Using AutoDock for Ligand‐Receptor Docking

Experimental evidence has been presented on the difference in intermolecular ion-water distances obtained from X-ray and neutron diffraction methods. Simultaneous least squares fitting procedures were performed for X-ray and neutron interference terms observed for (NaCl)x(*H2O)1−x, (x = 0, 0.02, 0.05, and 0.098) and (KCl)x(*H2O)1−x, (x = 0, 0.02, 0.05, and 0.075) solutions at 25 °C, respectively. The null-water mixture was employed for neutron diffraction measurements for these solutions to eliminate structural contribution from hydrogen atoms. It has been revealed that the hydration numbers of Na+ and K+ are concentration dependent and the values for lower-concentration limit are 5 and 6, respectively. The nearest neighbor Na+⋯H2O and K+⋯H2O distances are obtained to be 2.36–2.37 and 2.75–2.82 Å, respectively. In order to examine the effect of the separate treatment of interactions between ion-oxygen and ion-hydrogen atoms in the X-ray model function, simultaneous fitting procedures were carried out for X-ray and neutron diffraction data observed for 9.8 mol% NaCl and 7.5 mol% KCl solutions by employing the individual atom model for the X-ray interference term. Obtained Na+⋯O and K+⋯O distances are ca. 0.02 Å shorter than those determined by the simultaneous fit employing the usual united model for water molecules. The nearest neighbor Cl−⋯O distance derived from the simultaneous fit by means of the individual atom model exhibits ca. 0.1 Å shorter than that obtained from the fit using the united atom model. The present Cl−⋯O distance agrees with that obtained from neutron diffraction measurements on 35Cl/37Cl isotopically substituted aqueous 5 mol% Na*Cl solutions in D2O. The simultaneous fitting analyses employing X-ray model function with the united and individual atom models of water molecule have revealed that the ion-oxygen (water) internuclear distance is significantly shorter than the average separation of electron clouds between ion and neighboring water molecule. The present results indicate that the difference in ion-water distance observed from X-ray and neutron diffraction studies mainly arises from the united atom model of X-ray diffraction data analysis assuming a spherical electron density around oxygen atom within the water molecules.

The development of green polymers is a crucial long-term solution to address the problem of plastic waste. In particular, the bio-based polymer polyethylene vanillic (PEV) has garnered interest due to its comparable mechanical and thermal properties to polyethylene terephthalate (PET), a widely used single-use plastic. Molecular dynamics (MD) simulations are commonly employed to study the molecular structure and dynamic properties of materials, offering cost-effective applications. However, the accuracy of MD simulation results heavily relies on the chosen force field model. The all-atom (AA) force field, while providing insights into molecular interactions, demands significant computational resources, especially for large systems like polymers. This study aimed to employ the united atom (UA) model with revised OPLS-UA force field parameters for the biopolymer PEV, aiming to reduce computational time in dynamic and physical investigations. Consequently, the UA model successfully folded the PEV polymer in a manner resembling the single-chain PEV treated with the AA model, while also predicting a glass transition temperature (Tg) close to the experimental value of 348 K. These findings underscore the potential of the UA model for simulating PEV and its promising implications.

Hydroxyl-radical footprinting (HRF) is a powerful method for probing structures of nucleic acid-protein complexes with single-nucleotide resolution in solution. To tap the full quantitative potential of HRF, we describe a protocol, hydroxyl-radical footprinting interpretation for DNA (HYDROID), to quantify HRF data and integrate them with atomistic structural models. The stages of the HYDROID protocol are extraction of the lane profiles from gel images, quantification of the DNA cleavage frequency at each nucleotide and theoretical estimation of the DNA cleavage frequency from atomistic structural models, followed by comparison of experimental and theoretical results. Example scripts for each step of HRF data analysis and interpretation are provided for several nucleosome systems; they can be easily adapted to analyze user data. As input, HYDROID requires polyacrylamide gel electrophoresis (PAGE) images of HRF products and optionally can use a molecular model of the DNA-protein complex. The HYDROID protocol can be used to quantify HRF over DNA regions of up to 100 nucleotides per gel image. In addition, it can be applied to the analysis of RNA-protein complexes and free RNA or DNA molecules in solution. Compared with other methods reported to date, HYDROID is unique in its ability to simultaneously integrate HRF data with the analysis of atomistic structural models. HYDROID is freely available. The complete protocol takes ~3 h. Users should be familiar with the command-line interface, the Python scripting language and Protein Data Bank (PDB) file formats. A graphical user interface (GUI) with basic functionality (HYDROID_GUI) is also available.

A leading cause of mortality around the globe is coronary artery disease (CAD). Major improvements in CAD therapy have been developed during the last ten years. According to the degree, kind, and clinical manifestation of CAD, the current treatments are either chemical treatments, surgical, or a combination of both. The bark of the Indian medicinal plant “Terminalia arjuna” has been used for decades as a heart stimulant. Several medicinal elements, including saponins and flavonoids, have been isolated from the bark. Terminalia arjuna has been the subject of numerous experimental and clinical research for its ability to treat cardiovascular diseases. Because protein-ligand interactions are important in structure-based treatment discovery, we used molecular docking to analyze Arjunolic acid (phytochemical found in T. arjuna) and examined their binding affinity against the cardiovascular target protein. The three-dimensional (3D) structure of the Human Angiotensin Converting Enzyme (Target Cardiovascular Protein) was obtained from Protein Data Bank and docked using the Autodock tool. As a result of our research, T. arjuna seems to be a good option for producing broad-spectrum medicines to treat cardiovascular disease.

Traditional medicine has employed Ficus reliosa (Moraceae) to treat a wide range of diseases such as central nervous system disorder, infectious disease, endocrine system, respiratory system, etc. This study aimed to evaluate phytosterol constituents that may serve as lead-drug such as 28-Isofucosterol, Stigmasterol, Sitosterol, and Campesterol towards their pharmacokinetic properties and chemopreventive activity to regulate nuclear receptor factor 2 (Nrf2) uptake bound by Kelch-like Activating Protein (Keap1). Structures were studied to evaluate their respective bioactivity using Milliprot followed by a docking study with Autodock Tools for macromolecule obtained from Protein Data Bank in 2FLU PDB file name. Chemopreventive activity from Sitosterol, Stigmasterol, Campesterol and 28-Isofucosterol in each respective bioactivity towards nuclear receptor target : 0.72; 0.74; 0.71; 0.91. A computational study performed by Autodock Tools against Keap1 targeted macromolecule covalent binding toward threonine 609 to obtain inhibition results. As respective Inhibition constant obtained (pM) : 522.97; 286.75; 620.16; 369.55. Free energy obtained (kcal/mol) : -8.57; -13.02; -12.56; -12.87. Hence the results revealed from the study showed that phytosterol compound from Ficus religiosa proven to be useful as a chemopreventive agent to promote antioxidant gene activity through regulation of Nrf2 factor by competing for receptor site with Keap1 during stressed conditions.

Staphylococcus aureus is the most dangerous of all staphylococci and can be potentially fatal if it enters the bloodstream, lungs or heart. In order to develop natural inhibitors of Staphylococcus aureus, a quantitative structure-activity relationship (2D-QSAR) study was carried out on a series of chrysin derivatives. 2D-QSAR models were developed using multiple linear regression (MLR) and artificial neural networks (ANN). Descriptors were selected using principal component analysis (PCA). Molecular docking was performed using Autodock Tools, Autodock vina and discovery studio, and the structure of Enoyl-[acyl carrier protein] reductase (FabI) from Staphylococcus aureus (4FS3) was extracted from the Protein Data Bank (PDB). The predicted of absorption, distribution, metabolism, excretion and toxicity (ADME/Tox properties) were determined using pkCSM and SwissADME and compared with those of the most active molecule in the original database. This study showed that there is a significant correlation between activity and three descriptors (atomic contribution to logP (GCUT_SLOGP_2), number of hydrogen bond acceptor atoms (a_acc) and value of diameter - radius / radius (petitjeanSC)). The RLM model has a correlation coefficient of R2 =0.78, root mean square error (RMSE=0.07) and R2test=0.85 and R2=0.96 for the ANN model. We then designed new derivatives with improved in silico activity. Molecular docking showed that they fit perfectly into the active pocket of the receptor with low binding energy (-10.3 Kcal/mol). The predicted ADME/Tox properties of the compounds show that they have satisfactory predicted properties (non-toxic and have no carcinogenic potential), which confirms that the new derivatives have interesting anti-Staphylococcus aureus activity.

The outbreak of Severe Acute Respiratory Syndrome Corona Virus (SARS-CoV-2) responsible for Coronavirus disease 2019 (COVID-19) constitute a major public health threat and economic burden. Despite the multifold research all over the world. COVID-19 has remained without a treatment. Hence, the urgent need to repurpose the FDA-approved drugs through in silico computational technique to identify inhibitors of SARS-CoV-2. Bioinformatics techniques were used to screen 2015 FDA- approved drugs against SARS-CoV-2 proteins and associated human protein, angiotensin-converting enzyme 2 (ACE2) to identify drug for new indication. The ligands were downloaded from Selleckchem drug data bank in SDF format. Open Babel was used to separate the ligands into their compounds and prepared for docking simulation using shell script and saved as PDBQT. Experimental crystal structures of the receptors (ACE2 and Spike proteins) were retrieved from Protein Data Bank (PDB), Autodock tools was used to prepare the proteins and saved as PDBQT files. Docking protocols were validated, molecular docking simulations were performed on a Linux platform using AutoDock Vina in quadruplicate. The binding energies (BEs) kcal/mol were calculated and expressed as mean ± standard deviation. Discovery studio was used for visualization. Density Functional Theory (DFT) was used to determine the predictive maximal inhibitory concentration (pIC50) of best hits. Ninety-one (91) new hits were identified for prophylaxis. Four (4) compounds were subjected to DFT based IC50 calculation with molnupiravir as a reference compound. Significantly, calculated IC50 of molnupiravir in Vero cells and Calu-3 were 0.299 and 0.008µM respectively while the experimental IC50 in Vero cells and Calu-3 as reported in literature were 0.3 and 0.08 µM respectively. The newly identified hits are promising therapeutic agents and there is no significant difference between the experimental IC50 of molnupiravir and the calculated IC50.

Diabetes mellitus is a multifactorial metabolic disorder characterized by chronic hyperglycemia and impaired metabolism of proteins, fats, and carbohydrates due to inadequate insulin secretion or function. The condition causes severe physical distress and imposes financial burdens on individuals and healthcare systems. Common symptoms include polyuria, polydipsia, and polyphagia. The global prevalence of diabetes has more than doubled over the past 30 years. Although several classes of antidiabetics are available, many are associated with side effects, high cost, limited accessibility and reduced effectiveness over long-term use. This study focuses on utilizing computational methods to investigate the potential antidiabetic effects of phytochemical compounds derived from Gongronema latifolium on the peroxisome proliferator-activated receptor gamma (PPARγ), a receptor involved in insulin regulation. The target protein structure (6t1s) was obtained from the Protein Data Bank and prepared using Chimera 1.10.1, PyMol 2.3.0, and AutoDock Tools 1.5.6. A reference ligand (EDK) was similarly prepared. Phytochemicals from Gongronema latifolium were sourced from the PubChem database and DrugBank database, then screened for drug-likeness using Lipinski’s Rule of Five and toxicity criteria via DataWarrior. Selected compounds were further prepared for docking. Docking protocol validation was conducted, and molecular docking was performed using AutoDock Vina 4.2.6 on Ubuntu Linux 20.04. Results were analyzed in Excel and visualized with PyMol. The reference ligand demonstrated a mean binding energy of -11.9 kcal/mol. The top ten phytochemicals, with binding energies ranging from -10.1 to -9.4 kcal/mol, showed promising binding affinities. In conclusion, the front-runners can be predicted to have antidiabetic effects; however, camptothecin exhibited the highest binding affinity of -10.1 kcal/mol, indicating its potential as an antidiabetic agent targeting the PPARγ receptor.

Introduction: Antibiotic resistance, particularly carbapenem resistance, poses a serious global health challenge, with the OXA-48 carbapenemase being a key factor in providing this resistance. This study employed molecular docking approaches to identify novel compounds capable of inhibiting the OXA-48 carbapenemase. Methods: This study was descriptive-analytical. To investigate the binding mode of the selected phenazine compounds, the crystal structure of the OXA-48 carbapenemase was retrieved from the Protein Data Bank and prepared using AutoDock Tools 4.2. The compounds’ chemical structures were obtained from the PubChem database and optimized with ChemDraw (3D). Molecular docking was performed utilizing AutoDock Vina. Results: The findings from the docking studies indicated that hydrophobic interactions played a crucial role in the binding of phenazine compounds to the OXA-48 carbapenemase. Among all the compounds analyzed, Aotaphenazine, PhenazinolinE, and BaraphenazineD exhibited the most favorable docking results. These compounds showed a higher affinity for binding to the key amino acids of the enzyme's active site, with binding energies of -9.9, -6.9, and -6.9 kcal/mol, respectively. Conclusion: The molecular docking results suggest that the selected phenazine compounds, particularly Aotaphenazine, PhenazinolinE, and BaraphenazineD, may serve as potential inhibitors of the OXA-48 carbapenemase. Nonetheless, the therapeutic efficacy of these compounds needs to be confirmed through in vitro and in vivo studies.

Lowering blood pressure (BP) using antihypertensive medicines lowers the risk of target organ failure as well as the occurrence of cardiovascular disease. The most common modifiable risk factor for death and disability is hypertension, which is associated with strokes, increased coronary and systemic atherosclerosis, heart problems, and chronic kidney diseases (CKD). The majority of deaths and disabilities globally now result from cardiovascular diseases (CVDs), mainly in low- and middle-income nations. Numerous genetic, behavioral, and environmental risk factors all contribute to hypertension, a significant component in the advancement of CVD. Given the significance of protein-ligand interactions in structure-based therapy development, we molecularly docked nifedipine to the cardiovascular target protein to determine the drug’s binding affinity. The Angiotensin Converting Enzyme (Target hypertension Protein) three-dimensional (3D) structure was docked using the Autodock tool, which was retrieved from the Protein Data Bank (PDB). Our analysis indicates that nifedipine is an effective choice for treating hypertension and reducing the symptoms of angina (chest pain).

The creative process of developing novel drugs based on the identification of a biological target is known as “drug design.” Drug design, in its most basic form, is the design of molecules that complement the molecular target that they interact and bind to in terms of charge and shape. The process of locating chemical entities with the potential to become medicinal agents is known as drug discovery. The identification of novel molecular entities that could prove valuable in treating illnesses that meet the criteria for unmet medical needs is a primary objective of drug discovery initiatives. In response to this demand for greater quality, the drug discovery process has incorporated absorption, distribution, metabolism, and elimination studies as well as, more and more, safety data. Examine the chemical structure drawing method and use the ChemSketch app to draw. The world’s largest publicly available chemical information database is called PubChem. Chemicals can be found using their names, structures, molecular formulas, and other identifiers. Discover information about biological activity, safety and toxicity, chemical and physical qualities, patents, literature citations, and more. Large biological molecules like proteins and nucleic acids have three-dimensional structural data stored in a database called the Protein Data Bank (PDB). The process of docking involves predicting a ligand’s preferred orientation towards a target when they are bonded together to create a stable complex. Using the ChemSketch, PubChem, and pdb databases in the autodock tool, molecular docking is accomplished make additional adjustments by selecting and swapping out words. Amantadine, initially showcased as an antiviral specialist for flu A, is Adamantan-1amine. 1-Adamantylamine may be a synthetic tricyclic amine, and it may be a part of the lesson of adamantanes. Amantadine, initially promoted as an antiviral operator for flu A, is an elective pharmaceutical for antipsychotic-induced parkinsonism and dodges the antagonistic central anxious framework and fringe impacts of anticholinergic solutions. Its component of activity is vague but shows up to Include presynaptic dopamine (DA) reuptake barricade, help of DA discharge, postsynaptic DA agonism, and receptor tweak.

The Protein Data Bank (PDB) file format remains a popular format used and supported by many software to represent coordinates of macromolecular structures. It however suffers from drawbacks such as error-prone manual editing. Because of that, various software toolkits have been developed to facilitate its editing and manipulation, but, to date, there is no online tool available for this purpose.Here we present PDB-Tools Web, a flexible online service for manipulating PDB files. It offers a rich and user-friendly graphical user interface that allows users to mix-and-match more than 40 individual tools from the pdb-tools suite. Those can be combined in a few clicks to perform complex pipelines, which can be saved and uploaded. The resulting processed PDB files can be visualized online and downloaded. The web server is freely available at https://wenmr.science.uu.nl/pdbtools.

Quercetin's antibiofilm effectiveness against drug resistant Staphylococcus aureus and its validation by in silico modeling

We introduce Nanopolysaccharide Builder (NPB), a user-friendly software tool designed to construct polysaccharide nanostructures─mainly those based on cellulose, chitin, and chitosan─using experimental data or user-defined parameters. NPB enables the generation of cellulose and chitin allomorphs with customizable biochemical topologies and also facilitates the construction of large bundles that replicate nanostructures found in biological support systems, including plant cell walls and arthropod cuticles. The software outputs atomic Cartesian coordinates in Protein Data Bank (PDB) format and also provides atom connectivity files in PSF and PARM formats, ensuring seamless integration with major molecular dynamics (MD) engines such as NAMD, CHARMM, GROMACS, AMBER, OpenMM, and LAMMPS. Built on an interactive visualization framework, NPB features a graphical user interface (GUI) and supports both macOS and Linux operating systems. By enabling detailed atomic-scale studies of polysaccharide evolution in extracellular matrices and cell walls of algae, bacteria, fungi, and plants, NPB is poised to advance AI-guided research in sustainable chemical development and biomass utilization.

Biologics and molecular recognition: Mike Brigham‐Burke memorial issue