HCV genotypes are differently prone to the development of resistance to linear and macrocyclic protease inhibitors

Because of the extreme genetic variability of hepatitis C virus (HCV), we analyzed whether specific HCV-genotypes are differently prone to develop resistance to linear and macrocyclic protease-inhibitors (PIs)

The Cysteine-Rich Protein Thimet Oligopeptidase as a Model of the Structural Requirements for S-glutathiolation and Oxidative Oligomerization

Thimet oligopeptidase (EP24.15) is a cysteine-rich metallopeptidase containing fifteen Cys residues and no intra-protein disulfide bonds. Previous work on this enzyme revealed that the oxidative oligomerization of EP24.15 is triggered by S-glutathiolation at physiological GSSG levels (10–50 µM) via a mechanism based on thiol-disulfide exchange. In the present work, our aim was to identify EP24.15 Cys residues that are prone to S-glutathiolation and to determine which structural features in the cysteinyl bulk are responsible for the formation of mixed disulfides through the reaction with GSSG and, in this particular case, the Cys residues within EP24.15 that favor either S-glutathiolation or inter-protein thiol-disulfide exchange. These studies were conducted by in silico structural analyses and simulations as well as site-specific mutation. S-glutathiolation was determined by mass spectrometric analyses and western blotting with anti-glutathione antibody. The results indicated that the stabilization of a thiolate sulfhydryl and the solvent accessibility of the cysteines are necessary for S-thiolation. The Solvent Access Surface analysis of the Cys residues prone to glutathione modification showed that the S-glutathiolated Cys residues are located inside pockets where the sulfur atom comes into contact with the solvent and that the positively charged amino acids are directed toward these Cys residues. The simulation of a covalent glutathione docking onto the same Cys residues allowed for perfect glutathione posing. A mutation of the Arg residue 263 that forms a saline bridge to the Cys residue 175 significantly decreased the overall S-glutathiolation and oligomerization of EP24.15. The present results show for the first time the structural requirements for protein S-glutathiolation by GSSG and are consistent with our previous hypothesis that EP24.15 oligomerization is dependent on the electron transfer from specific protonated Cys residues of one molecule to previously S-glutathionylated Cys residues of another one

Iodine Atoms: A New Molecular Feature for the Design of Potent Transthyretin Fibrillogenesis Inhibitors

The thyroid hormone and retinol transporter protein known as transthyretin (TTR) is in the origin of one of the 20 or so known amyloid diseases. TTR self assembles as a homotetramer leaving a central hydrophobic channel with two symmetrical binding sites. The aggregation pathway of TTR into amiloid fibrils is not yet well characterized but in vitro binding of thyroid hormones and other small organic molecules to TTR binding channel results in tetramer stabilization which prevents amyloid formation in an extent which is proportional to the binding constant. Up to now, TTR aggregation inhibitors have been designed looking at various structural features of this binding channel others than its ability to host iodine atoms. In the present work, greatly improved inhibitors have been designed and tested by taking into account that thyroid hormones are unique in human biochemistry owing to the presence of multiple iodine atoms in their molecules which are probed to interact with specific halogen binding domains sitting at the TTR binding channel. The new TTR fibrillogenesis inhibitors are based on the diflunisal core structure because diflunisal is a registered salicylate drug with NSAID activity now undergoing clinical trials for TTR amyloid diseases. Biochemical and biophysical evidence confirms that iodine atoms can be an important design feature in the search for candidate drugs for TTR related amyloidosis

Novel Druggable Hot Spots in Avian Influenza Neuraminidase H5N1 Revealed by Computational Solvent Mapping of a Reduced and Representative Receptor Ensemble

The influenza virus subtype H5N1 has raised concerns of a possible human pandemic threat because of its high virulence and mutation rate. Although several approved anti-influenza drugs effectively target the neuraminidase, some strains have already acquired resistance to the currently available anti-influenza drugs. In this study, we present the synergistic application of extended explicit solvent molecular dynamics (MD) and computational solvent mapping (CS-Map) to identify putative ‘hot spots’ within flexible binding regions of N1 neuraminidase. Using representative conformations of the N1 binding region extracted from a clustering analysis of four concatenated 40-ns MD simulations, CS-Map was utilized to assess the ability of small, solvent-sized molecules to bind within close proximity to the sialic acid binding region. Mapping analyses of the dominant MD conformations reveal the presence of additional hot spot regions in the 150- and 430-loop regions. Our hot spot analysis provides further support for the feasibility of developing high-affinity inhibitors capable of binding these regions, which appear to be unique to the N1 strain

The Interaction Properties of the Human Rab GTPase Family – A Comparative Analysis Reveals Determinants of Molecular Binding Selectivity

Rab GTPases constitute the largest subfamily of the Ras protein superfamily. Rab proteins regulate organelle biogenesis and transport, and display distinct binding preferences for effector and activator proteins, many of which have not been elucidated yet. The underlying molecular recognition motifs, binding partner preferences and selectivities are not well understood.Comparative analysis of the amino acid sequences and the three-dimensional electrostatic and hydrophobic molecular interaction fields of 62 human Rab proteins revealed a wide range of binding properties with large differences between some Rab proteins. This analysis assists the functional annotation of Rab proteins 12, 14, 26, 37 and 41 and provided an explanation for the shared function of Rab3 and 27. Rab7a and 7b have very different electrostatic potentials, indicating that they may bind to different effector proteins and thus, exert different functions. The subfamily V Rab GTPases which are associated with endosome differ subtly in the interaction properties of their switch regions, and this may explain exchange factor specificity and exchange kinetics.We have analysed conservation of sequence and of molecular interaction fields to cluster and annotate the human Rab proteins. The analysis of three dimensional molecular interaction fields provides detailed insight that is not available from a sequence-based approach alone. Based on our results, we predict novel functions for some Rab proteins and provide insights into their divergent functions and the determinants of their binding partner selectivity

Optimal assignment methods for ligand-based virtual screening

<p>Abstract</p> <p>Background</p> <p>Ligand-based virtual screening experiments are an important task in the early drug discovery stage. An ambitious aim in each experiment is to disclose active structures based on new scaffolds. To perform these "scaffold-hoppings" for individual problems and targets, a plethora of different similarity methods based on diverse techniques were published in the last years. The optimal assignment approach on molecular graphs, a successful method in the field of quantitative structure-activity relationships, has not been tested as a ligand-based virtual screening method so far.</p> <p>Results</p> <p>We evaluated two already published and two new optimal assignment methods on various data sets. To emphasize the "scaffold-hopping" ability, we used the information of chemotype clustering analyses in our evaluation metrics. Comparisons with literature results show an improved early recognition performance and comparable results over the complete data set. A new method based on two different assignment steps shows an increased "scaffold-hopping" behavior together with a good early recognition performance.</p> <p>Conclusion</p> <p>The presented methods show a good combination of chemotype discovery and enrichment of active structures. Additionally, the optimal assignment on molecular graphs has the advantage to investigate and interpret the mappings, allowing precise modifications of internal parameters of the similarity measure for specific targets. All methods have low computation times which make them applicable to screen large data sets.</p

Deciphering the Arginine-Binding Preferences at the Substrate-Binding Groove of Ser/Thr Kinases by Computational Surface Mapping

Protein kinases are key signaling enzymes that catalyze the transfer of γ-phosphate from an ATP molecule to a phospho-accepting residue in the substrate. Unraveling the molecular features that govern the preference of kinases for particular residues flanking the phosphoacceptor is important for understanding kinase specificities toward their substrates and for designing substrate-like peptidic inhibitors. We applied ANCHORSmap, a new fragment-based computational approach for mapping amino acid side chains on protein surfaces, to predict and characterize the preference of kinases toward Arginine binding. We focus on positions P−2 and P−5, commonly occupied by Arginine (Arg) in substrates of basophilic Ser/Thr kinases. The method accurately identified all the P−2/P−5 Arg binding sites previously determined by X-ray crystallography and produced Arg preferences that corresponded to those experimentally found by peptide arrays. The predicted Arg-binding positions and their associated pockets were analyzed in terms of shape, physicochemical properties, amino acid composition, and in-silico mutagenesis, providing structural rationalization for previously unexplained trends in kinase preferences toward Arg moieties. This methodology sheds light on several kinases that were described in the literature as having non-trivial preferences for Arg, and provides some surprising departures from the prevailing views regarding residues that determine kinase specificity toward Arg. In particular, we found that the preference for a P−5 Arg is not necessarily governed by the 170/230 acidic pair, as was previously assumed, but by several different pairs of acidic residues, selected from positions 133, 169, and 230 (PKA numbering). The acidic residue at position 230 serves as a pivotal element in recognizing Arg from both the P−2 and P−5 positions

Rapid and Accurate Prediction and Scoring of Water Molecules in Protein Binding Sites

Water plays a critical role in ligand-protein interactions. However, it is still challenging to predict accurately not only where water molecules prefer to bind, but also which of those water molecules might be displaceable. The latter is often seen as a route to optimizing affinity of potential drug candidates. Using a protocol we call WaterDock, we show that the freely available AutoDock Vina tool can be used to predict accurately the binding sites of water molecules. WaterDock was validated using data from X-ray crystallography, neutron diffraction and molecular dynamics simulations and correctly predicted 97% of the water molecules in the test set. In addition, we combined data-mining, heuristic and machine learning techniques to develop probabilistic water molecule classifiers. When applied to WaterDock predictions in the Astex Diverse Set of protein ligand complexes, we could identify whether a water molecule was conserved or displaced to an accuracy of 75%. A second model predicted whether water molecules were displaced by polar groups or by non-polar groups to an accuracy of 80%. These results should prove useful for anyone wishing to undertake rational design of new compounds where the displacement of water molecules is being considered as a route to improved affinity

A Unified Model of the GABA(A) Receptor Comprising Agonist and Benzodiazepine Binding Sites

We present a full-length α(1)β(2)γ(2) GABA receptor model optimized for agonists and benzodiazepine (BZD) allosteric modulators. We propose binding hypotheses for the agonists GABA, muscimol and THIP and for the allosteric modulator diazepam (DZP). The receptor model is primarily based on the glutamate-gated chloride channel (GluCl) from C. elegans and includes additional structural information from the prokaryotic ligand-gated ion channel ELIC in a few regions. Available mutational data of the binding sites are well explained by the model and the proposed ligand binding poses. We suggest a GABA binding mode similar to the binding mode of glutamate in the GluCl X-ray structure. Key interactions are predicted with residues α(1)R66, β(2)T202, α(1)T129, β(2)E155, β(2)Y205 and the backbone of β(2)S156. Muscimol is predicted to bind similarly, however, with minor differences rationalized with quantum mechanical energy calculations. Muscimol key interactions are predicted to be α(1)R66, β(2)T202, α(1)T129, β(2)E155, β(2)Y205 and β(2)F200. Furthermore, we argue that a water molecule could mediate further interactions between muscimol and the backbone of β(2)S156 and β(2)Y157. DZP is predicted to bind with interactions comparable to those of the agonists in the orthosteric site. The carbonyl group of DZP is predicted to interact with two threonines α(1)T206 and γ(2)T142, similar to the acidic moiety of GABA. The chlorine atom of DZP is placed near the important α(1)H101 and the N-methyl group near α(1)Y159, α(1)T206, and α(1)Y209. We present a binding mode of DZP in which the pending phenyl moiety of DZP is buried in the binding pocket and thus shielded from solvent exposure. Our full length GABA(A) receptor is made available as Model S1