Biology subjects

Jaishankar, P.

Publications and source records attributed to Jaishankar, P..

8 recordsLinked to original sources

bioRxiv2025

Large scale prospective evaluation of co-folding across 557 Mac1-ligand complexes and three virtual screens

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 cut-off dates. AlphaFold3 (AF3), Boltz-2, and Chai-1 each reproduced >50% of the Mac1 ligand poses to better than 2 [A] 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 {beta}-lactamase, the dopamine D4 and the {sigma}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 suggests that integrating physics-based and deep-learning approaches may help with hit prioritization and subsequent optimization in structure-based ligand discovery.

biophysics↗

bioRxiv2025

Crystallographic Ensembles Reveal the Structural Basis of Binding Entropy in SARS-CoV2 Macrodomain

Structure-based drug design has traditionally focused on optimizing static, enthalpic interactions between ligands and proteins or on displacing binding site solvent molecules to entropically favor binding. A potentially large contributor to binding thermodynamics is the difference in conformational entropy of the protein upon binding a ligand; however, this has been difficult to quantify especially in high throughput. Here, through multiconformer ensemble modeling of hundreds of ligand-bound SARS-CoV-2 Macrodomain (Mac1) X-ray structures, we show how ligand binding reorganizes both protein conformational entropy and water molecules. By applying an optimal transport-based clustering algorithm, we show how specific protein-ligand interactions patterns drive the magnitude and spatial redistribution of conformational entropy and solvent networks. Using isothermal titration calorimetry (ITC), we demonstrate a correlation between experimental binding thermodynamics and conformational entropy estimated from structural ensemble models, showing that increased conformational heterogeneity and a less connected hydrogen-bonded water network lead to more entropic binding. These results establish a framework for extracting thermodynamically meaningful information from crystallographic ensembles, enabling the integration of entropic effects into prospective, ensemble-aware drug discovery.

biophysics↗

bioRxiv2025

Discovery of AVI-6451, a Potent and Selective Inhibitor of the SARS-CoV-2 ADP-Ribosylhydrolase Mac1 with Oral Efficacy in vivo

The COVID-19 pandemic made plain the need for effective antivirals acting on novel antiviral targets, among which viral macrodomains have attracted considerable attention. We recently described AVI-4206 (1), a potent and selective inhibitor of the SARS-CoV-2 ADP-ribosylhydrolase Mac1 based on a 9H-pyrimido[4,5-b]indole core, the first Mac1 inhibitor to demonstrate antiviral efficacy in mouse models of SARS-CoV-2 infection, but requiring IP administration and frequent dosing. Herein we describe an extensive, structurally enabled medicinal chemistry effort to identify orally bioavailable Mac1 inhibitors by addressing permeability and efflux liabilities of 1 and many of its analogs. Multiple strategies were pursued to overcome these issues, including replacing a urea function to reduce hydrogen bond donor count. While heterocyclic urea mimetics could deliver analogs like AVI-6318 (3) with potencies and ADME profiles similar to 1, abrogation of the P-gp liability was finally achieved with entirely non-polar substituents in place of urea. Thus, AVI-6451 (4) is a potent Mac1 inhibitor lead with low intrinsic clearance, high oral bioavailability, and antiviral efficacy with once-daily oral administration in a mouse model of SARS-CoV-2 infection.

pharmacology and toxicology↗

bioRxiv2025

Identifying a next-generation antimalarial trioxolane in a landscape of artemisinin partial resistance

For over two decades, artemisinin-based combination therapy (ACT) has been the standard of care for the treatment of uncomplicated falciparum malaria. However, artemisinin partial resistance (ART-R) is now prevalent in Southeast Asia and has emerged in eastern Africa, threatening ACT efficacy. Mechanistically, ART-R results from an endocytosis defect that limits concentrations of host-derived free heme in the parasite digestive vacuole, allowing early ring-stage parasites to survive exposure to the artemisinin component of ACT. The artemisinin-inspired 1,2,4-trioxolane artefenomel exhibits an extended pharmacokinetic exposure profile that predicts efficacy against ART-R parasites. Unfortunately, the development of artefenomel was halted recently after almost a decade of clinical trials. Herein, we describe the discovery of RLA-4735 and its single-enantiomer form RLA-5764, next-generation antimalarial trioxolanes that exhibit excellent in vitro potency against Plasmodium falciparum and single-exposure efficacy in a murine P. berghei model, thus retaining many of the favorable pharmacokinetic and pharmacodynamic properties of artefenomel while markedly improving solubility and development potential. In P. falciparum samples collected from patients in Uganda in 2019 and 2023, ex vivo ring-stage survival assays revealed the emergence of the ART-R phenotype over this timeframe, and furthermore demonstrated markedly superior activity of artefenomel and RLA-4735 as compared to dihydroartemisinin (the active metabolite of artemisinin components of ACTs) against ART-R parasites. Overall, our findings suggest a role for next-generation trioxolanes in addressing ART-R, and present a potent new, artefenomel-adjacent chemotype with good potential to deliver new development candidates. Summary SentenceKlope et. al. described the discovery and in vivo characterization of antimalarial endoperoxides effective against artemisinin-resistant parasites as potential development candidates for uncomplicated, blood-stage malaria.

pharmacology and toxicology↗

bioRxiv2024

Expanding Automated Multiconformer Ligand Modeling to Macrocycles and Fragments

Small molecule ligands exhibit a diverse range of conformations in solution. Upon binding to a target protein, this conformational diversity is reduced. However, ligands can retain some degree of conformational flexibility even when bound to a receptor. In the Protein Data Bank (PDB), a small number of ligands have been modeled with distinct alternative conformations that are supported by macromolecular X-ray crystallography density maps. However, the vast majority of structural models are fit to a single ligand conformation, potentially ignoring the underlying conformational heterogeneity present in the sample. We previously developed qFit-ligand to sample diverse ligand conformations and to select a parsimonious ensemble consistent with the density. While this approach indicated that many ligands populate alternative conformations, limitations in our sampling procedures often resulted in non-physical conformations and could not model complex ligands like macrocycles. Here, we introduce several improvements to qFit-ligand, including integrating RDKit for stochastic conformational sampling. This new sampling method greatly enriches low energy conformations of small molecules and macrocycles. We further extended qFit-ligand to identify alternative conformations in PanDDA-modified density maps from high throughput X-ray fragment screening experiments, as well as single-particle cryo-electron microscopy (cryo-EM) density maps. The new version of qFit-ligand improves fit to electron density and reduces torsional strain relative to deposited single conformer models and our prior version of qFit-ligand. These advances enhance the analysis of residual conformational heterogeneity present in ligand-bound structures, which can provide important insights for the rational design of therapeutic agents.

biophysics↗

bioRxiv2024

Extensive exploration of structure activity relationships for the SARS-CoV-2 macrodomain from shape-based fragment merging and active learning

The macrodomain contained in the SARS-CoV-2 non-structural protein 3 (NSP3) is required for viral pathogenesis and lethality. Inhibitors that block the macrodomain could be a new therapeutic strategy for viral suppression. We previously performed a large-scale X-ray crystallography-based fragment screen and discovered a sub-micromolar inhibitor by fragment linking. However, this carboxylic acid-containing lead had poor membrane permeability and other liabilities that made optimization difficult. Here, we developed a shape- based virtual screening pipeline - FrankenROCS - to identify new macrodomain inhibitors using fragment X-ray crystal structures. We used FrankenROCS to exhaustively screen the Enamine high-throughput screening (HTS) collection of 2.1 million compounds and selected 39 compounds for testing, with the most potent compound having an IC50 value equal to 130 M. We then paired FrankenROCS with an active learning algorithm (Thompson sampling) to efficiently search the Enamine REAL database of 22 billion molecules, testing 32 compounds with the most potent having an IC50 equal to 220 M. Further optimization led to analogs with IC50 values better than 10 M, with X-ray crystal structures revealing diverse binding modes despite conserved chemical features. These analogs represent a new lead series with improved membrane permeability that is poised for optimization. In addition, the collection of 137 X-ray crystal structures with associated binding data will serve as a resource for the development of structure-based drug discovery methods. FrankenROCS may be a scalable method for fragment linking to exploit ever-growing synthesis-on- demand libraries.

biophysics↗

bioRxiv2024

The Mac1 ADP-ribosylhydrolase is a Therapeutic Target for SARS-CoV-2

SARS-CoV-2 continues to pose a threat to public health. Current therapeutics remain limited to direct acting antivirals that lack distinct mechanisms of action and are already showing signs of viral resistance. The virus encodes an ADP-ribosylhydrolase macrodomain (Mac1) that plays an important role in the coronaviral lifecycle by suppressing host innate immune responses. Genetic inactivation of Mac1 abrogates viral replication in vivo by potentiating host innate immune responses. However, it is unknown whether this can be achieved by pharmacologic inhibition and can therefore be exploited therapeutically. Here we report a potent and selective lead small molecule, AVI-4206, that is effective in an in vivo model of SARS-CoV-2 infection. Standard cellular models indicate that AVI-4206 has high target engagement and can weakly inhibit viral replication in a gamma interferon- and Mac1 catalytic activity-dependent manner. However, a stronger antiviral effect for AVI-4206 is observed in human airway organoids and peripheral blood monocyte-derived macrophages. In an animal model of severe SARS-CoV-2 infection, AVI-4206 reduces viral replication, potentiates innate immune responses, and leads to a survival benefit. Our results provide pharmacological proof of concept that Mac1 is a valid therapeutic target via a novel immune-restoring mechanism that could potentially synergize with existing therapies targeting distinct, essential aspects of the coronaviral life cycle. This approach could be more widely used to target other viral macrodomains to develop antiviral therapeutics beyond COVID-19.

immunology↗

bioRxiv2017

Structure of the nucleotide exchange factor eIF2B reveals mechanism of memory-enhancing molecule

Regulation by the integrated stress response (ISR) converges on the phosphorylation of translation initiation factor eIF2 in response to a variety of stresses. Phosphorylation converts eIF2 from substrate to competitive inhibitor of its dedicated guanine nucleotide exchange factor, eIF2B, inhibiting translation. ISRIB, a drug-like eIF2B activator, reverses the effects of eIF2 phosphorylation and, remarkably, in rodents enhances cognition and corrects cognitive deficits after brain injury. To determine its mechanism of action, we solved an atomic-resolution structure of ISRIB bound in a deep cleft within decameric human eIF2B by electron cryo-microscopy. Structural and biochemical analyses revealed that formation of fully active, decameric eIF2B holoenzyme depended on the assembly of two identical tetrameric subcomplexes, and that ISRIB promoted this step by cross-bridging a central symmetry interface. Regulation of eIF2B assembly emerges as a rheostat for eIF2B activity that tunes translation during the ISR and that can be further modulated by ISRIB.

biochemistry↗