Ligand-Guided Investigation of a Series of Formamidine-Based Thiuram Disulfides as Potential Dual-Inhibitors of COX-1and COX-2

A series of thiuram disulfides 1–6 which had been previously synthesized and characterized,^[1] were studied for their potential therapeutic properties. Target-fishing analyses through HitPick and SwissTarget prediction identified COX1 and COX2, which are essential biomolecules in cancer-related inflammations, as the possible targets for compounds 1 and 4 among all the compounds tested. These two proteins have enjoyed interest as targets for treating some neoplastic cancer types such as breast, colorectal, skin, pancreatic, haematological and head cancers. The inhibitory potency of 1 and 4 as lead anticancer drug candidates with dual-target ability against COX1 and COX2 was examined through molecular docking, molecular dynamics simulation and post-MD analyses such as binding energy calculation, RMSD, RMSF, and RoG. The two compounds had better docking scores and binding energies than the known inhibitors of COX1 and COX2. Insights from the RMSD, RMSF, and RoG suggested that both 1 and 4 showed observable influence on the structural stability of these targets throughout the simulation. The reported observations of the effects of 1 and 4 on the structures of COX1 and COX2 indicate their probable inhibitory properties against these target proteins and their potential as lead anticancer drug candidates.     

Identification of selective MMP-9 inhibitors through multiple e-pharmacophore,ligand-based pharmacophore,molecular docking,and density functional theory approaches

Matrix metalloproteinase-9 (MMP-9) is a significant target for the development of drugs for the treatment of arthritis, CNS disorders, and cancer metastasis. The structure-based and ligand-based methods were used for the virtual screening (VS) of database compounds to obtain potent and selective MMP-9 inhibitors. Experimentally known MMP-9 inhibitors were used to grow up ligand-based three pharmacophore models utilizing Schrodinger suite. The X-ray crystallographic structures of MMP-9 with different inhibitors were used to develop five energy-optimized structure-based (e-pharmacophore) models. All developed pharmacophores were validated and applied to screen the Zinc database. Pharmacophore matched compounds were subjected to molecular docking to retrieve hits with novel scaffolds. The molecules with diverse structures, high docking scores and low binding energies for various crystal structures of MMP-9, were selected as final hits. The Induced fit docking (IFD) analysis provided significant information about the driving of inhibitor to approve a suitable bioactive conformational position in the active site of protein. Since charge transfer reaction occurs during receptor–ligand interaction, therefore, electronic features of hits (ligands) are interesting parameters to explain the binding interactions. Density functional theory (DFT) at B3LYP/6-31G* level was utilized to explore electronic features of hits. The docking study of hits using AutoDock was helpful to establish the binding interactions. The study illustrates that the combined pharmacophore approach is advantageous to identify diverse hits which have better binding affinity to the active site of the enzyme for all possible bioactive conformations. The approach used in the study is worthy to design drugs for other targets.     

Rapid protein-ligand docking using soft modes from molecular dynamics simulations to account for protein deformability: binding of FK506 to FKBP

Most current docking methods to identify possible ligands and putative binding sites on a receptor molecule assume a rigid receptor structure to allow virtual screening of large ligand databases. However, binding of a ligand can lead to changes in the receptor protein conformation that are sterically necessary to accommodate a bound ligand. An approach is presented that allows relaxation of the protein conformation in precalculated soft flexible degrees of freedom during ligand-receptor docking. For the immunosuppressant FK506-binding protein FKBP, the soft flexible modes are extracted as principal components of motion from a molecular dynamics simulation. A simple penalty function for deformations in the soft flexible mode is used to limit receptor protein deformations during docking that avoids a costly recalculation of the receptor energy by summing over all receptor atom pairs at each step. Rigid docking of the FK506 ligand binding to an unbound FKBP conformation failed to identify a geometry close to experiment as favorable binding site. In contrast, inclusion of the flexible soft modes during systematic docking runs selected a binding geometry close to experiment as lowest energy conformation. This has been achieved at a modest increase of computational cost compared to rigid docking. The approach could provide a computationally efficient way to approximately account for receptor flexibility during docking of large numbers of putative ligands and putative docking geometries.     

Scoring optimisation of unbound protein-protein docking including protein binding site predictions

The prediction of the structure of the protein-protein complex is of great importance to better understand molecular recognition processes. During systematic protein-protein docking, the surface of a protein molecule is scanned for putative binding sites of a partner protein. The possibility to include external data based on either experiments or bioinformatic predictions on putative binding sites during docking has been systematically explored. The external data were included during docking with a coarse-grained protein model and on the basis of force field weights to bias the docking search towards a predicted or known binding region. The approach was tested on a large set of protein partners in unbound conformations. The significant improvement of the docking performance was found if reliable data on the native binding sites were available. This was possible even if data for single key amino acids at a binding interface are included. In case of binding site predictions with limited accuracy, only modest improvement compared with unbiased docking was found. The optimisation of the protocol to bias the search towards predicted binding sites was found to further improve the docking performance resulting in approximately 40% acceptable solutions within the top 10 docking predictions compared with 22% in case of unbiased docking of unbound protein structures.     

Expanding GPCR homology model binding sites via a balloon potential: A molecular dynamics refinement approach

Homology modeling of G protein-coupled receptors is becoming a widely used tool in drug discovery. However, unrefined models built using the bovine rhodopsin crystal structure as the template, often have binding sites that are too small to accommodate known ligands. Here, we present a novel systematic method to refine model active sites based on a pressure-guided molecular dynamics simulation. A distinct advantage of this approach is the ability to introduce systematic perturbations in model backbone atoms in addition to side chain adjustments. The method is validated on two test cases: (1) docking of retinal into an MD-relaxed structure of opsin and (2) docking of known ligands into a homology model of the CCR2 receptor. In both cases, we show that the MD expansion algorithm makes it possible to dock the ligands in poses that agree with the crystal structure or mutagenesis data.     

Improving accuracy and efficiency of blind protein-ligand docking by focusing on predicted binding sites

The use of predicted binding sites (binding sites calculated from the protein structure alone) is evaluated here as a tool to focus the docking of small molecule ligands into protein structures, simulating cases where the real binding sites are unknown. The resulting approach consists of a few independent docking runs carried out on small boxes, centered on the predicted binding sites, as opposed to one larger blind docking run that covers the complete protein structure. The focused and blind approaches were compared using a set of 77 known protein-ligand complexes and 19 ligand-free structures. The focused approach is shown to: (1) identify the correct binding site more frequently than blind docking; (2) produce more accurate docking poses for the ligand; (3) require less computational time. Additionally, the results show that very few real binding sites are missed in spite of focusing on only three predicted binding sites per target protein. Overall the results indicate that, by improving the sampling in regions that are likely to correspond to binding sites, the focused docking approach increases accuracy and efficiency of protein ligand docking for those cases where the ligand-binding site is unknown. This is especially relevant in applications such as reverse virtual screening and structure-based functional annotation of proteins.     

Ligand binding to anti-cancer target CD44 investigated by molecular simulations

CD44 is a cell-surface glycoprotein and receptor for hyaluronan, one of the major components of the tumor extracellular matrix. There is evidence that the interaction between CD44 and hyaluronan promotes breast cancer metastasis. Recently, the molecule F-19848A was shown to inhibit hyaluronan binding to receptor CD44 in a cell-based assay. In this study, we investigated the mechanism and energetics of F-19848A binding to CD44 using molecular simulation. Using the molecular mechanics/Poisson Boltzmann surface area (MM-PBSA) method, we obtained the binding free energy and inhibition constant of the complex. The van der Waals (vdW) interaction and the extended portion of F-19848A play key roles in the binding affinity. We screened natural products from a traditional Chinese medicine database to search for CD44 inhibitors. From combining pharmaceutical requirements with docking and molecular dynamics simulations, we found ten compounds that are potentially better or equal to the F-19848A ligand at binding to CD44 receptor. Therefore, we have identified new candidates of CD44 inhibitors, based on molecular simulation, which may be effective small molecules for the therapy of breast cancer.     

Novel allosteric sites on Ras for lead generation

Aberrant Ras activity is a hallmark of diverse cancers and developmental diseases. Unfortunately, conventional efforts to develop effective small molecule Ras inhibitors have met with limited success. We have developed a novel multi-level computational approach to discover potential inhibitors of previously uncharacterized allosteric sites. Our approach couples bioinformatics analysis, advanced molecular simulations, ensemble docking and initial experimental testing of potential inhibitors. Molecular dynamics simulation highlighted conserved allosteric coupling of the nucleotide-binding switch region with distal regions, including loop 7 and helix 5. Bioinformatics methods identified novel transient small molecule binding pockets close to these regions and in the vicinity of the conformationally responsive switch region. Candidate binders for these pockets were selected through ensemble docking of ZINC and NCI compound libraries. Finally, cell-based assays confirmed our hypothesis that the chosen binders can inhibit the downstream signaling activity of Ras. We thus propose that the predicted allosteric sites are viable targets for the development and optimization of new drugs.     

Homology modeling, molecular docking and electrostatic potential analysis of MurF ligase from Klebsiella pneumonia

In spite of availability of moderately protective vaccine and antibiotics, new antibacterial agents are urgently needed to decrease the global incidence of Klebsiella pneumonia infections. MurF ligase, a key enzyme, which participates in the bacterial cell wall assembly, is indispensable to existence of K. pneumonia. MurF ligase lack mammalian vis-à-vis and have high specificity, uniqueness, and occurrence only in eubacteria, epitomizing them as promising therapeutic targets for intervention. In this study, we present a unified approach involving homology modeling and molecular docking studies on MurF ligase enzyme. As part of this study, a homology model of K. pneumonia (MurF ligase) enzyme was predicted for the first time in order to carry out structurebased drug design. The accuracy of the model was further validated using different computational approaches. The comparative molecular docking study on this enzyme was undertaken using different phyto-ligands from Desmodium sp. and a known antibiotic Ciprofloxacin. The docking analysis indicated the importance of hotspots (HIS 281 and ASN 282) within the MurF binding pocket. The Lipinski's rule of five was analyzed for all ligands considered for this study by calculating the ADME/Tox, drug likeliness using Qikprop simulation. Only ten ligands were found to comply with the Lipinski rule of five. Based on the molecular docking results and Lipinki values 6-Methyltetrapterol A was confirmed as a promising lead compound. The present study should therefore play a guiding role in the experimental design and development of 6-Methyltetrapterol A as a bactericidal agent.     

Differential activities of the two closely related withanolides, withaferin a and withanone: bioinformatics and experimental evidences

BACKGROUND AND PURPOSE: Withanolides are naturally occurring chemical compounds. They are secondary metabolites produced via oxidation of steroids and structurally consist of a steroid-backbone bound to a lactone or its derivatives. They are known to protect plants against herbivores and have medicinal value including anti-inflammation, anti-cancer, adaptogenic and anti-oxidant effects. Withaferin A (Wi-A) and Withanone (Wi-N) are two structurally similar withanolides isolated from Withania somnifera, also known as Ashwagandha in Indian Ayurvedic medicine. Ashwagandha alcoholic leaf extract (i-Extract), rich in Wi-N, was shown to kill cancer cells selectively. Furthermore, the two closely related purified phytochemicals, Wi-A and Wi-N, showed differential activity in normal and cancer human cells in vitro and in vivo. We had earlier identified several genes involved in cytotoxicity of i-Extract in human cancer cells by loss-of-function assays using either siRNA or randomized ribozyme library. METHODOLOGY/PRINCIPAL FINDINGS: In the present study, we have employed bioinformatics tools on four genes, i.e., mortalin, p53, p21 and Nrf2, identified by loss-of-function screenings. We examined the docking efficacy of Wi-N and Wi-A to each of the four targets and found that the two closely related phytochemicals have differential binding properties to the selected cellular targets that can potentially instigate differential molecular effects. We validated these findings by undertaking parallel experiments on specific gene responses to either Wi-N or Wi-A in human normal and cancer cells. We demonstrate that Wi-A that binds strongly to the selected targets acts as a strong cytotoxic agent both for normal and cancer cells. Wi-N, on the other hand, has a weak binding to the targets; it showed milder cytotoxicity towards cancer cells and was safe for normal cells. The present molecular docking analyses and experimental evidence revealed important insights to the use of Wi-A and Wi-N for cancer treatment and development of new anti-cancer phytochemical cocktails.     

Discrete molecular dynamics distinguishes nativelike binding poses from decoys in difficult targets

Virtual screening is one of the major tools used in computer-aided drug discovery. In structure-based virtual screening, the scoring function is critical to identifying the correct docking pose and accurately predicting the binding affinities of compounds. However, the performance of existing scoring functions has been shown to be uneven for different targets, and some important drug targets have proven especially challenging. In these targets, scoring functions cannot accurately identify the native or near-native binding pose of the ligand from among decoy poses, which affects both the accuracy of the binding affinity prediction and the ability of virtual screening to identify true binders in chemical libraries. Here, we present an approach to discriminating native poses from decoys in difficult targets for which several scoring functions failed to correctly identify the native pose. Our approach employs Discrete Molecular Dynamics simulations to incorporate protein-ligand dynamics and the entropic effects of binding. We analyze a collection of poses generated by docking and find that the residence time of the ligand in the native and nativelike binding poses is distinctly longer than that in decoy poses. This finding suggests that molecular simulations offer a unique approach to distinguishing the native (or nativelike) binding pose from decoy poses that cannot be distinguished using scoring functions that evaluate static structures. The success of our method emphasizes the importance of protein-ligand dynamics in the accurate determination of the binding pose, an aspect that is not addressed in typical docking and scoring protocols.     

An integrated approach of network-based systems biology,molecular docking,and molecular dynamics approach to unravel the role of existing antiviral molecules against AIDS-associated cancer

A serious challenge in cancer treatment is to reposition the activity of various already known drug candidates against cancer. There is a need to rewrite and systematically analyze the detailed mechanistic aspect of cellular networks to gain insight into the novel role played by various molecules. Most Human Immunodeficiency Virus infection-associated cancers are caused by oncogenic viruses like Human Papilloma Viruses and Epstein–Bar Virus. As the onset of AIDS-associated cancers marks the severity of AIDS, there might be possible interconnections between the targets and mechanism of both the diseases. We have explored the possibility of certain antiviral compounds to act against major AIDS-associated cancers: Kaposi’s Sarcoma, Non-Hodgkin Lymphoma, and Cervical Cancer with the help of systems pharmacology approach that includes screening for targets and molecules through the construction of a series of drug–target and drug–target–diseases network. Two molecules (Calanolide A and Chaetochromin B) and the target “HRAS” were finally screened with the help of molecular docking and molecular dynamics simulation. The results provide novel antiviral molecules against HRAS target to treat AIDS defining cancers and an insight for understanding the pharmacological, therapeutic aspects of similar unexplored molecules against various cancers.     

Molecular dynamics simulations reveal structural insights into inhibitor binding modes and mechanism of casein kinase II inhibitors

Casein kinase-II, a member of protein kinase family, plays significant role in different cellular processes such as cell growth, differentiation, proliferation, gene expression, and embryogenesis. Being a potent suppressor of apoptosis, it serves as a significant link for its association with various types of malignancies such as colorectal and breast cancer. To overcome its pathological role in various cancerous diseases, CK-II procures great consideration as a therapeutic target. This study aimed at understanding the binding mechanism and structural properties of benzimidazole derivatives by utilizing various computational tools including docking simulation, three-dimensional quantitative structure activity relationships and molecular dynamic simulation. Structure-based 3D-QSAR techniques such as CoMFA and CoMSIA models, were established from the conformations gained by protein–ligand docking approach. The attained models have showed a good extrapolative power for internal as well as external validation. Moreover, MD simulation was carried out to explain the detailed binding mechanism and the comparison of inhibitor’s binding mode with diverse biological activities. A good correlation was observed among docking studies, MD results, and contour map analysis. Interestingly new molecules were designed using detail structural information from MD simulation, showed higher potency of inhibition (pIC₅₀ 7.6–7.7) compare to the most active compound of the series.     

Plant derived anti-cancerous secondary metabolites as multipronged inhibitor of COX,Topo, and aromatase: molecular modeling and dynamics simulation analyses

In the present study, 300 plant derived secondary metabolites (100 each of alkaloid, flavonoid, and terpenoid), have been screened for their anti-cancerous activity through inhibition of selected key enzymatic targets, namely cyclooxygenases (COXs), topoisomerases (Topos), and aromatase by molecular docking approach. Furthermore, the stability of the complexes of top hits, from each class of secondary metabolites, with their respective enzymatic targets was analyzed using molecular dynamics (MD) simulation analyses and binding free energy calculations. Analysis of the results of the docking in light of the pharmacokinetically screened 18 alkaloids, 26 flavonoids, and 9 terpenoids, revealed that the flavonoid, curcumin, was the most potent inhibitor for all the selected enzymatic targets. The stability of the complexes of COX-1, COX-2, Topo I, Topo IIβ and aromatase with the most potent inhibitor curcumin and those of the respective drugs, namely ibuprofen, aspirin, topotecan, etoposide, and exemestane were also analyzed through MD simulation analyses which revealed better stability of curcumin complexes than those of respective drugs. Binding energy calculations of the complexes of the curcumin with all the targets, except those of Topos, exhibited lower binding energies for the curcumin complexes than those of respective drugs which corroborated with the results of molecular docking analyses. Thus, the present study affirms the versatile and multipronged nature of curcumin, the traditionally used herbal medicine, as anti-cancer molecule directed against these enzymatic targets.     

Computational screening,ensemble docking and pharmacophore analysis of potential gefitinib analogues against epidermal growth factor receptor

The observable mutated isoforms of epidermal growth factor receptor (EGFR) are important considerable therapeutic benchmarks in moderating the non-small cell lung cancer (NSCLC). Recently, quinazoline-based ATP competitive inhibitors have been developed against the EGFR; however, these imply the mutation probabilities, which contribute to the discovery of high probable novel inhibitors for EGFR mutants. Therefore, SAR-based bioactivity analysis, molecular docking and computational toxicogenomics approaches were performed to identify and evaluate new analogs of gefitinib against the ligand-binding domain of the EGFR double-mutated model. From the diverse groups of molecular clustering and molecular screening strategies, top high-binding gefitinib-analogues were identified and studied against EGFR core cavity through three-phase ensemble docking approach. Resulted high possible leads showed good binding orientations than gefitinib (positive control) thus they were subjected to pharmacophore analysis that possesses possible molecular assets to tight binding with EGFR domain. Residues Ser720, Arg841 and Trp880 were observed as novel hot spots and involved in H-bonds, pi-stacking and π-cation interactions that contribute additional electrostatic potency to sustain stability and complexity of protein-ligand complexes, thus they have ability to profoundly adopted by pharmacophoric features. Furthermore, lead molecules have an inhibition percent probability, anticancer potency, toxic impacts, flexible pharmacokinetics, potential gene-chemical interactions towards EGFR were revealed by computational systems biology tools. Our multiple screening strategies confirmed that the druggable sub-pocket was crucial to strong EGFR-ligand binding. The essential pharmacophoric features of ligands provided viewpoints for new inhibitors envisaging, and predicted scaffolds could used as anticancer agents against selected EGFR mutated isoforms.     

Modulation of interaction of mutant TP53 and wild type BRCA1 by alkaloids: a computational approach towards targeting protein-protein interaction as a futuristic therapeutic intervention strategy for breast cancer impediment

Protein–protein interactions (PPI) are a new emerging class of novel therapeutic targets. In order to probe these interactions, computational tools provide a convenient and quick method towards the development of therapeutics. Keeping this in view the present study was initiated to analyse interaction of tumour suppressor protein p53 (TP53) and breast cancer associated protein (BRCA1) as promising target against breast cancer. Using computational approaches such as protein–protein docking, hot spot analyses, molecular docking and molecular dynamics simulation (MDS), stepwise analyses of the interactions of the wild type and mutant TP53 with that of wild type BRCA1 and their modulation by alkaloids were done. Protein–protein docking method was used to generate both wild type and mutant complexes of TP53-BRCA1. Subsequently, the complexes were docked using sixteen different alkaloids, fulfilling ADMET and Lipinski’s rule of five criteria, and were compared with that of a well-known inhibitor of PPI, namely nutlin. The alkaloid dicentrine was found to be the best docked alkaloid among all the docked alklaloids as well as that of nutlin. Furthermore, MDS analyses of both wild type and mutant complexes with the best docked alkaloid i.e. dicentrine, revealed higher stability of mutant complex than that of the wild one, in terms of average RMSD, RMSF and binding free energy, corroborating the results of docking. Results suggested more pronounced interaction of BRCA1 with mutant TP53 leading to increased expression of mutated TP53 thus showing a dominant negative gain of function and hampering wild type TP53 function leading to tumour progression.     

Transient pockets on XIAP-BIR2: toward the characterization of putative binding sites of small-molecule XIAP inhibitors

Protein-protein interactions are abundant in signal transduction pathways and thus of crucial importance in the regulation of apoptosis. However, designing small-molecule inhibitors for these potential drug targets is very challenging as such proteins often lack well-defined binding pockets. An example for such an interaction is the binding of the anti-apoptotic BIR2 domain of XIAP to the pro-apoptotic caspase-3 that results in the survival of damaged cells. Although small-molecule inhibitors of this interaction have been identified, their exact binding sites on XIAP are not known as its crystal structures reveal no suitable pockets. Here, we apply our previously developed protocol for identifying transient binding pockets to XIAP-BIR2. Transient pockets were identified in snapshots taken during four different molecular dynamics simulations that started from the caspase-3:BIR2 complex or from the unbound BIR2 structure and used water or methanol as solvent. Clustering of these pockets revealed that surprisingly many pockets opened in the flexible linker region that is involved in caspase-3 binding. We docked three known inhibitors into these transient pockets and so determined five putative binding sites. In addition, by docking two inactive compounds of the same series, we show that this protocol is also able to distinguish between binders and nonbinders which was not possible when docking to the crystal structures. These findings represent a first step toward the understanding of the binding of small-molecule XIAP-BIR2 inhibitors on a molecular level and further highlight the importance of considering protein flexibility when designing small-molecule protein-protein interaction inhibitors.     

Molecular dynamics simulations show how the FMRP Ile304Asn mutation destabilizes the KH2 domain structure and affects its function

Mutations or deletions of FMRP, involved in the regulation of mRNA metabolism in brain, lead to the Fragile X syndrome (FXS), the most frequent form of inherited intellectual disability. A severe manifestation of the disease has been associated with the Ile304Asn mutation, located on the KH2 domain of the protein. Several hypotheses have been proposed to explain the possible molecular mechanism responsible for the drastic effect of this mutation in humans. Here, we performed a molecular dynamics simulation and show that the Ile304Asn mutation destabilizes the hydrophobic core producing a partial unfolding of two α-helices and a displacement of a third one. The affected regions show increased residue flexibility and motion. Molecular docking analysis revealed strongly reduced binding to a model single-stranded nucleic acid in agreement with known data that the two partially unfolded helices form the RNA-binding surface. The third helix, which we show here to be also affected, is involved in the PAK1 protein interaction. These two functional binding sites on the KH2 domain do not overlap spatially, and therefore, they can simultaneously bind their targets. Since the Ile304Asn mutation affects both binding sites, this may justify the severe clinical manifestation observed in the patient in which both mRNA metabolism activity and cytoskeleton remodeling would be affected.     

Evaluation of binding and antagonism/downregulation of brilanestrant molecule in estrogen receptor-α via quantum mechanics/molecular mechanics,molecular dynamics and binding free energy calculations

Abstract

The resistance to the endocrine therapy of breast cancer leads to the emergence of new class of drugs that downregulates the estrogen receptor action known as selective estrogen receptor downregulators (SERDs). The first approved SERD is fluvestrant; after this, there are several downregulators evolved and are in clinical trials, in which the brilanestrant (BRI) molecule shows nM range of binding affinity and efficacy. In the present study, to understand the binding nature of BRI molecule in the active site of ERα, the molecular docking analysis has been performed. Further, the QM/MM calculations were performed for the BRI–ERα complex to analyze the charge density distribution of intermolecular interactions. The molecular dynamics (MD) simulation was employed to understand the stability and binding mechanism of BRI molecule through root-mean-square deviation (RMSD), root-mean-square fluctuation (RMSF) and binding free energy calculations. From the MD simulation trajectory analysis, the alterations of Helix12 conformation and the key residue (Lys529), which is responsible for the ERα downregulation, have been identified. Further, the interaction between the H3 and H12 regions was identified for the antagonism of BRI molecule. The current study led us to understand the binding mechanism, antagonism and downregulation of BRI molecule, and this knowledge is essential to design novel SERDs for the treatment of endocrine-resistant positive breast cancer.

Communicated by Ramaswamy H. Sarma