Ivani Pauli

Drug-binding databases.

Recent developments in computer power and chemoinformatics methodology make possible that a huge amount of data become available through internet. These databases are devoted to a wide spectrum of scientific fields. Here we are concerned with databases related to protein-drug interactions. More specifically, databases where potential new molecules could be accessed to be used in virtual screening initiatives. In the past decade several databases have been developed where molecules to be used in the virtual screening could be easily identified, downloaded and even purchased. This review describes and summarizes the recent advances in the development of these databases, and also the main applications related to virtual screening projects.

Molecular Modeling as a Tool for Drug Discovery

With the progression of structural genomics projects, comparative modeling remains an increasingly important method of choice to obtain 3D structure of proteins. It helps to bridge the gap between the available sequence and structure information by providing reliable and accurate protein models. Comparative modeling based on more than 30% sequence identity is now approaching its natural template-based limits and further improvements require the development of effective refinement techniques capable of driving models toward native structure. For difficult targets, for which the most significant progress in recent years has been observed, optimal template selection and alignment accuracy are still the major problems. The past year has seen a maturation of molecular modeling, with an increasing number of comparative studies between established methods becoming possible, together with an explosion of new works especially in the areas of combinatorial chemistry and molecular diversity. To achieve this, knowledge about three-dimensional protein structures is crucial for the understanding of their functional mechanisms, and for a rational drug design. This review described recent progress in molecular modeling methodology.

In silico and in vitro: identifying new drugs.

Drug development is a high cost and laborious process, requiring a number of tests until a drug is made available in the market. Therefore, the use of methods to screen large number of molecules with less cost is crucial for faster identification of hits and leads. One strategy to identify drug-like molecules is the search for molecules able to interfere with a protein function, since protein interactions control most biological processes. Ideally the use of in silico screenings would make drug development faster and less expensive. Currently, however, the confirmation of biological activity is still needed. Due to the complexity of the task of drug discovery, an integrated and multi-disciplinary approach is ultimately required. Here we discuss examples of drugs developed through a combination of in silico and in vitro strategies. The potential use of these methodologies for the identification of active compounds as well as for early toxicity and bioavailability is also reviewed.

Protein-Drug Interaction Studies for Development of Drugs Against Plasmodium falciparum

The study of protein-drug interaction is of pivotal importance to understand the structural features essential for ligand affinity. The explosion of information about protein structures has paved the way to develop structure-based virtual screening approaches. Parasitic protein kinases have been pointed out as potential targets for antiparasitic development. The identification of protein kinases in the Plasmodium falciparum genome has opened the possibility to test new families of inhibitors as potential antimalarial drugs. In addition, other key enzymes which play roles in biosynthetic pathways, such as enoyl reductase and chorismate synthase, can be valuable targets for drug development. This review is focused on these protein targets that may help to materialize new generations of antimalarial drugs.

Molecular Recognition Models: A Challenge to Overcome

Molecular recognition process describes the interaction involving two molecules. In the case of biomolecules, these pairs of molecules could be protein-protein, protein-ligand or protein-nucleic acid. The first model to capture the essential features, behind the molecular recognition problem, was the lock-and-key paradigm. The overall analysis protein-protein, protein-nucleic acid and protein-ligand interaction based on the three-dimensional structures and physicochemical parameters, such as binding affinity, opened the possibility to provide further insights in this basic phenomenon. The main ideas behind the molecular recognition are discussed in the present review.

Genomic databases and the search of protein targets for protozoan parasites.

The development of databases devoted to biological information opened the possibility to integrate, query and analyze biological data obtained from several sources that otherwise would be scattered through the web. Several issues arise in the handling of biological information, mainly due to the diversity of biological subject matter and the complexity of biological approaches towards phenomena of the living world. The integration of genomic data, three-dimensional structures of proteins, biological activity, and drugs availability allows a system approach to the study of the biology. Here we review the current status of these research efforts to develop genomic databases for protozoan parasites, such as the apicomplexan parasites, Trypanosoma cruzi and Leishmania spp. These databases may help in the discovery and development of new drugs against parasite-mediated diseases.

Bioinformatics Tools for Screening of Antiparasitic Drugs

Drug development has become the Holy Grail of many structural bioinformatics groups. The explosion of information about protein structures, ligand-binding affinity, parasite genome projects, and biological activity of millions of molecules opened the possibility to correlate this scattered information in order to generate reliable computational models to predict the likelihood of being able to modulate a target with a small-molecule drug. Computational methods have shown their potential in drug discovery and development allied with in vitro and in vivo methodologies. The present review discusses the main bioinformatics tools available for drug discovery and development.

Molecular modeling and structure–activity relationships for a series of benzimidazole derivatives as cruzain inhibitors

AIM Chagas disease is endemic in Latin America and no effective treatment is available. Efforts in drug research have focused on several enzymes from Trypanosoma cruzi, among which cruzain is a validated pharmacological target. METHODOLOGY Chemometric analyses were performed on the data set using the hologram quantitative structure-activity relationship, comparative molecular field analysis and comparative molecular similarity index analysis methods. Docking simulations were executed using the crystallographic structure of cruzain in complex with a benzimidazole inhibitor. The top-scoring enzyme-inhibitor complexes were selected for the development of the 3D quantitative structure-activity relationship (QSAR) models and to assess the inhibitor binding modes and intermolecular interactions. RESULTS Benzimidazole derivatives as cruzain inhibitors were used in molecular docking and QSAR studies. Significant statistical indicators were obtained, and the best models demonstrated high predictive ability for an external test set (r 2pred = 0.65, 0.94 and 0.82 for hologram QSAR, comparative molecular field analysis and comparative molecular similarity index analysis, respectively). Additionally, the graphical information of the chemometric analyses demonstrated substantial complementarity with the enzyme-binding site. CONCLUSION These results demonstrate the relevance of the QSAR models to guide the design of structurally related benzimidazole derivatives with improved potency.

Experimental, DFT and docking simulations of the binding of diapocynin to human serum albumin: induced circular dichroism

Diapocynin has been regarded as the active principle of apocynin, which is the most used inhibitor of NADPH oxidase. Here we performed a comprehensive study of the interaction of diapocynin with human serum albumin (HSA). We found that diapocynin binds with higher efficacy to site I of HSA and its binding constant (8.5 × 105 mol−1 L) was almost 100-fold higher compared to apocynin. By interacting with this chiral cavity of the protein, diapocynin became a chiral molecule, which was evidenced by its induced circular dichroism spectrum. The axial chirality was theoretically confirmed by searching the most stable conformations adopted by diapocynin using Density Functional Theory (DFT). The four minimum energy conformers, which presented dihedral angles of 58.00° and 302.00° (syn-aS and syn-aR enantiomers pair bearing 2,2′-dihydroxyl groups at the same side) and 132.86° and 227.14° (anti-aS and anti-aR enantiomers pair bearing 2,2′-dihydroxyl groups at opposite sides) were used as initial conformations for the docking simulations. The highest scored docking pose was obtained for site 1 and the dihedral angle was 106.44°, i.e., an anti-aS chiral conformer. In conclusion, diapocynin is a strong ligand of HSA. An unprecedented combination of DFT calculation and docking simulation was used to explain the acquired chirality of diapocynin when bound to HSA.

Functional, thermodynamics, structural and biological studies of in silico-identified inhibitors of Mycobacterium tuberculosis enoyl-ACP(CoA) reductase enzyme

Novel chemotherapeutics agents are needed to kill Mycobacterium tuberculosis, the main causative agent of tuberculosis (TB). The M. tuberculosis 2-trans-enoyl-ACP(CoA) reductase enzyme (MtInhA) is the druggable bona fide target of isoniazid. New chemotypes were previously identified by two in silico approaches as potential ligands to MtInhA. The inhibition mode was determined by steady-state kinetics for seven compounds that inhibited MtInhA activity. Dissociation constant values at different temperatures were determined by protein fluorescence spectroscopy. van’t Hoff analyses of ligand binding to MtInhA:NADH provided the thermodynamic signatures of non-covalent interactions (ΔH°, ΔS°, ΔG°). Phenotypic screening showed that five compounds inhibited in vitro growth of M. tuberculosis H37Rv strain. Labio_16 and Labio_17 compounds also inhibited the in vitro growth of PE-003 multidrug-resistant strain. Cytotoxic effects on Hacat, Vero and RAW 264.7 cell lines were assessed for the latter two compounds. The Labio_16 was bacteriostatic and Labio_17 bactericidal in an M. tuberculosis-infected macrophage model. In Zebrafish model, Labio_16 showed no cardiotoxicity whereas Labio_17 showed dose-dependent cardiotoxicity. Accordingly, a model was built for the MtInhA:NADH:Labio_16 ternary complex. The results show that the Labio_16 compound is a direct inhibitor of MtInhA, and it may represent a hit for the development of chemotherapeutic agents to treat TB.