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.

Nifuroxazide as JAK2 inhibitor: A binding mode proposal and Hel cell proliferation assay.

Nifuroxazide has been employed as an anti-diarrheic agent since 1966, but in the last decade has brought to the research spotlight again due to its recently described antitumoral activity through the JAK2 inhibitory potential. Since 2008, more than 70 papers have been published about the issue and more are expected to the following years. Herein we discuss the findings of molecular modelling studies which were performed to elucidate the potential binding mode of this drug into the JAK2 ATP recognition site and also into the allosteric region near the catalytic site. Molecular modelling followed by dynamics simulations indicated the NFZ could bind at both sites, such as a Type II kinase inhibitor since residues from both ATP and modulatory site would exhibit contacts with the drug when in a stable complex. Synthesis of NFZ and its sulfur bioisosteric analogue GPQF-63 were performed and experimental assays against HEL cells indicate the potential of NFZ and, mainly of its analogue GPQF-63 in acting as inhibitors of cell growth. HEL-cells present the JAK2 V617F mutation which leads to an enhanced JAK/STAT pathway and they have never been tested by the NFZ activity before. A mechanistic approach was also performed and revealed that both compounds induce cell apoptosis.Taken together, both the theoretical and experimental approaches point out the N-acylhydrazones as good starting points in the search for JAK2 modulatory small molecules which could then, be studied as promising leads toward new alternatives to control the JAK-STAT pathway related pathologies. This is the first study, as far as we have known, to propose a potential binding mode for NFZ as well as reporting the activity of this drug against HEL cells, which are a usual cellular model to human erythroleukemia and other myeloproliferative diseases.

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.

Drift-diffusion (DrDiff) framework determines kinetics and thermodynamics of two-state folding trajectory and tunes diffusion models.

The stochastic drift-diffusion (DrDiff) theory is an approach used to characterize the dynamical properties of simulation data. With new features in transition times analyses, the framework characterized the thermodynamic free-energy profile [F(Q)], the folding time (τf), and transition path time (τTP) by determining the coordinate-dependent drift-velocity [v(Q)] and diffusion [D(Q)] coefficients from trajectory time traces. In order to explore the DrDiff approach and to tune it with two other methods (Bayesian analysis and fep1D algorithm), a numerical integration of the Langevin equation with known D(Q) and F(Q) was performed and the inputted coefficients were recovered with success by the diffusion models. DrDiff was also applied to investigate the prion protein (PrP) kinetics and thermodynamics by analyzing folding/unfolding simulations. The protein structure-based model, the well-known Go¯-model, was employed in a coarse-grained Cα level to generate long constant-temperature time series. PrP was chosen due to recent experimental single-molecule studies in D and τTP that stressed the importance and the difficulty of probing these quantities and the rare transition state events related to prion misfolding and aggregation. The PrP thermodynamic double-well F(Q) profile, the "X" shape of τf(T), and the linear shape of τTP(T) were predicted with v(Q) and D(Q) obtained by the DrDiff algorithm. With the advance of single-molecule techniques, the DrDiff framework might be a useful ally for determining kinetic and thermodynamic properties by analyzing time observables of biomolecular systems. The code is freely available at https://github.com/ronaldolab/DrDiff.

Methyl divanillate: redox properties and binding affinity with albumin of an antioxidant and potential NADPH oxidase inhibitor.

Vanillic acid is a widely used food additive (flavouring agent, JECFA number: 959) with many reported beneficial biological effects. The same is true for its ester derivative (methyl vanillate, JECFA number: 159). Based on the increasing evidence that diapocynin, the dimer of apocynin (NADPH oxidase inhibitor), has some improved pharmacological properties compared to its monomer, here the dimer of methyl vanillate (MV), i.e., methyl divanillate (dimer of methyl vanillate, DMV) was synthesized and studied in the context of its redox properties and binding affinity with human serum albumin (HSA). We found that the antioxidant potency of DMV was significantly increased compared to MV. In this regard, the reduction of 2,2-diphenyl-1-picrylhydrazyl (DPPH) free radical by DMV was 30-fold more effective compared to MV. Ferric ion reduction was 4-fold higher and peroxyl radical reduction was 2.7-fold higher. The interaction with HSA was significantly improved (Stern-Vomer constants, 3.8 × 105 mol-1 L and 2.3 × 104 mol-1 L, for DMV and MV, respectively). The complexation between DMV and HSA was also evidenced by induced circular dichroism (ICD) signal generation in the former due to its fixation in the asymmetric protein pocket. Density-functional calculations (TD-DFT) showed that the ICD spectrum was related to a DMV conformation bearing a dihedral angle of approximately -60°. Similar dihedral angles were obtained in the lowest and most populated DMV cluster poses obtained by molecular docking simulations. The computational studies and experimental displacement studies revealed that DMV binds preferentially at site I. In conclusion, besides being a powerful antioxidant, DMV is also a strong ligand of HSA. This is the first study on the chemical and biophysical properties of DMV, a compound with potential beneficial biological effects.

Interaction between 1-pyrenesulfonic acid and albumin: Moving inside the protein.

Due to the high sensitivity to alterations in microenvironment polarity of macromolecules, pyrene and its derivatives have long been applied in biosciences. Human serum albumin (HSA), besides its numerous physiological functions, is the main responsible by transport of endogenous and exogenous compounds in the circulatory system. Here, a comprehensive study was carry out to understand the interaction between HSA and the pyrene derivative 1-pyrenesulfonic acid (PMS), which showed a singular behaviour when bound to this protein. The complexation of PMS with HSA was studied by steady state, time-resolved and anisotropy fluorescence, induction of circular dichroism (ICD) and molecular docking. The fluorescence quenching of PMS by HSA was abnormal, being stronger at lower concentration of the quencher. Similar behaviour was obtained by measuring the ICD signal and fluorescence lifetime of PMS complexed in HSA. The displacement of PMS by site-specific drugs showed that this probe occupied both sites, but with higher affinity for site II. The movement of PMS between these main binding sites was responsible by the abnormal effect. Using the holo (PDB: ID 1A06) and apo (PDB: ID 1E7A) HSA structures, the experimental results were corroborated by molecular docking simulation. The abnormal spectroscopic behaviour of PMS is related to its binding in different regions in the protein. The movement of PMS into the protein can be traced by alteration in the spectroscopic signals. These findings bring a new point of view about the use of fluorescence quenching to characterize the interaction between albumin and ligands.

Effects of pH and aggregation in the human prion conversion into scrapie form: a study using molecular dynamics with excited normal modes.

There are two different prion conformations: (1) the cellular natural (PrPC) and (2) the scrapie (PrPSc), an infectious form that tends to aggregate under specific conditions. PrPC and PrPSc are widely different regarding secondary and tertiary structures. PrPSc contains more and longer ß-strands compared to PrPC. The lack of solved PrPSc structures precludes a proper understanding of the mechanisms related to the transition between cellular and scrapie forms, as well as the aggregation process. In order to investigate the conformational transition between PrPC and PrPSc, we applied MDeNM (molecular dynamics with excited normal modes), an enhanced sampling simulation technique that has been recently developed to probe large structural changes. These simulations yielded new structural rearrangements of the cellular prion that would have been difficult to obtain with standard MD simulations. We observed an increase in ß-sheet formation under low pH (≤ 4) and upon oligomerization, whose relevance was discussed on the basis of the energy landscape theory for protein folding. The characterization of intermediate structures corresponding to transition states allowed us to propose a conversion model from the cellular to the scrapie prion, which possibly ignites the fibril formation. This model can assist the design of new drugs to prevent neurological disorders related to the prion aggregation mechanism.

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.

Use of machine learning approaches for novel drug discovery.

INTRODUCTION: The use of computational tools in the early stages of drug development has increased in recent decades. Machine learning (ML) approaches have been of special interest, since they can be applied in several steps of the drug discovery methodology, such as prediction of target structure, prediction of biological activity of new ligands through model construction, discovery or optimization of hits, and construction of models that predict the pharmacokinetic and toxicological (ADMET) profile of compounds. AREAS COVERED: This article presents an overview on some applications of ML techniques in drug design. These techniques can be employed in ligand-based drug design (LBDD) and structure-based drug design (SBDD) studies, such as similarity searches, construction of classification and/or prediction models of biological activity, prediction of secondary structures and binding sites docking and virtual screening. EXPERT OPINION: Successful cases have been reported in the literature, demonstrating the efficiency of ML techniques combined with traditional approaches to study medicinal chemistry problems. Some ML techniques used in drug design are: support vector machine, random forest, decision trees and artificial neural networks. Currently, an important application of ML techniques is related to the calculation of scoring functions used in docking and virtual screening assays from a consensus, combining traditional and ML techniques in order to improve the prediction of binding sites and docking solutions.