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Hydrogen peroxide (H2O2) is a major reactive oxygen species (ROS) in living systems with broad roles spanning both oxidative stress and redox signaling. Indeed, owing to its potent redox activity, regulating local sites of H2O2 generation and trafficking is critical to determining downstream physiological and/or pathological consequences. We now report the design, synthesis, and biological evaluation of Microtubule Peroxy Yellow 1 (MT-PY1), an activity-based sensing fluorescent probe bearing a microtubule-targeting moiety for detection of H2O2 in living cells. MT-PY1 utilizes a boronate trigger to show a selective and robust turn-on response to H2O2 in aqueous solution and in living cells. Live-cell microscopy experiments establish that the probe co-localizes with microtubules and retains its localization after responding to changes in levels of H2O2, including detection of endogenous H2O2 fluxes produced upon growth factor stimulation. This work adds to the arsenal of activity-based sensing probes for biological analytes that enable selective molecular imaging with subcellular resolution.

FAK, a nonreceptor tyrosine kinase, has been recognized as a novel target class for the development of targeted anticancer agents. Overexpression of FAK is a common occurrence in several solid tumors, in which the kinase has been implicated in promoting metastases. Consequently, designing and developing potent FAK inhibitors is becoming an attractive goal, and FAK inhibitors are being recognized as a promising tool in our armamentarium for treating diverse cancers. This review comprehensively summarizes the different classes of synthetically derived compounds that have been reported as potent FAK inhibitors in the last three decades. Finally, the future of FAK-targeting smart drugs that are designed to slow down the emergence of drug resistance is discussed.

Surface adsorption is a crucial step in numerous processes, including heterogeneous catalysis, where the adsorption of key species is often used as a descriptor of efficiency. We present here an automated adsorption workflow for semiconductors which employs density functional theory calculations to generate adsorption data in a high-throughput manner. Starting from a bulk structure, the workflow performs an exhaustive surface search, followed by an adsorption structure construction step, which generates a minimal energy landscape to determine the optimal adsorbate-surface distance. An extensive set of energy-based, charge-based, geometric, and electronic descriptors tailored toward catalysis research are computed and saved to a personal user database. The application of the workflow to zinc telluride, a promising CO2 reduction photocatalyst, is presented as a case study to illustrate the capabilities of this method and its potential as a material discovery tool.

In this paper, we applied an innovative nuclear magnetic resonance (NMR)-guided screening and ligand design approach, named focused high-throughput screening by NMR (fHTS by NMR), to derive potent, low-molecular-weight ligands capable of mimicking interactions elicited by ephrin ligands on the receptor tyrosine kinase EphA4. The agents bind with nanomolar affinity, trigger receptor activation in cellular assays with motor neurons, and provide remarkable motor neuron protection from amyotrophic lateral sclerosis (ALS) patient-derived astrocytes. Structural studies on the complex between EphA4 ligand-binding domain and a most active agent provide insights into the mechanism of the agents at a molecular level. Together with preliminary in vivo pharmacology studies, the data form a strong foundation for the translation of these agents for the treatment of ALS and potentially other human diseases.

The synthesis of two isomeric testosterone dimers and an androstenedione dimer is reported. The design takes advantage of an efficient transformation of testosterone leading to the synthesis of the key diene, 7α-(buta-1,3-dienyl)-4-androsten-17β-ol-3-one, through an elimination reaction. It was found that in some instances the same reaction led to partial epimerization of the 17β-hydroxyl group into the 17α-hydroxyl group. The specific orientation of the hydroxyl function was confirmed by NMR spectroscopy. Capitalizing on this unforeseen side reaction, several dimers were assembled using an olefin metathesis reaction with Hoveyda-Grubbs catalyst. This led to the formation of two isomeric testosterone dimers with 17α-OH or 17β-OH (14α and 14β) as well as an androstenedione dimer (14). The new dimers and their respective precursors were tested on androgen-dependent (LNCaP) and androgen independent (PC3 and DU145) prostate cancer cells. It was discovered that the most active dimer was made of the natural hormone testosterone (14β) with an average IC50 of 13.3 μM. In LNCaP cells, 14β was ∼5 times more active than the antiandrogen drug cyproterone acetate (IC50 of 12.0 μM vs. 59.6 μM, respectively). At low concentrations (0.25-0.5 μM), 14α and 14β were able to completely inhibit LNCaP cell growth induced by testosterone or dihydrotestosterone. Furthermore, cross-reactivity of androgen-based dimers with sterol-metabolizing cytochrome P450 3A4 was explored and the results are disclosed herein.

Positron emission tomographic (PET) studies of amyloid β (Aβ) accumulation in Alzheimer's disease (AD) have shown clinical utility. The aim of this study was to develop and evaluate the effectiveness of a new fluorine-18 radiotracer [18F]Flotaza (2-{2-[2-[18F]fluoroethoxy]ethoxy}ethoxy)-4'-N,N-dimethylaminoazobenzene), for Aβ plaque imaging. Nucleophilic [18F]fluoride was used in a one-step radiosynthesis for [18F]flotaza. Using post mortem human AD brain tissues consisting of anterior cingulate (AC) and corpus callosum (CC), binding affinity of Flotaza, Ki = 1.68 nM for human Aβ plaques and weak (>10-5 M) for Tau protein. Radiosynthesis of [18F]Flotaza was very efficient in high radiochemical yields (>25%) with specific activities >74 GBq/μmol. Brain slices from all AD subjects were positively immunostained with anti-Aβ. Ratio of [18F]Flotaza in gray matter AC to white matter CC was >100 in all the 6 subjects. Very little white matter binding was seen. [18F]Flotaza binding in AC strongly correlated with anti-Aβ immunostains. [18F]Flotaza is therefore a suitable fluorine-18 PET radiotracer for PET imaging studies of human Aβ plaques.

In a cytopathic effect inhibition assay, a standardized Rhodiola rosea root and rhizome extract, also known as roseroot extract (SHR-5), exerted distinct anti-influenza A virus activity against HK/68 (H3N2) (IC50 of 2.8 µg/mL) without being cytotoxic. For fast and efficient isolation and identification of the extract's bioactive constituents, a high-performance countercurrent chromatographic separation method was developed. It resulted in a three-stage gradient elution program using a mobile phase solvent system composed of ethyl acetate/n-butanol/water (1 : 4 : 5 → 2 : 3 : 5 → 3 : 2 : 5) in the reversed-phase mode. The elaborated high-performance countercurrent chromatographic method allowed for fractionation of the complex roseroot extract in a single chromatographic step in a way that only one additional orthogonal isolation/purification step per fraction yielded 12 isolated constituents. They cover a broad polarity range and belong to different structural classes, namely, the phenylethanoid tyrosol and its glucoside salidroside, the cinnamyl alcohol glycosides rosavin, rosarin, and rosin as well as gallic acid, the cyanogenic glucoside lotaustralin, the monoterpene glucosides rosiridin and kenposide A, and the flavonoids tricin, tricin-5-O-β-D-glucopyranoside, and rhodiosin. The most promising anti-influenza activities were determined for rhodiosin, tricin, and tricin-5-O-β-D-glucopyranoside with IC50 values of 7.9, 13, and 15 µM, respectively. The herein established high-performance countercurrent chromatographic protocol enables fast and scalable access to major as well as minor roseroot constituents. This is of particular relevance for extract standardization, quality control, and further in-depth pharmacological investigations of the metabolites of this popular traditional herbal remedy.

The SARS-CoV-2 pandemic has rapidly spread across the globe, posing an urgent health concern. Many quests to computationally identify treatments against the virus rely on in silico small molecule docking to experimentally determined structures of viral proteins. One limit to these approaches is that protein dynamics are often unaccounted for, leading to overlooking transient, druggable conformational states. Using Gaussian accelerated molecular dynamics to enhance sampling of conformational space, we identified cryptic pockets within the SARS-CoV-2 main protease, including some within regions far from the active site. These simulations sampled comparable dynamics and pocket volumes to conventional brute force simulations carried out on two orders of magnitude greater timescales.

Non-steroidal anti-inflammatory drugs (NSAIDs) are a powerful class of inhibitors targeting two isoforms of the family of cyclooxygenase enzymes (COX-1 and COX-2). While NSAIDs are widely used in the management of pain, in particular as a treatment for osteo- and rheumatoid arthritis, their long-term use has been associated with numerous on- and off-target effects. As the carboxylic acid moiety present in common NSAIDs is responsible for some of their adverse effects, but is not required for their anti-inflammatory activity, we sought to mask this group through direct coupling to glucosamine, which is thought to prevent cartilage degradation. We report herein the conjugation of commonly prescribed NSAIDs to glucosamine hydrochloride and the use of molecular docking to show that addition of the carbohydrate moiety to the parent NSAID can enhance binding in the active site of COX-2. In a preliminary, in vitro screening assay, the diclofenac-glucosamine bioconjugate exhibited 10-fold greater activity toward COX-2, making it an ideal candidate for future in vivo studies. Furthermore, in an intriguing result, we observed that the mefenamic acid-glucosamine bioconjugate displayed enhanced activity toward COX-1 rather than COX-2.

The Statistical Assessment of Modeling of Proteins and Ligands (SAMPL) challenges focuses the computational modeling community on areas in need of improvement for rational drug design. The SAMPL7 physical property challenge dealt with prediction of octanol-water partition coefficients and pKa for 22 compounds. The dataset was composed of a series of N-acylsulfonamides and related bioisosteres. 17 research groups participated in the log P challenge, submitting 33 blind submissions total. For the pKa challenge, 7 different groups participated, submitting 9 blind submissions in total. Overall, the accuracy of octanol-water log P predictions in the SAMPL7 challenge was lower than octanol-water log P predictions in SAMPL6, likely due to a more diverse dataset. Compared to the SAMPL6 pKa challenge, accuracy remains unchanged in SAMPL7. Interestingly, here, though macroscopic pKa values were often predicted with reasonable accuracy, there was dramatically more disagreement among participants as to which microscopic transitions produced these values (with methods often disagreeing even as to the sign of the free energy change associated with certain transitions), indicating far more work needs to be done on pKa prediction methods.

Insulin-degrading enzyme (IDE) is a human mononuclear Zn2+ -dependent metalloenzyme that is widely regarded as the primary peptidase responsible for insulin degradation. Despite its name, IDE is also critically involved in the hydrolysis of several other disparate peptide hormones, including glucagon, amylin, and the amyloid β-protein. As such, the study of IDE inhibition is highly relevant to deciphering the role of IDE in conditions such as type-2 diabetes mellitus and Alzheimer disease. There have been few reported IDE inhibitors, and of these, inhibitors that directly target the active-site Zn2+ ion have yet to be fully explored. In an effort to discover new, zinc-targeting inhibitors of IDE, a library of ∼350 metal-binding pharmacophores was screened against IDE, resulting in the identification of 1-hydroxypyridine-2-thione (1,2-HOPTO) as an effective Zn2+ -binding scaffold. Screening a focused library of HOPTO compounds identified 3-sulfonamide derivatives of 1,2-HOPTO as inhibitors of IDE (Ki values of ∼50 μM). Further structure-activity relationship studies yielded several thiophene-sulfonamide HOPTO derivatives with good, broad-spectrum activity against IDE that have the potential to be useful pharmacological tools for future studies of IDE.

The N-acylsulfonamide functional group is a feature of the pharmacophore of several biologically active molecules, including marketed drugs. Although this acidic moiety presents multiple points of attachments that could be exploited to introduce structural diversification, depending on the circumstances, the replacement of the functional group itself with a suitable surrogate, or bioisostere, may be desirable. A number of N-acylsulfonamide bioisosteres have been developed over the years that provide opportunities to modulate both structure and physicochemical properties of this important structural motif. To enable an assessment of the relative impact on physicochemical properties that these replacements may have compared to the N-acylsulfonamide group, we conducted a structure-property relationship study based on matched molecular pairs, in which the N-acylsulfonamide moiety of common template reference structures is replaced with a series of bioisosteres. The data presented, which include an assessment of relative changes in acidity, permeability, lipophilicity and intrinsic solubility, provides a basis for informed decisions when deploying N-acylsulfonamides, or surrogates thereof, in analog design.

In an effort to probe the biophysical mechanisms of inhibition for ten previously-reported inhibitors of metallo-β-lactamases (MBL) with MBL IMP-1, equilibrium dialysis, metal analyses coupled with atomic absorption spectroscopy (AAS), native state mass spectrometry (native MS), and ultraviolet-visible spectrophotometry (UV-VIS) were used. 6-(1H-tetrazol-5-yl) picolinic acid (1T5PA), ANT431, D/l-captopril, thiorphan, and tiopronin were shown to form IMP-1/Zn(II)/inhibitor ternary complexes, while dipicolinic acid (DPA) and 4-(3-aminophenyl)pyridine-2,6-dicarboxylic acid (3AP-DPA) stripped some metal from the active site of IMP but also formed ternary complexes. DPA and 3AP-DPA stripped less metal from IMP-1 than from VIM-2 but stripped more metal from IMP-1 than from NDM-1. In contrast to a previous report, pterostilbene does not appear to bind to IMP-1 under our conditions. These results, along with previous studies, demonstrate similar mechanisms of inhibition toward different MBLs for different MBL inhibitors.

The molecular chaperone, Heat Shock Protein 70 (Hsp70), is an emerging drug target for neurodegenerative diseases, because of its ability to promote degradation of microtubule-associated protein tau (MAPT/tau). Recently, we reported YM-08 as a brain penetrant, allosteric Hsp70 inhibitor, which reduces tau levels. However, the benzothiazole moiety of YM-08 is vulnerable to metabolism by CYP3A4, limiting its further application as a chemical probe. In this manuscript, we designed and synthesized seventeen YM-08 derivatives by systematically introducing halogen atoms to the benzothiazole ring and shifting the position of the heteroatom in a distal pyridine. In microsome assays, we found that compound JG-23 has 12-fold better metabolic stability and it retained the ability to reduce tau levels in two cell-based models. These chemical probes of Hsp70 are expected to be useful tools for studying tau homeostasis.

This paper describes the structure-based design of a preliminary drug candidate against COVID-19 using free software and publicly available X-ray crystallographic structures. The goal of this tutorial is to disseminate skills in structure-based drug design and to allow others to unleash their own creativity to design new drugs to fight the current pandemic. The tutorial begins with the X-ray crystallographic structure of the main protease (Mpro) of the SARS coronavirus (SARS-CoV) bound to a peptide substrate and then uses the UCSF Chimera software to modify the substrate to create a cyclic peptide inhibitor within the Mpro active site. Finally, the tutorial uses the molecular docking software AutoDock Vina to show the interaction of the cyclic peptide inhibitor with both SARS-CoV Mpro and the highly homologous SARS-CoV-2 Mpro. The supporting information provides an illustrated step-by-step protocol, as well as a video showing the inhibitor design process, to help readers design their own drug candidates for COVID-19 and the coronaviruses that will cause future pandemics. An accompanying preprint in bioRxiv [https://doi.org/10.1101/2020.08.03.234872] describes the synthesis of the cyclic peptide and the experimental validation as an inhibitor of SARS-CoV-2 Mpro.

Spliceostatins and thailanstatins are intriguing natural products due to their structural features as well as their biological significance. This family of natural products has been the subject of immense synthetic interest because they exhibit very potent cytotoxicity in representative human cancer cell lines. The cytotoxic properties of these natural products are related to their ability to inhibit spliceosomes. FR901564 and spliceostatins have been shown to inhibit spliceosomes by binding to their SF3B component. Structurally, these natural products contain two highly functionalized tetrahydropyran rings with multiple stereogenic centers joined by a diene moiety and an acyclic side chain linked with an amide bond. Total syntheses of this family of natural products led to the development of useful synthetic strategies, which enabled the synthesis of potent derivatives. The spliceosome modulating properties of spliceostatins and synthetic derivatives opened the door for understanding the underlying spliceosome mechanism as well as the development of new therapies based upon small-molecule splicing modulators. This review outlines the total synthesis of spliceostatins, synthetic studies of structural derivatives, and their bioactivity.

Trichomonas vaginalis causes the most common, nonviral sexually transmitted infection. Only metronidazole (Mz) and tinidazole are approved for treating trichomoniasis, yet resistance is a clinical problem. The gold(I) complex, auranofin, is active against T. vaginalis and other protozoa but has significant human toxicity. In a systematic structure-activity exploration, we show here that diversification of gold(I) complexes, particularly as halides with simple C1-C3 trialkyl phosphines or as bistrialkyl phosphine complexes, can markedly improve potency against T. vaginalis and selectivity over human cells compared to that of the existing antirheumatic gold(I) drugs. All gold(I) complexes inhibited the two most abundant isoforms of the presumed target enzyme, thioredoxin reductase, but a subset of compounds were markedly more active against live T. vaginalis than the enzyme, suggesting that alternative targets exist. Furthermore, all tested gold(I) complexes acted independently of Mz and were able to overcome Mz resistance, making them candidates for the treatment of Mz-refractory trichomoniasis.

Cryptococcosis is an invasive infection that accounts for 15% of AIDS-related fatalities. Still, treating cryptococcosis remains a significant challenge due to the poor availability of effective antifungal therapies and emergence of drug resistance. Interestingly, protease inhibitor components of antiretroviral therapy regimens have shown some clinical benefits in these opportunistic infections. We investigated Major aspartyl peptidase 1 (May1), a secreted Cryptococcus neoformans protease, as a possible target for the development of drugs that act against both fungal and retroviral aspartyl proteases. Here, we describe the biochemical characterization of May1, present its high-resolution X-ray structure, and provide its substrate specificity analysis. Through combinatorial screening of 11,520 compounds, we identified a potent inhibitor of May1 and HIV protease. This dual-specificity inhibitor exhibits antifungal activity in yeast culture, low cytotoxicity, and low off-target activity against host proteases and could thus serve as a lead compound for further development of May1 and HIV protease inhibitors.

The use of epigenetic bromodomain inhibitors as anticancer therapeutics has transitioned from targeting bromodomain extraterminal domain (BET) proteins into targeting non-BET bromodomains. The two most relevant non-BET bromodomain oncology targets are cyclic AMP response element-binding protein (CBP) and E1A binding protein P300 (EP300). To explore the growing CBP/EP300 interest, we developed a highly efficient two-step synthetic route for dimethylisoxazole-attached imidazo[1,2-a]pyridine scaffold-containing inhibitors. Our efficient two-step reactions enabled high-throughput synthesis of compounds designed by molecular modeling, which together with structure-activity relationship (SAR) studies facilitated an overarching understanding of selective targeting of CBP/EP300 over non-BET bromodomains. This led to the identification of a new potent and selective CBP/EP300 bromodomain inhibitor, UMB298 (compound 23, CBP IC50 72 nM and bromodomain 4, BRD4 IC50 5193 nM). The SAR we established is in good agreement with literature-reported CBP inhibitors, such as CBP30, and demonstrates the advantage of utilizing our two-step approach for inhibitor development of other bromodomains.

The pharmacological inhibition of soluble epoxide hydrolase (sEH) is efficient for the treatment of inflammatory and pain-related diseases. Numerous potent sEH inhibitors (sEHIs) present adamantyl or phenyl moieties, such as the clinical candidates AR9281 or EC5026. Herein, in a new series of sEHIs, these hydrophobic moieties have been merged in a benzohomoadamantane scaffold. Most of the new sEHIs have excellent inhibitory activities against sEH. Molecular dynamics simulations suggested that the addition of an aromatic ring into the adamantane scaffold produced conformational rearrangements in the enzyme to stabilize the aromatic ring of the benzohomoadamantane core. A screening cascade permitted us to select a candidate for an in vivo efficacy study in a murine model of cerulein-induced acute pancreatitis. The administration of 22 improved the health status of the animals and reduced pancreatic damage, demonstrating that the benzohomoadamantane unit is a promising scaffold for the design of novel sEHIs.

Small molecule inhibitors of the focal adhesion kinase are regarded as promising tools in our armamentarium for treating cancer. Here, we identified four 1,2,4-triazole derivatives that inhibit FAK kinase significantly and evaluated their therapeutic potential. Most tested compounds revealed potent antiproliferative activity in HepG2 and Hep3B liver cancer cells, in which 3c and 3d were the most potent (IC50 range; 2.88 ~ 4.83 µM). Compound 3d possessed significant FAK inhibitory activity with IC50 value of 18.10 nM better than the reference GSK-2256098 (IC50 = 22.14 nM). The preliminary mechanism investigation by Western blot analysis showed that both 3c and 3d repressed FAK phosphorylation comparable to GSK-2256098 in HepG2 cells. As a result of FAK inhibition, 3c and 3d inhibited the pro-survival pathways by decreasing the phosphorylation levels of PI3K, Akt, JNK, and STAT3 proteins. This effect led to apoptosis induction and cell cycle arrest. Taken together, these results indicate that 3d could serve as a potent preclinical candidate for the treatment of cancers.

Combinatorial fusion analysis (CFA) is an approach for combining multiple scoring systems using the rank-score characteristic function and cognitive diversity measure. One example is to combine diverse machine learning models to achieve better prediction quality. In this work, we apply CFA to the synthesis of metal halide perovskites containing organic ammonium cations via inverse temperature crystallization. Using a data set generated by high-throughput experimentation, four individual models (support vector machines, random forests, weighted logistic classifier, and gradient boosted trees) were developed. We characterize each of these scoring systems and explore 66 possible combinations of the models. When measured by the precision on predicting crystal formation, the majority of the combination models improves the individual model results. The best combination models outperform the best individual models by 3.9 percentage points in precision. In addition to improving prediction quality, we demonstrate how the fusion models can be used to identify mislabeled input data and address issues of data quality. In particular, we identify example cases where all single models and all fusion models do not give the correct prediction. Experimental replication of these syntheses reveals that these compositions are sensitive to modest temperature variations across the different locations of the heating element that can hinder or enhance the crystallization process. In summary, we demonstrate that model fusion using CFA can not only identify a previously unconsidered influence on reaction outcome but also be used as a form of quality control for high-throughput experimentation.

Development of new chemical entities is costly, time-consuming, and has a low success rate. Accurate prediction of pharmacokinetic properties is critical to progress compounds with favorable drug-like characteristics in lead optimization. Of particular importance is the prediction of hepatic clearance, which determines drug exposure and contributes to projection of dose, half-life, and bioavailability. The most commonly employed methodology to predict hepatic clearance is termed in vitro to in vivo extrapolation (IVIVE) that involves measuring drug metabolism in vitro, scaling-up this in vitro intrinsic clearance to a prediction of in vivo intrinsic clearance by reconciling the enzymatic content between the incubation and an average human liver, and applying a model of hepatic disposition to account for limitations of protein binding and blood flow to predict in vivo clearance. This manuscript reviews common in vitro techniques used to predict hepatic clearance as well as current challenges and recent theoretical advancements in IVIVE.

Small molecule colloidal aggregates adsorb and partially denature proteins, inhibiting them artifactually. Oddly, this inhibition is typically time-dependent. Two mechanisms might explain this: low concentrations of the colloid and enzyme might mean low encounter rates, or colloid-based protein denaturation might impose a kinetic barrier. These two mechanisms should have different concentration dependencies. Perplexingly, when enzyme concentration was increased, incubation times actually lengthened, inconsistent with both models and with classical chemical kinetics of solution species. We therefore considered molecular crowding, where colloids with lower protein surface density demand a shorter incubation time than more crowded colloids. To test this, we grew and shrank colloid surface area. As the surface area shrank, the incubation time lengthened, while as it increased, the converse was true. These observations support a crowding effect on protein binding to colloidal aggregates. Implications for drug delivery and for detecting aggregation-based inhibition will be discussed.

Metabolomics analysis detected tambjamine alkaloids in aqueous and EtOAc extracts of the marine invertebrates Virididentula dentata, Tambja stegosauriformis, Tambja brasiliensis, and Roboastra ernsti. Among several tambjamines, the new amino acid derivatives tambjamines M-O (17-19) were identified by Marfey's advanced analysis, UPLC-MS/MS analyses, and total synthesis. The tambjamine diversity increased from the bryozoan V. dentata to its nudibranch predators T. stegosauriformis and T. brasiliensis and attained a higher diversity in R. ernsti, the nudibranch that preys upon T. stegosauriformis and T. brasiliensis. The total tambjamine content also increases among the trophic levels, probably due to biomagnification. Tambjamines A (1), C (3), and D (4) are the major metabolites in the tissues of V. dentata, T. stegosauriformis, T. brasiliensis, and R. ernsti and are likely the main chemical defenses of these marine invertebrates.

Relative free energy calculations are fast becoming a critical part of early stage pharmaceutical design, making it important to know how to obtain the best performance with these calculations in applications that could span hundreds of calculations and molecules. In this work, we compared two different treatments of long-range electrostatics, Particle Mesh Ewald (PME) and Reaction Field (RF), in relative binding free energy calculations using a nonequilibrium switching protocol. We found simulations using RF achieve comparable results to those using PME but gain more efficiency when using CPU and similar performance using GPU. The results from this work encourage more use of RF in molecular simulations.

Intrinsically disordered proteins (IDPs) are widely distributed across eukaryotic cells, playing important roles in molecular recognition, molecular assembly, post-translational modification, and other biological processes. IDPs are also associated with many diseases such as cancers, cardiovascular diseases, and neurodegenerative diseases. Due to their structural flexibility, conventional experimental methods cannot reliably capture their heterogeneous structures. Molecular dynamics simulation becomes an important complementary tool to quantify IDP structures. This review covers recent force field strategies proposed for more accurate molecular dynamics simulations of IDPs. The strategies include adjusting dihedral parameters, adding grid-based energy correction map (CMAP) parameters, refining protein-water interactions, and others. Different force fields were found to perform well on specific observables of specific IDPs but also are limited in reproducing all available experimental observables consistently for all tested IDPs. We conclude the review with perspective areas for improvements for future force fields for IDPs.

The COVID-19 pathogen, SARS-CoV-2, requires its main protease (SC2MPro ) to digest two of its translated long polypeptides to form a number of mature proteins that are essential for viral replication and pathogenesis. Inhibition of this vital proteolytic process is effective in preventing the virus from replicating in infected cells and therefore provides a potential COVID-19 treatment option. Guided by previous medicinal chemistry studies about SARS-CoV-1 main protease (SC1MPro ), we have designed and synthesized a series of SC2MPro inhibitors that contain β-(S-2-oxopyrrolidin-3-yl)-alaninal (Opal) for the formation of a reversible covalent bond with the SC2MPro active-site cysteine C145. All inhibitors display high potency with Ki values at or below 100 nM. The most potent compound, MPI3, has as a Ki value of 8.3 nM. Crystallographic analyses of SC2MPro bound to seven inhibitors indicated both formation of a covalent bond with C145 and structural rearrangement from the apoenzyme to accommodate the inhibitors. Virus inhibition assays revealed that several inhibitors have high potency in inhibiting the SARS-CoV-2-induced cytopathogenic effect in both Vero E6 and A549/ACE2 cells. Two inhibitors, MPI5 and MPI8, completely prevented the SARS-CoV-2-induced cytopathogenic effect in Vero E6 cells at 2.5-5 μM and A549/ACE2 cells at 0.16-0.31 μM. Their virus inhibition potency is much higher than that of some existing molecules that are under preclinical and clinical investigations for the treatment of COVID-19. Our study indicates that there is a large chemical space that needs to be explored for the development of SC2MPro inhibitors with ultra-high antiviral potency.

Covalently acting inhibitors constitute a large and growing fraction of approved small-molecule therapeutics as well as useful tools for a variety of in vitro and in vivo applications. Here, we aimed to develop a covalent antagonist of CC chemokine receptor 2 (CCR2), a class A GPCR that has been pursued as a therapeutic target in inflammation and immuno-oncology. Based on a known intracellularly binding CCR2 antagonist, several covalent derivatives were synthesized and characterized by radioligand binding and functional assays. These studies revealed compound 14 as an intracellular covalent ligand for CCR2. In silico modeling followed by site-directed mutagenesis confirmed that 14 forms a covalent bond with one of three proximal cysteine residues, which can be engaged interchangeably. To our knowledge, compound 14 represents the first covalent ligand reported for CCR2. Due to its unique properties, it may represent a promising tool for ongoing and future studies of CCR2 pharmacology.

Destabilizing mutations in small heat shock proteins (sHsps) are linked to multiple diseases; however, sHsps are conformationally dynamic, lack enzymatic function and have no endogenous chemical ligands. These factors render sHsps as classically "undruggable" targets and make it particularly challenging to identify molecules that might bind and stabilize them. To explore potential solutions, we designed a multi-pronged screening workflow involving a combination of computational and biophysical ligand-discovery platforms. Using the core domain of the sHsp family member Hsp27/HSPB1 (Hsp27c) as a target, we applied mixed solvent molecular dynamics (MixMD) to predict three possible binding sites, which we confirmed using NMR-based solvent mapping. Using this knowledge, we then used NMR spectroscopy to carry out a fragment-based drug discovery (FBDD) screen, ultimately identifying two fragments that bind to one of these sites. A medicinal chemistry effort improved the affinity of one fragment by ~50-fold (16 µM), while maintaining good ligand efficiency (~0.32 kcal/mol/non-hydrogen atom). Finally, we found that binding to this site partially restored the stability of disease-associated Hsp27 variants, in a redox-dependent manner. Together, these experiments suggest a new and unexpected binding site on Hsp27, which might be exploited to build chemical probes.

Many molecular simulation methods use force fields to help model and simulate molecules and their behavior in various environments. Force fields are sets of functions and parameters used to calculate the potential energy of a chemical system as a function of the atomic coordinates. Despite the widespread use of force fields, their inadequacies are often thought to contribute to systematic errors in molecular simulations. Furthermore, different force fields tend to give varying results on the same systems with the same simulation settings. Here, we present a pipeline for comparing the geometries of small molecule conformers. We aimed to identify molecules or chemistries that are particularly informative for future force field development because they display inconsistencies between force fields. We applied our pipeline to a subset of the eMolecules database, and highlighted molecules that appear to be parameterized inconsistently across different force fields. We then identified over-represented functional groups in these molecule sets. The molecules and moieties identified by this pipeline may be particularly helpful for future force field parameterization.

Enrichment of ligands versus property-matched decoys is widely used to test and optimize docking library screens. However, the unconstrained optimization of enrichment alone can mislead, leading to false confidence in prospective performance. This can arise by over-optimizing for enrichment against property-matched decoys, without considering the full spectrum of molecules to be found in a true large library screen. Adding decoys representing charge extrema helps mitigate over-optimizing for electrostatic interactions. Adding decoys that represent the overall characteristics of the library to be docked allows one to sample molecules not represented by ligands and property-matched decoys but that one will encounter in a prospective screen. An optimized version of the DUD-E set (DUDE-Z), as well as Extrema and sets representing broad features of the library (Goldilocks), is developed here. We also explore the variability that one can encounter in enrichment calculations and how that can temper one's confidence in small enrichment differences. The new tools and new decoy sets are freely available at http://tldr.docking.org and http://dudez.docking.org.

Pantetheine is ubiquitous in nature in various forms of pantetheine-containing ligands (PCLs), including coenzyme A and phosphopantetheine. Lack of scalable force field libraries for PCLs has hampered the computational studies of biological macromolecules containing PCLs. We describe here the development of the first generation Pantetheine Force Field (PFF) library that is compatible with Amber force fields; parameterized using Gasteiger, AM1-BCC, or RESP charging methods combined with gaff2 and ff14SB parameter sets. In addition, a "plug-and-play" strategy was employed to enable the systematic charging of computationally expensive molecules sharing common substructural motifs. The validation studies performed on the PFF library showed promising performance where molecular dynamics (MD) simulations results were compared with experimental data of three representative systems. The PFF library represents the first force field library capable of modeling systems containing PCLs in silico and will aid in various applications including protein engineering and drug discovery.

The prediction of acid dissociation constants (pKa) is a prerequisite for predicting many other properties of a small molecule, such as its protein-ligand binding affinity, distribution coefficient (log D), membrane permeability, and solubility. The prediction of each of these properties requires knowledge of the relevant protonation states and solution free energy penalties of each state. The SAMPL6 pKa Challenge was the first time that a separate challenge was conducted for evaluating pKa predictions as part of the Statistical Assessment of Modeling of Proteins and Ligands (SAMPL) exercises. This challenge was motivated by significant inaccuracies observed in prior physical property prediction challenges, such as the SAMPL5 log D Challenge, caused by protonation state and pKa prediction issues. The goal of the pKa challenge was to assess the performance of contemporary pKa prediction methods for drug-like molecules. The challenge set was composed of 24 small molecules that resembled fragments of kinase inhibitors, a number of which were multiprotic. Eleven research groups contributed blind predictions for a total of 37 pKa distinct prediction methods. In addition to blinded submissions, four widely used pKa prediction methods were included in the analysis as reference methods. Collecting both microscopic and macroscopic pKa predictions allowed in-depth evaluation of pKa prediction performance. This article highlights deficiencies of typical pKa prediction evaluation approaches when the distinction between microscopic and macroscopic pKas is ignored; in particular, we suggest more stringent evaluation criteria for microscopic and macroscopic pKa predictions guided by the available experimental data. Top-performing submissions for macroscopic pKa predictions achieved RMSE of 0.7-1.0 pKa units and included both quantum chemical and empirical approaches, where the total number of extra or missing macroscopic pKas predicted by these submissions were fewer than 8 for 24 molecules. A large number of submissions had RMSE spanning 1-3 pKa units. Molecules with sulfur-containing heterocycles or iodo and bromo groups were less accurately predicted on average considering all methods evaluated. For a subset of molecules, we utilized experimentally-determined microstates based on NMR to evaluate the dominant tautomer predictions for each macroscopic state. Prediction of dominant tautomers was a major source of error for microscopic pKa predictions, especially errors in charged tautomers. The degree of inaccuracy in pKa predictions observed in this challenge is detrimental to the protein-ligand binding affinity predictions due to errors in dominant protonation state predictions and the calculation of free energy corrections for multiple protonation states. Underestimation of ligand pKa by 1 unit can lead to errors in binding free energy errors up to 1.2 kcal/mol. The SAMPL6 pKa Challenge demonstrated the need for improving pKa prediction methods for drug-like molecules, especially for challenging moieties and multiprotic molecules.

Water molecules can be found interacting with the surface and within cavities in proteins. However, water exchange between bulk and buried hydration sites can be slow compared to simulation timescales, thus leading to the inefficient sampling of the locations of water. This can pose problems for free energy calculations for computer-aided drug design. Here, we apply a hybrid method that combines nonequilibrium candidate Monte Carlo (NCMC) simulations and molecular dynamics (MD) to enhance sampling of water in specific areas of a system, such as the binding site of a protein. Our approach uses NCMC to gradually remove interactions between a selected water molecule and its environment, then translates the water to a new region, before turning the interactions back on. This approach of gradual removal of interactions, followed by a move and then reintroduction of interactions, allows the environment to relax in response to the proposed water translation, improving acceptance of moves and thereby accelerating water exchange and sampling. We validate this approach on several test systems including the ligand-bound MUP-1 and HSP90 proteins with buried crystallographic waters removed. We show that our BLUES (NCMC/MD) method enhances water sampling relative to normal MD when applied to these systems. Thus, this approach provides a strategy to improve water sampling in molecular simulations which may be useful in practical applications in drug discovery and biomolecular design.

The US Food and Drug Administration (FDA) Expanded Access (EA) Program, which allows for compassionate uses of unapproved therapeutics and diagnostics outside of clinical trials, has gained significant traction during the Coronavirus 2019 (COVID-19) pandemic. While development of vaccines has been the major focus, uncertainties around new vaccine safety and effectiveness have spawned interest in other pharmacological options. Experimental drugs can also be approved under the FDA Emergency Use Authorization (EUA) program, designed to combat infectious disease and other threats. Here, we review the US experience in 2020 with pharmacological EA and EUA approvals during the pandemic. We also provide a description of, and clinical rationale for, each of the EA- or EUA-approved drugs (e.g. remdesivir, convalescent plasma, propofol 2%, hydroxychloroquine, ruxolitinib, bamlanivimab, baricitinib, casirivimab plus imdevimab) during the pandemic and concluding reflections on the EA program and its potential future uses.

Studies in tau and Aβ plaque transgenic mouse models demonstrated that brain-penetrant microtubule (MT)-stabilizing compounds, including the 1,2,4-triazolo[1,5-a]pyrimidines, hold promise as candidate treatments for Alzheimer's disease and related neurodegenerative tauopathies. Triazolopyrimidines have already been investigated as anticancer agents; however, the antimitotic activity of these compounds does not always correlate with stabilization of MTs in cells. Indeed, previous studies from our laboratories identified a critical role for the fragment linked at C6 in determining whether triazolopyrimidines promote MT stabilization or, conversely, disrupt MT integrity in cells. To further elucidate the structure-activity relationship (SAR) and to identify potentially improved MT-stabilizing candidates for neurodegenerative disease, a comprehensive set of 68 triazolopyrimidine congeners bearing structural modifications at C6 and/or C7 was designed, synthesized, and evaluated. These studies expand upon prior understanding of triazolopyrimidine SAR and enabled the identification of novel analogues that, relative to the existing lead, exhibit improved physicochemical properties, MT-stabilizing activity, and pharmacokinetics.

Three families of RNA viruses, the Coronaviridae, Flaviviridae, and Filoviridae, collectively have great potential to cause epidemic disease in human populations. The current SARS-CoV-2 (Coronaviridae) responsible for the COVID-19 pandemic underscores the lack of effective medications currently available to treat these classes of viral pathogens. Similarly, the Flaviviridae, which includes such viruses as Dengue, West Nile, and Zika, and the Filoviridae, with the Ebola-type viruses, as examples, all lack effective therapeutics. In this review, we present fundamental information concerning the biology of these three virus families, including their genomic makeup, mode of infection of human cells, and key proteins that may offer targeted therapies. Further, we present the natural products and their derivatives that have documented activities to these viral and host proteins, offering hope for future mechanism-based antiviral therapeutics. By arranging these potential protein targets and their natural product inhibitors by target type across these three families of virus, new insights are developed, and crossover treatment strategies are suggested. Hence, natural products, as is the case for other therapeutic areas, continue to be a promising source of structurally diverse new anti-RNA virus therapeutics.

Soluble epoxide hydrolase (sEH) is an α/β hydrolase fold protein and widely distributed in numerous organs including the liver, kidney, and brain. The inhibition of sEH can effectively maintain endogenous epoxyeicosatrienoic acids (EETs) levels and reduce dihydroxyeicosatrienoic acids (DHETs) levels, resulting in therapeutic potentials for cardiovascular, central nervous system, and metabolic diseases. Therefore, since the beginning of this century, the development of sEH inhibitors is a hot research topic. A variety of potent sEH inhibitors have been developed by chemical synthesis or isolated from natural sources. In this review, we mainly summarized the interconnected aspects of sEH with cardiovascular, central nervous system, and metabolic diseases and then focus on representative inhibitors, which would provide some useful guidance for the future development of potential sEH inhibitors.

Microbial natural products are important for the understanding of microbial interactions, chemical defense and communication, and have also served as an inspirational source for numerous pharmaceutical drugs. Tropical marine cyanobacteria have been highlighted as a great source of new natural products, however, few reports have appeared wherein a multi-omics approach has been used to study their natural products potential (i.e., reports are often focused on an individual natural product and its biosynthesis). This study focuses on describing the natural product genetic potential as well as the expressed natural product molecules in benthic tropical cyanobacteria. We collected from several sites around the world and sequenced the genomes of 24 tropical filamentous marine cyanobacteria. The informatics program antiSMASH was used to annotate the major classes of gene clusters. BiG-SCAPE phylum-wide analysis revealed the most promising strains for natural product discovery among these cyanobacteria. LCMS/MS-based metabolomics highlighted the most abundant molecules and molecular classes among 10 of these marine cyanobacterial samples. We observed that despite many genes encoding for peptidic natural products, peptides were not as abundant as lipids and lipopeptides in the chemical extracts. Our results highlight a number of highly interesting biosynthetic gene clusters for genome mining among these cyanobacterial samples.

We investigate the binding of native β-cyclodextrin (β-CD) and eight novel β-CD derivatives with two different guest compounds, using isothermal calorimetry and 2D NOESY NMR. In all cases, the stoichiometry is 1:1 and binding is exothermic. Overall, modifications at the 3' position of β-CD, which is at the secondary face, weaken binding by several kJ/mol relative to native β-CD, while modifications at the 6' position (primary face) maintain or somewhat reduce the binding affinity. The variations in binding enthalpy are larger than the variations in binding free energy, so entropy-enthalpy compensation is observed. Characterization of the bound conformations with NOESY NMR shows that the polar groups of the guests may be situated at either face, depending on the host molecule, and, in some cases, both orientations are populated. The present results were used in the SAMPL7 blinded prediction challenge whose results are detailed in the same special issue of JCAMD.

MicroRNAs (miRNAs) are short noncoding RNAs that play a fundamental role in gene regulation. Deregulation of miRNA expression has a strong correlation with disease and antisense oligonucleotides that bind and inhibit miRNAs associated with disease have therapeutic potential. Current research on the chemical modification of anti-miRNA oligonucleotides (anti-miRs) is focused on alterations of the phosphodiester-ribose backbone to improve nuclease resistance and binding affinity to miRNA strands. Here we describe a structure-guided approach for modification of the 3'-end of anti-miRs by screening for modifications compatible with a nucleotide-binding pocket present on human Argonaute2 (hAgo2). We computationally screened a library of 190 triazole-modified nucleoside analogs for complementarity to the t1A-binding pocket of hAgo2. Seventeen top scoring triazoles were then incorporated into the 3' end of anti-miR21 and potency was evaluated for each in a cell-based assay for anti-miR activity. Four triazole-modified anti-miRs showed higher potency than anti-miR21 bearing a 3' adenosine. In particular, a triazole-modified nucleoside bearing an ester substituent imparted a nine-fold and five-fold increase in activity for both anti-miR21 and anti-miR122 at 300 and 5 nM, respectively. The ester group was shown to be critical as a similar carboxylic acid and amide were inactive. Furthermore, anti-miR 3' end modification with triazole-modified nucleoside analogs improved resistance to snake venom phosphodiesterase, a 3'-exonuclease. Thus, the modifications described here are good candidates for improvement of anti-miR activity.

The SAMPL challenges focus on testing and driving progress of computational methods to help guide pharmaceutical drug discovery. However, assessment of methods for predicting binding affinities is often hampered by computational challenges such as conformational sampling, protonation state uncertainties, variation in test sets selected, and even lack of high quality experimental data. SAMPL blind challenges have thus frequently included a component focusing on host-guest binding, which removes some of these challenges while still focusing on molecular recognition. Here, we report on the results of the SAMPL7 blind prediction challenge for host-guest affinity prediction. In this study, we focused on three different host-guest categories-a familiar deep cavity cavitand series which has been featured in several prior challenges (where we examine binding of a series of guests to two hosts), a new series of cyclodextrin derivatives which are monofunctionalized around the rim to add amino acid-like functionality (where we examine binding of two guests to a series of hosts), and binding of a series of guests to a new acyclic TrimerTrip host which is related to previous cucurbituril hosts. Many predictions used methods based on molecular simulations, and overall success was mixed, though several methods stood out. As in SAMPL6, we find that one strategy for achieving reasonable accuracy here was to make empirical corrections to binding predictions based on previous data for host categories which have been studied well before, though this can be of limited value when new systems are included. Additionally, we found that alchemical free energy methods using the AMOEBA polarizable force field had considerable success for the two host categories in which they participated. The new TrimerTrip system was also found to introduce some sampling problems, because multiple conformations may be relevant to binding and interconvert only slowly. Overall, results in this challenge tentatively suggest that further investigation of polarizable force fields for these challenges may be warranted.

We previously identified a spiro [piperidine-4,1-pyrido [3,4-b]indole] class of co-potentiators that function in synergy with existing CFTR potentiators such as VX-770 or GLGP1837 to restore channel activity of a defined subset of minimal function cystic fibrosis transmembrane conductance regulator (CFTR) mutants. Here, structure-activity studies were conducted to improve their potency over the previously identified compound, 20 (originally termed CP-A01). Targeted synthesis of 37 spiro [piperidine-4,1-pyrido [3,4-b]indoles] was generally accomplished using versatile two or three step reaction protocols with each step having high efficiency. Structure-activity relationship studies established that analog 2i, with 6'-methoxyindole and 2,4,5-trifluorobenzyl substituents, had the greatest potency for activation of N1303K-CFTR, with EC50 ∼600 nM representing an ∼17-fold improvement over the original compound identified in a small molecule screen.

Cardiovascular diseases (CVDs) are the result of complex pathophysiological processes in the tissues comprising the heart and blood vessels. Inflammation is the main culprit for the development of cardiovascular dysfunction, and it may be traced to cellular stress events including apoptosis, oxidative and shear stress, and cellular and humoral immune responses, all of which impair the system's structure and function. An intracellular chaperone, heat shock protein 60 (HSP60) is an intriguing example of a protein that may both be an ally and a foe for cardiovascular homeostasis; on one hand providing protection against cellular injury, and on the other triggering damaging responses through innate and adaptive immunity. In this review we will discuss the functions of HSP60 and its effects on cells and the immune system regulation, only to later address its implications in the development and progression of CVD. Lastly, we summarize the outcome of various studies targeting HSP60 as a potential therapeutic strategy for cardiovascular and other diseases.

BackgroundBone loss is the most common reason for broken bones among the elderly. An ideal agent for the treatment of bone loss should have both osteoclast inhibitory and osteoblast stimulatory functions. Leucine-rich repeat kinase 1 (LRRK1) is a novel target for alternative antiresorptive drugs to treat osteoporosis and osteoporotic fractures. Recently a chemical IN04, Methyl 3-[({([5-(3,5-dimethoxyphenyl)-1,3,4-oxadiazol-2-yl]-thio}-acetyl)-amino]-benzoate, has been identified as a potential LRRK1 inhibitor.ObjectiveThe aim of this work is to investigate how the chemical IN04 interacts with LRRK1 and inhibits its activity.MethodsA structural model of the LRRK1 kinase domain was constructed with SWISS-MODEL. The human protein kinase ROCO4 (PDB ID: 4YZN) was chosen as the template based on sequence homology, structural and phylogenetic analysis. In addition, a homology model of the LRRK1 ROC domain was also prepared based on the LRRK2 ROC domain structure (PDB ID: 2ZEJ). The interactions of IN04 with the active sites in the LRRK1 kinase domain and ROC domain were investigated by SwissDock.ResultsIN04 was docked into the active site of the LRRK1 kinase domain with similar interactions as ATP comparable to the ligand bound to homologous kinases. Many rational binding modes of IN04 to LRRK1 kinase domain were investigated and the most likely binding pose containing multiple hydrogen bonds and a salt bridge was discovered. However, IN04 cannot fit into the GDP-binding site of the ROC domain.ConclusionChemical IN04 inhibits LRRK1 by binding to the active site of the kinase domain but not the ROC domain.

Four new chlorinated meroterpenoids, merochlorins G-J (1-4), and 10, a dihydronaphthalenedione precursor, along with known merochlorins A (5) and C-F (6-9), were obtained from cultivation of the bacterium strain Streptomyces sp. CNH-189, which was isolated from marine sediment. The planar structures of compounds 1-4 and 10 were elucidated by interpretation of MS, UV, and NMR spectroscopic data. The relative configurations of compounds 1-4 were determined via analysis of nuclear Overhauser effect (NOE) spectroscopic data, after which their absolute configurations were established by comparing the experimental electronic circular dichroism (ECD) spectra of compounds 1-4 to those of previously reported possible enantiomer models and DP4 calculations. Compound 3 displayed strong antibacterial activities against Bacillus subtilis, Kocuria rhizophila, and Staphylococcus aureus, with MIC values of 1, 2, and 2 μg/mL, respectively, whereas compound 1 exhibited weak antibacterial effects on these three strains, with a 16-32 μg/mL MIC value range.

Multitask deep neural networks learn to predict ligand-target binding by example, yet public pharmacological data sets are sparse, imbalanced, and approximate. We constructed two hold-out benchmarks to approximate temporal and drug-screening test scenarios, whose characteristics differ from a random split of conventional training data sets. We developed a pharmacological data set augmentation procedure, Stochastic Negative Addition (SNA), which randomly assigns untested molecule-target pairs as transient negative examples during training. Under the SNA procedure, drug-screening benchmark performance increases from R2 = 0.1926 ± 0.0186 to 0.4269 ± 0.0272 (122%). This gain was accompanied by a modest decrease in the temporal benchmark (13%). SNA increases in drug-screening performance were consistent for classification and regression tasks and outperformed y-randomized controls. Our results highlight where data and feature uncertainty may be problematic and how leveraging uncertainty into training improves predictions of drug-target relationships.

CRISPR-Cas12a is a genome-editing system, recently also harnessed for nucleic acid detection, which is promising for the diagnosis of the SARS-CoV-2 coronavirus through the DETECTR technology. Here, a collective ensemble of multimicrosecond molecular dynamics characterizes the key dynamic determinants allowing nucleic acid processing in CRISPR-Cas12a. We show that DNA binding induces a switch in the conformational dynamics of Cas12a, which results in the activation of the peripheral REC2 and Nuc domains to enable cleavage of nucleic acids. The simulations reveal that large-amplitude motions of the Nuc domain could favor the conformational activation of the system toward DNA cleavages. In this process, the REC lobe plays a critical role. Accordingly, the joint dynamics of REC and Nuc shows the tendency to prime the conformational transition of the DNA target strand toward the catalytic site. Most notably, the highly coupled dynamics of the REC2 region and Nuc domain suggests that REC2 could act as a regulator of the Nuc function, similar to what was observed previously for the HNH domain in the CRISPR-associated nuclease Cas9. These mutual domain dynamics could be critical for the nonspecific binding of DNA and thereby for the underlying mechanistic functioning of the DETECTR technology. Considering that REC is a key determinant in the system's specificity, our findings provide a rational basis for future biophysical studies aimed at characterizing its function in CRISPR-Cas12a. Overall, our outcomes advance our mechanistic understanding of CRISPR-Cas12a and provide grounds for novel engineering efforts to improve genome editing and viral detection.

Identifying and purchasing new small molecules to test in biological assays are enabling for ligand discovery, but as purchasable chemical space continues to grow into the tens of billions based on inexpensive make-on-demand compounds, simply searching this space becomes a major challenge. We have therefore developed ZINC20, a new version of ZINC with two major new features: billions of new molecules and new methods to search them. As a fully enumerated database, ZINC can be searched precisely using explicit atomic-level graph-based methods, such as SmallWorld for similarity and Arthor for pattern and substructure search, as well as 3D methods such as docking. Analysis of the new make-on-demand compound sets by these and related tools reveals startling features. For instance, over 97% of the core Bemis-Murcko scaffolds in make-on-demand libraries are unavailable from "in-stock" collections. Correspondingly, the number of new Bemis-Murcko scaffolds is rising almost as a linear fraction of the elaborated molecules. Thus, an 88-fold increase in the number of molecules in the make-on-demand versus the in-stock sets is built upon a 16-fold increase in the number of Bemis-Murcko scaffolds. The make-on-demand library is also more structurally diverse than physical libraries, with a massive increase in disc- and sphere-like shaped molecules. The new system is freely available at zinc20.docking.org.