Antibody and T‐cell receptors (TCRs) are the primary recognition molecules of the adaptive immune system. Antibodies have been extensively characterized and are being developed for a large number of ...therapeutic applications. This has been possible because of the ability to manufacture stable, soluble, monoclonal antibodies which retain the antigen specificity of B cells. Unlike antibodies, TCRs are not expressed in a soluble form, but are anchored to the T‐cell surface by an insoluble trans‐membrane domain. Characterization and development of TCRs has been hampered by the lack of suitable methods for producing them as soluble and stable proteins. Here we report the engineering of soluble human TCRs suitable for crystallization studies and potentially for in vivo therapeutic use.
Abstract
SHP2 is a cytoplasmic non-receptor tyrosine phosphatase involved in the propagation of extracellular signaling through receptor tyrosine kinases. Aberrant SHP2 activity has been identified ...as a driver in multiple cancers and SHP2 has also been implicated in the PD-1/PD-L1-mediated exhaustion of effector T-cells, leading to immune system evasion of tumors. Recently, we reported the identification of SHP099, an allosteric inhibitor of SHP2 with in in vivo efficacy against multiple RTK-driven tumor xenograft models. Here we report the use of alternate screening paradigms to identify a novel allosteric inhibitor which binds to a previously uncharacterized pocket on SHP2. Like SHP099, the second allosteric inhibitor stabilizes a closed conformation of SHP2, which blocks access to the phosphatase active site. Structure based drug design led to improvements in potency, and combination studies in biochemical, biophysical and cellular assays confirm dual occupation of SHP099 and the second allosteric molecule, resulting in improved potency. This work highlights a rare opportunity for dual occupation of inhibitors for a single target and provides additional tools for the exploration of SHP2 biology.
Citation Format: Michelle Fodor, Edmund Price, Ping Wang, Hengyu Lu, Andreea Argintaru, Zhouliang Chen, Meir Glick, Huai-Xiang Hao, Mitsunori Kato, Robert Koenig, Jonathan R. LaRochelle, Gang Liu, Eric McNeill, Dyuti Majumdar, Gisele Nishiguchi, Lawrence Perez, Greg Paris, Christopher Quinn, Timothy Ramsey, Martin Sendzik, Michael Shultz, Sarah Williams, Travis Stams, Stephen C. Blacklow, Matthew J. LaMarche, Michael G. Acker. Simultaneous inhibition of SHP2 phosphatase at two allosteric sites abstract. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 2808.
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.
Though phenotypic and target-based high-throughput screening approaches have been employed to discover new antibiotics, the identification of promising therapeutic candidates remains challenging. ...Each approach provides different information, and understanding their results can provide hypotheses for a mechanism of action (MoA) and reveal actionable chemical matter. Here, we describe a framework for identifying efficacy targets of bioactive compounds. High throughput biophysical profiling against a broad range of targets coupled with machine learning was employed to identify chemical features with predicted efficacy targets for a given phenotypic screen. We validate the approach on data from a set of 55 000 compounds in 24 historical internal antibacterial phenotypic screens and 636 bacterial targets screened in high-throughput biophysical binding assays. Models were built to reveal the relationships between phenotype, target, and chemotype, which recapitulated mechanisms for known antibacterials. We also prospectively identified novel inhibitors of dihydrofolate reductase with nanomolar antibacterial efficacy against Mycobacterium tuberculosis. Molecular modeling provided structural insight into target–ligand interactions underlying selective killing activity toward mycobacteria over human cells.
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
Merck & Co., Inc., Kenilworth, NJ, USA, is undergoing a transformation in the way that it prosecutes R&D programs. Through the adoption of a “model-driven” culture, enhanced R&D productivity ...is anticipated, both in the form of decreased attrition at each stage of the process and by providing a rational framework for understanding and learning from the data generated along the way. This new approach focuses on the concept of a “Design Cycle” that makes use of all the data possible, internally and externally, to drive decision-making. These data can take the form of bioactivity, 3D structures, genomics, pathway, PK/PD, safety data, etc. Synthesis of high-quality data into models utilizing both well-established and cutting-edge methods has been shown to yield high confidence predictions to prioritize decision-making and efficiently reposition resources within R&D. The goal is to design an adaptive research operating plan that uses both modeled data and experiments, rather than just testing, to drive project decision-making. To support this emerging culture, an ambitious information management (IT) program has been initiated to implement a harmonized platform to facilitate the construction of cross-domain workflows to enable data-driven decision-making and the construction and validation of predictive models. These goals are achieved through depositing model-ready data, agile persona-driven access to data, a unified cross-domain predictive model lifecycle management platform, and support for flexible scientist-developed workflows that simplify data manipulation and consume model services. The end-to-end nature of the platform, in turn, not only supports but also drives the culture change by enabling scientists to apply predictive sciences throughout their work and over the lifetime of a project. This shift in mindset for both scientists and IT was driven by an early impactful demonstration of the potential benefits of the platform, in which expert-level early discovery predictive models were made available from familiar desktop tools, such as ChemDraw. This was built using a workflow-driven service-oriented architecture (SOA) on top of the rigorous registration of all underlying model entities.
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.
We introduce a novel strategy to sample bioactive chemical space, which follows-up on hits from fragment campaigns without the need for a crystal structure. Our results strongly suggest that ...screening a few hundred or thousand fragments can substantially improve the selection of small-molecule screening subsets. By combining fragment-based screening with virtual fragment linking and HTS fingerprints, we have developed an effective strategy not only to expand from low-affinity hits to potent compounds but also to hop in chemical space to substantially novel chemotypes. In benchmark calculations, our approach accessed subsets of compounds that were substantially enriched in chemically diverse hit compounds for various activity classes. Overall, half of the hits in the screening collection were found by screening only 10% of the library. Furthermore, a prospective application led to the discovery of two structurally novel histone deacetylase 4 inhibitors.
The crystal structure of the bovine zinc metalloproteinase carboxypeptidase A (CPA) has been refined to 1.25 Å resolution based on room‐temperature X‐ray synchrotron data. The significantly improved ...structure of CPA at this resolution (anisotropic temperature factors, R factor = 10.4%, Rfree = 14.5%) allowed the modelling of conformational disorders of side chains, improved the description of the protein solvent network (375 water molecules) and provided a more accurate picture of the interactions between the active‐site zinc and its ligands. The calculation of standard uncertainties in individual atom positions of the refined model of CPA allowed the deduction of the protonation state of some key residues in the active site and confirmed that Glu72 and Glu270 are negatively charged in the resting state of the enzyme at pH 7.5. These results were further validated by theoretical calculations that showed significant reduction of the pKa of these side chains relative to solution values. The distance between the zinc‐bound solvent molecule and the metal ion is strongly suggestive of a neutral water molecule and not a hydroxide ion in the resting state of the enzyme. These findings could support both the general acid/general base mechanism, as well as the anhydride mechanism suggested for CPA.
The technology underpinning high-throughput docking (HTD) has developed over the past few years to where it has become a vital tool in modern drug discovery. Although the performance of various ...docking algorithms is adequate, the ability to accurately and consistently rank compounds using a scoring function remains problematic. We show that by employing a simple machine learning method (naïve Bayes) it is possible to significantly overcome this deficiency. Compounds from the Available Chemical Directory (ACD), along with known active compounds, were docked into two protein targets using three software packages. In cases where HTD alone was able to show some enrichment, the application of naïve Bayes was able to improve upon the enrichment. The application of this methodology to enrich HTD results can be carried out without a priori knowledge of the activity of compounds and results in superior enrichment of known actives compared to the use of scoring methods alone.
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