A SARS-CoV-2 protein interaction map reveals targets for drug repurposing

The novel coronavirus SARS-CoV-2, the causative agent of COVID-19 respiratory disease, has infected over 2.3 million people, killed over 160,000, and caused worldwide social and economic disruption1,2. There are currently no antiviral drugs with proven clinical efficacy, nor are there vaccines for its prevention, and these efforts are hampered by limited knowledge of the molecular details of SARS-CoV-2 infection. To address this, we cloned, tagged and expressed 26 of the 29 SARS-CoV-2 proteins in human cells and identified the human proteins physically associated with each using affinity-purification mass spectrometry (AP-MS), identifying 332 high-confidence SARS-CoV-2-human protein-protein interactions (PPIs). Among these, we identify 66 druggable human proteins or host factors targeted by 69 compounds (29 FDA-approved drugs, 12 drugs in clinical trials, and 28 preclinical compounds). Screening a subset of these in multiple viral assays identified two sets of pharmacological agents that displayed antiviral activity: inhibitors of mRNA translation and predicted regulators of the Sigma1 and Sigma2 receptors. Further studies of these host factor targeting agents, including their combination with drugs that directly target viral enzymes, could lead to a therapeutic regimen to treat COVID-19

Modeling the expansion of virtual screening libraries

Recently, the growth of commercially-available molecules has been driven by “tangible” make-on-demand, virtual libraries. Such billion-molecule libraries can never be fully synthesized, tested, or even stored. The only way to explore this expanded chemical space is by computationally prioritizing particular molecules for synthesis and testing, often by docking. The success of this prioritization may depend on library properties: how diverse are the molecules, how similar are they to bio-like molecules, such as metabolites and drugs, how does receptor-fit improve with library size, and how does the presence of artifacts grow with library size? To begin to investigate these questions, we compare the characteristics and performance of a library of 3 million “in-stock” molecules with that of ever-larger tangible libraries, up to 3 billion molecules in size. The bias toward biologically precedented molecules of the 886-fold larger tangible library decreases 19,000-fold compared to the in-stock library. Looking at docking hits, and not the overall libraries, thousands of high-ranking synthesized and tested tangible compounds from five ultra-large library docking campaigns are also dissimilar to bio-like molecules. These observations imply that bio-likeness plays little role in the likelihood of binding, appearing to contradict multiple studies to the contrary. Another important aspect of library growth is whether screening ever-larger libraries leads to better ligands. Judged by docking score, better fitting molecules are found as the library grows, with score improving log-linearly with library size. Finally, it is possible to imagine that as library size increases, so too do the chances of rare events—molecules that cheat the scoring function and rank artifactually well. Both simulation and experimental results from ultra-large library screens suggest that this is true—as the libraries grow, more and more artifacts can crowd the very top-ranking molecules. Although the nature of these artifacts appears to change from target to target, the expectation of their occurrence does not, and simple strategies may be devised to minimize the impact of these rare-event artifacts on the success of large library screens

Intra-crystalline mesoporous SAPO-11 prepared by a grinding synthesis method as FCC promoters to increase iso-paraffin of gasoline

International audienceA hierarchical SAPO-11 was prepared by a grinding synthesis method (GSM) and added as a promoter to increase the iso-paraffin yield of gasoline in fluid catalytic cracking (FCC) reaction. Compared tothe classical hydrothermal method, the GSM leads to thecreation of intra-crystalline mesopores with the assistance of a surfactant (Cetyltrimethyl Ammonium Bromide, CTAB). CTAB acts asa structure directing agentand as a hard template during the crystallization of SAPO-11. CTAB micelles formedthe intra-crystalline mesoporesin the SAPO-11 crystals.Theintra-crystalline mesoporosity of the SAPO-11is preservedat 800 o C understeaming, which render the materialas an appropriate component of the FCC catalyst.Besides, the presence of mesopores provides isomerization active sites and accelerates the desorption of iso-paraffin. The mesoporous SAPO-11 showed a higher selectivity for iso-paraffin and the yield of iso-paraffin increased by 2%

Selectivity Challenges in Docking Screens for GPCR Targets and Antitargets.

To investigate large library docking's ability to find molecules with joint activity against on-targets and selectivity versus antitargets, the dopamine D2 and serotonin 5-HT2A receptors were targeted, seeking selectivity against the histamine H1 receptor. In a second campaign, κ-opioid receptor ligands were sought with selectivity versus the μ-opioid receptor. While hit rates ranged from 40% to 63% against the on-targets, they were just as good against the antitargets, even though the molecules were selected for their putative lack of binding to the off-targets. Affinities, too, were often as good or better for the off-targets. Even though it was occasionally possible to find selective molecules, such as a mid-nanomolar D2/5-HT2A ligand with 21-fold selectivity versus the H1 receptor, this was the exception. Whereas false-negatives are tolerable in docking screens against on-targets, they are intolerable against antitargets; addressing this problem may demand new strategies in the field

Selectivity Challenges in Docking Screens for GPCR Targets and Antitargets

To investigate large library docking’s ability to find molecules with joint activity against on-targets and selectivity versus antitargets, the dopamine D₂ and serotonin 5-HT_2A receptors were targeted, seeking selectivity against the histamine H₁ receptor. In a second campaign, κ-opioid receptor ligands were sought with selectivity versus the μ-opioid receptor. While hit rates ranged from 40% to 63% against the on-targets, they were just as good against the antitargets, even though the molecules were selected for their putative lack of binding to the off-targets. Affinities, too, were often as good or better for the off-targets. Even though it was occasionally possible to find selective molecules, such as a mid-nanomolar D₂/5-HT_2A ligand with 21-fold selectivity versus the H₁ receptor, this was the exception. Whereas false-negatives are tolerable in docking screens against on-targets, they are intolerable against antitargets; addressing this problem may demand new strategies in the field

Property-Unmatched Decoys in Docking Benchmarks

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

Selectivity Challenges in Docking Screens for GPCR Targets and Antitargets

To investigate large library docking’s ability to find molecules with joint activity against on-targets and selectivity versus antitargets, the dopamine D₂ and serotonin 5-HT_2A receptors were targeted, seeking selectivity against the histamine H₁ receptor. In a second campaign, κ-opioid receptor ligands were sought with selectivity versus the μ-opioid receptor. While hit rates ranged from 40% to 63% against the on-targets, they were just as good against the antitargets, even though the molecules were selected for their putative lack of binding to the off-targets. Affinities, too, were often as good or better for the off-targets. Even though it was occasionally possible to find selective molecules, such as a mid-nanomolar D₂/5-HT_2A ligand with 21-fold selectivity versus the H₁ receptor, this was the exception. Whereas false-negatives are tolerable in docking screens against on-targets, they are intolerable against antitargets; addressing this problem may demand new strategies in the field