitors against different molecules found in the literature.

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.

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.

There are many protein ligands and/or drugs described with very different affinity to a large number of target proteins or receptors. In this work, we selected Ligands or Drug-target pairs (DTPs/nDTPs) of drugs with high affinity/non-affinity for different targets. 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 target and/or have not been implemented in the form of public web server freely accessible online to the scientific community. 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 20:20-15-1:1. This MLP classifies correctly 611 out of 678 DTPs (sensitivity=90.12%) and 3083 out of 3408 nDTPs (specificity=90.46%), corresponding to training accuracy=90.41%. The validation of the model was carried out by means of external predicting series. The model classifies correctly 310 out of 338 DTPs (sensitivity=91.72%) and 1527 out of 1674 nDTP (specificity=91.22%) in validation series, corresponding to total accuracy=91.30% for validation series (predictability). This model favorably compares with other ANN models developed in this work and Machine Learning classifiers published before to address the same problem in different aspects. We implemented the present model at web portal Bio-AIMS in the form of an online server called: Non-Linear MARCH-INSIDE Nested Drug-Bank Exploration & Screening Tool (NL MIND-BEST), which is located at URL: http://miaja.tic.udc.es/Bio-AIMS/NL-MIND-BEST.php. This online tool is based on PHP/HTML/Python and MARCH-INSIDE routines. Finally we illustrated two practical uses of this server with two different experiments. In experiment 1, we report by first time Quantum QSAR study, synthesis, characterization, and experimental assay of antiplasmodial and cytotoxic activities of oxoisoaporphine alkaloids derivatives as well as NL MIND-BEST prediction of potential target proteins. In experiment 2, we report sampling, parasite culture, sample preparation, 2-DE, MALDI-TOF, and -TOF/TOF MS, MASCOT search, MM/MD 3D structure modeling, and NL MIND-BEST prediction for different peptides a new protein of the found in the proteome of the human parasite Giardia lamblia, which is promising for anti-parasite drug-targets discovery.

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.

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.