Structural determinants of dual incretin receptor agonism by tirzepatide.

SignificanceTirzepatide is a dual agonist of the glucose-dependent insulinotropic polypeptide receptor (GIPR) and the glucagon-like peptide-1 receptor (GLP-1R), which are incretin receptors that regulate carbohydrate metabolism. This investigational agent has proven superior to selective GLP-1R agonists in clinical trials in subjects with type 2 diabetes mellitus. Intriguingly, although tirzepatide closely resembles native GIP in how it activates the GIPR, it differs markedly from GLP-1 in its activation of the GLP-1R, resulting in less agonist-induced receptor desensitization. We report how cryogenic electron microscopy and molecular dynamics simulations inform the structural basis for the unique pharmacology of tirzepatide. These studies reveal the extent to which fatty acid modification, combined with amino acid sequence, determines the mode of action of a multireceptor agonist.

Structural insights into probe-dependent positive allosterism of the GLP-1 receptor.

Drugs that promote the association of protein complexes are an emerging therapeutic strategy. We report discovery of a G protein-coupled receptor (GPCR) ligand that stabilizes an active state conformation by cooperatively binding both the receptor and orthosteric ligand, thereby acting as a 'molecular glue'. LSN3160440 is a positive allosteric modulator of the GLP-1R optimized to increase the affinity and efficacy of GLP-1(9-36), a proteolytic product of GLP-1(7-36). The compound enhances insulin secretion in a glucose-, ligand- and GLP-1R-dependent manner. Cryo-electron microscopy determined the structure of the GLP-1R bound to LSN3160440 in complex with GLP-1 and heterotrimeric Gs. The modulator binds high in the helical bundle at an interface between TM1 and TM2, allowing access to the peptide ligand. Pharmacological characterization showed strong probe dependence of LSN3160440 for GLP-1(9-36) versus oxyntomodulin that is driven by a single residue. Our findings expand protein-protein modulation drug discovery to uncompetitive, active state stabilizers for peptide hormone receptors.

Are induced fit protein conformational changes caused by ligand-binding predictable? A molecular dynamics investigation.

In this work, the ability of molecular dynamics simulations (MD) to prospectively predict regions of ligand binding sites that could undergo induced fit effects was investigated. Conventional MD was run on 39 apo structures (no ligand), and the resulting trajectories were compared to a set of 147 holo X-ray structures (ligand-bound). It was observed from the simulations, in the absence of the ligands, that structures exhibiting large residue conformational changes indicated higher likelihood of induced fit effects. Nevertheless, the simulation results did not perform better than using the normalized crystallographic structural factors as predictors of active-site rigid residues (87% predictive power) and mobile residues (47% predictive power). While the simulations could not produce full active sites conformations similar to holo-like states, it was found that the simulations could reproduce bound state conformations of individual residues. These results suggest potential issues in the use of unligated simulation frames directly for drug design applications such as ligand docking, and an overall caution in the use of protein flexibility in docking protocols should be emphasized. © 2017 Wiley Periodicals, Inc.

Discovery of selective RIO2 kinase small molecule ligand.

We report the discovery and initial optimization of diphenpyramide and several of its analogs as hRIO2 kinase ligands. One of these analogs is the most selective hRIO2 ligand reported to date. Diphenpyramide is a Cyclooxygenase 1 and 2 inhibitor that was used as an anti-inflammatory agent. The RIO2 kinase affinity of diphenpyramide was discovered by serendipity while profiling of 13 marketed drugs on a large 456 kinase assay panel. The inhibition values also suggested a relative selectivity of diphenpyramide for RIO2 against the other kinases in the panel. Subsequently three available and eight newly synthesized analogs were assayed, one of which showed a 10 fold increased hRIO2 binding affinity. Additionally, this compound shows significantly better selectivity over assayed kinases, when compared to currently known RIO2 inhibitors. As RIO2 is involved in the biosynthesis of the ribosome and cell cycle regulation, our selective ligand may be useful for the delineation of the biological role of this kinase. This article is part of a Special Issue entitled: Inhibitors of Protein Kinases.

Perspective on computational and structural aspects of kinase discovery from IPK2014.

Recent advances in understanding the activity and selectivity of kinase inhibitors and their relationships to protein structure are presented. Conformational selection in kinases is studied from empirical, data-driven and simulation approaches. Ligand binding and its affinity are, in many cases, determined by the predetermined active and inactive conformation of kinases. Binding affinity and selectivity predictions highlight the current state of the art and advances in computational chemistry as it applies to kinase inhibitor discovery. Kinome wide inhibitor profiling and cell panel profiling lead to a better understanding of selectivity and allow for target validation and patient tailoring hypotheses. This article is part of a Special Issue entitled: Inhibitors of Protein Kinases.

Oxadiazoles Have Butyrate-Specific Conditional Activity against Mycobacterium tuberculosis.

Mycobacterium tuberculosis is a global pathogen of huge importance which can adapt to several host niche environments in which carbon source availability is likely to vary. We developed and ran a phenotypic screen using butyrate as the sole carbon source to be more reflective of the host lung environment. We screened a library of ∼87,000 small compounds and identified compounds which demonstrated good antitubercular activity against M. tuberculosis grown with butyrate but not with glucose as the carbon source. Among the hits, we identified an oxadiazole series (six compounds) which had specific activity against M. tuberculosis but which lacked cytotoxicity against mammalian cells.

Knowledge-Based Strategy to Improve Ligand Pose Prediction Accuracy for Lead Optimization.

Accurately predicting how a small molecule binds to its target protein is an essential requirement for structure-based drug design (SBDD) efforts. In structurally enabled medicinal chemistry programs, binding pose prediction is often applied to ligands after a related compound's crystal structure bound to the target protein has been solved. In this article, we present an automated pose prediction protocol that makes extensive use of existing X-ray ligand information. It uses spatial restraints during docking based on maximum common substructure (MCS) overlap between candidate molecule and existing X-ray coordinates of the related compound. For a validation data set of 8784 docking runs, our protocol's pose prediction accuracy (80-82%) is almost two times higher than that of one unbiased docking method software (43%). To demonstrate the utility of this protocol in a project setting, we show its application in a chronological manner for a number of internal drug discovery efforts. The accuracy and applicability of this algorithm (>70% of cases) to medicinal chemistry efforts make this the approach of choice for pose prediction in lead optimization programs.

What general conclusions can we draw from kinase profiling data sets?

Understanding general selectivity trends across the kinome has implications ranging from target selection, compound prioritization, toxicity and patient tailoring. Several recent publications have described the characterization of kinase inhibitors via large assay panels, offering a range of generalizations that influenced kinase inhibitor research trends. Since a subset of profiled inhibitors overlap across reports, we evaluated the concordance of activity results for the same compound-kinase pairs across four data sources generated from different kinase biochemical assay technologies. Overall, 77% of all results are within 3 fold or qualitatively in agreement across sources. However, the agreement for active compounds is only 37%, indicating that different profiling panels are in better agreement to determine a compound's lack of activity rather than degree of activity. Low concordance is also found when comparing the promiscuity of kinase targets evaluated from different sources, and the pharmacological similarity of kinases. In contrast, the overall promiscuity of kinase inhibitors was consistent across sources. We highlight the difficulty of drawing general conclusions from such data by showing that no significant selectivity difference distinguishes type I vs. type II inhibitors, and limited kinase space similarity that is consistent across different sources. This article is part of a Special Issue entitled: Inhibitors of Protein Kinases (2012).

Identification of ß-Lactams Active against Mycobacterium tuberculosis by a Consortium of Pharmaceutical Companies and Academic Institutions.

Rising antimicrobial resistance challenges our ability to combat bacterial infections. The problem is acute for tuberculosis (TB), the leading cause of death from infection before COVID-19. Here, we developed a framework for multiple pharmaceutical companies to share proprietary information and compounds with multiple laboratories in the academic and government sectors for a broad examination of the ability of ß-lactams to kill Mycobacterium tuberculosis (Mtb). In the TB Drug Accelerator (TBDA), a consortium organized by the Bill & Melinda Gates Foundation, individual pharmaceutical companies collaborate with academic screening laboratories. We developed a higher order consortium within the TBDA in which four pharmaceutical companies (GlaxoSmithKline, Sanofi, MSD, and Lilly) collectively collaborated with screeners at Weill Cornell Medicine, the Infectious Disease Research Institute (IDRI), and the National Institute of Allergy and Infectious Diseases (NIAID), pharmacologists at Rutgers University, and medicinal chemists at the University of North Carolina to screen ∼8900 ß-lactams, predominantly cephalosporins, and characterize active compounds. In a striking contrast to historical expectation, 18% of ß-lactams screened were active against Mtb, many without a ß-lactamase inhibitor. One potent cephaloporin was active in Mtb-infected mice. The steps outlined here can serve as a blueprint for multiparty, intra- and intersector collaboration in the development of anti-infective agents.

Structure-guided expansion of kinase fragment libraries driven by support vector machine models.

This work outlines a new de novo design process for the creation of novel kinase inhibitor libraries. It relies on a profiling paradigm that generates a substantial amount of kinase inhibitor data from which highly predictive QSAR models can be constructed. In addition, a broad diversity of X-ray structure information is needed for binding mode prediction. This is important for scaffold and substituent site selection. Borrowing from FBDD, the process involves fragmentation of known actives, proposition of binding mode hypotheses for the fragments, and model-driven recombination using a pharmacophore derived from known kinase inhibitor structures. The support vector machine method, using Merck atom pair derived fingerprint descriptors, was used to build models from activity from 6 kinase assays. These models were qualified prospectively by selecting and testing compounds from the internal compound collection. Overall hit and enrichment rates of 82% and 2.5%, respectively, qualified the models for use in library design. Using the process, 7 novel libraries were designed, synthesized and tested against these same 6 kinases. The results showed excellent results, yielding a 92% hit rate for the 179 compounds that made up the 7 libraries. The results of one library designed to include known literature compounds, as well as an analysis of overall substituent frequency, are discussed.

Identification of tyrosine kinase inhibitors that halt Plasmodium falciparum parasitemia.

Although current malaria therapies inhibit pathways encoded in the parasite's genome, we have looked for anti-malaria drugs that can target an erythrocyte component because development of drug resistance might be suppressed if the parasite cannot mutate the drug's target. In search for such erythrocyte targets, we noted that human erythrocytes express tyrosine kinases, whereas the Plasmodium falciparum genome encodes no obvious tyrosine kinases. We therefore screened a library of tyrosine kinase inhibitors from Eli Lilly and Co. in a search for inhibitors with possible antimalarial activity. We report that although most tyrosine kinase inhibitors exerted no effect on parasite survival, a subset of tyrosine kinase inhibitors displayed potent anti-malarial activity. Moreover, all inhibitors found to block tyrosine phosphorylation of band 3 specifically suppressed P. falciparum survival at the parasite egress stage of its intra-erythrocyte life cycle. Conversely, tyrosine kinase inhibitors that failed to block band 3 tyrosine phosphorylation but still terminated the parasitemia were observed to halt parasite proliferation at other stages of the parasite's life cycle. Taken together these results suggest that certain erythrocyte tyrosine kinases may be important to P. falciparum maturation and that inhibitors that block these kinases may contribute to novel therapies for P. falciparum malaria.

Crystal structure of human RIOK2 bound to a specific inhibitor.

The RIO kinases (RIOKs) are a universal family of atypical kinases that are essential for assembly of the pre-40S ribosome complex. Here, we present the crystal structure of human RIO kinase 2 (RIOK2) bound to a specific inhibitor. This first crystal structure of an inhibitor-bound RIO kinase reveals the binding mode of the inhibitor and explains the structure-activity relationship of the inhibitor series. The inhibitor binds in the ATP-binding site and forms extensive hydrophobic interactions with residues at the entrance to the ATP-binding site. Analysis of the conservation of active site residues reveals the reasons for the specificity of the inhibitor for RIOK2 over RIOK1 and RIOK3, and it provides a template for inhibitor design against the human RIOK family.

Chemical fragments as foundations for understanding target space and activity prediction.

The use of small inhibitors' fragment frequencies for understanding kinase potency and selectivity is described. By quantification of differences in the frequency of occurrence of fragments, similarities between small molecules and their targets can be determined. Naive Bayes models employing fragments provide highly interpretable and reliable means for predicting potency in individual kinases, as demonstrated in retrospective tests and prospective selections that were subsequently screened. Statistical corrections for prospective validation allowed us to accurately estimate success rates in the prospective experiment. Selectivity relationships between kinase targets are substantially explained by differences in the fragment composition of actives. By application of fragment similarities to the broader proteome, it is shown that targets related by sequence exhibit similar fragment preferences in small molecules. Of greater interest, certain targets unrelated by sequence are shown to have similar fragment preferences, even when the chemical similarity of ligands active at each target is low.

Predicting the Conformational Variability of Abl Tyrosine Kinase using Molecular Dynamics Simulations and Markov State Models.

Understanding protein conformational variability remains a challenge in drug discovery. The issue arises in protein kinases, whose multiple conformational states can affect the binding of small-molecule inhibitors. To overcome this challenge, we propose a comprehensive computational framework based on Markov state models (MSMs). Our framework integrates the information from explicit-solvent molecular dynamics simulations to accurately rank-order the accessible conformational variants of a target protein. We tested the methodology using Abl kinase with a reference and blind-test set. Only half of the Abl conformational variants discovered by our approach are present in the disclosed X-ray structures. The approach successfully identified a protein conformational state not previously observed in public structures but evident in a retrospective analysis of Lilly in-house structures: the X-ray structure of Abl with WHI-P154. Using a MSM-derived model, the free energy landscape and kinetic profile of Abl was analyzed in detail highlighting opportunities for targeting the unique metastable states.

Molecular Basis for Necitumumab Inhibition of EGFR Variants Associated with Acquired Cetuximab Resistance.

Acquired resistance to cetuximab, an antibody that targets the EGFR, impacts clinical benefit in head and neck, and colorectal cancers. One of the mechanisms of resistance to cetuximab is the acquisition of mutations that map to the cetuximab epitope on EGFR and prevent drug binding. We find that necitumumab, another FDA-approved EGFR antibody, can bind to EGFR that harbors the most common cetuximab-resistant substitution, S468R (or S492R, depending on the amino acid numbering system). We determined an X-ray crystal structure to 2.8 Å resolution of the necitumumab Fab bound to an S468R variant of EGFR domain III. The arginine is accommodated in a large, preexisting cavity in the necitumumab paratope. We predict that this paratope shape will be permissive to other epitope substitutions, and show that necitumumab binds to most cetuximab- and panitumumab-resistant EGFR variants. We find that a simple computational approach can predict with high success which EGFR epitope substitutions abrogate antibody binding. This computational method will be valuable to determine whether necitumumab will bind to EGFR as new epitope resistance variants are identified. This method could also be useful for rapid evaluation of the effect on binding of alterations in other antibody/antigen interfaces. Together, these data suggest that necitumumab may be active in patients who are resistant to cetuximab or panitumumab through EGFR epitope mutation. Furthermore, our analysis leads us to speculate that antibodies with large paratope cavities may be less susceptible to resistance due to mutations mapping to the antigen epitope. Mol Cancer Ther; 17(2); 521-31. ©2017 AACR.

Drugs in other drugs: a new look at drugs as fragments.

The authors of this review have examined the complete set of marketed drugs, with regards to looking for structural similarities between drugs. By comparing the structures of all drugs, it has been established how many times one marketed drug occurred as a substructure within another marketed drug. A total of 209 from 1386 marketed drugs sized between 100 and 1500 Da (i.e. 15% of the 1386 total) are contained within other drugs, differing by one or more continuous chemical fragment, and as many as 418 drugs from the total of 1386 (i.e. 30%) contain other drugs as substructure fragments. Many smaller drugs occur in multiple larger drugs. Most of the small changes tend to retain primary indicated pharmacology, whereas larger changes more often lead to different primary pharmacology. We identify a subset of drugs that can be used in fragment-based drug discovery strategies. In addition, the analysis enhances understanding of marketed drug space from the chemical building-block perspective.

Reconstruction of 3D structures of MET antibodies from electron microscopy 2D class averages.

Dynamics of three MET antibody constructs (IgG1, IgG2, and IgG4) and the IgG4-MET antigen complex was investigated by creating their atomic models with an integrative experimental and computational approach. In particular, we used two-dimensional (2D) Electron Microscopy (EM) images, image class averaging, homology modeling, Rapidly exploring Random Tree (RRT) structure sampling, and fitting of models to images, to find the relative orientations of antibody domains that are consistent with the EM images. We revealed that the conformational preferences of the constructs depend on the extent of the hinge flexibility. We also quantified how the MET antigen impacts on the conformational dynamics of IgG4. These observations allow to create testable hypothesis to investigate MET biology. Our protocol may also help describe structural diversity of other antigen systems at approximately 5 Å precision, as quantified by Root-Mean-Square Deviation (RMSD) among good-scoring models.

Dependence of molecular properties on proteomic family for marketed oral drugs.

An association of drugs with their proteomic family reveals that molecular properties of drugs targeting proteases, lipid and peptide G-protein-coupled receptors (GPCRs), and nuclear hormone receptors significantly exceed limits for some properties in the "rule of five", while drugs targeting cytochrome P450s, biogenic amine GPCRs, and transporters have significantly lower values for certain properties. Also, the variation in drug targets appears to be a factor explaining increasing molecular weight over time.

Kinomics: characterizing the therapeutically validated kinase space.

The annotation and visualization of medicinally relevant kinase space revealed that kinase inhibitors in the clinic are, on average, of higher molecular weight and more lipophilic than all other clinically investigated drugs. Tyrosine kinases from the vascular endothelial growth factor and epidermal growth factor receptor families are the most pursued targets. Furthermore, oncological indications account for 75% of all kinase-related clinical interest. In addition, analysis of the similarity between kinase targets with respect to sequence, selectivity and structure has revealed that kinases with > or =60% sequence identity are most likely to be inhibited by the same classes of compounds and have similar ATP-binding sites. The identification of this threshold, together with the widely accepted representation of the sequence-based kinase space, is expanding our understanding of the clinical and structural space of the kinome.

Kinomics-structural biology and chemogenomics of kinase inhibitors and targets.

Classifying kinases based entirely on small molecule selectivity data is a new approach to drug discovery that allows scientists to understand relationships between targets. This approach combines the understanding of small molecules and targets, and thereby assists the researcher in finding new targets for existing molecules or understanding selectivity and polypharmacology of molecules in related targets. Currently, structural information is available for relatively few of the protein kinases encoded in the human genome (7% of the estimated 518); however, even the current knowledge base, when paired with structure-based design techniques, can assist in the identification and optimization of novel kinase inhibitors across the entire protein class. Chemogenomics attempts to combine genomic data, structural biological data, classical dendrograms, and selectivity data to explore, define, and classify the medicinally relevant kinase space. Exploitation of this information in the discovery of kinase inhibitors defines practical kinase chemogenomics (kinomics). In this paper, we review the available information on kinase targets and their inhibitors, and present the relationships between the various classification schema for kinase space. In particular, we present the first dendrogram of kinases based entirely on small molecule selectivity data. We find that the selectivity dendrogram differs from sequence-based clustering mostly in the higher-level groupings of the smaller clusters, and remains very comparable for closely homologous targets. Highly homologous kinases are, on average, inhibited comparably by small molecules. This observation, although intuitive, is very important to the process of target selection, as one would expect difficulty in achieving inhibitor selectivity for kinases that share high sequence identity.