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How do you study protein ligand interaction through molecular docking?

Docking (molecular)

Oligopeptide binding

Molecular Docking Simulation

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Open access•Journal Article•DOI Insights into the Molecular Mechanisms of Protein-Ligand Interactions by Molecular Docking and Molecular Dynamics Simulation: A Case of Oligopeptide Binding Protein Yi Fu, Ji Zhao, Zhiguo Chen - Show less +2 more 04 Dec 2018-Computational and Mathematical Methods in Medicine 91 Citations	Our comparison of these complexes highlights differences in the protein-ligand interactions between the two docking methods.

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Docking study in dft chemistry4 answersDocking studies in density functional theory (DFT) chemistry have been conducted in several papers. In one study, the efficiency of obtained products against selected drug targets in anti-Alzheimer ligand-receptor interactions was investigated using molecular docking analysis. Another study evaluated the bioactivity of phytochemicals as inhibitors against SARS-CoV-2 using computational models and molecular docking. In a different study, molecular docking was performed to investigate the mode of binding of novel sulfonamide derivatives to paraoxonase 1 enzyme. Additionally, molecular docking was used to determine the interactions between AChE and BChE enzymes and a chemical compound. Furthermore, a docking study was conducted to identify protein-ligand interactions and determine the best binding score. These papers demonstrate the use of molecular docking in DFT chemistry for various applications.

How to do ligand docking in protein?5 answersLigand docking in proteins involves several steps. First, a suitable search strategy and scoring function are needed for efficient and effective docking. One approach is to use AutoDock Vina, which employs an exhaustiveness parameter and a grid box to define the ligand binding site. Another method, FitDock, utilizes a hierarchical multi-feature alignment approach to fit the initial conformation to a given template, resulting in improved docking success rates and faster performance. Additionally, flexible protein-ligand docking can be achieved using a deep learning model based on the prediction of an intermolecular Euclidean distance matrix, which outperforms traditional docking methods. Finally, computational molecular docking can be used to generate a density of binding states, which can be used to calculate binding strengths, conformations, and atomic interactions between the ligand and protein.

How moleculer docking used for diabetes type 2?5 answersMolecular docking is used for diabetes type 2 by predicting the interaction and binding affinity of compounds with target enzymes involved in the disease. Several studies have utilized molecular docking to identify potential drug candidates for diabetes type 2 treatment. For example, Kwatra et al. conducted docking-based screening using a library of approved drugs and compounds to identify potential antiviral activity against diabetes type 2. Nursanti et al. used molecular docking to study the interaction between compounds from various plants and the Peroxisome Proliferator-Actived Receptor-Gamma (PPAR-y), a receptor involved in diabetes treatment. Cen et al. employed network pharmacological analysis and molecular docking to explore the mechanism of a traditional Chinese medicine formula on diabetes type 2. Hartanti et al. investigated the inhibition mechanism of compounds from Garcinia cowa on the protein tyrosine phosphatase 1B (PTP1B) enzyme using molecular docking. Farahin et al. used molecular docking to predict the interaction between polyphenol compounds from Anacardium occidentale and alpha-glucosidase (AG) and dipeptidyl-peptidase IV (DPP-4) enzymes involved in diabetes type 2.

What are the main tools use in molecular docking?4 answersThe main tools used in molecular docking include molecular docking software and web services. These tools are employed to study complex biological and chemical systems, and to identify, characterize, and develop novel drugs and compounds. Some commonly used tools include AutoDock Vina in UCSF Chimera, the VEGA Online web service, and the VEGA Web Edition (WE) and Score tools. These tools provide features such as file format conversion, 2D/3D conversion, surface mapping, editing and preparing input files, and rescoring docking poses. Additionally, consensus methods, fragment-based approaches, and machine learning algorithms are also utilized in molecular docking. These tools and approaches contribute to an increase in accuracy and are expected to eventually accomplish the full potential of molecular docking.

What is the best method for molecular docking study?5 answersMolecular docking is a powerful computational method for studying the interactions between biomolecules. It is widely used in drug design, as well as predicting protein-protein and protein-nucleic acid interactions. High-throughput molecular docking has proven to be extremely useful in identifying novel bioactive compounds within large chemical libraries. Quantum mechanical-based molecular docking has gained attention for its ability to provide better accuracy compared to classical molecular mechanics methods. The AlteQ method, which calculates electron density using Slater's type atomic contributions, has been used to evaluate the quality of interactions in docking complexes. LeDock, DSX, and X-score have shown good performance in molecular docking, with the combination of LeDock and DSX or X-score improving prediction accuracy. A meta-docking approach that combines the results of multiple docking programs, such as AutoDock4.2, LeDock, and rDOCK, has shown superior performance in scoring, posing, and screening protein-ligand complexes.

How do you choose ligands for docking?6 answers

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How does molecular docking contribute to drug discovery and development?

Molecular 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.Docking study IC50,structure, deriative

The docking study involving IC50 values and structural analysis of various derivatives was conducted in multiple research papers. For instance, a study on 1,3-thiazole derivatives revealed that compound (4) exhibited good cytotoxicity against the MCF-7 cancer cell line with an IC50 value of 33.84 μM. Additionally, a study on betulin derivatives highlighted compounds 7a and 7b as potent against C-32 and SNB-19 cell lines, with IC50 values of 2.15 and 0.91 μM for 7a, and 0.76 and 0.8 μM for 7b. Furthermore, a study on 1,2,4-triazine derivatives identified compound G11 as a potent COX-2 inhibitor with 78% inhibition relative to COX-1. These studies showcase the importance of docking studies in evaluating the IC50 values and structural interactions of various derivatives in different biological contexts.How OIM1-6 affect MRSA?

OIM1-6, also known as fructose 1,6 biphosphate aldolase-II (FBA), plays a crucial role in combating MRSA infections. FBA has been identified as a potential drug target for community-acquired MRSA (CA-MRSA) due to its involvement in the glycolytic pathway. Studies have focused on predicting the 3D structure of FBA and conducting molecular dynamics simulations to assess its stability and interactions with potential inhibitors. Additionally, FBA has been found to be evolutionarily conserved in various pathogenic species, making it a promising therapeutic target not only for CA-MRSA but also for other Staphylococci species. The use of FBA as a drug target presents a novel approach to combatting antimicrobial resistance in MRSA infections, offering potential for the development of effective treatments against this challenging pathogen.Has autodock been used to dock the GLP1 receptor?

AutoDock, a widely utilized molecular modeling tool, has been extensively applied in docking simulations, including interactions with the GLP1 receptor. AutoDock has been adapted for specific protein targets, and its latest version employs a semi-empirical force field for evaluating protein-ligand binding affinity. Additionally, AutoDock-GPU, an accelerated version, has been developed to enhance performance in molecular docking, showcasing significant speedup factors on GPUs and CPUs. The AutoDock suite offers specialized tools for challenging systems, such as those with substantial receptor flexibility, which could be relevant for docking studies involving the GLP1 receptor. Overall, AutoDock's capabilities and adaptations make it a valuable tool for docking simulations, potentially including interactions with the GLP1 receptor.What is the binding energy range in molecular docking?

The binding energy range in molecular docking varies depending on the specific protein-ligand interactions studied. Research has shown diverse binding energies in different systems. For instance, in the study of antifungal proteins, binding energies ranged from -5.08 kcal/mol to -5.56 kcal/mol. In another investigation focusing on psychedelic drugs interacting with human serum albumin, the best minimum binding energies observed were -7.6 kcal/mol for LSD and -6.5 kcal/mol for psilocybin. Additionally, a study on binding free energies between T4 lysozyme and small organic molecules reported good agreement with previous calculations, with a correlation coefficient of approximately 0.9 for flexible complexes and 0.8 for flexible ligands with rigid receptor configurations. These findings highlight the variability in binding energies observed across different molecular docking studies.Docking techniques for proteins

Protein docking techniques are crucial for studying protein-protein interactions and designing drugs. Various methods have been developed to predict near-native structures of protein complexes. These techniques include black-box approaches based on tensor-train decomposition, multilevel scoring functions with k-means clustering, and energy functions considering protein flexibility and desolvation effects. Template-based docking is found to be less sensitive to structural inaccuracies in protein models compared to free docking. The field is evolving towards more accurate and reliable computational methods, with a focus on artificial intelligence-driven approaches. These advancements aim to enhance the accuracy and efficiency of predicting protein interactions, offering valuable insights for drug design and understanding biological processes.How effective is doxycycline as a protease inhibitor in treating dengue fever?

Doxycycline has shown effectiveness as a protease inhibitor in treating dengue fever. Research indicates that doxycycline inhibits the dengue virus serine protease with an IC50 value of 52.3 ± 6.2 μM at normal human temperature, displaying concentration-dependent inhibition. Additionally, in silico molecular docking studies revealed that doxycycline binds to an allosteric site on the NS2B-NS3 protease, interacting with a significant amino acid residue, Lys74. Clinical studies have further supported the efficacy of doxycycline in reducing complications of dengue fever, showing lower rates of bleeding, plasma leakage, thrombocytopenia, and organ involvement compared to control groups. These findings highlight the potential of doxycycline as a promising protease inhibitor for dengue fever treatment, warranting further experimental and clinical investigations.Does the quinolin yellow have any impact on the hyperactivity levels of individuals with Attention Deficit Hyperactivity Disorder (ADHD)?

Quinoline yellow (QY), a synthetic yellow dye used in various food products, has been studied for its potential impacts. Research indicates that QY, along with other synthetic dyes, may exacerbate hyperactivity symptoms in sensitive individuals, including those with Attention Deficit Hyperactivity Disorder (ADHD). Additionally, studies have shown that QY can act as a disruptor of aryl hydrocarbon receptor (AHR) and estrogen receptor alpha (ERα) signaling, potentially raising toxicological concerns. Furthermore, investigations into the interaction between QY and human serum albumin (HSA) suggest that QY binds to HSA, affecting its structural and functional properties, which could have implications for biological processes. Overall, while more research is needed to fully understand the impact of QY on hyperactivity levels in individuals with ADHD, existing data suggests a potential link between synthetic dyes like QY and behavioral symptoms.What is the effect of myricetin on prostate androgen receptors?

Myricetin, a natural compound, has shown significant effects on prostate cancer cells. It acts as a potent inhibitor of histone lysine demethylase family 4 (KDM4) members, crucial coactivators of androgen receptor (AR) signaling in castration-resistant prostate cancer (CRPC). Additionally, myricetin exhibits selective cytotoxic, pro-apoptotic, and anti-metastatic effects on prostate cancer cells by inhibiting PIM1 and disrupting the PIM1/CXCR4 interaction. Furthermore, myricetin has been identified as a multi-targeted inhibitor against receptor tyrosine kinases (RTKs) and AR, showing promising anticancer activity by down-regulating mRNA expression of EGFR, IR, mTOR, and Bcl-2. These findings collectively suggest that myricetin has a significant impact on prostate androgen receptors by inhibiting critical pathways involved in prostate cancer progression and resistance.What is the reference range of RMSD value for determining the best binding and stability of protein-ligand complexes?

The root mean square displacement (RMSD) value serves as a crucial metric for assessing the binding and stability of protein-ligand complexes. While there isn't a specific universal reference range for RMSD values, various factors influence what constitutes an optimal RMSD value. The study by Rocco Meli and Philip C. Biggin emphasizes the significance of RMSD calculations in evaluating protein-ligand docking, where different ligand poses are compared. Additionally, Maciej Majewski et al. highlight the complexity of protein-ligand complexes, indicating that ligands often combine a single anchoring point with looser regions, balancing order and disorder. Therefore, the ideal RMSD value for optimal binding and stability would depend on the specific characteristics of the protein-ligand complex under investigation.How does the reference range of RMSF value vary across different types of protein-ligand complexes?

The reference range of Root Mean Square Fluctuation (RMSF) values varies across different types of protein-ligand complexes due to the complexity and size of the systems. Computational analysis of protein-ligand interactions plays a crucial role in understanding binding energies. Accurate calculation of binding free energies from molecular dynamics simulations is challenging for larger systems like proteins. In single-molecule protein-unfolding experiments, the unfolding signatures of known marker domains can affect the measured force distributions, impacting the free energy landscape. While scoring functions in docking programs rely on various interactions to model binding affinity, empirical scoring functions aim to estimate free energy of binding through regression-based approaches, with machine-learning methods showing improved prediction accuracy. These factors collectively influence the range of RMSF values observed in different protein-ligand complexes.