Anna Vulpetti

Identification of compounds with binding affinity to proteins via magnetization transfer from bulk water

A powerful screening by NMR methodology (WaterLOGSY), based on transfer of magnetization from bulk water, for the identification of compounds that interact with target biomolecules (proteins, RNA and DNA fragments) is described. The method exploits efficiently the large reservoir of H2O magnetization. The high sensitivity of the technique reduces the amount of biomolecule and ligands needed for the screening, which constitutes an important requirement for high throughput screening by NMR of large libraries of compounds. Application of the method to a compound mixture against the cyclin-dependent kinase 2 (cdk2) protein is presented.

The Experimental Uncertainty of Heterogeneous Public Ki Data

The maximum achievable accuracy of in silico models depends on the quality of the experimental data. Consequently, experimental uncertainty defines a natural upper limit to the predictive performance possible. Models that yield errors smaller than the experimental uncertainty are necessarily overtrained. A reliable estimate of the experimental uncertainty is therefore of high importance to all originators and users of in silico models. The data deposited in ChEMBL was analyzed for reproducibility, i.e., the experimental uncertainty of independent measurements. Careful filtering of the data was required because ChEMBL contains unit-transcription errors, undifferentiated stereoisomers, and repeated citations of single measurements (90% of all pairs). The experimental uncertainty is estimated to yield a mean error of 0.44 pK(i) units, a standard deviation of 0.54 pK(i) units, and a median error of 0.34 pK(i) units. The maximum possible squared Pearson correlation coefficient (R(2)) on large data sets is estimated to be 0.81.

Predicting polypharmacology by binding site similarity: from kinases to the protein universe.

Polypharmacology is receiving increasing attention in the pharmaceutical industry, since finding new targets of a compound is useful not only for anticipating possible side effects but also for opening new therapeutic opportunities. Thus, while system biology and personalized medicine are becoming increasingly important, there is an urgent need to map the inhibition profile of a compound on a large panel of targets by using both experimental and computational methods. This is especially important for kinase inhibitors, given the high similarity at the binding site level for the 518 kinases in the human genome. In this paper, we propose and validate a new method to predict the inhibition map of a compound by comparison of binding pockets. We used a subset of the Ambit panel for the validation-17 inhibitors with K(d) measured on 189 kinases-and found that on average 37% of kinases inhibited with K(d) < 10 microM were retrieved at 10% ROC enrichment. These results make this method particularly suitable to rationalize and optimize the selectivity profile of a compound. In addition, the method was extended to explore all the proteins in the PDB by using as queries pockets occupied by compounds of biological interest (ATP and various marketed drugs). The profiling of compounds against the protein universe revealed that striking structural similarities at the subpocket level (RMSD < 0.5 A) may also occur among targets with different folds, which can be exploited not only to predict off-target effects but also to design novel inhibitors for the target of interest.

Design and NMR-Based Screening of LEF, a Library of Chemical Fragments with Different Local Environment of Fluorine

A novel strategy for the design of a fluorinated fragment library that takes into account the local environment of fluorine is described. The procedure, based on a fluorine fingerprints descriptor, and the criteria used in the design, selection, and construction of the library are presented. The library, named LEF (Local Environment of Fluorine), combined with (19)F NMR ligand-based screening experiments represents an efficient and sensitive approach for the initial fragment identification within a fragment-based drug discovery project and for probing the presence of fluorophilic protein environments. Proper setup of the method, according to described theoretical simulations, allows the detection of very weak-affinity ligands and the detection of multiple ligands present within the same tested mixture, thus capturing all the potential fragments interacting with the receptor. These NMR hits are then used in the FAXS experiments for the fragment optimization process and for the follow-up screening aimed at identifying other chemical scaffolds relevant for the binding to the receptor.

Comparability of Mixed IC50 Data – A Statistical Analysis

The biochemical half maximal inhibitory concentration (IC50) is the most commonly used metric for on-target activity in lead optimization. It is used to guide lead optimization, build large-scale chemogenomics analysis, off-target activity and toxicity models based on public data. However, the use of public biochemical IC50 data is problematic, because they are assay specific and comparable only under certain conditions. For large scale analysis it is not feasible to check each data entry manually and it is very tempting to mix all available IC50 values from public database even if assay information is not reported. As previously reported for Ki database analysis, we first analyzed the types of errors, the redundancy and the variability that can be found in ChEMBL IC50 database. For assessing the variability of IC50 data independently measured in two different labs at least ten IC50 data for identical protein-ligand systems against the same target were searched in ChEMBL. As a not sufficient number of cases of this type are available, the variability of IC50 data was assessed by comparing all pairs of independent IC50 measurements on identical protein-ligand systems. The standard deviation of IC50 data is only 25% larger than the standard deviation of Ki data, suggesting that mixing IC50 data from different assays, even not knowing assay conditions details, only adds a moderate amount of noise to the overall data. The standard deviation of public ChEMBL IC50 data, as expected, resulted greater than the standard deviation of in-house intra-laboratory/inter-day IC50 data. Augmenting mixed public IC50 data by public Ki data does not deteriorate the quality of the mixed IC50 data, if the Ki is corrected by an offset. For a broad dataset such as ChEMBL database a Ki- IC50 conversion factor of 2 was found to be the most reasonable.

Identification of N,1,4,4-Tetramethyl-8-{[4-(4-Methylpiperazin-1-Yl)Phenyl]Amino}-4,5-Dihydro-1H-Pyrazolo[4,3-H]Quinazoline-3-Carboxamide (Pha-848125), a Potent, Orally Available Cyclin Dependent Kinase Inhibitor.

The discovery of a novel class of inhibitors of cyclin dependent kinases (CDKs) is described. Starting from compound 1, showing good potency as inhibitor of CDKs but being poorly selective against a panel of serine-threonine and tyrosine kinases, new analogues were synthesized. Enhancement in selectivity, antiproliferative activity against A2780 human ovarian carcinoma cells, and optimization of the physical properties and pharmacokinetic profile led to the identification of highly potent and orally available compounds. Compound 28 (PHA-848125), which in the preclinical xenograft A2780 human ovarian carcinoma model showed good efficacy and was well tolerated upon repeated daily treatments, was identified as a drug candidate for further development. Compound 28 is currently undergoing phase I and phase II clinical trials.

Fluorine–Protein Interactions and 19F NMR Isotropic Chemical Shifts: An Empirical Correlation with Implications for Drug Design

An empirical correlation between the fluorine isotropic chemical shifts, measured by 19F NMR spectroscopy, and the type of fluorine–protein interactions observed in crystal structures is presented. The CF, CF2, and CF3 groups present in fluorinated ligands found in the Protein Data Bank were classified according to their 19F NMR chemical shifts and their close intermolecular contacts with the protein atoms. Shielded fluorine atoms, i.e., those with increased electron density, are observed primarily in close contact to hydrogen bond donors within the protein structure, suggesting the possibility of intermolecular hydrogen bond formation. Deshielded fluorines are predominantly found in close contact with hydrophobic side chains and with the carbon of carbonyl groups of the protein backbone. Correlation between the 19F NMR chemical shift and hydrogen bond distance, both derived experimentally and computed through quantum chemical methods, is also presented. The proposed “rule of shielding” provides some insight into and guidelines for the judicious selection of appropriate fluorinated moieties to be inserted into a molecule for making the most favorable interactions with the receptor.

Fluorine as a Hydrogen‐Bond Acceptor: Experimental Evidence and Computational Calculations

Hydrogen-bonding interactions play an important role in many chemical and biological systems. Fluorine acting as a hydrogen-bond acceptor in intermolecular and intramolecular interactions has been the subject of many controversial discussions and there are different opinions about it. Recently, we have proposed a correlation between the propensity of fluorine to be involved in hydrogen bonds and its (19)F NMR chemical shift. We now provide additional experimental and computational evidence for this correlation. The strength of hydrogen-bond complexes involving the fluorine moieties CH2F, CHF2, and CF3 was measured and characterized in simple systems by using established and novel NMR methods and compared to the known hydrogen-bond complex formed between acetophenone and p-fluorophenol. Implications of these results for (19)F NMR screening are analyzed in detail. Computed values of the molecular electrostatic potential at the different fluorine atoms and the analysis of the electron density topology at bond critical points correlate well with the NMR results.

Intermolecular and Intramolecular Hydrogen Bonds Involving Fluorine Atoms: Implications for Recognition, Selectivity, and Chemical Properties

A correlation between 19F NMR isotropic chemical shift and close intermolecular F⋅⋅⋅HX contacts (with X=N or O) has been identified upon analysis of the X‐ray crystal structures of fluorinated molecules listed in the Cambridge Structural Database (CSD). An optimal F⋅⋅⋅X distance involving primary and shielded secondary fluorine atoms in hydrogen‐bond formation along with a correlation between F⋅⋅⋅H distance and F⋅⋅⋅HX angle were also derived from the analysis. The hydrogen bonds involving fluorine are relevant, not only for the recognition mechanism and stabilization of a preferred conformation, but also for improvement in the permeability of the molecules, as shown with examples taken from a proprietary database. Results of an analysis of the small number of fluorine‐containing natural products listed in the Protein Data Bank (PDB) appear to strengthen the derived correlation between 19F NMR isotropic chemical shift and interactions involving fluorine (also known as the “rule of shielding”) and provides a hypothesis for the recognition mechanism and catalytic activity of specific enzymes. Novel chemical scaffolds, based on the rule of shielding, have been designed for recognizing distinct structural motifs present in proteins. It is envisaged that this approach could find useful applications in drug design for the efficient optimization of chemical fragments or promising compounds by increasing potency and selectivity against the desired biomolecular target.

Structure-based approaches to improve selectivity: CDK2-GSK3beta binding site analysis.

An evaluation and comparison of two different approaches, GRID/CPCA and GRIND/CPCA (CPCA = consensus principal component analysis; GRIND = GRid-INdependent Descriptors), suitable for visualizing the structural differences between related proteins is presented. Ten crystal structures of CDK2/cyclin A and GSK3beta solved in-house with different inhibitors were compared with the aim of highlighting regions that could be potential sites for gaining selectivity for CDK2 versus GSK3beta. The analyses pointed out remarkable differences in the backs of the CDK2-GSK3beta ATP binding pockets that guided the optimization toward a selective benzodipyrazole CDK2 inhibitor. The gain in selectivity can be associated with the two main differences in the ATP pocket between the enzymes. Phe80 of CDK2, the so-called gatekeeper residue often exploited for the design of kinase selective ligands, is replaced by a leucine in GSK3beta, and Ala144 is replaced by a cysteine. As a consequence of these mutations, CDK2 has a less elongated and less flat buried region at the back of the ATP pocket.