Protein Data Bank Research Articles

Homology modeling, binding site identification, molecular docking and molecular dynamics simulation study of emerging and promising drug target of Wnt signaling – Human Porcupine enzyme

Wnt signaling is a critical pathway involved in cell proliferation, differentiation and cellular homeostasis. To address treatment need of diseases associated with the dysregulated Wnt signaling like cancer, Alzheimer’s disease, Osteoporosis, Myocardial infarction etc.; small molecules that target the very first and unique component of the Wnt pathway, Porcupine enzyme, have been proven to be effective. Till date, none of the Porcupine inhibitor has reached to market. To design the novel Porcupine inhibitors, there was a need of crystal structure of Porcupine but in 2019, it was not resolved and deposited in the protein data bank. So, with an aim to predict its 3D structure, homology modeling study was performed using two distinct platforms; I-TASSER and Molsoft ICMPro so that we could use the best validated model for the designing of NCEs. Both the generated homology models were compared through Ramachandran plot, Protein health tool of Molsoft ICM and other tools available on metaserver, SAVES v6.0. Molsoft model was found better with 84.6 % residues in most favored region and only 0.3 % residues in disallowed region in Ramachandran plot in comparison to 75.9 % and 1.7 % residues respectively in I-TASSER model. The ICM model was further refined for 50 ns under MD simulation where it got stabilized after 36 ns. Quantification of protein was done by Radius of Gyration (ROG) where model showed larger ROG value of 23.25°A which indicated closely packed structure. Binding site in the predicted structure of Porcupine was identified using ICM binding pocket identifier and used for molecular docking of known Porcupine inhibitors like IWP-2, IWP-3, IWP-L6, and LKG974. Key binding site residues; Arg124, Phe257, Leu261, Val302, Trp305, Asn306, Ser310, Leu313, His341, Phen345, Ala349, Val350 etc. involved in the interaction were identified after MD simulation which can be useful in designing of novel porcupine inhibitors in future.

Potentiality of Resveratrol in Mitigating Exercise-Induced Inflammation - A Bioinformatic Study on Interleukin-6 (IL-6) in Athletes

Background: Athletes, engaging in vigorous exercise, confront elevated reactive oxygen species (ROS) and inflammation. Dietary antioxidants, abundant in diverse foods, offer protection. Resveratrol, a potent polyphenol from grapes and berries, explores its anti-inflammatory potential and mechanism through in-silico analysis in athletes. Method: Initial steps involved IL-6 data retrieval (PDB ID 1alu), receptor preparation, and ligand selection (Resveratrol, CID 445154). IL-6 receptor binding site identification utilized literature and PyMol. Molecular docking was facilitated by AutoDock, validated through re-docking and overlay methods. Lead molecule selection in in silico virtual screening was based on binding energy (-5 to -15 kcal/mol). The evaluation concluded with interaction assessment and binding affinity calculation. Result and Discussion: The study utilized IL-6 from the Protein Data Bank. Resveratrol was prepared for molecular docking. Docking outcomes showed optimal fit and interaction of resveratrol with IL-6, forming three bonds and exhibiting a low binding energy of -5.2 kcal/mol. The research addressed exercise-induced inflammation, emphasizing IL-6's role. Bioinformatics and molecular docking provided insights into IL-6 binding. Resveratrol demonstrated a potential inhibitory effect on IL-6 action, presenting a promising avenue for anti-inflammatory intervention in athletes. Conclusion: Exploring resveratrol's anti-inflammatory potential on IL-6, our study used molecular docking, suggesting its protective effects in athletes. Ongoing research is vital for validation and broader implications

Renoprotective effects of Cermai Leaves (Phyllanthus acidus(L.) Skeels) as a candidate for antidiabetic in silico

Renal complications are a major concern in individuals with diabetes, necessitating the search for effective antidiabetic agents with renoprotective properties.Phyllanthus acidus(L.) Skeels, commonly known as Cermai leaves, have been recognized for their medicinal properties, including potential antidiabetic effects. This in silico study aimed to investigate the renoprotective effects of Phyllanthus acidus as an antidiabetic agent by targeting the insulin-like growth factor-1 (IGF-1) (PDB code 1GZR) and angiotensin-converting enzyme 2 (ACE2) (PDB code 6M0J) receptors. The prediction of phytochemical properties was made based on the five laws of Lipinski as seen from the values of absorption, distribution, and metabolism with Swiss ADME. The in-silico method was conducted with the assistance of computer devices, databases like Protein Data Bank (PDB) and PubChem, and software like PyRx and Discovery Studio. The results showed the compounds kaempferol and cafestol meet the five laws of Lipinski. The compound kaempferol has the lowest binding affinity, with a value of -5.8 kcal/mol on IGF-1 and -6.1 kcal/mol on ACE2 and cafestol, -5.6 kcal/mol on IGF-1 and -5.8 kcal/mol on ACE2. It’s compared to the native ligand value -3.9 kcal/mol on IGF-1 and -5.2 kcal/mol on ACE2. Based on the results, it is known that this compound has a high potential to be a drug candidate for renoprotective.

An In Silico Study of Transforming Growth Factor-β Inhibitors: A Potential Target for Diabetic Nephropathy Treatment with Active Compounds from the Active Fraction of Physalis angulata.

Transforming growth factor beta (TGF-β) initiates epithelial-mesenchymal transition (EMT) in tubular and glomerular epithelial cells, resulting in excessive production and deposition of extracellular matrix through its interaction with TGF-β receptors, which play a crucial role in TGF-β signaling involving two receptor types, namely TGF-β type I (TβRI) and type II (TβRII). EMT contributes to the pathogenesis of interstitial renal fibrosis, a marker of end-stage kidney disease. This study aimed to identify the bioactive compounds in the active fraction of P. angulata and evaluate their ability to inhibit the TGF-β activity and their potential as drug candidates. The active components in the active fraction of P. angulata were analyzed using gas chromatography-mass spectrometry (GC-MS). The bioactive compound structures were obtained from the PubChem database, while the protein targets, TβRI and TβRII, were retrieved from the Protein Data Bank (PDB). The molecular docking analyses were performed using PyRx 0.8 and Discovery Studio. SwissADME was used to evaluate ligand properties and druglikeness. Three dominant active compounds were identified, namely palmitic acid, campesterol, and stigmasterol. In silico studies demonstrated strong energy bonds existed between TβRI and palmitic acid, campesterol, stigmasterol, and SB431542 with binding energy values of -5.7, -10, -9.4, and -10.9 kcal/mol, respectively. Similarly, they strongly bound to TβRII with binding energy values of -5.2, -7.1, -7.5, and -6.1 kcal/mol, respectively. All compounds meet Lipinski's criteria for druglikeness. Among the identified active compounds, campesterol exhibited the highest affinity for TβRI, while stigmasterol exhibited a strong affinity for TβRII. These findings suggested that the three compounds have potential as drug candidates.

Comprehensive structural and functional analysis of hVEGFR1: Insights into phosphorylation, molecular interactions, and potential inhibitors through docking and dynamics simulations

Vascular Endothelial Growth Factor Receptor 1 (VEGFR1), is an enzyme with tyrosine kinase activity that plays a pivotal role in angiogenesis, the process of new blood vessel formation. This receptor is of significant clinical importance as it is implicated in various cancers, particularly non-small cell lung cancer (NSCLC), where its dysregulation leads to uncontrolled cell growth through ligand-induced phosphorylation. While commercially available drugs target VEGFR1, their prolonged use often leads to drug resistance and the emergence of mutations in cancer patients. To address these challenges, researchers have identified the human tyrosine kinase (hTK) domain of VEGFR1 as a potential therapeutic marker for lung malignancies. The 3D crystal structure of the hTK domain, obtained from Protein Data Bank (PDB ID: 3HNG), has provided vital structural insights of hVEGFR1. This study has revealed variations within the hVEGFR1 tyrosine kinase domain, distinguishing between regions associated with phosphorylase kinase and transferase activities. We identified numerous potential phosphorylation sites within the TK domain, shedding light on the protein's regulation and signaling possible. Detailed molecular interaction analyses have elucidated the binding forces between lead molecules and hVEGFR1, including hydrogen bonds, electrostatic, hydrophobic, and π-sigma interactions. The stability observed during molecular dynamics simulations further underscores the biological relevance of these interactions. Furthermore, docked complexes has highlighted localized structural fluctuations, offering insight into potential allosteric effects and dynamic conformational changes induced by lead molecules. These findings not only provide a comprehensive characterization of hVEGFR1 but also pave the way for the development of targeted therapies. Eventually, this study has the potential in identifying drug to combat diseases associated with hVEGFR1 dysregulation, including cancer and angiogenesis-related disorders, contributing to effective treatment strategies.

Dock2D: Synthetic data for the molecular recognition problem.

Predicting the physical interaction of proteins is a cornerstone problem in computational biology. New classes of learning-based algorithms are actively being developed, and are typically trained end-to-end on protein complex structures extracted from the Protein Data Bank. These training datasets tend to be large and difficult to use for prototyping and, unlike image or natural language datasets, they are not easily interpretable by non-experts. We present Dock2D-IP and Dock2DIF, two "toy" datasets that can be used to select algorithms predicting protein-protein interactions-or any other type of molecular interactions. Using two-dimensional shapes as input, each example from Dock2D-IP ("interaction pose") describe the interaction pose of two shapes known to interact and each example from Dock2D-IF ("interaction fact") describes whether two shapes form a stable complex or not, regardless of how they bind. We propose a number of baseline solutions to the problem and show that the same underlying energy function can be learned either by solving the interaction pose task (formulated as an energy-minimization "docking" problem) or the fact-ofinteraction task (formulated as a binding free energy estimation problem).

Supporting the CIF file format of proteins in molecular dynamics simulations

Molecular dynamics (MD) simulations can capture the dynamic behavior of proteins in full atomic detail and at very fine temporal resolution, so they have become an important tool in the study of protein dynamics. To date, several MD packages are widely used. An MD simulation starts from an initial structure that is generally taken from the Protein Data Bank (PDB). Until 2014, the PDB format was the standard file format for protein structures. However, there are certain intrinsic limitations in the PDB format, such as the storage of structural information in a fixed-width format, which is an issue for very large protein complexes. Therefore, the CIF (crystallographic information framework) format has been proposed, which is characterized by its superior expansibility. To our knowledge, the current mainstream MD packages support only the PDB format but do not support the CIF format directly. In this study, we modified the source code of one of the MD packages, GROMACS, which enables it to support CIF-formatted structure files as input and subsequently generate molecular topology files. This work simplifies the preprocessing of large protein complexes for MD simulations.

Theory of density waves and organization of proteins in icosahedral virus capsids.

Understanding the physical principles underlying the structural organization of the proteinaceous viral shells is of major importance to advance antiviral strategies. Here, we develop a phenomenological thermodynamic theory, which considers structures of small and middle-size icosahedral viral shells as a result of condensation of a minimum number of protein density waves on a spherical surface. Each of these irreducible critical waves has icosahedral symmetry and can be expressed as a specific series of the spherical harmonics Ylm with the same wave number l. As we demonstrate, in small viral shells self-assembled from individual proteins, the maxima of one critical density wave determine the positions of proteins, while the spatial derivatives of the second one control the protein orientations on the shell surface. In contrast to the small shells, the middle-size ones are always formed from pentamers and hexamers (referred to as capsomers). Considering all such structures deposited in the Protein Data Bank, we unexpectedly found that the positions of capsomeres in these shells correspond to the maxima of interference patterns produced by no more than two critical waves with close wave numbers. This fact allows us to explain the observed limit size of the icosahedral shells assembled from pentamers and hexamers. We also construct nonequilibrium thermodynamic potentials describing the protein crystallization and discuss the reasons behind the specific handedness of the viral shells.

Inhibitory effect of thymoquinone from Nigella sativa against SARS-CoV-2 main protease. An in-silico study.

Nigella sativa is known for the safety profile, containing a wealth of useful antiviral compounds. The main protease (Mpro, 3CLpro) of severe acute respiratory syndrome 2 (SARS-CoV-2) is being considered as one of the most attractive viral target, processing the polyproteins during viral pathogenesis and replication. In the current investigation we analyzed the potency of active component, thymoquinone (TQ) of Nigella sativa against SARS-CoV-2 Mpro. The structures of TQ and Mpro was retrieved from PubChem (CID10281) and Protein Data Bank (PDB ID 6MO3) respectively. The Mpro and TQ were docked and the complex was subjected to molecular dynamic (MD) simulations for a period 50ns. Protein folding effect was analyzed using radius of gyration (Rg) while stability and flexibility was measured, using root means square deviations (RMSD) and root means square fluctuation (RMSF) respectively. The simulation results shows that TQ is exhibiting good binding activity against SARS-CoV-2 Mpro, interacting many residues, present in the active site (His41, Cys145) and also the Glu166, facilitating the pocket shape. Further, experimental approaches are needed to validate the role of TQ against virus infection. The TQ is interfering with pocket maintaining residues as well as active site of virus Mpro which may be used as a potential inhibitor against SARS-CoV-2 for better management of COVID-19.

Antimalarial Potential of Phytochemical Compounds from Garcinia atroviridis Griff ex. T. Anders Targeting Multiple Proteins of Plasmodium falciparum 3D7: An In Silico Approach

Abstract Background: Plasmodium falciparum is a malaria-causing unicellular parasite with strain 3D7 (Pf3D7) being the most lethal. Currently, antimalarial resistance has been reported which necessitates the development of novel antimalarial drugs to combat the spread of malaria. Garcinia atroviridis Griff. ex T. Anders contains phytochemical compounds that are useful for various activities, including targeting Pf3D7 proteins. This study explored novel antimalarial drugs from G. atroviridis against several target proteins of Pf3D7 in silico. Methods: Phytocompounds from G. atroviridis were selected as ligands. After retrieval from the Protein Data Bank, the protein sequence was screened using BLASTp NCBI. Molecular docking analysis was performed on PyRx to compute binding affinity and identify the chemical interactions involved. The stability of the ligand-protein complex was evaluated using dynamic molecular approaches. Results: Our findings showed that quercetin has a high binding affinity with apicoplast DNA polymerase (−8.3 kcal/mol), glutamyl-tRNA synthetase (−7.5 kcal/mol), and plasmepsin X (−7.8 kcal/mol). Kaempferol had a high binding affinity for the cytochrome c2 domain-swapped dimer (−8.4 kcal/mol). Conclusion: Collectively, quercetin and kaempferol are potential antimalarial candidates which warrant further investigation using in vitro and in vivo designs.

Analysis and Visualization of Protein Channels, Tunnels, and Pores with MOLEonline and ChannelsDB 2.0.

Channels, tunnels, and pores serve as pathways for the transport of molecules and ions through protein structures, thus participating to their functions. MOLEonline ( https://mole.upol.cz ) is an interactive web-based tool with enhanced capabilities for detecting and characterizing channels, tunnels, and pores within protein structures. MOLEonline has two distinct calculation modes for analysis of channel and tunnels or transmembrane pores. This application gives researchers rich analytical insights into channel detection, structural characterization, and physicochemical properties. ChannelsDB 2.0 ( https://channelsdb2.biodata.ceitec.cz/ ) is a comprehensive database that offers information on the location, geometry, and physicochemical characteristics of tunnels and pores within macromolecular structures deposited in Protein Data Bank and AlphaFill databases. These tunnels are sourced from manual deposition from literature and automatic detection using software tools MOLE and CAVER. MOLEonline and ChannelsDB visualization is powered by the LiteMol Viewerand Mol* viewer, ensuring a user-friendly workspace. This chapter provides an overview of user applications and usage.

Challenges of Protein-Protein Docking of the Membrane Proteins.

Despite the recent advances in the determination of high-resolution membrane protein (MP) structures, the structural and functional characterization of MPs remains extremely challenging, mainly due to the hydrophobic nature, low abundance, poor expression, purification, and crystallization difficulties associated with MPs. Whereby the major challenges/hurdles for MP structure determination are associated with the expression, purification, and crystallization procedures. Although there have beensignificant advances in the experimental determination of MP structures, only a limited number of MP structures (approximately less than 1% of all) are available in the Protein Data Bank (PDB). Therefore, the structures of a large number of MPs still remain unresolved, which leads to the availability of widely unplumbed structural and functional information related to MPs. As a result, recent developments in the drug discovery realm and the significant biological contemplation have led to the development of several novel, low-cost, and time-efficient computational methods that overcome the limitations of experimental approaches, supplement experiments, and provide alternatives for the characterization of MPs. Whereby the fine tuning and optimizations of these computational approaches remains an ongoing endeavor.Computational methods offer a potential way for the elucidation of structural features andthe augmentation of currently available MP information. However, the use of computational modeling can be extremely challenging for MPs mainly due to insufficient knowledge of (or gaps in) atomic structures of MPs. Despite the availability of numerous in silico methods for 3D structure determination the applicability of these methods to MPs remains relatively low since all methods are not well-suited or adequate for MPs. However, sophisticated methods for MP structure predictions are constantly being developed and updated to integrate the modifications required for MPs. Currently, different computational methods for (1) MP structure prediction, (2) stability analysis of MPs through molecular dynamics simulations, (3) modeling of MP complexes through docking, (4) prediction of interactions between MPs, and (5) MP interactions with its soluble partner are extensively used. Towards this end, MP docking is widely used. It is notable that the MP docking methods yet few in number might show greater potential in terms of filling the knowledge gap. In this chapter, MP docking methods and associated challenges have been reviewed to improve the applicability, accuracy, and the ability to model macromolecular complexes.

Targeting decaprenylphosphoryl-β-D-ribose 2'-epimerase for Innovative Drug Development Against Mycobacterium Tuberculosis Drug-Resistant Strains.

Tuberculosis (TB) remains a global health challenge with the emergence of drug-resistant Mycobacterium tuberculosis variants, necessitating innovative drug molecules. One potential target is the cell wall synthesis enzyme decaprenylphosphoryl-β-D-ribose 2'-epimerase (DprE1), crucial for virulence and survival. This study employed virtual screening of 111 Protein Data Bank (PDB) database molecules known for their inhibitory biological activity against DprE1 with known IC50 values. Six compounds, PubChem ID: 390820, 86287492, 155294899, 155522922, 162651615, and 162665075, exhibited promising attributes as drug candidates and validated against clinical trial inhibitors BTZ043, TBA-7371, PBTZ169, and OPC-167832. Concurrently, this research focused on DprE1 mutation effects using molecular dynamic simulations. Among the 10 mutations tested, C387N significantly influenced protein behavior, leading to structural alterations observed through root-mean-square deviation (RMSD), root-mean-square fluctuation (RMSF), radius of gyration (Rg), and solvent-accessible surface area (SASA) analysis. Ligand 2 (ID: 390820) emerged as a promising candidate through ligand-based pharmacophore analysis, displaying enhanced binding compared with reference inhibitors. Molecular dynamic simulations highlighted ligand 2's interaction with the C387N mutation, reducing fluctuations, augmenting hydrogen bonding, and influencing solvent accessibility. These collective findings emphasize ligand 2's efficacy, particularly against severe mutations, in enhancing protein-ligand complex stability. Integrated computational and pharmacophore methodologies offer valuable insights into drug candidates and their interactions within intricate protein environments. This research lays a strategic foundation for targeted interventions against drug-resistant TB, highlighting ligand 2's potential for advanced drug development strategies.

Computational Studies on 6-Pyruvoyl Tetrahydropterin Synthase (6-PTPS) of Plasmodium falciparum

6-Pyruvoyl tetrahydropterin synthase (6-PTPS) is a lyase involved in the synthesis of tetrahydrobiopterin. In Plasmodium species where dihydroneopterin aldolase (DHNA) is absent, it acts in the folate biosynthetic pathway necessary for the growth and survival of the parasite. The 6-pyruvoyl tetrahydropterin synthase of Plasmodium falciparum ( PfPTPS) has been identified as a potential antimalarial drug target. This study identified potential inhibitors of PfPTPS using molecular docking techniques. Molecular docking and virtual screening of 62 compounds including the control to the deposited Protein Data Bank (PDB) structure was carried out using AutoDock Vina in PyRx. Five of the compounds, N,N-dimethyl- N’-[4-oxo-6-(2,2,5-trimethyl-1,3-dioxolan-4-yl)-3H-pteridin-2-yl]methanimidamide (140296439), 2-amino-6-[(1R)-3-cyclohexyl-1-hydroxypropyl]-3H-pteridin-4-one (140296495), 2-(2,3-dihydroxypropyl)-8,9-dihydro-6H-pyrimido[2,1-b]pteridine-7,11-dione (144380406), 2-(dimethylamino)-6-[(2,2-dimethyl-1,3-dioxolan-4-yl)-hydroxymethyl]-3H-pteridin-4-one (135573878), and [1-acetyloxy-1-(2-methyl-4-oxo-3H-pteridin-6-yl)propan-2-yl] acetate (136075207), showed better binding affinity than the control ligand, biopterin (135449517), and were selected and screened. Three conformers of 140296439 with the binding energy of −7.2, −7.1, and −7.0 kcal/mol along with 140296495 were better than the control at −5.7 kcal/mol. In silico absorption, distribution, metabolism, excretion, and toxicity (ADMET) studies predicted good pharmacokinetic properties of all the compounds while reporting a high risk of irritant toxicity in 140296439 and 144380406. The study highlights the five compounds, 140296439, 140296495, 144380406, 135573878 and 136075207, as potential inhibitors of PfPTPS and possible compounds for antimalarial drug development.

Molecular Docking Study of New Pyrrolyl Benzohydrazide Schiff Bases as M. tuberculosis InhA Enzyme Inhibitors

Background Tuberculosis TB is an ancient chronic infectious disease affecting primarily the lungs of humans and rapidly co-evolved with humans since several million years. It is mainly caused by a group of bacterial species termed Mycobacterium tuberculosis a principal strain of this infection. Each second millions are at risk of acquiring new infection of becoming sick prone to death and a conclusive understanding of pathogenesis is still deficient.Objective To design a series of new pyrrole benzohydrazide schiff bases as M. tuberculosis InhA inhibitors.Methods Docking software Sybyl-X version 2.0 was used for molecular modeling. To dock intended compounds Surflex-Dock procedure of sybyl was castoff. M. tuberculosis enoyl reductase InhA inhibitor target protein 4TZK complexed with ligand 1-cyclohexyl-N-3 5-dichlorophenyl-5-oxopyrrolidine3-carboxamide was taken from Protein Data Bank PDB entry code 4TZK httpwww.rcsb.orgpdb.Results Docking scores of the compounds ranged between 5.47 - 3.32 summarizing the interface force field between enzyme and the ligands.Conclusion Sixteen molecules bearing pyrrole hydrazone moiety were docked showing promising M. tuberculosis inhibition activity. In silico molecular docking analysis exhibited that peptide linkage -CO-NH- and imine -CN- of pyrrole hydrazones were important in binding with the targeted receptor.

In silico design of new ketorolac bioisosteres as anti-inflammatory agents

Nonsteroidal anti-inflammatory drugs (NSAIDs) are over-the-counter drugs and one of the most widely used pharmacological groups worldwide, their chronic use is limited due to their adverse effects, with gastrointestinal effects being the most relevant. Ketorolac is one of the most consumed NSAIDs worldwide, the use of which has been increasing in the treatment of acute and chronic pain; however, its use has been limited by its gastrointestinal adverse effects. Modifications in the structure of ketorolac may help the search for new chemical entity candidates for anti-inflammatory analgesic drugs with less gastric effects. Forty-two ketorolac-derived bioisosteres were designed, using in silico techniques, their physicochemical parameters (molecular weight, polar surface area, and lipophilicity) were determined with SwissADME, the Way2Drug server was used to predict the activity, a 2D quantitative structure-activity relationship (QSAR) analysis was performed to quantify the anti-inflammatory activity of the derivatives, using the ChemBL database and the R language; and the molecular modeling for the prediction of the positioning in the active site and the ligand efficiency (LE), for which the 5IKR file of the protein data bank database was used, and the molecular coupling was carried out under the parameters of AutoDock Vina. Of the 42 ketorolac derivatives, 12 molecules demonstrated good predictive oral absorption, of which only 8 molecules showed less gastro-injurious effects with Way2Drug and, under 2D QSAR analysis, 3 demonstrated excellent anti-inflammatory activity. Using the molecule modeling techniques, the LE was verified, and it was determined that molecules 32 and 34 adequately inhibited the cyclooxygenase-2 enzyme; however, the pKa of molecule 34 is not greater than that of ketorolac, molecule 32 being the one that meets with all the parameters proposed in the present study, so it would be a candidate to continue in preclinical phase studies to corroborate the findings made in this investigation.

In silico simulation of hyperoside, isoquercetin, quercetin, and quercitrin as potential antivirals against the pNP868R protein of African swine fever virus.

African swine fever (ASF) causes disease in pigs with up to 100% mortality rates. There is no effective vaccine to protect against it. This study aimed to perform in silico docking of ASF virus (ASFV) pNP868R protein with potential flavonoid ligands to identify ligands that interfere with mRNA cap formation. The ASFV pNP868R protein was tested with hyperoside, isoquercetin, quercetin, and quercitrin in this in silico simulation. ASFV pNP868R protein was extracted from the Research Collaboration for Structural Bioinformatics Protein Data Bank (RCSB PDB) database with PDB ID 7D8U (https://www.rcsb.org/structure/7D8U). Standard ligands were separated from proteins using UCSF Chimera 1.13. The standard ligand was redocked to protein using AutoDockTools 1.5.6 with the AutoDock4 method for validation. In the docking process, the grid box size was 40 × 40 × 40 Å3 with x, y, and z coordinates of 16.433, -43.826, and -9.496, respectively. The molecular docking process of the proposed ligand-protein complex can proceed if the standard ligand position is not significantly different from its original position in the viral protein's pocket. The root mean square deviation (RMSD), root mean square fluctuation (RMSF), and radius of gyration (RoG) of the hyperoside with the lowest energy binding need to be analyzed with molecular dynamics using Groningen machine for chemical simulation 5.1.1. Molecular docking and dynamic simulation revealed that hyperoside had the most stable and compact binding to the pNP868R protein. Hyperoside binds to the protein at the minimum energy of -9.07 KJ/mol. The RMSD, RMSF, and RoG values of 0.281 nm, 0.2 nm, and 2.175 nm, respectively, indicate the stability and compactness of this binding. Hyperoside is the most likely antiviral candidate to bind to the pNP868R protein in silico. Therefore, it is necessary to test whether this flavonoid can inhibit mRNA capping in vitro and elicit the host immune response against uncapped viral mRNA.

In silico design of sitagliptin bioisosteres as new inhibitors of Mpro SARS-CoV-2

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the virus that caused the last major pandemic and has led to multiple efforts being carried out around the world to investigate molecules such as specific chemotherapy drugs against this virus, such as the drug Sitagliptin, which inhibits Mpro, a protease of the virus, by acting on a highly conservative substrate recognition pocket in different coronaviruses. The study aims to develop new compounds under bioisosteric replacement techniques and obtain new SARS-Cov-2 Mpro inhibitors that are more effective than Sitagliptin. For this, 50 bioisosteric derivatives of Sitagliptin were designed, and the pharmacodynamic, pharmacokinetic, and physicochemical properties were analyzed. First, the physicochemical properties of oral absorption were analyzed according to the principles of Lipinski and Veber. The molecular docking was carried out between the protease Mpro (Protein Data Bank Code 7BB2) and the 26 bioisosteres that had the best oral absorption, with the bioisosteres modified in 2,4,5-trifluorophenyl being those that presented a lower affinity energy than that of Sitagliptin. In addition, molecules with pyrrole-pyrazole or similar groups have better pharmacokinetic properties. It is concluded that the pyrrole-pyrazole carboxamide derivative molecule has better energy affinity and ligand efficiency than the other bioisosteres; additionally, it has adequate pharmacokinetic properties, so it would be the best candidate to continue in in vitro or in vivo studies.

Quantum Mechanical Calculations, Hirshfeld Surface Analysis, Molecular Docking, ADME and Toxicology Studies of the Ethyl 4-chloro-2-[(4-nitrophenyl)hydrazono]-3-oxobutrate Compound

The theoretical chemical activity parameters of the compound Ethyl 4-chloro-2-[(4-nitrophenyl)hydrazono]-3-oxobutrate, which had previously been synthesized and crystallographically studied, were determined using Density Functional Theory (DFT). With Hirshfeld surface analysis, the effects of molecular interactions on the surface were defined with surface maps and the percentage contribution of the interactions of atoms with each other was determined. In addition, second-order nonlinear optical properties (NLO) and thermodynamic parameters were examined at temperatures between 100 and 1000 K. In addition, molecular docking study was carried out by downloading structure of a B-DNA dodecamer (PDB ID: 1BNA) from PDB (Protein Data Bank). ADME and toxicology properties were examined using the complex, in-silico method and it was discussed whether it had drug properties.

Molecular Docking Approach Reveals The Potential Role Of Psidium Guajava Leaf Extract Compounds As Quorum-Sensing Inhibitors Targeting Pseudomonas Aeruginosa’s Lasr

Quorum-sensing LasR antagonism is evolving as a primary focus in promising new antivirulence approaches for treating bacterial infections. Pseudomonas aeruginosa’s LasR is a target receptor for developing alternative medicines in chronic wounds because it acts as an autoinducer in biofilm formation. Our research aims to predict the biofunction of Psidium guajava leaf water extract as an inhibitor of the quorum-sensing LasR of Pseudomonas aeruginosa. The 3D structures of five bioactive compounds (quercetin, gallocatechin, esculin, 3-sinapoylquinic acid, ellagic acid) and N-3-Oxo-Dodecanoyl-L-Homoserine Lactone (as positive control) were obtained from the PubChem Database. The Protein Data Bank database is used to download LasR. The active site of the LasR protein was determined using Molegro Virtual Docker 5.0. The docking simulation uses Molegro Virtual Docker 5.0 and Discovery Studio program version 21.1.1 for visualization. The results showed that the five compounds could bind to LasR at the active site and substrate binding, leading to the compound’s potential as an antibacterial for Pseudomonas aeruginosa by inhibiting the quorum-sensing receptor LasR. The 3-sinapoylquinic acid-LasR complex shows the lowest binding energy, namely -311.2 kJ/mol.