Computational targeting of iron uptake proteins in Covid-19 induced mucormycosis to identify inhibitors via molecular dynamics, molecular mechanics and density function theory studies.

Mucormycosis is a concerning invasive fungal infection with difficult diagnosis, high mortality rates, and limited treatment options. Iron availability is crucial for fungal growth that causes this disease. This study aimed to computationally target iron uptake proteins in Rhizopus arrhizus, Lichtheimia corymbifera, and Mucor circinelloides to identify inhibitors, thereby halting fungal growth and intervening in mucormycosis pathogenesis. Seven important iron uptake proteins were identified, modeled, and validated using Ramachandran plots. An in-house antifungal library of ~ 15,401 compounds was screened in molecular docking studies with these proteins. The best small molecule-protein complexes were simulated at 100 ns using Maestro, Schrodinger. Toxicity predictions suggested all six molecules, identified as the best binding compounds to seven proteins, belonged to lower toxicity levels per GHS classification. A molecular mechanics GBSA study for all seven complexes indicated low standard deviations after calculating free binding energies every 10 ns of the 100 ns trajectory. Density functional theory via quantum mechanics approaches highlighted the HOMO, LUMO, and other properties of the six best-bound molecules, revealing their binding capabilities and behaviour. This study sheds light on the molecular mechanisms and protein-ligand interactions, providing a multi-dimensional view towards the use of FDBD01920, FDBD01923, and FDBD01848 as stable antifungal ligands. Supplementary Information: The online version contains supplementary material available at 10.1007/s40203-024-00264-7.

In Silico Screening, Molecular Dynamics Simulation and Binding Free Energy Identify Single-Point Mutations That Destabilize p53 and Reduce Binding to DNA.

Considering p53's pivotal role as a tumor suppressor protein, proactive identification and characterization of potentially harmful p53 mutations are crucial before they appear in the population. To address this, four computational prediction tools-SIFT, Polyphen-2, PhD-SNP, and MutPred2-utilizing sequence-based and machine-learning algorithms, were employed to identify potentially deleterious p53 nsSNPs (nonsynonymous single nucleotide polymorphisms) that may impact p53 structure, dynamics, and binding with DNA. These computational methods identified three variants, namely, C141Y, C238S, and L265P, as detrimental to p53 stability. Furthermore, molecular dynamics (MD) simulations revealed that all three variants exhibited heightened structural flexibility compared to the native protein, especially the C141Y and L265P mutations. Consequently, due to the altered structure of mutant p53, the DNA-binding affinity of all three variants decreased by approximately 1.8 to 9.7 times compared to wild-type p53 binding with DNA (14 µM). Notably, the L265P mutation exhibited an approximately ten-fold greater reduction in binding affinity. Consequently, the presence of the L265P mutation in p53 could pose a substantial risk to humans. Given that p53 regulates abnormal tumor growth, this research carries significant implications for surveillance efforts and the development of anticancer therapies.

Computational screening of chemical constituents derived from berry fruits as allosteric caspace-3/-7 inhibitors.

With the aim of finding the plant-derived allosteric inhibitors of caspase-3/-7, we conducted computational investigations of bioactive compounds present in various berry fruits. In a molecular docking study, perulactone demonstrated excellent binding affinity scores of -12.1 kcal/mol and -9.1 kcal/mol for caspase 7 and 3, respectively, whereas FDA-approved allosteric inhibitors (DICA and FICA) were found to show lower docking scores (-5.6 and -6.1 kcal/mol) against caspase 7 while (-5.0 and -5.1 kcal/mol) for caspase 3, respectively. MD simulations were used to validate the binding stability of perulactone in the active sites of caspase-7/-3, and the results showed outstanding stability with lower ligand RMSDs of 1.270-3.088 Å and 2.426-9.850 Å against the targeted receptor. Furthermore, we performed MMGBSA free binding energy, where the perulactone values of ΔG Bind were determined to be -63.98 kcal/mol and -66.32 kcal/mol for both receptors (3IBF and 1NME), which are significantly better than the -45.16 kcal/mol and -39.51 kcal/mol for DICA as well as -26.37 kcal/mol and -15.50 kcal/mol for FICA, respectively. The drug resemblance of perulactone was effectively evaluated by ADMET. Thus, our findings indicated that perulactone could be an orally administered therapeutic candidate for regulating apoptosis in a variety of disorders. However, there may be an urgent need to study using in vitro and in vivo experiments. Supplementary Information: The online version contains supplementary material available at 10.1007/s13205-024-04067-7.

Evaluation of Anti-Hyperlipidemic Activity of the Seeds Extracts of Ficus carica: In Vitro and In Silico Approaches.

Obesity and hyperlipidemia have become major disorders predominantly causing prevailing cardiovascular diseases and ultimately death. The prolonged use of anti-obesity drugs and statins for reducing obesity and blood lipid levels is leading toward adverse effects of kidneys and muscles, specifically rhabdomyolysis. The objective of this study is to evaluate potential of seeds of Ficus carica against hyperlipidemia. Various extracts and isolated compounds from fig seeds were analyzed and evaluated for their anti-hyperlipidemic potential. Methanol extract and its ethyl acetate fraction showed maximum pancreatic lipase inhibition of 61.93% and 86.45% in comparison to reference drug Orlistat. Four compounds isolated by HPLC-PDA technique were determined as Gallic acid, Catechin, Epicatechin, and Quercetin also showed strong potential to inhibit enzyme pancreatic lipase comparable to Orlistat. These isolated compounds were further analyzed for molecular docking and MM-GBSA studies. Three ligands, namely Quercetin, Epicatechin, and Catechin were found more effective against pancreatic lipase as these possessed docking scores (-9.881, -9.741, -9.410) higher to that of the reference ligand Orlistat (-5.273). The binding free energies of these compounds were -55.03, -56.54, and 60.35 kcal/mol, respectively. The results have shown that Quercetin has the highest binding affinity correlating with the highest inhibition of pancreatic lipase enzyme 1LPB. Hence, it is suggested that seeds of F. carica have promising anti-hyperlipidemic potential and foremost in reducing obesity.

Pharmacophore-Based Study: An In Silico Perspective for the Identification of Potential New Delhi Metallo-ß-lactamase-1 (NDM-1) Inhibitors.

In the ongoing battle against antibiotic-resistant bacteria, New Delhi metallo-ß-lactamase-1 (NDM-1) has emerged as a significant therapeutic challenge due to its ability to confer resistance to a broad range of ß-lactam antibiotics. This study presents a pharmacophore-based virtual screening, docking, and molecular dynamics simulation approach for the identification of potential inhibitors targeting NDM-1, a critical enzyme associated with antibiotic resistance. Through the generation of a pharmacophore model and subsequent virtual screening of compound libraries, candidate molecules (ZINC29142850 (Z1), ZINC78607001 (Z2), and ZINC94303138 (Z3)) were prioritized based on their similarity to known NDM-1 binder (hydrolyzed oxacillin (0WO)). Molecular docking studies further elucidated the binding modes and affinities of the selected compounds towards the active site of NDM-1. These compounds demonstrated superior binding affinities to the enzyme compared to a control compound (-7.30 kcal/mol), with binding scores of -7.13, -7.92, and -8.10 kcal/mol, respectively. Binding interactions within NDM-1's active site showed significant interactions with critical residues such as His250, Asn220, and Trp93 for these compounds. Subsequent molecular dynamics simulations were conducted to assess the stability of the ligand-enzyme complexes, showing low root mean square deviation (RMSD) values between 0.5 and 0.7 nm for Z1, Z2, which indicate high stability. Z2's compactness in principal component analysis (PCA) suggests that it can stabilize particular protein conformations more efficiently. Z2 displays a very cohesive landscape with a notable deep basin, suggesting a very persistent conformational state induced by the ligand, indicating robust binding and perhaps efficient inhibition. Z2 demonstrates the highest binding affinity among the examined compounds with a binding free energy of -25.68 kcal/mol, suggesting that it could offer effective inhibition of NDM-1. This study highlights the efficacy of computational tools in identifying novel antimicrobial agents against resistant bacteria, accelerating drug discovery processes.

Cheminformatics-based analysis identified (Z)-2-(2,5-dimethoxy benzylidene)-6-(2-(4-methoxyphenyl)-2-oxoethoxy) benzofuran-3(2H)-one as an inhibitor of Marburg replication by interacting with NP.

The highly pathogenic Marburg virus (MARV) is a member of the Filoviridae family, a non-segmented negative-strand RNA virus. This article represents the computer-aided drug design (CADD) approach for identifying drug-like compounds that prevent the MARV virus disease by inhibiting nucleoprotein, which is responsible for their replication. This study used a wide range of in silico drug design techniques to identify potential drugs. Out of 368 natural compounds, 202 compounds passed ADMET, and molecular docking identified the top two molecules (CID: 1804018 and 5280520) with a high binding affinity of -6.77 and -6.672 kcal/mol, respectively. Both compounds showed interactions with the common amino acid residues SER_216, ARG_215, TYR_135, CYS_195, and ILE_108, which indicates that lead compounds and control ligands interact in the common active site/catalytic site of the protein. The negative binding free energies of CID: 1804018 and 5280520 were -66.01 and -31.29 kcal/mol, respectively. Two lead compounds were re-evaluated using MD modeling techniques, which confirmed CID: 1804018 as the most stable when complexed with the target protein. PC3 of the (Z)-2-(2,5-dimethoxybenzylidene)-6-(2-(4-methoxyphenyl)-2-oxoethoxy) benzofuran-3(2H)-one (CID: 1804018) was 8.74 %, whereas PC3 of the 2'-Hydroxydaidzein (CID: 5280520) was 11.25 %. In this study, (Z)-2-(2,5-dimethoxybenzylidene)-6-(2-(4-methoxyphenyl)-2-oxoethoxy) benzofuran-3(2H)-one (CID: 1804018) unveiled the significant stability of the proteins' binding site in ADMET, Molecular docking, MM-GBSA and MD simulation analysis studies, which also showed a high negative binding free energy value, confirming as the best drug candidate which is found in Angelica archangelica which may potentially inhibit the replication of MARV nucleoprotein.

Insight of the molecular mechanism of inhibitors located at different allosteric sites regulating the activity of wild type and mutant KRAS (G12).

As the important hub of many cellular signaling networks, KRAS (Kirsten rat sarcoma viral oncogene homologue) has been identified as a tumor biomarker. It is the frequently mutated oncogene in human cancers, and KRAS protein activation caused by mutations, such as G12D, has been found in many human tumors tissues. Although, there are two specific allosteric sites (AS1 and AS2) on the KRAS protein that can be used as the targets for inhibitor development, the difference of regulatory mechanisms between two individual allosteric sites still not be reported. Here, using molecular dynamics simulations combined with molecular mechanics generalized born surface area (MM/GBSA) analysis, we found that both of the inhibitors, located at AS1 and AS2, were able to reduce the binding free energy between wild type, mutant KRAS (G12/D/V/S/C) and GTP remarkably, however the effect of inhibitors on the binding free energy between wild type, mutant KRAS and GDP was limited. In addition, the degree of decrease of binding free energy between KRAS and GTP caused by inhibitors at AS2 was significantly greater than that caused by inhibitors at AS1. Further analysis revealed that both inhibitors at AS1 and AS2 were able to regulate the fluctuation of Switch Ⅰ and Switch Ⅱ to expand the pocket of the orthosteric site (GTP binding site), thereby reducing the binding of KRAS to GTP. Noteworthy there was significant differences in the regulatory preferences on Switch Ⅰ and Switch Ⅱ between two type inhibitor. The inhibitor at AS2 mainly regulated Switch Ⅱ to affect the pocket of the orthosteric site, while the inhibitor at AS1 mainly expand the pocket of the orthosteric site by regulating the fluctuation of Switch Ⅰ. Our study compared the differences between two type inhibitors in regulating the KRAS protein activity and revealed the advantages of the AS2 as the small molecule drug target, aiming to provide theoretical guidance for the research of novel KRAS protein inhibitors.

Development of Fusion-Based Assay as a Drug Screening Platform for Nipah Virus Utilizing Baculovirus Expression Vector System.

Nipah virus (NiV) is known to be a highly pathogenic zoonotic virus, which is included in the World Health Organization Research & Development Blueprint list of priority diseases with up to 70% mortality rate. Due to its high pathogenicity and outbreak potency, a therapeutic countermeasure against NiV is urgently needed. As NiV needs to be handled within a Biological Safety Level (BSL) 4 facility, we had developed a safe drug screening platform utilizing a baculovirus expression vector system (BEVS) based on a NiV-induced syncytium formation that could be handled within a BSL-1 facility. To reconstruct the NiV-induced syncytium formation in BEVS, two baculoviruses were generated to express recombinant proteins that are responsible for inducing the syncytium formation, including one baculovirus exhibiting co-expressed NiV fusion protein (NiV-F) and NiV attachment glycoprotein (NiV-G) and another exhibiting human EphrinB2 protein. Interestingly, syncytium formation was observed in infected insect cells when the medium was modified to have a lower pH level and supplemented with cholesterol. Fusion inhibitory properties of several compounds, such as phytochemicals and a polysulfonated naphthylamine compound, were evaluated using this platform. Among these compounds, suramin showed the highest fusion inhibitory activity against NiV-induced syncytium in the baculovirus expression system. Moreover, our in silico results provide a molecular-level glimpse of suramin's interaction with NiV-G's central hole and EphrinB2's G-H loop, which could be the possible reason for its fusion inhibitory activity.

High-Throughput Molecular Modeling and Evaluation of the Anti-Inflammatory Potential of Açaí Constituents against NLRP3 Inflammasome.

The search for bioactive compounds in natural products holds promise for discovering new pharmacologically active molecules. This study explores the anti-inflammatory potential of açaí (Euterpe oleracea Mart.) constituents against the NLRP3 inflammasome using high-throughput molecular modeling techniques. Utilizing methods such as molecular docking, molecular dynamics simulation, binding free energy calculations (MM/GBSA), and in silico toxicology, we compared açaí compounds with known NLRP3 inhibitors, MCC950 and NP3-146 (RM5). The docking studies revealed significant interactions between açaí constituents and the NLRP3 protein, while molecular dynamics simulations indicated structural stabilization. MM/GBSA calculations demonstrated favorable binding energies for catechin, apigenin, and epicatechin, although slightly lower than those of MCC950 and RM5. Importantly, in silico toxicology predicted lower toxicity for açaí compounds compared to synthetic inhibitors. These findings suggest that açaí-derived compounds are promising candidates for developing new anti-inflammatory therapies targeting the NLRP3 inflammasome, combining efficacy with a superior safety profile. Future research should include in vitro and in vivo validation to confirm the therapeutic potential and safety of these natural products. This study underscores the value of computational approaches in accelerating natural product-based drug discovery and highlights the pharmacological promise of Amazonian biodiversity.

Chemical analogue based drug design for cancer treatment targeting PI3K: integrating machine learning and molecular modeling.

Cancer is a generic term for a group of disorders defined by uncontrolled cell growth and the potential to invade or spread to other parts of the body. Gene and epigenetic alterations disrupt normal cellular control, leading to abnormal cell proliferation, resistance to cell death, blood vessel development, and metastasis (spread to other organs). One of the several routes that play an important role in the development and progression of cancer is the phosphoinositide 3-kinase (PI3K) signaling pathway. Moreover, the gene PIK3CG encodes the catalytic subunit gamma (p110γ) of phosphoinositide 3-kinase (PI3Kγ), a member of the PI3K family. Therefore, in this study, PIK3CG was targeted to inhibit cancer by identifying a novel inhibitor through computational methods. The study screened 1015 chemical fragments against PIK3CG using machine learning-based binding estimation and docking to select the potential compounds. Later, the analogues were generated from the selected hits, and 414 analogues were selected, which were further screened, and as most potential candidates, three compounds were obtained: (a) 84,332, 190,213, and 885,387. The protein-ligand complex's stability and flexibility were then investigated by dynamic modeling. The 100 ns simulation revealed that 885,387 exhibited the steadiest deviation and constant creation of hydrogen bonds. Compared to the other compounds, 885,387 demonstrated a superior binding free energy (ΔG = -18.80 kcal/mol) with the protein when the MM/GBSA technique was used. The study determined that 885,387 showed significant therapeutic potential and justifies further experimental investigation as a possible inhibitor of the PIK3CG target implicated in cancer.

Exploring Natural Compounds as Potential CDK4 Inhibitors for Therapeutic Intervention in Neurodegenerative Diseases through Computational Analysis.

CDK4 is a member of the serine-threonine kinase family, which has been found to be overexpressed in a plethora of studies related to neurodegenerative diseases. CDK4 is one of the most validated therapeutic targets for neurodegenerative diseases. Hence, the discovery of potent inhibitors of CDK4 is a promising candidate in the drug discovery field. Firstly, the reference drug Palbociclib was identified from the available literature as a potential candidate against target CDK4. In the present study, the Collection of Open Natural Products (COCONUT) database was accessed for determining potential CDK4 inhibitors using computational approaches based on the Tanimoto algorithm for similarity with the target drug, i.e., Palbociclib. The potential candidates were analyzed using SWISSADME, and the best candidates were filtered based on Lipinski's Rule of 5, Brenk, blood-brain barrier permeability, and Pains parameter. Further, the molecular docking protocol was accessed for the filtered compounds to anticipate the CDK4-ligand binding score, which was validated by the fastDRH web-based server. Based on the best docking score so obtained, the best four natural compounds were chosen for further molecular dynamic simulation to assess their stability with CDK4. In this study, two natural products, with COCONUT Database compound ID-CNP0396493 and CNP0070947, have been identified as the most suitable candidates for neuroprotection.

Phytoconstituents of Citrus limon (Lemon) as Potential Inhibitors Against Multi Targets of SARS-CoV-2 by Use of Molecular Modelling and In†Vitro Determination Approaches.

In the present work, phytoconstituents from Citrus limon are computationally tested against SARS-CoV-2 target protein such as Mpro - (5R82.pdb), Spike - (6YZ5.pdb) &RdRp - (7BTF.pdb) for COVID-19. Docking was done by glide model, QikProp was performed by in silico ADMET screening & Prime MM-GB/SA modules were used to define binding energy. When compared with approved COVID-19 drugs such as Remdesivir, Ritonavir, Lopinavir, and Hydroxychloroquine, plant-based constituents such as Quercetin, Rutoside, Naringin, Eriocitrin, and Hesperidin. bind with significant G-scores to the active SARS-CoV-2 place. The constituents Rutoside and Eriocitrin were studied in each MD simulation in 100 ns against 3 proteins 5R82.pdb, 6YZ5.pdb and 7BTF.pdb.We performed an assay with significant natural compounds from contacts and in silico results (Rutin, Eriocitrin, Naringin, Hesperidin) using 3CL protease assay kit (B.11529 Omicron variant). This kit contained 3CL inhibitor GC376 as Control. The IC50 value of the test compound was found to be Rutin -17.50 µM, Eriocitrin-37.91 µM, Naringin-39.58 µM, Hesperidine-140.20 µM, the standard inhibitory concentration of GC376 was 38.64 µM. The phytoconstituents showed important interactions with SARS-CoV-2 targets, and potential modifications could be beneficial for future development.

Anti-quorum sensing activity of α-amidoamides against Agrobacterium tumefaciensNT1: Insights from molecular docking and dynamic investigations to synergistic approach of metronidazole release from gel formulations.

A unique approach is imperative for the development of drugs aimed at inhibiting various stages of infection, rather than solely focusing on bacterial viability. Among the array of unconventional targets explored for formulating novel antimicrobial medications, blocking the quorum-sensing (QS) system emerges as a highly effective and promising strategy against a variety of pathogenic microbes. In this investigation, we have successfully assessed nine α-aminoamides for their anti-QS activity using Agrobacterium tumefaciensNT1 as a biosensor strain. Among these compounds, three (2, 3and, 4) have been identified as potential anti-QS candidates. Molecular docking studies have further reinforced these findings, indicating that these compounds exhibit favorable pharmacokinetic profiles. Additionally, we have assessed the ligand's stability within the protein's binding pocket using molecular dynamics (MD) simulations and MMGBSA analysis. Further, combination of antiquorum sensing properties with antibiotics viaself-assembly represents a promising approach to enhance antibacterial efficacy, overcome resistance, and mitigate the virulence of bacterial pathogens. The release study also reflects a slow and gradual release of the metronidazole at both pH 6.5 and pH 7.4, avoiding the peaks and troughs associated with more immediate release formulations.

Uncovering the anti-breast cancer activity potential of east Kalimantan propolis by In vitro and bioinformatics analysis.

Numerous side effects of breast cancer drugs have prompted researchers to explore more into new therapeutic approaches derived from natural substances. In this context, our study focused on uncovering the potential of East Kalimantan propolis from Trigona apicalis for breast cancer treatment including the underlying mechanisms through bioinformatics approached. We conducted integrated in vitro and bioinformatics analysis of network pharmacology, molecular docking, molecular dynamics and MM-GBSA analysis. Initially, in vitro cytotoxic assay demonstrated the anti-breast cancer activity potential of ethanol extract of East Kalimantan propolis, particularly its ethyl acetate fraction, which exhibited similar activity to doxorubicin, as indicated by their IC50 value. This study revealed eight propolis compounds, consisting of flavonoids and phenolic acids, in East Kalimantan propolis. By integrating microarray datasets (GSE29431, GSE36295, and GSE42568) analysis with potential targets derived from propolis compounds, 39 shared target genes were identified. Subsequently, GO and KEGG pathway, protein-protein interaction (PPI) network, core hub genes and gene expression analysis revealed three major targets, namely, PTGS2, CXCL2, and MMP9. Among them, only MMP9 was highly expressed in breast cancer than normal. Moreover, molecular docking revealed the six of propolis compounds which exhibited pronounced binding affinity towards MMP-9, better than marimastat as control drug. Dynamic simulation confirmed the stability of chrysin and quercetin as best compounds. Additionally, MM-GBSA analysis revealed a relative binding energy for chrysin (-25.6403 kcal/mol) that was comparable to marimastat (-27.3827 kcal/mol). In conclusion, this study reveals how East Kalimantan Propolis affect breast cancer and emphasizes MMP9 as a key target for future therapeutics.

Antiviral Activity, Pharmacoinformatics, Molecular Docking, and Dynamics Studies of Azadirachta indica Against Nipah Virus by Targeting Envelope Glycoprotein: Emerging Strategies for Developing Antiviral Treatment.

The Nipah virus (NiV) belongs to the Henipavirus genus is a serious public health concern causing numerous outbreaks with higher fatality rate. Unfortunately, there is no effective medication available for NiV. To investigate possible inhibitors of NiV infection, we used in silico techniques to discover treatment candidates in this work. As there are not any approved treatments for NiV infection, the NiV-enveloped attachment glycoprotein was set as target for our study, which is responsible for binding to and entering host cells. Our in silico drug design approach included molecular docking, post-docking molecular mechanism generalised born surface area (MM-GBSA), absorption, distribution, metabolism, excretion/toxicity (ADME/T), and molecular dynamics (MD) simulations. We retrieved 418 phytochemicals associated with the neem plant (Azadirachta indica) from the IMPPAT database, and molecular docking was used to ascertain the compounds' binding strength. The top 3 phytochemicals with binding affinities of -7.118, -7.074, and -6.894 kcal/mol for CIDs 5280343, 9064, and 5280863, respectively, were selected for additional study based on molecular docking. The post-docking MM-GBSA of those 3 compounds was -47.56, -47.3, and -43.15 kcal/mol, respectively. As evidence of their efficacy and safety, all the chosen drugs had favorable toxicological and pharmacokinetic (Pk) qualities. We also performed MD simulations to confirm the stability of the ligand-protein complex structures and determine whether the selected compounds are stable at the protein binding site. All 3 phytochemicals, Quercetin (CID: 5280343), Cianidanol (CID: 9064), and Kaempferol (CID: 5280863), appeared to have outstanding binding stability to the target protein than control ribavirin, according to the molecular docking, MM-GBSA, and MD simulation outcomes. Overall, this work offers a viable approach to developing novel medications for treating NiV infection.

Computational screening combined with well-tempered metadynamics simulations identifies potential TMPRSS2 inhibitors.

Type-II transmembrane serine proteases are effective pharmacological targets for host defence against viral entry and in certain cancer cell progressions. These serine proteases cleave viral spike proteins to expose the fusion peptide for cell entry, which is essential to the life cycle of the virus. TMPRSS2 inhibitors can also fight against respiratory viruses that employ them for cell entry. Our study combining virtual screening, all-atom molecular dynamics, and well-tempered metadynamics simulation identifies vicenin-2, neohesperidin, naringin, and rhoifolin as promising TMPRSS2 antagonists. The binding energies obtained are - 16.3, - 15.4, - 13.6, and - 13.8 kcal/mol for vicenin-2, neohesperidin, naringin, and rhoifolin respectively. The RMSD, RMSF, PCA, DCCM, and binding free energy profiles also correlate with the stable binding of these ligands at the active site of TMPRSS2. The study reveals that these molecules could be promising lead molecules for combating future outbreaks of coronavirus and other respiratory viruses.

In Silico Approach: Anti-Tuberculosis Activity of Caespitate in the H37Rv Strain.

Tuberculosis is a highly lethal bacterial disease worldwide caused by Mycobacterium tuberculosis (Mtb). Caespitate is a phytochemical isolated from Helichrysum caespititium, a plant used in African traditional medicine that shows anti-tubercular activity, but its mode of action remains unknown. It is suggested that there are four potential targets in Mtb, specifically in the H37Rv strain: InhA, MabA, and UGM, enzymes involved in the formation of Mtb's cell wall, and PanK, which plays a role in cell growth. Two caespitate conformational structures from DFT conformational analysis in the gas phase (GC) and in solution with DMSO (CS) were selected. Molecular docking calculations, MM/GBSA analysis, and ADME parameter evaluations were performed. The docking results suggest that CS is the preferred caespitate conformation when interacting with PanK and UGM. In both cases, the two intramolecular hydrogen bonds characteristic of caespitate's molecular structure were maintained to achieve the most stable complexes. The MM/GBSA study confirmed that PanK/caespitate and UGM/caespitate were the most stable complexes. Caespitate showed favorable pharmacokinetic characteristics, suggesting rapid absorption, permeability, and high bioavailability. Additionally, it is proposed that caespitate may exhibit antibacterial and antimonial activity. This research lays the foundation for the design of anti-tuberculosis drugs from natural sources, especially by identifying potential drug targets in Mtb.

In-silico study MM/GBSA binding free energy and molecular dynamics simulation of some designed remdesivir derivatives as the inhibitory potential of SARS-CoV-2 main protease.

Background and purpose: Coronavirus disease (COVID-19) is one of the greatest challenges of the twentieth century. Recently, in silico tools help to predict new inhibitors of SARS-CoV-2. In this study, the new compounds based on the remdesivir structure (12 compounds) were designed. Experimental approach: The main interactions of remdesivir and designed compounds were investigated in the 3CLpro active site. The binding free energy of compounds by the MM-GBSA method was calculated and the best compound (compound 12 with the value of -88.173 kcal/mol) was introduced to the molecular dynamics simulation study. Findings/Results: The simulation results were compared with the results of protein simulation without the presence of an inhibitor and in the presence of remdesivir. Additionally, the RMSD results for the protein backbone showed that compound 12 in the second 50 nanoseconds has less fluctuation than the protein alone and in the presence of remdesivir, which indicates the stability of the compound in the active site of the Mpro protein. Furthermore, protein compactness was investigated in the absence of compounds and the presence of compound 12 and remdesivir. The Rg diagram shows a fluctuation of approximately 0.05 A, which indicates the compressibility of the protein in the presence and absence of compounds. The results of the RMSF plot also show the stability of essential amino acids during protein binding. Conclusion and implications: Supported by the theoretical results, compound 12 could have the potential to inhibit the 3CLpro enzyme, which requires further in vitro studies and enzyme inhibition must also be confirmed at protein levels.

Investigating the Potential of 2-Ethylbutyl-(Phenoxy)phosphoryl-D-Alaninate Against RNA-Dependent RNA Polymerase (RdRp) of SARS-CoV-2.

There was an emergency call globally when COVID-19 was detected in December 2019. The SARS-CoV-2 virus, a modified virus, is what causes this contagious disease. Although research is being conducted throughout the world, the main target is still to find the promising candidate to target RNA-dependent RNA polymerase (RdRp) to provide possible drug against COVID-19. Aim of this work is to find a molecule to inhibit the translational process of viral protein synthesis. Density Functional Theory calculations revealed information about the formation of the desired ligand (RD). Molecular docking of RD with RdRp was performed and compared with some reported molecules and the data revealed that RD had the best docking score with RdRp (-6.7 kcal/mol). Further, molecular dynamics (MD) simulations of RD with RdRp of SARS-CoV-2 revealed the formation of stable complex with a maximum number of seven hydrogen bonds. Root mean square deviations values are in acceptable range and root mean square fluctuations has less fluctuation indicate stable complex formation. Further, based on MM-GBSA calculation, RD formed a stable complex with RdRp of nCoV with ΔG° of -12.28 kcal·mol-1.

Computational Insights into Papaveroline as an In Silico Drug Candidate for Alzheimer's Disease via Fyn Tyrosine Kinase Inhibition.

Alzheimer's disease (AD) poses a significant global health challenge, necessitating the exploration of novel therapeutic strategies. Fyn Tyrosine Kinase has emerged as a key player in AD pathogenesis, making it an attractive target for drug development. This study focuses on investigating the potential of Papaveroline as a drug candidate for AD by targeting Fyn Tyrosine Kinase. The research employed high-throughput virtual screening and QSAR analysis were conducted to identify compounds with optimal drug-like properties, emphasizing adherence to ADMET parameters for further evaluation. Molecular dynamics simulations to analyze the binding interactions between Papaveroline and Staurosporine with Fyn Tyrosine Kinase over a 200-ns period. The study revealed detailed insights into the binding mechanisms and stability of the Papaveroline-Fyn complex, showcasing the compound's potential as an inhibitor of Fyn Tyrosine Kinase. Comparative analysis with natural compounds and a reference compound highlighted Papaveroline's unique characteristics and promising therapeutic implications for AD treatment. Overall, the findings underscore Papaveroline's potential as a valuable drug candidate for targeting Fyn Tyrosine Kinase in AD therapy, offering new avenues for drug discovery in neurodegenerative diseases. This study contributes to advancing our understanding of molecular interactions in AD pathogenesis and paves the way for further research and development in this critical area.