A light-driven enzymatic enantioselective radical acylation.

Enzymes are recognized as exceptional catalysts for achieving high stereoselectivities1-3, but their ability to control the reactivity and stereoinduction of free radicals lags behind that of chemical catalysts4. Thiamine diphosphate (ThDP)-dependent enzymes5 are well-characterized systems that inspired the development of N-heterocyclic carbenes (NHCs)6-8 but have not yet been proved viable in asymmetric radical transformations. There is a lack of a biocompatible and general radical-generation mechanism, as nature prefers to avoid radicals that may be harmful to biological systems9. Here we repurpose a ThDP-dependent lyase as a stereoselective radical acyl transferase (RAT) through protein engineering and combination with organophotoredox catalysis10. Enzyme-bound ThDP-derived ketyl radicals are selectively generated through single-electron oxidation by a photoexcited organic dye and then cross-coupled with prochiral alkyl radicals with high enantioselectivity. Diverse chiral ketones are prepared from aldehydes and redox-active esters (35 examples, up to 97% enantiomeric excess (e.e.)) by this method. Mechanistic studies reveal that this previously elusive dual-enzyme catalysis/photocatalysis directs radicals with the unique ThDP cofactor and evolvable active site. This work not only expands the repertoire of biocatalysis but also provides a unique strategy for controlling radicals with enzymes, complementing existing chemical tools.

Photoenzymatic enantioselective intermolecular radical hydroalkylation.

Enzymes are increasingly explored for use in asymmetric synthesis1-3, but their applications are generally limited by the reactions available to naturally occurring enzymes. Recently, interest in photocatalysis4 has spurred the discovery of novel reactivity from known enzymes5. However, so far photoinduced enzymatic catalysis6 has not been used for the cross-coupling of two molecules. For example, the intermolecular coupling of alkenes with α-halo carbonyl compounds through a visible-light-induced radical hydroalkylation, which could provide access to important γ-chiral carbonyl compounds, has not yet been achieved by enzymes. The major challenges are the inherent poor photoreactivity of enzymes and the difficulty in achieving stereochemical control of the remote prochiral radical intermediate7. Here we report a visible-light-induced intermolecular radical hydroalkylation of terminal alkenes that does not occur naturally, catalysed by an 'ene' reductase using readily available α-halo carbonyl compounds as reactants. This method provides an efficient approach to the synthesis of various carbonyl compounds bearing a γ-stereocentre with excellent yields and enantioselectivities (up to 99 per cent yield with 99 per cent enantiomeric excess), which otherwise are difficult to access using chemocatalysis. Mechanistic studies suggest that the formation of the complex of the substrates (α-halo carbonyl compounds) and the 'ene' reductase triggers the enantioselective photoinduced radical reaction. Our work further expands the reactivity repertoire of biocatalytic, synthetically useful asymmetric transformations by the merger of photocatalysis and enzyme catalysis.

De novo protein fold design through sequence-independent fragment assembly simulations.

De novo protein design generally consists of two steps, including structure and sequence design. Many protein design studies have focused on sequence design with scaffolds adapted from native structures in the PDB, which renders novel areas of protein structure and function space unexplored. We developed FoldDesign to create novel protein folds from specific secondary structure (SS) assignments through sequence-independent replica-exchange Monte Carlo (REMC) simulations. The method was tested on 354 non-redundant topologies, where FoldDesign consistently created stable structural folds, while recapitulating on average 87.7% of the SS elements. Meanwhile, the FoldDesign scaffolds had well-formed structures with buried residues and solvent-exposed areas closely matching their native counterparts. Despite the high fidelity to the input SS restraints and local structural characteristics of native proteins, a large portion of the designed scaffolds possessed global folds completely different from natural proteins in the PDB, highlighting the ability of FoldDesign to explore novel areas of protein fold space. Detailed data analyses revealed that the major contributions to the successful structure design lay in the optimal energy force field, which contains a balanced set of SS packing terms, and REMC simulations, which were coupled with multiple auxiliary movements to efficiently search the conformational space. Additionally, the ability to recognize and assemble uncommon super-SS geometries, rather than the unique arrangement of common SS motifs, was the key to generating novel folds. These results demonstrate a strong potential to explore both structural and functional spaces through computational design simulations that natural proteins have not reached through evolution.

Decoding CRISPR-Cas PAM recognition with UniDesign.

The critical first step in Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR)-associated (CRISPR-Cas) protein-mediated gene editing is recognizing a preferred protospacer adjacent motif (PAM) on target DNAs by the protein's PAM-interacting amino acids (PIAAs). Thus, accurate computational modeling of PAM recognition is useful in assisting CRISPR-Cas engineering to relax or tighten PAM requirements for subsequent applications. Here, we describe a universal computational protein design framework (UniDesign) for designing protein-nucleic acid interactions. As a proof of concept, we applied UniDesign to decode the PAM-PIAA interactions for eight Cas9 and two Cas12a proteins. We show that, given native PIAAs, the UniDesign-predicted PAMs are largely identical to the natural PAMs of all Cas proteins. In turn, given natural PAMs, the computationally redesigned PIAA residues largely recapitulated the native PIAAs (74% and 86% in terms of identity and similarity, respectively). These results demonstrate that UniDesign faithfully captures the mutual preference between natural PAMs and native PIAAs, suggesting it is a useful tool for engineering CRISPR-Cas and other nucleic acid-interacting proteins. UniDesign is open-sourced at https://github.com/tommyhuangthu/UniDesign.

Computational redesign of cytochrome P450 CYP102A1 for highly stereoselective omeprazole hydroxylation by UniDesign.

Cytochrome P450 CYP102A1 is a prototypic biocatalyst that has great potential in chemical synthesis, drug discovery, and biotechnology. CYP102A1 variants engineered by directed evolution and/or rational design are capable of catalyzing the oxidation of a wide range of organic compounds. However, it is difficult to foresee the outcome of engineering CYP102A1 for a compound of interest. Here, we introduce UniDesign as a computational framework for enzyme design and engineering. We tested UniDesign by redesigning CYP102A1 for stereoselective metabolism of omeprazole (OMP), a proton pump inhibitor, starting from an active but nonstereoselective triple mutant (TM: A82F/F87V/L188Q). To shift stereoselectivity toward (R)-OMP, we computationally scanned three active site positions (75, 264, and 328) for mutations that would stabilize the binding of the transition state of (R)-OMP while destabilizing that of (S)-OMP and picked three variants, namely UD1 (TM/L75I), UD2 (TM/A264G), and UD3 (TM/A328V), for experimentation, based on computed energy scores and models. UD1, UD2, and UD3 exhibit high turnover rates of 55 ± 4.7, 84 ± 4.8, and 79 ± 5.7 min-1, respectively, for (R)-OMP hydroxylation, whereas the corresponding rates for (S)-OMP are only 2.2 ± 0.19, 6.0 ± 0.68, and 14 ± 2.8 min-1, yielding an enantiomeric excess value of 92, 87, and 70%, respectively. These results suggest the critical roles of L75I, A264G, and A328V in steering OMP in the optimal orientation for stereoselective oxidation and demonstrate the utility of UniDesign for engineering CYP102A1 to produce drug metabolites of interest. The results are discussed in the context of protein structures.

Modular Access to Chiral Amines via Imine Reductase-Based Photoenzymatic Catalysis.

The development of catalysts serves as the cornerstone of innovation in synthesis, as exemplified by the recent discovery of photoenzymes. However, the repertoire of naturally occurring enzymes repurposed by direct light excitation to catalyze new-to-nature photobiotransformations is currently limited to flavoproteins and keto-reductases. Herein, we shed light on imine reductases (IREDs) that catalyze the remote C(sp3)-C(sp3) bond formation, providing a previously elusive radical hydroalkylation of enamides for accessing chiral amines (45 examples with up to 99% enantiomeric excess). Beyond their natural function in catalyzing two-electron reductive amination reactions, upon direct visible-light excitation or in synergy with a synthetic photoredox catalyst, IREDs are repurposed to tune the non-natural photoinduced single-electron radical processes. By conducting wet mechanistic experiments and computational simulations, we unravel how engineered IREDs direct radical intermediates toward the productive and enantioselective pathway. This work represents a promising paradigm for harnessing nature's catalysts for new-to-nature asymmetric transformations that remain challenging through traditional chemocatalytic methods.

CAZ Composite Photocatalysts for H2 Production and Degradation under Visible Light.

g-C3N4/Ag-ZnO (CAZ) composite photocatalysts were synthesized successfully by the hydrothermal method. The photocatalytic performance of photocatalysts was assessed through experiments measuring both hydrogen (H2) production and the degradation of methylene blue (MB). The H2 production rate of 60% CAZ reached 2.450 mmol·g-1·h-1, which was 8.5 times that of g-C3N4. 25% CAZ degraded 99.14% of MB dye within 40 min, and its degradation rate constant was 12.4 times that of g-C3N4. CAZ composite photocatalysts have good synergistic properties in degradation and hydrogen production and exhibit better photocatalytic performance. A Z-scheme photocatalytic system mechanism of CAZ has been proposed for the enhanced H2 production and photocatalytic degradation rate.

Repurposing Visible-Light-Excited Ene-Reductases for Diastereo- and Enantioselective Lactones Synthesis.

Repurposing enzymes to catalyze non-natural asymmetric transformations that are difficult to achieve using traditional chemical methods is of significant importance. Although radical C-O bond formation has emerged as a powerful approach for constructing oxygen-containing compounds, controlling the stereochemistry poses a great challenge. Here we present the development of a dual bio-/photo-catalytic system comprising an ene-reductase and an organic dye for achieving stereoselective lactonizations. By integrating directed evolution and photoinduced single electron oxidation, we repurposed engineered ene-reductases to steer non-natural radical C-O formations (one C-O bond for hydrolactonizations and lactonization-alkylations while two C-O bonds for lactonization-oxygenations). This dual catalysis gave a new approach to a diverse array of enantioenhanced 5- and 6-membered lactones with vicinal stereocenters, part of which bears a quaternary stereocenter (up to 99 % enantiomeric excess, up to 12.9 : 1 diastereomeric ratio). Detailed mechanistic studies, including computational simulations, uncovered the synergistic effect of the enzyme and the externally added organophotoredox catalyst Rh6G.

Photobiocatalysis for Abiological Transformations.

Harnessing biocatalysts for novel abiological transformations is a longstanding goal of synthetic chemistry. Combining the merits of biocatalysis and photocatalysis allows for selective transformations fueled by visible light and offers many advantages including new reactivity, high enantioselectivity, greener syntheses, and high yields. Photoinduced electron or energy transfer enables synthetic methodologies that complement conventional two electron processes or offer orthogonal pathways for developing new reactions. Enzymes are well suited and can be tuned by directed evolution to exert control over open-shell intermediates, thereby suppressing undesirable reactions and delivering high chemo- and stereoselectivities. Within the past decade, the combination of biocatalysis and photocatalysis was mainly focused on exploiting light-regenerated cofactors to function native enzymatic activity. However, recent developments have demonstrated that the combination can unlock new-to-nature chemistry. Particularly, the discovery and application of new strategies are well poised to expand the applications of photobiocatalysis.In the past five years, our lab has been studying the combinations of photocatalysis and biocatalysis that can be applied to create new synthetic methodologies and solve challenges in synthetic organic chemistry. Our efforts have expanded the strategies for combining external photocatalysts with enzymes through the construction of a synergistic cooperative stereoconvergent reduction system consisting of photosensitized energy transfer and ene-reductase-catalyzed alkene reduction. Additionally, our efforts have also extended the capability of cofactor-dependent photoenzymatic systems to include enantioselective bimolecular radical hydroalkylations of alkenes by irradiating electron donor-acceptor complexes comprised of enzymatic redox active cofactors and unnatural substrates.In this Account, we highlight strategies developed by our group and others for combining biocatalysis and photocatalysis with the aim of introducing non-natural reactivity to enzymes. Presently, strategies applied to achieve this goal include the repurposing of natural photoenzymes, the elucidation of new photoreactivity within cofactor-dependent enzymes, the combination of external photocatalysts with enzymes, and the construction of artificial photoenzymes. By demonstrating the successful applications of these strategies for achieving selective new-to-nature transformations, we hope to spur interest in expanding the scope of photobiocatalytic systems through the use and extension of these strategies and creation of new strategies. Additionally, we hope to elucidate the intuition in synergizing the unique capabilities of biocatalysis and photocatalysis so that photobiocatalysis can be recognized as a potential solution to difficult challenges in synthetic organic chemistry.

The Versatile Biocatalyst of Cytochrome P450 CYP102A1: Structure, Function, and Engineering.

Wild-type cytochrome P450 CYP102A1 from Bacillus megaterium is a highly efficient monooxygenase for the oxidation of long-chain fatty acids. The unique features of CYP102A1, such as high catalytic activity, expression yield, regio- and stereoselectivity, and self-sufficiency in electron transfer as a fusion protein, afford the requirements for an ideal biocatalyst. In the past three decades, remarkable progress has been made in engineering CYP102A1 for applications in drug discovery, biosynthesis, and biotechnology. The repertoire of engineered CYP102A1 variants has grown tremendously, whereas the substrate repertoire is avalanched to encompass alkanes, alkenes, aromatics, organic solvents, pharmaceuticals, drugs, and many more. In this article, we highlight the major advances in the past five years in our understanding of the structure and function of CYP102A1 and the methodologies used to engineer CYP102A1 for novel applications. The objective is to provide a succinct review of the latest developments with reference to the body of CYP102A1-related literature.

Serum Metabolome Mediates the Antiobesity Effect of Celastrol-Induced Gut Microbial Alterations.

The antiobesity effect of celastrol has been reported in numerous studies, but the underlying mechanism remains unclear. It is widely accepted that gut dysbiosis is closely related to obesity. The potential effect of celastrol on microbiota is worth exploring. In this study, the celastrol-induced weight loss was validated in high-fat diet (HFD)-induced obese mice, with the detection of reported phenotypes including a reduction in food intake, augments in dyslipidemia and glucose metabolism, and adipose thermogenesis. The anti-inflammatory effect of celastrol was also proved based on the alterations in serum cytokines. Antibiotic interference showed that gut microbiota contributes to celastrol-induced weight loss. Several key bacteria were identified using shotgun metagenomic sequencing to display the alterations of the intestinal microbiome in obese mice treated with celastrol. Meanwhile, the fecal and serum metabolic profiles were generated by pseudotargeted metabolomics, and changes in some critical metabolites related to appetite and metabolism were detected. Importantly, we applied in silico bidirectional mediation analysis to identify the precise connections among the alterations in gut microbes, serum metabolome, and host phenotypes induced by celastrol treatment for the first time. Therefore, we concluded that the celastrol-induced microbial changes partially contribute to the antiobesity effect via the serum metabolome. The mass spectrometry data are deposited on MetaboLights (ID: MTBLS3278).

Protein structure prediction using deep learning distance and hydrogen-bonding restraints in CASP14.

In this article, we report 3D structure prediction results by two of our best server groups ("Zhang-Server" and "QUARK") in CASP14. These two servers were built based on the D-I-TASSER and D-QUARK algorithms, which integrated four newly developed components into the classical protein folding pipelines, I-TASSER and QUARK, respectively. The new components include: (a) a new multiple sequence alignment (MSA) collection tool, DeepMSA2, which is extended from the DeepMSA program; (b) a contact-based domain boundary prediction algorithm, FUpred, to detect protein domain boundaries; (c) a residual convolutional neural network-based method, DeepPotential, to predict multiple spatial restraints by co-evolutionary features derived from the MSA; and (d) optimized spatial restraint energy potentials to guide the structure assembly simulations. For 37 FM targets, the average TM-scores of the first models produced by D-I-TASSER and D-QUARK were 96% and 112% higher than those constructed by I-TASSER and QUARK, respectively. The data analysis indicates noticeable improvements produced by each of the four new components, especially for the newly added spatial restraints from DeepPotential and the well-tuned force field that combines spatial restraints, threading templates, and generic knowledge-based potentials. However, challenges still exist in the current pipelines. These include difficulties in modeling multi-domain proteins due to low accuracy in inter-domain distance prediction and modeling protein domains from oligomer complexes, as the co-evolutionary analysis cannot distinguish inter-chain and intra-chain distances. Specifically tuning the deep learning-based predictors for multi-domain targets and protein complexes may be helpful to address these issues.

FASPR: an open-source tool for fast and accurate protein side-chain packing.

MOTIVATION: Protein structure and function are essentially determined by how the side-chain atoms interact with each other. Thus, accurate protein side-chain packing (PSCP) is a critical step toward protein structure prediction and protein design. Despite the importance of the problem, however, the accuracy and speed of current PSCP programs are still not satisfactory. RESULTS: We present FASPR for fast and accurate PSCP by using an optimized scoring function in combination with a deterministic searching algorithm. The performance of FASPR was compared with four state-of-the-art PSCP methods (CISRR, RASP, SCATD and SCWRL4) on both native and non-native protein backbones. For the assessment on native backbones, FASPR achieved a good performance by correctly predicting 69.1% of all the side-chain dihedral angles using a stringent tolerance criterion of 20°, compared favorably with SCWRL4, CISRR, RASP and SCATD which successfully predicted 68.8%, 68.6%, 67.8% and 61.7%, respectively. Additionally, FASPR achieved the highest speed for packing the 379 test protein structures in only 34.3 s, which was significantly faster than the control methods. For the assessment on non-native backbones, FASPR showed an equivalent or better performance on I-TASSER predicted backbones and the backbones perturbed from experimental structures. Detailed analyses showed that the major advantage of FASPR lies in the optimal combination of the dead-end elimination and tree decomposition with a well optimized scoring function, which makes FASPR of practical use for both protein structure modeling and protein design studies. AVAILABILITY AND IMPLEMENTATION: The web server, source code and datasets are freely available at https://zhanglab.ccmb.med.umich.edu/FASPR and https://github.com/tommyhuangthu/FASPR. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

SSIPe: accurately estimating protein-protein binding affinity change upon mutations using evolutionary profiles in combination with an optimized physical energy function.

MOTIVATION: Most proteins perform their biological functions through interactions with other proteins in cells. Amino acid mutations, especially those occurring at protein interfaces, can change the stability of protein-protein interactions (PPIs) and impact their functions, which may cause various human diseases. Quantitative estimation of the binding affinity changes (ΔΔGbind) caused by mutations can provide critical information for protein function annotation and genetic disease diagnoses. RESULTS: We present SSIPe, which combines protein interface profiles, collected from structural and sequence homology searches, with a physics-based energy function for accurate ΔΔGbind estimation. To offset the statistical limits of the PPI structure and sequence databases, amino acid-specific pseudocounts were introduced to enhance the profile accuracy. SSIPe was evaluated on large-scale experimental data containing 2204 mutations from 177 proteins, where training and test datasets were stringently separated with the sequence identity between proteins from the two datasets below 30%. The Pearson correlation coefficient between estimated and experimental ΔΔGbind was 0.61 with a root-mean-square-error of 1.93 kcal/mol, which was significantly better than the other methods. Detailed data analyses revealed that the major advantage of SSIPe over other traditional approaches lies in the novel combination of the physical energy function with the new knowledge-based interface profile. SSIPe also considerably outperformed a former profile-based method (BindProfX) due to the newly introduced sequence profiles and optimized pseudocount technique that allows for consideration of amino acid-specific prior mutation probabilities. AVAILABILITY AND IMPLEMENTATION: Web-server/standalone program, source code and datasets are freely available at https://zhanglab.ccmb.med.umich.edu/SSIPe and https://github.com/tommyhuangthu/SSIPe. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

EvoEF2: accurate and fast energy function for computational protein design.

MOTIVATION: The accuracy and success rate of de novo protein design remain limited, mainly due to the parameter over-fitting of current energy functions and their inability to discriminate incorrect designs from correct designs. RESULTS: We developed an extended energy function, EvoEF2, for efficient de novo protein sequence design, based on a previously proposed physical energy function, EvoEF. Remarkably, EvoEF2 recovered 32.5%, 47.9% and 22.3% of all, core and surface residues for 148 test monomers, and was generally applicable to protein-protein interaction design, as it recapitulated 30.9%, 42.4%, 31.3% and 21.4% of all, core, interface and surface residues for 88 test dimers, significantly outperforming EvoEF on the native sequence recapitulation. We further used I-TASSER to evaluate the foldability of the 148 designed monomer sequences, where all of them were predicted to fold into structures with high fold- and atomic-level similarity to their corresponding native structures, as demonstrated by the fact that 87.8% of the predicted structures shared a root-mean-square-deviation less than 2 Å to their native counterparts. The study also demonstrated that the usefulness of physical energy functions is highly correlated with the parameter optimization processes, and EvoEF2, with parameters optimized using sequence recapitulation, is more suitable for computational protein sequence design than EvoEF, which was optimized on thermodynamic mutation data. AVAILABILITY AND IMPLEMENTATION: The source code of EvoEF2 and the benchmark datasets are freely available at https://zhanglab.ccmb.med.umich.edu/EvoEF. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Effects of SARS-CoV-2 mutations on protein structures and intraviral protein-protein interactions.

Since 2019, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) causing coronavirus disease 2019 (COVID-19) has infected 10 millions of people across the globe, and massive mutations in virus genome have occurred during the rapid spread of this novel coronavirus. Variance in protein sequence might lead to a change in protein structure and interaction, then further affect the viral physiological characteristics, which could bring tremendous influence on the pandemic. In this study, we investigated 20 nonsynonymous mutations in the SARS-CoV-2 genome in which incidence rates were all ≥ 1% as of September 1st, 2020, and then modeled and analyzed the mutant protein structures. The results showed that four types of mutations caused dramatic changes in protein structures (RMSD ≥ 5.0 Å), which were Q57H and G251V in open-reading frames 3a (ORF3a), S194L, and R203K/G204R in nucleocapsid (N). Next, we found that these mutations also affected the binding affinity of intraviral protein interactions. In addition, the hot spots within these docking mutant complexes were altered, among which the mutation Q57H was involved in both Orf3a-S and Orf3a-Orf8 protein interactions. Besides, these mutations were widely distributed all over the world, and their occurrences fluctuated as time went on. Notably, the incidences of R203K/G204R in N and Q57H in Orf3a were both over 50% in some countries. Overall, our findings suggest that SARS-CoV-2 mutations could change viral protein structure, binding affinity, and hot spots of the interface, thereby might have impacts on SARS-CoV-2 transmission, diagnosis, and treatment of COVID-19.

Identification of 13 Guanidinobenzoyl- or Aminidinobenzoyl-Containing Drugs to Potentially Inhibit TMPRSS2 for COVID-19 Treatment.

Positively charged groups that mimic arginine or lysine in a natural substrate of trypsin are necessary for drugs to inhibit the trypsin-like serine protease TMPRSS2 that is involved in the viral entry and spread of coronaviruses, including SARS-CoV-2. Based on this assumption, we identified a set of 13 approved or clinically investigational drugs with positively charged guanidinobenzoyl and/or aminidinobenzoyl groups, including the experimentally verified TMPRSS2 inhibitors Camostat and Nafamostat. Molecular docking using the C-I-TASSER-predicted TMPRSS2 catalytic domain model suggested that the guanidinobenzoyl or aminidinobenzoyl group in all the drugs could form putative salt bridge interactions with the side-chain carboxyl group of Asp435 located in the S1 pocket of TMPRSS2. Molecular dynamics simulations further revealed the high stability of the putative salt bridge interactions over long-time (100 ns) simulations. The molecular mechanics/generalized Born surface area-binding free energy assessment and per-residue energy decomposition analysis also supported the strong binding interactions between TMPRSS2 and the proposed drugs. These results suggest that the proposed compounds, in addition to Camostat and Nafamostat, could be effective TMPRSS2 inhibitors for COVID-19 treatment by occupying the S1 pocket with the hallmark positively charged groups.

[Effect of fucoxanthin on insulin resistance in obese mice induced by high fat diet].

The aim of this paper was to study the effect and mechanism of fucoxanthin on insulin resistance of obese mice induced by high-fat diet. Fifty C57 BL/6 J male mice were randomly divided into control group and high-fat diet group. The insulin resistance model was induced with high-fat diet for 12 weeks, and model mice were randomly divided into model group, fucoxanthin-0.2% group, fucoxanthin-0.4% group and metformin group. After dietary treatment for 6 weeks, the body weight and epididymal fat weight in each group were measured. Fasting blood glucose(FBG), fasting insulin(FINS), total cholesterol(TC), triglyceride(TG), low-density lipoprotein(LDL-C) and high-density lipoprotein(HDL-C) were measured, and insulin resistance index(HOMA-IR) was calcula-ted. The pathological morphology in liver was observed by hematoxylin eosin staining, and the expressions of some key proteins in insulin receptor substrate 1(IRS-1)/posphoinositide 3-kinase(PI3 K)/serine-threonine kinase(Akt) and peroxisome proliferators-activated receptor-γ(PPARγ)/sterol regulatory element binding protein-1(SREBP-1)/fatty acid synthetase(FAS) pathways in liver were detected by Western blot. According to the findings, compared with the model group, levels of body weight, epididymal fat weight, FBG, FINS, TC, TG, LDL-C and HOMA-IR, as well as protein expressions of PPARγ, SREBP-1 and FAS in liver were significantly reduced(P<0.05 or P<0.01), while level of HDL-C and protein expressions of p-IRS-1, IRS-1, PI3 K and p-Akt in liver were signi-ficantly increased after treatment with fucoxanthin(P<0.05 or P<0.01). And the pathological changes of liver tissue in fucoxanthin-treated mice were also improved obviously. The results showed that fucoxanthin could improve obesity, hyperglycemia and hyperlipidemia, and alleviate insulin resistance in obese mice, and its mechanism is possibly related to the regulation of IRS-1/PI3 K/Akt and PPARγ/SREBP-1/FAS pathways.

Identifying the Zoonotic Origin of SARS-CoV-2 by Modeling the Binding Affinity between the Spike Receptor-Binding Domain and Host ACE2.

Despite considerable research progress on SARS-CoV-2, the direct zoonotic origin (intermediate host) of the virus remains ambiguous. The most definitive approach to identify the intermediate host would be the detection of SARS-CoV-2-like coronaviruses in wild animals. However, due to the high number of animal species, it is not feasible to screen all the species in the laboratory. Given that binding to ACE2 proteins is the first step for the coronaviruses to invade host cells, we propose a computational pipeline to identify potential intermediate hosts of SARS-CoV-2 by modeling the binding affinity between the Spike receptor-binding domain (RBD) and host ACE2. Using this pipeline, we systematically examined 285 ACE2 variants from mammals, birds, fish, reptiles, and amphibians, and found that the binding energies calculated for the modeled Spike-RBD/ACE2 complex structures correlated closely with the effectiveness of animal infection as determined by multiple experimental data sets. Built on the optimized binding affinity cutoff, we suggest a set of 96 mammals, including 48 experimentally investigated ones, which are permissive to SARS-CoV-2, with candidates from primates, rodents, and carnivores at the highest risk of infection. Overall, this work not only suggests a limited range of potential intermediate SARS-CoV-2 hosts for further experimental investigation, but also, more importantly, it proposes a new structure-based approach to general zoonotic origin and susceptibility analyses that are critical for human infectious disease control and wildlife protection.

Protein Structure and Sequence Reanalysis of 2019-nCoV Genome Refutes Snakes as Its Intermediate Host and the Unique Similarity between Its Spike Protein Insertions and HIV-1.

As the infection of 2019-nCoV coronavirus is quickly developing into a global pneumonia epidemic, the careful analysis of its transmission and cellular mechanisms is sorely needed. In this Communication, we first analyzed two recent studies that concluded that snakes are the intermediate hosts of 2019-nCoV and that the 2019-nCoV spike protein insertions share a unique similarity to HIV-1. However, the reimplementation of the analyses, built on larger scale data sets using state-of-the-art bioinformatics methods and databases, presents clear evidence that rebuts these conclusions. Next, using metagenomic samples from Manis javanica, we assembled a draft genome of the 2019-nCoV-like coronavirus, which shows 73% coverage and 91% sequence identity to the 2019-nCoV genome. In particular, the alignments of the spike surface glycoprotein receptor binding domain revealed four times more variations in the bat coronavirus RaTG13 than in the Manis coronavirus compared with 2019-nCoV, suggesting the pangolin as a missing link in the transmission of 2019-nCoV from bats to human.