Large-Scale Target Identification of Herbal Medicine Using a Reverse Docking Approach

Identification and Analysis of Anticancer Therapeutic Targets from the Polysaccharide Krestin (PSK) and Polysaccharopeptide (PSP) Using Inverse Docking

The natural compounds PSK and PSP have antitumor and immunostimulant properties. These pharmacological benefits have been documented in vitro and in vivo, although there is no information in silico which describes the action mechanisms at the molecular level. In this study, the inverse docking method was used to identify the interactions of PSK and PSP with two local databases: BPAT with 66 antitumor proteins, and BPSIC with 138 surfaces and intracellular proteins. This led to the identification interactions and similarities of PSK and the AB680 inhibitor in the active site of CD73. It was also found that PSK binds to CD59, interacting with the amino acids APS22 and PHE23, which coincide with the rlLYd4 internalization inhibitor. With the isoform of the K-RAS protein, PSK bonded to the TYR32 amino acid at switch 1, while with BAK it bonded to the region of the α1 helix, while PSP bonded to the activation site and the C-terminal and N-terminal ends of that helix. In Bcl-2, PSK interacted at the binding site of the Venetoclax inhibitor, showing similarities with the amino acids ASP111, VAL133, LEU137, MET115, PHE112, and TYR108, while PSP had similarities with THR132, VAL133, LEU137, GLN118, MET115, APS111, PHE112, and PHE104.

AI empowering traditional Chinese medicine?

For centuries, Traditional Chinese Medicine (TCM) has been a prominent treatment method in China, incorporating acupuncture, herbal remedies, massage, and dietary therapy to promote holistic health and healing. TCM has played a major role in drug discovery, with over 60% of small-molecule drugs approved by the FDA from 1981 to 2019 being derived from natural products. However, TCM modernization faces challenges such as data standardization and the complexity of TCM formulations. The establishment of comprehensive TCM databases has significantly improved the efficiency and accuracy of TCM research, enabling easier access to information on TCM ingredients and encouraging interdisciplinary collaborations. These databases have revolutionized TCM research, facilitating advancements in TCM modernization and patient care. In addition, advancements in AI algorithms and database data quality have accelerated progress in AI for TCM. The application of AI in TCM encompasses a wide range of areas, including herbal screening and new drug discovery, diagnostic and treatment principles, pharmacological mechanisms, network pharmacology, and the incorporation of innovative AI technologies. AI also has the potential to enable personalized medicine by identifying patterns and correlations in patient data, leading to more accurate diagnoses and tailored treatments. The potential benefits of AI for TCM are vast and diverse, promising continued progress and innovation in the field.

Exploring 3-O-glycosylations of 20(R)-dammarane ginsenosides and the catalytic mechanism underlying the stereoselectivity with the combined assistance of AlphaFold 2 and molecular docking

Glycosylation at C3-OH is the favorable modification for pharmaceutical activities and diversity expansion of 20(R)-dammarane ginsenosides. The 3-O-glycosylation, exclusively occurring in 20(R)-PPD ginsenosides, has never been achieved in 20(R)-PPT ginsenosides. Herein, 3-O-glycosylation of 20(R)-PPT enabled by a glycosyltransferase (GT) OsSGT2 was achieved with the combined assistance of AlphaFold 2 and molecular docking. Firstly, we combined AlphaFold2 algorithm and molecular docking to predict interactions between 20(R)-PPT and candidate GTs. A catalytically favorable binding geometry was thus identified in the OsSGT2-20(R)-PPT complex, suggesting OsSGT2 might act on 20(R)-PPT. The enzymatic assays demonstrated that OsSGT2 reacted with varied sugar donors to form 20(R)-PPT 3-O-glycosides, exhibiting donor promiscuity. Additionally, OsSGT2 displayed acceptor promiscuity, catalyzing 3-O-glucosylation of 20(R/S)-PPT, 20(R/S)-PPD and 20(R/S)-Rh1, respectively. Protein engineering on OsSGT2 was thus performed to probe its catalytic mechanism underlying its stereoselectivity. The W207A mutant preferred 20(S)-dammarane aglycons, while F395Q/A396G(QG) displayed a conversion enhancement towards both 20(R/S)-dammarane aglycons. The QG mutant was then used to synthesize 20(R)-PPT 3-O-glucoside, which displayed a moderate angiotensin-converting enzyme inhibitory effect with an IC50 of 27.5 ± 4.7 μM, superior to that of its 20(S)-epimer, with the combined assistance of target fishing and reverse docking. The water solubility of 20(R)-PPT 3-O-glucoside increased as well.

TAIGET: A small-molecule target identification and annotation web server

Background: Accurate target identification of small molecules and downstream target annotation are important in pharmaceutical research and drug development.

Methods: We present TAIGET, a friendly and easy to operate graphical web interface, which consists of a docking module based on AutoDock Vina and LeDock, a target screen module based on a Bayesian–Gaussian mixture model (BGMM), and a target annotation module derived from >14,000 cancer-related literature works.

Results: TAIGET produces binding poses by selecting ≤5 proteins at a time from the UniProt ID-PDB network and submitting ≤3 ligands at a time with the SMILES format. Once the identification process of binding poses is complete, TAIGET then screens potential targets based on the BGMM. In addition, three medical experts and 10 medical students curated associations among drugs, genes, gene regulation, cancer outcome phenotype, 2,170 cancer cell types, and 73 cancer types from the PubMed literature, with the aim to construct a target annotation module. A target-related PPI network can be visualized by an interactive interface.

Conclusion: This online tool significantly lowers the entry barrier of virtual identification of targets for users who are not experts in the technical aspects of virtual drug discovery. The web server is available free of charge at http://www.taiget.cn/.

Applied Machine Learning Toward Drug Discovery Enhancement: Leishmaniases as a Case Study

Drug discovery (DD) research is a complex field with a high attrition rate. Machine learning (ML) approaches combined to chemoinformatics are of valuable input to this field. We, herein, focused on implementing multiple ML algorithms that shall learn from different molecular fingerprints (FPs) of 65 057 molecules that have been identified as active or inactive against Leishmania major promastigotes. We sought to build a classifier able to predict whether a given molecule has the potential of being anti-leishmanial or not. Using the RDkit library, we calculated 5 molecular FPs of the molecules. Then, we implemented 4 ML algorithms that we trained and tested for their ability to classify the molecules into active/inactive classes based on their chemical structure, encoded by the molecular FPs. Best performers were random forest (RF) and support vector machine (SVM), while atom-pair and topology torsion FPs were the best embedding functions. Both models were further assessed on different stratification levels of the dataset and showed stable performances. At last, we used them to predict the potential of molecules within the Food and Drug Administration (FDA)-approved drugs collection to present anti-Leishmania effects. We ranked these drugs according to their anti-Leishmanial probability and obtained in total seven anti-Leishmania agents, previously described in the literature, within the top 10 of each model. This validates the robustness of the approach, the algorithms, and FPs choices as well as the importance of the dataset size and content. We further engaged these molecules into reverse docking experiments on 3D crystal structures of seven well-studied Leishmania drug targets and could predict the molecular targets for 4 drugs. The results bring novel insights into anti-Leishmania compounds.

Improving protein-ligand docking and screening accuracies by incorporating a scoring function correction term

Scoring functions are important components in molecular docking for structure-based drug discovery. Traditional scoring functions, generally empirical- or force field-based, are robust and have proven to be useful for identifying hits and lead optimizations. Although multiple highly accurate deep learning- or machine learning-based scoring functions have been developed, their direct applications for docking and screening are limited. We describe a novel strategy to develop a reliable protein–ligand scoring function by augmenting the traditional scoring function Vina score using a correction term (OnionNet-SFCT). The correction term is developed based on an AdaBoost random forest model, utilizing multiple layers of contacts formed between protein residues and ligand atoms. In addition to the Vina score, the model considerably enhances the AutoDock Vina prediction abilities for docking and screening tasks based on different benchmarks (such as cross-docking dataset, CASF-2016, DUD-E and DUD-AD). Furthermore, our model could be combined with multiple docking applications to increase pose selection accuracies and screening abilities, indicating its wide usage for structure-based drug discoveries. Furthermore, in a reverse practice, the combined scoring strategy successfully identified multiple known receptors of a plant hormone. To summarize, the results show that the combination of data-driven model (OnionNet-SFCT) and empirical scoring function (Vina score) is a good scoring strategy that could be useful for structure-based drug discoveries and potentially target fishing in future.

LigTMap: ligand and structure-based target identification and activity prediction for small molecular compounds

Target prediction is a crucial step in modern drug discovery. However, existing experimental approaches to target prediction are time-consuming and costly. Here, we introduce LigTMap, an online server with a fully automated workflow that can identify protein targets of chemical compounds among 17 classes of therapeutic proteins extracted from the PDBbind database. It combines ligand similarity search with docking and binding similarity analysis to predict putative targets. In the validation experiment of 1251 compounds, targets were successfully predicted for more than 70% of the compounds within the top-10 list. The performance of LigTMap is comparable to the current best servers SwissTargetPrediction and SEA. When testing with our newly compiled compounds from recent literature, we get improved top 10 success rate (66% ours vs. 60% SwissTargetPrediction and 64% SEA) and similar top 1 success rate (45% ours vs. 51% SwissTargetPrediction and 41% SEA). LigTMap directly provides ligand docking structures in PDB format, so that the results are ready for further structural studies in computer-aided drug design and drug repurposing projects. The LigTMap web server is freely accessible at https://cbbio.online/LigTMap. The source code is released on GitHub (https://github.com/ShirleyWISiu/LigTMap) under the BSD 3-Clause License to encourage re-use and further developments.

How Physiologic Targets Can Be Distinguished from Drug-Binding Proteins

In clinical trials, some drugs owe their effectiveness to off-target activity. This and other observations raise a possibility that many studies identifying targets of drugs are incomplete. If off-target proteins are pharmacologically important, it will be worthwhile to identify them early in the development process to gain a better understanding of the molecular basis of drug action. Herein, we outline a multidisciplinary strategy for systematic identification of physiologic targets of drugs in cells. A drug-binding protein whose genetic disruption yields very similar molecular effects as treatment of cells with the drug may be defined as a physiologic target of the drug. For a drug developed with a rational approach, it is desirable to verify experimentally that a protein used for hit optimization in vitro remains the sole polypeptide recognized by the drug in a cell. SIGNIFICANCE STATEMENT: A body of evidence indicates that inactivation of many drug-binding proteins may not cause the pharmacological effects triggered by the drugs. A multidisciplinary cell-based approach can be of great value in identifying the physiologic targets of drugs, including those developed with target-based strategies.

Proteomic screening identifies the direct targets of chrysin anti-lipid depot in adipocytes

ETHNOPHARMACOLOGICAL RELEVANCE

Overweight/obesity was mentioned by many countries as an obstacle to good health and long life, which increases risk of diseases and disorders. Previous studies suggested that the chronic low-grade inflammation present in the body was considered as the essential pathogenesis for obesity. Chrysin is extracted from traditional Chinese medicine Oroxylum indicum (Linn.) Kurz and plays a superior anti-obesity role. Chrysin could reduce the lipid depot by inhibiting the obesity-related inflammation in adipose tissue. However, the target protein for chrysin to exert its anti-obesity role are not verified.

AIM OF STUDY

The present study aimed to screen and validate the target protein for chrysin to reduce the lipid depot in palmitic acid-induced 3T3-L1 adipocytes.

MATERIALS AND METHODS

Obesity model was established employing 0.5 mmol/L palmitic acid-induced 3T3-L1 adipocytes through "Cocktails" method. Two-dimensional gel electrophoresis (2-DE) combined with liquid chromatography-mass spectrometry (LC-MS) was applied to analyze the differentially expressed proteins for chrysin intervention by lipid formation in adipocytes. Gene silencing was utilized to decrease gene expression of the candidate proteins, then production of triglyceride in 3T3-L1 was detected by triglycerides assay to determine the target proteins. Ultraviolet (UV) absorption together with fluorescence spectra validated the direct target proteins of chrysin. They also computed the correlation constants of combination between chrysin and the target proteins. Molecular docking was further employed to identify the main binding amino acids between chrysin and the target protein.

RESULTS

2-DE combined with LC-MS screened four candidate proteins which were related to metabolism and inflammation. The production of triglycerides in 3T3-L1 was reduced after decreasing gene expression of Annexin A2 (ANXA2), 60 kDa heat shock protein (HSP-60) and succinyl-CoA:3-ketoacid coenzyme A transferase 1 (SCOT-S), respectively. UV spectrum showed that the absorbance spectra of ANXA2 from 260 to 300 nm shifted upwards along with the increase in chrysin concentration, meanwhile the absorbance spectra of HSP-60 from 200 to 220 nm and from 265 to 280 nm shifted slightly upwards along with the increase in chrysin concentrations. The results indicated the conjugated structures between chrysin and ANXA2 or HSP-60. Fluorescence quenching further suggested a spontaneous interaction between chrysin and ANXA2 or HSP-60. Finally, molecular docking identified the main binding amino acids between ANXA2 and chrysin were Ser22, Tyr24, Pro267, Val298, Asp299, and Lys302.

CONCLUSIONS

Chrysin can reduce the amount of triglycerides by directly downregulating the inflammation-related target proteins ANXA2 and HSP-60, exerting an anti-obesity role.

The latest automated docking technologies for novel drug discovery

INTRODUCTION

Molecular docking has been consolidated as one of the most important methods in the molecular modeling field. It has been recognized as a prominent tool in the study of protein-ligand complexes, to describe intermolecular interactions, to accurately predict poses of multiple ligands, to discover novel promising bioactive compounds. Molecular docking methods have evolved in terms of their accuracy and reliability; but there are pending issues to solve for improving the connection between the docking results and the experimental evidence.

AREAS COVERED

In this article, the author reviews very recent innovative molecular docking applications with special emphasis on reverse docking, treatment of protein flexibility, the use of experimental data to guide the selection of docking poses, the application of Quantum mechanics(QM) in docking, and covalent docking.

EXPERT OPINION

There are several issues being worked on in recent years that will lead to important breakthroughs in molecular docking methods in the near future These developments are related to more efficient exploration of large datasets and receptor conformations, advances in electronic description, and the use of structural information for guiding the selection of results.

In silico study for prediction of novel bioactivities of the endophytic fungal alkaloid, mycoleptodiscin B for human targets

Mycoleptodiscin B is a natural product extracted from the endophytic fungus Mycoleptodiscus sp. found in Sri Lanka and Panama with experimentally unexplored activities for human targets. In this study, a computational methodology was applied to determine druggable targets of mycoleptodiscin B. According to the computational toxicity and pharmacokinetics assessment, mycoleptodiscin B was proven to be a suitable drug candidate. Druggable targets for this compound, aromatase, acidic plasma glycoprotein and androgen receptor, were predicted using reverse docking. A two-step validation of those targets was performed using conventional molecular docking and molecular dynamic (MD) simulations, resulting in aromatase being determined as the potential therapeutic target. Based on molecular mechanics/Generalized Born Surface Area (GBSA) free energies and ligand stability inside the active site cavity during its 120 ns MD run, it can be concluded that mycoleptodiscin B is a potent aromatase inhibitor and could be subjected to further in vitro and in vivo experiments in the drug development pipeline. Consequently, natural product chemists can quickly identify the hidden medicinal properties of their miracle compounds using the computational approach applied in this research.

New Tricks for Old Guys: Recent Developments in the Chemistry, Biochemistry, Applications and Exploitation of Selected Species from the Lamiaceae Family

Lamiaceae is one of the largest families of flowering plants comprising about 250 genera and over 7,000 species. Most of the plants of this family are aromatic and therefore important source of essential oils. Lamiaceae are widely used as culinary herbs and reported as medicinal plants in several folk traditions. In the Mediterranean area oregano, sage, rosemary, thyme and lavender stand out for geographical diffusion and variety of uses. The aim of this review is to provide recent data dealing with the phytochemical and pharmacological studies, and the more recent applications of the essential oils and the non-volatile phytocomplexes. This literature survey suggests how the deeper understanding of biomolecular processes in the health and food sectors as per as pest control bioremediation of cultural heritage, or interaction with human microbiome, fields, leads to the rediscovery and new potential applications of well-known plants.

Identification of the Molecular Mechanisms of Peimine in the Treatment of Cough Using Computational Target Fishing

Peimine (also known as verticine) is the major bioactive and characterized compound of Fritillariae Thunbergii Bulbus, a traditional Chinese medicine that is most frequently used to relieve a cough. Nevertheless, its molecular targets and mechanisms of action for cough are still not clear. In the present study, potential targets of peimine for cough were identified using computational target fishing combined with manual database mining. In addition, protein-protein interaction (PPI), gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed using, GeneMANIA and Database for Annotation, Visualization and Integrated Discovery (DAVID) databases respectively. Finally, an interaction network of drug-targets-pathways was constructed using Cytoscape. The results identified 23 potential targets of peimine associated with cough, and suggested that MAPK1, AKT1 and PPKCB may be important targets of pemine for the treatment of cough. The functional annotations of protein targets were related to the regulation of immunological and neurological function through specific biological processes and related pathways. A visual representation of the multiple targets and pathways that form a network underlying the systematic actions of peimine was generated. In summary, peimine is predicted to exert its systemic pharmacological effects on cough by targeting a network composed of multiple proteins and pathways.

Casticin inhibits nasopharyngeal carcinoma growth by targeting phosphoinositide 3-kinase

Background

Casticin, an isoflavone compound extracted from the herb Fructus Viticis, has demonstrated anti-inflammatory and anticancer activities and properties. The aim of this study was to investigate the effects and mechanisms of casticin in nasopharyngeal carcinoma (NPC) cells and to determine its potential for targeted use as a medicine.

Methods

NPC cells were used to perform the experiments. The CCK‑8 assay and colony formation assays were used to assess cell viability. Flow cytometry was used to measure the cell cycle and apoptosis analysis (annexin V/PI assay). A three-dimensional (3D) tumour sphere culture system was used to characterize the effect of casticin on NPC stem cells. In silico molecular docking prediction and high-throughput KINOME scan assays were used to evaluate the binding of casticin to phosphoinositide 3-kinase (PI3K), including wild-type and most of mutants variants. We also used the SelectScreen assay to detect the IC50 of ATP activity in the active site of the target kinase. Western blotting was used to evaluate the changes in key proteins involved cell cycle, apoptosis, stemness, and PI3K/protein kinase B (AKT) signalling. The effect of casticin treatment in vivo was determined by using a xenograft mouse model.

Results

Our results indicate that casticin is a new and novel selective PI3K inhibitor that can significantly inhibit NPC proliferation and that it induces G2/GM arrest and apoptosis by upregulating Bax/BCL2 expression. Moreover, casticin was observed to affect the self-renewal ability of the nasopharyngeal carcinoma cell lines, and a combination of casticin with BYL719 was observed to induce a decrease in the level of the phosphorylation of mTORC1 downstream targets in BYL719-insensitive NPC cell lines.

Conclusion

Casticin is a newly emerging selective PI3K inhibitor with potential for use as a targeted therapeutic treatment for nasopharyngeal carcinoma. Accordingly, casticin might represent a novel and effective agent against NPC and likely has high potential for combined use with pharmacological agents targeting PI3K/AKT.