Sensitive Electrochemical Detection of Ammonia Nitrogen via a Platinum-Zinc Alloy Nanoflower-Modified Carbon Cloth Electrode.

As a common water pollutant, ammonia nitrogen poses a serious risk to human health and the ecological environment. Therefore, it is important to develop a simple and efficient sensing scheme to achieve accurate detection of ammonia nitrogen. Here, we report a simple fabrication electrode for the electrochemical synthesis of platinum-zinc alloy nanoflowers (PtZn NFs) on the surface of carbon cloth. The obtained PtZn NFs/CC electrode was applied to the electrochemical detection of ammonia nitrogen by differential pulse voltammetry (DPV). The enhanced electrocatalytic activity of PtZn NFs and the larger electrochemical active area of the self-supported PtZn NFs/CC electrode are conducive to improving the ammonia nitrogen detection performance of the sensitive electrode. Under optimized conditions, the PtZn NFs/CC electrode exhibits excellent electrochemical performance with a wide linear range from 1 to 1000 µM, a sensitivity of 21.5 µA µM-1 (from 1 µM to 100 µM) and a lower detection limit of 27.81 nM, respectively. PtZn NFs/CC electrodes show excellent stability and anti-interference. In addition, the fabricated electrochemical sensor can be used to detect ammonia nitrogen in tap water and lake water samples.

Single walled carbon nanotubes band gap width measurement and the influence of nitrogen doping research.

The adjustment and measurement of the band gap width of single-walled carbon nanotubes are crucial for optimizing the design and enhancing the performance of carbon-based devices. This study utilizes the relationship between the band gap and temperature of semiconductor-based carbon nanotubes. The electrical conductivity of carbon nanotubes was obtained at various temperatures, and the corresponding band gap width (0.57 eV) was determined. The introduction of nitrogen results in a reduction of the band gap width and an increase in current flow between the device source and drain electrodes. Theoretical calculation demonstrated that nitrogen doping not only increases the conductivity of carbon nanotubes but also effectively inhibits the Schottky barrier between carbon nanotubes and metal electrodes. The Schottky barrier and the internal electric field can be effectively modulated via nitrogen doping in carbon nanotubes, which enhances the performance of carbon-based devices.

Discovery of ZFD-10 of a pyridazino[4,5-b]indol-4(5H)-one derivative as an anti-ZIKV agent and a ZIKV NS5 RdRp inhibitor.

Zika virus (ZIKV) infection is associated with the birth defect microcephaly and Guillain-Barré syndrome in adults. There is no approved vaccine or specific antiviral agent against ZIKV. ZFD-10, a novel structural skeleton of 1H-pyridazino[4,5-b]indol-4(5H)-one, was firstly synthesized and discovered to be a potent anti-ZIKV inhibitor with very low cytotoxicity. ZFD-10's anti-ZIKV potency is independent of cell lines and ZFD-10 mainly targets the post-entry stages of ZIKV life cycle. Time-of-addition and time-of-withdrawal assays showed that 10 µM ZFD-10 displayed the ability to decrease mainly at the RNA level and weakly the viral progeny particle load. Furthermore, ZFD-10 could protect ZIKV NS5 from thermal unfolding and aggregation and increase the Tagg value of ZIKV NS5 protein from 44.6 to 49.3 °C, while ZFD-10 dose-dependently inhibits ZIKV NS5 RdRp activity using in vitro RNA polymerase assays. Molecular docking study suggests that ZFD-10 affects RdRp enzymatic function through interfering with the fingers and thumb subdomains. These results supported that ZFD-10's cell-based anti-ZIKV activity is related to its anti-RdRp activity of ZIKV NS5. The in vivo anti-ZIKV study shows that the middle-dose (4.77 mg/kg/d) of ZFD-10 protected mice from ZIKV infection and the viral loads of the blood, liver, kidney and brain in the middle-dose and high-dose (9.54 mg/kg/d) were significantly reduced compared to those of the ZIKV control. These results confirm that ZFD-10 has a certain antiviral effect against ZIKV infection in vivo.

Cyclic peptide structure prediction and design using AlphaFold.

Deep learning networks offer considerable opportunities for accurate structure prediction and design of biomolecules. While cyclic peptides have gained significant traction as a therapeutic modality, developing deep learning methods for designing such peptides has been slow, mostly due to the small number of available structures for molecules in this size range. Here, we report approaches to modify the AlphaFold network for accurate structure prediction and design of cyclic peptides. Our results show this approach can accurately predict the structures of native cyclic peptides from a single sequence, with 36 out of 49 cases predicted with high confidence (pLDDT > 0.85) matching the native structure with root mean squared deviation (RMSD) less than 1.5 Å. Further extending our approach, we describe computational methods for designing sequences of peptide backbones generated by other backbone sampling methods and for de novo design of new macrocyclic peptides. We extensively sampled the structural diversity of cyclic peptides between 7-13 amino acids, and identified around 10,000 unique design candidates predicted to fold into the designed structures with high confidence. X-ray crystal structures for seven sequences with diverse sizes and structures designed by our approach match very closely with the design models (root mean squared deviation < 1.0 Å), highlighting the atomic level accuracy in our approach. The computational methods and scaffolds developed here provide the basis for custom-designing peptides for targeted therapeutic applications.

Automatic and accurate ligand structure determination guided by cryo-electron microscopy maps.

Advances in cryo-electron microscopy (cryoEM) and deep-learning guided protein structure prediction have expedited structural studies of protein complexes. However, methods for accurately determining ligand conformations are lacking. In this manuscript, we develop EMERALD, a tool for automatically determining ligand structures guided by medium-resolution cryoEM density. We show this method is robust at predicting ligands along with surrounding side chains in maps as low as 4.5 Å local resolution. Combining this with a measure of placement confidence and running on all protein/ligand structures in the EMDB, we show that 57% of ligands replicate the deposited model, 16% confidently find alternate conformations, 22% have ambiguous density where multiple conformations might be present, and 5% are incorrectly placed. For five cases where our approach finds an alternate conformation with high confidence, high-resolution crystal structures validate our placement. EMERALD and the resulting analysis should prove critical in using cryoEM to solve protein-ligand complexes.

Antiviral effects of the fused tricyclic derivatives of indoline and imidazolidinone on ZIKV infection and RdRp activities of ZIKV and DENV.

The prevalence and ravages of Zika virus (ZIKV) seriously endanger human health, especially causing significant neurological defects in both neonates as pediatric microcephaly and adults as Guillain-Barré syndrome. In this work, we studied anti-ZIKV effects of the fused tricyclic derivatives of indoline and imidazolidinone and discovered that some of them are valuable leads for drug discovery of anti-ZIKV agents. The current results show that certain compounds are broad-spectrum inhibitors of ZIKV- and dengue virus (DENV)-infection while distinctive compounds are selective ZIKV inhibitors or selective DENV inhibitors. Compounds of 12, 17 and 28 are more active against Asian ZIKV SZ-VIV01 strain than African ZIKV MR766 strain. It is valued that silylation makes six TBS compounds of 4-nitrophenyl hydrazine series and phenyl hydrazine series more active against ZIKV infection than their phenols. Time-of-addition and withdrawal studies indicate that compound 12 majorly acts on post-infection of RNA synthesis stage of ZIKV life cycle. Moreover, compounds of 12, 17 and 18 are anti-ZIKV agents with the inhibitory activities to ZIKV NS5 RdRp while 12 doesn't inhibit DENV infection even though it is a DENV RdRp inhibitor, 17 is an active agent against DENV infection but is only a weak DENV NS5 RdRp inhibitor, and 28 is inactive against DENV infection and not a DENV NS5 RdRp inhibitor. As a result, a compound's antiviral difference between ZIKV and DENV is not always related to anti-RdRp difference between ZIKV RdRp and DENV RdRp, and structural features of a compound play important roles in executing antiviral and anti-RdRp functions. Further discovery of highly potent broad-spectrum or selective agents against infection by ZIKV and DENV will be facilitated.

Inhibiting cardiac autophagy suppresses Zika virus replication.

Zika Virus (ZIKV) infection is a global threat. Other than the congenital neurological disorders it causes, ZIKV infection has been reported to induce cardiac complications. However, the precise treatment plans are unclear. Thus, illustrating the pathogenic mechanism of ZIKV in the heart is critical to providing effective prevention and treatment of ZIKV infection. The mechanism of autophagy has been reported recently in Dengue virus infection. Whether or not autophagy participates in ZIKV infection and its role remains unrevealed. This study successfully established the in vitro cardiomyocytes and in vivo mouse models of ZIKV infection to investigate the involvement of autophagy in ZIKV infection. The results showed that ZIKV infection is both time and gradient-dependent. The key autophagy protein, LC3B, increased remarkably after ZIKV infection. Meanwhile, autophagic flux was detected by immunofluorescence. Applying autophagy inhibitors decreased the LC3B levels. Furthermore, the number of viral copies was quantified to evaluate the influence of autophagy during infection. We found that autophagy was actively involved in the ZIKV infection and the inhibition of autophagy could effectively reduce the viral copies, suggesting a potential intervention strategy for reducing ZIKV infection and the undesired complications caused by ZIKV.

Identification of 6ω-cyclohexyl-2-(phenylamino carbonylmethylthio)pyrimidin-4(3H)-ones targeting the ZIKV NS5 RNA dependent RNA polymerase.

Zika virus (ZIKV), a mosquito-borne flavivirus, is a global health concern because of its association with severe neurological disorders such as neonatal microcephaly and adult Guillain-Barre syndrome. Although many efforts have been made to combat ZIKV infection, there is currently no approved vaccines or antiviral drugs available and there is an urgent need to develop effective anti-ZIKV agents. In this study, 26 acetylarylamine-S-DACOs derivatives were prepared, and eight of them were found to have inhibitory activity against Zika virus. Among these substances, 2-[(4-cyclohexyl-5-ethyl-6-oxo-1,6-dihydropyrimidin-2-yl)thio]-N-(3,5-difluorophenyl)acetamide (4w) with the best anti-ZIKV activity was selected for in-depth study of antiviral activity and mechanism of action. Here, we discovered 4w targeted on the ZIKV NS5 RNA -dependent RNA polymerase (RdRp), which exhibited good in vitro antiviral activity without cell species specificity, both at the protein level and at the RNA level can significantly inhibit ZIKV replication. Preliminary molecular docking studies showed that 4w preferentially binds to the palm region of NS5A RdRp through hydrogen bonding with residues such as LYS468, PHE466, GLU465, and GLY467. ZIKV NS5 RdRp enzyme activity experiment showed that 4w could directly inhibit ZIKV RdRp activity with EC50 = 11.38 ± 0.51 µM. In antiviral activity studies, 4w was found to inhibit ZIKV RNA replication with EC50 = 6.87 ± 1.21 µM. ZIKV-induced plaque formation was inhibited with EC50 = 7.65 ± 0.31 µM. In conclusion, our study disclosed that acetylarylamine-S-DACOs is a new active scaffolds against ZIKV, among which compound 4w was proved to be a potent novel anti-ZIKV compound target ZIKV RdRp protein. These promising results provide a future prospective for the development of ZIKV RdRp inhibitors.

Discovery of dehydroandrographolide derivatives with C19 hindered ether as potent anti-ZIKV agents with inhibitory activities to MTase of ZIKV NS5.

Infection by Zika virus (ZIKV), a mosquito-transmitted arbovirus and a member of Flavivirus, could make pediatric microcephaly and Guillain-Barré syndrome, which remains an ongoing global threat. The efficient antivirals to ZIKV infection are of great medical need. Andrographolide and its analogues were discovered to be active against flaviviral infection. In this study, we discovered some dehydroandrographolide derivatives of 3-oximido- or 3-alcohol-19-hindered ether to be potent anti-ZIKV agents with low cytotoxicities (CC50 > 200 µM). Time of addition assay suggests that compound 5a and its analogues act on inhibition of post-entry stage of ZIKV life cycle. It is discovered by experimental and molecular docking studies that active anti-ZIKV compounds of 3a, 5a, 5b and 5c possess inhibitory activities of ZIKV NS5 MTase (methyl transferase) enzymatic activity. Preliminary SAR reveals that C19-modification with bulky groups is necessary for anti-ZIKV infection and replication, anti-ZIKV activity of 5a comes from itself bearing hindered trityl ether but not from its instability, the backbone of dehydroandrographolide is more effective against ZIKV infection than that of andrographolide, and 3-oxime derivatives are more active against ZIKV infection than 3-alcohol derivatives. To our knowledge, 5a is the first reported MTase inhibitor of andrographolide derivatives. More importantly, discovery of active compound 5b with acid-stable 19-OCHPh2 against ZIKV infection is valued and gives us a clue to design and discover generally acid-stable anti-ZIKV agents.

Design, synthesis, discovery and SAR of the fused tricyclic derivatives of indoline and imidazolidinone against DENV replication and infection.

Dengue virus, belonging to a genus Flavivirus, caused public health problem in recent years. One controversial vaccine of DENV was approved and there is no antiviral for the clinic treatment of DENV, therefore, efficient antivirals to DENV are of great medical significance. In this study, we conducted the design, synthesis, cell-based and target-based activity evaluation of 28 compounds based on indoline structural skeleton against DENV infection. Among them, 13 active compounds against DENV infection were discovered and their structure-activity relationship (SAR) was summarized. In this study, indoline carbohydrazine has derived more active compounds than indoline carboamide. It is discovered that TBS group exhibits a good pharmacophore to enhance anti-DENV activity. Further exploration indicated that post-treatment acts as effective time of addition and compound 15 targeting the post-entry stages of DENV2 viral life cycle. SPR imaging results support there are strong interaction of 13 and 15 with RdRp and compounds 13 and 15 reduce RdRp enzymatic activity, revealing that RdRp of DENV NS5 is the drug target for these series of compounds. Molecular docking deciphered the relationship of the structural feature with the putative binding mode by 13 and 15 with RdRp domain of DENV2 NS5 by hydrogen bonds and hydrophobic interactions to establish the fitted low energy conformation. Future studies will focus on designing more potent inhibitors for the treatment and prevention of dengue virus replication and infection, and understanding the more profound underlying structural features of inhibitors and drug action of the mechanism.

Force Field Optimization Guided by Small Molecule Crystal Lattice Data Enables Consistent Sub-Angstrom Protein-Ligand Docking.

Accurate and rapid calculation of protein-small molecule interaction free energies is critical for computational drug discovery. Because of the large chemical space spanned by drug-like molecules, classical force fields contain thousands of parameters describing atom-pair distance and torsional preferences; each parameter is typically optimized independently on simple representative molecules. Here, we describe a new approach in which small molecule force field parameters are jointly optimized guided by the rich source of information contained within thousands of available small molecule crystal structures. We optimize parameters by requiring that the experimentally determined molecular lattice arrangements have lower energy than all alternative lattice arrangements. Thousands of independent crystal lattice-prediction simulations were run on each of 1386 small molecule crystal structures, and energy function parameters of an implicit solvent energy model were optimized, so native crystal lattice arrangements had the lowest energy. The resulting energy model was implemented in Rosetta, together with a rapid genetic algorithm docking method employing grid-based scoring and receptor flexibility. The success rate of bound structure recapitulation in cross-docking on 1112 complexes was improved by more than 10% over previously published methods, with solutions within <1 Å in over half of the cases. Our results demonstrate that small molecule crystal structures are a rich source of information for guiding molecular force field development, and the improved Rosetta energy function should increase accuracy in a wide range of small molecule structure prediction and design studies.

Fluorinated Aromatic Monomers as Building Blocks To Control α-Peptoid Conformation and Structure.

Peptoids are peptidomimetics of interest in the fields of drug development and biomaterials. However, obtaining stable secondary structures is challenging, and designing these requires effective control of the peptoid tertiary amide cis/trans equilibrium. Herein, we report new fluorine-containing aromatic monomers that can control peptoid conformation. Specifically, we demonstrate that a fluoro-pyridine group can be used to circumvent the need for monomer chirality to control the cis/trans equilibrium. We also show that incorporation of a trifluoro-methyl group ( NCF3Rpe) rather than a methyl group ( NRpe) at the α-carbon of a monomer gives rise to a 5-fold increase in cis-isomer preference.

Elucidating the inhibition of peptidoglycan biosynthesis in Staphylococcus aureus by albocycline, a macrolactone isolated from Streptomyces maizeus.

Antibiotic resistance is a serious threat to global public health, and methicillin-resistant Staphylococcus aureus (MRSA) is a poignant example. The macrolactone natural product albocycline, derived from various Streptomyces strains, was recently identified as a promising antibiotic candidate for the treatment of both MRSA and vancomycin-resistant S. aureus (VRSA), which is another clinically relevant and antibiotic resistant strain. Moreover, it was hypothesized that albocycline's antimicrobial activity was derived from the inhibition of peptidoglycan (i.e., bacterial cell wall) biosynthesis. Herein, preliminary mechanistic studies are performed to test the hypothesis that albocycline inhibits MurA, the enzyme that catalyzes the first step of peptidoglycan biosynthesis, using a combination of biological assays alongside molecular modeling and simulation studies. Computational modeling suggests albocycline exists as two conformations in solution, and computational docking of these conformations to an ensemble of simulated receptor structures correctly predicted preferential binding to S. aureus MurA-the enzyme that catalyzes the first step of peptidoglycan biosynthesis-over Escherichia coli (E. coli) MurA. Albocycline isolated from the producing organism (Streptomyces maizeus) weakly inhibited S. aureus MurA (IC50 of 480 µM) but did not inhibit E. coli MurA. The antimicrobial activity of albocycline against resistant S. aureus strains was superior to that of vancomycin, preferentially inhibiting Gram-positive organisms. Albocycline was not toxic to human HepG2 cells in MTT assays. While these studies demonstrate that albocycline is a promising lead candidate against resistant S. aureus, taken together they suggest that MurA is not the primary target, and further work is necessary to identify the major biological target.

Bridging Microscopic and Macroscopic Mechanisms of p53-MDM2 Binding with Kinetic Network Models.

Under normal cellular conditions, the tumor suppressor protein p53 is kept at low levels in part due to ubiquitination by MDM2, a process initiated by binding of MDM2 to the intrinsically disordered transactivation domain (TAD) of p53. Many experimental and simulation studies suggest that disordered domains such as p53 TAD bind their targets nonspecifically before folding to a tightly associated conformation, but the microscopic details are unclear. Toward a detailed prediction of binding mechanisms, pathways, and rates, we have performed large-scale unbiased all-atom simulations of p53-MDM2 binding. Markov state models (MSMs) constructed from the trajectory data predict p53 TAD binding pathways and on-rates in good agreement with experiment. The MSM reveals that two key bound intermediates, each with a nonnative arrangement of hydrophobic residues in the MDM2 binding cleft, control the overall on-rate. Using microscopic rate information from the MSM, we parameterize a simple four-state kinetic model to 1) determine that induced-fit pathways dominate the binding flux over a large range of concentrations, and 2) predict how modulation of residual p53 helicity affects binding, in good agreement with experiment. These results suggest new ways in which microscopic models of peptide binding, coupled with simple few-state binding flux models, can be used to understand biological function in physiological contexts.

Diverted Total Synthesis of Carolacton-Inspired Analogs Yields Three Distinct Phenotypes in Streptococcus mutans Biofilms.

The oral microbiome is a dynamic environment inhabited by both commensals and pathogens. Among these is Streptococcus mutans, the causative agent of dental caries, the most prevalent childhood disease. Carolacton has remarkably specific activity against S. mutans, causing acid-mediated cell death during biofilm formation; however, its complex structure limits its utility. Herein, we report the diverted total synthesis and biological evaluation of a rationally designed library of simplified analogs that unveiled three unique biofilm phenotypes further validating the role of natural product synthesis in the discovery of new biological phenomena.

A Maximum-Caliber Approach to Predicting Perturbed Folding Kinetics Due to Mutations.

We present a maximum-caliber method for inferring transition rates of a Markov state model (MSM) with perturbed equilibrium populations given estimates of state populations and rates for an unperturbed MSM. It is similar in spirit to previous approaches, but given the inclusion of prior information, it is more robust and simple to implement. We examine its performance in simple biased diffusion models of kinetics and then apply the method to predicting changes in folding rates for several highly nontrivial protein folding systems for which non-native interactions play a significant role, including (1) tryptophan variants of the GB1 hairpin, (2) salt-bridge mutations of the Fs peptide helix, and (3) MSMs built from ultralong folding trajectories of FiP35 and GTT variants of the WW domain. In all cases, the method correctly predicts changes in folding rates, suggesting the wide applicability of maximum-caliber approaches to efficiently predict how mutations perturb protein conformational dynamics.

Using Kinetic Network Models To Probe Non-Native Salt-Bridge Effects on α-Helix Folding.

Salt-bridge interactions play an important role in stabilizing many protein structures, and have been shown to be designable features for protein design. In this work, we study the effects of non-native salt bridges on the folding of a soluble alanine-based peptide (Fs peptide) using extensive all-atom molecular dynamics simulations performed on the Folding@home distributed computing platform. Using Markov State Models, we show how non-native salt-bridges affect the folding kinetics of Fs peptide by perturbing specific conformational states. Furthermore, we present methods for the automatic detection and analysis of such states. These results provide insight into helix folding mechanisms and useful information to guide simulation-based computational protein design.

Insights into Peptoid Helix Folding Cooperativity from an Improved Backbone Potential.

Peptoids (N-substituted oligoglycines) are biomimetic polymers that can fold into a variety of unique structural scaffolds. Peptoid helices, which result from the incorporation of bulky chiral side chains, are a key peptoid structural motif whose formation has not yet been accurately modeled in molecular simulations. Here, we report that a simple modification of the backbone φ-angle potential in GAFF is able to produce well-folded cis-amide helices of (S)-N-(1-phenylethyl)glycine (Nspe), consistent with experiment. We validate our results against both QM calculations and NMR experiments. For this latter task, we make quantitative comparisons to sparse NOE data using the Bayesian Inference of Conformational Populations (BICePs) algorithm, a method we have recently developed for this purpose. We then performed extensive REMD simulations of Nspe oligomers as a function of chain length and temperature to probe the molecular forces driving cooperative helix formation. Analysis of simulation data by Lifson-Roig helix-coil theory show that the modified potential predicts much more cooperative folding for Nspe helices. Unlike peptides, per-residue entropy changes for helix nucleation and extension are mostly positive, suggesting that steric bulk provides the main driving force for folding. We expect these results to inform future work aimed at predicting and designing peptoid peptidomimetics and tertiary assemblies of peptoid helices.

Bayesian inference of conformational state populations from computational models and sparse experimental observables.

We present a Bayesian inference approach to estimating conformational state populations from a combination of molecular modeling and sparse experimental data. Unlike alternative approaches, our method is designed for use with small molecules and emphasizes high-resolution structural models, using inferential structure determination with reference potentials, and Markov Chain Monte Carlo to sample the posterior distribution of conformational states. As an application of the method, we determine solution-state conformational populations of the 14-membered macrocycle cineromycin B, using a combination of previously published sparse Nuclear Magnetic Resonance (NMR) observables and replica-exchange molecular dynamic/Quantum Mechanical (QM)-refined conformational ensembles. Our results agree better with experimental data compared to previous modeling efforts. Bayes factors are calculated to quantify the consistency of computational modeling with experiment, and the relative importance of reference potentials and other model parameters.

Surprisal Metrics for Quantifying Perturbed Conformational Dynamics in Markov State Models.

Markov state models (MSMs), which model conformational dynamics as a network of transitions between metastable states, have been increasingly used to model the thermodynamics and kinetics of biomolecules. In considering perturbations to molecular dynamics induced by sequence mutations, chemical modifications, or changes in external conditions, it is important to assess how transition rates change, independent of changes in metastable state definitions. Here, we present a surprisal metric to quantify the difference in metastable state transitions for two closely related MSMs, taking into account the statistical uncertainty in observed transition counts. We show that the surprisal is a relative entropy metric closely related to the Jensen-Shannon divergence between two MSMs, which can be used to identify conformational states most affected by perturbations. As examples, we apply the surprisal metric to a two-dimensional lattice model of a protein hairpin with mutations to hydrophobic residues, all-atom simulations of the Fs peptide α-helix with a salt-bridge mutation, and a comparison of protein G ß-hairpin with its trpzip4 variant. Moreover, we show that surprisal-based adaptive sampling is an efficient strategy to reduce the statistical uncertainty in the Jensen-Shannon divergence, which could be a useful strategy for molecular simulation-based ab initio design.