Application of binding free energy calculations to prediction of binding modes and affinities of MDM2 and MDMX inhibitors

Towards development of new antimalarial compounds through in silico and in vitro assays

Malaria is one of most widespread infectious disease in world. The antimalarial therapy presents a series of limitations, such as toxicity and the emergence of resistance, which makes the search for new drugs urgent. Thus, it becomes necessary to explore essential and exclusive therapeutic targets of the parasite to achieve selective inhibition. Enoyl-ACP reductase is an enzyme of the type II fatty acid biosynthetic pathway and is responsible for the rate-limiting step in the fatty acid elongation cycle. In this work, we use hierarchical virtual screening and drug repositioning strategies to prioritize compounds for phenotypic assays and molecular dynamics studies. The molecules were tested against chloroquine-resistant W2 strain of Plasmodium falciparum (EC50 between 330.05 and 13.92 µM). Nitrofurantoin was the best antimalarial activity at low micromolar range (EC50 = 13.92 µM). However, a hit compound against malaria must have a biological activity value below 1 µM. A large number of molecules present problems with permeability in biological membranes and reaching an effective concentration in their target's microenvironment. Nitrofurantoin derivatives with inclusions of groups which confer increased lipid solubility (methyl groups, halogens and substituted and unsubstituted aromatic rings) have been proposed. These derivatives were pulled through the lipid bilayer in molecular dynamics simulations. Molecules 14, 18 and 21 presented lower free energy values than nitrofurantoin when crossing the lipid bilayer.

Spectroscopic and Molecular Docking Studies of the Interaction of Non-steroidal Anti-inflammatory Drugs with a Carrier Protein: an Interesting Case of Inner Filter Effect and Intensity Enhancement in Protein Fluorescence

Interaction of diclofenac and indomethacin with lysozyme was studied using several spectroscopic and molecular docking methods. Difference UV-visible spectra showed that the absorption profile of lysozyme changed when both diclofenac and indomethacin were mixed with the former. The sequential addition of both drugs to the lysozyme solution caused the decrease of the intrinsic fluorescence of the latter, however, when the data were corrected for inner filter effect, an enhancement in the fluorescence of lysozyme was detected. Accordingly, the fluorescence enhancement data were analyzed using Benesi-Hildebrand equation. Both, diclofenac and indomethacin showed good interaction with lysozyme, although, the association constants of indomethacin were nearly two-fold higher as compared to that of diclofenac. The binding was slightly more spontaneous in case of indomethacin and the major forces involved in the binding of both drugs with lysozyme were hydrogen bonding and hydrophobic interactions. Secondary structural analysis revealed that both drugs partially unfolded lysozyme. Results obtained through molecular docking were also in good agreement with the experimental outcomes. Both, diclofenac and indomethacin, are bounded at the same site inside lysozyme which is located in the big hydrophobic cavity of the protein.

Efficient Refinement of Complex Structures of Flexible Histone Peptides Using Post-Docking Molecular Dynamics Protocols

Histones are keys to many epigenetic events and their complexes have therapeutic and diagnostic importance. The determination of the structures of histone complexes is fundamental in the design of new drugs. Computational molecular docking is widely used for the prediction of target-ligand complexes. Large, linear peptides like the tail regions of histones are challenging ligands for docking due to their large conformational flexibility, extensive hydration, and weak interactions with the shallow binding pockets of their reader proteins. Thus, fast docking methods often fail to produce complex structures of such peptide ligands at a level appropriate for drug design. To address this challenge, and improve the structural quality of the docked complexes, post-docking refinement has been applied using various molecular dynamics (MD) approaches. However, a final consensus has not been reached on the desired MD refinement protocol. In this present study, MD refinement strategies were systematically explored on a set of problematic complexes of histone peptide ligands with relatively large errors in their docked geometries. Six protocols were compared that differ in their MD simulation parameters. In all cases, pre-MD hydration of the complex interface regions was applied to avoid the unwanted presence of empty cavities. The best-performing protocol achieved a median of 32% improvement over the docked structures in terms of the change in root mean squared deviations from the experimental references. The influence of structural factors and explicit hydration on the performance of post-docking MD refinements are also discussed to help with their implementation in future methods and applications.

Inhibition of respiratory syncytial virus by Daclatasvir and its derivatives: synthesis of computational derivatives as a new drug development

The most common cause of respiratory tract illness in newborns and young children is the respiratory syncytial virus (RSV). There is no approved vaccination or specific antiviral medication for RSV infections. Here, an attempt has been made to explore the potential of currently marketed drugs as well as their probable derivatives to improve the possibility of developing stronger medications against RSV. From the 100 synthetic drug compounds library, the best drug molecule was identified through drug-likeness properties, toxicity, molecular docking and molecular dynamics simulations. Molecular Mechanics Generalized Born Surface Area (MM-GBSA) was also a method that was applied in this study. Daclatasvir showed the highest binding energy and appeared as the best drug to inhibit matrix protein and a fusion protein of RSV. Based on Daclatasvir, 40 computational derivatives were made. D28, D34 and D40 showed far better results than the actual drug. Changes in lipophilicity character increase the binding energy of derivatives. Molecular dynamic simulations showed their non-deviated, non-fluctuated and stable complex formation with target proteins. The high number of amino acid contacts throughout the trajectory increases the stability and effectiveness of derivatives. The key to producing a novel medicine to eradicate RSV is provided by derivatives. Daclatasvir will be employed as a potential RSV inhibitor up until that point.Communicated by Ramaswamy H. Sarma.

Molecular Dynamics and Other HPC Simulations for Drug Discovery

High performance computing (HPC) is taking an increasingly important place in drug discovery. It makes possible the simulation of complex biochemical systems with high precision in a short time, thanks to the use of sophisticated algorithms. It promotes the advancement of knowledge in fields that are inaccessible or difficult to access through experimentation and it contributes to accelerating the discovery of drugs for unmet medical needs while reducing costs. Herein, we report how computational performance has evolved over the past years, and then we detail three domains where HPC is essential. Molecular dynamics (MD) is commonly used to explore the flexibility of proteins, thus generating a better understanding of different possible approaches to modulate their activity. Modeling and simulation of biopolymer complexes enables the study of protein-protein interactions (PPI) in healthy and disease states, thus helping the identification of targets of pharmacological interest. Virtual screening (VS) also benefits from HPC to predict in a short time, among millions or billions of virtual chemical compounds, the best potential ligands that will be tested in relevant assays to start a rational drug design process.

Quantum mechanical approaches and molecular docking studies of metal based anticancer drugs cis-Diammine glycolato platinum and Diaminocyclohexane oxalatoplatinum structures

The optimized structures and theoretical studies of metal based anticancer drugs Nedaplatin (cis-Diammine glycolato platinum) (CDGP) and Oxaliplatin (Diaminocyclohexane oxalatoplatinum) (DCOP) structures by Density Functional Theory (DFT) method at the B3LYP level with LANL2DZ was applied to investigate the spectroscopic, structural optical properties, conducting properties for the chosen materials, Band Gap was predicted with the help of HOMO-LUMO values. From the calculated parameters of the title compounds CDGP and DCOP shows DCOP found to be highly reactive metal complexed anticancer drug over CDGP. On calculating the site of the lowest binding energy a receptor active site with ligand is done by Ligand-protein docking process. In this study, the difference in the coordinates of ligands and the binding (intermolecular) energy called as Root Mean Square Deviation (RMSD) estimated to estimate the drug-DNA interactions. Molecular docking is the broadly used screening studies in the visualizing of Drug-DNA interaction at an level of atom and drug designing based on structure. Chosen anticancer drugs with their binding affinities to the selected DNA and Drugs potential anticancer behavior were examined, Out of 2 drug compounds screened, CDGP and DCOP, DCOP have shown least binding energies as -6.88 kcal/mol than CDGP as -5.64 kcal/mol and the interactive studies of drug compounds conformations with RMSD values in accordance to crystal structures is less than or equal to 2.00 Å in focusing on the complete DNA structure (large grid box) done using the Autodock software.The compound with most least binding energy value with low inhibition constant found to have good binding affinity towards the DNA structure and the structural properties studied using DFT also shows good chemical reactivity in accordance with the Docking studies.From the results obtained DCOP found to have good chemical descriptors with Good binding affinity than CDGP.Thus DCOP is the best suited for biological molecular target.

Detailed Experimental and In Silico Investigation of Indomethacin Binding with Human Serum Albumin Considering Primary and Secondary Binding Sites

The interaction of indomethacin with human serum albumin (HSA) has been studied here considering the primary and secondary binding sites. The Stern–Volmer plots were linear in the lower concentration range of indomethacin while a downward curvature was observed in the higher concentration range, suggesting the presence of more than one binding site for indomethacin inside HSA due to which the microenvironment of the fluorophore changed slightly and some of its fraction was not accessible to the quencher. The Stern–Volmer quenching constants (KSV) for the primary and secondary sites were calculated from the two linear portions of the Stern–Volmer plots. There was around a two-fold decrease in the quenching constants for the low-affinity site as compared to the primary binding site. The interaction takes place via a static quenching mechanism and the KSV decreases at both primary and secondary sites upon increasing the temperature. The binding constants were also evaluated, which show strong binding at the primary site and fair binding at the secondary site. The binding was thermodynamically favorable with the liberation of heat and the ordering of the system. In principle, hydrogen bonding and Van der Waals forces were involved in the binding at the primary site while the low-affinity site interacted through hydrophobic forces only. The competitive binding was also evaluated using warfarin, ibuprofen, hemin, and a warfarin + hemin combination as site markers. The binding profile remained unchanged in the presence of ibuprofen, whereas it decreased in the presence of both warfarin and hemin with a straight line in the Stern–Volmer plots. The reduction in the binding was at a maximum when both warfarin and hemin were present simultaneously with the downward curvature in the Stern–Volmer plots at higher concentrations of indomethacin. The secondary structure of HSA also changes slightly in the presence of higher concentrations of indomethacin. Molecular dynamics simulations were performed at the primary and secondary binding sites of HSA which are drug site 1 (located in the subdomain IIA of the protein) and the hemin binding site (located in subdomain IB), respectively. From the results obtained from molecular docking and MD simulation, the indomethacin molecule showed more binding affinity towards drug site 1 followed by the other two sites.

Synthesis, molecular docking, and binding Gibbs free energy calculation of β-nitrostyrene derivatives: Potential inhibitors of SARS-CoV-2 3CL protease

The outbreak of novel coronavirus disease 2019 (COVID-19), caused by the novel coronavirus (SARS-CoV-2), has had a significant impact on human health and the economic development. SARS-CoV-2 3CL protease (3CLpro) is highly conserved and plays a key role in mediating the transcription of virus replication. It is an ideal target for the design and screening of anti-coronavirus drugs. In this work, seven β-nitrostyrene derivatives were synthesized by Henry reaction and β-dehydration reaction, and their inhibitory effects on SARS-CoV-2 3CL protease were identified by enzyme activity inhibition assay in vitro. Among them, 4-nitro-β-nitrostyrene (compound a) showed the lowest IC₅₀ values of 0.7297 μM. To investigate the key groups that determine the activity of β-nitrostyrene derivatives and their interaction mode with the receptor, the molecular docking using the CDOCKER protocol in Discovery Studio 2016 was performed. The results showed that the hydrogen bonds between β-NO₂ and receptor GLY-143 and the π-π stacking between the aryl ring of the ligand and the imidazole ring of receptor HIS-41 significantly contributed to the ligand activity. Furthermore, the ligand-receptor absolute binding Gibbs free energies were calculated using the Binding Affinity Tool (BAT.py) to verify its correlation with the activity of β-nitrostyrene 3CLpro inhibitors as a scoring function. The higher correlation(r²=0.6) indicates that the absolute binding Gibbs free energy based on molecular dynamics can be used to predict the activity of new β-nitrostyrene 3CLpro inhibitors. These results provide valuable insights for the functional group-based design, structure optimization and the discovery of high accuracy activity prediction means of anti-COVID-19 lead compounds.

Glucose dissociates DDX21 dimers to regulate mRNA splicing and tissue differentiation

Glucose is a universal bioenergy source; however, its role in controlling protein interactions is unappreciated, as are its actions during differentiation-associated intracellular glucose elevation. Azido-glucose click chemistry identified glucose binding to a variety of RNA binding proteins (RBPs), including the DDX21 RNA helicase, which was found to be essential for epidermal differentiation. Glucose bound the ATP-binding domain of DDX21, altering protein conformation, inhibiting helicase activity, and dissociating DDX21 dimers. Glucose elevation during differentiation was associated with DDX21 re-localization from the nucleolus to the nucleoplasm where DDX21 assembled into larger protein complexes containing RNA splicing factors. DDX21 localized to specific SCUGSDGC motif in mRNA introns in a glucose-dependent manner and promoted the splicing of key pro-differentiation genes, including GRHL3, KLF4, OVOL1, and RBPJ. These findings uncover a biochemical mechanism of action for glucose in modulating the dimerization and function of an RNA helicase essential for tissue differentiation.

Small Molecular Antimicrobial Ligands of YspD are Potential Therapeutic Agents Against Yersinia enterocolitica Infection

YspD is a hydrophilic translocator forming the platform for assemblage of functional translocon. Exposure to the extra-cellular milieu makes YspD a potential therapeutic target. DoGSiteScorer predicted best druggable pocket (P0) within YspD, encompassing predominantly the C-terminal helical bundles and the long helices-9 & 5. COACH metaserver also identified ligand binding residues within the aforementioned druggable pocket mapping to helix-9. Amino acids of helix-9 are involved in oligomerization of YspD. Interaction of helix-9 and parts of C-terminal of YspD with hydrophobic translocator protein (YspB), is essential for translocation of bacterial effectors to initiate an infection. Helices-9 & 5 form an intramolecular coiled-coil structure, required for protein-protein interaction. Targeting intramolecular coiled-coil and parts of C-terminal would be important for functional inactivation of YspD. Solvent exposed surface in YspD, particularly in P0, enhances its accessibility to ligands. Nine small molecular inhibitors of TIIISS were identified and retrieved from ZINC15 database (drug-library) as putative drug candidates. Molecular docking of potential ligands with P0 was done using SwissDock server and Achilles Blind Docking server. Considering the "Significance" threshold of binding score and region of interaction, Salicylidene Acyl Hydrazide derivatives (INP0400) and Phenoxyacetamide derivative (MBX1641) were found to bind effectively with YspD. These potential ligands interact with functional domains of YspD including parts of C-terminal and the intramolecular coiled-coil, which may affect the oligomerization of YspD and disrupt the interaction of YspD with YspB, inhibiting formation of functional translocon. The identified small molecular antimicrobial ligands of YspD could be tested in vivo to attenuate Y. enterocolitica infection by deregulation of Ysa-Ysp TIIISS.

Supplementary Information

The online version contains supplementary material available at 10.1007/s40011-022-01443-2.

Compounds in Indonesian Ginger Rhizome Extracts and Their Potential for Anti-Skin Aging Based on Molecular Docking

Skin aging is a condition caused by reactive oxygen species (ROS) and advanced glycation end products (AGEs). Indonesian gingers (Zingiber officinale), which consists of Gajah (GG), Red (MM), and Emprit (EE) ginger, are thought to produce anti-skin aging compounds through enzyme inhibition. The enzymes used in the molecular docking study were collagenase, hyaluronidase, elastase, and tyrosinase. This study aimed to determine the compounds contained in Indonesian ginger rhizome ethanolic extracts using liquid chromatography–mass spectrometry/mass spectrometry to differentiate metabolites contained in the different Indonesian ginger rhizome extracts. A principal component analysis (PCA) and a heat map analysis were used in order to determine which compounds and extracts contained potential anti-skin aging properties based on a molecular docking study. Ascorbic acid was used as a control ligand in the molecular docking study. Ninety-eight compounds were identified in three different ginger rhizomes extracts and were grouped into three separate quadrants. The most potent compound for anti-skin aging in the Indonesian ginger rhizome extracts was octinoxate. Octinoxate showed a high abundance in the EE ginger rhizome extract. Therefore, the EE ginger extract was the Indonesian ginger rhizome extract with the greatest potential for anti-skin aging.

Meta-Analysis Reveals That Absolute Binding Free-Energy Calculations Approach Chemical Accuracy

Systematic and quantitative analysis of the reliability of formally exact methods that in silico calculate absolute protein-ligand binding free energies remains lacking. Here, we provide, for the first time, evidence-based information on the reliability of these methods by statistically studying 853 cases from 34 different research groups through meta-analysis. The results show that formally exact methods approach chemical accuracy (error = 1.58 kcal/mol), even if people are challenging difficult tasks such as blind drug screening in recent years. The geometrical-pathway-based methods prove to possess a better convergence ability than the alchemical ones, while the latter have a larger application range. We also reveal the importance of always using the latest force fields to guarantee reliability and discuss the pros and cons of turning to an implicit solvent model in absolute binding free-energy calculations. Moreover, based on the meta-analysis, an evidence-based guideline for in silico binding free-energy calculations is provided.

Targeted Proteomic Approaches for Proteome-Wide Characterizations of the AMP-Binding Capacities of Kinases

Kinases play important roles in cell signaling, and adenosine monophosphate (AMP) is known to modulate cellular energy homeostasis through AMP-activated protein kinase (AMPK). Here, we explored novel AMP-binding kinases by employing a desthiobiotin-conjugated AMP acyl-phosphate probe to enrich efficiently AMP-binding proteins. Together with a parallel-reaction monitoring-based targeted proteomic approach, we uncovered 195 candidate AMP-binding kinases. We also enriched desthiobiotin-labeled peptides from adenine nucleotide-binding sites of kinases and analyzed them using LC-MS/MS in the multiple-reaction monitoring mode, which resulted in the identification of 44 peptides derived from 43 kinases displaying comparable or better binding affinities toward AMP relative to adenosine triphosphate (ATP). Moreover, our proteomic data revealed a potential involvement of AMP in the MAPK pathway through binding directly to the relevant kinases, especially MEK2 and MEK3. Together, we revealed the AMP-binding capacities of a large number of kinases, and our work built a strong foundation for understanding how AMP functions as a second messenger to modulate cell signaling.

Molecular docking of DS-3032B, a mouse double minute 2 enzyme antagonist with potential for oncology treatment development

BACKGROUND

It is known that p53 suppression is an important marker of poor prognosis of cancers, especially in solid tumors of the breast, lung, stomach, and esophagus; liposarcomas, glioblastomas, and leukemias. Because p53 has mouse double minute 2 (MDM2) as its primary negative regulator, this molecular docking study seeks to answer the following hypotheses: Is the interaction between DS-3032B and MDM2 stable enough for this drug to be considered as a promising neoplastic inhibitor?

AIM

To analyze, in silico, the chemical bonds between the antagonist DS-3032B and its binding site in MDM2.

METHODS

For molecular docking simulations, the file containing structures of MDM2 (receptor) and the drug DS-3032B (ligand) were selected. The three-dimensional structure of MDM2 was obtained from Protein Data Bank, and the one for DS-3032B was obtained from PubChem database. The location and dimensions of the Grid box was determined using AutoDock Tools software. In this case, the dimensions of the Grid encompassed the entire receptor. The ligand DS-3032B interacts with the MDM2 receptor in a physiological environment with pH 7.4; thus, to simulate more reliably, its interaction was made with the calculation for the prediction of its protonation state using the MarvinSketch^® software. Both ligands, with and without the protonation, were prepared for molecular docking using the AutoDock Tools software. This software detects the torsion points of the drug and calculates the angle of the torsions. Molecular docking simulations were performed using the tools of the AutoDock platform connected to the Vina software. The analyses of the amino acid residues involved in the interactions between the receptor and the ligand as well as the twists of the ligand, atoms involved in the interactions, and type, strength, and length of the interactions were performed using the PyMol software (pymol.org/2) and Discovery Studio from BIOVIA^®.

RESULTS

The global alignment indicated crystal structure 5SWK was more suitable for docking simulations by presenting the p53 binding site. The three-dimensional structure 5SWK for MDM2 was selected from Protein Data Bank and the three-dimensional structure of DS-3032B was selected from PubChem (Compound CID: 73297272; Milademetan). After molecular docking simulations, the most stable conformer was selected for both protonated and non-protonated DS-3032B. The interaction between MDM2 and DS-3032B occurs with high affinity; no significant difference was observed in the affinity energies between the MDM2/pronated DS-3032B (-9.9 kcal/mol) and MDM2/non-protonated DS-3032B conformers (-10.0 kcal/mol). Sixteen amino acid residues of MDM2 are involved in chemical bonds with the protonated DS-3032B; these 16 residues of MDM2 belong to the p53 biding site region and provide high affinity to interaction and stability to drug-protein complex.

CONCLUSION

Molecular docking indicated that DS-3032B antagonist binds to the same region of the p53 binding site in the MDM2 with high affinity and stability, and this suggests therapeutic efficiency.

Potential Therapeutic Targets of Rehmannia Formulations on Diabetic Nephropathy: A Comparative Network Pharmacology Analysis

Background: Previous retrospective cohorts showed that Rehmannia-6 (R-6, Liu-wei-di-huang-wan) formulations were associated with significant kidney function preservation and mortality reduction among chronic kidney disease patients with diabetes. This study aimed to investigate the potential mechanism of action of common R-6 variations in a clinical protocol for diabetic nephropathy (DN) from a system pharmacology approach.

Study Design and Methods: Disease-related genes were retrieved from GeneCards and OMIM by searching “Diabetic Nephropathy” and “Macroalbuminuria”. Variations of R-6 were identified from a published existing clinical practice guideline developed from expert consensus and pilot clinical service program. The chemical compound IDs of each herb were retrieved from TCM-Mesh and PubChem. Drug targets were subsequently revealed via PharmaMapper and UniProtKB. The disease gene interactions were assessed through STRING, and disease–drug protein–protein interaction network was integrated and visualized by Cytoscape. Clusters of disease–drug protein–protein interaction were constructed by Molecular Complex Detection (MCODE) extension. Functional annotation of clusters was analyzed by DAVID and KEGG pathway enrichment. Differences among variations of R-6 were compared. Binding was verified by molecular docking with AutoDock.

Results: Three hundred fifty-eight genes related to DN were identified, forming 11 clusters which corresponded to complement and coagulation cascades and signaling pathways of adipocytokine, TNF, HIF-1, and AMPK. Five variations of R-6 were analyzed. Common putative targets of the R-6 variations on DN included ACE, APOE, CCL2, CRP, EDN1, FN1, HGF, ICAM1, IL10, IL1B, IL6, INS, LEP, MMP9, PTGS2, SERPINE1, and TNF, which are related to regulation of nitric oxide biosynthesis, lipid storage, cellular response to lipopolysaccharide, inflammatory response, NF-kappa B transcription factor activity, smooth muscle cell proliferation, blood pressure, cellular response to interleukin-1, angiogenesis, cell proliferation, peptidyl-tyrosine phosphorylation, and protein kinase B signaling. TNF was identified as the seed for the most significant cluster of all R-6 variations. Targets specific to each formulation were identified. The key chemical compounds of R-6 have good binding ability to the putative protein targets.

Conclusion: The mechanism of action of R-6 on DN is mostly related to the TNF signaling pathway as a core mechanism, involving amelioration of angiogenesis, fibrosis, inflammation, disease susceptibility, and oxidative stress. The putative targets identified could be validated through clinical trials.

Genome sequencing and in silico analysis of isoprene degrading monooxygenase enzymes of Sphingobium sp. BHU LFT2

The whole genome sequencing of a novel isoprene degrading strain of Sphingobium sp. BHU LFT2, its in silico analysis for identifying and characterizing enzymes, especially isoprene monooxygenases (IsoMO), which initiate the degradation process, and in vitro validation with cell extract of optimal temperature and pH and analysis for utilizing isoprene as the preferential substrate, were conducted. The most efficient monooxygenase was identified through comparative analyses using molecular docking followed by molecular dynamics simulation approach. The in silico results revealed high thermostability for most of the monooxygenases. Most potent monooxygenase with locus ID JQK15_20300 exhibiting high sequence similarity with known monooxygenases of isoprene-degrading Rhodococcus sp. LB1 and SC4 strains was identified. Interaction energy of -17.25 kJ/mol for JQK15_20300 with isoprene, was almost similar as that analysed for above-mentioned similar known counterparts, was exhibited by the molecular docking. Molecular dynamic simulation of 100 ns and free energy analysis of JQK15_20300 in the complex with isoprene gave persistent interaction of isoprene with JQK15_20300 during the simulation with high average binding energy of -47.13 kJ/mol thus proving higher affinity of JQK15_20300 for isoprene. The study revealed that the highly efficient isoprene degrading strain of Sphingobium sp. BHU LFT2 having effective monooxygenase could be utilized for large-scale applications including detoxification of air contaminated with isoprene in closed working systems.Communicated by Ramaswamy H. Sarma.

Novel derivatives of arabinogalactan, pullulan & lactobionic acid for targeting asialoglycoprotein receptor: Biomolecular interaction, synthesis & evaluation

Targeted-drug administration to liver reduces side effects by minimising drug distribution to non-target organs and increases therapeutic efficacy by boosting drug concentration in target cells. In this study, arabinogalactan-(AG), pullulan-(PL) and lactobionic acid-(LA) were selected as natural ligands to target asialoglycoprotein receptor-(ASGPR-1) present on hepatocytes. In silico docking studies were performed and binding affinities of novel ligands viz. palmitoylated AG-(PAG), lauroylated AG-(LAG), palmitoylated PL-(PPL), lauroylated PL-(LPL) and lactobionic acid-adipic acid dihydrazide conjugate-(LAD) were compared with AG, PL and LA. These novel ligands were successfully synthesized and characterized. The ligands were incorporated into drug loaded nanostructured lipid carriers-(NLCs) for surface functionalization. HepG2 cellular internalization of hepatocyte-targeted NLCs was studied using fluorescence microscopy and LAD-decorated-drug loaded NLCs giving maximum cellular uptake were studied using confocal microscopy. Toxicity potential of LAD-decorated NLCs was assessed in vivo. Molecular docking results suggested that among the ligands, order of binding affinity was found to be LAD>PAG > PPL > LPL > LAG. Acute toxicity studies revealed hemocompatibility and absence of organ toxicity for ligand LAD. Additionally, the results establish proof-of-concept of enhanced targeting efficacy of novel ASGPR targeting ligands. These ligands can be used for surface modification of nanocarriers for future targeted delivery in treating various liver disorders.

Predicting Diagnostic Potential of Cathepsin in Epithelial Ovarian Cancer: A Design Validated by Computational, Biophysical and Electrochemical Data

Background: Epithelial ovarian cancer remains one of the leading variants of gynecological cancer with a high mortality rate. Feasibility and technical competence for screening and detection of epithelial ovarian cancer remain a major obstacle and the development of point of care diagnostics (POCD) may offer a simple solution for monitoring its progression. Cathepsins have been implicated as biomarkers for cancer progression and metastasis; being a protease, it has an inherent tendency to interact with Cystatin C, a cysteine protease inhibitor. This interaction was assessed for designing a POCD module. Methods: A combinatorial approach encompassing computational, biophysical and electron-transfer kinetics has been used to assess this protease-inhibitor interaction. Results: Calculations predicted two cathepsin candidates, Cathepsin K and Cathepsin L based on their binding energies and structural alignment and both predictions were confirmed experimentally. Differential pulse voltammetry was used to verify the potency of Cathepsin K and Cathepsin L interaction with Cystatin C and assess the selectivity and sensitivity of their electrochemical interactions. Electrochemical measurements indicated selectivity for both the ligands, but with increasing concentrations, there was a marked difference in the sensitivity of the detection. Conclusions: This work validated the utility of dry-lab integration in the wet-lab technique to generate leads for the design of electrochemical diagnostics for epithelial ovarian cancer.

Combining Experimental Isotherms, Minimalistic Simulations, and a Model to Understand and Predict Chemical Adsorption onto Montmorillonite Clays

An attractive approach to minimize human and animal exposures to toxic environmental contaminants is the use of safe and effective sorbent materials to sequester them. Montmorillonite clays have been shown to tightly bind diverse toxic chemicals. Due to their promise as sorbents to mitigate chemical exposures, it is important to understand their function and rapidly screen and predict optimal clay-chemical combinations for further testing. We derived adsorption free-energy values for a structurally and physicochemically diverse set of toxic chemicals using experimental adsorption isotherms performed in the current and previous studies. We studied the diverse set of chemicals using minimalistic MD simulations and showed that their interaction energies with calcium montmorillonite clays calculated using simulation snapshots in combination with their net charge and their corresponding solvent's dielectric constant can be used as inputs to a minimalistic model to predict adsorption free energies in agreement with experiments. Additionally, experiments and computations were used to reveal structural and physicochemical properties associated with chemicals that can be adsorbed to calcium montmorillonite clay. These properties include positively charged groups, phosphine groups, halide-rich moieties, hydrogen bond donor/acceptors, and large, rigid structures. The combined experimental and computational approaches used in this study highlight the importance and potential applicability of analogous methods to study and design novel advanced sorbent systems in the future, broadening their applicability for environmental contaminants.

Cholesterol-lowering activity of natural mono- and sesquiterpenoid compounds in essential oils: A review and investigation of mechanisms using in silico protein-ligand docking

Mono- and sesquiterpenoids are the main chemical constituents of essential oils. Essential oils and their constituents have received increasing attention for lipid-lowering properties in both cell and animal models. Despite the chemical diversity of essential oil compounds, the effects of many of these compounds on cholesterol metabolism are highly similar. In this report, we review the literature regarding the effects of essential oils and their terpenoid constituents on cholesterol homeostasis, and explore likely mechanisms using protein-ligand docking. We identified 98 experimental and seven clinical studies on essential oils, isolated compounds, and blends; 100 of these described improvements either in blood cholesterol levels or in sterol metabolic pathways. Our review and docking analysis confirmed two likely mechanisms common to many essential oil compounds: (1) direct agonism of peroxisome-proliferator-activated receptors, and (2) direct interaction with sterol-sensing domains, motifs found in key sterol regulatory proteins including sterol regulatory element binding protein cleavage activating protein and HMG-CoA reductase. Notably, these direct interactions lead to decreased transcription and accelerated degradation of HMG-CoA reductase. Our work suggests that terpene derivatives in essential oils have cholesterol-lowering activity and could potentially work synergistically with statins, however, further high quality studies are needed to establish their clinical efficacy.

Absolute Binding Free Energy Calculations for Highly Flexible Protein MDM2 and Its Inhibitors

Reliable prediction of binding affinities for ligand-receptor complex has been the primary goal of a structure-based drug design process. In this respect, alchemical methods are evolving as a popular choice to predict the binding affinities for biomolecular complexes. However, the highly flexible protein-ligand systems pose a challenge to the accuracy of binding free energy calculations mostly due to insufficient sampling. Herein, integrated computational protocol combining free energy perturbation based absolute binding free energy calculation with free energy landscape method was proposed for improved prediction of binding free energy for flexible protein-ligand complexes. The proposed method is applied to the dataset of various classes of p53-MDM2 (murine double minute 2) inhibitors. The absolute binding free energy calculations for MDMX (murine double minute X) resulted in a mean absolute error value of 0.816 kcal/mol while it is 3.08 kcal/mol for MDM2, a highly flexible protein compared to MDMX. With the integration of the free energy landscape method, the mean absolute error for MDM2 is improved to 1.95 kcal/mol.

Spectroscopic and Molecular Docking Investigation on the Noncovalent Interaction of Lysozyme with Saffron Constituent "Safranal"

Owing to the various beneficial properties of the popular spice saffron, the interaction of safranal, a secondary metabolite of the former, with hen egg white lysozyme was investigated. The formation of a complex was evidenced by UV-visible spectroscopy. Fluorescence quenching experiments were also performed to understand the binding mechanism and to evaluate the forces involved in binding. The strong absorption of safranal in the range of excitation and emission wavelengths of lysozyme fluorescence required the correction of the inner filter effect for fluorescence spectra to obtain the apparent extent of binding. There was a considerable difference between the observed spectra and corrected spectra, and a similar observation was found in the case of synchronous fluorescence spectra. From the analysis of quenching data, it was found that the mechanism involved in quenching was static with 1:1 binding between them. The interaction was found to be driven, mainly, by hydrophobic forces and hydrogen bonding. Safranal had negligible impact on the secondary structure of lysozyme. The interaction was also studied by molecular docking, and the results were in good agreement with the results obtained experimentally. The binding site of safranal was in the big hydrophobic cavity of lysozyme. The amino acids involved in the interaction were Asp52, Ile58, Gln57, Asn59, Trp62, Trp63, Trp108, Ile98, Asp101, and Ala107.

Improving Protein-Ligand Docking Results with High-Throughput Molecular Dynamics Simulations

Structure-based virtual screening relies on classical scoring functions that often fail to reliably discriminate binders from nonbinders. In this work, we present a high-throughput protein-ligand complex molecular dynamics (MD) simulation that uses the output from AutoDock Vina to improve docking results in distinguishing active from decoy ligands in a directory of useful decoy-enhanced (DUD-E) dataset. MD trajectories are processed by evaluating ligand-binding stability using root-mean-square deviations. We select 56 protein targets (of 7 different protein classes) and 560 ligands (280 actives, 280 decoys) and show 22% improvement in ROC AUC (area under the curve, receiver operating characteristics curve), from an initial value of 0.68 (AutoDock Vina) to a final value of 0.83. The MD simulation demonstrates a robust performance across all seven different protein classes. In addition, some predicted ligand-binding modes are moderately refined during MD simulations. These results systematically validate the reliability of a physics-based approach to evaluate protein-ligand binding interactions.

Molecular Docking: Shifting Paradigms in Drug Discovery

Molecular docking is an established in silico structure-based method widely used in drug discovery. Docking enables the identification of novel compounds of therapeutic interest, predicting ligand-target interactions at a molecular level, or delineating structure-activity relationships (SAR), without knowing a priori the chemical structure of other target modulators. Although it was originally developed to help understanding the mechanisms of molecular recognition between small and large molecules, uses and applications of docking in drug discovery have heavily changed over the last years. In this review, we describe how molecular docking was firstly applied to assist in drug discovery tasks. Then, we illustrate newer and emergent uses and applications of docking, including prediction of adverse effects, polypharmacology, drug repurposing, and target fishing and profiling, discussing also future applications and further potential of this technique when combined with emergent techniques, such as artificial intelligence.

Potential Application of Alchemical Free Energy Simulations to Discriminate GPCR Ligand Efficacy

G protein-coupled receptors (GPCRs) play fundamental roles in physiological processes by modulating diverse signaling pathways and thus have been one of the most important drug targets. Based on the fact that GPCR-mediated signaling is modulated in a ligand-specific manner such as agonist, inverse agonist, and neutral antagonist (termed ligand efficacy), quantitative characterization of the ligand efficacy is essential for rational design of selective modulators for GPCR targets. As experimental approaches for this purpose are time-, cost-, and labor-intensive, computational tools that can systematically predict GPCR ligand efficacy can have a big impact on GPCR drug design. Here, we have performed free energy perturbation molecular dynamics simulations to calculate absolute binding free energy of an inverse agonist, a neutral antagonist, and an agonist to β2-adrenergic receptor (β2-AR) active and inactive states, respectively, in explicit lipid bilayers. Relatively short alchemical free energy calculations reveal that both the time-series of the total binding free energy and decomposed energy contributions can be used as relevant physical properties to discriminate β2-AR ligand efficacy. This study illustrates a merit of the current approach over simple, fast docking calculations or highly expensive millisecond-time scale simulations.

Medicinal Chemistry Strategies to Disrupt the p53-MDM2/MDMX Interaction

The growth inhibitory activity of p53 tumor suppressor is tightly regulated by interaction with two negative regulatory proteins, murine double minute 2 (MDM2) and X (MDMX), which are overexpressed in about half of all human tumors. The elucidation of crystallographic structures of MDM2/MDMX complexes with p53 has been pivotal for the identification of several classes of inhibitors of the p53-MDM2/MDMX interaction. The present review provides in silico strategies and screening approaches used in drug discovery as well as an overview of the most relevant classes of small-molecule inhibitors of the p53-MDM2/MDMX interaction, their progress in pipeline, and highlights particularities of each class of inhibitors. Most of the progress made with high-throughput screening has led to the development of inhibitors belonging to the cis-imidazoline, piperidinone, and spiro-oxindole series. However, novel potent and selective classes of inhibitors of the p53-MDM2 interaction with promising antitumor activity are emerging. Even with the discovery of the 3D structure of complex p53-MDMX, only two small molecules were reported as selective p53-MDMX antagonists, WK298 and SJ-172550. Dual inhibition of the p53-MDM2/MDMX interaction has shown to be an alternative approach since it results in full activation of the p53-dependent pathway. The knowledge of structural requirements crucial to the development of small-molecule inhibitors of the p53-MDMs interactions has enabled the identification of novel antitumor agents with improved in vivo efficacy.

CHARMM-GUI PDB manipulator for advanced modeling and simulations of proteins containing nonstandard residues

CHARMM-GUI, http://www.charmm-gui.org, is a web-based graphical user interface to prepare molecular simulation systems and input files to facilitate the usage of common and advanced simulation techniques. Since it is originally developed in 2006, CHARMM-GUI has been widely adopted for various purposes and now contains a number of different modules designed to setup a broad range of simulations including free energy calculation and large-scale coarse-grained representation. Here, we describe functionalities that have recently been integrated into CHARMM-GUI PDB Manipulator, such as ligand force field generation, incorporation of methanethiosulfonate spin labels and chemical modifiers, and substitution of amino acids with unnatural amino acids. These new features are expected to be useful in advanced biomolecular modeling and simulation of proteins.

CHARMM-GUI Ligand Binder for absolute binding free energy calculations and its application

Advanced free energy perturbation molecular dynamics (FEP/MD) simulation methods are available to accurately calculate absolute binding free energies of protein-ligand complexes. However, these methods rely on several sophisticated command scripts implementing various biasing energy restraints to enhance the convergence of the FEP/MD calculations, which must all be handled properly to yield correct results. Here, we present a user-friendly Web interface, CHARMM-GUI Ligand Binder ( http://www.charmm-gui.org/input/gbinding ), to provide standardized CHARMM input files for calculations of absolute binding free energies using the FEP/MD simulations. A number of features are implemented to conveniently set up the FEP/MD simulations in highly customizable manners, thereby permitting an accelerated throughput of this important class of computations while decreasing the possibility of human errors. The interface and a series of input files generated by the interface are tested with illustrative calculations of absolute binding free energies of three nonpolar aromatic ligands to the L99A mutant of T4 lysozyme and three FK506-related ligands to FKBP12. Statistical errors within individual calculations are found to be small (~1 kcal/mol), and the calculated binding free energies generally agree well with the experimental measurements and the previous computational studies (within ~2 kcal/mol). Therefore, CHARMM-GUI Ligand Binder provides a convenient and reliable way to set up the ligand binding free energy calculations and can be applicable to pharmaceutically important protein-ligand systems.