Targeting cancer-specific glycans by cyclic peptide lectinomimics.

The transformation from normal to malignant phenotype in human cancers is associated with aberrant cell-surface glycosylation. Thus, targeting glycosylation changes in cancer is likely to provide not only better insight into the roles of carbohydrates in biological systems, but also facilitate the development of new molecular probes for bioanalytical and biomedical applications. In the reported study, we have synthesized lectinomimics based on odorranalectin 1; the smallest lectin-like cyclic peptide isolated from the frog Odorrana grahami skin, and assessed the ability of these peptides to bind specific carbohydrates on molecular and cellular levels. In addition, we have shown that the disulfide bond found in 1 can be replaced with a lactam bridge. However, the orientation of the lactam bridge, peptides 2 and 3, influenced cyclic peptide's conformation and thus these peptides' ability to bind carbohydrates. Naturally occurring 1 and its analog 3 that adopt similar conformation in water bind preferentially L-fucose, and to a lesser degree D-galactose and N-acetyl-D-galactosamine, typically found within the mucin O-glycan core structures. In cell-based assays, peptides 1 and 3 showed a similar binding profile to Aleuria aurantia lectin and these two peptides inhibited the migration of metastatic breast cancer cell lines in a Transwell assay. Altogether, the reported data demonstrate the feasibility of designing lectinomimics based on cyclic peptides.

Synthesis and biological evaluations of novel endomorphin analogues containing α-hydroxy-ß-phenylalanine (AHPBA) displaying mixed µ/δ opioid receptor agonist and δ opioid receptor antagonist activities.

A novel series of endomorphin-1 (EM-1) and endomorphin-2 (EM-2) analogues was synthesized, incorporating chiral α-hydroxy-ß-phenylalanine (AHPBA), and/or Dmt(1)-Tic(2) at different positions. Pharmacological activity and metabolic stability of the series was assessed. Consistent with earlier studies of ß-amino acid substitution into endomorphins, multiple analogues incorporation AHPBA displayed high affinity for µ and δ opioid receptors (MOR and DOR, respectively) in radioligand competition binding assays, and an increased stability in rat brain membrane homogenates, notably Dmt-Tic-(2R,3S)AHPBA-Phe-NH2 (compound 26). Intracerebroventricular (i.c.v.) administration of 26 produced antinociception (ED50 value (and 95% confidence interval) = 1.98 (0.79-4.15) nmol, i.c.v.) in the mouse 55 °C warm-water tail-withdrawal assay, equivalent to morphine (2.35 (1.13-5.03) nmol, i.c.v.), but demonstrated DOR-selective antagonism in addition to non-selective opioid agonism. The antinociception of 26 was without locomotor activity or acute antinociceptive tolerance. This novel class of peptides adds to the potentially therapeutically relevant collection of previously reported EM analogues.

Design and synthesis of α-conotoxin GID analogues as selective α4ß2 nicotinic acetylcholine receptor antagonists.

The α4ß2 nicotinic acetylcholine receptor (nAChR) is an important target for currently approved smoking cessation therapeutics. However, the development of highly selective α4ß2 nAChR antagonists remains a significant challenge. α-Conotoxin GID is an antagonist of α4ß2 nAChRs, though it is significantly more potent toward the α3ß2 and α7 subtypes. With the goal of obtaining further insights into α-conotoxin GID/nAChR interactions that could lead to the design of GID analogues with improved affinity for α4ß2 nAChRs, we built a homology model of the GID/α4ß2 complex using an X-ray co-crystal structure of an α-conotoxin/acetylcholine binding protein (AChBP) complex. Several additional interactions that could potentially enhance the affinity of GID for α4ß2 nAChRs were observed in our model, which led to the design and synthesis of 22 GID analogues. Seven analogues displayed inhibitory activity toward α4ß2 nAChRs that was comparable to GID. Significantly, both GID[A10S] and GID[V13I] demonstrated moderately improved selectivity toward α4ß2 over α3ß2 when compared with GID, while GID[V18N] exhibited no measurable inhibitory activity for the α3ß2 subtype, yet retained inhibitory activity for α4ß2. In this regard, GID[V18N] is the most α4ß2 nAChR selective α-conotoxin analogue identified to date.

Systematic mining of generally recognized as safe (GRAS) flavor chemicals for bioactive compounds.

Bioactive food compounds can be both therapeutically and nutritionally relevant. Screening strategies are widely employed to identify bioactive compounds from edible plants. Flavor additives contained in the so-called FEMA GRAS (generally recognized as safe) list of approved flavoring ingredients is an additional source of potentially bioactive compounds. This work used the principles of molecular similarity to identify compounds with potential mood-modulating properties. The ability of certain GRAS molecules to inhibit histone deacetylase-1 (HDAC1), proposed as an important player in mood modulation, was assayed. Two GRAS chemicals were identified as HDAC1 inhibitors in the micromolar range, results similar to what was observed for the structurally related mood prescription drug valproic acid. Additional studies on bioavailability, toxicity at higher concentrations, and off-target effects are warranted. The methodology described in this work could be employed to identify potentially bioactive flavor chemicals present in the FEMA GRAS list.

Ligand/kappa-opioid receptor interactions: insights from the X-ray crystal structure.

During the past five years, the three-dimensional structures of 14 different G-protein coupled receptors (GPCRs) have been resolved by X-ray crystallography. The most recently published structures, those of the opioid receptors (ORs), are remarkably important in pain modulation, drug addiction, and mood disorders. These structures, confirmed previously proposed key interactions conferring potency and antagonistic properties, including the well-known interaction with Asp138, conserved in all aminergic GPCRs. In addition, crystallization of the opioid receptors highlighted the potential function of the ECL2 and ICL2 loops. We have previously reported a set of potent and selective kappa opioid receptor peptide agonists, of which ff(D-nle)r-NH2 is among the most potent and selective ones. These peptides were identified from the deconvolution of a 6,250,000 tetrapeptide combinatorial library. A derivative of this set is currently the subject of a phase 2 clinical trial in the United States. In this work, we describe comparative molecular modeling studies of kappa-OR peptide agonists with the co-crystallized antagonist, JDTic, and also report structure-activity relationships of 23 tetrapeptides. The overall binding and contact interactions are sound and interactions known to favor selectivity and potency were observed. Additional modeling studies will reveal conformational changes that the kappa-OR undergoes upon binding to these peptide agonists.

Toward an efficient approach to identify molecular scaffolds possessing selective or promiscuous compounds.

The concept of a recurrent scaffold present in a series of structures is common in medicinal drug discovery. We present a scaffold analysis of compounds screened across 100 sequence-unrelated proteins to identify scaffolds that drive promiscuity or selectivity. Selectivity and promiscuity play a major role in traditional and poly-pharmacological drug design considerations. The collection employed here is the first publicly available data set containing the complete screening profiles of more than 15 000 compounds from different sources. In addition, no scaffold analysis of this data set has been reported. The protocol described here employs the Molecular Equivalence Index tool to facilitate the selection of Bemis-Murcko frameworks in the data set, which contain at least five compounds and Scaffold Hunter to generate a hierarchical tree of scaffolds. The annotation of the scaffold tree with protein-binding profile data enabled the successful identification of mostly highly specific compounds, due to data set constraints. We also applied this approach to a public set of 1497 small molecules screened non-uniformly across a panel of 172 protein kinases. The approach is general and can be applied to any other data sets and activity readout.

Rapid scanning structure-activity relationships in combinatorial data sets: identification of activity switches.

We present a general approach to describe the structure-activity relationships (SAR) of combinatorial data sets with activity for two biological endpoints with emphasis on the rapid identification of substitutions that have a large impact on activity and selectivity. The approach uses dual-activity difference (DAD) maps that represent a visual and quantitative analysis of all pairwise comparisons of one, two, or more substitutions around a molecular template. Scanning the SAR of data sets using DAD maps allows the visual and quantitative identification of activity switches defined as specific substitutions that have an opposite effect on the activity of the compounds against two targets. The approach also rapidly identifies single- and double-target R-cliffs, i.e., compounds where a single or double substitution around the central scaffold dramatically modifies the activity for one or two targets, respectively. The approach introduced in this report can be applied to any analogue series with two biological activity endpoints. To illustrate the approach, we discuss the SAR of 106 pyrrolidine bis-diketopiperazines tested against two formylpeptide receptors obtained from positional scanning deconvolution methods of mixture-based libraries.

Systematic characterization of structure-activity relationships and ADMET compliance: a case study.

Traditionally, activity landscape modeling has been focused on analyzing SAR, despite the fact that lead optimization in drug discovery involves concurrent enhancements of activity and ADMET properties of leads. As a case study, we discuss the systematic analysis of activity landscapes, incorporating ADMET considerations, using a dataset of 166 compounds screened for kappa-opioid receptor activity. Pairwise MACCS/Tanimoto structure similarities, property similarities utilizing 33 ADMET descriptors and a 35-dimensional 'violation bit vector' representing drug-likeness are analyzed. We address the question about the range of ADMET property violations that arise from structural changes, subtle and significant. Pairs of compounds are identified bearing identical, comparable and significantly different drug-likeness in the three informative regions of structure-activity landscapes.

Identification of a small molecule that selectively inhibits mouse PC2 over mouse PC1/3: a computational and experimental study.

The calcium-dependent serine endoproteases prohormone convertase 1/3 (PC1/3) and prohormone convertase 2 (PC2) play important roles in the homeostatic regulation of blood glucose levels, hence implicated in diabetes mellitus. Specifically, the absence of PC2 has been associated with chronic hypoglycemia. Since there is a reasonably good conservation of the catalytic domain between species translation of inhibitory effects is likely. In fact, similar results have been found using both mouse and human recombinant enzymes. Here, we employed computational structure-based approaches to screen 14,400 compounds from the Maybridge small molecule library towards mouse PC2. Our most remarkable finding was the identification of a potent and selective PC2 inhibitor. Kinetic data showed the compound to be an allosteric inhibitor. The compound identified is one of the few reported selective, small-molecule inhibitors of PC2. In addition, this new PC2 inhibitor is structurally different and of smaller size than those reported previously. This is advantageous for future studies where structural analogues can be built upon.

Molecular scaffold analysis of natural products databases in the public domain.

Natural products represent important sources of bioactive compounds in drug discovery efforts. In this work, we compiled five natural products databases available in the public domain and performed a comprehensive chemoinformatic analysis focused on the content and diversity of the scaffolds with an overview of the diversity based on molecular fingerprints. The natural products databases were compared with each other and with a set of molecules obtained from in-house combinatorial libraries, and with a general screening commercial library. It was found that publicly available natural products databases have different scaffold diversity. In contrast to the common concept that larger libraries have the largest scaffold diversity, the largest natural products collection analyzed in this work was not the most diverse. The general screening library showed, overall, the highest scaffold diversity. However, considering the most frequent scaffolds, the general reference library was the least diverse. In general, natural products databases in the public domain showed low molecule overlap. In addition to benzene and acyclic compounds, flavones, coumarins, and flavanones were identified as the most frequent molecular scaffolds across the different natural products collections. The results of this work have direct implications in the computational and experimental screening of natural product databases for drug discovery.

Data mining of protein-binding profiling data identifies structural modifications that distinguish selective and promiscuous compounds.

Activity profiling of compound collections across multiple targets is increasingly being used in probe and drug discovery. Herein, we discuss an approach to systematically analyzing the structure-activity relationships of a large screening profile data with emphasis on identifying structural changes that have a significant impact on the number of proteins to which a compound binds. As a case study, we analyzed a recently released public data set of more than 15 000 compounds screened across 100 sequence-unrelated proteins. The screened compounds have different origins and include natural products, synthetic molecules from academic groups, and commercial compounds. Similar synthetic structures from academic groups showed, overall, greater promiscuity differences than do natural products and commercial compounds. The method implemented in this work readily identified structural changes that differentiated highly specific from promiscuous compounds. This approach is general and can be applied to analyze any other large-scale protein-binding profile data.

Molecular recognition of the Thomsen-Friedenreich antigen-threonine conjugate by adhesion/growth regulatory galectin-3: nuclear magnetic resonance studies and molecular dynamics simulations.

Nuclear magnetic resonance (NMR) spectroscopy and molecular modeling methods have been strategically combined to elucidate the molecular recognition features of the binding of threonine O-linked Thomsen-Friedenreich (TF) antigen to chimera-type avian galectin-3 (CG-3). Saturation transfer difference (STD) NMR experiments revealed the highest intensities for the H4 protons of both the ß-D-Galp and α-D-GalpNAc moieties, with 100 and 71% of relative STD, respectively. The methyl protons of the threonine residue exhibited a small STD effect, <15%, indicating that the interaction of the amino acid with the protein is rather transient. Two-dimensional transferred nuclear Overhauser effect spectroscopy NMR experiments and molecular modeling suggested some differences in conformer populations between the free and bound states. A dynamic binding mode for the TF antigen-CG-3 complex consisting of two poses has been deduced. In one pose, intermolecular interactions were formed between the terminal threonine residue and the receptor. In the second pose, intermolecular interactions involved the internal GalpNAc. The difference in the trend of some shifts in the heteronuclear single-quantum coherence titration spectra indicates some disparities in the binding interactions of CG-3 with lactose and TF antigen. The results obtained from this model of the avian orthologue of human galectin-3 will allow detailed interspecies comparison to give sequence deviations in phylogeny a structural and functional meaning. Moreover, the results indicate that the peptide scaffold presenting TF antigen could be relevant for binding and thus provides a possible route for the design of galectin-3 inhibitors with improved affinity and selectivity.

Multitarget structure-activity relationships characterized by activity-difference maps and consensus similarity measure.

Dual and triple activity-difference (DAD/TAD) maps are tools for the systematic characterization of structure-activity relationships (SAR) of compound data sets screened against two or three targets. DAD and TAD maps are two- and three- dimensional representations of the pairwise activity differences of compound data sets, respectively. Adding pairwise structural similarity information into these maps readily reveals activity cliff regions in the SAR for one, two, or three targets. In addition, pairs of compounds in the smooth regions of the SAR and scaffold hops are also easily identified in these maps. Herein, DAD and TAD maps are employed for the systematic characterization of the SAR of a benchmark set of 299 compounds screened against dopamine, norepinephrine, and serotonin transporters. To reduce the well-known dependence of the activity landscape on the structural representation, five selected 2D and 3D structure representations were used to characterize the SAR. Systematic analysis of the DAD and TAD maps reveals regions in the landscape with similar SAR for two or the three targets as well as regions with inverse SAR, i.e., changes in structure that increase activity for one target, but decrease activity for the other target. Focusing the analysis on pairs of compounds with high structure similarity revealed the presence of single-, dual-, and triple-target activity cliffs, i.e., small changes in structure with high changes in potency for one, two, or the three targets, respectively. Triple-target scaffold hops are also discussed. Activity cliffs and scaffold hops were also quantified and represented using two recently proposed approaches namely, mean Structure Activity Landscape Index (mean SALI) and Consensus Structure-Activity Similarity (SAS) maps.

Consensus models of activity landscapes with multiple chemical, conformer, and property representations.

We report consensus Structure-Activity Similarity (SAS) maps that address the dependence of activity landscapes on molecular representation. As a case study, we characterized the activity landscape of 54 compounds with activities against human cathepsin B (hCatB), human cathepsin L (hCatL), and Trypanosoma brucei cathepsin B (TbCatB). Starting from an initial set of 28 descriptors we selected ten representations that capture different aspects of the chemical structures. These included four 2D (MACCS keys, GpiDAPH3, pairwise, and radial fingerprints) and six 3D (4p and piDAPH4 fingerprints with each including three conformers) representations. Multiple conformers are used for the first time in consensus activity landscape modeling. The results emphasize the feasibility of identifying consensus data points that are consistently formed in different reference spaces generated with several fingerprint models, including multiple 3D conformers. Consensus data points are not meant to eliminate data, disregarding, for example, "true" activity cliffs that are not identified by some molecular representations. Instead, consensus models are designed to prioritize the SAR analysis of activity cliffs and other consistent regions in the activity landscape that are captured by several molecular representations. Systematic description of the SARs of two targets give rise to the identification of pairs of compounds located in the same region of the activity landscape of hCatL and TbCatB suggesting similar mechanisms of action for the pairs involved. We also explored the relationship between property similarity and activity similarity and found that property similarities are suitable to characterize SARs. We also introduce the concept of structure-property-activity (SPA) similarity in SAR studies.

Integrating computational and mixture-based screening of combinatorial libraries.

Mixture-based synthetic combinatorial library (MB-SCL) screening is a well-established experimental approach for rapidly retrieving structure-activity relationships (SAR) and identifying hits. Virtual screening is also a powerful approach that is increasingly being used in drug discovery programs and has a growing number of successful applications. However, limited efforts have been made to integrate both techniques. To this end, we combined experimental data from a MB-SCL of bicyclic guanidines screened against the κ-opioid receptor and molecular similarity methods. The activity data and similarity analyses were integrated in a biometric analysis-similarity map. Such a map allows the molecules to be categorized as actives, activity cliffs, low similarity to the reference compounds, or missed hits. A compound with IC(50) = 309 nM was found in the "missed hits" region, showing that active compounds can be retrieved from a MS-SCL via computational approaches. The strategy presented in this work is general and is envisioned as a general-purpose approach that can be applied to other MB-SCLs.

Dynamic clustering threshold reduces conformer ensemble size while maintaining a biologically relevant ensemble.

Representing the 3D structures of ligands in virtual screenings via multi-conformer ensembles can be computationally intensive, especially for compounds with a large number of rotatable bonds. Thus, reducing the size of multi-conformer databases and the number of query conformers, while simultaneously reproducing the bioactive conformer with good accuracy, is of crucial interest. While clustering and RMSD filtering methods are employed in existing conformer generators, the novelty of this work is the inclusion of a clustering scheme (NMRCLUST) that does not require a user-defined cut-off value. This algorithm simultaneously optimizes the number and the average spread of the clusters. Here we describe and test four inter-dependent approaches for selecting computer-generated conformers, namely: OMEGA, NMRCLUST, RMS filtering and averaged-RMS filtering. The bioactive conformations of 65 selected ligands were extracted from the corresponding protein:ligand complexes from the Protein Data Bank, including eight ligands that adopted dissimilar bound conformations within different receptors. We show that NMRCLUST can be employed to further filter OMEGA-generated conformers while maintaining biological relevance of the ensemble. It was observed that NMRCLUST (containing on average 10 times fewer conformers per compound) performed nearly as well as OMEGA, and both outperformed RMS filtering and averaged-RMS filtering in terms of identifying the bioactive conformations with excellent and good matches (0.5 < RMSD < 1.0 A). Furthermore, we propose thresholds for OMEGA root-mean square filtering depending on the number of rotors in a compound: 0.8, 1.0 and 1.4 for structures with low (1-4), medium (5-9) and high (10-15) numbers of rotatable bonds, respectively. The protocol employed is general and can be applied to reduce the number of conformers in multi-conformer compound collections and alleviate the complexity of downstream data processing in virtual screening experiments.

Conformational landscape of platinum(II)-tetraamine complexes: DFT and NBO studies.

The potential energy surfaces of chiral tetraamine Pt(II) coordination complexes were computed at the B3LYP/LANL2DZ level of theory by a systematic variation of two dihedral angles: C12-C15-C34-C37 (theta) and C24-C17-C31-C48 (psi) employing a grid resolution of 30 degrees . Potential energy surfaces calculated using density functional theory methods and Boltzmann-derived populations revealed strong preference for one diasteromer of each series studied. In addition, natural bond orbital analysis show that the minima are stabilized predominantly by a combination of electronic interactions between two phenyl groups, the phenyl groups and the Pt(2+) ion, as well as with the amine groups. Additional experimental characterization of the diasteroisomers studied here is in progress and will permit further molecular modeling studies with the appropriate stereochemistry.

Modeling of peptides containing D-amino acids: implications on cyclization.

Cyclic peptides are therapeutically attractive due to their high bioavailability, potential selectivity, and scaffold novelty. Furthermore, the presence of D-residues induces conformational preferences not followed by peptides consisting of naturally abundant L-residues. Therefore, comprehending how amino acids induce turns in peptides, subsequently facilitating cyclization, is significant in peptide design. Here, we performed 20-ns explicit-solvent molecular dynamics simulations for three diastereomeric peptides with stereochemistries: LLLLL, LLLDL, and LDLDL. Experimentally LLLLL and LDLDL readily cyclize, whereas LLLDL cyclizes in low yield. Simulations at 310 K produced conformations with inter-terminal hydrogen bonds that correlated qualitatively with the experimental cyclization trend. Energies obtained for representative structures from quantum chemical (B3LYP/PCM/cc-pVTZ//HF/6-31G*) calculations predicted pseudo-cyclic and extended conformations as the most stable for LLLLL and LLLDL, respectively, in agreement with the experimental data. In contrast, the most stable conformer predicted for peptide LDLDL was not a pseudo-cyclic structure. Moreover, D-residues preferred the experimentally less populated alpha(L) rotamers even when simulations were performed at a higher temperature and with strategically selected starting conformations. Energies calculated with molecular mechanics were consistent only with peptide LLLLL. Thus, the conformational preferences obtained for the all L: -amino acid peptide were in agreement with the experimental observations. Moreover, refinement of the force field is expected to provide far-reaching conformational sampling of peptides containing D-residues to further develop force field-based conformational-searching methods.

Identification, structure-activity relationships and molecular modeling of potent triamine and piperazine opioid ligands.

Opioid receptors are important targets for pain management. Here, we report the synthesis and biological evaluation of three positional scanning combinatorial libraries, consisting of linear triamines and piperazines. A highly potent (14 nM) and selective (IC(50(mu))/IC(50(kappa))=71; IC(50(delta))/IC(50(kappa))=714) triamine for the kappa-opioid receptor was found. In addition, non-selective mu-kappa binders were obtained, with binding affinities of 54 nM and 22 nM for mu- and kappa-opioid receptors, respectively. Structure-activity relationships of each subset are described. 3D molecular alignments based on shape similarity to internal and external query molecules were carried out. For the combinatorial chemistry dataset studied here a 1.3 similarity cut-off value was observed to be efficient in the rocs-based alignment method. Interactions from the overlays analyzed in the binding sites of homology models of the receptors revealed specific substitution patterns for enhancing binding affinity in the piperazine series. Pharmacophore modeling of the compounds found from the three combinatorial libraries was also performed. The pharmacophore model indicated that the important feature for receptor binding activity with the mu-receptor was the presence of at least one hydrogen bond acceptor and one aromatic hydrophobic group. Whereas for the kappa-receptor two binding modes emerged with one set of compounds employing the hydrogen bond acceptor and aromatic hydrophobic group, and a second set possibly via interactions with the receptor by hydrophobic and ionic salt-bridges.

The conformational properties of methyl alpha-(2,8)-di/trisialosides and their N-acyl analogues: implications for anti-Neisseria meningitidis B vaccine design.

The conformational properties of di- and trisaccharide fragments of the polysialic acid O-antigen capsular polysaccharide (CPS) of Neisseria meningitidis B (NmB) have been investigated by a combination of solution phase NMR spectroscopy and explicit-solvent molecular dynamics (MD) simulations. Simulations employing 100 ns of conventional MD, as well as 160 ns of replica exchange MD (REMD), with the GLYCAM06 force field were shown to be in agreement with experimental NMR scalar J-coupling and NOE values. The presence of conformational families has been determined by monitoring interglycosidic torsion angles, by comparing structural superimpositions, as well as via a Bayesian statistical analysis of the torsional data. Attempts to augment the immunogenicity of NmB CPS often involve chemical modifications of the N-acetyl moiety. Here the effects of these chemical group modifications on the conformational properties of the trisialoside have been probed via REMD simulations of the N-glycolyl, N-propionyl, N-propyl and N-butanoyl analogues. Although there were conformational families unique to each non-native analogue, the chemical modifications resulted in largely equivalent overall conformational phase-spaces compared to the native trisialoside. On the basis of the conformational distributions, these shared conformational properties suggest that a recurrent global conformational epitope may be present in both the native and chemically modified CPS fragments. Explanations are therefore provided for monoclonal antibody cross-reactivity, in terms of recognition of a shared global CPS conformation, as well as for lack of cross-reactivity, in terms of fine structural differences associated with the N-acyl groups, which may be dominant in highly matured antibody responses.