Pharmacophore modeling, molecular docking, and molecular dynamics simulation approaches for identifying new lead compounds for inhibiting aldose reductase 2

Pharmacophore-based virtual screening, molecular docking and molecular dynamics studies for the discovery of novel neuraminidase inhibitors

The in silico evaluation of 27 p-aminosalicylic acid derivatives, also referred to as neuraminidase inhibitors was the focus of the current study. To search and predict new potential neuraminidase inhibitors, this study was based on the ligand-based pharmacophore modeling, 3D QSAR, molecular docking, ADMET and MD simulation studies. The data was generated from recently reported inhibitors and divided into two groups, one of these group has 17 compounds for training and the second group has 10 compounds for testing purpose. The generated pharmacophore has known as ADDPR_4 was found statistically significant 3D-QSAR model owing the high trust scores (R2 = 0.974, Q2 = 0.905, RMSE = 0.23). Morever external validation was also employed to evaluate the prediction capacity of the built pharmacophore model (R2pred = 0.905). In addition, in silico ADMET, analyses were employed to evaluate the obtained hits for drug likeness properties. The stability of formed complexes was further evaluated using molecular dynamics. Top two hits showed stable complexes with Neuraminidase based on calculated total binding energy by MM-PBSA.Communicated by Ramaswamy H. Sarma.

Structure-based pharmacophore modeling, virtual screening and simulation studies for the identification of potent anticancerous phytochemical lead targeting cyclin-dependent kinase 2

Cyclin-dependent kinases are of critical importance in directing various cell cycle phases making them as potential tumor targets. Cyclin-dependent kinase 2 (CDK2) in particular plays a significant part during cell cycle events and its imbalance roots out tumorogenic environment. Herein, we built a structure-based pharmacophore model complementing the ATP pocket site of CDK2 with four pharmacophoric features, using a series of structures obtained from cluster analysis during MD simulation assessment. This was followed by its validation and further database screening against Taiwan indigenous plants database (5284 compounds). The screened compounds were subjected toward Lipinski's rule (RO5) and ADMET filter followed by docking analysis and simulation study. In filtering hits (10 compounds) via molecular docking against CDK2, Schinilenol with -8.1 kcal/mol fetched out as a best lead phytoinhibitor in the presence of standard drug (Dinaciclib). Additionally, pharmacophore mapping analysis also indicated relative fit values of dinaciclib and schinilenol as 2.37 and 2.31, respectively. Optimization, flexibility prediction and the stability of CDK2 in complex with the ligands were also ascertained by means of molecular dynamics for 50 ns, which further proposed schinilenol having better binding stability than dinaciclib with RMSD values ranging from 0.31 to 0.34 nm. Reactivity site, biological activity detection and cardiotoxicity assessment also proposed schinilenol as a better phytolead inhibitor than the existing dinaciclib. Abbreviations: CDK2: Cyclin dependent kinase2; ATP: Adenosine triphosphate; MD: Molecular dynamics, RO5: Rule of five; ADMET: Absorption, distribution, metabolism, and excretion; RMSD: Root mean square deviation; DS: Discovery Studio; SOM: Site of metabolism; RBPM: receptor based pharmacophore model; TIP: Schinilenol; hERG: human Ether-à-go-go - Related GeneCommunicated by Ramaswamy H. Sarma.

Understanding Aldose Reductase-Inhibitors interactions with free energy simulation

Aldose Reductase (AR) reduces a variety of substrates, such as aldehydes, aldoses and corticosteroids. It is the first and rate-limiting enzyme of the polyol pathway and is an important target enzyme for diabetes-associated complications, including retinopathy, neuropathy, and nephropathy. Inhibitors targeting this enzyme are structurally different and some of them have side effects. In existing publications, computational techniques are applied to investigate the binding affinities of existing inhibitors and predicting the affinities of newly designed ligands. However, these calculations only employ coarse and approximated methods such as docking and MM/PBSA. Brute force simulations are employed to study the dynamics of the system but no converged statistics is obtained. As a result, these computations provide results not consistent with experimental values and large discrepancies exist. In the current work, we employ the enhanced sampling technique of alchemical free energy simulation to calculate the binding affinities of several ligands targeting AR. The statistical error is defined with care and the correlation in the time-series data is fully considered. The statistically optimal estimators are used to extract the free energy estimates and the predicted results are in agreement with the experimental values. Less computationally demanding end-point free energy methods are also performed to compare their efficiency with the alchemical methods. As is expected, the end-point methods are of less accuracy and reliability compared with the alchemical free energy methods. The decomposition of the free energy difference in each alchemical transformation into the enthalpic and entropic components gives further insights on the thermodynamics. The enthalpy-entropy compensation is observed in this case. As the structural data obtained from experiments are only snapshots and more details are needed to understand the dynamics of the protein-ligand system, the conformational ensemble is analyzed. We identify important residues involved in the protein-ligand binding case and short-lived interactions formed due to fluctuations in the conformational ensemble. The current work shed light on the atomic detailed understanding of the dynamics of AR-inhibitors interactions.

Computational prediction models for assessing endocrine disrupting potential of chemicals

Endocrine disrupting chemicals (EDCs) mimic natural hormones and disrupt endocrine function. Humans and wildlife are exposed to EDCs might alter endocrine functions through various mechanisms and lead to an adverse effects. Hence, EDCs identification is important to protect the ecosystem and to promote the public health. Leveraging in-vitro and in-vivo experiments to identify potential EDCs is time consuming and expensive. Hence, quantitative structure-activity relationship is applied to screen the potential EDCs. Here, we summarize the predictive models developed using various algorithms to forecast the binding activity of chemicals to the estrogen and androgen receptors, alpha-fetoprotein, and sex hormone binding globulin.

Combiphore (Structure and Ligand Based Pharmacophore) - Approach for the Design of GPR40 Modulators in the Management of Diabetes

BACKGROUND

Pharmacophore mapping and molecular docking can be synergistically integrated to improve the drug design and discovery process. A rational strategy, combiphore approach, derived from the combined study of Structure and Ligand based pharmacophore has been described to identify novel GPR40 modulators.

METHODS

DISCOtech module from Discovery studio was used for the generation of the Structure and Ligand based pharmacophore models which gave hydrophobic aromatic, ring aromatic and negative ionizable as essential pharmacophoric features. The generated models were validated by screening active and inactive datasets, GH scoring and ROC curve analysis. The best model was exposed as a 3D query to screen the hits from databases like GLASS (GPCR-Ligand Association), GPCR SARfari and Mini-Maybridge. Various filters were applied to retrieve the hit molecules having good drug-like properties. A known protein structure of hGPR40 (pdb: 4PHU) having TAK-875 as ligand complex was used to perform the molecular docking studies; using SYBYL-X 1.2 software.

RESULTS AND CONCLUSION

Clustering both the models gave RMSD of 0.89. Therefore, the present approach explored the maximum features by combining both ligand and structure based pharmacophore models. A common structural motif as identified in combiphore for GPR40 modulation consists of the para-substituted phenyl propionic acid scaffold. Therefore, the combiphore approach, whereby maximum structural information (from both ligand and biological protein) is explored, gives maximum insights into the plausible protein-ligand interactions and provides potential lead candidates as exemplified in this study.

Probing the influence of carboxyalkyl groups on the molecular flexibility and the charge density of apigenin derivatives

Apigenin is an important flavonoids due to its antidiabetic bioactivity. It was reported experimentally that the 7-substituent derivative of apigenin has higher biological activity than 4'- and 5-substituted derivatives while introducing sole carboxyalkyl group -(CH₂)₇COOH into the parent structure. Molecular docking studies indicated that the other two derivatives have lower binding affinities than the 7-substituent derivative (-7.52 kcal mol^-1), which is considered to be a better inhibitor than the parent molecule. Almost all of the carbon atoms and oxygen atoms are coplaner for all three molecules in solution phase, however, all carboxyalkyl groups bend inside into the parent molecules in the active site, and the jagged geometries of the carbon chains are destroyed correspondingly. In addition, most of the electron densities of the chemical bonds for all molecules are decreased, especially the 7-substituent derivative. In contrast, most of the Laplacian values for three molecules are increased in the active site, which suggests that the charge densities at the bond critical point (bcp) are much more depleted than the solution phase. Dipole moments of derivatives are all increased in the active site, suggesting strong intermolecular interactions. After interacting with the S. cerevisiae α-glucosidase, only the 7-substituent derivative has the lowest energy gap ΔE _HOMO-LUMO, which indicates the lowest stability and the highest inhibition activity. Graphical abstract Probing the influence of carboxyalkyl groups on the molecular flexibility and the charge density of apigenin derivatives.

Discovery of Anti-Hypertensive Oligopeptides from Adlay Based on In Silico Proteolysis and Virtual Screening

Adlay (Coix larchryma-jobi L.) was the commonly used Traditional Chinese Medicine (TCM) with high content of seed storage protein. The hydrolyzed bioactive oligopeptides of adlay have been proven to be anti-hypertensive effective components. However, the structures and anti-hypertensive mechanism of bioactive oligopeptides from adlay were not clear. To discover the definite anti-hypertensive oligopeptides from adlay, in silico proteolysis and virtual screening were implemented to obtain potential oligopeptides, which were further identified by biochemistry assay and molecular dynamics simulation. In this paper, ten sequences of adlay prolamins were collected and in silico hydrolyzed to construct the oligopeptide library with 134 oligopeptides. This library was reverse screened by anti-hypertensive pharmacophore database, which was constructed by our research team and contained ten anti-hypertensive targets. Angiotensin-I converting enzyme (ACE) was identified as the main potential target for the anti-hypertensive activity of adlay oligopeptides. Three crystal structures of ACE were utilized for docking studies and 19 oligopeptides were finally identified with potential ACE inhibitory activity. According to mapping features and evaluation indexes of pharmacophore and docking, three oligopeptides were selected for biochemistry assay. An oligopeptide sequence, NPATY (IC50 = 61.88 ± 2.77 µM), was identified as the ACE inhibitor by reverse-phase high performance liquid chromatography (RP-HPLC) assay. Molecular dynamics simulation of NPATY was further utilized to analyze interactive bonds and key residues. ALA354 was identified as a key residue of ACE inhibitors. Hydrophobic effect of VAL518 and electrostatic effects of HIS383, HIS387, HIS513 and Zn2+ were also regarded as playing a key role in inhibiting ACE activities. This study provides a research strategy to explore the pharmacological mechanism of Traditional Chinese Medicine (TCM) proteins based on in silico proteolysis and virtual screening, which could be beneficial to reveal the pharmacological action of TCM proteins and provide new lead compounds for peptides-based drug design.

Targeting of AMP-activated protein kinase: prospects for computer-aided drug design

INTRODUCTION

Dysregulation of energy homeostasis has been implicated in a number of human chronic diseases including diabetes, obesity, cancer, and inflammation. Given the functional attributes as a central regulator of energy homeostasis, AMP-activated protein kinase (AMPK) is emerging as a therapeutic target for these diseases, and lines of evidence have highlighted the need for rational and robust screening systems for identifying specific AMPK modulators with a therapeutic potential for preventing and/or curing these diseases. Areas covered: Here, the authors review the recent advances in the understanding of three-dimensional structures of AMPK in relationship with the regulatory mechanisms, potentials of AMPK as a therapeutic target in human chronic diseases, and prospects of computer-based drug design for AMPK. Expert opinion: Accumulating information of AMPK structure has provided us with deep insight into the molecular basis underlying the regulatory mechanisms, and further discloses several structural domains, which can be served for a target site for computer-based drug design. Molecular docking and simulations provides useful information about the binding sites between potent drugs and AMPK as well as a rational screening format to discover isoform-specific AMPK modulators. For these reasons, the authors suggest that computer-aided virtual screening methods hold promise as a rational approach for discovering more specific AMPK modulators.

Inhibition of Paracoccidioides lutzii Pb01 isocitrate lyase by the natural compound argentilactone and its semi-synthetic derivatives

The dimorphic fungus Paracoccidioides spp. is responsible for paracoccidioidomycosis, the most prevalent systemic mycosis in Latin America, causing serious public health problems. Adequate treatment of mycotic infections is difficult, since fungi are eukaryotic organisms with a structure and metabolism similar to those of eukaryotic hosts. In this way, specific fungus targets have become important to search of new antifungal compound. The role of the glyoxylate cycle and its enzymes in microbial virulence has been reported in many fungal pathogens, including Paracoccidioides spp. Here, we show the action of argentilactone and its semi-synthetic derivative reduced argentilactone on recombinant and native isocitrate lyase from Paracoccidioides lutzii Pb01 (PbICL) in the presence of different carbon sources, acetate and glucose. Additionally, argentilactone and its semi-synthetic derivative reduced argentilactone exhibited relevant inhibitory activity against P. lutzii Pb01 yeast cells and dose-dependently influenced the transition from the mycelium to yeast phase. The other oxygenated derivatives tested, epoxy argentilactone and diol argentilactone-, did not show inhibitory action on the fungus. The results were supported by in silico experiments.

Development of a human dihydroorotate dehydrogenase (hDHODH) pharma-similarity index approach with scaffold-hopping strategy for the design of novel potential inhibitors

Human dihydroorotate dehydrogenase (hDHODH) is a class-2 dihydroorotate dehydrogenase. Because it is extensively used by proliferating cells, its inhibition in autoimmune and inflammatory diseases, cancers, and multiple sclerosis is of substantial clinical importance. In this study, we had two aims. The first was to develop an hDHODH pharma-similarity index approach (PhSIA) using integrated molecular dynamics calculations, pharmacophore hypothesis, and comparative molecular similarity index analysis (CoMSIA) contour information techniques. The approach, for the discovery and design of novel inhibitors, was based on 25 diverse known hDHODH inhibitors. Three statistical methods were used to verify the performance of hDHODH PhSIA. Fischer’s cross-validation test provided a 98% confidence level and the goodness of hit (GH) test score was 0.61. The q², r², and predictive r² values were 0.55, 0.97, and 0.92, respectively, for a partial least squares validation method. In our approach, each diverse inhibitor structure could easily be aligned with contour information, and common substructures were unnecessary. For our second aim, we used the proposed approach to design 13 novel hDHODH inhibitors using a scaffold-hopping strategy. Chemical features of the approach were divided into two groups, and the Vitas-M Laboratory fragment was used to create de novo inhibitors. This approach provides a useful tool for the discovery and design of potential inhibitors of hDHODH, and does not require docking analysis; thus, our method can assist medicinal chemists in their efforts to identify novel inhibitors.

Intermolecular interactions of the malate synthase of Paracoccidioides spp

Background

The fungus Paracoccidioides spp is the agent of paracoccidioidomycosis (PCM), a pulmonary mycosis acquired by the inhalation of fungal propagules. Paracoccidioides malate synthase (PbMLS) is important in the infectious process of Paracoccidioides spp because the transcript is up-regulated during the transition from mycelium to yeast and in yeast cells during phagocytosis by murine macrophages. In addition, PbMLS acts as an adhesin in Paracoccidioides spp. The evidence for the multifunctionality of PbMLS indicates that it could interact with other proteins from the fungus and host. The objective of this study was to identify and analyze proteins that possibly bind to PbMLS (PbMLS-interacting proteins) because protein interactions are intrinsic to cell processes, and it might be possible to infer the function of a protein through the identification of its ligands.

Results

The search for interactions was performed using an in vivo assay with a two-hybrid library constructed in S. cerevisiae; the transcripts were sequenced and identified. In addition, an in vitro assay using pull-down GST methodology with different protein extracts (yeast, mycelium, yeast-secreted proteins and macrophage) was performed, and the resulting interactions were identified by mass spectrometry (MS). Some of the protein interactions were confirmed by Far-Western blotting using specific antibodies, and the interaction of PbMLS with macrophages was validated by indirect immunofluorescence and confocal microscopy. In silico analysis using molecular modeling, dynamics and docking identified the amino acids that were involved in the interactions between PbMLS and PbMLS-interacting proteins. Finally, the interactions were visualized graphically using Osprey software.

Conclusion

These observations indicate that PbMLS interacts with proteins that are in different functional categories, such as cellular transport, protein biosynthesis, modification and degradation of proteins and signal transduction. These data suggest that PbMLS could play different roles in the fungal cell.

Prospecting for novel plant-derived molecules of Rauvolfia serpentina as inhibitors of Aldose Reductase, a potent drug target for diabetes and its complications

Aldose Reductase (AR) is implicated in the development of secondary complications of diabetes, providing an interesting target for therapeutic intervention. Extracts of Rauvolfia serpentina, a medicinal plant endemic to the Himalayan mountain range, have been known to be effective in alleviating diabetes and its complications. In this study, we aim to prospect for novel plant-derived inhibitors from R. serpentina and to understand structural basis of their interactions. An extensive library of R. serpentina molecules was compiled and computationally screened for inhibitory action against AR. The stability of complexes, with docked leads, was verified using molecular dynamics simulations. Two structurally distinct plant-derived leads were identified as inhibitors: indobine and indobinine. Further, using these two leads as templates, 16 more leads were identified through ligand-based screening of their structural analogs, from a small molecules database. Thus, we obtained plant-derived indole alkaloids, and their structural analogs, as potential AR inhibitors from a manually curated dataset of R. serpentina molecules. Indole alkaloids reported herein, as a novel structural class unreported hitherto, may provide better insights for designing potential AR inhibitors with improved efficacy and fewer side effects.