Two enzymes of unknown function from the cog1735 subset of the amidohydrolase superfamily (AHS), LMOf2365_2620 (Lmo2620) from Listeria monocytogenes str. 4b F2365 and Bh0225 from Bacillus halodurans C-125, were cloned, expressed and purified to homogeneity. The catalytic functions of these two enzymes were interrogated by an integrated strategy encompassing bioinformatics, computational docking to three-dimensional crystal structures, and library screening. The three-dimensional structure of Lmo2620 was determined at a resolution of 1.6 Å with two phosphates and a binuclear zinc center in the active site. The proximal phosphate bridges the binuclear metal center and is 7.1 Å away from the distal phosphate. The distal phosphate hydrogen bonds with Lys-242, Lys-244, Arg-275 and Tyr-278. Enzymes within cog1735 of the AHS have previously been shown to catalyze the hydrolysis of substituted lactones. Computational docking of the high energy intermediate (HEI) form of the KEGG database to the three-dimensional structure of Lmo2620 highly enriched anionic lactones versus other candidate substrates. The active site structure and the computational docking results suggested that probable substrates would likely include phosphorylated sugar lactones. A small library of diacid sugar lactones and phosphorylated sugar lactones was synthesized and tested for substrate activity with Lmo2620 and Bh0225. Two substrates were identified for these enzymes, d-lyxono-1,4-lactone-5-phosphate and l-ribono-1,4-lactone-5-phosphate. The kcat/Km values for the cobalt-substituted enzymes with these substrates are ~105 M−1 s−1.
Applications in structural biology and medicinal chemistry require protein-ligand scoring functions for two distinct tasks: (i) ranking different poses of a small molecule in a protein binding site; and (ii) ranking different small molecules by their complementarity to a protein site. Using probability theory, we developed two atomic distance-dependent statistical scoring functions: PoseScore was optimized for recognizing native binding geometries of ligands from other poses and RankScore was optimized for distinguishing ligands from nonbinding molecules. Both scores are based on a set of 8,885 crystallographic structures of protein-ligand complexes, but differ in the values of three key parameters. Factors influencing the accuracy of scoring were investigated, including the maximal atomic distance and non-native ligand geometries used for scoring, as well as the use of protein models instead of crystallographic structures for training and testing the scoring function. For the test set of 19 targets, RankScore improved the ligand enrichment (logAUC) and early enrichment (EF1) scores computed by DOCK 3.6 for 13 and 14 targets, respectively. In addition, RankScore performed better at rescoring than each of seven other scoring functions tested. Accepting both the crystal structure and decoy geometries with all-atom root-mean-square errors of up to 2 Å from the crystal structure as correct binding poses, PoseScore gave the best score to a correct binding pose among 100 decoys for 88% of all cases in a benchmark set containing 100 protein-ligand complexes. PoseScore accuracy is comparable to that of DrugScoreCSD and ITScore/SE, and superior to 12 other tested scoring functions. Therefore, RankScore can facilitate ligand discovery, by ranking complexes of the target with different small molecules; PoseScore can be used for protein-ligand complex structure prediction, by ranking different conformations of a given protein-ligand pair. The statistical potentials are available through the Integrative Modeling Platform (IMP) software package (http://salilab.org/imp/) and the LigScore web server (http://salilab.org/ligscore/).
statistical potential; reference state; binding pose; ligand enrichment
Discovering the unintended “off-targets” that predict adverse drug reactions (ADRs) is daunting by empirical methods alone. Drugs can act on multiple protein targets, some of which can be unrelated by traditional molecular metrics, and hundreds of proteins have been implicated in side effects. We therefore explored a computational strategy to predict the activity of 656 marketed drugs on 73 unintended “side effect” targets. Approximately half of the predictions were confirmed, either from proprietary databases unknown to the method or by new experimental assays. Affinities for these new off-targets ranged from 1 nM to 30 μM. To explore relevance, we developed an association metric to prioritize those new off-targets that explained side effects better than any known target of a given drug, creating a Drug-Target-ADR network. Among these new associations was the prediction that the abdominal pain side effect of the synthetic estrogen chlorotrianisene was mediated through its newly discovered inhibition of the enzyme COX-1. The clinical relevance of this inhibition was borne-out in whole human blood platelet aggregation assays. This approach may have wide application to de-risking toxicological liabilities in drug discovery.
The Enzyme Function Initiative (EFI) was recently established to address the challenge of assigning reliable functions to enzymes discovered in bacterial genome projects; in this Current Topic we review the structure and operations of the EFI. The EFI includes the Superfamily/Genome, Protein, Structure, Computation, and Data/Dissemination Cores that provide the infrastructure for reliably predicting the in vitro functions of unknown enzymes. The initial targets for functional assignment are selected from five functionally diverse superfamilies (amidohydrolase, enolase, glutathione transferase, haloalkanoic acid dehalogenase, and isoprenoid synthase), with five superfamily-specific Bridging Projects experimentally testing the predicted in vitro enzymatic activities. The EFI also includes the Microbiology Core that evaluates the in vivo context of in vitro enzymatic functions and confirms the functional predictions of the EFI. The deliverables of the EFI to the scientific community include: 1) development of a large-scale, multidisciplinary sequence/structure-based strategy for functional assignment of unknown enzymes discovered in genome projects (target selection, protein production, structure determination, computation, experimental enzymology, microbiology, and structure-based annotation); 2) dissemination of the strategy to the community via publications, collaborations, workshops, and symposia; 3) computational and bioinformatic tools for using the strategy; 4) provision of experimental protocols and/or reagents for enzyme production and characterization; and 5) dissemination of data via the EFI’s website, enzymefunction.org. The realization of multidisciplinary strategies for functional assignment will begin to define the full metabolic diversity that exists in nature and will impact basic biochemical and evolutionary understanding, as well as a wide range of applications of central importance to industrial, medicinal and pharmaceutical efforts.
Target identification is a core challenge in chemical genetics. Here we use chemical similarity to predict computationally the targets of 586 compounds active in a zebrafish behavioral assay. Of 20 predictions tested, 11 had activities ranging from 1 to 10,000nM on the predicted targets. The role of two of these targets was tested in the original zebrafish phenotype. Prediction of targets from chemotype is rapid and may be generally applicable.
Ligand enrichment among top-ranking hits is a key metric of molecular docking. To avoid bias, decoys should resemble ligands physically, so that enrichment is not simply a separation of gross features, yet be chemically distinct from them, so that they are unlikely to be binders. We have assembled a directory of useful decoys (DUD), with 2950 ligands for 40 different targets. Every ligand has 36 decoy molecules that are physically similar but topologically distinct, leading to a database of 98,266 compounds. For most targets, enrichment was at least half a log better with uncorrected databases such as the MDDR than with DUD, evidence of bias in the former. These calculations also allowed forty-by-forty cross docking, where the enrichments of each ligand set could be compared for all 40 targets, enabling a specificity metric for the docking screens. DUD is freely available online as a benchmarking set for docking at http://blaster.docking.org/dud/.
virtual screening; molecular docking; docking decoy; automation; enrichment; binding pose
G-Protein coupled receptors (GPCRs) are intensely studied as drug targets and for their role in signaling. With the determination of the first crystal structures, interest in structure-based ligand discovery has increased. Unfortunately, most GPCRs lack experimental structures. The determination of the D3 receptor structure, and a community challenge to predict it, enabled a fully prospective comparison of ligand discovery from a modeled structure versus that of the subsequently released crystal structure. Over 3.3 million molecules were docked against a homology model, and 26 of the highest ranking were tested for binding. Six had affinities from 0.2 to 3.1μM. Subsequently, the crystal structure was released and the docking screen repeated. Of the 25 compounds selected, five had affinities from 0.3 to 3.0μM. One of the novel ligands from the homology model screen was optimized for affinity to 81nM. The feasibility of docking screens against modeled GPCRs more generally is considered.
To compare virtual and high-throughput screening in an unbiased way, 50,000 compounds were docked into the 3-dimensional structure of dihydrofolate reductase prospectively, and the results were compared to a subsequent experimental screening of the same library. Undertaking these calculations demanded careful database curation and control calculations with annotated inhibitors. These ultimately led to a ranked list of more likely and less likely inhibitors and to the prediction that relatively few inhibitors would be found in the empirical screen. The latter prediction turned out to be correct, with arguably no validated inhibitors found experimentally. Subsequent retesting of high-scoring docked molecules may have found 2 true inhibitors, although this remains uncertain due to experimental ambiguities. The implications of this study for screening campaigns are considered. (Journal of Biomolecular Screening 2005:667-674)
high-throughput screening; HTS; virtual screening; molecular docking; database preparation
Two enzymes of unknown function from the amidohydrolase superfamily were discovered to catalyze the deamination of N-6-methyladenine to hypoxanthine and methyl amine. The methylation of adenine in bacterial DNA is a common modification for the protection of host DNA against restriction endonucleases. The enzyme from Bacillus halodurans, Bh0637, catalyzes the deamination of N-6-methyladenine with a kcat of 185 s−1 and a kcat/Km of 2.5 × 106 M−1 s−1. Bh0637 catalyzes the deamination of N-6-methyladenine two orders of magnitude faster than adenine. A comparative model of Bh0637 was computed using the three-dimensional structure of Atu4426 (PDB code: 3NQB) as a structural template and computational docking was used to rationalize the preferential utilization of N-6-methyladenine over adenine. This is the first identification of an N-6-methyladenine deaminase (6-MAD).
Clozapine, by virtue of its absence of extrapyramidal side effects and greater efficacy, revolutionized the treatment of schizophrenia, although the mechanisms underlying this exceptional activity remain controversial. Combining an unbiased cheminformatics and physical screening approach, we evaluated clozapine's activity at >2350 distinct molecular targets. Clozapine, and the closely related atypical antipsychotic drug olanzapine, interacted potently with a unique spectrum of molecular targets. This distinct pattern, which was not shared with the typical antipsychotic drug haloperidol, suggested that the serotonergic neuronal system was a key determinant of clozapine's actions. To test this hypothesis, we used pet1−/− mice, which are deficient in serotonergic presynaptic markers. We discovered that the antipsychotic-like properties of the atypical antipsychotic drugs clozapine and olanzapine were abolished in a pharmacological model that mimics NMDA-receptor hypofunction in pet1−/− mice, whereas haloperidol's efficacy was unaffected. These results show that clozapine's ability to normalize NMDA-receptor hypofunction, which is characteristic of schizophrenia, depends on an intact presynaptic serotonergic neuronal system.
serotonin; schizophrenia; clozapine; pet1; antipsychotic drugs; receptor pharmacology; schizophrenia; antipsychotics; drug discovery; development; molecular & cellular neurobiology; chemical biology
We investigated a series of sulfonamide boronic acids that resulted from the merging of two unrelated AmpC β-lactamase inhibitor series. The new boronic acids differed in the replacement of the canonical carboxamide, found in all penicillin and cephalosporin antibiotics, with a sulfonamide. Surprisingly, these sulfonamides had a highly distinct structure-activity relationship from the previously explored carboxamides, high ligand efficiencies (up to 0.91), Ki values down to 25 nM and up to 23 times better for smaller analogs. Conversely, Ki values were 10 to 20 times worse for larger molecules than in the carboxamide congener series. X-ray crystal structures (1.6–1.8 Å) of AmpC with three of the new sulfonamides suggest that this altered structure-activity relationship results from the different geometry and polarity of the sulfonamide versus the carboxamide. The most potent inhibitor reversed β-lactamase-mediated resistance to third generation cephalosporins, lowering their minimum inhibitory concentrations up to 32-fold in cell culture.
AmpC; structure-based inhibitor design; structure-based drug discovery; β-lactamase inhibition; X-ray crystallography; antimicrobial; antibiotic resistance
A small set of boronic acids acting as low nanomolar inhibitors of AmpC β-lactamase were designed and synthesized in the effort to improve affinity, pharmacokinetic properties, and to provide a valid lead compound. X-ray crystallography revealed the binary complex of the best inhibitor bound to the enzyme, highlighting possibilities for its further rational derivatization and chemical optimization.
Dr0930, a member of the amidohydrolase superfamily in Deinococcus radiodurans, was cloned, expressed and purified to homogeneity. The enzyme crystallized in the space group P3121 and the structure was determined to a resolution of 2.1 Å. The protein folds as a (β/α)7β-barrel and a binuclear metal center is found at the C-terminal end of the β-barrel. The purified protein contains a mixture of zinc and iron and is intensely purple at high concentrations. The purple color was determined to be due to a charge transfer complex between iron in the β-metal position and Tyr-97. Mutation of Tyr-97 to phenylalanine or complexation of the metal center with manganese abolished the absorbance in the visible region of the spectrum. Computational docking was used to predict potential substrates for this previously unannotated protein. The enzyme was found to catalyze the hydrolysis of δ- and γ-lactones with an alkyl substitution at the carbon adjacent to the ring oxygen. The best substrate was δ-nonanoic lactone with a kcat/Km of 1.6 × 106 M−1 s−1. Dr0930 was also found to catalyze the very slow hydrolysis of paraoxon with values of kcat and kcat/Km of 0.07 min−1 and 0.8 M−1 s−1, respectively. The amino acid sequence identity to the phosphotriesterase (PTE) from Pseudomonas diminuta is ~30%. The eight substrate specificity loops were transplanted from PTE to Dr0930 but no phosphotriesterase activity could be detected in the chimeric PTE-Dr0930 hybrid. Mutation of Phe-26 and Cys-72 in Dr0930 to residues found in the active site of PTE enhanced the kinetic constants for the hydrolysis of paraoxon. The F26G/C72I mutant catalyzed the hydrolysis of paraoxon with a kcat of 1.14 min−1, an increase of 16-fold over the wild type enzyme. These results support previous proposals that phosphotriesterase activity evolved from an ancestral parent enzyme possessing lactonase activity.
The Similarity Ensemble Approach (SEAa) relates proteins based on the set-wise chemical similarity among their ligands. It can be used to rapidly search large compound databases and to build cross-target similarity maps. The emerging maps relate targets in ways that reveal relationships one might not recognize based on sequence or structural similarities alone. SEA has previously revealed cross talk between drugs acting primarily on G-protein coupled receptors (GPCRs). Here we used SEA to look for potential off-target inhibition of the enzyme protein farnesyltransferase (PFTase) by commercially available drugs. The inhibition of PFTase has profound consequences for oncogenesis, as well as a number of other diseases. In the present study, two commercial drugs, Loratadine and Miconazole, were identified as potential ligands for PFTase and subsequently confirmed as such experimentally. These results point towards the applicability of SEA for the prediction of not only GPCR-GPCR drug cross talk, but also GPCR-enzyme and enzyme-enzyme drug cross talk.
An enzyme from Pseudomonas aeruginosa, Pa0142 (gi|9945972) has been identified for the first time that is able to catalyze the deamination of 8-oxoguanine (8-oxoG) to uric acid. 8-Oxoguanine is formed by the oxidation of guanine residues within DNA by reactive oxygen species and this lesion results in the G:C to T:A transversions. The value of kcat/Km for the deamination of 8-oxoG by Pa0142 at pH 8.0 and 30 °C is 2.0 × 104 M−1 s−1. This enzyme can also catalyze the deamination of isocystosine and guanine at rates that are approximately an order of magnitude slower. The three-dimensional structure of a homologous enzyme (gi|44264246) from the Sargasso Sea has been determined by x-ray diffraction methods to a resolution of 2.2Å (PDB code: 3h4u). The enzyme folds as a (β/α)8− barrel and it is a member of the amidohydrolase superfamily with a single zinc in the active site. This enzyme catalyzes the deamination of 8-oxoG with a value of kcat/Km of 2.7 × 105 M−1 s−1. Computational docking of potential high energy intermediates for the deamination reaction to the x-ray crystal structure suggests that the active site binding of 8-oxoG is facilitated by hydrogen bond interactions from a conserved glutamine that follows β-strand 1 with O6, a conserved tyrosine that follows β-strand 2 with N7, and a conserved cysteine residue that follows β-strand 4 with O8. A bioinformatic analysis of available protein sequences suggest that approximately 200 other bacteria possess an enzyme capable of catalyzing the deamination of 8-oxoG.
Pre-organization of enzyme active sites for substrate recognition typically comes at a cost to the stability of the folded form of the protein, and consequently enzymes can be dramatically stabilized by substitutions that attenuate the size and pre-organization “strain” of the active site. How this stability-activity trade-off constrains enzyme evolution has remained less certain, and it is unclear whether one should expect major stability insults as enzymes mutate towards new activities, or how these new activities manifest structurally. These questions are both germane and easy to study in β-lactamases, which are evolving on the timescale of years to confer resistance to an ever-broader spectrum of β-lactam antibiotics. To explore whether stability is a substantial constraint on this antibiotic resistance evolution, we investigated extended-spectrum mutants of class C β-lactamases which had evolved new activity versus third-generation cephalosporins. Five mutant enzymes had between 100- to 200-fold increased activity against the antibiotic cefotaxime in enzyme assays, and the mutant enzymes all lost thermodynamic stability – from 1.7 to 4.1 kcal/mol – consistent with the function-stability hypothesis. Intriguingly, several of the substitutions were 10 – 20 Å from the catalytic serine; the question arose how they conferred extended-spectrum activity. Eight structures, including complexes with inhibitors and extended-spectrum antibiotics, were determined by x-ray crystallography. Distinct mechanisms of action are revealed for each mutant, including changes in the flexibility and ground state structures of the enzyme. These results explain the structural bases for the antibiotic resistance conferred by these substitutions, and their corresponding decrease in protein stability, which will constrain the evolution of new antibiotic resistance.
protein stability; AmpC beta-lactamase; antibiotic resistance; evolution; action-at-a-distance
Human renal dipeptidase, an enzyme associated with glutathione metabolism and the hydrolysis of β-lactams, is similar in sequence to a cluster of ~400 microbial proteins currently annotated as nonspecific dipeptidases within the amidohydrolase superfamily. The closest homologue to the human renal dipeptidase from a fully sequenced microbe is Sco3058 from Streptomyces coelicolor. Dipeptide substrates of Sco3058 were identified by screening a comprehensive series of L-Xaa-L-Xaa, L-Xaa-D-Xaa and D-Xaa-L-Xaa dipeptide libraries. The substrate specificity profile shows that Sco3058 hydrolyzes a broad range of dipeptides with a marked preference for an L-amino acid at the N-terminus and a D-amino acid at the C-terminus. The best substrate identified was L-Arg-D-Asp (kcat/Km = 7.6 × 105 M−1 s−1). The three-dimensional structure of Sco3058 was determined in the absence and presence of the inhibitors citrate and a phosphinate mimic of L-Ala-D-Asp. The enzyme folds as a (β/α)8-barrel and two zinc ions are bound in the active site. Site-directed mutagenesis was used to probe the importance of specific residues that have direct interactions with the substrate analogues in the active site (Asp-22, His-150, Arg-223 and Asp-320). Solvent viscosity and kinetic effects by D2O indicate that substrate binding is relatively sticky and that proton transfers do not occurr during the rate-limiting step. A bell-shaped pH-rate profile for kcat and kcat/Km indicated that one group needs to be deprotonated and a second group must be protonated for optimal turnover. Computational docking of high-energy intermediate forms of L/D-Ala-L/D-Ala to the three dimensional structure of Sco3058 identified the structural determinants for the stereochemical preferences for substrate binding and turnover.
A model binding site was used to investigate charge–charge interactions in molecular docking. This simple site, a small (180 Å3) engineered cavity in cyctochrome c peroxidase (CCP), is negatively charged and completely buried from solvent, allowing us to explore the balance between electrostatic energy and ligand desolvation energy in a system where many of the common approximations in docking do not apply. A database with about 5300 molecules was docked into this cavity. Retrospective testing with known ligands and decoys showed that overall the balance between electrostatic interaction and desolvation energy was captured. More interesting were prospective docking scre”ens that looked for novel ligands, especially those that might reveal problems with the docking and energy methods. Based on screens of the 5300 compound database, both high-scoring and low-scoring molecules were acquired and tested for binding. Out of 16 new, high-scoring compounds tested, 15 were observed to bind. All of these were small heterocyclic cations. Binding constants were measured for a few of these, they ranged between 20 μM and 60 μM. Crystal structures were determined for ten of these ligands in complex with the protein. The observed ligand geometry corresponded closely to that predicted by docking. Several low-scoring alkyl amino cations were also tested and found to bind. The low docking score of these molecules owed to the relatively high charge density of the charged amino group and the corresponding high desolvation penalty. When the complex structures of those ligands were determined, a bound water molecule was observed interacting with the amino group and a backbone carbonyl group of the cavity. This water molecule mitigates the desolvation penalty and improves the interaction energy relative to that of the “naked” site used in the docking screen. Finally, six low-scoring neutral molecules were also tested, with a view to looking for false negative predictions. Whereas most of these did not bind, two did (phenol and 3-fluorocatechol). Crystal structures for these two ligands in complex with the cavity site suggest reasons for their binding. That these neutral molecules do, in fact bind, contradicts previous results in this site and, along with the alkyl amines, provides instructive false negatives that help identify weaknesses in our scoring functions. Several improvements of these are considered.
molecular docking; electrostatic; solvation; cyctochrome c peroxidase; X-ray crystallography
Trypanosoma cruzi and Trypanosoma brucei are parasites that cause Chagas’ disease and African sleeping sickness, respectively. Both parasites rely on essential cysteine proteases for survival, cruzain for T. cruzi and TbCatB/rhodesain for T. brucei. A recent quantitative high-throughput screen of cruzain identified triazine nitriles, which are known inhibitors of other cysteine proteases, as reversible inhibitors of the enzyme. Structural modifications detailed herein, including core scaffold modification from triazine to purine, improved the in vitro potency against both cruzain and rhodesain by 350-fold, while also gaining activity against T. brucei parasites. Selected compounds were screened against a panel of human cysteine and serine proteases to determine selectivity, and a co-crystal was obtained of our most potent analog bound to Cruzain.
We present a combined experimental and modeling study of organic ligand molecules binding to a slightly polar engineered cavity site in T4 lysozyme (L99A/M102Q). For modeling, we computed alchemical absolute binding free energies. These were blind tests performed prospectively on 13 diverse, previously untested candidate ligand molecules. We predicted that eight compounds would bind to the cavity and five would not; 11 of 13 predictions were correct at this level. The RMS error to the measurable absolute binding energies was 1.8 kcal/mol. In addition, we computed relative binding free energies for six phenol derivatives starting from two known ligands: phenol and catechol. The average RMS error in the relative free energy prediction was 2.5 (phenol) and 1.1 (catechol) kcal/mol. To understand these results at atomic resolution, we obtained x-ray co-complex structures for nine of the diverse ligands and for all six phenol analogs. The average RMSD of the predicted pose to the experiment was 2.0Å (diverse set), 1.8Å (phenol derived predictions) and 1.2Å (catechol derived predictions). We found that to predict accurate affinities and rank-orderings required near-native starting orientations of the ligand in the binding site. Unanticipated binding modes, multiple ligand binding, and protein conformational change all proved challenging for the free energy methods. We believe these results can help guide future improvements in physics-based absolute binding free energy methods.
Alchemical free energy; free energy calculation; T4 lysozyme; model cavity site; hydrophobic and polar
The perceived and actual burden of false positives in high-throughput screening has received considerable attention; however, few studies exist on the contributions of distinct mechanisms of non-specific effects like chemical reactivity, assay signal interference, and colloidal aggregation. Here, we analyze the outcome of a screen of 197,861 diverse compounds in a concentration-response format against the cysteine protease cruzain, a target expected to be particularly sensitive to reactive compounds and using an assay format with light detection in the short-wavelength region where significant compound autofluorescence is typically encountered. Approximately 1.9% of all compounds screened were detergent-sensitive inhibitors. The contribution from autofluorescence and compounds bearing reactive functionalities was dramatically lower: of all hits, only 1.8% were autofluorescent and 1.48% contained reactive or undesired functional groups. The distribution of false positives was relatively constant across library sources. The simple step of including detergent in the assay buffer suppressed the nonspecific effect of approximately 93% of the original hits.
Computer-based docking screens are now widely used to discover new ligands for targets of known structure; in the last two years alone, the discovery of ligands for over 20 proteins have been reported. Recently, investigators have also turned to predicting new substrates and for enzymes of unknown function, taking docking in a wholly new direction. Increasingly, the hit-rates, the true- and the false-positives from the docking screens are being compared to those from empirical, high-throughput screens, revealing the strengths, weaknesses and complementarities of both techniques. The recent efflorescence of GPCR structures has made these quintessential drug targets available to structure-based approaches. Consistent with their “druggability”, the docking screens have returned high hit-rates and potent molecules. Finally, in the last several years, an approach almost exactly opposite to docking has also appeared; this pharmacological network approach begins not with the structure of the target but rather those of drug molecules and asks, given a pattern of chemistry in the ligands, what targets may a particular drug bind to? This method, which returns to an older, pharmacology logic, has been surprisingly successful in predicting new “off-targets” for established drugs.
Whereas drugs are intended to be selective, at least some bind to several physiologic targets, explaining both side effects and efficacy. As many drug-target combinations exist, it would be useful to explore possible interactions computationally. Here, we compared 3,665 FDA-approved and investigational drugs against hundreds of targets, defining each target by its ligands. Chemical similarities between drugs and ligand sets predicted thousands of unanticipated associations. Thirty were tested experimentally, including the antagonism of the β1 receptor by the transporter inhibitor Prozac, the inhibition of the 5-HT transporter by the ion channel drug Vadilex, and antagonism of the histamine H4 receptor by the enzyme inhibitor Rescriptor. Overall, 23 new drug-target associations were confirmed, five of which were potent (< 100 nM). The physiological relevance of one such, the drug DMT on serotonergic receptors, was confirmed in a knock-out mouse. The chemical similarity approach is systematic and comprehensive, and may suggest side-effects and new indications for many drugs.
In lead discovery, libraries of 106 molecules are screened for biological activity. Given the over 1060 drug-like molecules thought possible, such screens might never succeed. That they do, even occasionally, implies a biased selection of library molecules. Here a method is developed to quantify the bias in screening libraries towards biogenic molecules. With this approach, we consider what is missing from screening libraries and how they can be optimized.
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 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 more than 25% sequence identity with the target.
comparative modeling; docking screens; consensus enrichment