Florbela Pereira

Triterpenes from Acacia Dealbata

Abstract From the leaves, flowers and seeds of A. dealbata four lupene type triterpenes, lupenone, lupeol, lupenyl palpimate and lupenyl cinnamate, not previously reported in this genus, have been isolated. Their structures were established by spectroscopic means and by comparison with literature references.

A chemoinformatics approach to the discovery of lead-like molecules from marine and microbial sources en route to antitumor and antibiotic drugs.

The comprehensive information of small molecules and their biological activities in the PubChem database allows chemoinformatic researchers to access and make use of large-scale biological activity data to improve the precision of drug profiling. A Quantitative Structure–Activity Relationship approach, for classification, was used for the prediction of active/inactive compounds relatively to overall biological activity, antitumor and antibiotic activities using a data set of 1804 compounds from PubChem. Using the best classification models for antibiotic and antitumor activities a data set of marine and microbial natural products from the AntiMarin database were screened—57 and 16 new lead compounds for antibiotic and antitumor drug design were proposed, respectively. All compounds proposed by our approach are classified as non-antibiotic and non-antitumor compounds in the AntiMarin database. Recently several of the lead-like compounds proposed by us were reported as being active in the literature.

Estimation of Mayr electrophilicity with a quantitative structure-property relationship approach using empirical and DFT descriptors.

Quantitative structure-property relationships (QSPRs) were investigated for the estimation of the Mayr electrophilicity parameter using a data set of 64 compounds, all currently available uncharged electrophiles in Mayrs Database of Reactivity Parameters. Three collections of empirical descriptors were employed, from Dragon, Adriana.Code, and CDK. Models were built with multilinear regressions, k nearest neighbors, model trees, random forests, support vector machines (SVMs), associative neural networks, and counterpropagation neural networks. Quantum chemical descriptors were calculated with density functional theory (DFT) methods and incorporated in QSPR models. The best results were achieved with SVM using seven empirical and DFT descriptors; an R(2) of 0.92 was obtained for the test set (21 compounds). The final seven descriptors were the Parr electrophilicity index, ε(LUMO), hardness, and four CDK descriptors (FNSA-3, ATSc5, Kier2, and nAtomLAC). Screening of correlations between individual descriptors and Mayr electrophilicity revealed the highest absolute value of correlation for DFT ε(LUMO) (R = -0.82) and comparable correlations for some empirical descriptors, e.g., Dragons folding degree index (R = -0.80), Kier flexibility index (R = -0.78), and Kier S2K index (R = -0.78). High correlations were observed in the training set between reactivity descriptors calculated by the PM6 semiempirical and DFT methods (R = 0.96 for ε(LUMO) and 0.94 for the electrophilicity index).

Machine Learning Methods to Predict Density Functional Theory B3LYP Energies of HOMO and LUMO Orbitals

Machine learning algorithms were explored for the fast estimation of HOMO and LUMO orbital energies calculated by DFT B3LYP, on the basis of molecular descriptors exclusively based on connectivity. The whole project involved the retrieval and generation of molecular structures, quantum chemical calculations for a database with >111 000 structures, development of new molecular descriptors, and training/validation of machine learning models. Several machine learning algorithms were screened, and an applicability domain was defined based on Euclidean distances to the training set. Random forest models predicted an external test set of 9989 compounds achieving mean absolute error (MAE) up to 0.15 and 0.16 eV for the HOMO and LUMO orbitals, respectively. The impact of the quantum chemical calculation protocol was assessed with a subset of compounds. Inclusion of the orbital energy calculated by PM7 as an additional descriptor significantly improved the quality of estimations (reducing the MAE in >30%).

Encapsulation of sodium nimesulide and precursors in β-cyclodextrin

Crystalline 1 : 1 inclusion complexes with β-cyclodextrin (β-CD) and the sodium salt of nimesulide (4-nitro-2-phenoxymethanesulfonanilide), and the sodium salt of the derivative 2-phenoxymethanesulfonanilide, have been prepared by co-precipitation from aqueous solution. The presence of true inclusion complexes was supported by elemental analysis, thermogravimetry and powder X-ray diffraction. FTIR and 13C CP MAS NMR spectroscopy confirmed that no chemical modification of the guests occurred upon formation of inclusion complexes. The reaction of the precursors 2-phenoxynitrobenzene and 2-phenoxyaniline with β-CD was also studied and crystalline inclusion complexes with a 2 : 1 (host-to-guest) stoichiometry were isolated. The interaction of the different guest species with β-CD host molecules was studied theoretically by carrying out ab initio calculations. Favourable inclusion geometries were obtained for the four guests mentioned above. On the other hand, it was found that the inclusion of the neutral guests nimesulide and 2-phenoxymethanesulfonanilide was considerably less favourable. This is in agreement with the experimentally observed difficulty in isolating true inclusion complexes containing these guests and β-CD. The calculated lower stability is attributed to the different steric hindrance arising from the different conformational preferences of neutral and anionic forms.

QSAR-Assisted Virtual Screening of Lead-Like Molecules from Marine and Microbial Natural Sources for Antitumor and Antibiotic Drug Discovery

A Quantitative Structure-Activity Relationship (QSAR) approach for classification was used for the prediction of compounds as active/inactive relatively to overall biological activity, antitumor and antibiotic activities using a data set of 1746 compounds from PubChem with empirical CDK descriptors and semi-empirical quantum-chemical descriptors. A data set of 183 active pharmaceutical ingredients was additionally used for the external validation of the best models. The best classification models for antibiotic and antitumor activities were used to screen a data set of marine and microbial natural products from the AntiMarin database—25 and four lead compounds for antibiotic and antitumor drug design were proposed, respectively. The present work enables the presentation of a new set of possible lead like bioactive compounds and corroborates the results of our previous investigations. By other side it is shown the usefulness of quantum-chemical descriptors in the discrimination of biologically active and inactive compounds. None of the compounds suggested by our approach have assigned non-antibiotic and non-antitumor activities in the AntiMarin database and almost all were lately reported as being active in the literature.

The Madeira Archipelago As a Significant Source of Marine-Derived Actinomycete Diversity with Anticancer and Antimicrobial Potential

Marine-derived actinomycetes have demonstrated an ability to produce novel compounds with medically relevant biological activity. Studying the diversity and biogeographical patterns of marine actinomycetes offers an opportunity to identify genera that are under environmental pressures, which may drive adaptations that yield specific biosynthetic capabilities. The present study describes research efforts to explore regions of the Atlantic Ocean, specifically around the Madeira Archipelago, where knowledge of the indigenous actinomycete diversity is scarce. A total of 400 actinomycetes were isolated, sequenced, and screened for antimicrobial and anticancer activities. The three most abundant genera identified were Streptomyces, Actinomadura, and Micromonospora. Phylogenetic analyses of the marine OTUs isolated indicated that the Madeira Archipelago is a new source of actinomycetes adapted to life in the ocean. Phylogenetic differences between offshore (>100 m from shore) and nearshore (< 100 m from shore) populations illustrates the importance of sampling offshore in order to isolate new and diverse bacterial strains. Novel phylotypes from chemically rich marine actinomycete groups like MAR4 and the genus Salinispora were isolated. Anticancer and antimicrobial assays identified Streptomyces, Micromonospora, and Salinispora as the most biologically active genera. This study illustrates the importance of bioprospecting efforts at unexplored regions of the ocean to recover bacterial strains with the potential to produce novel and interesting chemistry.

Prediction of the anomeric configuration, type of linkage, and residues in disaccharides from 1D 13C NMR data

A machine learning approach was explored for the prediction of the anomeric configuration, residues, and type of linkages of disaccharides using (13)C NMR chemical shifts. For this study, 154 pyranosyl disaccharides were used that are dimers of the α or β anomers of d-glucose, d-galactose or d-mannose residues bonded through α or β glycosidic linkages of types 1→2, 1→3, 1→4, or 1→6, as well as methoxylated disaccharides. The (13)C NMR chemical shifts of the training set were calculated using the casper (Computer Assisted SPectrum Evaluation of Regular polysaccharides) program, and chemical shifts of the test set were experimental values obtained from the literature. Experiments were performed for (1) classification of the anomeric configuration, (2) classification of the type of linkage, and (3) classification of the residues. Classification trees could correctly classify 67%, 74%, and 38% of the test set for the three tasks, respectively, on the basis of unassigned chemical shifts. The results for the same experiments using Random Forests were 93%, 90%, and 68%, respectively.

Computational Methodologies in the Exploration of Marine Natural Product Leads

Computational methodologies are assisting the exploration of marine natural products (MNPs) to make the discovery of new leads more efficient, to repurpose known MNPs, to target new metabolites on the basis of genome analysis, to reveal mechanisms of action, and to optimize leads. In silico efforts in drug discovery of NPs have mainly focused on two tasks: dereplication and prediction of bioactivities. The exploration of new chemical spaces and the application of predicted spectral data must be included in new approaches to select species, extracts, and growth conditions with maximum probabilities of medicinal chemistry novelty. In this review, the most relevant current computational dereplication methodologies are highlighted. Structure-based (SB) and ligand-based (LB) chemoinformatics approaches have become essential tools for the virtual screening of NPs either in small datasets of isolated compounds or in large-scale databases. The most common LB techniques include Quantitative Structure–Activity Relationships (QSAR), estimation of drug likeness, prediction of adsorption, distribution, metabolism, excretion, and toxicity (ADMET) properties, similarity searching, and pharmacophore identification. Analogously, molecular dynamics, docking and binding cavity analysis have been used in SB approaches. Their significance and achievements are the main focus of this review.

NavMol 3.0: enabling the representation of metabolic reactions by blind users

Summary The representation of metabolic reactions strongly relies on visualization, which is a major barrier for blind users. The NavMol software renders the communication and interpretation of molecular structures and reactions accessible by integrating chemoinformatics and assistive technology. NavMol 3.0 provides a molecular editor for metabolic reactions. The user can start with templates of reactions and build from such cores. Atom-to-atom mapping enables changes in the reactants to be reflected in the products (and vice-versa) and the reaction centres to be automatically identified. Blind users can easily interact with the software using the keyboard and text-to-speech technology. Availability and implementation NavMol 3.0 is free and open source under the GNU general public license (GPLv3), and can be downloaded at http://sourceforge.net/projects/navmol as a JAR file. Contact joao@airesdesousa.com.