Novartis Institutes for BioMedical Research 220 Mass Ave Cambridge, MA 02139
jeremy.jenkins@novartis.com
I am a Senior Investigator I at Novartis Institutes for BioMedical Research. My group studies Chemical Biology Informatics in the Developmental & Molecular Pathways Department. We are interested in applying informatics to the problems of assessing phenotypic screens and identifying compound targets/MOA.
Abstract: Since the advent of High Throughput Screening (HTS), there has been an urgent need for methods that facilitate the interrogation of large-scale chemical biology data to build a mode of action (MoA) hypothesis. This can be done either prior to the HTS by subset design of compounds with known MoA or post HTS by data annotation and mining. To enable this process, we develop a tool that compares compounds solely based on their bioactivity -the chemical biological descriptors "High-Throughput Screening Fingerprints" (HTS-FP). In the current embodiment, data are aggregated from 195 biochemical and cell-based assays developed at Novartis and can be used to identify bioactivity relationships among the in-house collection comprising ~1.5 million compounds. We demonstrate the value of the HTS-FP for virtual screening and in particular scaffold hopping. HTS-FP outperforms state of the art methods in several aspects, retrieving bioactive compounds with remarkable chemical dissimilarity to a probe structure. We also apply HTS-FP for the design of screening subsets in HTS. Using retrospective data, we show that a biodiverse selection of plates performs significantly better than a chemically diverse selection of plates, both in terms of number of hits and diversity of chemotypes retrieved. This is also true in the case of hit expansion predictions using HTS-FP similarity. Sets of compounds clustered with HTS-FP are biologically meaningful, in a sense that these clusters enrich for genes and gene ontology (GO) terms, showing that compounds that are bioactively similar also tend to target proteins that operate together in the cell. HTS-FP are not only valuable because of their predictive power, but mainly because they relate compounds solely based on bioactivity, harnessing the accumulated knowledge of a high-throughput screening facility towards the understanding of how compounds interact with the proteome.
Abstract: ABSTRACT: The identification of molecular target and mechanism of action of compounds is a key hurdle in drug discovery. Multiplexed techniques for bead-based expression profiling allow the measurement of transcriptional signatures of compound-treated cells in high-throughput mode. Such profiles can be used to gain insight into compounds' mode of action and the protein targets they are modulating. Through the proxy of target prediction from such gene signatures we explored important aspects of the use of transcriptional profiles to capture biological variability of perturbed cellular assays. We found that signatures derived from expression data and signatures derived from biological interaction networks performed equally well, and we showed that gene signatures can be optimised using a genetic algorithm. Gene signatures of approximately 128 genes seemed to be most generic, capturing a maximum of the perturbation inflicted on cells through compound treatment. Moreover, we found evidence for oxidative phosphorylation to be one of the most general ways to capture compound perturbation.
Abstract: From a medicinal chemistry point of view, one of the primary goals of high throughput screening (HTS) hit list assessment is the identification of chemotypes with an informative structure-activity relationship (SAR). Such chemotypes may enable optimization of the primary potency, as well as selectivity and phamacokinetic properties. A common way to prioritize them is molecular clustering of the hits. Typical clustering techniques, however, rely on a general notion of chemical similarity or standard rules of scaffold decomposition and are thus insensitive to molecular features that are enriched in biologically active compounds. This hinders SAR analysis, because compounds sharing the same pharmacophore might not end up in the same cluster and thus are not directly compared to each other by the medicinal chemist. Similarly, common chemotypes that are not related to activity may contaminate clusters, distracting from important chemical motifs. We combined molecular similarity and Bayesian models and introduce (I) a robust, activity-aware clustering approach and (II) a feature mapping method for the elucidation of distinct SAR determinants in polypharmacologic compounds. We evaluated the method on 462 dose-response assays from the Pubchem Bioassay repository. Activity-aware clustering grouped compounds sharing molecular cores that were specific for the target or pathway at hand, rather than grouping inactive scaffolds commonly found in compound series. Many of these core structures we also found in literature that discussed SARs of the respective targets. A numerical comparison of cores allowed for identification of the structural prerequisites for polypharmacology, i.e., distinct bioactive regions within a single compound, and pointed toward selectivity-conferring medchem strategies. The method presented here is generally applicable to any type of activity data and may help bridge the gap between hit list assessment and designing a medchem strategy.
Abstract: Introduction: The goal of early predictive safety assessment (PSA) is to keep compounds with detectable liabilities from progressing further in the pipeline. Such compounds jeopardize the core of pharmaceutical research and development and limit the timely delivery of innovative therapeutics to the patient. Computational methods are increasingly used to help understand observed data, generate new testable hypotheses of relevance to safety pharmacology, and supplement and replace costly and time-consuming experimental procedures. Areas covered: The authors survey methods operating on different scales of both physical extension and complexity. After discussing methods used to predict liabilities associated with structures of individual compounds, the article reviews the use of adverse event data and safety profiling panels. Finally, the authors examine the complexities of toxicology data from animal experiments and how these data can be mined. Expert opinion: A significant obstacle for data-driven safety assessment is the absence of integrated data sets due to a lack of sharing of data and of using standard ontologies for data relevant to safety assessment. Informed decisions to derive focused sets of compounds can help to avoid compound liabilities in screening campaigns, and improved hit assessment of such campaigns can benefit the early termination of undesirable compounds.
Abstract: Given the tremendous growth of bioactivity databases, the use of computational tools to predict protein targets of small molecules has been gaining importance in recent years. Applications span a wide range, from the 'designed polypharmacology' of compounds to mode-of-action analysis. In this review, we firstly survey databases that can be used for ligand-based target prediction and which have grown tremendously in size in the past. We furthermore outline methods for target prediction that exist, both based on the knowledge of bioactivities from the ligand side and methods that can be applied in situations when a protein structure is known. Applications of successful in silico target identification attempts are discussed in detail, which were based partly or in whole on computational target predictions in the first instance. This includes the authors' own experience using target prediction tools, in this case considering phenotypic antibacterial screens and the analysis of high-throughput screening data. Finally, we will conclude with the prospective application of databases to not only predict, retrospectively, the protein targets of a small molecule, but also how to design ligands with desired polypharmacology in a prospective manner.
Abstract: The design of a high-quality screening collection is of utmost importance for the early drug-discovery process and provides, in combination with high-quality assay systems, the foundation of future discoveries. Herein, we review recent trends and observations to successfully expand the access to bioactive chemical space, including the feedback from hit assessment interviews of high-throughput screening campaigns; recent successes with chemogenomics target family approaches, the identification of new relevant target/domain families, diversity-oriented synthesis and new emerging compound classes, and non-classical approaches, such as fragment-based screening and DNA-encoded chemical libraries. The role of in silico library design approaches are emphasized.
Abstract: We present here a comprehensive analysis of proteases in the peptide substrate space and demonstrate its applicability for lead discovery. Aligned octapeptide substrates of 498 proteases taken from the MEROPS peptidase database were used for the in silico analysis. A multiple-category naïve Bayes model, trained on the two-dimensional chemical features of the substrates, was able to classify the substrates of 365 (73%) proteases and elucidate statistically significant chemical features for each of their specific substrate positions. The positional awareness of the method allows us to identify the most similar substrate positions between proteases. Our analysis reveals that proteases from different families, based on the traditional classification (aspartic, cysteine, serine, and metallo), could have substrates that differ at the cleavage site (P1-P1') but are similar away from it. Caspase-3 (cysteine protease) and granzyme B (serine protease) are previously known examples of cross-family neighbors identified by this method. To assess whether peptide substrate similarity between unrelated proteases could reliably translate into the discovery of low molecular weight synthetic inhibitors, a lead discovery strategy was tested on two other cross-family neighbors--namely cathepsin L2 and matrix metallo proteinase 9, and calpain 1 and pepsin A. For both these pairs, a naïve Bayes classifier model trained on inhibitors of one protease could successfully enrich those of its neighbor from a different family and vice versa, indicating that this approach could be prospectively applied to lead discovery for a novel protease target with no known synthetic inhibitors.
Abstract: The elucidation of drug targets is important both to optimize desired compound action and to understand drug side-effects. In this study, we created statistical models which link chemical substructures of ligands to protein domains in a probabilistic manner and employ the model to triage the results of affinity chromatography experiments. By annotating targets with their InterPro domains, general rules of ligand-protein domain associations were derived and successfully employed to predict protein targets outside the scope of the training set. This methodology was then tested on a proteomics affinity chromatography data set containing 699 compounds. The domain prediction model correctly detected 31.6% of the experimental targets at a specificity of 46.8%. This is striking since 86% of the predicted targets are not part of them (but share InterPro domains with them), and thus could not have been predicted by conventional target prediction approaches. Target predictions improve drastically when significance (FDR) scores for target pulldowns are employed, emphasizing their importance for eliminating artifacts. Filament proteins (such as actin and tubulin) are detected to be 'frequent hitters' in proteomics experiments and their presence in pulldowns is not supported by the target predictions. On the other hand, membrane-bound receptors such as serotonin and dopamine receptors are noticeably absent in the affinity chromatography sets, although their presence would be expected from the predicted targets of compounds. While this can partly be explained by the experimental setup, we suggest the computational methods employed here as a complementary step of identifying protein targets of small molecules. Affinity chromatography results for gefitinib are discussed in detail and while two out of the three kinases with the highest affinity to gefitinib in biochemical assays are detected by affinity chromatography, also the possible involvement of NSF as a target for modulating cancer progressions via beta-arrestin can be proposed by this method.
Abstract: We present a workflow that leverages data from chemogenomics based target predictions with Systems Biology databases to better understand off-target related toxicities. By analyzing a set of compounds that share a common toxic phenotype and by comparing the pathways they affect with pathways modulated by nontoxic compounds we are able to establish links between pathways and particular adverse effects. We further link these predictive results with literature data in order to explain why a certain pathway is predicted. Specifically, relevant pathways are elucidated for the side effects rhabdomyolysis and hypotension. Prospectively, our approach is valuable not only to better understand toxicities of novel compounds early on but also for drug repurposing exercises to find novel uses for known drugs.
Abstract: Different molecular descriptors capture different aspects of molecular structures, but this effect has not yet been quantified systematically on a large scale. In this work, we calculate the similarity of 37 descriptors by repeatedly selecting query compounds and ranking the rest of the database. Euclidean distances between the rank-ordering of different descriptors are calculated to determine descriptor (as opposed to compound) similarity, followed by PCA for visualization. Four broad descriptor classes are identified, which are circular fingerprints; circular fingerprints considering counts; path-based and keyed fingerprints; and pharmacophoric descriptors. Descriptor behavior is much more defined by those four classes than the particular parametrization. Using counts instead of the presence/absence of fingerprints significantly changes descriptor behavior, which is crucial for performance of topological autocorrelation vectors, but not circular fingerprints. Four-point pharmacophores (piDAPH4) surprisingly lead to much higher retrieval rates than three-point pharmacophores (28.21% vs 19.15%) but still similar rank-ordering of compounds (retrieval of similar actives). Looking into individual rankings, circular fingerprints seem more appropriate than path-based fingerprints if complex ring systems or branching patterns are present; count-based fingerprints could be more suitable in databases with a large number of repeated subunits (amide bonds, sugar rings, terpenes). Information-based selection of diverse fingerprints for consensus scoring (ECFP4/TGD fingerprints) led only to marginal improvement over single fingerprint results. While it seems to be nontrivial to exploit orthogonal descriptor behavior to improve retrieval rates in consensus virtual screening, those descriptors still each retrieve different actives which corroborates the strategy of employing diverse descriptors individually in prospective virtual screening settings.
Abstract: Typically, screening collections of pharmaceutical companies contain more than a million compounds today. However, for certain high-throughput screening (HTS) campaigns, constraints posed by the assay throughput and/or the reagent costs make it impractical to screen the entire deck. Therefore, it is desirable to effectively screen subsets of the collection based on a hypothesis or a diversity selection. How to select compound subsets is a subject of ongoing debate. The authors present an approach based on extended connectivity fingerprints to carry out diversity selection on a per plate basis (instead of a per compound basis). HTS data from 35 Novartis screens spanning 5 target classes were investigated to assess the performance of this approach. The analysis shows that selecting a fingerprint-diverse subset of 250K compounds, representing 20% of the screening deck, would have achieved significantly higher hit rates for 86% of the screens. This measure also outperforms the Murcko scaffold-based plate selection described previously, where only 49% of the screens showed similar improvements. Strikingly, the 2-fold improvement in average hit rates observed for 3 of 5 target classes in the data set indicates a target bias of the plate (and thus compound) selection method. Even though the diverse subset selection lacks any target hypothesis, its application shows significantly better results for some targets-namely, G-protein-coupled receptors, proteases, and protein-protein interactions-but not for kinase and pathway screens. The synthetic origin of the compounds in the diverse subset appears to influence the screening hit rates. Natural products were the most diverse compound class, with significantly higher hit rates compared to the compounds from the traditional synthetic and combinatorial libraries. These results offer empirical guidelines for plate-based diversity selection to enhance hit rates, based on target class and the library type being screened.
Abstract: Chemogenomics knowledge-based drug discovery approaches aim to extract the knowledge gained from one target and to apply it for the discovery of ligands and hopefully drugs of a new target which is related to the parent target by homology or conserved molecular recognition. Herein, we demonstrate the potential of knowledge-based virtual screening by applying it to the MDM4-p53 protein-protein interaction where the MDM2-p53 protein-protein interaction constitutes the parent reference system; both systems are potentially relevant to cancer therapy. We show that a combination of virtual screening methods, including homology based similarity searching, QSAR (Quantitative Structure-Activity Relationship) methods, HTD (High Throughput Docking), and UNITY pharmacophore searching provide a successful approach to the discovery of inhibitors. The virtual screening hit list is of the magnitude of 50,000 compounds picked from the corporate compound library of approximately 1.2 million compounds. Emphasis is placed on the facts that such campaigns are only feasible because of the now existing HTCP (High throughput Cherry-Picking) automation systems in combination with robust MTS (Medium Throughput Screening) fluorescence-based assays. Given that the MDM2-p53 system constitutes the reference system, it is not surprising that significantly more and stronger hits are found for this interaction compared to the MDM4-p53 system. Novel, selective and dual hits are discovered for both systems. A hit rate analysis will be provided compared to the full HTS (High-throughput Screening).
Abstract: We present a novel method to better investigate adverse drug reactions in chemical space. By integrating data sources about adverse drug reactions of drugs with an established cheminformatics modeling method, we generate a data set that is then visualized with a systems biology tool. Thereby new insights into undesired drug effects are gained. In this work, we present a global analysis linking chemical features to adverse drug reactions.
Abstract: Understanding the safety of newly developed compounds is a key task in each early drug discovery project. In early stages, pharmaceutical companies address this task by using so-called preclinical safety profiling, in which compounds are screened in inexpensive large-scale assays to understand possible liabilities. This process generates a large amount of binding data on various compounds against a panel of targets - usually thousands or tens of thousands of compounds profiled against approximately 100 different targets. This data matrix is highly valuable and elicits further analysis. After briefly introducing the nature of safety profiling data, we describe several computational methods used internally at Novartis to analyze it. We showcase protocols that can be used to understand compound promiscuity on a chemical structure level and protocols to evaluate the promiscuity of targets used in safety profiling. We also describe a method to quickly determine the chemical similarity of compounds active against different targets. Next, it is shown what protocols can be used to evaluate global chemical similarity of targets. The above approaches can be used either to optimize the composition of a panel of targets or to better understand certain toxicities. Finally, we will explain a simple method to elucidate hidden patterns in safety profiling data.
Abstract: We present a workflow that leverages data from chemogenomics based target predictions with Systems Biology databases to better understand off-target related toxicities. By analyzing a set of compounds that share a common toxic phenotype and by comparing the pathways they affect with pathways modulated by nontoxic compounds we are able to establish links between pathways and particular adverse effects. We further link these predictive results with literature data in order to explain why a certain pathway is predicted. Specifically, relevant pathways are elucidated for the side effects rhabdomyolysis and hypotension. Prospectively, our approach is valuable not only to better understand toxicities of novel compounds early on but also for drug repurposing exercises to find novel uses for known drugs.
Abstract: BACKGROUND: Bacillus thuringiensis Cry1Aa insecticidal protein is the most active known B. thuringiensis toxin against the forest insect pest Lymantria dispar (gypsy moth), unfortunately it is also highly toxic against the non-target insect Bombyx mori (silk worm). RESULTS: Surface exposed hydrophobic residues over domains II and III were targeted for site-directed mutagenesis. Substitution of a phenylalanine residue (F328) by alanine reduced binding to the Bombyx mori cadherin by 23-fold, reduced biological activity against B. mori by 4-fold, while retaining activity against Lymantria dispar. CONCLUSION: The results identify a novel receptor-binding epitope and demonstrate that virtual elimination of binding to cadherin BR-175 does not completely remove toxicity in the case of B. mori.
Abstract: In this work we explore the possibilities of using fragment-based screening data to prioritize compounds from a full HTS library, a method we call virtual fragment linking (VFL). The ability of VFL to identify compounds of nanomolar potency based on micromolar fragment binding data was tested on 75 target classes from the WOMBAT database and succeeded in 57 cases. Further, the method was demonstrated for seven drug targets from in-house screening programs that performed both FBS of 8800 fragments and screens of the full library. VFL captured between 28% and 67% of the hits (IC 50 < 10microM) in the top 5% of the ranked library for four of the targets (enrichment between 5-fold and 13-fold). Our findings lead us to conclude that proper coverage of chemical space by the fragment library is crucial for the VFL methodology to be successful in prioritizing HTS libraries from fragment-based screening data.
Abstract: High-content screening is transforming drug discovery by enabling simultaneous measurement of multiple features of cellular phenotype that are relevant to therapeutic and toxic activities of compounds. High-content screening studies typically generate immense datasets of image-based phenotypic information, and how best to mine relevant phenotypic data is an unsolved challenge. Here, we introduce factor analysis as a data-driven tool for defining cell phenotypes and profiling compound activities. This method allows a large data reduction while retaining relevant information, and the data-derived factors used to quantify phenotype have discernable biological meaning. We used factor analysis of cells stained with fluorescent markers of cell cycle state to profile a compound library and cluster the hits into seven phenotypic categories. We then compared phenotypic profiles, chemical similarity and predicted protein binding activities of active compounds. By integrating these different descriptors of measured and potential biological activity, we can effectively draw mechanism-of-action inferences.
Abstract: High-throughput screening (HTS) is a well-established hit-finding approach used in the pharmaceutical industry. In this article, recent experience at Novartis with respect to factors influencing the success of HTS campaigns is discussed. An inherent measure of HTS quality could be defined by the assay Z and Z' factors, the number of hits and their biological potencies; however, such measures of quality do not always correlate with the advancement of hits to the later stages of drug discovery. Also, for many target classes, such as kinases, it is easy to identify hits, but, as a result of selectivity, intellectual property and other issues, the projects do not result in lead declarations. In this article, HTS success is defined as the fraction of HTS campaigns that advance into the later stages of drug discovery, and the major influencing factors are examined. Interestingly, screening compounds in individual wells or in mixtures did not have a major impact on the HTS success and, equally interesting, there was no difference in the progression rates of biochemical and cell-based assays. Particular target types, assay technologies, structure-activity relationships and powder availability had a much greater impact on success as defined above. In addition, significant mutual dependencies can be observed - while one assay format works well with one target type, this situation might be completely reversed for a combination of the same readout technology with a different target type. The results and opinions presented here should be regarded as groundwork, and a plethora of factors that influence the fate of a project, such as biophysical measurements, chemical attractiveness of the hits, strategic reasons and safety pharmacology, are not covered here. Nonetheless, it is hoped that this information will be used industry-wide to improve success rates in terms of hits progressing into exploratory chemistry and beyond. The support that can be obtained from new in silico approaches to phase transitions are also described, along with the gaps they are designed to fill.
Abstract: We compared two algorithms for ligand-target prediction, namely, the Laplacian-modified Bayesian classifier and the Winnow algorithm. A dataset derived from the WOMBAT database, spanning 20 pharmaceutically relevant activity classes with 13 000 compounds, was used for performance assessment in 24 different experiments, each of which was assessed using a 15-fold Monte Carlo cross-validation. Compounds were described by different circular fingerprints, ECFP_4 and MOLPRINT 2D. A detailed analysis of the resulting approximately 2.4 million predictions led to very similar measures for overall accuracy for both classifiers, whereas we observed significant differences for individual activity classes. Moreover, we analyzed our data with respect to the numbers of compounds which are exclusively retrieved by either of the algorithmsbut never by the otheror by neither of them. This provided detailed information that can never be obtained by considering the overall performance statistics alone.
Abstract: Development of a pharmacophore hypothesis related to small-molecule activity is pivotal to chemical optimization of a series, since it defines features beneficial or detrimental to activity. Although crystal structures may provide detailed 3D interaction information for one molecule with its receptor, docking a different ligand to that model often leads to unreliable results due to protein flexibility. Graham Richards' lab was one of the first groups to utilize "fuzzy" pattern recognition algorithms taken from the field of image processing to solve problems in protein modeling. Thus, descriptor "fuzziness" was partly able to emulate conformational flexibility of the target while simultaneously enhancing the speed of the search. In this work, we extend these developments to a ligand-based method for describing and aligning molecules in flexible chemical space termed FEature POint PharmacophoreS (FEPOPS), which allows exploration of dynamic biological space. We develop a novel, combinatorial algorithm for molecular comparisons and evaluate it using the WOMBAT dataset. The new approach shows superior retrospective virtual screening performance than earlier shape-based or charge-based algorithms. Additionally, we use target prediction to evaluate how FEPOPS alignments match the molecules biological activity by identifying the atoms and features that make the key contributions to overall chemical similarity. Overall, we find that FEPOPS are sufficiently fuzzy and flexible to find not only new ligand scaffolds, but also challenging molecules that occupy different conformational states of dynamic biological space as from induced fits.
Abstract: Classification methods for data sets of molecules according to their chemical structure were evaluated for their biological relevance, including rule-based, scaffold-oriented classification methods and clustering based on molecular descriptors. Three data sets resulting from uniformly determined in vitro biological profiling experiments were classified according to their chemical structures, and the results were compared in a Pareto analysis with the number of classes and their average spread in the profile space as two concurrent objectives which were to be minimized. It has been found that no classification method is overall superior to all other studied methods, but there is a general trend that rule-based, scaffold-oriented methods are the better choice if classes with homogeneous biological activity are required, but a large number of clusters can be tolerated. On the other hand, clustering based on chemical fingerprints is superior if fewer and larger classes are required, and some loss of homogeneity in biological activity can be accepted.
Abstract: This work describes a novel semi-sequential technique for in silico enhancement of high-throughput screening (HTS) experiments now employed at Novartis. It is used in situations in which the size of the screen is limited by the readout (e.g., high-content screens) or the amount of reagents or tools (proteins or cells) available. By performing computational chemical diversity selection on a per plate basis (instead of a per compound basis), 25% of the 1,000,000-compound screening was optimized for general initial HTS. Statistical models are then generated from target-specific primary results (percentage inhibition data) to drive the cherry picking and testing from the entire collection. Using retrospective analysis of 11 HTS campaigns, the authors show that this method would have captured on average two thirds of the active compounds (IC(50) < 10 microM) and three fourths of the active Murcko scaffolds while decreasing screening expenditure by nearly 75%. This result is true for a wide variety of targets, including G-protein-coupled receptors, chemokine receptors, kinases, metalloproteinases, pathway screens, and protein-protein interactions. Unlike time-consuming "classic" sequential approaches that require multiple iterations of cherry picking, testing, and building statistical models, here individual compounds are cherry picked just once, based directly on primary screening data. Strikingly, the authors demonstrate that models built from primary data are as robust as models built from IC(50) data. This is true for all HTS campaigns analyzed, which represent a wide variety of target classes and assay types.
Abstract: High throughput screening (HTS) data is often noisy, containing both false positives and negatives. Thus, careful triaging and prioritization of the primary hit list can save time and money by identifying potential false positives before incurring the expense of followup. Of particular concern are cell-based reporter gene assays (RGAs) where the number of hits may be prohibitively high to be scrutinized manually for weeding out erroneous data. Based on statistical models built from chemical structures of 650 000 compounds tested in RGAs, we created "frequent hitter" models that make it possible to prioritize potential false positives. Furthermore, we followed up the frequent hitter evaluation with chemical structure based in silico target predictions to hypothesize a mechanism for the observed "off target" response. It was observed that the predicted cellular targets for the frequent hitters were known to be associated with undesirable effects such as cytotoxicity. More specifically, the most frequently predicted targets relate to apoptosis and cell differentiation, including kinases, topoisomerases, and protein phosphatases. The mechanism-based frequent hitter hypothesis was tested using 160 additional druglike compounds predicted by the model to be nonspecific actives in RGAs. This validation was successful (showing a 50% hit rate compared to a normal hit rate as low as 2%), and it demonstrates the power of computational models toward understanding complex relations between chemical structure and biological function.
Abstract: Preclinical Safety Pharmacology (PSP) attempts to anticipate adverse drug reactions (ADRs) during early phases of drug discovery by testing compounds in simple, in vitro binding assays (that is, preclinical profiling). The selection of PSP targets is based largely on circumstantial evidence of their contribution to known clinical ADRs, inferred from findings in clinical trials, animal experiments, and molecular studies going back more than forty years. In this work we explore PSP chemical space and its relevance for the prediction of adverse drug reactions. Firstly, in silico (computational) Bayesian models for 70 PSP-related targets were built, which are able to detect 93% of the ligands binding at IC(50) < or = 10 microM at an overall correct classification rate of about 94%. Secondly, employing the World Drug Index (WDI), a model for adverse drug reactions was built directly based on normalized side-effect annotations in the WDI, which does not require any underlying functional knowledge. This is, to our knowledge, the first attempt to predict adverse drug reactions across hundreds of categories from chemical structure alone. On average 90% of the adverse drug reactions observed with known, clinically used compounds were detected, an overall correct classification rate of 92%. Drugs withdrawn from the market (Rapacuronium, Suprofen) were tested in the model and their predicted ADRs align well with known ADRs. The analysis was repeated for acetylsalicylic acid and Benperidol which are still on the market. Importantly, features of the models are interpretable and back-projectable to chemical structure, raising the possibility of rationally engineering out adverse effects. By combining PSP and ADR models new hypotheses linking targets and adverse effects can be proposed and examples for the opioid mu and the muscarinic M2 receptors, as well as for cyclooxygenase-1 are presented. It is hoped that the generation of predictive models for adverse drug reactions is able to help support early SAR to accelerate drug discovery and decrease late stage attrition in drug discovery projects. In addition, models such as the ones presented here can be used for compound profiling in all development stages.
Abstract: Chemogenomics comprises a systematic relationship between targets and ligands that are used as target modulators in living systems such as cells or organisms. In recent years, data on small molecule-bioactivity relationships have become increasingly available, and consequently so have the number of approaches used to translate bioactivity data into knowledge. This review will focus on two aspects of chemogenomics. Firstly, in cases such as cell-based screens, the question of which target(s) a compound is modulating in order to cause the observed phenotype is crucial. In silico target prediction tools can suggest likely biological targets of small molecules via data mining in target-annotated chemical databases. This review presents some of the current tools available for this task and shows some sample applications relevant to a pharmaceutical industry setting. These applications are the prediction of false-positives in cell-based reporter gene assays, the prediction of targets by linking bioassay data with protein domain annotations, and the direct prediction of adverse reactions. Secondly, in recent years a shift from structure-derived chemical descriptors to biological descriptors has occurred. Here, the effect of a compound on a number of biological endpoints is used to make predictions about other properties, such as putative targets, associated adverse reactions, and pathways modulated by the compound. This review further summarizes these "performance" descriptors and their applications, focusing on gene expression profiles and high-content screening data. The advent of such biological fingerprints suggests that the field of drug discovery is currently at a crossroads, where single target bioassay results are supplanted by multidimensional biological fingerprints that reflect a new awareness of biological networks and polypharmacology.
Abstract: This study describes a method for mining and modeling binding data obtained from a large panel of targets (in vitro safety pharmacology) to distinguish differences between promiscuous and selective compounds. Two naïve Bayes models for promiscuity and selectivity were generated and validated on a test set as well as publicly available drug databases. The model shows a higher score (lower promiscuity) for marketed drugs than for compounds in early development or compounds that failed during clinical development. Such models can be used in triaging high-throughput screening data or for lead optimization.
Abstract: High-throughput screening (HTS) plays a pivotal role in lead discovery for the pharmaceutical industry. In tandem, cheminformatics approaches are employed to increase the probability of the identification of novel biologically active compounds by mining the HTS data. HTS data is notoriously noisy, and therefore, the selection of the optimal data mining method is important for the success of such an analysis. Here, we describe a retrospective analysis of four HTS data sets using three mining approaches: Laplacian-modified naive Bayes, recursive partitioning, and support vector machine (SVM) classifiers with increasing stochastic noise in the form of false positives and false negatives. All three of the data mining methods at hand tolerated increasing levels of false positives even when the ratio of misclassified compounds to true active compounds was 5:1 in the training set. False negatives in the ratio of 1:1 were tolerated as well. SVM outperformed the other two methods in capturing active compounds and scaffolds in the top 1%. A Murcko scaffold analysis could explain the differences in enrichments among the four data sets. This study demonstrates that data mining methods can add a true value to the screen even when the data is contaminated with a high level of stochastic noise.
Abstract: Target identification is a critical step following the discovery of small molecules that elicit a biological phenotype. The present work seeks to provide an in silico correlate of experimental target fishing technologies in order to rapidly fish out potential targets for compounds on the basis of chemical structure alone. A multiple-category Laplacian-modified naïve Bayesian model was trained on extended-connectivity fingerprints of compounds from 964 target classes in the WOMBAT (World Of Molecular BioAcTivity) chemogenomics database. The model was employed to predict the top three most likely protein targets for all MDDR (MDL Drug Database Report) database compounds. On average, the correct target was found 77% of the time for compounds from 10 MDDR activity classes with known targets. For MDDR compounds annotated with only therapeutic or generic activities such as "antineoplastic", "kinase inhibitor", or "anti-inflammatory", the model was able to systematically deconvolute the generic activities to specific targets associated with the therapeutic effect. Examples of successful deconvolution are given, demonstrating the usefulness of the tool for improving knowledge in chemogenomics databases and for predicting new targets for orphan compounds.
Abstract: Lead discovery in the pharmaceutical environment is largely an industrial-scale process in which it is typical to screen 1-5 million compounds in a matter of weeks using High Throughput Screening (HTS). This process is a very costly endeavor. Typically a HTS campaign of 1 million compounds will cost anywhere from $500000 to $1000000. There is consequently a great deal of pressure to maximize the return on investment by finding fast and more effective ways to screen. A panacea that has emerged over the past few years to help address this issue is in silico screening. In silico screening is now incorporated in all areas of lead discovery; from target identification and library design, to hit analysis and compound profiling. However, as lead discovery has evolved over the past few years, so has the role of in silico screening.
Abstract: Conventional similarity searching of molecules compares single (or multiple) active query structures to each other in a relative framework, by means of a structural descriptor and a similarity measure. While this often works well, depending on the target, we show here that retrieval rates can be improved considerably by incorporating an external framework describing ligand bioactivity space for comparisons ("Bayes affinity fingerprints"). Structures are described by Bayes scores for a ligand panel comprising about 1000 activity classes extracted from the WOMBAT database. The comparison of structures is performed via the Pearson correlation coefficient of activity classes, that is, the order in which two structures are similar to the panel activity classes. Compound retrieval on a recently published data set could be improved by as much as 24% relative (9% absolute). Knowledge about the shape of the "bioactive chemical universe" is thus beneficial to identifying similar bioactivities. Principal component analysis was employed to further analyze activity space with the objective to define orthogonal ligand bioactive chemical space, leading to nine major (roughly orthogonal) activity axes. Employing only those nine activity classes, retrieval rates are still comparable to original Bayes affinity fingerprints; thus, the concept of orthogonal bioactive ligand chemical space was validated as being an information-rich but low-dimensional representation of bioactivity space. Correlations between activity classes are a major determinant to gauge whether the desired multitarget activity of drugs is (on the basis of current knowledge) a feasible concept because it measures the extent to which activities can be optimized independently, or only by strongly influencing one another.
Abstract: Bridging chemical and biological space is the key to drug discovery and development. Typically, cheminformatics methods operate under the assumption that similar chemicals have similar biological activity. Ideally then, one could predict a drug's biological function(s) given only its chemical structure by similarity searching in libraries of compounds with known activities. In practice, effectively choosing a similarity metric is case dependent. This work compares both 2D and 3D chemical descriptors as tools for predicting the biological targets of ligand probes, on the basis of their similarity to reference molecules in a 46,000 compound, biologically annotated chemical database. Overall, we found that the 2D methods employed here outperform the 3D (88% vs 67% success) in correct target prediction. However, the 3D descriptors proved superior in cases of probes with low structural similarity to other compounds in the database (singletons). Additionally, the 3D method (FEPOPS) shows promise for providing pharmacophoric alignment of the small molecules' chemical features consistent with those seen in experimental ligand/ receptor complexes. These results suggest that querying annotated chemical databases with a systematic combination of both 2D and 3D descriptors will prove more effective than employing single methods.
Abstract: A primary goal of 3D similarity searching is to find compounds with similar bioactivity to a reference ligand but with different chemotypes, i.e., "scaffold hopping". However, an adequate description of chemical structures in 3D conformational space is difficult due to the high-dimensionality of the problem. We present an automated method that simplifies flexible 3D chemical descriptions in which clustering techniques traditionally used in data mining are exploited to create "fuzzy" molecular representations called FEPOPS (feature point pharmacophores). The representations can be used for flexible 3D similarity searching given one or more active compounds without a priori knowledge of bioactive conformations or pharmacophores. We demonstrate that similarity searching with FEPOPS significantly enriches for actives taken from in-house high-throughput screening datasets and from MDDR activity classes COX-2, 5-HT3A, and HIV-RT, while also scaffold or ring-system hopping to new chemical frameworks. Further, inhibitors of target proteins (dopamine 2 and retinoic acid receptor) are recalled by FEPOPS by scaffold hopping from their associated endogenous ligands (dopamine and retinoic acid). Importantly, the method excels in comparison to commonly used 2D similarity methods (DAYLIGHT, MACCS, Pipeline Pilot fingerprints) and a commercial 3D method (Pharmacophore Distance Triplets) at finding novel scaffold classes given a single query molecule.
Abstract: "Hit lists" generated by high-throughput screening (HTS) typically contain a large percentage of false positives, making follow-up assays necessary to distinguish active from inactive substances. Here we present a method for improving the accuracy of HTS hit lists by computationally based virtual screening (VS) of the corresponding chemical libraries and selecting hits by HTS/VS consensus. This approach was applied in a case study on the target-enzyme angiogenin, a potent inducer of angiogenesis. In conjunction with HTS of the National Cancer Institute Diversity Set and ChemBridge DIVERSet E (approximately 18,000 compounds total), VS was performed with two flexible library docking/scoring methods, DockVision/Ludi and GOLD. Analysis of the results reveals that dramatic enrichment of the HTS hit rate can be achieved by selecting compounds in consensus with one or both of the VS functions. For example, HTS hits ranked in the top 2% by GOLD included 42% of the true hits, but only 8% of the false positives; this represents a sixfold enrichment over the HTS hit rate. Notably, the HTS/VS method was effective in selecting out inhibitors with midmicromolar dissociation constants typical of leads commonly obtained in primary screens.
Abstract: The most potent low molecular weight inhibitors of pancreatic RNase superfamily enzymes reported to date are synthetic derivatives of adenosine 5(')-pyrophosphate. Here we have investigated the effects of six natural nucleotides that also incorporate this moiety (NADP(+), NADPH, ATP, Ap(3)A, Ap(4)A, and Ap(5)A) on the activities of RNase A and two of its homologues, eosinophil-derived neurotoxin and angiogenin. With eosinophil-derived neurotoxin and angiogenin, Ap(5)A is comparable to the tightest binding inhibitors identified previously (K(i) values at pH 5.9 are 370 nM and 100 microM, respectively); it ranks among the strongest small antagonists of RNase A as well (K(i)=230 nM). The K(i) for NADPH with angiogenin is similar to that of Ap(5)A. These findings suggest that Ap(5)A and NADPH may serve as useful new leads for inhibitor design. Examination of inhibition under physiological conditions indicates that NADPH, ATP, and Ap(5)A may suppress intracellular RNase activity significantly in vivo.
Abstract: Human angiogenin (Ang) is a tumor-promoting RNase in the pancreatic RNase superfamily. Efforts to develop nucleotide-based inhibitors of Ang as potential anticancer drugs have been hampered by the lack of direct structural information on Ang-nucleotide complexes. Here, we have used heteronuclear NMR spectroscopy with (15)N- and (15)N/(13)C-labeled Ang to map the interactions of Ang with the phosphate ion, seven adenosine mononucleotides (the 2'-, 3'-, and 5'-monophosphates, the 2',5'- and 3',5'-diphosphates, the 5'-diphosphate, and the 2'-monophospho-5'-diphosphate), and the dinucleotide 2'-deoxyuridine 3'-pyrophosphate (P' --> 5') adenosine-2'-phosphate (dUppA-2'-p). The 2'-phosphate based derivatives, which bind more tightly than the corresponding 3'-phosphate isomers, induced characteristic large resonance perturbations of the backbone amide proton of Leu(115), the backbone (15)N of His(114), and the Gln(12) side-chain NH(2) group in the Ang active site. In contrast, adenosine derivatives with only 3'- or 5'-phosphates produced much less dramatic perturbations of Leu(115) and His(114) resonances, along with modest perturbations of additional residues both within and beyond the active site. Measurements of NOEs together with molecular docking analyses revealed the three-dimensional structures of the complexes of Ang with adenosine 2',5'-diphosphate and dUppA-2'-p; the binding modes of these inhibitors differ substantially from those predicted in earlier studies. Most notably, the 2'-phosphate rather than the 5'-phosphate occupies the P(1) catalytic subsite of Ang, and the side chain of His(114) has undergone a conformational transition that positions it outside P(1) and allows it to form stacking interactions with the adenine ring of the inhibitor. Strikingly, the 2'-deoxyuridine moiety of dUppA-2'-p makes only a few contacts with Ang, and these involve residues outside the B(1) subsite where the pyrimidine ring of substrates normally binds.
Abstract: Angiogenin (ANG) is a potent inducer of angiogenesis and an RNase A homologue whose ribonucleolytic activity is essential for its biological action. Recently, we reported the identification of small non-nucleotide inhibitors of the enzymatic activity of ANG by high-throughput screening (HTS) [Kao, R. Y. T., et al. (2002) Proc. Natl. Acad. Sci. U.S.A. 99, 10066-10071]. Two of the inhibitors that were obtained, National Cancer Institute compound NSC-65828 [8-amino-5-(4'-hydroxybiphenyl-4-ylazo)naphthalene-2-sulfonate] and ChemBridge compound C-181431 [4,4'-dicarboxy-3,3'-bis(naphthylamido)diphenylmethanone], were judged to be suitable for further development, and one of these (NSC-65828) was shown to possess antitumor activity in mice. Here we have used computational docking as a guide for the identification of available NSC-65828 and C-181431 analogues that bind more tightly to ANG, and for the characterization of inhibitor binding modes. Numerous analogues were found to have greater avidity than the HTS compounds or any small nucleotide inhibitors; four were considered to be of interest as potential leads (K(i) = 5-25 microM). Two of these analogues bind more tightly to ANG than to RNase A, and are the first small molecules shown to exhibit this selectivity. The predicted binding orientations of the HTS compounds and the new lead inhibitors were evaluated by determining the effects of ANG active site mutations on inhibitory potency. The results with ANG variants R5A, H8A, N68A, and des(121-123) are highly consistent with the docking models. Affinity changes observed with Q12A and Q117G reveal aspects of active site function that are not apparent from the free ANG crystal structure or from the modeled complexes. These findings should prove to be useful in the design of more effective and specific ANG antagonists.
Abstract: The results of previous preclinical and clinical studies have identified angiogenin (ANG) as a potentially important target for anticancer therapy. Here we report the design and implementation of a high-throughput screening assay to identify small molecules that bind to the ribonucleolytic active site of ANG, which is critically involved in the induction of angiogenesis by this protein. Screening of 18,310 compounds from the National Cancer Institute (NCI) Diversity Set and ChemBridge DIVERSet yielded 15 hits that inhibit the enzymatic activity of ANG with K(i) values <100 microM. One of these, NCI compound 65828 [8-amino-5-(4'-hydroxybiphenyl-4-ylazo)naphthalene-2-sulfonate; K(i) = 81 microM], was selected for more detailed studies. Minor changes in ANG or ligand structure markedly reduced potency, demonstrating that inhibition reflects active-site rather than nonspecific binding; these observations are consistent with a computationally generated model of the ANG.65828 complex. Local treatment with modest doses of 65828 significantly delayed the formation of s.c. tumors from two distinct human cancer cell types in athymic mice. ANG is the likely target involved because (i) a 65828 analogue with much lower potency against the enzymatic activity of ANG failed to exert any antitumor effect, (ii) tumors from 65828-treated mice had fewer interior blood vessels than those from control mice, and (iii) 65828 appears to have no direct effect on the tumor cells. Our findings provide considerable support for the targeting of the enzymatic active site of ANG as a strategy for developing new anticancer drugs.
Abstract: Recently, 3',5'-pyrophosphate-linked 2'-deoxyribodinucleotides were shown to be >100-fold more effective inhibitors of RNase A superfamily enzymes than were the corresponding monophosphate-linked (i.e., standard) dinucleotides. Here, we have investigated two ribo analogues of these compounds, cytidine 3'-pyrophosphate (P'-->5') adenosine (CppA) and uridine 3'-pyrophosphate (P'-->5') adenosine (UppA), as potential substrates for RNase A and angiogenin. CppA and UppA are cleaved efficiently by RNase A, yielding as products 5'-AMP and cytidine or uridine cyclic 2',3'-phosphate. The k(cat)/K(m) values are only 4-fold smaller than for the standard dinucleotides CpA and UpA, and the K(m) values (10-16 microM) are lower than those reported for any earlier small substrates (e.g., 500-700 microM for CpA and UpA). The k(cat)/K(m) value for CppA with angiogenin is also only severalfold smaller than for CpA, but the effect of lengthening the internucleotide linkage on K(m) is more modest. Ribonucleotide 3',5'-pyrophosphate linkages were proposed previously to exist in nature as chemically labile intermediates in the pathway for the generation of cyclic 2',3'-phosphate termini in various RNAs. We demonstrate that in fact they are relatively stable (t(1/2) > 15 days for uncatalyzed degradation of UppA at pH 6 and 25 degrees C) and that cleavage in vivo is most likely enzymatic. Replacements of the RNase A catalytic residues His12 and His119 by alanine reduce activity toward UppA by approximately 10(5)-and 10(3.3)-fold, respectively. Thus, both residues play important roles. His12 probably acts as a base catalyst in cleavage of UppA (as with RNA). However, the major function of His119 in RNA cleavage, protonation of the 5'-O leaving group, is not required for UppA cleavage because the pK(a) of the leaving group is much lower than that for RNA substrates. A crystal structure of the complex of RNase A with 2'-deoxyuridine 3'-pyrophosphate (P'-->5') adenosine (dUppA), determined at 1.7 A resolution, together with models of the UppA complex based on this structure suggest that His119 contributes to UppA cleavage through a hydrogen bond with a nonbridging oxygen atom in the pyrophosphate and through pi-pi stacking with the six-membered ring of adenine.
Abstract: BACKGROUND: To better understand the molecular interactions of Bt toxins with non-target insects, we have examined the real-time binding specificity and affinity of Cry1 toxins to native silkworm (Bombyx mori) midgut receptors. Previous studies on B. mori receptors utilized brush border membrane vesicles or purifed receptors in blot-type assays. RESULTS: The Bombyx mori (silkworm) aminopeptidase N (APN) and cadherin-like receptors for Bacillus thuringiensis insecticidal Cry1Aa toxin were purified and their real-time binding affinities for Cry toxins were examined by surface plasmon resonance. Cry1Ab and Cry1Ac toxins did not bind to the immobilized native receptors, correlating with their low toxicities. Cry1Aa displayed moderate affinity for B. mori APN (75 nM), and unusually tight binding to the cadherin-like receptor (2.6 nM), which results from slow dissociation rates. The binding of a hybrid toxin (Aa/Aa/Ac) was identical to Cry1Aa. CONCLUSIONS: These results indicate domain II of Cry1Aa is essential for binding to native B. mori receptors and for toxicity. Moreover, the high-affinity binding of Cry1Aa to native cadherin-like receptor emphasizes the importance of this receptor class for Bt toxin research.
Abstract: The functional role of the alpha8 loop residues in domain II of Bacillus thuringiensis Cry1Ac toxin was examined. Alanine substitution mutations were introduced in the residues from 275 to 293. Among the mutant toxins, substitutions at R281 and R289 affected toxicity to Manduca sexta and Lymantria dispar. Loss of toxicity by these mutant toxins was well correlated with reductions in binding affinity for brush border membrane vesicles and the purified receptor, aminopeptidase N (APN), from both insects. These data suggest that the two arginine residues in the alpha8 loop region are important in toxicity and APN binding in L. dispar and M. sexta.
Abstract: BTR-270, a gypsy moth (Lymantria dispar) brush border membrane molecule that binds Bacillus thuringiensis (Bt) Cry1A toxins with high affinity, was purified by preparative gel electrophoresis. Rabbit antibodies specific for the Bt toxin-binding molecule were raised. Attempts to label BTR-270 by protein-directed techniques were futile, but it was degraded by proteases with broad specificity indicating the presence of a peptide. Carbohydrate was detected by labeling with digoxigenin hydrazide following periodate oxidation. Mild alkaline hydrolysis destroyed toxin and antibody binding, suggesting O-linked glycans are involved in the activity. GC/MS composition analysis showed that the predominant sugars were galactose, glucose, and N-acetyl galactosamine with lesser amounts of N-acetyl glucosamine, glucuronic acid, xylose, and fucose. The carbohydrate moiety accounted for 73% of its total mass. Amino acid analysis showed a high content of aspartic/asparagine, threonine, and serine residues in the protein moiety. The purified glycoconjugate was not visualized using Coomassie or silver staining procedures, but stained "blue" using the cationic dye Stains-all. BTR-270 was labeled with biotin and used as a diagnostic probe for screening and identifying toxins that bind to the receptor. Toxin-binding kinetics obtained using a biosensor demonstrated that the receptor binds Cry1Aa and Cry1Ab toxins with high affinity, and displays a weaker affinity for Cry1Ac, in correlation with the toxicity of these toxins towards gypsy moth. Arch.
Abstract: Specificity for target insects of Bacillus thuringiensis insecticidal Cry toxins is largely determined by toxin affinity for insect midgut receptors. The mode of binding for one such toxin-receptor complex was investigated by extensive toxin mutagenesis, followed by real-time receptor binding analysis using an optical biosensor (BIAcore). Wild-type Cry1Ac, a three-domain, lepidopteran-specific toxin, bound purified gypsy moth (Lymantria dispar) aminopeptidase N (APN) biphasically. Site 1 displayed fast association and dissociation kinetics, while site 2 possessed slower kinetics, yet tighter affinity. We empirically determined that two Cry1Ac surface regions are involved in in vivo toxicity and APN binding. Mutations within domain III affected binding rates to APN site 1, whereas mutations in domain II affected binding rates to APN site 2. Furthermore, domain III contact is completely inhibited in the presence of N-acetylgalactosamine, indicating loss of domain III binding eliminates all APN binding. Based upon these observations, the following model is proposed. A cavity in lectin-like domain III initiates docking through recognition of an N-acetylgalactosamine moiety on L. dispar APN. Following primary docking, a higher affinity domain II binding mechanism occurs, which is critical for insecticidal activity.
Abstract: Two arginine residues (368-369) of Cry1Ab and Cry1Ac were mutated to alanine, glutamic acid and lysine by site-directed mutagenesis. Insecticidal activities of the mutant toxins on Manduca sexta and Lymantria dispar larvae were examined. Cry1Ac mutant toxins (c)RR-AA and (c)RR-EE and Cry1Ab mutant toxins (b)RR-AA and (b)RR-EE showed great reductions in toxicity against both insects. In contrast, conservatively changed (c)RR-KK and (b)RR-KK mutants did not alter toxicity to either insect. Binding assays with brush border membrane vesicles (BBMVs) prepared from L. dispar midguts demonstrated that (c)RR-AA, (c)RR-EE, (b)RR-AA and (b)RR-EE bound with lower affinities compared with their respective wild-type toxins. To M. sexta BBMVs, (c)RR-AA and (c)RR-EE showed great reductions in BBMV binding. However, (b)RR-AA and (b)RR-EE did not alter BBMV competition patterns, despite their reduced toxicity. Further binding assays were performed with aminopeptidase N (APN) purified from L. dispar and M. sexta BBMVs using surface plasmon resonance (BIAcore). Direct correlation between toxicity and APN binding was observed for the mutant toxins using this technique. The inconsistency between BBMV and APN binding data with Cry1Ab to M. sexta suggests the possibility of a different Cry1Ab toxin-binding mechanism or the importance of another receptor in M. sexta.
Abstract: Bacillus thuringiensis Cry1Ac delta-endotoxin specifically binds a 115-kDa aminopeptidase-N purified from Manduca sexta midgut. Cry1Ac domain III mutations were constructed around a putative sugar-binding pocket and binding to purified aminopeptidase-N and brush border membrane vesicles (BBMV) was compared to toxicity. Q509A, R511A, Y513A, and 509-511 (QNR-AAA) eliminated aminopeptidase-N binding and reduced binding to BBMV. However, toxicity decreased no more than two-fold, indicating activity is not directly correlated with aminopeptidase-N binding. Analysis of toxin binding to aminopeptidase-N in M. sexta is therefore insufficient for predicting toxicity. Mutants retained binding, however, to another BBMV site, suggesting alternative receptors may compensate in vivo.
