Lessons learned during the journey of data: from experiment to model for predicting kinase affinity, selectivity, polypharmacology, and resistance.

Recent advances in machine learning (ML) are reshaping drug discovery. Structure-based ML methods use physically-inspired models to predict binding affinities from protein:ligand complexes. These methods promise to enable the integration of data for many related targets, which addresses issues related to data scarcity for single targets and could enable generalizable predictions for a broad range of targets, including mutants. In this work, we report our experiences in building KinoML, a novel framework for ML in target-based small molecule drug discovery with an emphasis on structure-enabled methods. KinoML focuses currently on kinases as the relative structural conservation of this protein superfamily, particularly in the kinase domain, means it is possible to leverage data from the entire superfamily to make structure-informed predictions about binding affinities, selectivities, and drug resistance. Some key lessons learned in building KinoML include: the importance of reproducible data collection and deposition, the harmonization of molecular data and featurization, and the choice of the right data format to ensure reusability and reproducibility of ML models. As a result, KinoML allows users to easily achieve three tasks: accessing and curating molecular data; featurizing this data with representations suitable for ML applications; and running reproducible ML experiments that require access to ligand, protein, and assay information to predict ligand affinity. Despite KinoML focusing on kinases, this framework can be applied to other proteins. The lessons reported here can help guide the development of platforms for structure-enabled ML in other areas of drug discovery.

Stabilization of interdomain closure by a G protein inhibitor.

Inhibitors of heterotrimeric G proteins are being developed as therapeutic agents. Epitomizing this approach are YM-254890 (YM) and FR900359 (FR), which are efficacious in models of thrombosis, hypertension, obesity, asthma, uveal melanoma, and pain, and under investigation as an FR-antibody conjugate in uveal melanoma clinical trials. YM/FR inhibits the Gq/11/14 subfamily by interfering with GDP (guanosine diphosphate) release, but by an unknown biophysical mechanism. Here, we show that YM inhibits GDP release by stabilizing closure between the Ras-like and α-helical domains of a Gα subunit. Nucleotide-free Gα adopts an ensemble of open and closed configurations, as indicated by single-molecule Förster resonance energy transfer and molecular dynamics simulations, whereas GDP and GTPγS (guanosine 5'-O-[gamma-thio]triphosphate) stabilize distinct closed configurations. YM stabilizes closure in the presence or absence of GDP without requiring an intact interdomain interface. All three classes of mammalian Gα subunits that are insensitive to YM/FR possess homologous but degenerate YM/FR binding sites, yet can be inhibited upon transplantation of the YM/FR binding site of Gq. Novel YM/FR analogs tailored to each class of G protein will provide powerful new tools for therapeutic investigation.

Drug Discovery in Low Data Regimes: Leveraging a Computational Pipeline for the Discovery of Novel SARS-CoV-2 Nsp14-MTase Inhibitors.

The COVID-19 pandemic, caused by the SARS-CoV-2 virus, has led to significant global morbidity and mortality. A crucial viral protein, the non-structural protein 14 (nsp14), catalyzes the methylation of viral RNA and plays a critical role in viral genome replication and transcription. Due to the low mutation rate in the nsp region among various SARS-CoV-2 variants, nsp14 has emerged as a promising therapeutic target. However, discovering potential inhibitors remains a challenge. In this work, we introduce a computational pipeline for the rapid and efficient identification of potential nsp14 inhibitors by leveraging virtual screening and the NCI open compound collection, which contains 250,000 freely available molecules for researchers worldwide. The introduced pipeline provides a cost-effective and efficient approach for early-stage drug discovery by allowing researchers to evaluate promising molecules without incurring synthesis expenses. Our pipeline successfully identified seven promising candidates after experimentally validating only 40 compounds. Notably, we discovered NSC620333, a compound that exhibits a strong binding affinity to nsp14 with a dissociation constant of 427 ± 84 nM. In addition, we gained new insights into the structure and function of this protein through molecular dynamics simulations. We identified new conformational states of the protein and determined that residues Phe367, Tyr368, and Gln354 within the binding pocket serve as stabilizing residues for novel ligand interactions. We also found that metal coordination complexes are crucial for the overall function of the binding pocket. Lastly, we present the solved crystal structure of the nsp14-MTase complexed with SS148 (PDB:8BWU), a potent inhibitor of methyltransferase activity at the nanomolar level (IC50 value of 70 ± 6 nM). Our computational pipeline accurately predicted the binding pose of SS148, demonstrating its effectiveness and potential in accelerating drug discovery efforts against SARS-CoV-2 and other emerging viruses.

Death by a thousand cuts through kinase inhibitor combinations that maximize selectivity and enable rational multitargeting.

Kinase inhibitors are successful therapeutics in the treatment of cancers and autoimmune diseases and are useful tools in biomedical research. However, the high sequence and structural conservation of the catalytic kinase domain complicate the development of selective kinase inhibitors. Inhibition of off-target kinases makes it difficult to study the mechanism of inhibitors in biological systems. Current efforts focus on the development of inhibitors with improved selectivity. Here, we present an alternative solution to this problem by combining inhibitors with divergent off-target effects. We develop a multicompound-multitarget scoring (MMS) method that combines inhibitors to maximize target inhibition and to minimize off-target inhibition. Additionally, this framework enables optimization of inhibitor combinations for multiple on-targets. Using MMS with published kinase inhibitor datasets we determine potent inhibitor combinations for target kinases with better selectivity than the most selective single inhibitor and validate the predicted effect and selectivity of inhibitor combinations using in vitro and in cellulo techniques. MMS greatly enhances selectivity in rational multitargeting applications. The MMS framework is generalizable to other non-kinase biological targets where compound selectivity is a challenge and diverse compound libraries are available.

MEN1 mutations mediate clinical resistance to menin inhibition.

Chromatin-binding proteins are critical regulators of cell state in haematopoiesis1,2. Acute leukaemias driven by rearrangement of the mixed lineage leukaemia 1 gene (KMT2Ar) or mutation of the nucleophosmin gene (NPM1) require the chromatin adapter protein menin, encoded by the MEN1 gene, to sustain aberrant leukaemogenic gene expression programs3-5. In a phase 1 first-in-human clinical trial, the menin inhibitor revumenib, which is designed to disrupt the menin-MLL1 interaction, induced clinical responses in patients with leukaemia with KMT2Ar or mutated NPM1 (ref. 6). Here we identified somatic mutations in MEN1 at the revumenib-menin interface in patients with acquired resistance to menin inhibition. Consistent with the genetic data in patients, inhibitor-menin interface mutations represent a conserved mechanism of therapeutic resistance in xenograft models and in an unbiased base-editor screen. These mutants attenuate drug-target binding by generating structural perturbations that impact small-molecule binding but not the interaction with the natural ligand MLL1, and prevent inhibitor-induced eviction of menin and MLL1 from chromatin. To our knowledge, this study is the first to demonstrate that a chromatin-targeting therapeutic drug exerts sufficient selection pressure in patients to drive the evolution of escape mutants that lead to sustained chromatin occupancy, suggesting a common mechanism of therapeutic resistance.

Death by a Thousand Cuts â€" Combining Kinase Inhibitors for Selective Target Inhibition and Rational Polypharmacology.

Kinase inhibitors are successful therapeutics in the treatment of cancers and autoimmune diseases and are useful tools in biomedical research. The high sequence and structural conservation of the catalytic kinase domain complicates the development of specific kinase inhibitors. As a consequence, most kinase inhibitors also inhibit off-target kinases which complicates the interpretation of phenotypic responses. Additionally, inhibition of off-targets may cause toxicity in patients. Therefore, highly selective kinase inhibition is a major goal in both biomedical research and clinical practice. Currently, efforts to improve selective kinase inhibition are dominated by the development of new kinase inhibitors. Here, we present an alternative solution to this problem by combining inhibitors with divergent off-target activities. We have developed a multicompound-multitarget scoring (MMS) method framework that combines inhibitors to maximize target inhibition and to minimize off-target inhibition. Additionally, this framework enables rational polypharmacology by allowing optimization of inhibitor combinations against multiple selected on-targets and off-targets. Using MMS with previously published chemogenomic kinase inhibitor datasets we determine inhibitor combinations that achieve potent activity against a target kinase and that are more selective than the most selective single inhibitor against that target. We validate the calculated effect and selectivity of a combination of inhibitors using the in cellulo NanoBRET assay. The MMS framework is generalizable to other pharmacological targets where compound specificity is a challenge and diverse compound libraries are available.

Mechanistic Basis for ATP-Dependent Inhibition of Glutamine Synthetase by Tabtoxinine-ß-lactam.

Tabtoxinine-ß-lactam (TßL), also known as wildfire toxin, is a time- and ATP-dependent inhibitor of glutamine synthetase produced by plant pathogenic strains of Pseudomonas syringae. Here we demonstrate that recombinant glutamine synthetase from Escherichia coli phosphorylates the C3-hydroxyl group of the TßL 3-(S)-hydroxy-ß-lactam (3-HßL) warhead. Phosphorylation of TßL generates a stable, noncovalent enzyme-ADP-inhibitor complex that resembles the glutamine synthetase tetrahedral transition state. The TßL ß-lactam ring remains intact during enzyme inhibition, making TßL mechanistically distinct from traditional ß-lactam antibiotics such as penicillin. Our findings could enable the design of new 3-HßL transition state inhibitors targeting enzymes in the ATP-dependent carboxylate-amine ligase superfamily with broad therapeutic potential in many disease areas.

Quantifying Allosteric Communication via Both Concerted Structural Changes and Conformational Disorder with CARDS.

Allosteric (i.e., long-range) communication within proteins is crucial for many biological processes, such as the activation of signaling cascades in response to specific stimuli. However, the physical basis for this communication remains unclear. Existing computational methods for identifying allostery focus on the role of concerted structural changes, but recent experimental work demonstrates that disorder is also an important factor. Here, we introduce the Correlation of All Rotameric and Dynamical States (CARDS) framework for quantifying correlations between both the structure and disorder of different regions of a protein. To quantify disorder, we draw inspiration from methods for quantifying "dynamic heterogeneity" from chemical physics to classify segments of a dihedral's time evolution as being in either ordered or disordered regimes. To demonstrate the utility of this approach, we apply CARDS to the Catabolite Activator Protein (CAP), a transcriptional activator that is regulated by Cyclic Adenosine MonoPhosphate (cAMP) binding. We find that CARDS captures allosteric communication between the two cAMP-Binding Domains (CBDs). Importantly, CARDS reveals that this coupling is dominated by disorder-mediated correlations, consistent with NMR experiments that establish allosteric coupling between the CBDs occurs without a concerted structural change. CARDS also recapitulates an enhanced role for disorder in the communication between the DNA-Binding Domains (DBDs) and CBDs in the S62F variant of CAP. Finally, we demonstrate that using CARDS to find communication hotspots identifies regions of CAP that are in allosteric communication without foreknowledge of their identities. Therefore, we expect CARDS to be of great utility for both understanding and predicting allostery.