Eric R. Hantz

Accurate prediction of ligand-bound protein complexes and ranking them by affinity are central problems in drug discovery. While deep-learning co-folding methods can help address these challenges, their evaluation has been hampered by the difficulties in assessing independence from training data and insufficiently large test sets. Here, we test the ability of co-folding methods to predict the structures of 551 ligands, 489 of which are of sufficient quality for evaluation, bound to the SARS-CoV-2 NSP3 macrodomain (Mac1) that were determined after the training cutoff dates. AlphaFold3 (AF3), Boltz-2, and Chai-1 each reproduced >50% of the Mac1 ligand poses to better than 2 Å root-mean-square deviation of experiment. Despite the potential for co-folding to describe protein conformational changes that stabilize ligand binding, we did not find that common conformational rearrangements, including peptide flip and a large loop opening, were recapitulated by the co-folding prediction. For AF3 and Chai-1, ligand pose prediction confidence weakly, but significantly, tracked experimental potency, while DOCK3.7 energies were only weakly correlated. Boltz-2 affinity predictions showed the strongest correlation with measured potency and, after calibration, achieved lower mean absolute error than a baseline predictor. We next assessed whether co-folding scores could rescore docking hit lists to distinguish true ligands from non-binders among hundreds of molecules prospectively experimentally tested against AmpC β-lactamase, the dopamine D4, and the σ ₂ receptors. AF3 ligand pose confidence values did not separate true ligands from high-scoring false positives as effectively as docking scores or Boltz-2 affinity predictions did. Taken together, the modest, but independent correlations of docking score and co-folding confidence or affinity suggest that integrating physics-based and deep-learning approaches may help with hit prioritization and subsequent optimization in structure-based ligand discovery.

Carbapenems are essential β-lactam antibiotics whose clinical utility is now threatened by emerging carbapenemases, including the Class A serine β-lactamase KPC-2 and the Class B metallo-β-lactamase NDM-1. Here, we describe a comprehensive, structure-based assessment of active-site binding determinants for KPC-2 and NDM-1 in the context of reversible, noncovalent inhibition by a quinoline-2(1H)-one phosphonate scaffold. Systematic substitution of the scaffold core revealed the N1 and C7 positions as most tolerant of diverse substitution, while the less tolerant C3 and C6 positions nevertheless drive affinity and binding mode when modified with a C3-methyl or C6-fluoro substituent, respectively. We also describe biophysical and computational studies aimed at determining the pharmacological significance of the 2:1 ligand binding stoichiometry observed in several NDM-1 complex structures, concluding that only one of these binding events is relevant for potent NDM-1 inhibition. Although the current inhibitors lack significant whole-cell activity, the structural and biochemical findings described provide valuable information for the targeting of evolutionarily and mechanistically diverse carbapenemases.

The main proteases (M^Pro) of coronaviruses are clinically validated targets for antiviral discovery. Herein, we detail the in vivo optimization of uracil-core M^Pro inhibitors derived from AVI-4516, an in vivo active lead bearing an unactivated propargyl warhead. To expand the anticoronaviral spectrum, we introduced diverse C6 substitution to target the S1' pocket in M^Pro and observed enhanced cellular activity against various nirmatrelvir-resistant mutants. Pharmacokinetic profiling of 12 analogs revealed overall inferior exposure of the C6 aryl analogs. However, PK profiling across three species identified the improved atropisomeric lead (M)-AVI-4773 (5-(5,6-difluoro-1H-benzo[d][1,2,3]triazol-1-yl)-3-((M)-isoquinolin-4-yl)-6-methyl-1-(prop-2-yn-1-yl)pyrimidine-2,4(1H,3H)-dione), which exhibits rapid-onset oral efficacy in both SARS-CoV-2 and Middle East respiratory syndrome (MERS) mouse models, highlighting a promising chemotype with the potential to deliver anticoronaviral development candidates.

Accurate prediction of ligand-bound protein complexes and ranking them by affinity are central problems in drug discovery. While deep learning co-folding methods can help address these challenges, their evaluation has been hampered by the difficulties in assessing independence from training data and insufficiently large test sets. Here we test the ability of co-folding methods to predict the structures of 557 ligands bound to the SARS-CoV-2 NSP3 macrodomain (Mac1) that were determined after the training cut-off dates. AlphaFold3 (AF3), Boltz-2, and Chai-1 each reproduced >50% of the Mac1 ligand poses to better than 2 Å RMSD of experiment. Despite the potential for co-folding to describe protein conformational changes that stabilize ligand binding, we did not find that common conformational rearrangements, including peptide flip and a large loop opening, were recapitulated by the co-folding prediction. For AF3 and Chai-1, ligand pose prediction confidence weakly, but significantly, tracked experimental potency, while DOCK3.7 energies were only weakly correlated. Boltz-2 affinity predictions showed the strongest correlation with measured potency and, after calibration, achieved lower mean absolute error than a baseline predictor. We next assessed whether co-folding scores could rescore docking hit-lists to distinguish true ligands from non-binders among hundreds of molecules prospectively experimentally tested against AmpC β-lactamase, the dopamine D4 and the σ₂ receptors. AF3 ligand pose confidence values did not separate true ligands from high-scoring false-positives as effectively as docking scores or Boltz-2 affinity predictions did. Taken together, the modest, but independent correlations of docking score and co-folding confidence or affinity suggests that integrating physics-based and deep-learning and approaches may help with hit prioritization and subsequent optimization in structure-based ligand discovery.

The COVID-19 pandemic emphasized the need for effective antivirals acting at new targets. We recently described AVI-4206, a 9H-pyrimido[4,5-b]indole-based inhibitor of the SARS-CoV-2 macrodomain Mac1, an ADP-ribosylhydrolase thought to blunt the host immune response to viral infection. While AVI-4206 represents the first Mac1 inhibitor to demonstrate in vivo efficacy in mouse infection models, its low oral bioavailability necessitated IP administration. Herein, we describe an extensive medicinal chemistry campaign to identify new leads with improved cellular permeability and high oral bioavailability. Various tactics were employed toward these ends, including an exploration of urea isosteres to reduce hydrogen bond donor count and total polar surface area. These efforts ultimately produced AVI-6451, a potent Mac1 inhibitor with low intrinsic clearance and high oral bioavailability that achieves superior antiviral efficacy with once-daily oral administration in a mouse model of SARS-CoV-2 infection.

The main protease (M ^Pro ) of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a validated drug target. Starting with a lead-like dihydrouracil chemotype identified in a large-library docking campaign, we improved M ^Pro inhibition >1000-fold by engaging additional M ^Pro subsites and using a latent electrophile to engage Cys ¹⁴⁵ . Advanced leads from this series show pan-coronavirus antiviral activity, low clearance in mice, and for AVI-4773 , a rapid reduction in viral titers >1,000,000 after just three doses. Both compounds are well distributed in mouse tissues, including brain, where concentrations >1000× the 90% effective concentration are observed 8 hours after oral dosing for AVI-4773 . AVI-4516 shows minimal inhibition of major cytochrome P450s and human proteases. AVI-4516 also exhibits synergy with the RNA-dependent RNA polymerase inhibitor, molnupiravir, in cellular infection models. Related analogs strongly inhibit nirmatrelvir-resistant M ^Pro mutant virus. The properties of this chemotype are differentiated from existing clinical and preclinical M ^Pro inhibitors and will advance therapeutic development against emerging SARS-CoV-2 variants and other coronaviruses.

The main protease (M ^Pro ) of SARS-CoV-2 is crucial for viral replication and is the target of nirmatrelvir (the active ingredient of Paxlovid) and ensitrelvir. The identification of new agents with differentiated pharmacokinetic and drug resistance profiles will increase therapeutic options for COVID-19 patients and bolster pandemic preparedness generally. Starting with a lead-like dihydrouracil chemotype from a large-library docking campaign, we improved M ^Pro inhibition >1,000-fold by engaging additional sub-sites in the M ^Pro active site, most notably by employing a latent propargyl electrophile to engage the catalytic Cys145. Advanced leads from this series, including AVI-4516 and AVI-4773 show pan-coronavirus antiviral activity in cells, very low clearance in mice, and for AVI-4773 a rapid reduction in viral titers more than a million-fold after just three doses, more rapidly and effectively than the approved drugs, nirmatrelvir and ensitrelvir. Both AVI-4516 and AVI-477 3 are well distributed in mouse tissues, including brain, where concentrations ten or fifteen-thousand times the EC ₉₀ , respectively, are observed eight hours after an oral dose. As exemplar of the series, AVI-4516 shows minimal inhibition of major CYP isoforms and human cysteine and serine proteases, likely due to its latent–electrophilic warhead. AVI-4516 also exhibits synergy in cellular infection models in combination with the RdRp inhibitor molnupiravir, while related analogs strongly inhibit nirmatrelvir-resistant M ^Pro mutant virus in cells. The in vivo and antiviral properties of this new chemotype are differentiated from existing clinical and pre-clinical M ^Pro inhibitors, and will advance new therapeutic development against emerging SARS-CoV-2 variants and other coronaviruses. One sentence summary: This manuscript describes the discovery of a new class of potent inhibitors of the SARS-CoV-2 major proteases (M ^Pro ) with a unique mechanism of inhibition, pan coronaviral activity in cellulo , exquisite selectivity vs. the human proteome, and exceptional in vivo efficacy in SARS-CoV-2 infection models that surpasses that of currently approved agents.

Despite large investments from academia and industry,heart failure,which results from a disruption of the contractile apparatus, remainsa leading cause of death. Cardiac muscle contraction is a calcium-dependentmechanism, which is regulated by the troponin protein complex (cTn)and specifically by the N-terminal domain of its calcium-binding subunit(cNTnC). There is an increasing need for the development of smallmolecules that increase calcium sensitivity without altering the systoliccalcium concentration, thereby strengthening the cardiac function.Here, we examined the effect of our previously identified calcium-sensitizingsmall molecule, ChemBridge compound 7930079, in the context of severalhomologous muscle systems. The effect of this molecule on force generationin isolated cardiac trabeculae and slow skeletal muscle fibers wasmeasured. Furthermore, we explored the use of Gaussian acceleratedmolecular dynamics in sampling highly predictive receptor conformationsbased on NMR-derived starting structures. Additionally, we took arational computational approach for lead optimization based on lipophilicdiphenyl moieties. This integrated structural-biochemical-physiologicalapproach led to the identification of three novel low-affinity binders,which had similar binding affinities to the known positive inotropetrifluoperazine. The most potent identified calcium sensitizer wascompound 16 with an apparent affinity of 117 & PLUSMN; 17 & mu;M.

Despite large investments from academia and industry, heart failure, which results from a disruption of the contractile apparatus, remains a leading cause of death. Cardiac muscle contraction is a calcium-dependent mechanism, which is regulated by the troponin protein complex (cTn) and specifically by the N-terminal domain of its calcium binding subunit (cNTnC). There is an increasing need for the development of small molecules that increase calcium sensitivity without altering systolic calcium concentration, thereby strengthening cardiac function. Here, we examined the effect of our previously identified calcium sensitizing small molecule, ChemBridge compound 7930079, in the context of several homologous muscle systems. The effect of this molecule on force generation in isolated cardiac trabeculae and slow skeletal muscle fibers was measured. Furthermore, we explored the use of Gaussian accelerated molecular dynamics in sampling highly predictive receptor conformations based on NMR derived starting structures. Additionally, we took a rational computational approach for lead optimization based on lipophilic diphenyl moieties. This led to the identification of three novel low affinity binders, which had similar binding affinities to known positive inotrope trifluoperazine. The most potent identified calcium sensitizer was compound 16 with an apparent affinity of 117 ± 17 μM .

Calcium-dependent heart muscle contraction is regulated by the cardiac troponin protein complex (cTn) and specifically by the N-terminal domain of its calcium binding subunit (cNTnC). cNTnC contains one calcium binding site (site II), and altered calcium binding in this site has been studied for decades. It has been previously shown that cNTnC mutants, which increase calcium sensitization may have therapeutic benefits, such as restoring cardiac muscle contractility and functionality post-myocardial infarction events. Here, we computationally characterized eight mutations for their potential effects on calcium binding affinity in site II of cNTnC. We utilized two distinct methods to estimate calcium binding: adaptive steered molecular dynamics (ASMD) and thermodynamic integration (TI). We observed a sensitizing trend for all mutations based on the employed ASMD methodology. The TI results showed excellent agreement with experimentally known calcium binding affinities in wild-type cNTnC. Based on the TI results, five mutants were predicted to increase calcium sensitivity in site II. This study presents an interesting comparison of the two computational methods, which have both been shown to be valuable tools in characterizing the impacts of calcium sensitivity in mutant cNTnC systems.