Ligand-target Interactions Research Articles

Analisis Docking Molekuler Senyawa Derivat Phthalimide sebagai Inhibitor Non-Nukleosida HIV-1 Reverse Transcriptase

Senyawa derivat phthalimide dilaporkan sebagai kelas baru inhibitor nonnukleosida reverse transcriptase. Analisis docking molekuler senyawa derivat phthalimide terhadap enzim reverse transcriptase diperlukan untuk mengetahui afinitas dan pola interaksi antara senyawa di atas dengan enzim reverse transcriptase.
 Senyawa derivat phthalimide dioptimasi geometri menggunakan perangkat lunak VegaZZ selanjutnya dilakukan dengan cara preparasi target, preparasi ligan, validasi metode docking, dan analisis docking menggunakan PyRx-Python 0.8 - AutoDock Vina sehingga didapatkan interaksi ligan dengan target, energi bebas pengikatan, ikatan hidrogen, dan pola interaksi. Pola interaksi dilihat dari tiga puluh tiga senyawa derivat phthalimide dengan enzim reverse transcriptase menunjukkan ikatan hidrogen dengan asam amino Lys101 dimana interaksi tersebut mirip dengan interaksi senyawa TIBO R 86183 yang merupakan ligan asli protein target.

Truly Target-Focused Pharmacophore Modeling: A Novel Tool for Mapping Intermolecular Surfaces.

Pharmacophore models are an accurate and minimal tridimensional abstraction of intermolecular interactions between chemical structures, usually derived from a group of molecules or from a ligand-target complex. Only a limited amount of solutions exists to model comprehensive pharmacophores using the information of a particular target structure without knowledge of any binding ligand. In this work, an automated and customable tool for truly target-focused (T²F) pharmacophore modeling is introduced. Key molecular interaction fields of a macromolecular structure are calculated using the AutoGRID energy functions. The most relevant points are selected by a newly developed filtering cascade and clustered to pharmacophore features with a density-based algorithm. Using five different protein classes, the ability of this method to identify essential pharmacophore features was compared to structure-based pharmacophores derived from ligand-target interactions. This method represents an extremely valuable instrument for drug design in a situation of scarce ligand information available, but also in the case of underexplored therapeutic targets, as well as to investigate protein allosteric pockets and protein-protein interactions.

Probing specific ligand-protein interactions by native-denatured exchange mass spectrometry

Probing ligand-target protein interactions provides essential information for deep understanding of biochemical machinery and design of drug screening assays. Native electrospray ionization-mass spectrometry (ESI-MS) is promising for direct analysis of ligand-protein complexes. However, it lacks the ability to distinguish between specific and non-specific ligand-protein interactions, and to further recognize the specifically bound proteins as drug target candidates, which remains as a major challenge in the field of drug developments by far. Herein we report a native-denatured exchange (NDX) mass spectrometry (MS) acquisition approach using a liquid sample-desorption electrospray ionization (LS-DESI) setup, and demonstrate its capability in enabling a change from native detection of noncovalent ligand-protein complexes to denatured analysis using three model ligand-protein complexes including myoglobin, CDP-ribonuclease and N,N′,N″-triacetylchitotriose (NAG3)-lysozyme. Notably, we found the NDX-MS approach can readily discriminate specific ligand-protein interactions from nonspecific ones, as revealed by their distinct dynamic profiles of Kd as a function of the DESI spraying flow rate. Consequently, this NDX-MS approach holds promise for future applications to discovering specific protein targets for ligands of interest, and to screening compounds with high specificity to drug targets and thus eliminates off-target effects.

Lost in translation: What's in an EC50? Innovative PK/PD reasoning in the drug development context

Translation across species and from in vitro to in vivo is a central tenet in drug discovery pharmacology. Successful implementation requires proper assessment of both in vivo potency and efficacy. This notwithstanding, in vivo data is typically defined mostly in terms of ligand-to-target binding affinity, similar to in vitro studies. As in vivo potency and efficacy involve a combination not only of drug, but also partitioning, target, and drug-target-complex events and processes, ignoring some of the central differences between in vivo and in vitro may result in serious miscalculations of in vivo efficacious exposure for translational predictions.We compare potency measures derived from two basic pharmacodynamic model situations: A ‘closed’ in vitro system defining target binding of a ligand when both concentrations remain essentially static, and an ‘open’ in vivo system where target turnover dynamics and elimination of the drug-target complex are also included. Corresponding equilibrium (steady-state) expressions in the central pharmacokinetic compartment are derived and presented. Three representative variants of ‘open’ in vivo systems are discussed, showing relationships for ligand-target complex and ligand for each of the systems and graphically illustrating corresponding shapes. The examples include i) two ligands competing for one target, ii) two targets competing for one ligand (/drug), and iii) target-ligand (/drug) interactions in a peripheral PK compartment. The expanded in vivo potency EC50 expression emphasises the contribution from target-related biology parameters that need accounting for, and particularly that ‘closed’ system (in vitro) properties should not be first choice when ranking compounds in vivo (‘open’ system).

New Equilibrium Models of Drug-Receptor Interactions Derived from Target-Mediated Drug Disposition

In vivo analyses of pharmacological data are traditionally based on a closed system approach not incorporating turnover of target and ligand-target kinetics, but mainly focussing on ligand-target binding properties. This study incorporates information about target and ligand-target kinetics parallel to binding. In a previous paper, steady-state relationships between target- and ligand-target complex versus ligand exposure were derived and a new expression of in vivo potency was derived for a circulating target. This communication is extending the equilibrium relationships and in vivo potency expression for (i) two separate targets competing for one ligand, (ii) two different ligands competing for a single target and (iii) a single ligand-target interaction located in tissue. The derived expressions of the in vivo potencies will be useful both in drug-related discovery projects and mechanistic studies. The equilibrium states of two targets and one ligand may have implications in safety assessment, whilst the equilibrium states of two competing ligands for one target may cast light on when pharmacodynamic drug-drug interactions are important. The proposed equilibrium expressions for a peripherally located target may also be useful for small molecule interactions with extravascularly located targets. Including target turnover, ligand-target complex kinetics and binding properties in expressions of potency and efficacy will improve our understanding of within and between-individual (and across species) variability. The new expressions of potencies highlight the fact that the level of drug-induced target suppression is very much governed by target turnover properties rather than by the target expression level as such.

Synthesis, crystal structure, computational analysis and biological properties of 1-(4-chlorobenzoyl)-3-[2-(2-{2-[3-(4-chlorobenzoyl)-thioureido]-ethoxy}ethoxy)ethyl]-thiourea and its Ni(II) and Cu(II) complexes

A new dianionic ligand, 1-(4-chlorobenzoyl)-3-[2-(2-{2-[3-(4-chlorobenzoyl)-thioureido]-ethoxy}ethoxy)ethyl]-thiourea(CBEDEA), and its Ni(II) and Cu(II) complexes have been synthesized. X-ray single crystal analysis of CBEDEA shows that the molecule crystallized in the triclinic crystal system, space group P-1, with two molecules per unit cell. The studied compounds were further characterized by FT-IR, UV-VIS,1H NMR and mass spectroscopy. The metal complexes were isolated as four coordinated ML(M: Ni(II), Cu(II), L:CBEDEA) molecules. Computational studies on CBEDEA gave electrostatic potential energy isovalues which showed that there is a higher probability of metal coordination around the carbonyl and thione groups. Results of non-covalent interaction studies revealed the presence of significant amount of hydrogen bonding and other weak non-covalent interactions in the molecule. Docking calculations on CBEDEA and its Ni(II) and Cu(II) metal complexes revealed that they have affinity for beta-lactamase, a protein implicated in antibiotic drug-resistant mechanism. Complexation with the metal ion shrank the size of the molecule and enabled the metal complexes to fit more appropriately within the binding groove of the protein resulting in the improved affinity over CBEDEA ligand. Target-ligand binding interactions resulted from hydrophobicity and possibility of hydrogen bonding of the molecules. In vitro screening of the compounds against 17 bacteria and 4 yeasts confirmed their antimicrobial potency against more susceptible Gram-positive bacteria. Results of this study suggest that the metal complexes could be developed into novel antimicrobial compounds.

Investigation and molecular docking studies of Bassianolide from Lecanicillium lecanii against Plutella xylostella (Lepidoptera: Plutellidae)

Entomopathogenic fungi are rich sources of bioactive secondary metabolites that possess insecticidal properties. The present study reported a novel approach for the identification of insecticidal compounds produced by Lecanicillium lecanii 09 and to assess their toxicity against the diamondback moth Plutella xylostella L. The cyclic peptides groups of toxic substances were separated from L. lecanii 09 through submerged liquid state fermentation. The most abundant toxic metabolite, Bassianolide was purified by high-performance liquid chromatography (HPLC) and its molecular weight and purity were determined by Liquid chromatography – mass spectroscopy (LC-MS), Fourier transformed infrared spectroscopy (FT-IR), and H1 nuclear magnetic resonance (NMR) respectively. Subsequently, the toxicity of bassianolide was tested against third instar larvae of P. xylostella at three different concentrations (0.01, 0.1, 0.5 mg/ml). The results showed that higher concentration of 0.5 mg/ml had significant maximum mortality at 120 hour post inoculation. Furthermore, we investigated the ligand-target interaction of secondary metabolite binding with target insect immune receptor proteins and predicted the role of toxicity against insect host. This is the first study to report the infection process and the interaction of fungal mediated cyclicdepsipeptide compound (bassianolide) from L. lecanii 09 against the insect host P. xylostella. This novel approach provides a potential impact on biological control using natural toxic compound which acts as good inhibitor on pest insect and prevents toxicity hazards, pollution as well as ecocidal effects killing several beneficial insects.

Improved Dose-Response Relationship of (+)-Discodermolide-Taxol Hybrid Congeners

(+)-Discodermolide is a microtubule-stabilizing agent with potential for the treatment of taxol-refractory malignancies. (+)-Discodermolide congeners containing the C-3'-phenyl side chain of taxol (paclitaxel) were synthesized based on computational docking models predicting this moiety would fill an aromatic pocket of β-tubulin insufficiently occupied by (+)-discodermolide, thereby conferring improved ligand-target interaction. It was recently demonstrated, however, that the C-3'-phenyl side chain occupied a different space, instead extending toward the M-loop of β-tubulin, where it induced a helical conformation, hypothesized to improve lateral contacts between adjacent microtubule protofilaments. This insight led us to evaluate the biological activity of hybrid congeners using a panel of genetically diverse cancer cell lines. Hybrid molecules retained the same tubulin-polymerizing profile as (+)-discodermolide. Since (+)-discodermolide is a potent inducer of accelerated senescence, a fate that contributes to drug resistance, congeners were also screened for senescence induction. Flow cytometric and transcriptional analysis revealed that the hybrids largely retained the senescence-inducing properties of (+)-discodermolide. In taxol-sensitive cell models, the congeners had improved dose-response parameters relative to (+)-discodermolide and, in some cases, were superior to taxol. However, in cells susceptible to senescence, EMax increased without concomitant improvements in EC50 such that overall dose-response profiles resembled that of (+)-discodermolide.

Unraveling the Binding, Proton Blockage, and Inhibition of Influenza M2 WT and S31N by Rimantadine Variants.

Recently, the binding kinetics of a ligand-target interaction, such as the residence time of a small molecule on its protein target, are seen as increasingly important for drug efficacy. Here, we investigate these concepts to explain binding and proton blockage of rimantadine variants bearing progressively larger alkyl groups to influenza A virus M2 wild type (WT) and M2 S31N protein proton channel. We showed that resistance of M2 S31N to rimantadine analogues compared to M2 WT resulted from their higher koff rates compared to the kon rates according to electrophysiology (EP) measurements. This is due to the fact that, in M2 S31N, the loss of the V27 pocket for the adamantyl cage resulted in low residence time inside the M2 pore. Both rimantadine enantiomers have similar channel blockage and binding kon and koff against M2 WT. To compare the potency between the rimantadine variants against M2, we applied approaches using different mimicry of M2, i.e., isothermal titration calorimetry and molecular dynamics simulation, EP, and antiviral assays. It was also shown that a small change in an amino acid at site 28 of M2 WT, which does not line the pore, seriously affects M2 WT blockage kinetics.

X-ray Structures of Target-Ligand Complexes Containing Compounds with Assay Interference Potential.

Pan assay interference compounds (PAINS) have become a paradigm for compound classes that might cause artifacts in biological assays. PAINS-defining substructures are typically contained in larger compounds. We have systematically examined X-ray structures of protein-ligand complexes for compounds containing PAINS motifs. In 2874 X-ray structures, 1107 unique ligands with PAINS substructures belonging to 70 different classes were identified. PAINS most frequently detected in crystallographic ligands included a number of prominent candidates such as quinones, catechols, or Mannich bases. However, on the basis of X-ray data, the presence of specific ligand-target interactions and reactivity under assay conditions were not mutually exclusive. In some instances, reactivity of ligands was likely responsible for complex formation. Different categories of PAINS-containing ligands were distinguished, which aided in the interpretation of specific interactions versus potential assay artifacts. Careful consideration of structural data adds another dimension to the analysis of interference compounds.

Selection of Informative Examples in Chemogenomic Datasets.

High-throughput and high-content screening campaigns have resulted in the creation of large chemogenomic matrices. These matrices form the training data which is used to build ligand-target interaction models for pharmacological and chemical biology research. While academic, government, and industrial efforts continuously add to the ligand-target data pairs available for modeling, major research efforts are devoted to improving machine learning techniques to cope with the sparseness, heterogeneity, and size of available datasets as well as inherent noise and bias. This "race of arms" has led to the creation of algorithms to generate highly complex models with high prediction performance at the cost of training efficiency as well as interpretability.In contrast, recent studies have challenged the necessity for "big data" in chemogenomic modeling and found that models built on larger numbers of examplesdo not necessarily result in better predictive abilities. Automated adaptive selection of the training data (ligand-target instances) used for model creation can result in considerably smaller training sets that retain prediction performance on par with training using hundreds of thousands of data points. In this chapter, we describe the protocols used for one such iterative chemogenomic selection technique, including model construction and update as well as possible techniques for evaluations of constructed models and analysis of the iterative model construction.

Multivalency Increases the Binding Strength of RGD Peptidomimetic-Paclitaxel Conjugates to Integrin αV β3.

This work reports the synthesis of three multimeric RGD peptidomimetic‐paclitaxel conjugates featuring a number of αVβ3 integrin ligands ranging from 2 to 4. These constructs were assembled by conjugation of the integrin αVβ3 ligand cyclo[DKP‐RGD]‐CH2NH2 with paclitaxel via a 2′‐carbamate with a self‐immolative spacer, the lysosomally cleavable Val‐Ala dipeptide linker, a multimeric scaffold, a triazole linkage, and finally a PEG spacer. Two monomeric conjugates were also synthesized as reference compounds. Remarkably, the new multimeric conjugates showed a binding affinity for the purified integrin αVβ3 receptor that increased with the number of integrin ligands (reaching a minimum IC50 value of 1.2 nm for the trimeric), thus demonstrating that multivalency is an effective strategy to strengthen the ligand–target interactions.

Linking High-Throughput Screens to Identify MoAs and Novel Inhibitors of Mycobacterium tuberculosis Dihydrofolate Reductase.

Though phenotypic and target-based high-throughput screening approaches have been employed to discover new antibiotics, the identification of promising therapeutic candidates remains challenging. Each approach provides different information, and understanding their results can provide hypotheses for a mechanism of action (MoA) and reveal actionable chemical matter. Here, we describe a framework for identifying efficacy targets of bioactive compounds. High throughput biophysical profiling against a broad range of targets coupled with machine learning was employed to identify chemical features with predicted efficacy targets for a given phenotypic screen. We validate the approach on data from a set of 55 000 compounds in 24 historical internal antibacterial phenotypic screens and 636 bacterial targets screened in high-throughput biophysical binding assays. Models were built to reveal the relationships between phenotype, target, and chemotype, which recapitulated mechanisms for known antibacterials. We also prospectively identified novel inhibitors of dihydrofolate reductase with nanomolar antibacterial efficacy against Mycobacterium tuberculosis. Molecular modeling provided structural insight into target-ligand interactions underlying selective killing activity toward mycobacteria over human cells.

The potential role of in silico approaches to identify novel bioactive molecules from natural resources.

In recent years, integration of in silico approaches to natural product (NP) research reawakened the declined interest in NP-based drug discovery efforts. In particular, advancements in cheminformatics enabled comparison of NP databases with contemporary small-molecule libraries in terms of molecular properties and chemical space localizations. Virtual screening and target fishing approaches were successful in recognizing the untold macromolecular targets for NPs to exploit the unmet therapeutic needs. Developments in molecular docking and scoring methods along with molecular dynamics enabled to predict the target-ligand interactions more accurately taking into consideration the remarkable structural complexity of NPs. Hence, innovative in silico strategies have contributed valuably to the NP research in drug discovery processes as reviewed herein. [Formula: see text].

Synthesis and Biological Evaluation of 7-Deoxy-Epothilone Analogues.

The synthesis of two deoxygenated analogues of potent epothilones is reported in an effort to analyze the relative importance of molecular conformation and ligand–target interactions to biological activity. 7-deoxy-epothilone D and 7-deoxy-(S)-14-methoxy-epothilone D were prepared through total synthesis and shown to maintain the conformational preferences of their biologically active parent congeners through computer modeling and nuclear magnetic resonance (NMR) studies. The significant decrease in observed potency for each compound suggests that a hydrogen bond between the C7-hydroxyl group and the tubulin binding site plays a critical role in the energetics of binding in the epothilone class of polyketides.

Active learning for computational chemogenomics.

Computational chemogenomics models the compound-protein interaction space, typically for drug discovery, where existing methods predominantly either incorporate increasing numbers of bioactivity samples or focus on specific subfamilies of proteins and ligands. As an alternative to modeling entire large datasets at once, active learning adaptively incorporates a minimum of informative examples for modeling, yielding compact but high quality models. Results/methodology: We assessed active learning for protein/target family-wide chemogenomic modeling by replicate experiment. Results demonstrate that small yet highly predictive models can be extracted from only 10-25% of large bioactivity datasets, irrespective of molecule descriptors used. Chemogenomic active learning identifies small subsets of ligand-target interactions in a large screening database that lead to knowledge discovery and highly predictive models.

Virtual Screening in Search of Allosteric Modulators of Nav1.1 Channels

Voltage-gated Na+ (Nav) channels provide the basis for neuronal excitability in the brain. Of the nine Nav channel isoforms (Nav1.1-Nav1.9), Nav1.1 exhibits cell-specific distribution in fast-spiking parvalbumin (PV) interneurons in the cortical circuit. Reduced function of PV interneurons results in impairment of the cognitive domain, a bio-signature common to a variety of neuropsychiatric disorders. Thus, selective allosteric modulators targeting Nav1.1 might provide new means to rescue PV interneuron function and improve cognition. Previous studies have identified fibroblast growth factor 14 (FGF14) as a functionally relevant regulator of Nav1.1 channels. Binding of FGF14 to the Nav1.1 C-terminal tail results in modulation of Nav1.1-mediated peak transient currents and biophysical properties of the channel activation and steady-state inactivation. The FGF14:Nav1.1 protein:protein interaction (PPI) interface might therefore provide a novel target for the development of Nav1.1 allosteric modulators.In search of small molecules targeting Nav1.1, we conducted a ligand-based high-throughput virtual screening using a FGF14:Nav1.1 homology model based on available crystal structures of homologous proteins. The UCSF chimera software was used to determine the region of interaction between FGF14 and Nav1.1, and Autodock was used to create a grid box surrounding this area to identify critical amino acids at the PPI surface of FGF14.This region of interest was submitted to the Texas Advanced Computing Center (TACC) drug discovery database, which identified 1001 ZINC compounds out of 642,759 possible ligands against the interaction site. Scores ranged from −13 to 0, with lower scores indicating greater likelihood of binding to the FGF14 surface, and thus interfering with Nav 1.1 binding. On-going counter and orthogonal screenings using in heterologous systems as well as Schrodinger Advanced Drug Discovery Suite are being used to provide in-cell validation of 14 identified hits and understanding of the target-ligand interactions. These small molecules might represent a new class of PPI-based Nav1.1-specific allosteric modulators with applicability as cognitive enhancers.

Analysis of Differential Efficacy and Affinity of GABAA (α1/α2) Selective Modulators.

Selective modulators of the γ-amino butyric acid (GABAA) family of receptors have the potential to treat a range of disease states related to cognition, pain, and anxiety. While the development of various α subunit-selective modulators is currently underway for the treatment of anxiety disorders, a mechanistic understanding of the correlation between their bioactivity and efficacy, based on ligand-target interactions, is currently still lacking. In order to alleviate this situation, in the current study we have analyzed, using ligand- and structure-based methods, a data set of 5440 GABAA modulators. The Spearman correlation (ρ) between binding activity and efficacy of compounds was calculated to be 0.008 and 0.31 against the α1 and α2 subunits of GABA receptor, respectively; in other words, the compounds had little diversity in structure and bioactivity, but they differed significantly in efficacy. Two compounds were selected as a case study for detailed interaction analysis due to the small difference in their structures and affinities (ΔpKi(comp1_α1-comp2_α1) = 0.45 log units, ΔpKi(comp1_α2-comp2_α2) = 0 log units) as compared to larger relative efficacies (ΔRE(comp1_α1-comp2_α1) = 1.03, ΔRE(comp1_α2-comp2_α2) = 0.21). Docking analysis suggested that His-101 is involved in a characteristic interaction of the α1 receptor with both compounds 1 and 2. Residues such as Phe-77, Thr-142, Asn-60, and Arg-144 of the γ chain of the α1γ2 complex also showed interactions with heterocyclic rings of both compounds 1 and 2, but these interactions were disturbed in the case of α2γ2 complex docking results. Binding pocket stability analysis based on molecular dynamics identified three substitutions in the loop C region of the α2 subunit, namely, G200E, I201T, and V202I, causing a reduction in the flexibility of α2 compared to α1. These amino acids in α2, as compared to α1, were also observed to decrease the vibrational and dihedral entropy and to increase the hydrogen bond content in α2 in the apo state. However, freezing of both α1 and α2 was observed in the ligand-bound state, with an increased number of internal hydrogen bonds and increased entropy. Therefore, we hypothesize that the amino acid differences in the loop C region of α2 are responsible for conformational changes in the protein structure compared to α1, as well as for the binding modes of compounds and hence their functional signaling.

Screening Complex Biological Samples with Peptide Microarrays: The Favorable Impact of Probe Orientation via Chemoselective Immobilization Strategies on Clickable Polymeric Coatings.

The generation of robust analytical data using microarray platforms strictly relies on optimal ligand-target interaction at the sensor surface, which, in turn, is inherently bound to the correct immobilization scheme of the interrogated bioprobes. In the present work, we performed a rigorous comparative analysis of the impact of peptide ligands immobilization strategy in the screening of Burkholderia cepacia complex (BCC) infections in patients affected by cystic fibrosis (CF). We generated arrays of previously validated Burkholderia derived peptide probes that were selectively oriented on polymeric coatings by means of different click-type reactions including thiol maleimide, copper-catalyzed azide-alkyne cycloaddition (CuAAC), and strain-promoted azide-alkyne cycloaddition (SPAAC). We compared immobilization efficiency among the different chemoselective reactions, and we evaluated diagnostic performances at a statistically significant level, also in contrast to random immobilization strategies. Our findings clearly support the favorable role of correct bioprobe orientation in discriminating seronegative from infected individuals and, in the last analysis, in generating more-reliable and more-reproducible data. Spacing biomolecules from the sensor surface by means of small hydrophilic linkers also positively affects the analytical performance and leads to increased statistical significance of data. Overall, all of the click immobilization strategies that were considered displayed a good efficiency. Interestingly, SPAAC-mediated conjugation using DBCO cyclooctyne for some peptides resulted in sequence-dependent autofluorescence in the Cy5 emission range wavelength, which could be circumvented by using a different fluorescence detection channel. On the basis of our results, we critically discuss the immobilization parameters that need to be carefully considered for peptide ligand immobilization purposes.

Multi-fields model for predicting target–ligand interaction

Predicting target–ligand interactions is a critical task of chemogenomics and plays a key role in virtual drug discovery. Moreover, it is important to take insights into the molecular recognition mechanisms between chemical substructures of ligands and binding sites of targets. In this work, we suppose the interaction between a ligand and a target is the result of the comprehensive effect of multiple fields between the ligand and the binding site of the target, and propose a multi-field interaction model (MFIM) to predict the target–ligand interaction. The evaluation result on the same data set shows that MFIM outperforms other two representative methods. The derived fragment interaction network is robust to the parameter fluctuation and the connections in the network are sparse. Moreover, the edge weights of the network might reflect the fragment interaction intensity and most of the significant edges are chemical interpretable.