scispace - formally typeset
Search or ask a question

What is the role of molecular docking in predicting the binding affinity of methoxyflavone to the TGF-beta receptor? 


Best insight from top research papers

Molecular docking plays a crucial role in predicting the binding affinity of compounds like methoxyflavone to the TGF-beta receptor. It is a computational tool used to estimate binding poses and affinities of small molecules within specific receptor targets . Studies have shown that molecular docking, coupled with molecular dynamics simulations, can identify lead compounds with high binding affinities to the TGF-beta receptor, such as Epicatechin, Fisetin, Luteolin, Curcumin, Curcumin Pyrazole, and Demethoxycurcumin . These compounds exhibit strong interactions with the receptor and show promising potential for therapeutic development against conditions like kidney fibrosis and oral sub-mucous fibrosis . By utilizing docking methodologies, researchers can screen and identify potential drug candidates with optimal binding affinities to target receptors, paving the way for the development of effective treatments for various diseases.

Answers from top 5 papers

More filters
Papers (5)Insight
Not addressed in the paper.
Molecular docking predicts binding affinity of compounds like methoxyflavone to TGF-beta receptors. Curcumin derivatives showed high affinity in docking studies for TGF-β receptors in Oral Submucous Fibrosis.
Molecular docking predicts binding affinity; methoxyflavone's interaction with TGFβR-1 was not specifically mentioned. However, Epicatechin, Fisetin, and Luteolin showed high affinity.
Open accessJournal ArticleDOI
301 Citations
Molecular docking predicts binding modes and affinities of ligands like methoxyflavone to receptors. It aids in drug design by optimizing lead compounds and screening for biologically active molecules.
Open accessJournal ArticleDOI
Tatu Pantsar, Antti Poso 
30 Jul 2018-Molecules
259 Citations
Not addressed in the paper.

Related Questions

How does molecular docking contribute to drug discovery and development?5 answersMolecular docking plays a crucial role in drug discovery and development by predicting the interaction between molecules. It aids in virtual screening of large compound libraries, identifying potential lead compounds, and optimizing their interactions with target molecules to enhance pharmacological activity and reduce side effects. Through structure-activity relationship (SAR) analysis, docking helps in modifying molecules for improved properties like efficacy and potency. Additionally, molecular docking assists in rationalizing ligand activity, forecasting ligand-protein interactions, and revealing novel therapeutic compounds, thus aiding in structure-based drug design. The technique also enables accurate modeling of molecular structures and precise prediction of biological activities of drug molecules, contributing significantly to the drug discovery process.
How can molecular docking be used to predict the binding of a ligand to a receptor?4 answersMolecular docking is a computational method used to predict the binding of a ligand to a receptor. It involves simulating the interaction between the two molecules to generate a binding model. The docking process starts with the ligand and receptor separated by a physical distance, and the ligand explores different conformations to find its position in the receptor's active site. The goal is to identify the most favorable orientation and binding conformation of the ligand against the receptor. This is achieved by evaluating the strength of the interaction and counting favorable intermolecular interactions using scoring functions. The docking simulations can predict the top binding conformations or poses of the ligand against the receptor. The ultimate aim is to obtain an optimized docked conformer with the highest affinity and a stable complex with minimized free energy.
How to get retinoid receptors molecular docking?4 answersRetinoid receptors can be studied using molecular docking techniques. Several papers have utilized molecular docking to investigate the binding of retinoids to retinoid X receptors (RXRs) and retinoic acid receptors (RARs). These studies have used computational methods to predict the binding affinities and interactions between retinoids and the receptor proteins. The docking simulations have provided insights into the conformational flexibility and binding modes of different retinoids, including natural and synthetic analogues. The results from these docking studies have been validated using experimental measurements of ligand affinity and recruitment of coactivator proteins. The findings have helped in understanding the structural requirements for retinoid activity and selectivity, which can aid in the design and development of more active and selective synthetic retinoids for therapeutic applications.
Meaning of Molecular docking?3 answersMolecular docking is a computational method used in drug discovery to predict the interaction between a small molecule (ligand) and a larger molecule (macromolecule or target). It involves positioning the 3D structure of the ligand and target in different orientations to find the optimal conformation. The goal is to determine the binding mode and affinity of the ligand to the target. Molecular docking is an important step in computer-aided drug design, as it helps in rationalizing ligand activity, identifying potential therapeutic compounds, and predicting ligand-protein interactions. It also aids in the development of structure-activity relationships (SARs) and lead optimization. By simulating the binding process, molecular docking provides insights into the feasibility of biochemical reactions and can be used to guide experimental research.
Do machine learning play role after molecular docking?3 answersMachine learning plays a role after molecular docking in drug discovery. It is used to develop and improve protein-ligand scoring functions, which are crucial for the accuracy and predictive power of molecular docking. Machine learning models have been employed to identify novel antiviral agents against African swine fever virus (ASFV) based on structural information data. Additionally, machine learning methods have been combined with traditional structure-based virtual screening to effectively identify potential hits from large compound databases for a given target. Furthermore, machine learning models, such as long short-term memory (LSTM) neural networks and XGBoost, have been used to predict docking scores without explicitly performing docking calculations, accelerating the process of virtual screening. Overall, machine learning techniques have proven to be valuable in enhancing the efficiency and effectiveness of molecular docking and virtual screening in drug discovery.
Docking molecular and natural products5 answersComputational approaches such as docking and molecular dynamics have been used to discover bioactive molecules and understand their mode of action in natural products. These approaches have been applied to study the biological activities and pancreatic lipase inhibition of various natural compounds, including eugenol, gingerol, ascorbic acid, oleuropein, piperine, hesperidin, quercetin, luteolin, and curcumin. Additionally, molecular docking has been used to estimate the binding energy of natural product analogues against the enzyme α-chymotrypsin. These computational methods provide valuable insights into the potential biological functions of food constituents and can aid in the discovery of new functional foods that may help prevent disorders such as diabetes, hypercholesterolemia, and obesity.

See what other people are reading

How does the use of molecular dynamics simulations affect the accuracy of AutoDCK Vina-based docking results?
5 answers
The incorporation of molecular dynamics (MD) simulations following AutoDock Vina docking significantly enhances the accuracy of ligand-binding predictions. MD simulations evaluate ligand-binding stability through root-mean-square deviations, refining predicted binding modes and improving the discrimination between active and decoy ligands. This approach results in a notable 22% enhancement in ROC AUC values, indicating a substantial boost in predictive performance across various protein classes. Additionally, the use of MD simulations aids in evaluating protein-ligand interactions more reliably, offering a physics-based method to validate binding interactions and enhance the accuracy of docking results.
How does molecular docking and scoring with Autodock Vina contribute to the discovery of novel drug candidates?
5 answers
Molecular docking and scoring with AutoDock Vina play a crucial role in the discovery of novel drug candidates. AutoDock Vina, known for its accuracy and speed, is widely utilized in academia and industry for drug design optimization. By utilizing machine learning approaches like TensorFlow and XGBoost, AutoDock Vina evaluates the inhibitory potential of compounds against specific targets, such as the SARS-CoV-2 main protease. Additionally, the acceleration of AutoDock Vina with GPUs, as seen in Vina-GPU 2.0, significantly speeds up the virtual screening process, enabling the identification of potential drug candidates with enhanced efficiency and precision. This integrated approach aids in reducing the number of compounds that require further investigation, thereby streamlining the drug discovery process.
What is the significance of molecular docking studies in drug discovery and development?
5 answers
Molecular docking studies play a crucial role in drug discovery and development by predicting the interaction between drug molecules and their target receptors. This computational method aids in accurate modeling of molecular structures, predicting biological activities of drug molecules, and identifying potential therapeutic compounds. Molecular docking is essential for virtual screening of large compound libraries, lead optimization, and structure-activity relationship (SAR) analysis, which are vital steps in computer-aided drug design. By utilizing molecular docking, researchers can efficiently explore high-dimensional spaces, estimate binding affinities, and optimize lead compounds to enhance pharmacological activity while reducing side effects. Overall, molecular docking significantly contributes to rationalizing ligand activity, identifying novel therapeutic agents, and advancing drug development processes in a cost-effective and efficient manner.
What is the latest research on Gamma Oryzanol and Diabetes or Cardiovascular disease?
5 answers
Recent research has highlighted the potential of Gamma Oryzanol (γ-OZ) in managing diabetes and cardiovascular diseases. Studies have shown that γ-OZ possesses antioxidant, anti-inflammatory, and anti-diabetic properties, making it a promising therapeutic agent. Furthermore, computational predictions suggest that γ-OZ could act as an agonist of the human peroxisome proliferator-activated receptor gamma (PPAR-γ), a key player in glucose metabolism and adipogenesis. Nano-encapsulation techniques have been explored to enhance the stability and bioavailability of γ-OZ, indicating its potential application in improving cardiovascular health. Overall, the research underscores the multifaceted benefits of γ-OZ in combating diabetes and cardiometabolic complications, paving the way for further investigations into its therapeutic potential.
Which carbonic anhydrase is more efficient than bovine CA from erythrocytes to capture carbon dioxide?
5 answers
The zinc tetraphenylporphyrin (Zn-TPP) solubilized by GroEL protein cage, mimicking carbonic anhydrase (CA), exhibits higher efficiency in capturing carbon dioxide compared to bovine CA from erythrocytes. The Zn-TPP-GroEL complex shows hydrase activity catalyzing CO2 hydration, with a catalytic activity about half of bovine CA at 25°C. Notably, at 60°C, a temperature close to industrial CO2 absorption conditions, Zn-TPP-GroEL outperforms the natural enzyme, showcasing higher thermal stability and better catalytic performance. Additionally, the GroEL-solubilized Zn-TPP accelerates CO2 precipitation as CaCO3, demonstrating superior long-term performance compared to bovine CA. This innovative nano-caged system presents a promising alternative for efficient carbon capture applications.
Why salicylaldehyde is good for the sythesis of schiff base ligand?
5 answers
Salicylaldehyde is favored for synthesizing Schiff base ligands due to its versatile nature and ability to form stable complexes with various metal ions, enhancing the antibacterial and antimicrobial activities of the resulting compounds. Schiff base ligands derived from salicylaldehyde have shown excellent antibacterial properties against both gram-positive and gram-negative bacterial strains. Additionally, salicylaldehyde-based sensors have demonstrated selective sensing abilities for specific metal ions, such as Cd2+ and Ni2+, showcasing the potential for targeted detection applications. Furthermore, Schiff base compounds bearing the salicylaldehyde moiety have exhibited potent anti-inflammatory effects, making them promising candidates for pharmaceutical applications. The unique properties of salicylaldehyde make it a valuable building block for the synthesis of diverse and biologically active Schiff base ligands.
What is passonline app for biological activity prediction including in silico methods?
5 answers
The PASS online application is a valuable tool for predicting biological activity spectra of drug-like compounds, including in silico methods. It utilizes a biotransformation network to analyze the integrated activity profile of compounds, considering metabolites as well. The application predicts a wide range of biological activities with high accuracy, aiding in the identification of potential pharmaceutical agents or drug repurposing. Moreover, PASS has been used to predict biological activities associated with the treatment of HIV-associated comorbidities, demonstrating high mean accuracy in these predictions. Additionally, PASS was employed to assess the potential antiviral activity of compounds from the Etlingera elatior plant against SARS-CoV-2 main protease, highlighting its role in antiviral therapy.
What is molocler docking?
5 answers
Molecular docking is a computational modeling tool in bioinformatics that predicts the interaction between two or more molecules to form a stable complex. It aids in determining the 3D structure of the complex based on the binding characteristics of the ligand and target. This method is crucial in various fields like drug discovery, where it simulates interactions between small molecules and macromolecules like proteins, facilitating early-stage feasibility assessments before experimental research. Molecular docking plays a significant role in predicting the strength and type of signals produced by molecules, aiding in understanding signal transduction and structure-based drug design. Overall, molecular docking is a versatile tool with applications in structure-based drug design, biochemical pathway assessment, lead optimization, and de novo drug design.
What are the resideus of akt ative binding site?
5 answers
The residues involved in the active binding site of Akt include Lys14, Arg25, Tyr38, Arg48, Arg86, Thr21, and Arg23. These residues play a crucial role in interacting with phospholipids generated by PI3-K, thereby regulating Akt activity and promoting cell survival. Additionally, the Akt PH domain consists of specific loops and pockets that accommodate the phospholipids through hydrogen-bonding interactions, with key residues like Lys14, Arg25, and Tyr38 forming the bottom of the binding pocket. Furthermore, the Akt pathway, where Akt is the central protein, is essential in diseases like Alzheimer's, Parkinson's, and Diabetes, with small molecules binding to the PH domain of Akt to facilitate its phosphorylation and upregulate the pathway.
What is Molecular docking?
4 answers
Molecular docking is a computational method extensively used to predict and analyze the interactions between molecules, such as proteins, enzymes, DNA, RNA, and ligands, either natural or synthetic. It aids in understanding binding mechanisms, predicting binding conformations, and assessing binding affinities. This technique plays a crucial role in drug discovery by modeling the interaction between a drug and its target receptor, helping in accurate structure modeling and biological activity prediction of drug molecules. Initially focusing on rigid interactions, advancements in computational capabilities now allow for dynamic simulations of ligand-protein interactions over time, enhancing the understanding of molecular interactions in drug design. Molecular docking serves as a valuable tool in designing materials at the mesoscale for various applications by elucidating intermolecular interactions in hybrid systems.
Docking and molecular dynamics simulation of compounds inhibiting InhA enzyme of drug-resistant Mycobacterium tuberculosis: An in-silico approach.
5 answers
Docking and molecular dynamics simulations were utilized in an in-silico approach to identify compounds inhibiting the InhA enzyme of drug-resistant Mycobacterium tuberculosis. Various studies focused on developing mutant-specific inhibitors to combat resistance, with approaches including mutation impact modeling, virtual screening, and 3D-pharmacophore searches. Compounds like triclosan derivatives and natural products from Brucea javanica were investigated for their inhibitory potential against InhA, showing promising results in silico. Additionally, computational techniques like homology modeling, molecular docking, and molecular dynamics simulations were employed to study the behavior of specific proteins like Rv1250 from M. tuberculosis, aiding in the design of novel enzyme inhibitors for pathogenesis prevention.