Docking Server Research Articles

Deciphering significant interaction between Clp1 (CF IA) and Ssu72 (CPF) in pre-mRNA processing via in silico approaches

The cleavage and polyadenylation step are indispensable for pre-mRNA processing in eukaryotes. Defective 3′- end maturation of precursor mRNA has catastrophic effects, leading to several diseases in humans. This processing is orchestrated by a complex machinery comprising more than 20 proteins in Saccharomyces cerevisiae. Endonucleolytic cleavage followed by the addition of poly(A) tail at the 3′-end of the precursor mRNA requires CPF, CF IA and CF IB proteins. Clp1, a protein factor of the CF IA sub-unit is indispensable for the functioning of this machinery. Based on in silico analysis including molecular docking via different docking servers and molecular dynamics (MD) simulations, the current study provides key evidence of the Clp1 N-terminal (1–100 amino acids) domain’s interaction with Ssu72. MD simulations consolidate this binding between Clp1 and Ssu72. Our study presents strong evidence of a model where Clp1 (CF IA) associates with Ssu72 (CPF) and both the proteins are vital for tethering the complex for mediating cleavage and polyadenylation reaction during the key events of pre-mRNA 3′-end processing. These findings may pave the way to decipher the individual roles of Clp1 and Ssu72 during pre-mRNA maturation.

Screening miRNAs to Hinder the Tumorigenesis of Renal Clear Cell Carcinoma Associated with KDR Expression.

This study delved into the role of Kinase Insert Domain Receptor (KDR) and its associated miRNAs in renal cell carcinoma through an extensive computational analysis. The potential of our findings to guide future research in this area is significant. Our methods, which included the use of UALCAN and GEPIA2 databases, as well as miRDB, MirDIP, miRNet v2.0, miRTargetLink, MiEAA v2.1, TarBase v8.0, INTERNET, and miRTarBass, were instrumental in identifying the regulation of miRNA associated with KDR expression. The predicted miRNA was validated with the TCGA-KIRC patients' samples by implementing CancerMIRNome. The TargetScanHuman v8.0 was implemented to identify the associations between human miRNAs and KDR. A Patch Dock server analyzed the interactions between hsa-miR-200b-3p-KDR and hsa-miR-200b-3p with KDR. The KDR expression rate was investigated in the Kidney Renal Cell Carcinoma (KIRC) samples, and adjacent normal tissues revealed that the expression rate was significantly higher than the normal samples, which was evident from the strong statistical significance (P = 1.63e-12). Likely, the KDR ex-pression rate was estimated as high at tumor grade 1 and gradually decreased till the metastasis grade, reducing the survival rate of the KIRC patients. To identify these signals early, we predicted a miRNA that could trigger the expression of KDR. Furthermore, we uncovered the potential associations between miR-200c-3p expressions by regulating KDR towards the progression of KIRC. Upon examining the outcome, it became evident that miR-200c-3p was significantly down-regulated in KIRC compared to the normal samples. Moreover, the negative correlation was obtained for hsa-miR-200c-3p (R = - 0.276) along with the KDR expression describing that the increased rate of hsa-miR-200c-3p might reduce the KDR expression rate, which may suppress the KIRC initiation or progres-sion. The in-silico analysis indicated that the significant increase in KDR expression during the initiation of KIRC could serve as an early diagnostic marker. Moreover, KDR could be utilized to identify advancements in KIRC stages. Additionally, hsa-miR-200c-3p was identified as a potential regulator capable of downregulating and upregulating KDR expression among the 24 miRNAs screened. This find-ing holds promise for future research endeavors. Concurrent administration of the FDA-approved 5-fluor-ouracil with KIRC drugs, such as sorafenib, zidovudine, and everolimus, may have the potential to en-hance the therapeutic efficacy in downregulating hsa-miR-200c-3p. However, further in vitro studies are imperative to validate these findings and gain a comprehensive understanding of the intricate regulatory interplay involving hsa-miR-200c-3p, KDR, 5-fluorouracil, and other FDA-approved drugs for the treat-ment of KIRC. This will facilitate the identification of KIRC stage progression and its underlying pre-ventative mechanisms.

ATM kinase phosphorylates Ser15 of p53 in a pH-dependent manner

The phosphorylation of Ser15 in the transactivation domain (TAD) of the tumor suppressor protein 53 (p53) by ataxia-telangiectasia mutated (ATM) kinase is a crucial step in the tumor suppressor function of p53. An understanding of the factors that affect the rate of Ser15 phosphorylation may provide new strategies for the manipulation of the ATM-p53 pathway in cancer therapy. In this study, the effect of electrostatic interactions between ATM and p53 was investigated by measuring the phosphorylation of Ser15 at varying pH ranges from 5 to 9. To achieve this, two different kinase assay methods were utilized: the ELISA technique, which directly quantifies the phosphorylated Ser15, and the Universal Kinase Assay, which assesses the formation of ADP. The results revealed that Ser15 phosphorylation was pH-dependent, with higher phosphorylation rates observed in the alkaline range. To ascertain whether the lower phosphorylation rates observed at acidic pH were due to protein denaturation, a pH-dependent solubility profile was generated using the CamSol server. The obtained results demonstrated comparable solubility rates within the pH range of the kinase assays performed. Furthermore, the significance of negatively charged residues in TAD1-39 was evaluated by substituting Asp and Glu residues with hydrophobic and uncharged hydrophilic residues in TAD1-39 using ChimeraX and subsequently comparing their interactions with the ATM using the protein-protein docking server HADDOCK2.4. The results of the docking simulations indicated that the alteration of negatively charged residues with uncharged ones resulted in a reduction in the efficiency of the interaction between the ATM and TAD1-39. In conclusion, it can be stated that electrostatic interactions between the ATM and TAD are important for optimal Ser15 phosphorylation.

2-Propyl-1H-imidazole-4,5-dicarboxylate acid supported cu/ni/fe/co complexes: synthesis, biological and catalytic evaluation

Herein, we report 2-propyl-1H-imidazole-4,5-dicarboxylate complexes of Cu/Co/Fe/Ni metal ions. The complexes have been characterized using powder X-ray diffraction, FTIR, and mass spectrometry. Thermal gravimetric analysis (TGA) established their thermal stability, enduring temperatures of up to 650 °C. The interaction of Bovine Serum Albumin (BSA) and DNA with these complexes was examined, revealing a significant binding affinity up to 70%. Fluorescence quenching experiments also showed the binding of complexes with biomolecules. These interactions were substantiated by molecular docking studies against the proteins 3v03 (BSA) and 1d28 (DNA), shedding light on the essential interactions driving their potential medicinal activity. The Cu complex displayed a notably high GSC score and a substantial area as determined through the Patch Dock server. Free radical interaction with 2,2-diphenylpicrylhydrazyl (DPPH) shows their antioxidant activity where Cu and Fe complexes expressed greater activity as compared to others. The catalytic properties of the compounds were assessed using p-nitrophenol (PNP) where the complexes exhibited reducing activity, leading to a conversion of over 90% of PNP into p-aminophenol. Analysis revealed a pseudo-second-order model closely aligned with experimental values, indicating second-order reduction kinetics.

Specific interaction from different Aβ42 peptide fragments to α7nAChR-A study of molecular dynamics simulation.

Existing researches confirmed that β amyloid (Aβ) has a high affinity for the α7 nicotinic acetylcholine receptor (α7nAChR), associating closely to Alzheimer's disease. The majority of related studies focused on the experimental reports on the neuroprotective role of Aβ fragment (Aβx), however, with a lack of investigation into the most suitable binding region and mechanism of action between Aβ fragment and α7nAChR. In the study, we employed four Aβ1-42 fragments Aβx, Aβ1-16, Aβ10-16, Aβ12-28, and Aβ30-42, of which the first three were confirmed to play neuroprotective roles upon directly binding, to interact with α7nAChR. The protein-ligand docking server of CABS-DOCK was employed to obtain the α7nAChR-Aβx complexes. Only the top α7nAChR-Aβx complexes were used to perform all-atom GROMACS dynamics simulation in combination with Charmm36 force field, by which α7nAChR-Aβx interactions' dynamic behavior and specific locations of these different Aβx fragments were identified. MM-PBSA calculations were also done to estimate the binding free energies and the different contributions from the residues in the Aβx. Two distinct results for the first three and fourth Aβx fragments in binding site, strength, key residue, and orientation, account for why the fourth fails to play a neuroprotective role at the molecular level.

Designing a Conserved Immunogenic Peptide Construct from the Nucleocapsid Protein of Puumala orthohantavirus.

Puumala orthohantavirus (PUUV) is an emerging zoonotic virus endemic to Europe and Russia that causes nephropathia epidemica, a mild form of hemorrhagic fever with renal syndrome (HFRS). There are limited options for treatment and diagnosis of orthohantavirus infection, making the search for potential immunogenic candidates crucial. In the present work, various bioinformatics tools were employed to design conserved immunogenic peptides containing multiple epitopes of PUUV nucleocapsid protein. Eleven conserved peptides (90% conservancy) of the PUUV nucleocapsid protein were identified. Three conserved peptides containing multiple T and B cell epitopes were selected using a consensus epitope prediction algorithm. Molecular docking using the HPEP dock server demonstrated strong binding interactions between the epitopes and HLA molecules (ten alleles for each class I and II HLA). Moreover, an analysis of population coverage using the IEDB database revealed that the identified peptides have over 90% average population coverage across six continents. Molecular docking and simulation analysis reveal a stable interaction with peptide constructs of chosen immunogenic peptides and Toll-like receptor-4. These computational analyses demonstrate selected peptides' immunogenic potential, which needs to be validated in different experimental systems.

Enhancing explainable SARS-CoV-2 vaccine development leveraging bee colony optimised Bi-LSTM, Bi-GRU models and bioinformatic analysis

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a single-stranded RNA virus that caused the outbreak of the coronavirus disease 2019 (COVID-19). The COVID-19 outbreak has led to millions of deaths and economic losses globally. Vaccination is the most practical solution, but finding epitopes (antigenic peptide regions) in the SARS-CoV-2 proteome is challenging, costly, and time-consuming. Here, we proposed a deep learning method based on standalone Recurrent Neural networks to predict epitopes from SARS-CoV-2 proteins easily. We optimised the standalone Bidirectional Long Short-Term Memory (Bi-LSTM) and Bidirectional Gated Recurrent Unit (Bi-GRU) with a bioinspired optimisation algorithm, namely, Bee Colony Optimization (BCO). The study shows that LSTM-based models, particularly BCO-Bi-LSTM, outperform all other models and achieve an accuracy of 0.92 and AUC of 0.944. To overcome the challenge of understanding the model predictions, explainable AI using the Shapely Additive Explanations (SHAP) method was employed to explain how Blackbox models make decisions. Finally, the predicted epitopes led to the development of a multi-epitope vaccine. The multi-epitope vaccine effectiveness evaluation is based on vaccine toxicity, allergic response risk, and antigenic and biochemical characteristics using bioinformatic tools. The developed multi-epitope vaccine is non-toxic and highly antigenic. Codon adaptation, cloning, gel electrophoresis assess genomic sequence, protein composition, expression and purification while docking and IMMSIM servers simulate interactions and immunological response, respectively. These investigations provide a conceptual framework for developing a SARS-CoV-2 vaccine.

In Silico Molecular Docking of Phytochemicals of Murraya koenigii Against Streptococcus mutans.

Background The curry leaf tree, Murraya koenigii, is a tropical to subtropical tree in the family Rutaceae that is native to Asia. The plant parts are shown to have potential antimicrobial, antioxidant, antifungal, antidiarrheal, antidiabetic, anticancer, and anti-inflammatory properties. Streptococcus mutans is a facultative anaerobic, Gram-positive cocci, a common inhabitant of the human oral cavity that forms biofilms, contributing to dental caries. Aim The study aimed to analyze the inhibitory potential of phytocompounds in M. koenigii against the oral pathogen S. mutans. Materials and methods The protein and ligand were prepared, and molecular docking was carried out using the Hex protein docking server. The PyMOL program was used to view, analyze, and annotate the docked complex. The interaction of the drug, including the mechanism of action, and predicted adverse effects were predicted using the Way2Drug PASS Online server. The absorption, distribution, metabolism, excretion, and toxicity properties of the drug candidates were analyzed using the SwissADME online server. Results The study identified O-methyl murrayamine, koenigine, koenigicine, and murrayone as having inhibitory potential against the glycosyltransferase protein of S. mutans. Among the four compounds analyzed for docking, koenigicine had the lowest E-score, indicating a strong interaction with the receptor. Among the four compounds analyzed, murrayone had a high topological polar surface area score, while all four compounds had similar bioavailability scores. Conclusion This study concluded that O-methyl murrayamine, koenigine, koenigicine, and murrayone exhibit potent inhibitory potential against S. mutans. M. koenigiileaf extract can be used in toothpaste as an antibacterial agent to protect teeth against dental caries. These findings are important for the potential use of the above compound to act as an anticariogenic agent in oral health applications.

Exploring Neophytadiene from Ampelocissus araneosa: A Molecular Docking Approach to Inhibit Biofilm Formation in Staphylococcus aureus

Background: The urgent need to combat antimicrobial resistance and biofilm production has prompted the exploration of alternative remedies, such as plant extracts, as viable substitutes for antibiotics.
 Aim: This study aims to assess the phytocompound from A. araneosa and elucidate its inhibitory mechanisms against biofilm formation by S. aureus through molecular docking techniques.
 Methodology: Plant material collection and extract preparation were conducted, followed by GCMS analysis of A. araneosa and protein modeling of Biofilm_IcaR from S. aureus. Drug 3D prediction and drug docking studies were performed using Neophytadiene and Metronidazole, with the Patch Dock server for the exploration of molecular interactions between the compounds and protein targets.
 Results and Implications: The study identified bioactive compounds from A. araneosa, including neophytadiene, 3-eicosyne, phytol, and stigmasterol, known for their robust antimicrobial properties. Molecular docking studies demonstrated that neophytadiene exhibited a higher binding affinity to the Biofilm_IcaR protein compared to the control drug, Metronidazole, suggesting its efficacy as an inhibitor. These findings provide molecular insights into the potential of neophytadiene as a therapeutic agent against biofilm-producing S. aureus, with implications for the development of novel antimicrobial treatments.

Investigation of the Effect of Main Components of Wild Thyme on Covid-19 by Computational Methods

Aromatic plant species of the genus thymus have an important role as they have therapeutic properties such as antirheumatic, antiseptic, antispasmodic, antimicrobial, cardiac, carminative, diuretic and expectorant. It is also known that such plants strengthen the immune system and help cope with infectious diseases such as colds and flu. In this study, the effects of thymol, p-cymene, -terpinene, bornyl acetate, borneol, carvacrol, thymol methyl ether, thymol acetate, which are the main components of wild thyme (thymus serpyllum L.), on Covid-19 were investigated at the molecular level. Optimizations and molecular docking were done in Docking Server with the MMFF94 method. Major components of wild thyme were docked separately against 6LU7 protein representing the first gene form of Covid-19 and 7KDL protein representing the mutated form. Docking poses and binding energies between target proteins and wild thyme components were calculated. The results were compared with favipiravir, an antiviral drug developed against influenza virus and also used in the treatment of Covid-19. It was found that the thymol molecule, one of the main components of wild thyme, has the highest biological activity against both 6LU7 and 7KDL protein chains of Covid-19.

Design and Discovery of Genistein-based Drugs as a Potential Tyrosine Kinase Inhibitor for Lung Adenocarcinoma through Hybrid In-silico Methods

This research employs a comprehensive approach to investigate potential therapeutic candidates for lung adenocarcinoma through drug-drug transcriptomic similarity analysis and molecular docking simulations. Using the Connectivity Map Touchstone tool, we identified compounds with high transcriptomic similarity to genistein, revealing potential shared mechanisms of action. The selected compounds, including avrainvillamide-analog-2, were further assessed through molecular docking simulations against the tyrosine kinase inhibitor (TKI) enzyme. Avrainvillamide-analog-2 exhibited a remarkable binding affinity in pocket C2, interacting with key amino acids. The results provide valuable insights into the pharmacological properties of the identified compounds, laying the groundwork for future experimental validations and drug development initiatives. Additionally, cavities detection by CB Dock server and structural refinement by PDB REDO contribute to the overall understanding of ligand binding and protein structure. This integrative approach offers a holistic perspective for identifying potential lead compounds and understanding their molecular interactions, facilitating the rational design of novel therapeutics for lung adenocarcinoma.

Cryptococcal proteases exhibit the potential to activate the latent SARS-CoV-2 spike protein

BackgroundThe COVID-19 pandemic has affected more than 650 million people and resulted in over 6.8 million deaths. Notably, the disease could co-manifest with microbial infections, like cryptococcosis, which also presents as a primary lung infection. ObjectiveIn this contribution, we sought to determine if cryptococcal supernatant (which contains secreted furin-like proteases) could activate the SARS-CoV-2 spike protein. MethodsMolecular docking of the crystal structures of the SARS-CoV-2 spike protein (target) and selected cryptococcal proteases (ligands) was executed using the high ambiguity driven protein-protein docking (HADDOCK) server, with the furin protease serving as a reference ligand. The furin protease is found in human cells and typically activates the SARS-CoV-2 spike protein.Importantly, in order to provide experimental evidence for enzymatic activity, we also assessed the biochemical efficiency of cryptococcal proteases to initiate viral entry into HEK-293 T cells by SARS-CoV-2 spike pseudotyped Lentivirus. ResultsWe show that the selected cryptococcal proteases could interact with the spike protein, and some had a better or comparable binding affinity for the spike protein than furin protease following an in silico comparative analysis of the molecular docking parameters. Furthermore, it was noted that the biochemical efficiency of the cryptococcal supernatant to transduce HEK-293 T cells with SARS-CoV-2 pseudovirions was comparable (p > 0.05) to that of recombinant furin. ConclusionsTaken together, these data show that cryptococcal proteases could activate the SARS-CoV-2 spike protein. In practice, it may be critical to determine if patients have an underlying cryptococcal infection, as this microbe could secrete proteases that may further activate the SARS-CoV-2 viral particles, thus undermining COVID-19 intervention measures.

Exploration and validation of key genes associated with early lymph node metastasis in thyroid carcinoma using weighted gene co-expression network analysis and machine learning.

Thyroid carcinoma (THCA), the most common endocrine neoplasm, typically exhibits an indolent behavior. However, in some instances, lymph node metastasis (LNM) may occur in the early stages, with the underlying mechanisms not yet fully understood. LNM potential was defined as the tumor's capability to metastasize to lymph nodes at an early stage, even when the tumor volume is small. We performed differential expression analysis using the 'Limma' R package and conducted enrichment analyses using the Metascape tool. Co-expression networks were established using the 'WGCNA' R package, with the soft threshold power determined by the 'pickSoftThreshold' algorithm. For unsupervised clustering, we utilized the 'ConsensusCluster Plus' R package. To determine the topological features and degree centralities of each node (protein) within the Protein-Protein Interaction (PPI) network, we used the CytoNCA plugin integrated with the Cytoscape tool. Immune cell infiltration was assessed using the Immune Cell Abundance Identifier (ImmuCellAI) database. We applied the Least Absolute Shrinkage and Selection Operator (LASSO), Support Vector Machine (SVM), and Random Forest (RF) algorithms individually, with the 'glmnet,' 'e1071,' and 'randomForest' R packages, respectively. Ridge regression was performed using the 'oncoPredict' algorithm, and all the predictions were based on data from the Genomics of Drug Sensitivity in Cancer (GDSC) database. To ascertain the protein expression levels and subcellular localization of genes, we consulted the Human Protein Atlas (HPA) database. Molecular docking was carried out using the mcule 1-click Docking server online. Experimental validation of gene and protein expression levels was conducted through Real-Time Quantitative PCR (RT-qPCR) and immunohistochemistry (IHC) assays. Through WGCNA and PPI network analysis, we identified twelve hub genes as the most relevant to LNM potential from these two modules. These 12 hub genes displayed differential expression in THCA and exhibited significant correlations with the downregulation of neutrophil infiltration, as well as the upregulation of dendritic cell and macrophage infiltration, along with activation of the EMT pathway in THCA. We propose a novel molecular classification approach and provide an online web-based nomogram for evaluating the LNM potential of THCA (http://www.empowerstats.net/pmodel/?m=17617_LNM). Machine learning algorithms have identified ERBB3 as the most critical gene associated with LNM potential in THCA. ERBB3 exhibits high expression in patients with THCA who have experienced LNM or have advanced-stage disease. The differential methylation levels partially explain this differential expression of ERBB3. ROC analysis has identified ERBB3 as a diagnostic marker for THCA (AUC=0.89), THCA with high LNM potential (AUC=0.75), and lymph nodes with tumor metastasis (AUC=0.86). We have presented a comprehensive review of endocrine disruptor chemical (EDC) exposures, environmental toxins, and pharmacological agents that may potentially impact LNM potential. Molecular docking revealed a docking score of -10.1 kcal/mol for Lapatinib and ERBB3, indicating a strong binding affinity. In conclusion, our study, utilizing bioinformatics analysis techniques, identified gene modules and hub genes influencing LNM potential in THCA patients. ERBB3 was identified as a key gene with therapeutic implications. We have also developed a novel molecular classification approach and a user-friendly web-based nomogram tool for assessing LNM potential. These findings pave the way for investigations into the mechanisms underlying differences in LNM potential and provide guidance for personalized clinical treatment plans.

Epitopes mapping for identification of potential cross-reactive peptide against leptospirosis

Leptospira, the pathogenic helical spirochetes that cause leptospirosis, is an emerging zoonotic disease with effective dissemination tactics in the host and can infect humans and animals with moderate or severe illnesses. Thus, peptide-based vaccines may be the most effective strategy to manage the immune response against Leptospira to close these gaps. In the current investigation, highly immunogenic proteins from the proteome of Leptospira interorgan serogroup Icterohaemorrhagie serovar Lai strain 56601 were identified using immunoinformatic methods. It was discovered that the conserved and most immunogenic outer membrane Lepin protein was both antigenic and non-allergenic by testing 15 linear B-cells and the ten best T-cell (Helper-lymphocyte (HTL) with the most significant number of HLA-DR binding alleles and the eight cytotoxic T lymphocyte (CTL)) epitopes. Furthermore, a 3D structural model of CTL epitopes was created using the Pep-Fold3 platform. Using the Autodock 4.2 docking server, research was conducted to determine how well the top-ranked CTL peptide models attach to HLA-A*0201 (PDB ID: 4U6Y). With HLA-A*0201, the epitope SSGTGNLHV binds with a binding energy of −1.29 kcal/mol. Utilizing molecular dynamics modeling, the projected epitope-allele docked complex structure was optimized, and the stability of the complex system was assessed. Therefore, this epitope can trigger an immunological response and produce effective Leptospira vaccine candidates. Overall, this study offers a unique vaccination candidate and may encourage additional research into leptospirosis vaccines. Communicated by Ramaswamy H. Sarma

Computational screening of foxtail millet (Setaria italica (L.) P.Beauv.) polyphenols for finding β-secretase (BACE 1) inhibitors that target the amyloidogenic pathway in Alzheimer's disease

Abstract BACKGROUND: Foxtail millet (FM; Setaria italica (L.) P.Beauv.) is known for its antioxidant and neuroprotective action against Alzheimer's disease (AD); hence the present study aims to screen its polyphenols to find less toxic and potentially effective anti-Alzheimer's drugs. MATERIALS AND METHODS: The details about phyto-compounds of FM and the β-secretase enzyme (BACE 1) of AD have been retrieved from the PubChem compound database and protein data bank, respectively. The Patch Dock server was used to carry out docking experiments. Initially, all the FM phytocompounds and reference drug (MK-8931) were subjected to docking, and subsequently, the best-docked complexes were selected based on their binding energy (in kcal/mol). Rule of five and adsorption, distribution, metabolism, excretion, and toxicity (ADMET) of selected compounds were analyzed using various computational tools. The final docking complexes were developed by using PyMOL. RESULTS: The molecular docking results revealed that among the 24 polyphenols studied, two compounds, namely N′-p-coumaroyl-N″-caffeoylspermidine (NPCNCS) and di-P-coumaroylspermidine (DPCS), demonstrated strong binding to the active site of β-secretase. and exhibited higher binding energies of -300.75 kcal/mol and -193.41 kcal/mol, respectively, in comparison to the reference drug MK-8931, with a binding energy of -154.36 kcal/mol. These compounds established hydrogen bond interactions with catalytic dyad (Asp 32 and Asp 228) and other accessory interactions with residues of the “flap” hairpin loop and “10s loop” of the β-secretase active site involved in the stabilization of substrate/inhibitor binding. In a nutshell, the NPCNCS and DPCS have shown more robust binding affinity than MK-8931 and docked well into the interior side of the active site. On the other hand, NPCNCS and DPCS have satisfied the RO5 and ADMET filters. CONCLUSIONS: The in silico analysis of FM polyphenols revealed that the N′-p-coumaroyl-N″-feruloylspermidine and di-P-coumaroylspermidine compounds exhibited non-toxic, permeability to blood-brain barrier and solid binding affinity toward β-secretase enzyme thereby; these millet compounds might be considered as β-secretase inhibitors for treating AD.

Protein-Ligand Blind Docking Using CB-Dock2.

Protein-ligand blind docking is a widely used method for studying the binding sites and poses of ligands and receptors in pharmaceutical and biological research. Recently, our new blind docking server named CB-Dock2 has been released and is currently being utilized by researchers worldwide. CB-Dock2 outperforms state-of-the-art methods due to its accuracy in binding site identification and binding pose prediction, which are enabled by its knowledge-based docking engine. This highly automated server offers interactive and intuitive input and output web interfaces, making it an efficient and user-friendly tool for the bioinformatics and cheminformatics communities. This chapter provides a brief overview of the methods, followed by a detailed guide on using the CB-Dock2 server. Additionally, we present a case study that evaluates the performance of protein-ligand blind docking using this tool.

Integrated Transcriptome and Molecular Docking to Identify the Hub Superimposed Attenuation Targets of Curcumin in Breast Cancer Cells.

Numerous in vitro and in vivo studies have shown that curcumin primarily activates apoptotic pathways in cancer cells and inhibits cancer progression by modulating various molecular targets. In this study, we utilized reverse docking servers to predict 444 human proteins that may potentially be targeted by curcumin. Then, high-throughput assays were conducted by using RNA-seq technology on curcumin-treated MCF-7 (human breast cancer ER (+)) and MDA-MB-231 (human breast cancer ER(-)/TNBC) cancer cell lines. Enrichment analysis identified seven and eight significantly down-regulated signaling pathways in these two cell lines, where the enriched genes were used to construct protein-protein interaction networks. From these networks, the MCODE algorithm screened out 42 hub targets, which are core genes of the RTK-(PI3K-AKT)/(MEK/ERK1/2) crosstalk network. Genetic alteration and expression patterns of hub targets of curcumin may be closely related to the overall pathogenesis and prognosis of breast cancer. MAPKAPK3, AKT3, CDK5, IGF1R, and MAPK11 are potential prognostic markers and therapeutic targets of curcumin in patients with triple-negative breast cancer. Molecular docking and transcriptomic results confirmed that curcumin can inhibit these high-scoring targets at the protein level. Additionally, these targets can act as self-feedback factors, relying on the cascading repressive effects in the network to limit their own transcription at the mRNA level. In conclusion, the integration of transcriptomic and molecular docking approaches enables the rapid identification of dual or multiple inhibitory targets of curcumin in breast cancer. Our study provides the potential elucidation of the anti-cancer mechanism of curcumin.

Screen natural terpenoids to identify potential Jab1 inhibitors for treating breast cancer

Jab1 (c-Jun activation domain-binding protein-1) overexpression has been extensively linked to cancer development (or metastasis) in various malignancies by positively regulating cancer cell proliferation or inactivating several tumor suppressors. Recent research has focused on utilizing plant products to target crucial elements of dysregulated signaling pathways to elucidate a potent cancer therapeutic approach. Terpenoids have shown significant anti-inflammatory and anti-cancerous properties in a broader range of carcinomas by inducing apoptosis. Through an extensive literature search, we have selected only those terpenoids (from the NPACT database) that have not been explored against Jab1 (CSN5, COP9 signalosome subunit 5) in breast cancer for our research study. We have used two docking servers, PATCH DOCK, and CB DOCK, to find the binding interaction between selected terpenoids and Jab1. Further, we have also used SWISS ADME to investigate the pharmacokinetics of selected ligands. Amongst all selected ligands, lutein (belongs to the xanthophylls class) has displayed maximum binding energy in both CB Dock and Patch Dock analysis. Hence, our preliminary in silico results have shown lutein as the potent lead candidate for developing a better drug against breast cancer. However, more in silico and in vitro studies are still needed to validate the inhibitory potential of lutein terpenoid against Jab1 in breast cancer.

Construction of Protein–Protein Interaction Networks on Capsicum Against Sepsis by Regulating of Molecular Mechanisms: Bioinformatics Approaches

We aimed to create the construction of protein–protein interaction networks for target proteins and to analyze the molecular characteristics of capsicum, screen and identify the potential targets of capsicum against sepsis using bioinformatics approaches. The potential anti-sepsis targets of capsicum were screened by chemical protein interaction analysis and matched with the published anti-sepsis related disease targets in the Online Mendelian Inheritance in Man database (OMIM) and the Genecards Database to screen out the anti-sepsis targets of capsicum. The potential targets of capsicum against sepsis were further identified by molecular docking server. Capsicum exhibited good medicinal properties. Overall, 14 potential targets were screened and 3453 disease targets were obtained from OMIM and Genecards databases. Subsequently, we found that there were 14 proteins that were both molecular targets and disease targets. The molecular docking server identified that the active ingredient beta-carotene had the ability to bind to 11 targeted proteins, which was a potential target of capsicum against sepsis. Capsicum can exert its therapeutic effects by interacting with key amino acid residues of the targeted proteins. Capsicum can inhibit tissue and organ damage of sepsis by regulating CAV1 and CTNNB1, and reduce inflammatory response of sepsis by regulating MYC, VEGFA, NR3C1 and DUSP1d, and ultimately prevent and treat sepsis.

NCoVDock2: a docking server to predict the binding modes between COVID-19 targets and its potential ligands.

The rapid emergence of SARS-CoV-2 variants with multi-sites mutations is considered as a major obstacle for the development of drugs and vaccines. Although most of the functional proteins essential for SARS-CoV-2 have been determined, the understanding of the COVID-19 target-ligand interactions remains a key challenge. The old version of this COVID-19 docking server was built in 2020, and free and open to all users. Here, we present nCoVDock2, a new docking server to predict the binding modes for targets from SARS-CoV-2. First, the new server supports more targets. We replaced the modeled structures with newly resolved structures and added more potential targets of COVID-19, especially for the variants. Second, for small molecule docking, Autodock Vina was upgraded to the latest version 1.2.0, and a new scoring function was added for peptide or antibody docking. Third, the input interface and molecular visualization were updated for a better user experience. The web server, together with an extensive help and tutorial, are freely available at: https://ncovdock2.schanglab.org.cn.