Franco Henrique Andrade Leite

Identification of Novel Protein Kinase Receptor Type 2 Inhibitors Using Pharmacophore and Structure-Based Virtual Screening

The Protein Kinase Receptor type 2 (RIPK2) plays an important role in the pathogenesis of inflammatory diseases; it signals downstream of the NOD1 and NOD2 intracellular sensors and promotes a productive inflammatory response. However, excessive NOD2 signaling has been associated with various diseases, including sarcoidosis and inflammatory arthritis; the pharmacological inhibition of RIPK2 is an affinity strategy that demonstrates an increased expression of pro-inflammatory secretion activity. In this study, a pharmacophoric model based on the crystallographic pose of ponatinib, a potent RIPK2 inhibitor, and 30 other ones selected from the BindingDB repository database, was built. Compounds were selected based on the available ZINC compounds database and in silico predictions of their pharmacokinetic, toxicity and potential biological activity. Molecular docking was performed to identify the probable interactions of the compounds as well as their binding affinity with RIPK2. The compounds were analyzed to ponatinib and WEHI-345, which also used as a control. At least one of the compounds exhibited suitable pharmacokinetic properties, low toxicity and an interesting binding affinity and high fitness compared with the crystallographic pose of WEHI-345 in complex with RIPK2. This compound also possessed suitable synthetic accessibility, rendering it a potential and very promising RIPK2 inhibitor to be further investigated in regards to different diseases, particularly inflammatory ones.

Caffeoylquinic acids from antiplasmodial active extract of Xanthium cavanillesii fruits and their molecular modelling studies

Abstract The antiplasmodial active extract of Xanthium cavanillesii contains 3,4-dicaffeoyl quinic acid (3,4-DCQA), 3,5-dicaffeoyl quinic acid (3,5-DCQA) and 1,3,5-tricaffeoyl quinic acid (1,3,5-TCQA). These results inspired us to investigate the interaction of these molecules with a promising validated target of Plasmodium, PfATP6 orthologue of mammalian Ca+2-ATPase. Models of this receptor were obtained through comparative modelling. Afterwards, molecular docking studies were used to identify possible interaction modes of these caffeoyl quinic derivatives on the binding site. The 1,3,5-TCQA had the best energy, but all of these had better energy than thapsigargin, a non-competitive inhibitor of the sarco/endoplasmatic reticulum Ca+2-ATPase (SERCA).

Virtual Screening for the Selection of New Candidates to Trypanosoma cruzi Farnesyl Pyrophosphate Synthase Inhibitors

Chagas disease is a tropical parasitic disease that is caused by Trypanosoma cruzi and causes 12,000 deaths per year, mainly in Latin America. The available drugs for treating have severe limitations, including poor efficacy and high toxicity. One way to overcome these limitations is targeting priority molecules with computational tools to direct in vitro assays against validated targets. Farnesyl pyrophosphate synthase (E.C. 2.5.1.10) is an enzyme that participates in the initial stage of sterol biosynthesis, and its inhibition causes damage to membrane integrity, leading to parasite death. With the aim to identify potential inhibitors against this target from T. cruzi, hierarchical virtual screening approaches were performed through a combination of ligand-based pharmacophore models and molecular docking. First, pharmacophore model filtering resulted in 15,154 molecules that had the minimum structural requirements for inhibition (QFIT > 0). These molecules were subsequently submitted to molecular docking routine, which resulted in 11,762 molecules (Grid Score between –232.74 to –0.96 kcal mol). The top 30 ranked molecules in these approaches were grouped in self-organizing maps. These analyses showed four promising compounds from natural products that mimic the major interactions present in the substrate/ inhibitor, which indicates that these molecules can be assayed by in vitro experiments.

Computational design of new protein kinase 2 inhibitors for the treatment of inflammatory diseases using QSAR, pharmacophore-structure-based virtual screening, and molecular dynamics

Receptor-interacting protein kinase 2 (RIPK2) plays an essential role in autoimmune response and is suggested as a target for inflammatory diseases. A pharmacophore model was built from a dataset with ponatinib (template) and 18 RIPK2 inhibitors selected from BindingDB database. The pharmacophore model validation was performed by multiple linear regression (MLR). The statistical quality of the model was evaluated by the correlation coefficient (R), squared correlation coefficient (R2), explanatory variance (adjusted R2), standard error of estimate (SEE), and variance ratio (F). The best pharmacophore model has one aromatic group (LEU24 residue interaction) and two hydrogen bonding acceptor groups (MET98 and TYR97 residues interaction), having a score of 24.739 with 14 aligned inhibitors, which were used in virtual screening via ZincPharmer server and the ZINC database (selected in function of the RMSD value). We determined theoretical values of biological activity (logRA) by MLR, pharmacokinetic and toxicology properties, and made molecular docking studies comparing binding affinity (kcal/mol) results with the most active compound of the study (ponatinib) and WEHI-345. Nine compounds from the ZINC database show satisfactory results, yielding among those selected, the compound ZINC01540228, as the most promising RIPK2 inhibitor. After binding free energy calculations, the following molecular dynamics simulations showed that the receptor protein’s backbone remained stable after the introduction of ligands.

Identification of Lutzomyia longipalpis Odorant Binding Protein Modulators by Comparative Modeling, Hierarchical Virtual Screening, and Molecular Dynamics

Visceral leishmaniasis (VL) is the second most important vector-borne disease in the world. It is transmitted by Lutzomyia longipalpis in America; therefore, controlling the vector is essential to prevent the disease, especially using traps with chemical attractants. It is known that odorant binding proteins (OBPs) act at the first odor selection level, so in silico methodology was used to identify putative vector chemical modulators based on OBPs on known ligand structures. Therefore, 3D structures of L. longipalpis OBP were predicted through different comparative modeling methods. The best model was subjected to molecular dynamics studies. Then, a hierarchical virtual screening approach filtered OBP modulator-like compounds from ZINC12 biogenic database based in global chemical space, using principal components from ChemGPS-NP server. Such compounds then were evaluated and ranked according to their affinity with the OBP orthosteric site by molecular docking in DOCK 6.7. The compounds were scored by Grid Score function and top five ranked poses had their intermolecular complex interactions analyzed in PLIP server. Most ligands in the top of the rank were lysophospholipids, which could potentially interact with the OBP hydrophobic pocket through Phe72, Tyr76, Ile79, Ala87, Lys88, Asp92, Phe61, Leu75, Trp113, His120, and Phe122 residues and H-bonding with His120 and Phe122. Next, compounds in the top of the rank were evaluated by 50 ns MD and the results showed that the phosphate group of these compounds could set a salt bridge with His110. Additionally, Tyr76, Ala87, Met91, Trp113, and Phe122 were important to hydrophobic interactions with the ligand. These results highlight the importance of accurate assessments such as MD studies in order to analyze the docking results in the identification of new odorant modulators.