Estimation of Electrostatic Interaction Energies on a Trapped-Ion Quantum Computer.

We present the first hardware implementation of electrostatic interaction energies by using a trapped-ion quantum computer. As test system for our computation, we focus on the reduction of NO to N2O catalyzed by a nitric oxide reductase (NOR). The quantum computer is used to generate an approximate ground state within the NOR active space. To efficiently measure the necessary one-particle density matrices, we incorporate fermionic basis rotations into the quantum circuit without extending the circuit length, laying the groundwork for further efficient measurement routines using factorizations. Measurements in the computational basis are then used as inputs for computing the electrostatic interaction energies on a classical computer. Our experimental results strongly agree with classical noise-less simulations of the same circuits, finding electrostatic interaction energies within chemical accuracy despite hardware noise. This work shows that algorithms tailored to specific observables of interest, such as interaction energies, may require significantly fewer quantum resources than individual ground state energies would require in the straightforward supermolecular approach.

Towards holistic Compound Quality Scores: Extending ligand efficiency indices with compound pharmacokinetic characteristics.

The suitability of small molecules as oral drugs is often assessed by simple physicochemical rules, the application of ligand efficiency scores or by composite scores based on physicochemical compound properties. These rules and scores are empirical and typically lack mechanistic background, such as information on pharmacokinetics (PK). We introduce new types of Compound Quality Scores (CQS, specifically called dose scores and cmax scores), which explicitly include predicted or, when available, experimental PK parameters and combine these with on-target potency. These CQS scores are surrogates for an estimated dose and corresponding cmax and allow prioritizing of compounds within test cascades as well as before synthesis. We demonstrate the complementarity and, in most cases, superior performance relative to existing efficiency metrics by project examples.

Discovery of a Novel Potent and Selective HSD17B13 Inhibitor, BI-3231, a Well-Characterized Chemical Probe Available for Open Science.

Genome-wide association studies in patients revealed HSD17B13 as a potential new target for the treatment of nonalcoholic steatohepatitis (NASH) and other liver diseases. However, the physiological function and the disease-relevant substrate of HSD17B13 remain unknown. In addition, no suitable chemical probe for HSD17B13 has been published yet. Herein, we report the identification of the novel potent and selective HSD17B13 inhibitor BI-3231. Through high-throughput screening (HTS), using estradiol as substrate, compound 1 was identified and selected for subsequent optimization resulting in compound 45 (BI-3231). In addition to the characterization of compound 45 for its functional, physicochemical, and drug metabolism and pharmacokinetic (DMPK) properties, NAD+ dependency was investigated. To support Open Science, the chemical HSD17B13 probe BI-3231 will be available to the scientific community for free via the opnMe platform, and thus can help to elucidate the pharmacology of HSD17B13.

BIreactive: Expanding the Scope of Reactivity Predictions to Propynamides.

We present the first comprehensive study on the prediction of reactivity for propynamides. Covalent inhibitors like propynamides often show improved potency, selectivity, and unique pharmacologic properties compared to their non-covalent counterparts. In order to achieve this, it is essential to tune the reactivity of the warhead. This study shows how three different in silico methods can predict the in vitro properties of propynamides, a covalent warhead class integrated into approved drugs on the market. Whereas the electrophilicity index is only applicable to individual subclasses of substitutions, adduct formation and transition state energies have a good predictability for the in vitro reactivity with glutathione (GSH). In summary, the reported methods are well suited to estimate the reactivity of propynamides. With this knowledge, the fine tuning of the reactivity is possible which leads to a speed up of the design process of covalent drugs.

BRD4: quantum mechanical protein-ligand binding free energies using the full-protein DFT-based QM-PBSA method.

Fully quantum mechanical approaches to calculating protein-ligand free energies of binding have the potential to reduce empiricism and explicitly account for all physical interactions responsible for protein-ligand binding. In this study, we show a realistic test of the linear-scaling DFT-based QM-PBSA method to estimate quantum mechanical protein-ligand binding free energies for a set of ligands binding to the pharmaceutical drug-target bromodomain containing protein 4 (BRD4). We show that quantum mechanical QM-PBSA is a significant improvement over traditional MM-PBSA in terms of accuracy against experiment and ligand rank ordering and that the quantum and classical binding energies are converged to a similar degree. We test the interaction entropy and normal mode entropy correction terms to QM- and MM-PBSA.

Reliably assessing the electronic structure of cytochrome P450 on today's classical computers and tomorrow's quantum computers.

An accurate assessment of how quantum computers can be used for chemical simulation, especially their potential computational advantages, provides important context on how to deploy these future devices. To perform this assessment reliably, quantum resource estimates must be coupled with classical computations attempting to answer relevant chemical questions and to define the classical algorithms simulation frontier. Herein, we explore the quantum computation and classical computation resources required to assess the electronic structure of cytochrome P450 enzymes (CYPs) and thus define a classical-quantum advantage boundary. This is accomplished by analyzing the convergence of density matrix renormalization group plus n-electron valence state perturbation theory (DMRG+NEVPT2) and coupled-cluster singles doubles with noniterative triples [CCSD(T)] calculations for spin gaps in models of the CYP catalytic cycle that indicate multireference character. The quantum resources required to perform phase estimation using qubitized quantum walks are calculated for the same systems. Compilation into the surface code provides runtime estimates to compare directly to DMRG runtimes and to evaluate potential quantum advantage. Both classical and quantum resource estimates suggest that simulation of CYP models at scales large enough to balance dynamic and multiconfigurational electron correlation has the potential to be a quantum advantage problem and emphasizes the important interplay between classical computations and quantum algorithms development for chemical simulation.

Protein-ligand free energies of binding from full-protein DFT calculations: convergence and choice of exchange-correlation functional.

The accurate prediction of protein-ligand binding free energies with tractable computational methods has the potential to revolutionize drug discovery. Modeling the protein-ligand interaction at a quantum mechanical level, instead of relying on empirical classical-mechanics methods, is an important step toward this goal. In this study, we explore the QM-PBSA method to calculate the free energies of binding of seven ligands to the T4-lysozyme L99A/M102Q mutant using linear-scaling density functional theory on the whole protein-ligand complex. By leveraging modern high-performance computing we perform over 2900 full-protein (2600 atoms) DFT calculations providing new insights into the convergence, precision and reproducibility of the QM-PBSA method. We find that even at moderate sampling over 50 snapshots, the convergence of QM-PBSA is similar to traditional MM-PBSA and that the DFT-based energy evaluations are very reproducible. We show that in the QM-PBSA framework, the physically-motivated GGA exchange-correlation functional PBE outperforms the more modern, dispersion-including non-local and meta-GGA-nonlocal functionals VV10 and B97M-rV. Different empirical dispersion corrections perform similarly well but the three-body dispersion term, as included in Grimme's D3 dispersion, is significant and improves results slightly. Inclusion of an entropy correction term sampled over less than 25 snapshots is detrimental while an entropy correction sampled over the same 50 or 100 snapshots as the enthalpies improves the accuracy of the QM-PBSA method. As full-protein DFT calculations can now be performed on modest computational resources our study demonstrates that they can be a useful addition to the toolbox of free energy calculations.

Covalent inhibitor reactivity prediction by the electrophilicity index-in and out of scope.

Drug discovery is an expensive and time-consuming process. To make this process more efficient quantum chemistry methods can be employed. The electrophilicity index is one property that can be calculated by quantum chemistry methods, and if calculated correctly gives insight into the reactivity of covalent inhibitors. Herein we present the usage of the electrophilicity index on three common warheads, i.e., acrylamides, 2-chloroacetamides, and propargylamides. We thoroughly examine the properties of the electrophilicity index, show which pitfalls should be avoided, and what the requirements to successfully apply the electrophilicity index are.

BIreactive: A Machine-Learning Model to Estimate Covalent Warhead Reactivity.

In the past decade, the pharmaceutical industry has paid closer attention to covalent drugs. Differently from standard noncovalent drugs, these compounds can exhibit peculiar properties, such as higher potency or longer duration of target inhibition with a potentially lower dosage. These properties are mainly driven by the reactive functional group present in the compound, the so-called warhead that forms a covalent bond with a specific nucleophilic amino-acid on the target. In this work, we report the possibility to combine ab initio activation energies with machine-learning to estimate covalent compound intrinsic reactivity. The idea behind this approach is to have a precise estimation of the transition state barriers, and thus of the compound reactivity, but with the speed of a machine-learning algorithm. We call this method "BIreactive". Here, we demonstrate this approach on acrylamides and 2-chloroacetamides, two warhead classes that possess different reaction mechanisms. In combination with our recently implemented truncation algorithm, we also demonstrate the possibility to use BIreactive not only for fragments but also for lead-like molecules. The generic nature of this approach allows also the extension to several other warheads. The combination of these factors makes BIreactive a valuable tool for the covalent drug discovery process in a pharmaceutical context.

Current and Future Challenges in Modern Drug Discovery.

Drug discovery is an expensive, time-consuming, and risky business. To avoid late-stage failure, learnings from past projects and the development of new approaches are crucial. New modalities and emerging new target spaces allow the exploration of unprecedented indications or to address so far undrugable targets. Late-stage attrition is usually attributed to the lack of efficacy or to compound-related safety issues. Efficacy has been shown to be related to a strong genetic link to human disease, a better understanding of the target biology, and the availability of biomarkers to bridge from animals to humans. Compound safety can be improved by ligand optimization, which is becoming increasingly demanding for difficult targets. Therefore, new strategies include the design of allosteric ligands, covalent binders, and other modalities. Design methods currently heavily rely on artificial intelligence and advanced computational methods such as free energy calculations and quantum chemistry. Especially for quantum chemical methods, a more detailed overview is given in this chapter.

A Fast Ab Initio Predictor Tool for Covalent Reactivity Estimation of Acrylamides.

Thanks to their unique mode of action, covalent drugs represent an exceptional opportunity for drug design. After binding to a biologically relevant target system, covalent compounds form a reversible or irreversible covalent bond with a nucleophilic amino acid. Due to the inherently large binding energy of a covalent bond, covalent binders exhibit higher potencies and thus allow potentially lower drug dosages. However, a proper balancing of compound reactivity is key for the design of covalent binders, to achieve high levels of target inhibition while minimizing promiscuous covalent binding to nontarget proteins. In this work, we demonstrated the possibility to apply the electrophilicity index concept to estimate covalent compound reactivity. We tested this approach on acrylamides, one of the most prominent classes of covalent warheads. Our study clearly demonstrated that, for compounds with molecular weight (MW) below 250 Da, the electrophilicity index can be directly used to estimate compound reactivity. On the other hand, for leadlike molecules (MW > 250 Da) we implemented a new truncation algorithm that has to be applied before reactivity calculations. This algorithm can ensure the localization of HOMO/LUMO orbitals on the compound warhead and thus a correct estimation of its reactivity. Our results also indicate that caution should be used when employing the electrophilicity index to estimate the reactivity of nonterminal acrylamides. The nonparametric nature of this method and its reasonable computational cost make it a suitable tool to support covalent drug design.

Second M3 muscarinic receptor binding site contributes to bronchoprotection by tiotropium.

BACKGROUND AND PURPOSE: The bronchodilator tiotropium binds not only to its main binding site on the M3 muscarinic receptor but also to an allosteric site. Here, we have investigated the functional relevance of this allosteric binding and the potential contribution of this behaviour to interactions with long-acting ß-adrenoceptor agonists, as combination therapy with anticholinergic agents and ß-adrenoceptor agonists improves lung function in chronic obstructive pulmonary disease. EXPERIMENTAL APPROACH: ACh, tiotropium, and atropine binding to M3 receptors were modelled using molecular dynamics simulations. Contractions of bovine and human tracheal smooth muscle strips were studied. KEY RESULTS: Molecular dynamics simulation revealed extracellular vestibule binding of tiotropium, and not atropine, to M3 receptors as a secondary low affinity binding site, preventing ACh entry into the orthosteric binding pocket. This resulted in a low (allosteric binding) and high (orthosteric binding) functional affinity of tiotropium in protecting against methacholine-induced contractions of airway smooth muscle, which was not observed for atropine and glycopyrrolate. Moreover, antagonism by tiotropium was insurmountable in nature. This behaviour facilitated functional interactions of tiotropium with the ß-agonist olodaterol, which synergistically enhanced bronchoprotective effects of tiotropium. This was not seen for glycopyrrolate and olodaterol or indacaterol but was mimicked by the interaction of tiotropium and forskolin, indicating no direct ß-adrenoceptor-M3 receptor crosstalk in this effect. CONCLUSIONS AND IMPLICATIONS: We propose that tiotropium has two binding sites at the M3 receptor that prevent ACh action, which, together with slow dissociation kinetics, may contribute to insurmountable antagonism and enhanced functional interactions with ß-adrenoceptor agonists.

Enhancing Drug Residence Time by Shielding of Intra-Protein Hydrogen Bonds: A Case Study on CCR2 Antagonists.

The target residence time (RT) for a given ligand is one of the important parameters that have to be optimized during drug design. It is well established that shielding the receptor-ligand hydrogen bond (H-bond) interactions from water has been one of the factors in increasing ligand RT. Building on this foundation, here we report that shielding an intra-protein H-bond, which confers rigidity to the binding pocket and which is not directly involved in drug-receptor interactions, can strongly influence RT for CCR2 antagonists. Based on our recently solved CCR2 structure with MK-0812 and molecular dynamics (MD) simulations, we show that the RT for this and structurally related ligands is directly dependent on the shielding of the Tyr120-Glu291 H-bond from the water. If solvated this H-bond is often broken, making the binding pocket flexible and leading to shorter RT.

Allosteric Activation of Striatal-Enriched Protein Tyrosine Phosphatase (STEP, PTPN5) by a Fragment-like Molecule.

Protein tyrosine phosphatase non-receptor type 5 (PTPN5, STEP) is a brain specific phosphatase that regulates synaptic function and plasticity by modulation of N-methyl-d-aspartate receptor (NMDAR) and α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid receptor (AMPAR) trafficking. Dysregulation of STEP has been linked to neurodegenerative and neuropsychiatric diseases, highlighting this enzyme as an attractive therapeutic target for drug discovery. Selective targeting of STEP with small molecules has been hampered by high conservation of the active site among protein tyrosine phosphatases. We report the discovery of the first small molecule allosteric activator for STEP that binds to the phosphatase domain. Allosteric binding is confirmed by both X-ray and 15N NMR experiments, and specificity has been demonstrated by an enzymatic test cascade. Molecular dynamics simulations indicate stimulation of enzymatic activity by a long-range allosteric mechanism. To allow the scientific community to make use of this tool, we offer to provide the compound in the course of an open innovation initiative.

Crystal Structure of CC Chemokine Receptor 2A in Complex with an Orthosteric Antagonist Provides Insights for the Design of Selective Antagonists.

We determined two crystal structures of the chemokine receptor CCR2A in complex with the orthosteric antagonist MK-0812. Full-length CCR2A, stabilized by rubredoxin and a series of five mutations were resolved at 3.3 Å. An N- and C-terminally truncated CCR2A construct was crystallized in an alternate crystal form, which yielded a 2.7 Å resolution structure using serial synchrotron crystallography. Our structures provide a clear structural explanation for the observed key role of residue E2917.39 in high-affinity binding of several orthosteric CCR2 antagonists. By combining all the structural information collected, we generated models of co-structures for the structurally diverse pyrimidine amide class of CCR2 antagonists. Even though the representative Ex15 overlays well with MK-0812, it also interacts with the non-conserved H1213.33, resulting in a significant selectivity over CCR5. Insights derived from this work will facilitate drug discovery efforts directed toward highly selective CCR2 antagonists with potentially superior efficacy.

Engineering Salt Bridge Networks between Transmembrane Helices Confers Thermostability in G-Protein-Coupled Receptors.

Introduction of specific point mutations has been an effective strategy in enhancing the thermostability of G-protein-coupled receptors (GPCRs). Our previous work showed that a specific residue position on transmembrane helix 6 (TM6) in class A GPCRs consistently yields thermostable mutants. The crystal structure of human chemokine receptor CCR5 also showed increased thermostability upon mutation of two positions, A233D6.33 and K303E7.59. With the goal of testing the transferability of these two thermostabilizing mutations in other chemokine receptors, we tested the mutations A237D6.33 and R307E7.59 in human CCR3 for thermostability and aggregation properties in detergent solution. Interestingly, the double mutant exhibited a 6-10-fold decrease in the aggregation propensity of the wild-type protein. This is in stark contrast to the two single mutants whose aggregation properties resemble the wild type (WT). Moreover, unlike in CCR5, the two single mutants separately showed no increase in thermostability compared to the wild-type CCR3, while the double-mutant A237D6.33/R307E7.59 confers an increase of 2.6 °C in the melting temperature compared to the WT. Extensive all-atom molecular dynamics (MD) simulations in detergent micelles show that a salt bridge network between transmembrane helices TM3, TM6, and TM7 that is absent in the two single mutants confers stability in the double mutant. The free energy surface of the double mutant shows conformational homogeneity compared to the single mutants. An annular n-dodecyl maltoside detergent layer packs tighter to the hydrophobic surface of the double-mutant CCR3 compared to the single mutants providing additional stability. The purification of other C-C chemokine receptors lacking such stabilizing residues may benefit from the incorporation of these two point mutations.

Late-Stage Functionalization of Drug-Like Molecules Using Diversinates.

Late-stage functionalization (LSF) is a powerful method to quickly generate new analogues of a lead structure without resorting to de novo synthesis. We have leveraged Baran Diversinates to carry out late-stage functionalizations on lead structures from internal drug discovery projects and accurately predicted regioselectivities using computational methods. Our functionalization successfully afforded specific regioisomers which were in line with our predictions. To enhance reactivity, decrease reaction time, and increase reaction yields, we have developed new functionalization conditions involving iron(III) catalysis. Finally, we demonstrate how our LSF reactions using Baran Diversinates can lead to new analogues with improved in vitro DMPK parameters.

Multiple Binding Sites Contribute to the Mechanism of Mixed Agonistic and Positive Allosteric Modulators of the Cannabinoid CB1 Receptor.

The cannabinoid CB1 receptor (CB1R) is an abundant metabotropic G-protein-coupled receptor that has been difficult to address therapeutically because of CNS side effects exerted by orthosteric drug candidates. Recent efforts have focused on developing allosteric modulators that target CB1R. Compounds from the recently discovered class of mixed agonistic and positive allosteric modulators (Ago-PAMs) based on 2-phenylindoles have shown promising functional and binding properties as CB1R ligands. Here, we identify binding modes of both the CP 55,940 agonist and GAT228, a 2-phenylindole allosteric modulator, by using our metadynamics simulation protocol, and quantify their affinity and cooperativity by atomistic simulations. We demonstrate the involvement of multiple adjunct binding sites in the Ago-PAM characteristics of the 2-phenylindole modulators and explain their ability to compete with orthosteric agonists at higher concentrations. We validate these results experimentally by showing the contribution of multiple sites on the allosteric binding of ZCZ011, another homologous member of the class, together with the orthosteric agonist.

Identifying Functional Hotspot Residues for Biased Ligand Design in G-Protein-Coupled Receptors.

G-protein-coupled receptors (GPCRs) mediate multiple signaling pathways in the cell, depending on the agonist that activates the receptor and multiple cellular factors. Agonists that show higher potency to specific signaling pathways over others are known as "biased agonists" and have been shown to have better therapeutic index. Although biased agonists are desirable, their design poses several challenges to date. The number of assays to identify biased agonists seems expensive and tedious. Therefore, computational methods that can reliably calculate the possible bias of various ligands ahead of experiments and provide guidance, will be both cost and time effective. In this work, using the mechanism of allosteric communication from the extracellular region to the intracellular transducer protein coupling region in GPCRs, we have developed a computational method to calculate ligand bias ahead of experiments. We have validated the method for several ß-arrestin-biased agonists in ß2-adrenergic receptor (ß2AR), serotonin receptors 5-HT1B and 5-HT2B and for G-protein-biased agonists in the κ-opioid receptor. Using this computational method, we also performed a blind prediction followed by experimental testing and showed that the agonist carmoterol is ß-arrestin-biased in ß2AR. Additionally, we have identified amino acid residues in the biased agonist binding site in both ß2AR and κ-opioid receptors that are involved in potentiating the ligand bias. We call these residues functional hotspots, and they can be used to derive pharmacophores to design biased agonists in GPCRs.

GPCR Homology Model Generation for Lead Optimization.

The vast increase of recently solved GPCR X-ray structures forms the basis for GPCR homology modeling to atomistic accuracy. Nowadays, homology models can be employed for GPCR-ligand optimization and have been reported as invaluable tools for drug design in the last few years. Elucidation of the complex GPCR pharmacology and the associated GPCR conformations made clear that different homology models have to be constructed for different activation states of the GPCRs. Therefore, templates have to be chosen accordingly to their sequence homology as well as to their activation state. The subsequent ligand placement is nontrivial, as some recent X-ray structures show very unusual ligand binding sites and solvent involvement, expanding the space of the putative ligand binding site from the generic retinal binding pocket to the whole receptor. In the present study, a workflow is presented starting from the selection of the target sequence, guiding through the GPCR modeling process, and finishing with ligand placement and pose validation.