Specificity and mechanism of TonB-dependent ferric catecholate uptake by Fiu.

We studied the Escherichia coli outer membrane protein Fiu, a presumed transporter of monomeric ferric catecholates, by introducing Cys residues in its surface loops and modifying them with fluorescein maleimide (FM). Fiu-FM bound iron complexes of the tricatecholate siderophore enterobactin (FeEnt) and glucosylated enterobactin (FeGEnt), their dicatecholate degradation product Fe(DHBS)2 (FeEnt*), the monocatecholates dihydroxybenzoic acid (FeDHBA) and dihydroxybenzoyl serine (FeDHBS), and the siderophore antibiotics cefiderocol (FDC) and MB-1. Unlike high-affinity ligand-gated porins (LGPs), Fiu-FM had only micromolar affinity for iron complexes. Its apparent KD values for FeDHBS, FeDHBA, FeEnt*, FeEnt, FeGEnt, FeFDC, and FeMB-1 were 0.1, 0.7, 0.7, 1.0, 0.3, 0.4, and 4 µM, respectively. Despite its broad binding abilities, the transport repertoires of E. coli Fiu, as well as those of Cir and FepA, were less broad. Fiu only transported FeEnt*. Cir transported FeEnt* and FeDHBS (weakly); FepA transported FeEnt, FeEnt*, and FeDHBA. Both Cir and FepA bound FeGEnt, albeit with lower affinity. Related transporters of Acinetobacter baumannii (PiuA, PirA, BauA) had similarly moderate affinity and broad specificity for di- or monomeric ferric catecholates. Both microbiological and radioisotopic experiments showed Fiu's exclusive transport of FeEnt*, rather than ferric monocatecholate compounds. Molecular docking and molecular dynamics simulations predicted three binding sites for FeEnt*in the external vestibule of Fiu, and a fourth site deeper in its interior. Alanine scanning mutagenesis in the outermost sites (1a, 1b, and 2) decreased FeEnt* binding affinity as much as 20-fold and reduced or eliminated FeEnt* uptake. Finally, the molecular dynamics simulations suggested a pathway of FeEnt* movement through Fiu that may generally describe the process of metal transport by TonB-dependent receptors.

Drug Discovery in Low Data Regimes: Leveraging a Computational Pipeline for the Discovery of Novel SARS-CoV-2 Nsp14-MTase Inhibitors.

The COVID-19 pandemic, caused by the SARS-CoV-2 virus, has led to significant global morbidity and mortality. A crucial viral protein, the non-structural protein 14 (nsp14), catalyzes the methylation of viral RNA and plays a critical role in viral genome replication and transcription. Due to the low mutation rate in the nsp region among various SARS-CoV-2 variants, nsp14 has emerged as a promising therapeutic target. However, discovering potential inhibitors remains a challenge. In this work, we introduce a computational pipeline for the rapid and efficient identification of potential nsp14 inhibitors by leveraging virtual screening and the NCI open compound collection, which contains 250,000 freely available molecules for researchers worldwide. The introduced pipeline provides a cost-effective and efficient approach for early-stage drug discovery by allowing researchers to evaluate promising molecules without incurring synthesis expenses. Our pipeline successfully identified seven promising candidates after experimentally validating only 40 compounds. Notably, we discovered NSC620333, a compound that exhibits a strong binding affinity to nsp14 with a dissociation constant of 427 ± 84 nM. In addition, we gained new insights into the structure and function of this protein through molecular dynamics simulations. We identified new conformational states of the protein and determined that residues Phe367, Tyr368, and Gln354 within the binding pocket serve as stabilizing residues for novel ligand interactions. We also found that metal coordination complexes are crucial for the overall function of the binding pocket. Lastly, we present the solved crystal structure of the nsp14-MTase complexed with SS148 (PDB:8BWU), a potent inhibitor of methyltransferase activity at the nanomolar level (IC50 value of 70 ± 6 nM). Our computational pipeline accurately predicted the binding pose of SS148, demonstrating its effectiveness and potential in accelerating drug discovery efforts against SARS-CoV-2 and other emerging viruses.

Resistance gene-guided genome mining reveals the roseopurpurins as inhibitors of cyclin-dependent kinases.

With the significant increase in the availability of microbial genome sequences in recent years, resistance gene-guided genome mining has emerged as a powerful approach for identifying natural products with specific bioactivities. Here, we present the use of this approach to reveal the roseopurpurins as potent inhibitors of cyclin-dependent kinases (CDKs), a class of cell cycle regulators implicated in multiple cancers. We identified a biosynthetic gene cluster (BGC) with a putative resistance gene with homology to human CDK2. Using targeted gene disruption and transcription factor overexpression in Aspergillus uvarum, and heterologous expression of the BGC in Aspergillus nidulans, we demonstrated that roseopurpurin C (1) is produced by this cluster and characterized its biosynthesis. We determined the potency, specificity, and mechanism of action of 1 as well as multiple intermediates and shunt products produced from the BGC. We show that 1 inhibits human CDK2 with a Kiapp of 44 nM, demonstrates selectivity for clinically relevant members of the CDK family, and induces G1 cell cycle arrest in HCT116 cells. Structural analysis of 1 complexed with CDK2 revealed the molecular basis of ATP-competitive inhibition.

Development, Characterization, and Structural Analysis of a Genetically Encoded Red Fluorescent Peroxynitrite Biosensor.

Boronic acid-containing fluorescent molecules have been widely used to sense hydrogen peroxide and peroxynitrite, which are important reactive oxygen and nitrogen species in biological systems. However, it has been challenging to gain specificity. Our previous studies developed genetically encoded, green fluorescent peroxynitrite biosensors by genetically incorporating a boronic acid-containing noncanonical amino acid (ncAA), p-boronophenylalanine (pBoF), into the chromophore of circularly permuted green fluorescent proteins (cpGFPs). In this work, we introduced pBoF to amino acid residues spatially close to the chromophore of an enhanced circularly permuted red fluorescent protein (ecpApple). Our effort has resulted in two responsive ecpApple mutants: one bestows reactivity toward both peroxynitrite and hydrogen peroxide, while the other, namely, pnRFP, is a selective red fluorescent peroxynitrite biosensor. We characterized pnRFP in vitro and in live mammalian cells. We further studied the structure and sensing mechanism of pnRFP using X-ray crystallography, 11B-NMR, and computational methods. The boron atom in pnRFP adopts an sp2-hybridization geometry in a hydrophobic pocket, and the reaction of pnRFP with peroxynitrite generates a product with a twisted chromophore, corroborating the observed "turn-off" fluorescence response. Thus, this study extends the color palette of genetically encoded peroxynitrite biosensors, provides insight into the response mechanism of the new biosensor, and demonstrates the versatility of using protein scaffolds to modulate chemoreactivity.

Computational approaches for the design of modulators targeting protein-protein interactions.

BACKGROUND: Protein-protein interactions (PPIs) are intriguing targets for designing novel small-molecule inhibitors. The role of PPIs in various infectious and neurodegenerative disorders makes them potential therapeutic targets . Despite being portrayed as undruggable targets, due to their flat surfaces, disorderedness, and lack of grooves. Recent progresses in computational biology have led researchers to reconsider PPIs in drug discovery. AREAS COVERED: In this review, we introduce in-silico methods used to identify PPI interfaces and present an in-depth overview of various computational methodologies that are successfully applied to annotate the PPIs. We also discuss several successful case studies that use computational tools to understand PPIs modulation and their key roles in various physiological processes. EXPERT OPINION: Computational methods face challenges due to the inherent flexibility of proteins, which makes them expensive, and result in the use of rigid models. This problem becomes more significant in PPIs due to their flexible and flat interfaces. Computational methods like molecular dynamics (MD) simulation and machine learning can integrate the chemical structure data into biochemical and can be used for target identification and modulation. These computational methodologies have been crucial in understanding the structure of PPIs, designing PPI modulators, discovering new drug targets, and predicting treatment outcomes.

Bee-safe peptidomimetic acaricides achieved by comparative genomics.

The devastating Varroa mite (Varroa destructor Anderson and Trueman) is an obligatory ectoparasite of the honey bee, contributing to significant colony losses in North America and throughout the world. The limited number of conventional acaricides to reduce Varroa mites and prevent disease in honey bee colonies is challenged with wide-spread resistance and low target-site selectivity. Here, we propose a biorational approach using comparative genomics for the development of honey bee-safe and selective acaricides targeting the Varroa mite-specific neuropeptidergic system regulated by proctolin, which is lacking in the honey bee. Proctolin is a highly conserved pentapeptide RYLPT (Arg-Tyr-Leu-Pro-Thr) known to act through a G protein-coupled receptor to elicit myotropic activity in arthropod species. A total of 33 different peptidomimetic and peptide variants were tested on the Varroa mite proctolin receptor. Ligand docking model and mutagenesis studies revealed the importance of the core aromatic residue Tyr2 in the proctolin ligand. Peptidomimetics were observed to have significant oral toxicity leading to the paralysis and death of Varroa mites, while there were no negative effects observed for honey bees. We have demonstrated that a taxon-specific physiological target identified by advanced genomics information offers an opportunity to develop Varroa mite-selective acaricides, hence, expedited translational processes.

A high-performance genetically encoded fluorescent indicator for in vivo cAMP imaging.

cAMP is a key second messenger that regulates diverse cellular functions including neural plasticity. However, the spatiotemporal dynamics of intracellular cAMP in intact organisms are largely unknown due to low sensitivity and/or brightness of current genetically encoded fluorescent cAMP indicators. Here, we report the development of the new circularly permuted GFP (cpGFP)-based cAMP indicator G-Flamp1, which exhibits a large fluorescence increase (a maximum ΔF/F0 of 1100% in HEK293T cells), decent brightness, appropriate affinity (a Kd of 2.17 µM) and fast response kinetics (an association and dissociation half-time of 0.20 and 0.087 s, respectively). Furthermore, the crystal structure of the cAMP-bound G-Flamp1 reveals one linker connecting the cAMP-binding domain to cpGFP adopts a distorted ß-strand conformation that may serve as a fluorescence modulation switch. We demonstrate that G-Flamp1 enables sensitive monitoring of endogenous cAMP signals in brain regions that are implicated in learning and motor control in living organisms such as fruit flies and mice.

A Bright, Nontoxic, and Non-aggregating red Fluorescent Protein for Long-Term Labeling of Fine Structures in Neurons.

Red fluorescent proteins are useful as morphological markers in neurons, often complementing green fluorescent protein-based probes of neuronal activity. However, commonly used red fluorescent proteins show aggregation and toxicity in neurons or are dim. We report the engineering of a bright red fluorescent protein, Crimson, that enables long-term morphological labeling of neurons without aggregation or toxicity. Crimson is similar to mCherry and mKate2 in fluorescence spectra but is 100 and 28% greater in molecular brightness, respectively. We used a membrane-localized Crimson-CAAX to label thin neurites, dendritic spines and filopodia, enhancing detection of these small structures compared to cytosolic markers.

A review of epidemiology and public health control measures of COVID-19 variants in Hong Kong, December 2020 to June 2021.

Background: The emergence of coronavirus disease 2019 (COVID-19) variants posed considerable threats to the global public health. We reviewed the epidemiology of variant cases and control measures implemented in Hong Kong. Methods: The epidemiological characteristics and the temporal trend of the COVID-19 variant cases and local clusters in Hong Kong, and the corresponding public health control measures were reviewed. Results: Between December 2020 and June 2021, 393 variant cases were reported, including 153, 59 and 79 cases of Alpha, Beta and Delta variants with no Gamma variant. The vast majority (378, 96.2%) were imported cases. Since early June 2021, Delta variant had taken over Alpha as the dominant strain. Public health control measures, including risk-stratified quarantine and testing requirements for inbound travellers, banning of flights from extremely high-risk areas, enhanced contact tracing and quarantine, were implemented. Among the 3 clusters involving local transmissions, 2 were linked to imported cases while the source of the remaining one was unknown. Discussion: Amid the global surges of COVID-19 variants, Hong Kong had continued to limit and prevent the occurrence of community-wide outbreak. Ongoing control strategies should be constantly reviewed and adjusted in response to the global and local COVID-19 situation.

Co-circulation of two SARS-CoV-2 variant strains within imported pet hamsters in Hong Kong.

During the investigation of a pet shop outbreak of severe acute respiratory coronavirus 2 (SARS-CoV-2) with probable hamster-to-human transmission, the environmental and hamster samples in epidemiologically linked pet shops were found positive for SARS-CoV-2 Delta variant AY.127 strains which are phylogenetically closely related to patients and reported European strains. This interspecies' spill-over has triggered transmission in 58 patients epidemiologically linked to three pet shops. Incidentally, three dwarf hamsters imported from the Netherlands and centralized in a warehouse distributing animals to pet shops were positive for SARS-CoV-2 spike variant phylogenetically related to European B.1.258 strains from March 2020. This B.1.258 strain almost disappeared in July 2021. While no hamster-to-human transmission of B.1.258-like strain was found in this outbreak, molecular docking showed that its spike receptor-binding domain (RBD) has a similar binding energy to human ACE2 compared to that of Delta variant AY.127. Therefore, the potential of this B.1.258-related spike variant for interspecies jumping cannot be ignored. The co-circulation of B.1.258-related spike variants with Delta AY.127, which originated in Europe and was not previously found in Hong Kong, suggested that hamsters in our wholesale warehouse and retail pet shops more likely have acquired these viruses in the Netherlands or stopovers during delivery by aviation than locally. The risk of human-to-hamster reverse zoonosis by multiple SARS-CoV-2 variants leading to further adaptive spike mutations with subsequent transmission back to humans cannot be underestimated as an outbreak source of COVID-19. Testing imported pet animals susceptible to SARS-CoV-2 is warranted to prevent future outbreaks.

An Open Drug Discovery Competition: Experimental Validation of Predictive Models in a Series of Novel Antimalarials.

The Open Source Malaria (OSM) consortium is developing compounds that kill the human malaria parasite, Plasmodium falciparum, by targeting PfATP4, an essential ion pump on the parasite surface. The structure of PfATP4 has not been determined. Here, we describe a public competition created to develop a predictive model for the identification of PfATP4 inhibitors, thereby reducing project costs associated with the synthesis of inactive compounds. Competition participants could see all entries as they were submitted. In the final round, featuring private sector entrants specializing in machine learning methods, the best-performing models were used to predict novel inhibitors, of which several were synthesized and evaluated against the parasite. Half possessed biological activity, with one featuring a motif that the human chemists familiar with this series would have dismissed as "ill-advised". Since all data and participant interactions remain in the public domain, this research project "lives" and may be improved by others.

Locating ligand binding sites in G-protein coupled receptors using combined information from docking and sequence conservation.

GPCRs (G-protein coupled receptors) are the largest family of drug targets and share a conserved structure. Binding sites are unknown for many important GPCR ligands due to the difficulties of GPCR recombinant expression, biochemistry, and crystallography. We describe our approach, ConDockSite, for predicting ligand binding sites in class A GPCRs using combined information from surface conservation and docking, starting from crystal structures or homology models. We demonstrate the effectiveness of ConDockSite on crystallized class A GPCRs such as the beta2 adrenergic and A2A adenosine receptors. We also demonstrate that ConDockSite successfully predicts ligand binding sites from high-quality homology models. Finally, we apply ConDockSite to predict the ligand binding sites on a structurally uncharacterized GPCR, GPER, the G-protein coupled estrogen receptor. Most of the sites predicted by ConDockSite match those found in other independent modeling studies. ConDockSite predicts that four ligands bind to a common location on GPER at a site deep in the receptor cleft. Incorporating sequence conservation information in ConDockSite overcomes errors introduced from physics-based scoring functions and homology modeling.

Mechanisms of allosteric and mixed mode aromatase inhibitors.

Aromatase (CYP19) catalyzes the last biosynthetic step of estrogens in mammals and is a primary drug target for hormone-related breast cancer. However, treatment with aromatase inhibitors is often associated with adverse effects and drug resistance. In this study, we used virtual screening targeting a predicted cytochrome P450 reductase binding site on aromatase to discover four novel non-steroidal aromatase inhibitors. The inhibitors have potencies comparable to the noncompetitive tamoxifen metabolite, endoxifen. Our two most potent inhibitors, AR11 and AR13, exhibit both mixed-type and competitive-type inhibition. The cytochrome P450 reductase-CYP19 coupling interface likely acts as a transient binding site. Our modeling shows that our inhibitors bind better at different sites near the catalytic site. Our results predict the location of multiple ligand binding sites on aromatase. The combination of modeling and experimental results supports the important role of the reductase binding interface as a low affinity, promiscuous ligand binding site. Our new inhibitors may be useful as alternative chemical scaffolds that may show different adverse effects profiles than current clinically used aromatase inhibitors.

Structural studies of full-length receptor tyrosine kinases and their implications for drug design.

Receptor tyrosine kinases (RTKs) are important drug targets for cancer and immunological disorders. Crystal structures of individual RTK domains have contributed greatly to the structure-based drug design of clinically used drugs. Low-resolution structures from electron microscopy are now available for the RTKs, EGFR, PDGFR, and Kit. However, there are still no high-resolution structures of full-length RTKs due to the technical challenges of working with these complex, membrane proteins. Here, we review what has been learned from structural studies of these three RTKs regarding their mechanisms of ligand binding, activation, oligomerization, and inhibition. We discuss the implications for drug design. More structural data on full-length RTKs may facilitate the discovery of druggable sites and drugs with improved specificity and effectiveness against resistant mutants.

RORγ Structural Plasticity and Druggability.

Retinoic acid receptor-related orphan receptor γ (RORγ) is a transcription factor regulating the expression of the pro-inflammatory cytokine IL-17 in human T helper 17 (Th17) cells. Activating RORγ can induce multiple IL-17-mediated autoimmune diseases but may also be useful for anticancer therapy. Its deep immunological functions make RORɣ an attractive drug target. Over 100 crystal structures have been published describing atomic interactions between RORɣ and agonists and inverse agonists. In this review, we focus on the role of dynamic properties and plasticity of the RORɣ orthosteric and allosteric binding sites by examining structural information from crystal structures and simulated models. We discuss the possible influences of allosteric ligands on the orthosteric binding site. We find that high structural plasticity favors the druggability of RORɣ, especially for allosteric ligands.

Decoding allosteric communication pathways in protein lysine acetyltransferase.

In bacteria, protein lysine acetylation circuits can control core processes such as carbon metabolism. In E. coli, cyclic adenosine monophosphate (cAMP) controls the transcription level and activity of protein lysine acetyltransferase (PAT). The M. tuberculosis PatA (Mt-PatA) resides in two different conformations; the activated state and autoinhibited state. However, the mechanism of cAMP allosteric regulation of Mt-PatA remains mysterious. Here, we performed extensive all-atom molecular dynamics (MD) simulations (three independent run for each system) and built a residue-residue dynamic correlation network to show how cAMP mediates allosteric activation. cAMP binds at the regulatory site in the regulatory domain, which is 32 Å away from the catalytic site. An extensive conformational restructuring relieves autoinhibition caused by a molecular Lid (residues 161-203) that shelters the substrate-binding surface. In the activated state, the regulatory domain rotates (~40°) around Ser144, which links both domains. Rotation removes the C-terminus from the cAMP site and relieves the autoinhibited state. Also, the molecular Lid refolds and creates an activator binding site. A conserved residue, His173, was mutated into Lys in the Lid, and during an MD trajectory of the activated state, positioned itself near an acetyl donor molecule in the catalytic domain, suggesting a direct mechanism for acetylation. This study describes the allosteric framework for Mt-PatA and prerequisite intermediate states that permit long-distance signal transmission.

Deep neural network affinity model for BACE inhibitors in D3R Grand Challenge 4.

Drug Design Data Resource (D3R) Grand Challenge 4 (GC4) offered a unique opportunity for designing and testing novel methodology for accurate docking and affinity prediction of ligands in an open and blinded manner. We participated in the beta-secretase 1 (BACE) Subchallenge which is comprised of cross-docking and redocking of 20 macrocyclic ligands to BACE and predicting binding affinity for 154 macrocyclic ligands. For this challenge, we developed machine learning models trained specifically on BACE. We developed a deep neural network (DNN) model that used a combination of both structure and ligand-based features that outperformed simpler machine learning models. According to the results released by D3R, we achieved a Spearman's rank correlation coefficient of 0.43(7) for predicting the affinity of 154 ligands. We describe the formulation of our machine learning strategy in detail. We compared the performance of DNN with linear regression, random forest, and support vector machines using ligand-based, structure-based, and combining both ligand and structure-based features. We compared different structures for our DNN and found that performance was highly dependent on fine optimization of the L2 regularization hyperparameter, alpha. We also developed a novel metric of ligand three-dimensional similarity inspired by crystallographic difference density maps to match ligands without crystal structures to similar ligands with known crystal structures. This report demonstrates that detailed parameterization, careful data training and implementation, and extensive feature analysis are necessary to obtain strong performance with more complex machine learning methods. Post hoc analysis shows that scoring functions based only on ligand features are competitive with those also using structural features. Our DNN approach tied for fifth in predicting BACE-ligand binding affinities.

In-silico design of peptide inhibitors of K-Ras target in cancer disease.

Cancer is a leading cause of death, over one million individuals analyzed, and around 500,000 deaths happen due to cancer every year alone in the United States. The Ras is a significant protein in the signaling transduction pathways and has a leading role in cell proliferation. Above 30% of all human tumors arises due to the mutations in genes that encode a Ras protein that operate signaling cascades necessary for malignant transformation, tumor angiogenesis, and metastasis. The Ras gene family comprised of 36 total genes in human. The N-Ras, K-Ras, and H-Ras are accounted for to assume noticeable function in human cancer. The mutation in K-Ras protein is most commonly found in tumors. K-Ras is the most crucial driver in lung and pancreatic cancers. Among the mutations of N-Ras, H-Ras, and K-Ras, the mutant K-Ras is the most prevalent target for the development of Lungs, colon, and pancreatic cancers. The study aimed to develop the peptide inhibitors of the K-Ras G12D. The crystal structure of the mutant K-Ras/R11.1.6 G12D complex was retrieved from the protein databank. The protein R11.1.6 directly blocks interaction with Raf and diminishes signaling through the Raf-MEK-ERK signaling pathway. Here, in this study, we designed novel peptides from the truncated reference peptide (R11.1.6) through residue scan methodology. The top ten designed peptides (based on binding free energies) were subjected to molecular dynamics simulations using AMBER to evaluate stability. Our results indicate that the top ten selected peptides have strong interactions with K-Ras than the reference peptide (R11.1.6) and have the potency to prevent the binding of Raf and K-Ras.Communicated by Ramaswamy H. Sarma.

G Protein-Coupled Estrogen Receptor Production Using an Escherichia coli Cell-Free Expression System.

Heterologous expression of the G protein-coupled estrogen receptor (GPER) comes with a suite of challenges intrinsic to membrane proteins. This receptor's low expression levels and tendency to form insoluble aggregates in Escherichia coli and yeast make it a difficult receptor-target to study. In this unit, we detail steps to produce monomeric GPER using a precipitation-based cell-free system. We provide information on the DNA construct for expression, the pipetting scheme for the reaction supplements to generate a master mix, and the cell-free reaction setup. In the last portion of this unit, we outline steps for solubilization and purification, and we provide a viable method for qualitatively observing functionality by liquid chromatography-mass spectrometry detection. © 2019 by John Wiley & Sons, Inc.