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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

A high-throughput screen against pantothenate synthetase (PanC) identifies 3-biphenyl-4-cyanopyrrole-2-carboxylic acids as a new class of inhibitor with activity against Mycobacterium tuberculosis.

The enzyme pantothenate synthetase, PanC, is an attractive drug target in Mycobacterium tuberculosis. It is essential for the in vitro growth of M. tuberculosis and for survival of the bacteria in the mouse model of infection. PanC is absent from mammals. We developed an enzyme-based assay to identify inhibitors of PanC, optimized it for high-throughput screening, and tested a large and diverse library of compounds for activity. Two compounds belonging to the same chemical class of 3-biphenyl-4- cyanopyrrole-2-carboxylic acids had activity against the purified recombinant protein, and also inhibited growth of live M. tuberculosis in manner consistent with PanC inhibition. Thus we have identified a new class of PanC inhibitors with whole cell activity that can be further developed.

Selectivity data: assessment, predictions, concordance, and implications.

Could high-quality in silico predictions in drug discovery eventually replace part or most of experimental testing? To evaluate the agreement of selectivity data from different experimental or predictive sources, we introduce the new metric concordance minimum significant ratio (cMSR). Empowered by cMSR, we find the overall level of agreement between predicted and experimental data to be comparable to that found between experimental results from different sources. However, for molecules that are either highly selective or potent, the concordance between different experimental sources is significantly higher than the concordance between experimental and predicted values. We also show that computational models built from one data set are less predictive for other data sources and highlight the importance of bias correction for assessing selectivity data. Finally, we show that small-molecule target space relationships derived from different data sources and predictive models share overall similarity but can significantly differ in details.

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).

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.

Kinase inhibitor data modeling and de novo inhibitor design with fragment approaches.

A reconstructive approach based on computational fragmentation of existing inhibitors and validated kinase potency models to recombine and create "de novo" kinase inhibitor small molecule libraries is described. The screening results from model selected molecules from the corporate database and seven computationally derived small molecule libraries were used to evaluate this approach. Specifically, 1895 model selected database molecules were screened at 20 microM in six kinase assays and yielded an overall hit rate of 84%. These models were then used in the de novo design of seven chemical libraries consisting of 20-50 compounds each. Then 179 compounds from synthesized libraries were tested against these six kinases with an overall hit rate of 92%. Comparing predicted and observed selectivity profiles serves to highlight the strengths and limitations of the methodology, while analysis of functional group contributions from the libraries suggest general principles governing binding of ATP competitive compounds.

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.

Lessons in molecular recognition. 2. Assessing and improving cross-docking accuracy.

Docking methods are used to predict the manner in which a ligand binds to a protein receptor. Many studies have assessed the success rate of programs in self-docking tests, whereby a ligand is docked into the protein structure from which it was extracted. Cross-docking, or using a protein structure from a complex containing a different ligand, provides a more realistic assessment of a docking program's ability to reproduce X-ray results. In this work, cross-docking was performed with CDocker, Fred, and Rocs using multiple X-ray structures for eight proteins (two kinases, one nuclear hormone receptor, one serine protease, two metalloproteases, and two phosphodiesterases). While average cross-docking accuracy is not encouraging, it is shown that using the protein structure from the complex that contains the bound ligand most similar to the docked ligand increases docking accuracy for all methods ("similarity selection"). Identifying the most successful protein conformer ("best selection") and similarity selection substantially reduce the difference between self-docking and average cross-docking accuracy. We identify universal predictors of docking accuracy (i.e., showing consistent behavior across most protein-method combinations), and show that models for predicting docking accuracy built using these parameters can be used to select the most appropriate docking method.