Search

In their recent commentary in Protein Science, Jaskolski et al. analyzed three randomly picked diffraction data sets from fragment-screening group depositions from the PDB and, based on that, they claimed that such data are principally problematic. We demonstrate here that if such data are treated properly, none of the proclaimed criticisms persist.

Despite unprecedented efforts, our therapeutic arsenal against SARS-CoV-2 remains limited. The conserved macrodomain 1 (Mac1) in NSP3 is an enzyme exhibiting ADP-ribosylhydrolase activity and a possible drug target. To determine the therapeutic potential of Mac1 inhibition, we generated recombinant viruses and replicons encoding catalytically inactive NSP3 Mac1 domain by mutating a critical asparagine in the active site. While substitution to alanine (N40A) reduced activity by ∼10-fold, mutations to aspartic acid (N40D) reduced the catalytic activity by ∼100-fold relative to wildtype activity. Importantly, the N40A mutation rendered Mac1 unstable in vitro and lowered expression levels in bacterial and mammalian cells. When incorporated into SARS-CoV-2 molecular clones, the N40D mutant only modestly affected viral fitness in immortalized cell lines, but reduced viral replication in human airway organoids by 10-fold. In mice, N40D replicated at >1000-fold lower levels while inducing a robust interferon response, and all infected animals survived infection with the mutant, but not the wildtype virus. Our data validate SARS-CoV-2 NSP3 Mac1 domain as a critical viral pathogenesis factor and a reasonable target to develop antivirals, while emphasizing the importance of amino acid identity in viral mutagenesis studies and underscoring the limitations of solely relying on in vitro viral replication studies for target validation.

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 Å 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.

The process of finding molecules that bind to a target protein is a challenging first step in drug discovery. Crystallographic fragment screening is a strategy based on elucidating binding modes of small polar compounds and then building potency by expanding or merging them. Recent advances in high-throughput crystallography enable screening of large fragment libraries, reading out dense ensembles of fragments spanning the binding site. However, fragments typically have low affinity thus the road to potency is often long and fraught with false starts. Here, we take advantage of high-throughput crystallography to reframe fragment-based hit discovery as a denoising problem – identifying significant pharmacophore distributions from a fragment ensemble amid noise due to weak binders – and employ an unsupervised machine learning method to tackle this problem. Our method screens potential molecules by evaluating whether they recapitulate those fragment-derived pharmacophore distributions. We retrospectively validated our approach on an open science campaign against SARS-CoV-2 main protease (Mpro), showing that our method can distinguish active compounds from inactive ones using only structural data of fragment-protein complexes, without any activity data. Further, we prospectively found novel hits for Mpro and the Mac1 domain of SARS-CoV-2 non-structural protein 3. More broadly, our results demonstrate how unsupervised machine learning helps interpret high throughput crystallography data to rapidly discover of potent chemical modulators of protein function.

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, few have been described. Here, we report the structure-based development of several chemical scaffolds exhibiting low- to sub-micromolar affinity for Mac1 through iterations of computer-aided design, structural characterization by ultra-high resolution X-ray protein crystallography, and binding evaluation with in-solution assays. Potent scaffolds were designed withComputational fragment-linking and ultra-large library docking identifies potent inhibitors of the SARS-CoV-2 macrodomain.

The nonstructural protein 3 (NSP3) macrodomain of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) (Mac1) removes adenosine diphosphate (ADP) ribosylation posttranslational modifications, playing a key role in the immune evasion capabilities of the virus responsible for the coronavirus disease 2019 pandemic. Here, we determined neutron and x-ray crystal structures of the SARS-CoV-2 NSP3 macrodomain using multiple crystal forms, temperatures, and pHs, across the apo and ADP-ribose–bound states. We characterize extensive solvation in the Mac1 active site and visualize how water networks reorganize upon binding of ADP-ribose and non-native ligands, inspiring strategies for displacing waters to increase the potency of Mac1 inhibitors. Determining the precise orientations of active site water molecules and the protonation states of key catalytic site residues by neutron crystallography suggests a catalytic mechanism for coronavirus macrodomains distinct from the substrate-assisted mechanism proposed for human MacroD2. These data provoke a reevaluation of macrodomain catalytic mechanisms and will guide the optimization of Mac1 inhibitors.

The title alkaloids, 1 and 2 respectively, have been prepared using cross-coupling chemistries and together with various analogues they have been evaluated for their capacity to inhibit acetylcholinesterase. Contrary to an earlier report, we have found that biaryl 1 is not a significant inhibitor of this enzyme and nor are any of its congeners, including alkaloid 2.

Oligomerization has been suggested to be an important mechanism for increasing or maintaining the thermostability of proteins. Although it is evident that protein-protein contacts can result in substantial stabilization in many extant proteins, evidence for evolutionary selection for oligomerization is largely indirect and little is understood of the early steps in the evolution of oligomers. A laboratory-directed evolution experiment that selected for increased thermostability in the αE7 carboxylesterase from the Australian sheep blowfly, Lucilia cuprina, resulted in a thermostable variant, LcαE7-4a, that displayed increased levels of dimeric and tetrameric quaternary structure. A trade-off between activity and thermostability was made during the evolution of thermostability, with the higher-order oligomeric species displaying the greatest thermostability and lowest catalytic activity. Analysis of monomeric and dimeric LcαE7-4a crystal structures revealed that only one of the oligomerization-inducing mutations was located at a potential protein-protein interface. This work demonstrates that by imposing a selective pressure demanding greater thermostability, mutations can lead to increased oligomerization and stabilization, providing support for the hypothesis that oligomerization is a viable evolutionary strategy for protein stabilization.

This review focuses on recent work that has begun to establish specific functional roles for protein conformational dynamics, specifically how the conformational landscapes that proteins can sample can evolve under laboratory based evolutionary selection. We discuss recent technical advances in computational and biophysical chemistry, which have provided us with new ways to dissect evolutionary processes. Finally, we offer some perspectives on the emerging view of conformational dynamics and evolution, and the challenges that we face in rationally engineering conformational dynamics.

Insecticides allow control of agricultural pests and disease vectors and are vital for global food security and health. The evolution of resistance to insecticides, such as organophosphates (OPs), is a serious and growing concern. OP resistance often involves sequestration or hydrolysis of OPs by carboxylesterases. Inhibiting carboxylesterases could, therefore, restore the effectiveness of OPs for which resistance has evolved. Here, we use covalent virtual screening to produce nano-/picomolar boronic acid inhibitors of the carboxylesterase αE7 from the agricultural pest Lucilia cuprina as well as a common Gly137Asp αE7 mutant that confers OP resistance. These inhibitors, with high selectivity against human acetylcholinesterase and low to no toxicity in human cells and in mice, act synergistically with the OPs diazinon and malathion to reduce the amount of OP required to kill L. cuprina by up to 16-fold and abolish resistance. The compounds exhibit broad utility in significantly potentiating another OP, chlorpyrifos, against the common pest, the peach–potato aphid ( Myzus persicae ). These compounds represent a solution to OP resistance as well as to environmental concerns regarding overuse of OPs, allowing significant reduction of use without compromising efficacy.

Previous electrophysiological and behavioural studies implicate esterase 6 in the processing of the pheromone cis-vaccenyl acetate and various food odorants that affect aggregation and reproductive behaviours. Here we show esterase 6 has relatively high activity against many of the short-mid chain food esters, but negligible activity against cis-vaccenyl acetate. The crystal structure of esterase 6 confirms its substrate-binding site can accommodate many short-mid chain food esters but not cis-vaccenyl acetate. Immunohistochemical assays show esterase 6 is expressed in non-neuronal cells in the third antennal segment that could be accessory or epidermal cells surrounding numerous olfactory sensilla, including basiconics involved in food odorant detection. Esterase 6 is also produced in trichoid sensilla, but not in the same cell types as the cis-vaccenyl acetate binding protein LUSH. Our data support a model in which esterase 6 acts as a direct odorant degrading enzyme for many bioactive food esters, but not cis-vaccenyl acetate.

Summary The proper function of enzymes often depends upon their efficient interconversion between particular conformational sub-states on a free-energy landscape. Experimentally characterizing these sub-states is challenging, which has limited our understanding of the role of protein dynamics in many enzymes. Here, we have used a combination of kinetic crystallography and detailed analysis of crystallographic protein ensembles to map the accessible conformational landscape of an insect carboxylesterase ( Lc αE7) as it traverses all steps in its catalytic cycle . Lc αE7 is of special interest because of its evolving role in organophosphate insecticide resistance. Our results reveal that a dynamically coupled network of residues extends from the substrate-binding site to a surface loop. Interestingly, the coupling of this network that is apparent in the apoenzyme appears to be reduced in the phosphorylated enzyme intermediate. Altogether, the results of this work highlight the importance of protein dynamics to enzyme function and the evolution of new activity.

The evolution of new enzymatic activity is rarely observed outside of the laboratory. In the agricultural pest Lucilia cuprina, a naturally occurring mutation (Gly137Asp) in α-esterase 7 (LcαE7) results in acquisition of organophosphate hydrolase activity and confers resistance to organophosphate insecticides. Here, we present an X-ray crystal structure of LcαE7:Gly137Asp that, along with kinetic data, suggests that Asp137 acts as a general base in the new catalytic mechanism. Unexpectedly, the conformation of Asp137 observed in the crystal structure obstructs the active site and is not catalytically productive. Molecular dynamics simulations reveal that alternative, catalytically competent conformers of Asp137 are sampled on the nanosecond time scale, although these states are less populated. Thus, although the mutation introduces the new reactive group responsible for organophosphate detoxification, the catalytic efficiency appears to be limited by conformational disorganization: the frequent sampling of low-energy nonproductive states. This result is consistent with a model of molecular evolution in which initial function-changing mutations can result in enzymes that display only a fraction of their catalytic potential due to conformational disorganization.