Gianni Podda

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 (ΔGk) is introduced, which circumvent this problem. Based on ΔGk, 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.

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.

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.

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.

2D-RNA-coupling numbers: a new computational chemistry approach to link secondary structure topology with biological function.

Methods for prediction of proteins, DNA, or RNA function and mapping it onto sequence often rely on bioinformatics alignment approach instead of chemical structure. Consequently, it is interesting to develop computational chemistry approaches based on molecular descriptors. In this sense, many researchers used sequence‐coupling numbers and our group extended them to 2D proteins representations. However, no coupling numbers have been reported for 2D‐RNA topology graphs, which are highly branched and contain useful information. Here, we use a computational chemistry scheme: (a) transforming sequences into RNA secondary structures, (b) defining and calculating new 2D‐RNA‐coupling numbers, (c) seek a structure‐function model, and (d) map biological function onto the folded RNA. We studied as example 1‐aminocyclopropane‐1‐carboxylic acid (ACC) oxidases known as ACO, which control fruit ripening having importance for biotechnology industry. First, we calculated τk(2D‐RNA) values to a set of 90‐folded RNAs, including 28 transcripts of ACO and control sequences. Afterwards, we compared the classification performance of 10 different classifiers implemented in the software WEKA. In particular, the logistic equation ACO = 23.8 · τ1(2D‐RNA) + 41.4 predicts ACOs with 98.9%, 98.0%, and 97.8% of accuracy in training, leave‐one‐out and 10‐fold cross‐validation, respectively. Afterwards, with this equation we predict ACO function to a sequence isolated in this work from Coffea arabica (GenBank accession DQ218452). The τ1(2D‐RNA) also favorably compare with other descriptors. This equation allows us to map the codification of ACO activity on different mRNA topology features. The present computational‐chemistry approach is general and could be extended to connect RNA secondary structure topology to other functions.

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.

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.

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.

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 IC50 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 potent compound (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.

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.