María Auxiliadora Dea-Ayuela

MIND-BEST: Web Server for Drugs and Target Discovery; Design, Synthesis, and Assay of MAO-B Inhibitors and Theoretical−Experimental Study of G3PDH Protein from Trichomonas gallinae

Many drugs with very different affinity to a large number of receptors are described. Thus, in this work, we selected drug-target pairs (DTPs/nDTPs) of drugs with high affinity/nonaffinity for different targets. Quantitative structure-activity relationship (QSAR) models become a very useful tool in this context because they substantially reduce time and resource-consuming experiments. Unfortunately, most QSAR models predict activity against only one protein target and/or they have not been implemented on a public Web server yet, freely available online to the scientific community. To solve this problem, we developed a multitarget QSAR (mt-QSAR) classifier combining the MARCH-INSIDE software for the calculation of the structural parameters of drug and target with the linear discriminant analysis (LDA) method in order to seek the best model. The accuracy of the best LDA model was 94.4% (3,859/4,086 cases) for training and 94.9% (1,909/2,012 cases) for the external validation series. In addition, we implemented the model into the Web portal Bio-AIMS as an online server entitled MARCH-INSIDE Nested Drug-Bank Exploration & Screening Tool (MIND-BEST), located at http://miaja.tic.udc.es/Bio-AIMS/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 for the first time a MIND-BEST prediction, synthesis, characterization, and MAO-A and MAO-B pharmacological assay of eight rasagiline derivatives, promising for anti-Parkinson drug design. In experiment 2, we report sampling, parasite culture, sample preparation, 2-DE, MALDI-TOF and -TOF/TOF MS, MASCOT search, 3D structure modeling with LOMETS, and MIND-BEST prediction for different peptides as new protein of the found in the proteome of the bird parasite Trichomonas gallinae, which is promising for antiparasite drug targets discovery.

HP-Lattice QSAR for dynein proteins: Experimental proteomics (2D-electrophoresis, mass spectrometry) and theoretic study of a Leishmania infantum sequence

The toxicity and inefficacy of actual organic drugs against Leishmaniosis justify research projects to find new molecular targets in Leishmania species including Leishmania infantum (L. infantum) and Leishmaniamajor (L. major), both important pathogens. In this sense, quantitative structure-activity relationship (QSAR) methods, which are very useful in Bioorganic and Medicinal Chemistry to discover small-sized drugs, may help to identify not only new drugs but also new drug targets, if we apply them to proteins. Dyneins are important proteins of these parasites governing fundamental processes such as cilia and flagella motion, nuclear migration, organization of the mitotic splinde, and chromosome separation during mitosis. However, despite the interest for them as potential drug targets, so far there has been no report whatsoever on dyneins with QSAR techniques. To the best of our knowledge, we report here the first QSAR for dynein proteins. We used as input the Spectral Moments of a Markov matrix associated to the HP-Lattice Network of the protein sequence. The data contain 411 protein sequences of different species selected by ClustalX to develop a QSAR that correctly discriminates on average between 92.75% and 92.51% of dyneins and other proteins in four different train and cross-validation datasets. We also report a combined experimental and theoretic study of a new dynein sequence in order to illustrate the utility of the model to search for potential drug targets with a practical example. First, we carried out a 2D-electrophoresis analysis of L. infantum biological samples. Next, we excised from 2D-E gels one spot of interest belonging to an unknown protein or protein fragment in the region M<20,200 and pI<4. We used MASCOT search engine to find proteins in the L. major data base with the highest similarity score to the MS of the protein isolated from L. infantum. We used the QSAR model to predict the new sequence as dynein with probability of 99.99% without relying upon alignment. In order to confirm the previous function annotation we predicted the sequences as dynein with BLAST and the omniBLAST tools (96% alignment similarity to dyneins of other species). Using this combined strategy, we have successfully identified L. infantum protein containing dynein heavy chain, and illustrated the potential use of the QSAR model as a complement to alignment tools.

Prediction of enzyme classes from 3D structure: a general model and examples of experimental-theoretic scoring of peptide mass fingerprints of Leishmania proteins.

The number of protein and peptide structures included in Protein Data Bank (PDB) and Gen Bank without functional annotation has increased. Consequently, there is a high demand for theoretical models to predict these functions. Here, we trained and validated, with an external set, a Markov Chain Model (MCM) that classifies proteins by their possible mechanism of action according to Enzyme Classification (EC) number. The methodology proposed is essentially new, and enables prediction of all EC classes with a single equation without the need for an equation for each class or nonlinear models with multiple outputs. In addition, the model may be used to predict whether one peptide presents a positive or negative contribution of the activity of the same EC class. The model predicts the first EC number for 106 out of 151 (70.2%) oxidoreductases, 178/178 (100%) transferases, 223/223 (100%) hydrolases, 64/85 (75.3%) lyases, 74/74 (100%) isomerases, and 100/100 (100%) ligases, as well as 745/811 (91.9%) nonenzymes. It is important to underline that this method may help us predict new enzyme proteins or select peptide candidates that improve enzyme activity, which may be of interest for the prediction of new drugs or drug targets. To illustrate the models application, we report the 2D-Electrophoresis (2DE) isolation from Leishmania infantum as well as MADLI TOF Mass Spectra characterization and theoretical study of the Peptide Mass Fingerprints (PMFs) of a new protein sequence. The theoretical study focused on MASCOT, BLAST alignment, and alignment-free QSAR prediction of the contribution of 29 peptides found in the PMF of the new protein to specific enzyme action. This combined strategy may be used to identify and predict peptides of prokaryote and eukaryote parasites and their hosts as well as other superior organisms, which may be of interest in drug development or target identification.

3D entropy and moments prediction of enzyme classes and experimental-theoretic study of peptide fingerprints in Leishmania parasites.

The number of protein 3D structures without function annotation in Protein Data Bank (PDB) has been steadily increased. This fact has led in turn to an increment of demand for theoretical models to give a quick characterization of these proteins. In this work, we present a new and fast Markov chain model (MCM) to predict the enzyme classification (EC) number. We used both linear discriminant analysis (LDA) and/or artificial neural networks (ANN) in order to compare linear vs. non-linear classifiers. The LDA model found is very simple (three variables) and at the same time is able to predict the first EC number with an overall accuracy of 79% for a data set of 4755 proteins (859 enzymes and 3896 non-enzymes) divided into both training and external validation series. In addition, the best non-linear ANN model is notably more complex but has an overall accuracy of 98.85%. It is important to emphasize that this method may help us to predict not only new enzyme proteins but also to select peptide candidates found on the peptide mass fingerprints (PMFs) of new proteins that may improve enzyme activity. In order to illustrate the use of the model in this regard, we first report the 2D electrophoresis (2DE) and MADLI-TOF mass spectra characterization of the PMF of a new possible malate dehydrogenase sequence from Leishmania infantum. Next, we used the models to predict the contribution to a specific enzyme action of 30 peptides found in the PMF of the new protein. We implemented the present model in a server at portal Bio-AIMS (http://miaja.tic.udc.es/Bio-AIMS/EnzClassPred.php). This free on-line tool is based on PHP/HTML/Python and MARCH-INSIDE routines. This combined strategy may be used to identify and predict peptides of prokaryote and eukaryote parasites and their hosts as well as other superior organisms, which may be of interest in drug development or target identification.

Generalized lattice graphs for 2D-visualization of biological information

Abstract Several graph representations have been introduced for different data in theoretical biology. For instance, complex networks based on Graph theory are used to represent the structure and/or dynamics of different large biological systems such as protein–protein interaction networks. In addition, Randic, Liao, Nandy, Basak, and many others developed some special types of graph-based representations. This special type of graph includes geometrical constrains to node positioning in space and adopts final geometrical shapes that resemble lattice-like patterns. Lattice networks have been used to visually depict DNA and protein sequences but they are very flexible. However, despite the proved efficacy of new lattice-like graph/networks to represent diverse systems, most works focus on only one specific type of biological data. This work proposes a generalized type of lattice and illustrates how to use it in order to represent and compare biological data from different sources. We exemplify the following cases: protein sequence; mass spectra (MS) of protein peptide mass fingerprints (PMF); molecular dynamic trajectory (MDTs) from structural studies; mRNA microarray data; single nucleotide polymorphisms (SNPs); 1D or 2D-Electrophoresis study of protein polymorphisms and protein-research patent and/or copyright information. We used data available from public sources for some examples but for other, we used experimental results reported herein for the first time. This work may break new ground for the application of Graph theory in theoretical biology and other areas of biomedical sciences.

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.

Hemolytic and pharmacokinetic studies of liposomal and particulate amphotericin B formulations.

Amphotericin B (AmB) is a very effective antifungal and antiparasitic drug with a narrow therapeutic window. To improve its efficacy/toxicity balance, new controlled release formulations have been developed based on different encapsulation systems, aggregation states and particle sizes modifications. The kinetics of the hemolytic process was studied not only to characterize the toxicity of different formulations but also as an indicator of drug release. Pharmacokinetic studies in beagle dogs were carried out with those formulations that exhibited the least hemolytic toxicity: liposomal formulation (AmBisome), poly-aggregated AmB and encapsulated particulate AmB formulation. A novel poly-aggregated AmB formulation proved to be comparable in terms of low hemolytic activity with the marketed gold standard formulation: AmBisome. Its pharmacokinetic profile, characterized by a smaller area under the curve and larger volume of distribution, was markedly different from AmBisome, resulting in a cost-effective alternative for the treatment of leishmaniasis which can enhance the AmB passive target by the uptake by the cells of the reticulo-endothelial system. Effects of different variables such as type of formulation, dose, microencapsulation, anesthesia and dogs healthy state on AmB pharmacokinetics were studied.

New amphotericin B-gamma cyclodextrin formulation for topical use with synergistic activity against diverse fungal species and Leishmania spp

Amphotericin B (AmB) has a broad antifungal and leishmanicidal activity with low incidence of clinical resistance. Its parenteral administration has high risk of nephrotoxicity that limits its use. In order to treat cutaneous infections, AmB topical administration is a safer therapy because of the low systemic absorption of the drug across mucous membranes. Moreover, in some developing countries both fungal topical infections and cutaneous leishmaniasis are an important health problem. The aim of this work is to formulate a topical amphotericin preparation and test its in vitro antifungal (against 11 different fungal species) and antileishmanial activity. γ-Cyclodextrin (γ-CD) was chosen to solubilise AmB. Furthermore, γ-CD has shown a synergistic effect on membrane destabilization with AmB. Topical novel formulations based on AmB-CD complex have exhibited greater antifungal activity (48%, 28% and 60% higher) when compared to AmB Neo-Sensitabs(®) disks, AmB dissolved in dimethyl sulfoxide (DMSO) and Clotrimazole(®) cream, respectively. Furthermore, AmB-CD methyl cellulose gel has shown significantly higher inhibition activity on biofilm formation, larger penetration through yeast biofilms and higher fungicidal activity on biofilm cells compared to AmB dissolved in DMSO. In addition, AmB-CD gel exhibited both high in vitro leishmanicidal efficacy with wider therapeutic index (between 2 and 8-fold higher than AmB deoxycholate depending on Leishmania spp.) and also in vivo activity in an experimental model of cutaneous leishmaniasis. These results illustrate the feasibility of a topical AmB formulation easy to prepare, physicochemically stable over 6 months, safe and effective against diverse fungal and parasitic cutaneous infections.

Changes in the proteome and infectivity of Leishmania infantum induced by in vitro exposure to a nitric oxide donor

Leishmania species are protozoan parasites that exhibit an intracellular amastigote form within mammalian macrophages and an extracellular promastigote form inside the sandfly vector. The generation of nitric oxide (NO) upon activation of macrophages is surely the principal killing effector of intracellular amastigotes but little is known about the potential action of NO against the promastigote phase during its multiplication inside the digestive tract of the sandfly vector. Therefore, we have approached this issue by using an in vitro model to study the effect of an NO donor, 3-morpholinosydnonimine (SIN-1), on the proteome and infectivity of promastigotes of Leishmania infantum. Exposure of promastigotes to SIN-1 during its logarithmic growth phase caused a dramatic effect on parasite protein expression and viability, consequently killing about 60-70% of the promastigotes. The significant changes in the proteome included the over-expression of enolase, peroxidoxin precursors, and heat-shock protein 70 (HSP70), under-expression of 20S proteasome alpha 5 unit, and phosphomannomutase and induced expression of 3-hydroxy-3-methyglutaryl-CoA (HMG-CoA) synthase and prostaglandine f2-alpha (PGD2) synthase. Interestingly, promastigotes that resisted treatment showed enhanced infectivity to J774 macrophages in comparison to the controls. This finding together with the appearance of the PGD2S and an over-expression of HSP70 isoforms in treated promastigotes led us to speculate the existence of NO-mediated programmed cell death (PCD) events as a potential mechanism of population regulation and selection of properly infecting forms that predominantly operate on the promastigote stage.

Multilocus genotyping of Giardia duodenalis in lambs from Spain reveals a high heterogeneity

Fecal specimens from 120 lambs in Valencia (Spain) were analyzed for Giardia duodenalis by IFA and nested-PCR using the beta giardin (bg), glutamate dehydrogenase (gdh), triose phosphate isomerase (tpi) and small subunit ribosomal RNA (ssurRNA) genes. The highest prevalence was obtained using the ssurRNA gene (89.2%), whereas values from other techniques ranged from 64.1% to 69.2%. Sequences of the ssurRNA showed a high proportion of assemblage A or mixed assemblage A/E samples (55.1% and 25.2%, respectively). When the other 3 loci were analyzed, between 6.5% and 15.4% were found to be assemblage A or A/E, respectively. Nested PCR for the tpi gene was the most variable of the targets employed. Twelve new sequences of gdh and tpi for G. duodenalis from sheep were found. Multilocus genotyping resulted in 63 patterns from the 71 samples sequenced at the four loci. This high variability among isolates possibly reflects the high frequency of mixed infections.