Inhibition of N-myristoyltransferase activity promotes androgen receptor degradation in prostate cancer.

BACKGROUND: Even though prostate cancer (PCa) patients initially respond to androgen deprivation therapy, some will eventually develop castration resistant prostate cancer (CRPC). Androgen receptor (AR) mediated cell signaling is a major driver in the progression of CRPC while only a fraction of PCa becomes AR negative. This study aimed to understand the regulation of AR levels by N-myristoyltransferase in PCa cells. METHODS: Two enantiomers, (1S,2S)- d-NMAPPD and (1R,2R)- d-NMAPPD (LCL4), were characterized by various methods (1 H and 13 C NMR, UHPLC, high-resolution mass spectra, circular dichroism) and evaluated for the ability to bind to N-myristoyltransferase 1 (NMT1) using computational docking analysis. structure-activity relationship analysis of these compounds led to the synthesis of (1R,2R)-LCL204 and evaluation as a potential NMT1 inhibitor utilizing the purified full length NMT1 enzyme. The NMT inhibitory activity wase determined by Click chemistry and immunoblotting. Regulation of NMT1 on tumor growth was evaluated in a xenograft tumor model. RESULTS: (1R,2R)- d-NMAPPD, but not its enantiomer (1S,2S)- d-NMAPPD, inhibited NMT1 activity and reduced AR protein levels. (1R,2R)-LCL204, a derivative of (1R,2R)- d-NMAPPD, inhibited global protein myristoylation. It also suppressed protein levels, nuclear translocation, and transcriptional activity of AR full-length or variants in PCa cells. This was due to enhanced ubiquitin and proteasome-mediated degradation of AR. Knockdown of NMT1 levels inhibited tumor growth and proliferation of cancer cells. CONCLUSION: Inhibitory efficacy on N-myristoyltransferase activity by d-NMAPPD is stereospecific. (1R,2R)-LCL204 reduced global N-myristoylation and androgen receptor protein levels at low micromolar concentrations in prostate cancer cells. pharmacological inhibition of NMT1 enhances ubiquitin-mediated proteasome degradation of AR. This study illustrates a novel function of N-myristoyltransferase and provides a potential strategy for treatment of CRPC.

Nicardipine is a putative EED inhibitor and has high selectivity and potency against chemoresistant prostate cancer in preclinical models.

BACKGROUND: It is imperative to develop novel therapeutics to overcome chemoresistance, a significant obstacle in the clinical management of prostate cancer (PCa) and other cancers. METHODS: A phenotypic screen was performed to identify novel inhibitors of chemoresistant PCa cells. The mechanism of action of potential candidate(s) was investigated using in silico docking, and molecular and cellular assays in chemoresistant PCa cells. The in vivo efficacy was evaluated in mouse xenograft models of chemoresistant PCa. RESULTS: Nicardipine exhibited high selectivity and potency against chemoresistant PCa cells via inducing apoptosis and cell cycle arrest. Computational, molecular, and cellular studies identified nicardipine as a putative inhibitor of embryonic ectoderm development (EED) protein, and the results are consistent with a proposed mechanism of action that nicardipine destabilised enhancer of zeste homologue 2 (EZH2) and inhibited key components of noncanonical EZH2 signalling, including transducer and activator of transcription 3, S-phase kinase-associated protein 2, ATP binding cassette B1, and survivin. As a monotherapy, nicardipine effectively inhibited the skeletal growth of chemoresistant C4-2B-TaxR tumours. As a combination regimen, nicardipine synergistically enhanced the in vivo efficacy of docetaxel against C4-2 xenografts. CONCLUSION: Our findings provided the first preclinical evidence supporting nicardipine as a novel EED inhibitor that has the potential to be promptly tested in PCa patients to overcome chemoresistance and improve clinical outcomes.

Structural and spectroscopic characterization of RufO indicates a new biological role in rufomycin biosynthesis.

Rufomycins constitute a class of cyclic heptapeptides isolated from actinomycetes. They are secondary metabolites that show promising treatment against Mycobacterium tuberculosis infections by inhibiting a novel drug target. Several nonproteinogenic amino acids are integrated into rufomycins, including a conserved 3-nitro-tyrosine. RufO, a cytochrome P450 (CYP)-like enzyme, was proposed to catalyze the formation of 3-nitro-tyrosine in the presence of O2 and NO. To define its biological function, the interaction between RufO and the proposed substrate tyrosine is investigated using various spectroscopic methods that are sensitive to the structural change of a heme center. However, a low- to high-spin state transition and a dramatic increase in the redox potential that are commonly found in CYPs upon ligand binding have not been observed. Furthermore, a 1.89-Å crystal structure of RufO shows that the enzyme has flexible surface regions, a wide-open substrate access tunnel, and the heme center is largely exposed to solvent. Comparison with a closely related nitrating CYP reveals a spacious and hydrophobic distal pocket in RufO, which is incapable of stabilizing a free amino acid. Molecular docking validates the experimental data and proposes a possible substrate. Collectively, our results disfavor tyrosine as the substrate of RufO and point to the possibility that the nitration occurs during or after the assembly of the peptides. This study indicates a new function of the unique nitrating enzyme and provides insights into the biosynthesis of nonribosomal peptides.

Identifying Drug Candidates for COVID-19 with Large-Scale Drug Screening.

Papain-like protease (PLpro) is critical to COVID-19 infection. Therefore, it is a significant target protein for drug development. We virtually screened a 26,193 compound library against the PLpro of SARS-CoV-2 and identified several drug candidates with convincing binding affinities. The three best compounds all had better estimated binding energy than those of the drug candidates proposed in previous studies. By analyzing the docking results for the drug candidates identified in this and previous studies, we demonstrate that the critical interactions between the compounds and PLpro proposed by the computational approaches are consistent with those proposed by the biological experiments. In addition, the predicted binding energies of the compounds in the dataset showed a similar trend as their IC50 values. The predicted ADME and drug-likeness properties also suggested that these identified compounds can be used for COVID-19 treatment.

Exploring and engineering PAM-diverse Streptococci Cas9 for PAM-directed bifunctional and titratable gene control in bacteria.

The RNA-guided Cas9s serve as powerful tools for programmable gene editing and regulation; their targeting scopes and efficacies, however, are always constrained by the PAM sequence stringency. Most Streptococci Cas9s, including the prototype SpCas9 from S. pyogenes, specifically recognize a canonical NGG PAM via a conserved RxR PAM-binding motif within the PAM-interaction (PI) domain. Here, SpCas9-based mining unveils three distinct and rarely presented PAM-binding motifs (QxxxR, QxQ and RxQ) among Streptococci Cas9 orthologs. With the catalytically-dead QxxxR-containing SedCas9 from S. equinus, we dissect its NAG PAM specificity and elucidate its underlying recognition mechanism via computational prediction and mutagenesis analysis. Replacing the SedCas9 PI domain with alternate PAM-binding motifs rewires its PAM specificity to NGG or NAA. Moreover, a semi-rational design with minimal mutation creates a SedCas9-NQ variant showing robust activity towards expanded NNG and NAA PAMs, based upon which we engineered a compact ω-SedCas9-NQ transcriptional regulator for PAM-directed bifunctional and titratable gene control. The ω-SedCas9-NQ mediated metabolic reprogramming of endogenous genes in Escherichia coli affords a 2.6-fold increase of 4-hydroxycoumarin production. This work reveals new Cas9 scaffolds with distinct PAM-binding motifs for PAM relaxation and creates a new PAM-diverse Cas9 variant for versatile gene control in bacteria.

Encapsulating Cas9 into extracellular vesicles by protein myristoylation.

CRISPR/Cas9 genome editing is a very promising avenue for the treatment of a variety of genetic diseases. However, it is still very challenging to encapsulate CRISPR/Cas9 machinery for delivery. Protein N-myristoylation is an irreversible co/post-translational modification that results in the covalent attachment of the myristoyl-group to the N-terminus of a target protein. It serves as an anchor for a protein to associate with the cell membrane and determines its intracellular trafficking and activity. Extracellular vesicles (EVs) are secreted vesicles that mediate cell-cell communication. In this study, we demonstrate that myristoylated proteins were preferentially encapsulated into EVs. The octapeptide derived from the leading sequence of the N-terminus of Src kinase was a favourable substrate for N-myristoyltransferase 1, the enzyme that catalyzes myristoylation. The fusion of the octapeptide onto the N-terminus of Cas9 promoted the myristoylation and encapsulation of Cas9 into EVs. Encapsulation of Cas9 and sgRNA-eGFP inside EVs was confirmed using protease digestion assays. Additionally, to increase the transfection potential, VSV-G was introduced into the EVs. The encapsulated Cas9 in EVs accounted for 0.7% of total EV protein. Importantly, the EVs coated with VSV-G encapsulating Cas9/sgRNA-eGFP showed up to 42% eGFP knock out efficiency with limited off-target effects in recipient cells. Our study provides a novel approach to encapsulate CRISPR/Cas9 protein and sgRNA into EVs. This strategy may open an effective avenue to utilize EVs as vehicles to deliver CRISPR/Cas9 for genome-editing-based gene therapy.

DeepFlu: a deep learning approach for forecasting symptomatic influenza A infection based on pre-exposure gene expression.

BACKGROUND AND OBJECTIVE: Not everyone gets sick after an exposure to influenza A viruses (IAV). Although KLRD1 has been identified as a potential biomarker for influenza susceptibility, it remains unclear whether forecasting symptomatic flu infection based on pre-exposure host gene expression might be possible. METHOD: To examine this hypothesis, we developed DeepFlu using the state-of-the-art deep learning approach on the human gene expression data infected with IAV subtype H1N1 or H3N2 viruses to forecast who would catch the flu prior to an exposure to IAV. RESULTS: The results indicated that such forecast is possible and, in other words, gene expression could reflect the strength of host immunity. In the leave-one-person-out cross-validation, DeepFlu based on deep neural network outperformed the models using convolutional neural network, random forest, or support vector machine, achieving 70.0% accuracy, 0.787 AUROC, and 0.758 AUPR for H1N1 and 73.8% accuracy, 0.847 AUROC, and 0.901 AUPR for H3N2. In the external validation, DeepFlu also reached 71.4% accuracy, 0.700 AUROC, and 0.723 AUPR for H1N1 and 73.5% accuracy, 0.732 AUROC, and 0.749 AUPR for H3N2, surpassing the KLRD1 biomarker. In addition, DeepFlu which was trained only by pre-exposure data worked the best than by other time spans and mixed training data of H1N1 and H3N2 did not necessarily enhance prediction. DeepFlu is available at https://github.com/ntou-compbio/DeepFlu. CONCLUSIONS: DeepFlu is a prognostic tool that can moderately recognize individuals susceptible to the flu and may help prevent the spread of IAV.

Polyphenols as Potential Inhibitors of SARS-CoV-2 RNA Dependent RNA Polymerase (RdRp).

An increasing number of studies have demonstrated the antiviral nature of polyphenols, and many polyphenols have been proposed to inhibit SARS-CoV or SARS-CoV-2. Our previous study revealed the inhibitory mechanisms of polyphenols against DNA polymerase α and HIV reverse transcriptase to show that polyphenols can block DNA elongation by competing with the incoming NTPs. Here we applied computational approaches to examine if some polyphenols can also inhibit RNA polymerase (RdRp) in SARS-CoV-2, and we identified some better candidates than remdesivir, the FDA-approved drug against RdRp, in terms of estimated binding affinities. The proposed compounds will be further examined to develop new treatments for COVID-19.

Engineering a PAM-flexible SpdCas9 variant as a universal gene repressor.

The RNA-guided CRISPR-associated Cas9 proteins have been widely applied in programmable genome recombination, base editing or gene regulation in both prokaryotes and eukaryotes. SpCas9 from Streptococcus pyogenes is the most extensively engineered Cas9 with robust and manifold functionalities. However, one inherent limitation of SpCas9 is its stringent 5'-NGG-3' PAM requirement that significantly restricts its DNA target range. Here, to repurpose SpCas9 as a universal gene repressor, we generate and screen variants of the deactivated SpCas9 (SpdCas9) with relaxed 5'-CAT-3' PAM compatibility that can bind to the start codon ATG of almost any gene. Stepwise structure-guided mutations of the PAM-interacting residues and auxiliary PAM-proximal residues of the SpdNG (5'-NG-3' PAM) create a PAM-flexible variant SpdNG-LWQT that preferentially accommodates 5'-NRN-3' PAMs. SpdNG-LWQT is demonstrated to be effective in gene repression with the advantage of customizable sgRNA design in both Escherichia coli and Saccharomyces cerevisiae. This work validates the feasibility of purposeful PAM expansion of Cas9 towards signature PAMs and establishes a universal SpdCas9-based gene repressor.

Polyphenols as alternative treatments of COVID-19.

Although scientists around the world have put lots of effort into the development of new treatments for COVID-19 since the outbreak, no drugs except Veklury (remdesivir) have been approved by FDA. There is an urgent need to discover some alternative antiviral treatment for COVID-19. Because polyphenols have been shown to possess antiviral activities, here we conducted a large-scale virtual screening for more than 400 polyphenols. Several lead compounds such as Petunidin 3-O-(6″-p-coumaroyl-glucoside) were identified to have promising binding affinities and convincing binding mechanisms. Analyzing the docking results and ADME properties sheds light on the potential efficacy of the top-ranked drug candidates and pinpoints the key residues on the target proteins for the future of drug development.

DeepDISE: DNA Binding Site Prediction Using a Deep Learning Method.

It is essential for future research to develop a new, reliable prediction method of DNA binding sites because DNA binding sites on DNA-binding proteins provide critical clues about protein function and drug discovery. However, the current prediction methods of DNA binding sites have relatively poor accuracy. Using 3D coordinates and the atom-type of surface protein atom as the input, we trained and tested a deep learning model to predict how likely a voxel on the protein surface is to be a DNA-binding site. Based on three different evaluation datasets, the results show that our model not only outperforms several previous methods on two commonly used datasets, but also demonstrates its robust performance to be consistent among the three datasets. The visualized prediction outcomes show that the binding sites are also mostly located in correct regions. We successfully built a deep learning model to predict the DNA binding sites on target proteins. It demonstrates that 3D protein structures plus atom-type information on protein surfaces can be used to predict the potential binding sites on a protein. This approach should be further extended to develop the binding sites of other important biological molecules.

Pharmacological inhibition of noncanonical EED-EZH2 signaling overcomes chemoresistance in prostate cancer.

Rationale: Chemoresistance is a major obstacle in prostate cancer (PCa) treatment. We sought to understand the underlying mechanism of PCa chemoresistance and discover new treatments to overcome docetaxel resistance. Methods: We developed a novel phenotypic screening platform for the discovery of specific inhibitors of chemoresistant PCa cells. The mechanism of action of the lead compound was investigated using computational, molecular and cellular approaches. The in vivo toxicity and efficacy of the lead compound were evaluated in clinically-relevant animal models. Results: We identified LG1980 as a lead compound that demonstrates high selectivity and potency against chemoresistant PCa cells. Mechanistically, LG1980 binds embryonic ectoderm development (EED), disrupts the interaction between EED and enhancer of zeste homolog 2 (EZH2), thereby inducing the protein degradation of EZH2 and inhibiting the phosphorylation and activity of EZH2. Consequently, LG1980 targets a survival signaling cascade consisting of signal transducer and activator of transcription 3 (Stat3), S-phase kinase-associated protein 2 (SKP2), ATP binding cassette B 1 (ABCB1) and survivin. As a lead compound, LG1980 is well tolerated in mice and effectively suppresses the in vivo growth of chemoresistant PCa and synergistically enhances the efficacy of docetaxel in xenograft models. Conclusions: These results indicate that pharmacological inhibition of EED-EZH2 interaction is a novel strategy for the treatment of chemoresistant PCa. LG1980 and its analogues have the potential to be integrated into standard of care to improve clinical outcomes in PCa patients.

Structural and Functional Enrichment Analyses for Antimicrobial Peptides.

Whether there is any inclination between structures and functions of antimicrobial peptides (AMPs) is a mystery yet to be unraveled. AMPs have various structures associated with many different antimicrobial functions, including antibacterial, anticancer, antifungal, antiparasitic and antiviral activities. However, none has yet reported any antimicrobial functional tendency within a specific category of protein/peptide structures nor any structural tendency of a specific antimicrobial function with respect to AMPs. Here, we examine the relationships between structures categorized by three structural classification methods (CATH, SCOP, and TM) and seven antimicrobial functions with respect to AMPs using an enrichment analysis. The results show that antifungal activities of AMPs were tightly related to the two-layer sandwich structure of CATH, the knottin fold of SCOP, and the first structural cluster of TM. The associations with knottin and TM Cluster 1 even sustained through the AMPs with a low sequence identity. Moreover, another significant mutual enrichment was observed between the third cluster of TM and anti-Gram-positive-bacterial/anti-Gram-negative-bacterial activities. The findings of the structure-function inclination further our understanding of AMPs and could help us design or discover new therapeutic potential AMPs.

In silico identification of drug candidates against COVID-19.

The COVID-19 pandemic has caused unprecedented health and economic crisis throughout the world. However, there is no effective medication or therapeutic strategy for treatment of this disease currently. Here, to elucidate the inhibitory effects, we first tested binding affinities of 11 HIV-1 protease inhibitors or their pharmacoenhancers docked onto SARS-CoV-2 main protease (M pro ), and 12 nucleotide-analog inhibitors docked onto RNA dependent RNA polymerase (RdRp). To further obtain the effective drug candidates, we screened 728 approved drugs via virtual screening on SARS-CoV-2 M pro . Our results demonstrate that remdesivir shows the best binding energy on RdRp and saquinvir is the best inhibitor of M pro . Based on the binding energies, we also list 10 top-ranked approved drugs which can be potential inhibitors for M pro . Overall, our results do not only propose drug candidates for further experiments and clinical trials but also pave the way for future lead optimization and drug design.

Elucidating the Inhibitory Effect of Resveratrol and Its Structural Analogs on Selected Nucleotide-Related Enzymes.

Resveratrol, the most widely studied natural phytochemical, has been shown to interact with different target proteins. Previous studies show that resveratrol binds and inhibits DNA polymerases and some other enzymes; however, the binding and functioning mechanisms remain unknown. The elucidated knowledge of inhibitory mechanisms of resveratrol will assist us in new drug discovery. We utilized molecular docking and molecular dynamics (MD) simulation to reveal how resveratrol and structurally similar compounds bind to various nucleotide-dependent enzymes, specifically, DNA polymerases, HIV-1 reverse transcriptase, and ribonucleotide reductase. The results show that resveratrol and its analogs exert their inhibitory effects by competing with the substrate dNTPs in these enzymes and blocking elongation of chain polymerization. In addition, the results imply that resveratrol binds to a variety of other ATP-/NTP-binding proteins.

A Pilot Study of All-Computational Drug Design Protocol-From Structure Prediction to Interaction Analysis.

Speeding up the drug discovery process is of great significance. To achieve that, high-efficiency methods should be exploited. The conventional wet-bench methods hardly meet the high-speed demand due to time-consuming experiments. Conversely, in silico approaches are much more efficient for drug discovery and design. However, in silico approaches usually serve as a supportive role in research processes. To fully exert the strength of computational methods, we propose a protocol which integrates various in silico approaches, from de novo protein structure prediction to ligand-protein interaction simulation. As a proof of concept, human SK2/calmodulin complex was used as a target for validation. First, we obtained a predicted structure of SK2/calmodulin and predicted binding sites which were consistent with the literature data. Then we investigated the ligand-protein interaction via virtual mutagenesis, flexible docking, and binding affinity calculation. As a result, the binding energies of mutants have similar trends compared with the EC50 values (R = 0.6 for NS309 in V481 mutants). The results indicate that our protocol can be applied to the drug design of structure unknown proteins. Our study also demonstrates that the integration of in silico approaches is feasible and it facilitates the acceleration of new drug discovery.

Integrating Structural Information to Study the Dynamics of Protein-Protein Interactions in Cells.

The information of how two proteins interact is embedded in the atomic details of their binding interfaces. These interactions, spatial-temporally coordinating each other as a network in a variable cytoplasmic environment, dominate almost all biological functions. A feasible and reliable computational model is highly demanded to realistically simulate these cellular processes and unravel the complexities beneath them. We therefore present a multiscale framework that integrates simulations on two different scales. The higher-resolution model incorporates structural information of proteins and energetics of their binding, while the lower-resolution model uses a highly simplified representation of proteins to capture the long-time-scale dynamics of a system with multiple proteins. Through a systematic benchmark test and two practical applications of biomolecular systems with specific cellular functions, we demonstrated that this method could be a powerful approach to understand molecular mechanisms of dynamic interactions between biomolecules and their functional impacts with high computational efficiency.

Predicting Protein-protein Association Rates using Coarse-grained Simulation and Machine Learning.

Protein-protein interactions dominate all major biological processes in living cells. We have developed a new Monte Carlo-based simulation algorithm to study the kinetic process of protein association. We tested our method on a previously used large benchmark set of 49 protein complexes. The predicted rate was overestimated in the benchmark test compared to the experimental results for a group of protein complexes. We hypothesized that this resulted from molecular flexibility at the interface regions of the interacting proteins. After applying a machine learning algorithm with input variables that accounted for both the conformational flexibility and the energetic factor of binding, we successfully identified most of the protein complexes with overestimated association rates and improved our final prediction by using a cross-validation test. This method was then applied to a new independent test set and resulted in a similar prediction accuracy to that obtained using the training set. It has been thought that diffusion-limited protein association is dominated by long-range interactions. Our results provide strong evidence that the conformational flexibility also plays an important role in regulating protein association. Our studies provide new insights into the mechanism of protein association and offer a computationally efficient tool for predicting its rate.

Understand protein functions by comparing the similarity of local structural environments.

The three-dimensional structures of proteins play an essential role in regulating binding between proteins and their partners, offering a direct relationship between structures and functions of proteins. It is widely accepted that the function of a protein can be determined if its structure is similar to other proteins whose functions are known. However, it is also observed that proteins with similar global structures do not necessarily correspond to the same function, while proteins with very different folds can share similar functions. This indicates that function similarity is originated from the local structural information of proteins instead of their global shapes. We assume that proteins with similar local environments prefer binding to similar types of molecular targets. In order to testify this assumption, we designed a new structural indicator to define the similarity of local environment between residues in different proteins. This indicator was further used to calculate the probability that a given residue binds to a specific type of structural neighbors, including DNA, RNA, small molecules and proteins. After applying the method to a large-scale non-redundant database of proteins, we show that the positive signal of binding probability calculated from the local structural indicator is statistically meaningful. In summary, our studies suggested that the local environment of residues in a protein is a good indicator to recognize specific binding partners of the protein. The new method could be a potential addition to a suite of existing template-based approaches for protein function prediction.

A Computational Model for Kinetic Studies of Cadherin Binding and Clustering.

Cadherin is a cell-surface transmembrane receptor that mediates calcium-dependent cell-cell adhesion and is a major component of adhesive junctions. The formation of intercellular adhesive junctions is initiated by trans binding between cadherins on adjacent cells, which is followed by the clustering of cadherins via the formation of cis interactions between cadherins on the same cell membranes. Moreover, classical cadherins have multiple glycosylation sites along their extracellular regions. It was found that aberrant glycosylation affects the adhesive function of cadherins and correlates with metastatic phenotypes of several cancers. However, a mechanistic understanding of cadherin clustering during cell adhesion and the role of glycosylation in this process is still lacking. Here, we designed a kinetic model that includes multistep reaction pathways for cadherin clustering. We further applied a diffusion-reaction algorithm to numerically simulate the clustering process using a recently developed coarse-grained model. Using experimentally measured rates of trans binding between soluble E-cadherin extracellular domains, we conducted simulations of cadherin-mediated cell-cell binding kinetics, and the results are quantitatively comparable to experimental data from micropipette experiments. In addition, we show that incorporating cadherin clustering via cis interactions further increases intercellular binding. Interestingly, a two-phase kinetic profile was derived under the assumption that glycosylation regulates the kinetic rates of cis interactions. This two-phase profile is qualitatively consistent with experimental results from micropipette measurements. Therefore, our computational studies provide new, to our knowledge, insights into the molecular mechanism of cadherin-based cell adhesion.