A terpenoids database with the chemical content as a novel agronomic trait.

Natural products play a pivotal role in drug discovery, and the richness of natural products, albeit significantly influenced by various environmental factors, is predominantly determined by intrinsic genetics of a series of enzymatic reactions and produced as secondary metabolites of organisms. Heretofore, few natural product-related databases take the chemical content into consideration as a prominent property. To gain unique insights into the quantitative diversity of natural products, we have developed the first TerPenoids database embedded with Content information (TPCN) with features such as compound browsing, structural search, scaffold analysis, similarity analysis and data download. This database can be accessed through a web-based computational toolkit available at http://www.tpcn.pro/. By conducting meticulous manual searches and analyzing over 10 000 reference papers, the TPCN database has successfully integrated 6383 terpenoids obtained from 1254 distinct plant species. The database encompasses exhaustive details including isolation parts, comprehensive molecule structures, chemical abstracts service registry number (CAS number) and 7508 content descriptions. The TPCN database accentuates both the qualitative and quantitative dimensions as invaluable phenotypic characteristics of natural products that have undergone genetic evolution. By acting as an indispensable criterion, the TPCN database facilitates the discovery of drug alternatives with high content and the selection of high-yield medicinal plant species or phylogenetic alternatives, thereby fostering sustainable, cost-effective and environmentally friendly drug discovery in pharmaceutical farming. Database URL: http://www.tpcn.pro/.

Conformational plasticity of SpyCas9 induced by AcrIIA4 and AcrIIA2: Insights from molecular dynamics simulation.

CRISPR-Cas9 systems constitute bacterial adaptive immune systems that protect against phage infections. Bacteriophages encode anti-CRISPR proteins (Acrs) that mitigate the bacterial immune response. However, the structural basis for their inhibitory actions from a molecular perspective remains elusive. In this study, through microsecond atomistic molecular dynamics simulations, we demonstrated the remarkable flexibility of Streptococcus pyogenes Cas9 (SpyCas9) and its conformational adaptability during interactions with AcrIIA4 and AcrIIA2. Specifically, we demonstrated that the binding of AcrIIA4 and AcrIIA2 to SpyCas9 induces a conformational rearrangement that causes spatial separation between the nuclease and cleavage sites, thus making the endonuclease inactive. This separation disrupts the transmission of signals between the protospacer adjacent motif recognition and nuclease domains, thereby impeding the efficient processing of double-stranded DNA. The simulation also reveals that AcrIIA4 and AcrIIA2 cause different structural variations of SpyCas9. Our research illuminates the precise mechanisms underlying the suppression of SpyCas9 by AcrIIA4 and AcrIIA2, thus presenting new possibilities for controlling genome editing with higher accuracy.

Sequence variety in the CC' loop of Siglec-8/9/3 determines the recognitions to sulfated oligosaccharides.

Siglecs are important lectins found in different types of immune cells and function as regulatory molecules by recognizing self-associated glycans and converting extracellular interactions into signals for inhibiting immune cell functions. Although many Siglecs have been found to show broad specificities and recognize different types of sulfated oligosaccharides, Siglec-8 and Siglec-9 displayed a high degree of specificity for sialyl N-acetyllactosamine (sLacNAc) with sulfations at O6-positions of the galactose (6'-sulfation) and N-acetylglucosamine (6-sulfation), respectively. Siglec-3 was recently discovered to bind sLacNAc both sulfations. In addition to a conserved arginine residue for binding to sialic acid residue, the sequence variety in the CC' loop may provide binding specificities to sulfated oligosaccharides in Siglecs. Thus, the present study employed molecular models to study the impact of different residues in the CC' loops of Siglec-8/9/3 to the recognitions of 6-sulfations in Gal and/or GlcNAc of sLacNAc. The negatively charged residues in the CC' loop of Siglec-9 formed unfavorable electrostatic repulsions with the 6-sulfate in Gal and resulted no recognitions, in contrast to the favorable interactions formed between the positively charged residues in the CC' loop of Siglec-8 and the 6-sulfate in Gal resulting strong specificity. A two-state binding model was proposed for Siglec-3 recognizing 6-sulfations in Gal and GlcNAc of sLacNAc, as the neutral residues in the CC' loop of Siglec-3 could not form strong favorable interactions to lock the 6-sulfate in Gal within a single binding pose or strong unfavorable interactions to repel the 6-sulfate in Gal. The oligosaccharide adopted two distinctive binding poses and oriented the sulfate groups to form interactions with residues in the CC' loop and G-strand. The present study provided a structural mechanism for the sequence variety in the CC' loop of Siglec-8/9/3 determining the recognitions to the sulfated oligosaccharides and offered insights into the binding specificities for Siglecs.

Yin/Yang associated differential responses to Psoralea corylifolia Linn. In rat models: an integrated metabolomics and transcriptomics study.

ETHNOPHARMACOLOGICAL RELEVANCE: Psoralea corylifolia Linn. (BGZ) is a commonly used traditional Chinese medicine (TCM) for the treatment of kidney-yang deficiency syndrome (Yangsyn) with good curative effect and security. However, BGZ was also reported to induce liver injury in recent years. According to TCM theory, taking BGZ may induce a series of adverse reactions in patients with kidney-yin deficiency syndrome (Yinsyn), which suggests that BGZ-induced liver damage may be related to its unreasonable clinical use. AIM OF THE STUDY: Liver injury caused by TCM is a rare but potentially serious adverse drug reaction, and the identification of predisposed individuals for drug-induced liver injury (DILI) remains challenging. The study aimed to investigate the differential responses to BGZ in Yangsyn and Yinsyn rat models and identify the corresponding characteristic biomarkers. MATERIALS AND METHODS: The corresponding animal models of Yangsyn and Yinsyn were induced by hydrocortisone and thyroxine + reserpine respectively. Body weight, organ index, serum biochemistry, and Hematoxylin and Eosin (HE) staining were used to evaluate the liver toxicity effect of BGZ on rats with Yangsyn and Yinsyn. Transcriptomics and metabonomics were used to screen the representative biomarkers (including metabolites and differentially expressed genes (DEGs)) changed by BGZ in Yangsyn and Yinsyn rats, respectively. RESULTS: The level changes of liver organ index, alanine aminotransferase (ALT), and aspartate aminotransferase (AST), suggested that BGZ has liver-protective and liver-damaging effects on Yangsyn and Yinsyn rats, respectively, and the results also were confirmed by the pathological changes of liver tissue. The results showed that 102 DEGs and 27 metabolites were significantly regulated related to BGZ's protective effect on Yangsyn, which is mainly associated with the glycerophospholipid metabolism, arachidonic acid metabolism, pantothenate, and coenzyme A (CoA) biosynthesis pathways. While 28 DEGs and 31 metabolites, related to the pathway of pantothenate and CoA biosynthesis, were significantly regulated for the BGZ-induced liver injury in Yinsyn. Furthermore, 4 DEGs (aldehyde dehydrogenase 1 family member B1 (Aldh1b1), solute carrier family 25 member 25 (Slc25a25), Pim-3 proto-oncogene, serine/threonine kinase (Pim3), out at first homolog (Oaf)) and 4 metabolites (phosphatidate, phosphatidylcholine, N-Acetylleucine, biliverdin) in the Yangsyn group and 1 DEG [galectin 5 (Lgals5)] and 1 metabolite (5-amino-1-(5-phospho-D-ribosyl)imidazole-4-carboxylate) in Yinsyn group were significantly correlated to the ALT and AST levels of BGZ treated and untreated groups (receiver operating characteristic (ROC) ≥ 0.9). CONCLUSIONS: Yinsyn and Yangsyn are the predisposed syndromes for BGZ to exert liver damage and liver protection respectively, which are mainly related to the regulation of amino acid metabolism, lipid metabolism, energy metabolism, and metabolism of cofactors and vitamins. The results further suggest that attention should be paid to the selection of predisposed populations when using drugs related to the regulation of energy metabolism, and the Yinsyn/Yangsyn animal models based on the theory of TCM syndromes may be a feasible method for identifying the susceptible population to receive TCM.

Binding Mechanism of CD47 with SIRPα Variants and Its Antibody: Elucidated by Molecular Dynamics Simulations.

The intricate complex system of the differentiation 47 (CD47) and the signal-regulatory protein alpha (SIRPα) cluster is a crucial target for cancer immunotherapy. Although the conformational state of the CD47-SIRPα complex has been revealed through crystallographic studies, further characterization is needed to fully understand the binding mechanism and to identify the hot spot residues involved. In this study, molecular dynamics (MD) simulations were carried out for the complexes of CD47 with two SIRPα variants (SIRPαv1, SIRPαv2) and the commercially available anti-CD47 monoclonal antibody (B6H12.2). The calculated binding free energy of CD47-B6H12.2 is lower than that of CD47-SIRPαv1 and CD47-SIRPαv2 in all the three simulations, indicating that CD47-B6H12.2 has a higher binding affinity than the other two complexes. Moreover, the dynamical cross-correlation matrix reveals that the CD47 protein shows more correlated motions when it binds to B6H12.2. Significant effects were observed in the energy and structural analyses of the residues (Glu35, Tyr37, Leu101, Thr102, Arg103) in the C strand and FG region of CD47 when it binds to the SIRPα variants. The critical residues (Leu30, Val33, Gln52, Lys53, Thr67, Arg69, Arg95, and Lys96) were identified in SIRPαv1 and SIRPαv2, which surround the distinctive groove regions formed by the B2C, C'D, DE, and FG loops. Moreover, the crucial groove structures of the SIRPα variants shape into obvious druggable sites. The C'D loops on the binding interfaces undergo notable dynamical changes throughout the simulation. For B6H12.2, the residues Tyr32LC, His92LC, Arg96LC, Tyr32HC, Thr52HC, Ser53HC, Ala101HC, and Gly102HC in its initial half of the light and heavy chains exhibit obvious energetic and structural impacts upon binding with CD47. The elucidation of the binding mechanism of SIRPαv1, SIRPαv2, and B6H12.2 with CD47 could provide novel perspectives for the development of inhibitors targeting CD47-SIRPα.

A chemoinformatic analysis on natural glycosides with respect to biological origin and structural class.

Covering: up to 202216.19% of reported natural products (NPs) in the Dictionary of Natural Products (DNP) are glycosides. As one of the most important NPs' structural modifications, glycosylation can change the NPs' polarity, making the aglycones more amphipathic. However, until now, little is known about the general distribution profile of the natural glycosides in different biological sources or structural types. The reason, structural or species preferences of the natural glycosylation remain unclear. In this highlight, chemoinformatic methods were employed to analyze the natural glycosides from DNP, the most comprehensively annotated NP database. We found that the glycosylation ratios of NPs from plants, bacteria, animals and fungi decrease successively, which are 24.99%, 20.84%, 8.40% and 4.48%, respectively. Echinoderm-derived NPs (56.11%) are the most frequently glycosylated, while those produced by molluscs (1.55%), vertebrates (2.19%) and Rhodophyta (3.00%) are the opposite. Among the diverse structural types, a large proportion of steroids (45.19%), tannins (44.78%) and flavonoids (39.21%) are glycosides, yet aminoacids and peptides (5.16%), alkaloids (5.66%) are comparatively less glycosylated. Even within the same biological source or structural type, their glycosylation rates fluctuate drastically between sub- or cross-categories. The substitute patterns of flavonoid and terpenoid glycosides and the most frequently glycosylated scaffolds were identified. NPs with different glycosylation levels occupy different chemical spaces of physicochemical property and scaffold. These findings could help us to interpret the preference of NPs' glycosylation and investigate how NP glycosylation could aid NP-based drug discovery.

Learning with uncertainty to accelerate the discovery of histone lysine-specific demethylase 1A (KDM1A/LSD1) inhibitors.

Machine learning including modern deep learning models has been extensively used in drug design and screening. However, reliable prediction of molecular properties is still challenging when exploring out-of-domain regimes, even for deep neural networks. Therefore, it is important to understand the uncertainty of model predictions, especially when the predictions are used to guide further experiments. In this study, we explored the utility and effectiveness of evidential uncertainty in compound screening. The evidential Graphormer model was proposed for uncertainty-guided discovery of KDM1A/LSD1 inhibitors. The benchmarking results illustrated that (i) Graphormer exhibited comparative predictive power to state-of-the-art models, and (ii) evidential regression enabled well-ranked uncertainty estimates and calibrated predictions. Subsequently, we leveraged time-splitting on the curated KDM1A/LSD1 dataset to simulate out-of-distribution predictions. The retrospective virtual screening showed that the evidential uncertainties helped reduce false positives among the top-acquired compounds and thus enabled higher experimental validation rates. The trained model was then used to virtually screen an independent in-house compound set. The top 50 compounds ranked by two different ranking strategies were experimentally validated, respectively. In general, our study highlighted the importance to understand the uncertainty in prediction, which can be recognized as an interpretable dimension to model predictions.

[Potential molecular mechanisms of QiZhenYuanDan in treatment of atherosclerosis based on network pharmacology].

Objective: By means of network pharmacology, potential targets and molecular pathways of QiZhenYuanDan in the treatment of atherosclerosis (AS) were studied. Methods: TCMSP database was used to obtain the main active components and target information of Astragali Radix, Fructus Ligustri Lucidi, Corydalis Rhizoma and Salvia Miltiorrhiza in QiZhenYuanDan. Disease targets were retrieved by OMIM and other databases. Molecular networks were constructed using Cytoscape. STRING database was searched and PPI network diagram was drawn to obtain the key targets of QiZhenYuanDan in the treatment of AS; and the targets were uploaded to Metascape data platform for GO and KEGG analysis. Results: There were 118 targets of intersection between QiZhenYuanDan and AS, which were used as the predicted targets of QiZhenYuanDan on AS. GO analysis showed that the biological functions of QiZhenYuanDan in the treatment of AS targets mainly involved biological processes, such as the cytokine-mediated signaling pathway, cytokine receptor binding. KEGG pathway was mainly enriched in 155 signaling pathways, including PI3K-Akt, HIF-1, NF-κB signal pathway and inflammatory bowel disease pathway. Conclusion: Based on the result of network pharmacology study, the mechanisms of Qizhenyuandan for AS treatment was preliminarily revealed. The active ingredients such as quercetin and kaempferol act on targets such as IL-6 and PI3K-Akt, and exert anti-AS effects by inhibiting apoptosis, oxidative stress, as well as inflammatory responses. Our result indicates that QiZhenYuanDan exhibits anti-AS effect via a multi-component, multi-target and multi-route synergistic process.

Oxymatrine ameliorates experimental autoimmune encephalomyelitis by rebalancing the homeostasis of gut microbiota and reducing blood-brain barrier disruption.

Background: Increasing evidence suggests that gut dysbiosis can directly or indirectly affect the immune system through the brain-gut axis and play a role in the occurrence and development of Multiple sclerosis (MS). Oxymatrine (OMAT) has been shown to ameliorate the symptoms of MS in the classical experimental autoimmune encephalomyelitis (EAE) model of MS, but whether its therapeutic role is through the correction of gut dysbiosis, is unclear. Methods: The effects of OMAT on intestinal flora and short-chain fatty acids in EAE model mice were evaluated by 16S rRNA sequencing and GC-MS/MS, respectively, and the function change of the blood-brain barrier and intestinal epithelial barrier was further tested by immunohistochemical staining, Evans Blue leakage detection, and RT-qPCR. Results: The alpha and beta diversity in the feces of EAE mice were significantly different from that of the control group but recovered substantially after OMAT treatment. Besides, the OMAT treatment significantly affected the gut functional profiling and the abundance of genes associated with energy metabolism, amino acid metabolism, the immune system, infectious diseases, and the nervous system. OMAT also decreased the levels of isobutyric acid and isovaleric acid in EAE mice, which are significantly related to the abundance of certain gut microbes and were consistent with the reduced expression of TNF-a, IL-6, and IL-1b. Furthermore, OMAT treatment significantly increased the expression of ZO-1 and occludin in the brains and colons of EAE mice and decreased blood-brain barrier permeability. Conclusion: OMAT may alleviate the clinical and pathological symptoms of MS by correcting dysbiosis, restoring gut ecological and functional microenvironment, and inhibiting immune cell-mediated inflammation to remodel the brain-gut axis.

EnMCB: an R/bioconductor package for predicting disease progression based on methylation correlated blocks using ensemble models.

MOTIVATION: Based on the concept that contiguous cytosine-phosphorothioate-guanine (CpG) sites in the same DNA strand may be modified by a methyltransferase or demethylase together, current study found that the combination of multiple CpGs into a single block may promote cancer diagnosis. However, there is no R package available for building models based on methylation correlated blocks. RESULTS: Here, we present a package named stacked ensemble of machine learning models for methylation correlated blocks (EnMCB) to build signatures based on DNA methylation correlated blocks for survival prediction. The Cox regression, support vector regression, mboost and elastic-net model were combined in the ensemble model. Methylation profiles from The Cancer Genome Atlas were used as real datasets. The package automatically partitions the genome into blocks of tightly co-methylated CpG sites, termed methylation correlated blocks. After partitioning and modeling, the diagnostic capacities for predicting patients' survivals are given. AVAILABILITY AND IMPLEMENTATION: EnMCB is freely available for download at GitHub (https://github.com/whirlsyu/EnMCB/) and Bioconductor (http://bioconductor.org/packages/release/bioc/html/EnMCB.html). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Predicting lung adenocarcinoma disease progression using methylation-correlated blocks and ensemble machine learning classifiers.

Applying the knowledge that methyltransferases and demethylases can modify adjacent cytosine-phosphorothioate-guanine (CpG) sites in the same DNA strand, we found that combining multiple CpGs into a single block may improve cancer diagnosis. However, survival prediction remains a challenge. In this study, we developed a pipeline named "stacked ensemble of machine learning models for methylation-correlated blocks" (EnMCB) that combined Cox regression, support vector regression (SVR), and elastic-net models to construct signatures based on DNA methylation-correlated blocks for lung adenocarcinoma (LUAD) survival prediction. We used methylation profiles from the Cancer Genome Atlas (TCGA) as the training set, and profiles from the Gene Expression Omnibus (GEO) as validation and testing sets. First, we partitioned the genome into blocks of tightly co-methylated CpG sites, which we termed methylation-correlated blocks (MCBs). After partitioning and feature selection, we observed different diagnostic capacities for predicting patient survival across the models. We combined the multiple models into a single stacking ensemble model. The stacking ensemble model based on the top-ranked block had the area under the receiver operating characteristic curve of 0.622 in the TCGA training set, 0.773 in the validation set, and 0.698 in the testing set. When stratified by clinicopathological risk factors, the risk score predicted by the top-ranked MCB was an independent prognostic factor. Our results showed that our pipeline was a reliable tool that may facilitate MCB selection and survival prediction.

[The Antileukemia Activity of ZSTK474 on U937 Cells].

OBJECTIVE: To investigate the antileukemia activity of phosphatidylinositol-3 kinase (PI3K) inhibitor ZSTK474 on human leukemia cell line U937. METHODS: MTT, soft agar assay, flow cytometric analysis and western blot were used to detect the effect of ZSTK474 on U937 cell proliferation, tumorigenicity, cell cycle, cell apoptosis and phosphorylation levels of the key factor of PI3K/AKT pathway. Chou-Talalay method was used to evaluate the combination of ZSTK474 with Cytarabine or Homoharringtonine. RESULTS: PI3K inhibitor ZSTK474 could inhibit the proliferation and tumorigenicity of U937 cell, induce G1 cell cycle arrest and promote cell apoptosis, and enhance intracellular ROS production and decrease MMP, downregulate Cyclin D1, p-Rb, BCL-2 and upregulate p27, caspase-9, caspase-3, PARP and BAX. Furthermore, the phosphorylation of PDK1, GSK-3ß, AKT and mTOR could be downregulated by ZSTK474. The combination of ZSTK474 with Homoharringtonine was synergistic. CONCLUSION: ZSTK474 can inhibit the pathway of PI3K/AKT, ZSTK474 alone or in combination with Homoharringtonine shows potential antileukemia activity on U937 cells.

Accelerating the identification of subtype selective inhibitors via Three-Dimensional Biologically Relevant Spectrum (BRS-3D): The monoamine oxidase subtypes as a case study.

Subtype-selective drugs are of great therapeutic importance as they are expected to be more effective and with less side-effects. However, discovery of subtype selective inhibitors was hampered by the high similarity of the binding sites within subfamilies. In this study, we further evaluated the applicability of "Three-Dimensional Biologically Relevant Spectrum (BRS-3D)" for the identification of subtype-selective inhibitors. A case study was performed on monoamine oxidase, which has two subtypes related to distinct diseases. The inhibitory activity against MAO-A/B of 347 compounds experimentally tested in this research was reported. Compound M124 (5H-thiazolo[3,2-a]pyrimidin-5-one) with IC50 less than 100 nM (SI = 23) was selected as a probe to investigate the structure selectivity relationship. Similarity search led to the identification of compound M229 and M249 with IC50 values of 7.4 nM, 4 nM and acceptable selectivity index over MAO-A (M229 SI > 1351, M249 SI > 2500). The molecular basis for subtype selectivity was explored through docking study and attention based DNN model. Additionally, in silico ADME properties were characterized. Accordingly, it is found that BRS-3D is a robust method for subtype selectivity in the early stage of drug discovery and the compounds reported here can be promising leads for further experimental analysis.

Multifaceted anti-colorectal tumor effect of digoxin on HCT8 and SW620 cells in vitro.

BACKGROUND: Colorectal cancer (CRC) is one of the leading causes of cancer death worldwide. Novel drugs for CRC therapy are urgently needed. Digoxin has been in clinical use for treatment of heart failure and atrial arrhythmias for many years. Fragmentary reports suggested that digoxin might have antitumor efficacy on CRC. Here, we aimed to investigate the antitumor effect of digoxin on human CRC cells and the underlying mechanism. METHODS: Cell viability was determined using 3-(4,5-dimethyl-2-thiazolyl)-2,5-diphenyl-2-H-tetrazolium bromide (MTT) assay and plate colony formation assay. The effects of digoxin on cell-cycle distribution and apoptosis were analysed by flow cytometry. The anti-metastatic effect on tumor cells was determined by wound-healing assay and transwell assay. Anti-angiogenic effect was examined by determining the inhibition against proliferation, migration, and tube formation of human umbilical vein endothelial cells (HUVECs). Mechanism study was performed by Western blot, enzyme-linked immunosorbent assay (ELISA), and gelatin-zymography assay. RESULTS: Digoxin potently inhibited cell proliferation, induced G1-phase and G2/M-phase arrest in colorectal-cancer HCT8 and SW620 cells, respectively. No obvious apoptosis was observed in the treated cells. Anti-metastatic activities were shown on HCT8 cells by inhibiting the migration and invasion. Meanwhile, the expression of MMP2, MMP9, and phosphorylated Integrinß1 were decreased. Digoxin inhibited the proliferation, migration, and tube formation of HUVECs and reduced HIF1α expression and vascular endothelial growth factor A (VEGF-A) secretion in HCT8 cells, suggesting anti-angiogenic activity. Furthermore, digoxin significantly reversed ABCB1-mediated multidrug resistance on SW620/Ad300 cells. CONCLUSION: Our findings suggest that digoxin has the potential to be applied as an antitumor drug via inhibiting proliferation and metastasis as well as reversing the ABCB1-mediated multidrug resistance of colorectal cancer.

Novel C7-Substituted Coumarins as Selective Monoamine Oxidase Inhibitors: Discovery, Synthesis and Theoretical Simulation.

There is a continued need to develop new selective human monoamine oxidase (hMAO) inhibitors that could be beneficial for the treatment of neurological diseases. However, hMAOs are closely related with high sequence identity and structural similarity, which hinders the development of selective MAO inhibitors. "Three-Dimensional Biologically Relevant Spectrum (BRS-3D)" method developed by our group has demonstrated its effectiveness in subtype selectivity studies of receptor and enzyme ligands. Here, we report a series of novel C7-substituted coumarins, either synthesized or commercially purchased, which were identified as selective hMAO inhibitors. Most of the compounds demonstrated strong activities with IC50 values (half-inhibitory concentration) ranging from sub-micromolar to nanomolar. Compounds, FR1 and SP1, were identified as the most selective hMAO-A inhibitors, with IC50 values of 1.5 nM (selectivity index (SI) < -2.82) and 19 nM (SI < -2.42), respectively. FR4 and FR5 showed the most potent hMAO-B inhibitory activity, with IC50 of 18 nM and 15 nM (SI > 2.74 and SI > 2.82). Docking calculations and molecular dynamic simulations were performed to elucidate the selectivity preference and SAR profiles.

Identification of novel monoamine oxidase selective inhibitors employing a hierarchical ligand-based virtual screening strategy.

Aim: Due to the pivotal role in the oxidative deamination of monoamine neurotransmitters, two distinct monoamine oxidase (MAO) subtypes, MAO-A and MAO-B, present a significant pharmacological interest. Here, we reported a hierarchical and time-efficient ligand-based virtual screening strategy to identify potent selective and reversible MAO inhibitors. Result: A total of 130 compounds were assessed in dose-response biochemical assay against MAOs. Among them, 70 compounds were active with inhibition higher than 70%, involving 25 compounds with IC50 values less than 1 µM. Conclusion: Our research demonstrated the validity of Biologically Relevant Spectrum (BRS-3D) in predicting subtype-selective ligands and afforded a novel highly efficient way to develop selective inhibitors in the early stage of drug discovery.

Integrating Multifaceted Information to Predict Mycobacterium tuberculosis-Human Protein-Protein Interactions.

Tuberculosis (TB) is one of the biggest infectious disease killers caused by Mycobacterium tuberculosis (MTB). Studying the protein-protein interactions (PPIs) between MTB and human can deepen our understanding of the pathogenesis of TB and offer new clues to the treatment against MTB infection, but the experimentally validated interactions are especially scarce in this regard. Herein we proposed an integrated framework that combined template-, domain-domain interaction-, and machine learning-based methods to predict MTB-human PPIs. As a result, we established a network composed of 13 758 PPIs including 451 MTB proteins and 3167 human proteins ( http://liulab.hzau.edu.cn/MTB/ ). Compared to known human targets of various pathogens, our predicted human targets show a similar tendency in terms of the network topological properties and enrichment in important functional genes. Additionally, these human targets largely have longer sequence lengths, more protein domains, more disordered residues, lower evolutionary rates, and older protein ages. Functional analysis demonstrates that these proteins show strong preferences toward the phosphorylation, kinase activity, and signaling transduction processes and the disease and immune related pathways. Dissecting the cross-talk among top-ranked pathways suggests that the cancer pathway may serve as a bridge in MTB infection. Triplet analysis illustrates that the paired targets interacting with the same partner are adjacent to each other in the intraspecies network and tend to share similar expression patterns. Finally, we identified 36 potential anti-MTB human targets by integrating known drug target information and molecular properties of proteins.

Deciphering the Odorant Binding, Activation, and Discrimination Mechanism of Dhelobp21 from Dastarus Helophoroides.

Odorant-binding proteins (OBPs) play a pivotal role in transporting odorants through the sensillar lymph of insect chemosensory sensilla and increasing the sensitivity of the olfactory system. To address the ligand binding, activation, and release mechanisms of OBPs, we performed a set of conventional molecular dynamics simulations for binding of the odorant-binding protein DhelOBP21 from Dastarcus helophoroides with 18 ligands (1-NPN and 17 volatiles), as well as four constant-pH molecular dynamics simulations. We found that the open pocket DhelOBP21 at pH 5.0 could bind volatiles and form a closed pocket complex via transformation of its N-terminus into regular Helix at pH 7.0 and vice versa. Moreover, the discrimination of volatiles (selectivity and promiscuity) was determined by the characteristics of both the volatiles and the 'essential' and 'selective' amino acid residues in OBP binding pockets, rather than the binding affinity of the volatiles. This study put forward a new hypothesis that during the binding of volatiles there are two transitions for the DhelOBP21 amino-terminus: pH- and odorant binding-dependent random-coil-to-helix. Another important finding is providing a framework for the exploration of the complete coil-to-helix transition process and theoretically analyzing its underlying causes at molecular level.

Cheminformatic Insight into the Differences between Terrestrial and Marine Originated Natural Products.

This is a new golden age for drug discovery based on natural products derived from both marine and terrestrial sources. Herein, a straightforward but important question is "what are the major structural differences between marine natural products (MNPs) and terrestrial natural products (TNPs)?" To answer this question, we analyzed the important physicochemical properties, structural features, and drug-likeness of the two types of natural products and discussed their differences from the perspective of evolution. In general, MNPs have lower solubility and are often larger than TNPs. On average, particularly from the perspective of unique fragments and scaffolds, MNPs usually possess more long chains and large rings, especially 8- to 10-membered rings. MNPs also have more nitrogen atoms and halogens, notably bromines, and fewer oxygen atoms, suggesting that MNPs may be synthesized by more diverse biosynthetic pathways than TNPs. Analysis of the frequently occurring Murcko frameworks in MNPs and TNPS also reveals a striking difference between MNPs and TNPs. The scaffolds of the former tend to be longer and often contain ester bonds connected to 10-membered rings, while the scaffolds of the latter tend to be shorter and often bear more stable ring systems and bond types. Besides, the prediction from the naïve Bayesian drug-likeness classification model suggests that most compounds in MNPs and TNPs are drug-like, although MNPs are slightly more drug-like than TNPs. We believe that MNPs and TNPs with novel drug-like scaffolds have great potential to be drug leads or drug candidates in drug discovery campaigns.