Biology subjects

Irwin, J. J.

Publications and source records attributed to Irwin, J. J..

7 recordsLinked to original sources

bioRxiv2023

Structure-based discovery of CFTR potentiators and inhibitors

The cystic fibrosis transmembrane conductance regulator (CFTR) is a crucial ion channel whose loss of function leads to cystic fibrosis, while its hyperactivation leads to secretory diarrhea. Small molecules that improve CFTR folding (correctors) or function (potentiators) are clinically available. However, the only potentiator, ivacaftor, has suboptimal pharmacokinetics and inhibitors have yet to be clinically developed. Here we combine molecular docking, electrophysiology, cryo-EM, and medicinal chemistry to identify novel CFTR modulators. We docked [~]155 million molecules into the potentiator site on CFTR, synthesized 53 test ligands, and used structure-based optimization to identify candidate modulators. This approach uncovered novel mid-nanomolar potentiators as well as inhibitors that bind to the same allosteric site. These molecules represent potential leads for the development of more effective drugs for cystic fibrosis and secretory diarrhea, demonstrating the feasibility of large-scale docking for ion channel drug discovery.

biophysics↗

bioRxiv2023

Structure-based discovery of cannabinoid-1 receptor agonists with reduced side effects

Large library docking can reveal unexpected chemotypes that complement the structures of biological targets. Seeking new agonists for the cannabinoid-1 receptor (CB1R), we docked 74 million tangible molecules, prioritizing 46 high ranking ones for de novo synthesis and testing. Nine were active by radioligand competition, a 20% hit-rate. Structure-based optimization of one of the most potent of these (Ki = 0.7 {micro}M) led to 4042, a 1.9 nM ligand and a full CB1R agonist. A cryo-EM structure of the purified enantiomer of 4042 ( 1350) in complex with CB1R-Gi1 confirmed its docked pose. The new agonist was strongly analgesic, with generally a 5-10-fold therapeutic window over sedation and catalepsy and no observable conditioned place preference. These findings suggest that new cannabinoid chemotypes may disentangle characteristic cannabinoid side-effects from their analgesia, supporting the further development of cannabinoids as pain therapeutics.

pharmacology and toxicology↗

bioRxiv2023

Structure-based discovery of inhibitors of the SARS-CoV-2 Nsp14 N7-methyltransferase

An under-explored target for SARS-CoV-2 is non-structural protein 14 (Nsp14), a crucial enzyme for viral replication that catalyzes the methylation of N7-guanosine of the viral RNA at 5'-end; this enables the virus to evade the host immune response by mimicking the eukaryotic post-transcriptional modification mechanism. We sought new inhibitors of the S-adenosyl methionine (SAM)-dependent methyltransferase (MTase) activity of Nsp14 with three large library docking strategies. First, up to 1.1 billion make-on-demand ("tangible") lead-like molecules were docked against the enzymes SAM site, seeking reversible inhibitors. On de novo synthesis and testing, three inhibitors emerged with IC50 values ranging from 6 to 43 M, each with novel chemotypes. Structure-guided optimization and in vitro characterization supported their non-covalent mechanism. In a second strategy, docking a library of 16 million tangible fragments revealed nine new inhibitors with IC50 values ranging from 12 to 341 M and ligand efficiencies from 0.29 to 0.42. In a third strategy, a newly created library of 25 million tangible, virtual electrophiles were docked to covalently modify Cys387 in the SAM binding site. Seven inhibitors emerged with IC50 values ranging from 3.2 to 39 M, the most potent being a reversible aldehyde. Initial optimization of a second series yielded a 7 M acrylamide inhibitor. Three inhibitors characteristic of the new series were tested for selectivity against 30 human protein and RNA MTases, with one showing partial selectivity and one showing high selectivity. Overall, 32 inhibitors encompassing eleven chemotypes had IC50 values <50 M and 5 inhibitors in four chemotypes had IC50 values <10 M. These molecules are among the first non-SAM-like inhibitors of Nsp14, providing multiple starting points for optimizing towards antiviral activity.

biophysics↗

bioRxiv2022

Large library docking for novel SARS-CoV-2 main protease non-covalent and covalent inhibitors

Antiviral therapeutics to treat SARS-CoV-2 are much desired for the on-going pandemic. A well-precedented viral enzyme is the main protease (MPro), which is now targeted by an approved drug and by several investigational drugs. With the inevitable liabilities of these new drugs, and facing viral resistance, there remains a call for new chemical scaffolds against MPro. We virtually docked 1.2 billion non-covalent and a new library of 6.5 million electrophilic molecules against the enzyme structure. From these, 29 non-covalent and 11 covalent inhibitors were identified in 37 series, the most potent having an IC50 of 29 M and 20 M, respectively. Several series were optimized, resulting in inhibitors active in the low micromolar range. Subsequent crystallography confirmed the docking predicted binding modes and may template further optimization. Together, these compounds reveal new chemotypes to aid in further discovery of MPro inhibitors for SARS-CoV-2 and other future coronaviruses.

biophysics↗

bioRxiv2022

Structure-based inhibitor optimization for the Nsp3 Macrodomain of SARS-CoV-2

The nonstructural protein 3 (NSP3) of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) contains a conserved macrodomain enzyme (Mac1) that is critical for pathogenesis and lethality. While small molecule inhibitors of Mac1 have great therapeutic potential, at the outset of the COVID-19 pandemic there were no well-validated inhibitors for this protein nor, indeed, the macrodomain enzyme family, making this target a pharmacological orphan. Here, we report the structure-based discovery and development of several different chemical scaffolds exhibiting low- to sub-micromolar affinity for Mac1 through iterations of computer-aided design, structural characterization by ultra-high resolution protein crystallography, and binding evaluation. Potent scaffolds were designed with in silico fragment linkage and by ultra-large library docking of over 450 million molecules. Both techniques leverage the computational exploration of tangible chemical space and are applicable to other pharmacological orphans. Overall, 160 ligands in 119 different scaffolds were discovered, and 152 Mac1-ligand complex crystal structures were determined, typically to 1 [A] resolution or better. Our analyses discovered selective and cell-permeable molecules, unexpected ligand-mediated protein dynamics within the active site, and key inhibitor motifs that will template future drug development against Mac1. Significance StatementSARS-CoV-2 encodes a viral macrodomain protein (Mac1) that hydrolyzes ribo-adenylate marks on viral proteins, disrupting the innate immune response to the virus. Catalytic mutations in the enzyme make the related SARS-1 virus less pathogenic and non-lethal in animals, suggesting that Mac1 will be a good antiviral target. However, no potent inhibitors of this protein class have been described, and pharmacologically the enzyme remains an orphan. Here, we computationally designed potent inhibitors of Mac1, determining 150 inhibitor-enzyme structures to ultra-high resolution by crystallography. In silico fragment linking and molecular docking of > 450 million virtual compounds led to inhibitors with submicromolar activity. These molecules may template future drug discovery efforts against this crucial but understudied viral target.

biophysics↗

bioRxiv2021

Crystal structures of the σ2 receptor template large-library docking for selective chemotypes active in vivo

The {sigma}2 receptor is a poorly understood transmembrane receptor that has attracted intense interest in many areas of biology including cancer imaging, Alzheimers disease, schizophrenia, and neuropathic pain. However, little is known regarding the molecular details of the receptor, and few highly selective ligands are available. Here, we report the crystal structure of the {sigma}2 receptor in complex with the clinical drug candidate roluperidone and the probe compound PB28. These structures, in turn, templated a large-scale docking screen of 490 million make-on-demand molecules. Of these, 484 compounds were synthesized and tested, prioritizing not only high-ranking docked molecules, but also those with mediocre and poor scores. Overall, 127 compounds with binding affinities superior to 1 M were identified, all in new chemotypes, 31 of which had affinities superior to 50 nM. Intriguingly, hit rate fell smoothly and monotonically with docking score. Seeking to develop selective and biologically active probe molecules, we optimized three of the original docking hits for potency and for selectivity, achieving affinities in the 3 to 48 nM range and to up to 250-fold selectivity vs. the {sigma}1 receptor. Crystal structures of the newly discovered ligands bound to the {sigma}2 receptor were subsequently determined, confirming the docked poses. To investigate the contribution of the {sigma}2 receptor in pain processing, and to distinguish it from the contribution of the {sigma}1 receptor, two potent {sigma}2-selective and one potent {sigma}1/{sigma}2 non-selective ligand were tested for efficacy in a mouse model of neuropathic pain. All three ligands demonstrated timedependent decreases in mechanical hypersensitivity in the spared nerve injury model, supporting a role for the {sigma}2 receptor in nociception, and a possible role for {sigma}1/{sigma}2 polypharmacology. This study illustrates the opportunities for rapid discovery of in vivo active and selective probes to study under-explored areas of biology using structurebased screens of diverse, ultra-large libraries following the elucidation of protein structures.

pharmacology and toxicology↗

bioRxiv2021

Ligand Strain Energy in Large Library Docking

While small molecule internal strain is crucial to molecular docking, using it in evaluating ligand scores has remained elusive. Here, we investigate a technique that calculates strain using relative torsional populations in the Cambridge Structural Database, enabling fast pre-calculation of these energies. In retrospective studies of large docking screens of the dopamine D4 receptor and of AmpC {beta}-lactamase, where close to 600 docking hits were tested experimentally, including such strain energies improved hit rates by preferentially reducing high-scoring decoy molecules that were strained. In a 40 target subset of the DUD-E benchmark, we found two thresholds that usefully distinguished between ligands and decoys: one based on the total strain energy of the small molecules, and one based on the maximum strain allowed for any given torsion within them. Using these criteria, about 75% of the benchmark targets had improved enrichment after strain filtering. Relying on pre-calculated population distributions, this approach is rapid, taking less than 0.04 second to evaluate a conformation on a standard core, making it pragmatic for pre-calculating strain in even ultra-large libraries. Since it is scoring function agnostic, it may be useful to multiple docking approaches; it is openly available at http://tldr.docking.org

bioinformatics↗