Review of computer-aided models for predicting collagen stability.

Collagen is the most abundant protein in the whole human body and its instability is involved in many important diseases, such as Osteogenesis imperfecta, Ehlers-Danlos syndrome, and collagenopathy. The stability of the collagen triple helix is strictly related to its amino acid sequence, especially the main Gly-X-Y motif. Many groups have used computational methods to investigate collagen's structure and the relationship between its stability and structure. In this study, we initially reviewed the most important computational methods that have been applied in this field. We then assembled data on a large number of collagen-like peptides to build the first Markov chain model for predicting the stability of the collagen at different temperatures, simply by analyzing the amino acid sequence. We used the literature to assemble a set of 102 peptides and their relative melting temperatures were determined experimentally, indicating a great variance with the main motif of the collagen. This dataset was then split in two classes, stable and unstable, according to their melting temperatures and the dataset was then used to build artificial neural network (ANN) models to predict collagen stability. We built models to predict stability at temperatures of 38°C, 35°C, 30°C, and 25°C degrees, and all models had an accuracy between 82% and 92%. Several cross-validation procedures were performed to validate the model. This method facilitates fast and accurate predictions of collagen stability at different temperatures.

Synthesis, human monoamine oxidase inhibitory activity and molecular docking studies of 3-heteroarylcoumarin derivatives.

Monoamine oxidase (MAO) is an important drug target for the treatment of neurological disorders. Series of 3-indolyl and 3-thiophenylcoumarins were synthesized and evaluated as inhibitors of the two human MAO isoforms, hMAO-A and hMAO-B. In general, the derivatives were found to be selective hMAO-B inhibitors with IC(50) values in the nanoMolar (nM) to microMolar (µM) range. Docking experiments were carried out in order to compare the theoretical and experimental affinity of these compounds to the hMAO-B protein. According to our results, docking experiments could be an interesting approach to try to predict the activity of this class of coumarins against MAO-B receptors.

Synthesis and biological evaluation of a novel series of bis-salicylaldehydes as mushroom tyrosinase inhibitors.

A series of novel bis-salicylaldehydes were synthesised and evaluated as tyrosinase inhibitors using a tyrosinase-dependent L-DOPA oxidation assay. The bis-salicylaldehydes exhibited greater inhibitory activity than salicylaldehyde. Our data suggests that these novel compounds may serve as a structural template for the design and development of novel tyrosinase inhibitors.

Lipophilic phenolic antioxidants: correlation between antioxidant profile, partition coefficients and redox properties.

Lipophilic compounds structurally based on caffeic, hydrocaffeic, ferulic and hydroferulic acids were synthesized. Subsequently, their antioxidant activity was evaluated as well as their partition coefficients and redox potentials. The structure-property-activity relationship (SPAR) results revealed the existence of a clear correlation between the redox potentials and the antioxidant activity. In addition, some compounds showed a proper lipophilicity to cross the blood-brain barrier. Their predicted ADME properties are also in accordance with the general requirements for potential CNS drugs. Accordingly, one can propose these phenolic compounds as potential antioxidants for tackling the oxidative status linked to the neurodegenerative processes.

Review of QSAR models for enzyme classes of drug targets: Theoretical background and applications in parasites, hosts, and other organisms.

The number of protein 3D structures without function annotation in Protein Data Bank (PDB) has been steadily increased. Many of these proteins are relevant for Pharmaceutical Design because they may be enzymes of different classes that could become drug targets. 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 review and discussion of Alignment-Free Methods (AFMs) for fast prediction of the Enzyme Classification (EC) number from structural patterns. We referred to both methods based on linear techniques such as Linear Discriminant Analysis (LDA) and/or non-linear models like Artificial Neural Networks (ANN) or Support Vector Machine (SVM) in order to compare linear vs. non-linear classifiers. We also detected which of these models have been implemented as Web Servers free to the public and compiled a list of some of these web sites. For instance, we reviewed the servers implemented at portal Bio-AIMS (http://miaja.tic.udc.es/Bio-AIMS/EnzClassPred.php) and the server EzyPred (http://www.csbio.sjtu.edu.cn/bioinf/EzyPred/).

Review of MARCH-INSIDE & complex networks prediction of drugs: ADMET, anti-parasite activity, metabolizing enzymes and cardiotoxicity proteome biomarkers.

In this communication we carry out an in-depth review of a very versatile QSPR-like method. The method name is MARCH-INSIDE (MARkov CHains Ivariants for Network Selection and DEsign) and is a simple but efficient computational approach to the study of QSPR-like problems in biomedical sciences. The method uses the theory of Markov Chains to generate parameters that numerically describe the structure of a system. This approach generates two principal types of parameters Stochastic Topological Indices (sto-TIs). The use of these parameters allows the rapid collection, annotation, retrieval, comparison and mining structures of molecular, macromolecular, supramolecular, and non-molecular systems within large databases. Here, we review and comment by the first time on the several applications of MARCH-INSIDE to predict drugs ADMET, Activity, Metabolizing Enzymes, and Toxico-Proteomics biomarkers discovery. The MARCH-INSIDE models reviewed are: a) drug-tissue distribution profiles, b) assembling drug-tissue complex networks, c) multi-target models for anti-parasite/anti-microbial activity, c) assembling drug-target networks, d) drug toxicity and side effects, e) web-server for drug metabolizing enzymes, f) models in drugs toxico-proteomics. We close the review with some legal remarks related to the use of this class of QSPR-like models.

Synthesis and vasorelaxant and platelet antiaggregatory activities of a new series of 6-halo-3-phenylcoumarins.

A series of 6-halo-3-hydroxyphenylcoumarins (resveratrol-coumarins hybrid derivatives) was synthesized in good yields by a Perkin reaction followed by hydrolysis. The new compounds were evaluated for their vasorelaxant activity in intact rat aorta rings pre-contracted with phenylephrine (PE), as well as for their inhibitory effects on platelet aggregation induced by thrombin in washed human platelets. These compounds concentration-dependently relaxed vascular smooth muscle and some of them showed a platelet antiaggregatory activity that was up to thirty times higher than that shown by trans-resveratrol and some other previously synthesized derivatives.

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 model's 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.

Tyrosinase inhibitor activity of coumarin-resveratrol hybrids.

In the present work we report on the contribution of the coumarin moiety to tyrosinase inhibition. Coumarin-resveratrol hybrids 1-8 have been resynthesized to investigate the structure-activity relationships and the IC(50) values of these compounds were measured. The results showed that these compounds exhibited tyrosinase inhibitory activity. Compound 3-(3',4',5'-trihydroxyphenyl)-6,8-dihydroxycoumarin (8)is the most potentcompound (0.27 mM), more so than umbelliferone (0.42 mM), used as reference compound. The kinetic studies revealed that compound 8 caused non-competitive tyrosinase inhibition.

Evaluation of photoluminescence properties of some poly(ethylene glycol)-supported coumarin derivatives.

The immobilization of some coumarin derivatives on modified poly(ethylene glycol)s is reported and the influence of the polymeric support on the photoluminescence activity of the compounds is discussed. Upon ultraviolet excitation, the derivatives showed coumarin-related emission properties whose peak position and efficiency depended on the loading of the polymer and on the mesomeric effects of the substituents.

Formation of 2-substituted benzofuran fragment ions from 6-alkyl- and 6-aryldibenzo(d,f)(1,3)dioxepine derivatives under electron ionization-a useful precursor ion for isomeric differentiation.

Tandem mass spectrometry has been applied to differentiate three sets of o-, m- and p-methyl, -methoxy and -nitro-substituted-6-phenyl-dibenzo(d,f)(1,3)dioxepines. Collision-induced dissociation (CID) experiments have been carried out on 2-phenylbenzo[b]furan fragment ions, which originate from the decomposition of the molecular ions after their EI-induced isomerization to spirocyclic structures. With the exception of m- and p-methylphenylbenzo[b]furan isomers, which display identical CID mass spectra, the three isomeric methoxy- and nitrophenylbenzo[b]furan fragment ions display very characteristic CID behavior which allows unequivocal differentiation of the 6-phenyl-dibenzo(d,f)(1,3)dioxepine isomers. 6-(o-nitrophenyl)-dibenzo(d,f)(1,3)dioxepine isomer, does not form a 2-(o-nitrophenyl)benzo[b]furan ion and, therefore, it can be differentiated from the m- and p- isomers based on the mere EI mass spectra. Furthermore, it shows a characteristic ion most likely due to an ortho effect between the nitro group and the dioxepine ring. Multiple stage mass spectrometric techniques (MSn), labeled derivatives and reference compounds were used in order to gain additional information on the structures of product ion from the CID fragmentation.

PEG-immobilization of cardol and soluble polymer-supported synthesis of some cardol-coumarin derivatives: preliminary evaluation of their inhibitory activity on mushroom tyrosinase.

In this work, the PEG-immobilization and the liquid phase synthesis of some coumarins derived from cardol are presented. Some preliminary results on their tyrosinase inhibitory activity are also included.

Computational chemistry study of 3D-structure-function relationships for enzymes based on Markov models for protein electrostatic, HINT, and van der Waals potentials.

In a significant work, Dobson and Doig (J Mol Biol 2003, 330, 771) illustrated protein prediction as enzymatic or not from spatial structure without resorting to alignments. They used 52 protein features and a nonlinear support vector machine model to classify more than 1000 proteins collected from the PDB with a 77% overall accuracy. The most useful features were: the secondary-structure content, the amino acid frequencies, the number of disulphide bonds, and the largest cleft size. Working on the same dataset used by D&D, in this article we reported a good and simple model, based on the Markov chain models (MCM), to classify protein 3D structures as enzymatic or not, taking into consideration the spatial structure without resorting to alignments. Here we define, for the first time, a general MCM to calculate the electrostatic potential, molecular vibrations, van der Waals (vdw) interactions, and hydrophobic interactions (HINT) and use them in comparative studies of potential fields and/or protein function prediction. The dataset is composed of 1371 proteins divided into 689 enzymes and 682 nonenzymes, all proteins were collected from the PDB. The best model we found was a linear model carried out with the linear discriminant analysis; it was able to classify 74.18% of the proteins using only two electrostatic potentials. In the work described here, we define 3D-HINT potentials (mu(k)) and use them for the first time to derive a classifier for protein enzymes. We analyzed ROC curves, domain of applicability, parametric assumptions, desirability maps, and also tested other nonlinear artificial neural network models which did not improve the linear model. In closing, this MCM allows a fast calculation and comparison of different potentials deriving into accurate protein 3D structure-function relationships, notably simpler than the previous.

Using spectral moments of spiral networks based on PSA/mass spectra outcomes to derive quantitative proteome-disease relationships (QPDRs) and predicting prostate cancer.

In prostate cancer (PCa), prognostic (predictive) factors are particularly important given the marked heterogeneity of this disease at clinical, morphologic, and biomolecular levels. Blood contains a treasure of previously unstudied biomarkers that could reflect the ongoing physiological state of all tissue. The serum prostate-specific antigen (PSA) measurement is a very good biomarker for PCa, but the percentage of bad classification is somewhat high. The blood proteome mass spectra (MS) represent a potential tool for detection of diseases; however the identification of a single biomarker from the complex output from MS is often difficult. In this paper, we propose a general strategy, based on computational chemistry techniques, which should improve the predictive power of PSA. Our group adapted the square-spiral graph to represent human serum-plasma-proteome MS for healthy and PCa patients. These graphs were previously applied to DNA and/or protein sequences. In this work, we calculated different classes of connectivity indices (CIs), and created various models based on the spectral moments. The best QPDRs model found showed accuracy values ranging from 71.7% to 97.2%, and 70.4% to 99.2% of specificity. This methodology might be useful for several applications in computational chemistry.

Synthesis and complete assignment of the 1H and 13C NMR signals of some oxopyrancoumarin and oxofuropyrancoumarin derivatives.

The synthesis of four pyranocoumarins starting from phloroglucinol and the complete (1)H and (13)C NMR assignment of seven pyranocoumarins has been performed using 1D and 2D NMR techniques including COSY, HMQC and HMBC experiments.

MMM-QSAR recognition of ribonucleases without alignment: comparison with an HMM model and isolation from Schizosaccharomyces pombe, prediction, and experimental assay of a new sequence.

The study of type III RNases constitutes an important area in molecular biology. It is known that the pac1+ gene encodes a particular RNase III that shares low amino acid similarity with other genes despite having a double-stranded ribonuclease activity. Bioinformatics methods based on sequence alignment may fail when there is a low amino acidic identity percentage between a query sequence and others with similar functions (remote homologues) or a similar sequence is not recorded in the database. Quantitative structure-activity relationships (QSAR) applied to protein sequences may allow an alignment-independent prediction of protein function. These sequences of QSAR-like methods often use 1D sequence numerical parameters as the input to seek sequence-function relationships. However, previous 2D representation of sequences may uncover useful higher-order information. In the work described here we calculated for the first time the spectral moments of a Markov matrix (MMM) associated with a 2D-HP-map of a protein sequence. We used MMMs values to characterize numerically 81 sequences of type III RNases and 133 proteins of a control group. We subsequently developed one MMM-QSAR and one classic hidden Markov model (HMM) based on the same data. The MMM-QSAR showed a discrimination power of RNAses from other proteins of 97.35% without using alignment, which is a result as good as for the known HMM techniques. We also report for the first time the isolation of a new Pac1 protein (DQ647826) from Schizosaccharomyces pombe strain 428-4-1. The MMM-QSAR model predicts the new RNase III with the same accuracy as other classical alignment methods. Experimental assay of this protein confirms the predicted activity. The present results suggest that MMM-QSAR models may be used for protein function annotation avoiding sequence alignment with the same accuracy of classic HMM models.

Computational chemistry development of a unified free energy Markov model for the distribution of 1300 chemicals to 38 different environmental or biological systems.

Predicting tissue and environmental distribution of chemicals is of major importance for environmental and life sciences. Most of the molecular descriptors used in computational prediction of chemicals partition behavior consider molecular structure but ignore the nature of the partition system. Consequently, computational models derived up-to-date are restricted to the specific system under study. Here, a free energy-based descriptor (DeltaG(k)) is introduced, which circumvent this problem. Based on DeltaG(k), we developed for the first time a single linear classification model to predict the partition behavior of a broad number of structurally diverse drugs and other chemicals (1300) for 38 different partition systems of biological and environmental significance. The model presented training/predicting set accuracies of 91.79/88.92%. Parametrical assumptions were checked. Desirability analysis was used to explore the levels of the predictors that produce the most desirable partition properties. Finally, inversion of the partition direction for each one of the 38 partition systems evidences that our models correctly classified 89.08% of compounds with an uncertainty of only +/-0.17% independently of the direction of the partition process used to seek the model. Other 10 different classification models (linear, neural networks, and genetic algorithms) were also tested for the same purposes. None of these computational models favorably compare with respect to the linear model indicating that our approach capture the main aspects that govern chemicals partition in different systems.

Computational chemistry comparison of stable/nonstable protein mutants classification models based on 3D and topological indices.

In principle, there are different protein structural parameters that can be used in computational chemistry studies to classify protein mutants according to thermal stability including: sequence, connectivity, and 3D descriptors. Connectivity parameters (called topological indices, TIs) are simpler than 3D parameters being then less computationally expensive. However, TIs ignore important aspects of protein structure and hence are expected to be inaccurate. In any case, a comparison of 3D and TIs has not been reported with respect to the power of discrimination of proteins according to stability. In this study, we compare both classes of indices in this sense by the first time. The best model found, based on 3D spectral moments correctly classified 507 out of 525 (96.6%) proteins while TIs model correctly classified 404 out of 525 (77.0%) proteins. We have shown that, in fact, 3D descriptor models gave more accurate results than TIs but interestingly, TIs give acceptable results in a timely way in spite of their simplicity.

2-substituted benzofuran fragment ion formation in the electron ionization mass spectra of 6-alkyl- and 6-aryldibenzo(d,f)(1,3)dioxepine derivatives. 1. Spirocyclization of the molecular ions in the gas phase.

The formation of 2-substituted benzo[b]furan ions in the electron ionization (EI) mass spectra of a series of 6-alkyl- and 6-aryldibenzo(d,f)(1,3)dioxepines has been studied by means of exact mass measurements and multiple-stage mass spectrometry conditions using an ion trap mass spectrometer. The proposed mechanism of formation of benzo[b]furan ions requires the formation of a spirocyclic cyclohexadienone system, which undergoes elimination of a cyclopentadienone molecule. A parallel with the chemical conversion of arylmethyl-substituted dibenzo(d,f)(1,3)dioxepines into an analogous spirocyclic system was also underlined.

On the applicability of QSAR for recognition of miRNA bioorganic structures at early stages of organism and cell development: embryo and stem cells.

Quantitative structure-activity-relationship (QSAR) models have application in bioorganic chemistry mainly to the study of small sized molecules while applications to biopolymers remain not very developed. MicroRNAs (miRNAs), which are non-coding small RNAs, regulate a variety of biological processes and constitute good candidates to scale up the application of QSAR to biopolymers. The propensity of a small RNA sequence to act as miRNA depends on its secondary structure, which one can explain in terms of folding thermodynamic parameters. Then, thermodynamic QSAR can be used, for instance, for fast identification of miRNAs at early stages of development such as embryos and stem cells (called here esmiRNAs), and gain clarity inside cellular differentiation processes and diseases such as cancer. First, we calculated folding free energies (DeltaG), enthalpies (DeltaH), and entropies (DeltaS) as well as melting temperatures (T(m)) for 2623 small RNA sequences (including 623 esmiRNAs and 2000 negative control sequences). Next, we seek a QSAR classification model: esmiRNA=0.035 x T(m)-0.078 x DeltaS-8.748. The model correctly recognized 543 (87.2%) of esmiRNAs and 935 (93.5%) of non-esmiRNAs divided into both training and validation series. The model also recognized 908 out of 1000 additional negative control sequences. ROC curve analysis (area=0.93) demonstrated that the present model significantly differentiates from a random classifier. In addition, we map the influence of thermodynamic parameters over esmiRNA activity. Last, a double ordinate Cartesian plot of cross-validated residuals (first ordinate), standard residuals (second ordinate), and leverages (abscissa) defined the domain of applicability of the model as a squared area within +/-2 band for residuals and a leverage threshold of h=0.0074. The present is the first QSAR model for quickly accurate selection of new esmiRNAs with potential use in bioorganic and medicinal chemistry.