Showing papers on "Docking (molecular) published in 2009"

Application of the PM6 semi-empirical method to modeling proteins enhances docking accuracy of AutoDock

Abstract: Molecular docking methods are commonly used for predicting binding modes and energies of ligands to proteins. For accurate complex geometry and binding energy estimation, an appropriate method for calculating partial charges is essential. AutoDockTools software, the interface for preparing input files for one of the most widely used docking programs AutoDock 4, utilizes the Gasteiger partial charge calculation method for both protein and ligand charge calculation. However, it has already been shown that more accurate partial charge calculation - and as a consequence, more accurate docking- can be achieved by using quantum chemical methods. For docking calculations quantum chemical partial charge calculation as a routine was only used for ligands so far. The newly developed Mozyme function of MOPAC2009 allows fast partial charge calculation of proteins by quantum mechanical semi-empirical methods. Thus, in the current study, the effect of semi-empirical quantum-mechanical partial charge calculation on docking accuracy could be investigated. The docking accuracy of AutoDock 4 using the original AutoDock scoring function was investigated on a set of 53 protein ligand complexes using Gasteiger and PM6 partial charge calculation methods. This has enabled us to compare the effect of the partial charge calculation method on docking accuracy utilizing AutoDock 4 software. Our results showed that the docking accuracy in regard to complex geometry (docking result defined as accurate when the RMSD of the first rank docking result complex is within 2 A of the experimentally determined X-ray structure) significantly increased when partial charges of the ligands and proteins were calculated with the semi-empirical PM6 method. Out of the 53 complexes analyzed in the course of our study, the geometry of 42 complexes were accurately calculated using PM6 partial charges, while the use of Gasteiger charges resulted in only 28 accurate geometries. The binding affinity estimation was not influenced by the partial charge calculation method - for more accurate binding affinity prediction development of a new scoring function for AutoDock is needed. Our results demonstrate that the accuracy of determination of complex geometry using AutoDock 4 for docking calculation greatly increases with the use of quantum chemical partial charge calculation on both the ligands and proteins.

Comparison of several molecular docking programs: pose prediction and virtual screening accuracy.

Abstract: Molecular docking programs are widely used modeling tools for predicting ligand binding modes and structure based virtual screening. In this study, six molecular docking programs (DOCK, FlexX, GLIDE, ICM, PhDOCK, and Surflex) were evaluated using metrics intended to assess docking pose and virtual screening accuracy. Cognate ligand docking to 68 diverse, high-resolution X-ray complexes revealed that ICM, GLIDE, and Surflex generated ligand poses close to the X-ray conformation more often than the other docking programs. GLIDE and Surflex also outperformed the other docking programs when used for virtual screening, based on mean ROC AUC and ROC enrichment values obtained for the 40 protein targets in the Directory of Useful Decoys (DUD). Further analysis uncovered general trends in accuracy that are specific for particular protein families. Modifying basic parameters in the software was shown to have a significant effect on docking and virtual screening results, suggesting that expert knowledge is critical...

Efficient docking of peptides to proteins without prior knowledge of the binding site

Abstract: Reliability in docking of ligand molecules to proteins or other targets is an important challenge for molecular modeling. Applications of the docking technique include not only prediction of the binding mode of novel drugs, but also other problems like the study of protein-protein interactions. Here we present a study on the reliability of the results obtained with the popular AutoDock program. We have performed systematical studies to test the ability of AutoDock to reproduce eight different protein/ligand complexes for which the structure was known, without prior knowledge of the binding site. More specifically, we look at factors influencing the accuracy of the final structure, such as the number of torsional degrees of freedom in the ligand. We conclude that the Autodock program package is able to select the correct complexes based on the energy without prior knowledge of the binding site. We named this application blind docking, as the docking algorithm is not able to "see" the binding site but can still find it. The success of blind docking represents an important finding in the era of structural genomics.

Validation of molecular docking programs for virtual screening against dihydropteroate synthase.

Abstract: Dihydropteroate synthase (DHPS) is the target of the sulfonamide class of antibiotics and has been a validated antibacterial drug target for nearly 70 years. The sulfonamides target the p-aminobenzoic acid (pABA) binding site of DHPS and interfere with folate biosynthesis and ultimately prevent bacterial replication. However, widespread bacterial resistance to these drugs has severely limited their effectiveness. This study explores the second and more highly conserved pterin binding site of DHPS as an alternative approach to developing novel antibiotics that avoid resistance. In this study, five commonly used docking programs, FlexX, Surflex, Glide, GOLD, and DOCK, and nine scoring functions, were evaluated for their ability to rank-order potential lead compounds for an extensive virtual screening study of the pterin binding site of B. anthracis DHPS. Their performance in ligand docking and scoring was judged by their ability to reproduce a known inhibitor conformation and to efficiently detect known active compounds seeded into three separate decoy sets. Two other metrics were used to assess performance; enrichment at 1% and 2% and Receiver Operating Characteristic (ROC) curves. The effectiveness of postdocking relaxation prior to rescoring and consensus scoring were also evaluated. Finally, we have developed a straightforward statistical method of including the inhibition constants of the known active compounds when analyzing enrichment results to more accurately assess scoring performance, which we call the 'sum of the sum of log rank' or SSLR. Of the docking and scoring functions evaluated, Surflex with Surflex-Score and Glide with GlideScore were the best overall performers for use in virtual screening against the DHPS target, with neither combination showing statistically significant superiority over the other in enrichment studies or pose selection. Postdocking ligand relaxation and consensus scoring did not improve overall enrichment.

Structure-based discovery of β2-adrenergic receptor ligands

Abstract: Aminergic G protein-coupled receptors (GPCRs) have been a major focus of pharmaceutical research for many years. Due partly to the lack of reliable receptor structures, drug discovery efforts have been largely ligand-based. The recently determined X-ray structure of the β2-adrenergic receptor offers an opportunity to investigate the advantages and limitations inherent in a structure-based approach to ligand discovery against this and related GPCR targets. Approximately 1 million commercially available, “lead-like” molecules were docked against the β2-adrenergic receptor structure. On testing of 25 high-ranking molecules, 6 were active with binding affinities <4 μM, with the best molecule binding with a Ki of 9 nM (95% confidence interval 7–10 nM). Five of these molecules were inverse agonists. The high hit rate, the high affinity of the most potent molecule, the discovery of unprecedented chemotypes among the new inhibitors, and the apparent bias toward inverse agonists among the docking hits, have implications for structure-based approaches against GPCRs that recognize small organic molecules.

Molecular docking and ligand specificity in fragment-based inhibitor discovery

Abstract: Fragment screens have successfully identified new scaffolds in drug discovery, often with relatively high hit rates (5%) using small screening libraries (1,000–10,000 compounds). This raises two questions: would other noteworthy chemotypes be found were one to screen all commercially available fragments (>300,000), and does the success rate imply low specificity of fragments? We used molecular docking to screen large libraries of fragments against CTX-M β-lactamase. We identified ten millimolar-range inhibitors from the 69 compounds tested. The docking poses corresponded closely to the crystallographic structures subsequently determined. Notably, these initial low-affinity hits showed little specificity between CTX-M and an unrelated β-lactamase, AmpC, which is unusual among β-lactamase inhibitors. This is consistent with the idea that the high hit rates among fragments correlate to a low initial specificity. As the inhibitors were progressed, both specificity and affinity rose together, yielding to our knowledge the first micromolar-range noncovalent inhibitors against a class A β-lactamase.

Soft protein-protein docking in internal coordinates.

Abstract: The association of two biological macromolecules is a fundamental biological phenomenon and an unsolved theoretical problem. Docking methods for ab initio prediction of association of two independently determined protein structures usually fail when they are applied to a large set of complexes, mostly because of inaccuracies in the scoring function and/or difficulties on simulating the rearrangement of the interface residues on binding. In this work we present an efficient pseudo-Brownian rigid-body docking procedure followed by Biased Probability Monte Carlo Minimization of the ligand interacting side-chains. The use of a soft interaction energy function precalculated on a grid, instead of the explicit energy, drastically increased the speed of the procedure. The method was tested on a benchmark of 24 protein-protein complexes in which the three-dimensional structures of their subunits (bound and free) were available. The rank of the near-native conformation in a list of candidate docking solutions was <20 in 85% of complexes with no major backbone motion on binding. Among them, as many as 7 out of 11 (64%) protease-inhibitor complexes can be successfully predicted as the highest rank conformations. The presented method can be further refined to include the binding site predictions and applied to the structures generated by the structural proteomics projects. All scripts are available on the Web.

Toward high-resolution homology modeling of antibody Fv regions and application to antibody-antigen docking.

Abstract: High-resolution homology models are useful in structure-based protein engineering applications, especially when a crystallographic structure is unavailable. Here, we report the development and implementation of RosettaAntibody, a protocol for homology modeling of antibody variable regions. The protocol combines comparative modeling of canonical complementarity determining region (CDR) loop conformations and de novo loop modeling of CDR H3 conformation with simultaneous optimization of VL-VH rigid-body orientation and CDR backbone and side-chain conformations. The protocol was tested on a benchmark of 54 antibody crystal structures. The median root-mean-square-deviation (rmsd) of the antigen binding pocket comprised of all the CDR residues was 1.5 A with 80% of the targets having an rmsd lower than 2.0 A. The median backbone heavy atom global rmsd of the CDR H3 loop prediction was 1.6 A, 1.9 A, 2.4 A, 3.1 A and 6.0 A for very short (4–6 residues), short (7–9), medium (10–11), long (12–14) and very long (17–22) loops respectively. When the set of ten top-scoring antibody homology models are used in local ensemble docking to antigen, a moderate to high accuracy docking prediction was achieved in seven of fifteen targets. This success in computational docking with high-resolution homology models is encouraging, but challenges still remain in modeling antibody structures for sequences with long H3 loops. This first large-scale antibody-antigen docking study using homology models reveals the level of “functional accuracy” of these structural models towards protein engineering applications.

Chalcones: a valid scaffold for monoamine oxidases inhibitors.

Abstract: A large series of substituted chalcones have been synthesized and tested in vitro for their ability to inhibit human monoamine oxidases A and B (hMAO-A and hMAO-B). While all the compounds showed hMAO-B selective activity in the micro- and nanomolar ranges, the best results were obtained in the presence of chlorine and hydroxyl or methoxyl substituents. To better understand the enzyme-inhibitor interaction and to explain the selectivity of the most active compounds toward hMAO-B, molecular modeling studies were carried out on new, high resolution, hMAO-B crystallographic structures. For the only compound that also showed activity against hMAO-A as well as low selectivity, the molecular modeling study was also performed on the hMAO-A crystallographic structure. The docking technique provided new insight on the inhibition mechanism and the rational drug design of more potent/selective hMAO inhibitors based on the chalcone scaffold.

Four-dimensional docking: a fast and accurate account of discrete receptor flexibility in ligand docking.

Abstract: Many available methods aimed at incorporating the receptor flexibility in ligand docking are computationally expensive, require a high level of user intervention, and were tested only on benchmarks of limited size and diversity. Here we describe the Four-dimensional (4D) docking approach that allows seamless incorporation of receptor conformational ensembles in a single docking simulation and reduces the sampling time while preserving the accuracy of traditional ensemble docking. The approach was tested on a benchmark of 99 therapeutically relevant proteins and 300 diverse ligands (half of them experimental or marketed drugs). The conformational variability of the binding pockets was represented by the available crystallographic data, with the total of 1113 receptor structures. The 4D docking method reproduced the correct ligand binding geometry in 77.3% of the benchmark cases, matching the success rate of the traditional approach, but employed on average only one fourth of the time during the ligand sampling phase.

A novel intermembrane space-targeting signal docks cysteines onto Mia40 during mitochondrial oxidative folding.

Abstract: Mia40 imports Cys-containing proteins into the mitochondrial intermembrane space (IMS) by ensuring their Cys-dependent oxidative folding. In this study, we show that the specific Cys of the substrate involved in docking with Mia40 is substrate dependent, the process being guided by an IMS-targeting signal (ITS) present in Mia40 substrates. The ITS is a 9-aa internal peptide that (a) is upstream or downstream of the docking Cys, (b) is sufficient for crossing the outer membrane and for targeting nonmitochondrial proteins, (c) forms an amphipathic helix with crucial hydrophobic residues on the side of the docking Cys and dispensable charged residues on the other side, and (d) fits complementary to the substrate cleft of Mia40 via hydrophobic interactions of micromolar affinity. We rationalize the dual function of Mia40 as a receptor and an oxidase in a two step–specific mechanism: an ITS-guided sliding step orients the substrate noncovalently, followed by docking of the substrate Cys now juxtaposed to pair with the Mia40 active Cys.

Docking studies on DNA-ligand interactions: building and application of a protocol to identify the binding mode.

Abstract: Despite DNA being an important target for several drugs, most of the docking programs are validated only for proteins and their ligands. In this paper, we used AutoDock 4.0 to perform self-dockings and cross dockings between two DNA ligands (a minor groove binder and an intercalator) and four distinct receptors: 1) crystallographic DNA without intercalation gap; 2) crystallographic DNA with intercalation gap; 3) canonical B-DNA; and 4) modified B-DNA with intercalation gap. Besides being efficient in self-dockings, AutoDock is capable of correctly identifying two of the main DNA binding modes with the condition that the target possesses an artificial intercalation gap. Therefore, we suggest a default protocol to identify DNA binding modes which uses a modified canonical DNA (with gap) as receptor. This protocol was applied to dock two different Troger bases to DNA and the predicted binding modes agree with those suggested, yet not established, by experimental data. We also applied the protocol to dock aflatoxin B(1) exo-8,9-epoxide, and the results are in complete agreement with experimental data from the literature. We propose that this approach can be used to investigate other ligands whose binding mode to DNA remains unknown, yielding a suitable starting point for further theoretical studies such as molecular dynamics simulations.

Protein-protein docking using region-based 3D Zernike descriptors

Abstract: Protein-protein interactions are a pivotal component of many biological processes and mediate a variety of functions. Knowing the tertiary structure of a protein complex is therefore essential for understanding the interaction mechanism. However, experimental techniques to solve the structure of the complex are often found to be difficult. To this end, computational protein-protein docking approaches can provide a useful alternative to address this issue. Prediction of docking conformations relies on methods that effectively capture shape features of the participating proteins while giving due consideration to conformational changes that may occur. We present a novel protein docking algorithm based on the use of 3D Zernike descriptors as regional features of molecular shape. The key motivation of using these descriptors is their invariance to transformation, in addition to a compact representation of local surface shape characteristics. Docking decoys are generated using geometric hashing, which are then ranked by a scoring function that incorporates a buried surface area and a novel geometric complementarity term based on normals associated with the 3D Zernike shape description. Our docking algorithm was tested on both bound and unbound cases in the ZDOCK benchmark 2.0 dataset. In 74% of the bound docking predictions, our method was able to find a near-native solution (interface C-α RMSD ≤ 2.5 A) within the top 1000 ranks. For unbound docking, among the 60 complexes for which our algorithm returned at least one hit, 60% of the cases were ranked within the top 2000. Comparison with existing shape-based docking algorithms shows that our method has a better performance than the others in unbound docking while remaining competitive for bound docking cases. We show for the first time that the 3D Zernike descriptors are adept in capturing shape complementarity at the protein-protein interface and useful for protein docking prediction. Rigorous benchmark studies show that our docking approach has a superior performance compared to existing methods.

Cracking the Phosphatase Code: Docking Interactions Determine Substrate Specificity

Abstract: Many biological processes are regulated through alterations in the phosphorylation state of their protein components. Identifying the protein kinases and phosphatases that add and remove phosphate groups to these proteins is critical to understand cellular regulation. Most phosphoserine- and phosphothreonine-specific dephosphorylation is performed by a handful of protein phosphatases, each of which dephosphorylates sites that share little similarity in amino acid sequence. How do these enzymes recognize such diverse substrates with specificity? Studies show that several regions of the phosphatase that are not involved in catalysis are required for its recognition of protein partners. These regions, or docking surfaces, are conserved, and each binds weakly to a small, degenerate sequence motif (that is, a docking site) in a substrate or regulator of a phosphatase. Several of these weak interactions combine to achieve overall binding specificity. This Review, which contains 3 figures and 67 references, discusses genetic and structural studies that identify conserved docking surfaces in protein phosphatase type 1 (PP1) and the Ca 2+ -calmodulin–regulated phosphatase calcineurin, and show that some phosphatase inhibitors—for example, the immunosuppressants FK506 and cyclosporin A—act by interfering with substrate docking rather than with catalytic activity.

Energetic analysis of fragment docking and application to structure-based pharmacophore hypothesis generation.

Abstract: We have developed a method that uses energetic analysis of structure-based fragment docking to elucidate key features for molecular recognition. This hybrid ligand- and structure-based methodology uses an atomic breakdown of the energy terms from the Glide XP scoring function to locate key pharmacophoric features from the docked fragments. First, we show that Glide accurately docks fragments, producing a root mean squared deviation (RMSD) of <1.0 A for the top scoring pose to the native crystal structure. We then describe fragment-specific docking settings developed to generate poses that explore every pocket of a binding site while maintaining the docking accuracy of the top scoring pose. Next, we describe how the energy terms from the Glide XP scoring function are mapped onto pharmacophore sites from the docked fragments in order to rank their importance for binding. Using this energetic analysis we show that the most energetically favorable pharmacophore sites are consistent with features from known tight binding compounds. Finally, we describe a method to use the energetically selected sites from fragment docking to develop a pharmacophore hypothesis that can be used in virtual database screening to retrieve diverse compounds. We find that this method produces viable hypotheses that are consistent with known active compounds. In addition to retrieving diverse compounds that are not biased by the co-crystallized ligand, the method is able to recover known active compounds from a database screen, with an average enrichment of 8.1 in the top 1% of the database.

Influence of protonation, tautomeric, and stereoisomeric states on protein-ligand docking results.

Abstract: In this work, we present a systematical investigation of the influence of ligand protonation states, stereoisomers, and tautomers on results obtained with the two protein−ligand docking programs GOLD and PLANTS. These different states were generated with a fully automated tool, called SPORES (Structure PrOtonation and Recognition System). First, the most probable protonations, as defined by this rule based system, were compared to the ones stored in the well-known, manually revised CCDC/ASTEX data set. Then, to investigate the influence of the ligand protonation state on the docking results, different protonation states were created. Redocking and virtual screening experiments were conducted demonstrating that both docking programs have problems in identifying the correct protomer for each complex. Therefore, a preselection of plausible protomers or the improvement of the scoring functions concerning their ability to rank different molecules/states is needed. Additionally, ligand stereoisomers were tested...

Structure and allosteric effects of low-molecular-weight activators on the protein kinase PDK1

Abstract: Protein phosphorylation transduces a large set of intracellular signals. One mechanism by which phosphorylation mediates signal transduction is by prompting conformational changes in the target protein or interacting proteins. Previous work described an allosteric site mediating phosphorylation-dependent activation of AGC kinases. The AGC kinase PDK1 is activated by the docking of a phosphorylated motif from substrates. Here we present the crystallography of PDK1 bound to a rationally developed low-molecular-weight activator and describe the conformational changes induced by small compounds in the crystal and in solution using a fluorescence-based assay and deuterium exchange experiments. Our results indicate that the binding of the compound produces local changes at the target site, the PIF binding pocket, and also allosteric changes at the ATP binding site and the activation loop. Altogether, we present molecular details of the allosteric changes induced by small compounds that trigger the activation of PDK1 through mimicry of phosphorylation-dependent conformational changes.

Molecular docking screens using comparative models of proteins.

Abstract: Two orders of magnitude more protein sequences can be modeled by comparative modeling than have been determined by X-ray crystallography and NMR spectroscopy. Investigators have nevertheless been cautious about using comparative models for ligand discovery because of concerns about model errors. We suggest how to exploit comparative models for molecular screens, based on docking against a wide range of crystallographic structures and comparative models with known ligands. To account for the variation in the ligand-binding pocket as it binds different ligands, we calculate “consensus” enrichment by ranking each library compound by its best docking score against all available comparative models and/or modeling templates. For the majority of the targets, the consensus enrichment for multiple models was better than or comparable to that of the holo and apo X-ray structures. Even for single models, the models are significantly more enriching than the template structure if the template is paralogous and shares ...

Chemical probes that competitively and selectively inhibit Stat3 activation.

Abstract: Signal transducer and activator of transcription (Stat) 3 is an oncogene constitutively activated in many cancer systems where it contributes to carcinogenesis. To develop chemical probes that selectively target Stat3, we virtually screened 920,000 small drug-like compounds by docking each into the peptide-binding pocket of the Stat3 SH2 domain, which consists of three sites—the pY-residue binding site, the +3 residue-binding site and a hydrophobic binding site, which served as a selectivity filter. Three compounds satisfied criteria of interaction analysis, competitively inhibited recombinant Stat3 binding to its immobilized pY-peptide ligand and inhibited IL-6-mediated tyrosine phosphorylation of Stat3. These compounds were used in a similarity screen of 2.47 million compounds, which identified 3 more compounds with similar activities. Examination of the 6 active compounds for the ability to inhibit IFN-γ-mediated Stat1 phosphorylation revealed that 5 of 6 were selective for Stat3. Molecular modeling of the SH2 domains of Stat3 and Stat1 bound to compound revealed that compound interaction with the hydrophobic binding site was the basis for selectivity. All 5 selective compounds inhibited nuclear-to-cytoplasmic translocation of Stat3, while 3 of 5 compounds induced apoptosis preferentially of breast cancer cell lines with constitutive Stat3 activation. Thus, virtual ligand screening of compound libraries that targeted the Stat3 pY-peptide binding pocket identified for the first time 3 lead compounds that competitively inhibited Stat3 binding to its pY-peptide ligand; these compounds were selective for Stat3 vs. Stat1 and induced apoptosis preferentially of breast cancer cells lines with constitutively activated Stat3.

High-Performance Drug Discovery: Computational Screening by Combining Docking and Molecular Dynamics Simulations

Abstract: Virtual compound screening using molecular docking is widely used in the discovery of new lead compounds for drug design. However, this method is not completely reliable and therefore unsatisfactory. In this study, we used massive molecular dynamics simulations of protein-ligand conformations obtained by molecular docking in order to improve the enrichment performance of molecular docking. Our screening approach employed the molecular mechanics/Poisson-Boltzmann and surface area method to estimate the binding free energies. For the top-ranking 1,000 compounds obtained by docking to a target protein, approximately 6,000 molecular dynamics simulations were performed using multiple docking poses in about a week. As a result, the enrichment performance of the top 100 compounds by our approach was improved by 1.6–4.0 times that of the enrichment performance of molecular dockings. This result indicates that the application of molecular dynamics simulations to virtual screening for lead discovery is both effective and practical. However, further optimization of the computational protocols is required for screening various target proteins.

Electrostatics in protein-protein docking.

Abstract: A novel geometric-electrostatic docking algorithm is presented, which tests and quantifies the electrostatic complementarity of the molecular surfaces together with the shape complementarity. We represent each molecule to be docked as a grid of complex numbers, storing information regarding the shape of the molecule in the real part and information regarding the electrostatic character of the molecule in the imaginary part. The electrostatic descriptors are derived from the electrostatic potential of the molecule. Thus, the electrostatic character of the molecule is represented as patches of positive, neutral, or negative values. The potential for each molecule is calculated only once and stored as potential spheres adequate for exhaustive rotation/translation scans. The geometric-electrostatic docking algorithm is applied to 17 systems, starting form the structures of the unbound molecules. The results-in terms of the complementarity scores of the nearly correct solutions, their ranking in the lists of sorted solutions, and their statistical uniqueness-are compared with those of geometric docking, showing that the inclusion of electrostatic complementarity in docking is very important, in particular in docking of unbound structures. Based on our results, we formulate several "good electrostatic docking rules": The geometric-electrostatic docking procedure is more successful than geometric docking when the potential patches are large and when the potential extends away from the molecular surface and protrudes into the solvent. In contrast, geometric docking is recommended when the electrostatic potential around the molecules to be docked appears homogeneous, that is, with a similar sign all around the molecule.

Protein–protein docking dealing with the unknown

Abstract: Protein-protein binding is one of the critical events in biology, and knowledge of proteic complexes three-dimensional structures is of fundamental importance for the biochemical study of pharmacologic compounds. In the past two decades there was an emergence of a large variety of algorithms designed to predict the structures of protein-protein complexes--a procedure named docking. Computational methods, if accurate and reliable, could play an important role, both to infer functional properties and to guide new experiments. Despite the outstanding progress of the methodologies developed in this area, a few problems still prevent protein-protein docking to be a widespread practice in the structural study of proteins. In this review we focus our attention on the principles that govern docking, namely the algorithms used for searching and scoring, which are usually referred as the docking problem. We also focus our attention on the use of a flexible description of the proteins under study and the use of biological information as the localization of the hot spots, the important residues for protein-protein binding. The most common docking softwares are described too.

Molecular interactions of isoxazolcurcumin with human serum albumin: spectroscopic and molecular modeling studies

Abstract: Curcumin is a nontoxic natural product with diverse pharmacological potencies. We report the interaction of a potent synthetic derivative of curcumin, isoxazolcurcumin (IOC) with human serum albumin (HSA) using various biophysical methods. The observed fluorescence quenching of HSA by IOC is due to a complex formation by a static quenching process with a quenching constant of the order of 10(5) M(-1). The binding affinity and the number of binding sites were obtained from a Scatchard analysis. Thermodynamics reveals that the interaction is entropy driven with predominantly hydrophobic forces. From the observed Forster-type fluorescence resonance energy transfer (FRET), the donor (Trp 214 in HSA) to acceptor (IOC) distance is calculated to be 3.2 nm. The conformational changes of HSA due to the interaction were investigated qualitatively from synchronous fluorescence spectra along with a quantitative estimation of the secondary structure from Fourier Transform Infrared (FTIR) and circular dichroism (CD) spectroscopies. Molecular docking studies were performed to obtain information on the possible residues involved in the interaction process, and changes in accessible surface area of the interacting residues were calculated. The preferred binding site of IOC was analyzed by ligand displacement experiments with 1-anilino-8-naphthalenesulfonate (ANS) and warfarin-bound HSA.

Mechanism of p300 Specific Histone Acetyltransferase Inhibition by Small Molecules

Abstract: Dysfunction of histone acetyltransferases (HATs) leads to several diseases including cancer, diabetes, and asthma. Therefore, small molecule inhibitors and activators of HATs are being considered as new generation therapeutics. Here, we report the molecular mechanisms of p300 HAT inhibition by specific and nonspecific HAT inhibitors: garcinol, isogarcinol, and 1 (LTK14). The p300 specific HAT inhibitor 1 behaves as a noncompetitive inhibitor for both acetyl-CoA and histone, unlike nonspecific HAT inhibitors garcinol and isogarcinol. The isothermal calorimetric data suggest that there is a high affinity enthalpy driven single binding site for 1 on p300HAT domain in contrast to two binding sites for garcinol and isogarcinol. Furthermore, the precise nature of molecular interactions was determined by using fluorescence, docking, and mutational studies. On the basis of these observations, we have proposed the mechanisms of specific versus nonspecific HAT inhibition by these small molecule compounds, which may be useful to design therapeutically favorable HAT inhibitors.

Structure-based design, synthesis, and biological evaluation of a series of novel and reversible inhibitors for the severe acute respiratory syndrome-coronavirus papain-like protease.

Abstract: We describe here the design, synthesis, molecular modeling, and biological evaluation of a series of small molecule, nonpeptide inhibitors of SARS-CoV PLpro. Our initial lead compound was identified via high-throughput screening of a diverse chemical library. We subsequently carried out structure−activity relationship studies and optimized the lead structure to potent inhibitors that have shown antiviral activity against SARS-CoV infected Vero E6 cells. Upon the basis of the X-ray crystal structure of inhibitor 24-bound to SARS-CoV PLpro, a drug design template was created. Our structure-based modification led to the design of a more potent inhibitor, 2 (enzyme IC50 = 0.46 μM; antiviral EC50 = 6 μM). Interestingly, its methylamine derivative, 49, displayed good enzyme inhibitory potency (IC50 = 1.3 μM) and the most potent SARS antiviral activity (EC50 = 5.2 μM) in the series. We have carried out computational docking studies and generated a predictive 3D-QSAR model for SARS-CoV PLpro inhibitors.

Studies on the molecular recognition between bioactive peptides and angiotensin-converting enzyme

Abstract: High blood pressure or hypertension is a condition affecting many individuals and represents a controllable risk factor for cardiovascular diseases such as coronary heart disease and stroke. A non-pharmacological approach to manage these includes the application of food components with antihypertensive activity. Milk protein-derived peptides have been exploited as natural hypotensive agents, namely the peptides Val-Pro-Pro (VPP) and Ile-Pro-Pro (IPP), already commercialized in functional foods as a potential alternative to synthetic drugs. These bioactive peptides inhibit in vitro and in vivo the Angiotensin I-converting enzyme (ACE), a protein with an important role in blood pressure regulation. In this work, we attempted to elucidate the possible mode of interaction between the peptides and ACE, including mechanisms of binding to the cofactor Zn2+, and further contrast this with the known mode of inhibition exerted by synthetic drugs (Captopril, Enalaprilat and Lisinopril). The bioactive peptide Ala-Leu-Pro-Met-His-Ile-Arg (ALPMHIR), also known to inhibit the enzyme ACE but with a lower efficiency than VPP and IPP, was utilized in the docking studies for comparison. It was observed that the best docking poses obtained for VPP and IPP were located at the ACE catalytic site with very high resemblance to the drugs mode of interaction, including the coordination with Zn2+. As for ALPMHIR, the best docking poses were located in the narrow ACE channel outside the catalytic site, representing higher affinity energies and fewer resemblances with the interaction established by drugs.

Molecular Modeling and Molecular Dynamics Studies of Hydralazine with Human DNA Methyltransferase 1

Abstract: DNA methyltransferases (DNMTs) are a family of enzymes that methylate DNA at the C5 position of cytosine residues, and their inhibition is a promising strategy for the treatment of various developmental and proliferative diseases, particularly cancers. In the present study, a binding model for hydralazine, with a validated homology model of human DNMT, was developed by the use of automated molecular docking and molecular dynamics simulations. The docking protocol was validated by predicting the binding mode of 2'-deoxycytidine, 5-azacytidine, and 5-aza-2'-deoxycytidine. The inhibitory activity of hydralazine toward DNMT may be rationalized at the molecular level by similar interactions within the binding pocket (e.g., by a similar pharmacophore) as established by substrate-like deoxycytidine analogues. These interactions involve a complex network of hydrogen bonds with arginine and glutamic acid residues that also play a major role in the mechanism of DNA methylation. Despite the different scaffolds of other non-nucleoside DNMT inhibitors such as procaine and procainamide, the current modeling work reveals that these drugs exhibit similar interactions within the DNMT1 binding site. These findings are valuable in guiding the rational design and virtual screening of novel DNMT inhibitors.