Correction: Molecular modelling of the HCMV IL-10 protein isoforms and analysis of their interaction with the human IL-10 receptor.

[This corrects the article DOI: 10.1371/journal.pone.0277953.].

MASSA Algorithm: an automated rational sampling of training and test subsets for QSAR modeling.

QSAR models capable of predicting biological, toxicity, and pharmacokinetic properties were widely used to search lead bioactive molecules in chemical databases. The dataset's preparation to build these models has a strong influence on the quality of the generated models, and sampling requires that the original dataset be divided into training (for model training) and test (for statistical evaluation) sets. This sampling can be done randomly or rationally, but the rational division is superior. In this paper, we present MASSA, a Python tool that can be used to automatically sample datasets by exploring the biological, physicochemical, and structural spaces of molecules using PCA, HCA, and K-modes. The proposed algorithm is very useful when the variables used for QSAR are not available or to construct multiple QSAR models with the same training and test sets, producing models with lower variability and better values for validation metrics. These results were obtained even when the descriptors used in the QSAR/QSPR were different from those used in the separation of training and test sets, indicating that this tool can be used to build models for more than one QSAR/QSPR technique. Finally, this tool also generates useful graphical representations that can provide insights into the data.

Structural analysis of factors related to FAM3C/ILEI dimerization and identification of inhibitor candidates targeting cancer treatment.

FAM3 is a superfamily of four cytokines that maintain a single globular structure ß -ß -α of three classes: FAM3A, B, C and D. FAM3C was the first member of this family related to cancer and is functionally characterized as an essential factor for the epithelial-mesenchymal transition (EMT), leading to late delays in tumor progression. Due to its crucial role in EMT and metastasis, FAM3C has been termed an interleukin-like EMT (ILEI) inducer. There are several studies on the part of FAM3C in the progression of cancer and other diseases. However, little is known about its cellular receptors and possible inhibitors. In this study, based on in silico approaches, we performed structural analyses of factors related to FAM3C/ILEI dimerization. We also identified four possible inhibitor candidates, expected to be exciting prototypes and could be submitted to future biological tests targeting cancer treatment.

Molecular modelling of the HCMV IL-10 protein isoforms and analysis of their interaction with the human IL-10 receptor.

The human cytomegalovirus (HCMV) UL111A gene encodes several homologs of the cellular interleukin 10 (cIL-10). Alternative splicing in the UL111A region produces two relatively well-characterized transcripts designated cmvIL-10 (isoform A) and LAcmvIL-10 (isoform B). The cmvIL-10 protein is the best characterized, both structurally and functionally, and has many immunosuppressive activities similar to cIL-10, while LAcmvIL-10 has more restricted biological activities. Alternative splicing also results in five less studied UL111A transcripts encoding additional proteins homologous to cIL-10 (isoforms C to G). These transcripts were identified during productive HCMV infection of MRC-5 cells with the high passage laboratory adapted AD169 strain, and the structure and properties of the corresponding proteins are largely unknown. Moreover, it is unclear whether these protein isoforms are able to bind the cellular IL-10 receptor and induce signalling. In the present study, we investigated the expression spectrum of UL111A transcripts in fully permissive MRC-5 cells and semi permissive U251 cells infected with the low passage HCMV strain TB40E. We identified a new spliced transcript (H) expressed during productive infection. Using computational methods, we carried out molecular modelling studies on the three-dimensional structures of the HCMV IL-10 proteins encoded by the transcripts detected in our work (cmvIL-10 (A), LAcmvIL-10 (B), E, F and H) and on their interaction with the human IL-10 receptor (IL-10R1). The modelling predicts clear differences between the isoform structures. Furthermore, the in silico simulations (molecular dynamics simulation and normal-mode analyses) allowed us to evaluate regions that contain potential receptor binding sites in each isoform. The analyses demonstrate that the complexes between the isoforms and IL-10R1 present different types of molecular interactions and consequently different affinities and stabilities. The knowledge about structure and expression of specific viral IL-10 isoforms has implications for understanding of their properties and role in HCMV immune evasion and pathogenesis.

Relationship between Hormonal Modulation and Gastroprotective Activity of Malvidin and Cyanidin Chloride: In Vivo and In Silico Approach.

Peptic ulcers are lesions that affect the gastrointestinal tract and that can be triggered by external factors such as alcohol use. This study investigated the gastroprotective role of two anthocyanidins, malvidin and cyanidin chloride, in an ethanol-induced gastric ulcer model in male and female mice (ovariectomized and supplemented with 17ß-estradiol or not) and aimed to evaluate the effectiveness of anthocyanidins in preventing the formation of lesions and to identify the underlying mechanisms, while considering hormonal differences. Moreover, in silico comparative analysis was performed to predict the properties and biological behaviors of the molecules. We observed that the hormonal status did not interfere with the gastroprotective action of malvidin, although antioxidant mechanisms were modulated differently depending on sex. On the other hand, cyanidin showed gastroprotective activity at different doses, demonstrating that, for the same experimental model, there is a need to adjust the effective dose depending on sex. In silico analysis showed that, despite being structurally similar, the interaction with receptors and target proteins in this study (myeloperoxidase, superoxide dismutase, catalase, and reduced glutathione) differed between the two molecules, which explains the difference observed in in vivo treatments.

Virtual screening and in vitro assays of novel hits as promising DPP-4 inhibitors.

Diabetes is a metabolic disorder that presents hyperglycemia and vascular complications due to the non-production of insulin or its inappropriate use by the body. One of the strategies to treat diabetes is the inhibition of dipeptidyl peptidase-4 (DPP-4) and it is interesting to conduct virtual screening studies to search for new inhibitors of the DPP-4 enzyme. This study involves a virtual screening using the crystallographic structure of DPP-4 and a compound subset from the ZINC database. To filter this compound subset, we used some physicochemical properties, positioning at the three DPP-4 binding sites, molecular interactions, and ADME-Tox properties. The conformations of ligands obtained from AutoDock Vina were analyzed using a consensus with other algorithms (AutoDock and GOLD). The compounds selected from virtual screening were submitted to biological assays using the "DPPIV-Glo™ protease assay". Cytotoxicity tests were also performed. One promising compound (ZINC1572309) established interactions with important residues at the binding site. The results of the ADME-Tox prediction for ZINC1572309 were compared with a reference drug (sitagliptin). The cytotoxicity of sitagliptin and ZINC1572309 were evaluated using the XTT short-term cytotoxic assay, including normal and tumor cell lines to observe the cellular response to inhibitor treatment at different genetic bases. Both compounds (ZINC1572309 and the reference drug - sitagliptin) also inhibited DPP-4 activity, suggesting interesting biological effects of the selected compound at non-cytotoxic concentrations. Therefore, from in silico and in vitro studies, a potential hit as DPP-4 inhibitor was discovered and it can be structurally optimized to achieve suitable activity and pharmacokinetic profiles.

Competition Between Phenothiazines and BH3 Peptide for the Binding Site of the Antiapoptotic BCL-2 Protein.

The study of proteins and mechanisms involved in the apoptosis and new knowledge about cancer's biology are essential for planning new drugs. Tumor cells develop several strategies to gain proliferative advantages, including molecular alterations to evade from apoptosis. Failures in apoptosis could contribute to cancer pathogenesis, since these defects can cause the accumulation of dividing cells and do not remove genetic variants that have malignant potential. The apoptosis mechanism is composed by proteins that are members of BCL-2 and cysteine-protease families. BH3-only peptides are the "natural" intracellular ligands of BCL-2 family proteins. On the other hand, studies have proved that phenothiazine compounds influence the induction of cellular death. To understand the characteristics of phenothiazines and their effects on tumoral cells and organelles involved in the apoptosis, as well as evaluating their pharmacologic potential, we have carried out computational simulation with the purpose of relating the structures of the phenothiazines with their biological activity. Since the tridimensional (3D) structure of the target protein is known, we have employed the molecular docking approach to study the interactions between compounds and the protein's active site. Hereafter, the molecular dynamics technique was used to verify the temporal evolution of the BCL-2 complexes with phenothiazinic compounds and the BH3 peptide, the stability and the mobility of these molecules in the BCL-2 binding site. From these results, the calculation of binding free energy between the compounds and the biological target was carried out. Thus, it was possible to verify that thioridazine and trifluoperazine tend to increase the stability of the BCL-2 protein and can compete for the binding site with the BH3 peptide.

Integrated Protocol to Design Potential Inhibitors of Dipeptidyl Peptidase- 4 (DPP-4).

BACKGROUND: A strategy for the treatment of type II diabetes mellitus is the inhibition of the enzyme known as dipeptidyl peptidase-4 (DPP-4). AIMS: This study aims to investigate the main interactions between DPP-4 and a set of inhibitors, as well as proposing potential candidates to inhibit this enzyme. METHODS: We performed molecular docking studies followed by the construction and validation of CoMFA and CoMSIA models. The information provided from these models was used to aid in the search for new candidates to inhibit DPP-4 and the design of new bioactive ligands from structural modifications in the most active molecule of the studied series. RESULTS: We were able to propose a set of analogues with biological activity predicted by the CoMFA and CoMSIA models, suggesting that our protocol can be used to guide the design of new DPP-4 inhibitors as drug candidates to treat diabetes. CONCLUSION: Once the integration of the techniques mentioned in this article was effective, our strategy can be applied to design possible new DPP-4 inhibitors as candidates to treat diabetes.

Structural Dynamics of DPP-4 and Its Influence on the Projection of Bioactive Ligands.

Dipeptidyl peptidase-4 (DPP-4) is a target to treat type II diabetes mellitus. Therefore, it is important to understand the structural aspects of this enzyme and its interaction with drug candidates. This study involved molecular dynamics simulations, normal mode analysis, binding site detection and analysis of molecular interactions to understand the protein dynamics. We identified some DPP-4 functional motions contributing to the exposure of the binding sites and twist movements revealing how the two enzyme chains are interconnected in their bioactive form, which are defined as chains A (residues 40-767) and B (residues 40-767). By understanding the enzyme structure, its motions and the regions of its binding sites, it will be possible to contribute to the design of new DPP-4 inhibitors as drug candidates to treat diabetes.

Molecular docking studies and 2D analyses of DPP-4 inhibitors as candidates in the treatment of diabetes.

Dipeptidyl peptidase-4 (DPP-4) is an important biological target related to the treatment of diabetes as DPP-4 inhibitors can lead to an increase in the insulin levels and a prolonged activity of glucagon-like peptide-1 (GLP-1) and gastric inhibitory polypeptide (GIP), being effective in glycemic control. Thus, this study analyses the main molecular interactions between DPP-4 and a series of bioactive ligands. The methodology used here employed molecular modeling methods, such as HQSAR (Hologram Quantitative Structure-Activity) analyses and molecular docking, with the aim of understanding the main structural features of the compound series that are essential for the biological activity. Analyses of the main interactions in the active site of DPP-4, in particular, the contribution of the hydroxyl coordination between Tyr547 and Ser630 by the water molecule, which is described in the literature as important for the coordinated interactions in the active site, were performed. Significant correlation coefficients of the best 2D model (r(2) = 0.942 and q(2) = 0.836) were obtained, indicating the predictive power of this model for untested compounds. Therefore, the final model constructed in this study, along with the information from the contribution maps, could be useful in the design of novel DPP-4 ligands with improved activity.