Molecular basis for binding promiscuity of antagonist drugs

Polypharmacology: The science of multi-targeting molecules

Polypharmacology is a concept where a molecule can interact with two or more targets simultaneously. It offers many advantages as compared to the conventional single-targeting molecules. A multi-targeting drug is much more efficacious due to its cumulative efficacy at all of its individual targets making it much more effective in complex and multifactorial diseases like cancer, where multiple proteins and pathways are involved in the onset and development of the disease. For a molecule to be polypharmacologic in nature, it needs to possess promiscuity which is the ability to interact with multiple targets; and at the same time avoid binding to antitargets which would otherwise result in off-target adverse effects. There are certain structural features and physicochemical properties which when present would help researchers to predict if the designed molecule would possess promiscuity or not. Promiscuity can also be identified via advanced state-of-the-art computational methods. In this review, we also elaborate on the methods by which one can intentionally incorporate promiscuity in their molecules and make them polypharmacologic. The polypharmacology paradigm of "one drug-multiple targets" has numerous applications especially in drug repurposing where an already established drug is redeveloped for a new indication. Though designing a polypharmacological drug is much more difficult than designing a single-targeting drug, with the current technologies and information regarding different diseases and chemical functional groups, it is plausible for researchers to intentionally design a polypharmacological drug and unlock its advantages.

Strong Enrichment of Aromatic and Sulfur-Containing Residues in Ligand-Protein Binding Sites

While certain residues have clear involvement in determining the 3D structure of a macromolecule because they affect the folding topology or the overall protein stability, the role of different residues in ligand accommodation and binding has attracted less attention. On the basis of the assumption that drug-binding sites on target molecules have specific amino acid compositions, the incidence of each standard amino acid at the binding sites of small molecules and their correlations are calculated for an unprecedented large set of high-quality X-ray structures. Results show, for the first time, strong and highly correlated enrichments of aromatic and sulfur-containing residues, which play an important role in ligand binding and shape the nature of the chemical interactions.

In silico identification of promiscuous scaffolds as potential inhibitors of 1-deoxy-d-xylulose 5-phosphate reductoisomerase for treatment of Falciparum malaria

CONTEXT

Malaria remains one of the prevalent infectious diseases worldwide. Plasmodium falciparum 1-deoxy-d-xylulose-5-phosphate reductoisomerase (PfDXR) plays a role in isoprenoid biosynthesis in the malaria parasite, making this parasite enzyme an attractive target for antimalarial drug design. Fosmidomycin is a promising DXR inhibitor, which showed safety as well as efficacy against Plasmodium falciparum malaria in clinical trials. However, due to its poor oral bioavailability and non-drug-like properties, the focus of medicinal chemists is to develop inhibitors with improved pharmacological properties.

OBJECTIVE

This study described the computational design of new and potent inhibitors for deoxyxylulose 5-phosphate reductoisomerase and the prediction of their pharmacokinetic and pharmacodynamic properties.

MATERIAL AND METHODS

A complex-based pharmacophore model was generated from the complex X-ray crystallographic structure of PfDXR using MOE (Molecular Operating Environment). Furthermore, MOE-Dock was used as docking software to predict the binding modes of hits and target enzyme.

RESULTS

Finally, 14 compounds were selected as new and potent inhibitors of PfDXR on the basis of pharmacophore mapping, docking score, binding energy and binding interactions with the active site residues of the target protein. The predicted pharmacokinetic properties showed improved permeability by efficiently crossing blood-brain barrier. While, in silico promiscuity binding data revealed that these hits also have the ability to bind with other P. falciparum drug targets.

DISCUSSION AND CONCLUSION

In conclusion, innovative scaffolds with novel modes of action, improved efficacy and acceptable physiochemical/pharmacokinetic properties were computationally identified.

Low potency toxins reveal dense interaction networks in metabolism

BACKGROUND

The chemicals of metabolism are constructed of a small set of atoms and bonds. This may be because chemical structures outside the chemical space in which life operates are incompatible with biochemistry, or because mechanisms to make or utilize such excluded structures has not evolved. In this paper I address the extent to which biochemistry is restricted to a small fraction of the chemical space of possible chemicals, a restricted subset that I call Biochemical Space. I explore evidence that this restriction is at least in part due to selection again specific structures, and suggest a mechanism by which this occurs.

RESULTS

Chemicals that contain structures that our outside Biochemical Space (UnBiological groups) are more likely to be toxic to a wide range of organisms, even though they have no specifically toxic groups and no obvious mechanism of toxicity. This correlation of UnBiological with toxicity is stronger for low potency (millimolar) toxins. I relate this to the observation that most chemicals interact with many biological structures at low millimolar toxicity. I hypothesise that life has to select its components not only to have a specific set of functions but also to avoid interactions with all the other components of life that might degrade their function.

CONCLUSIONS

The chemistry of life has to form a dense, self-consistent network of chemical structures, and cannot easily be arbitrarily extended. The toxicity of arbitrary chemicals is a reflection of the disruption to that network occasioned by trying to insert a chemical into it without also selecting all the other components to tolerate that chemical. This suggests new ways to test for the toxicity of chemicals, and that engineering organisms to make high concentrations of materials such as chemical precursors or fuels may require more substantial engineering than just of the synthetic pathways involved.

Computational identification of proteins for selectivity assays

At the stage of optimization of a chemical series the compounds are normally assayed for binding or inhibition on the target protein as well as on several proteins from a selectivity panel. These proteins are normally identified on the basis of sequence homology to the target protein. Experimental selectivity data are also taken into account if available. Cases when a nonhomologous protein has a significant affinity to the compound series are going to be missed if the selectivity panel is identified by homology. Experimental data is usually either unavailable or limited to a small fraction of proteins that should be considered. We have developed a computational method of identification of selectivity panel proteins. It is based on the evaluation of binding site similarity to the target protein using docking scores of target-selected molecular probes. These probes are obtained by docking a large library of drug-like compounds to the target protein followed by selecting a diverse subset from the best virtual binders. Docking scores of these probes to other proteins measure binding site similarity to the target. Because the method does not require prior knowledge of either affinities or structures of inhibitors for the target, it can be applied to any protein with known 3D structure. Validation of the method includes rediscovery of nonhomologous proteins that bind common ligands: estradiol, tamoxifen, and riboflavin. Given 3D structures, the method can effectively discriminate proteins with similar binding sites from random proteins independent of sequence homology.

Occupation of the cytochrome P450 substrate pocket by diverse compounds at general anesthesia concentrations

Each of a diverse array of compounds, at concentrations reported to effect general anesthesia, when added to liver microsomes, forms a complex with cytochromes P450 to generate, with reference to a cuvette containing microsomes only, a characteristic absorbance-difference spectrum. This spectrum results from a change in the electron-spin state of the heme iron atom induced upon entry by the anesthetic molecule into the enzyme catalytic pocket. The difference spectrum, representing the anesthetic-P450 complex, is characteristic of substances that are substrates for the enzyme. For the group of compounds as a whole, the magnitudes of the absorbance-difference spectra vary only about twofold, although the anesthetic potencies vary by several orders of magnitude. The dissociation constants (Ks), calculated from absorbance data and representing affinities of the anesthetics for P450, agree closely with the respective EC50 (concentration that effects anesthesia in 50% of individuals) values, and with the respective Ki (concentration that inhibits P450 catalytic activities half-maximally) values reported by us previously. The absorbance complex resulting from the occupation of the catalytic pocket by endogenous substrates, androstenedione and arachidonic acid, is inhibited, competitively, by anesthetics. Occupation of and perturbation of the heme catalytic pocket by anesthetic, as monitored by the absorbance-difference spectrum, is rapidly reversible. The presumed in vivo consequences of perturbation by general anesthetics of heme proteins is suppression of the generation of chemical signals that determine cell sensitivity and response.

Protein affinity map of chemical space

Affinity fingerprinting is a quantitative method for mapping chemical space based on binding preferences of compounds for a reference panel of proteins. An effective reference panel of <20 proteins can be empirically selected which shows differential interaction with nearly all compounds. By using this map to iteratively sample the chemical space, identification of active ligands from a library of 30,000 candidate compounds has been accomplished for a wide spectrum of specific protein targets. In each case, <200 compounds were directly assayed against the target. Further, analysis of the fingerprint database suggests a strategy for effective selection of affinity chromatography ligands and scaffolds for combinatorial chemistry. With such a system, the large numbers of potential therapeutic targets emerging from genome research can be categorized according to ligand binding properties, complementing sequence based classification.

Deciphering cryptic similarities in protein binding sites

Rapid expansion in the number of plausible drug targets arising from genomics research has created new pressures for increased efficiency in discovery of high specificity candidate drug compounds. Improved understanding of conserved features among protein structures provides a promising route to achieving this goal. Indirect evidence implies that important similarities are now ripe for elucidation by emerging experimental approaches.

Enhancement of tumor growth by drugs with some common molecular actions

A group of structurally related drugs representing diverse therapeutic classes share, among a number of pharmacological properties, enhancement of tumor growth in several rodent models of malignancy. One common action, the inhibition of histamine binding to and catalytic activity of cytochrome P450 monooxygenases, is highly correlated with potency to enhance tumor growth. Among members of this drug ensemble, the antiestrogen tamoxifen has been shown in controlled clinical studies to increase the incidence of uterine and gastrointestinal cancer and to accelerate the course of gastric cancer, and the tamoxifen analogue clomiphene has been linked to neuroblastoma and the tricyclic group of antidepressants to ovarian cancer. The determination of drug affinities for protein modulators of cell growth, proliferation, and transformation suggests a strategy for identifying at least some classes of chemicals that impart oncologic risks to humans.

Predicting ligand binding to proteins by affinity fingerprinting

BACKGROUND

There are many ways to represent a molecule's properties, including atomic-connectivity drawings, NMR spectra, and molecular orbital models. Prior methods for predicting the biological activity of compounds have largely depended on these physical representations. Measuring a compound's binding potency against a small reference panel of diverse proteins defines a very different representation of the molecule, which we call an affinity fingerprint. Statistical analysis of such fingerprints provides new insights into aspects of binding interactions that are shared among a wide variety of proteins. These analyses facilitate prediction of the binding properties of these compounds assayed against new proteins.

RESULTS

Affinity fingerprints are reported for 122 structurally-diverse compounds using a reference panel of eight proteins that collectively are able to generate unique fingerprints for about 75% of the small organic compounds tested. Application of multivariate regression techniques to this database enables the creation of computational surrogates to represent new proteins that are surprisingly effective at predicting binding potencies. We illustrate this for two enzymes with no previously recognizable similarity to each other or to any of the reference proteins. Fitting of analogous computational surrogates to four other proteins confirms the generality of the method; when applied to a fingerprinted library of 5000 compounds, several sub-micromolar hits were correctly predicted.

CONCLUSIONS

An affinity fingerprint database, which provides a rich source of data defining operational similarities among proteins, can be used to test theories of cryptic homology unexpected from current understanding of protein structure. Practical applications to drug design include efficient pre-screening of large numbers of compounds against target proteins using fingerprint similarities, supplemented by a small number of empirical measurements, to select promising compounds for further study.

Amino acid preferences at protein binding sites

An analysis of the amino acid distribution at protein binding sites was carried out using 50 diverse macromolecules for which crystallographic data with a bound ligand are available. The purpose of this study is to determine whether differential trends in amino acid distributions exist at binding sites compared to other regions in the proteins. The results indicate that some residues, particularly Arg, His, Trp and Tyr are substantially more frequent at the binding sites, compared to the number of times these residues are present in proteins generally. These effects go beyond the differences seen comparing surface exposed residues to bulk protein. The resemblance in the residue utilization at the binding sites of unrelated proteins restricts the possible types of interactions with ligands, possibly accounting for the repetition of substructural motifs in chemicals with diverse pharmacological action. Further, the use of these diagnostic features may permit identification of ligand binding pockets in a protein structure deduced from sequence information or from data in the absence of a ligand. Some of these findings complement and extend previously described trends for antibody binding sites.

Re-examination of [3H]mepyramine binding assay for histamine H1 receptor using quinine

[3H]Mepyramine, a potent antagonist of the histamine H1 receptor, has been widely used as a radioligand binding assay for the H1 receptor. Previously, we purified a mepyramine binding protein (MBP) from rat liver, but found that its partial amino acid sequences were very similar to those of debrisoquine 4-hydroxylase isozymes (P450 db1 and db2), which are members of the superfamily of cytochrome P450. Using cloned histamine H1 receptor cDNA, we found that [3H]mepyramine could bind only the H1 receptor and did not bind MBP in the presence of 10(-5) M quinine, an inhibitor of debrisoquine 4-hydroxylase isozymes. We developed a method to determine the contents of the H1 receptor and MBP separately using [3H]mepyramine and quinine and found that MBP is abundant in certain areas of bovine brain.