Identification of novel peptide inhibitors of Plasmodium falciparum dihydrofolate reductase (PfDHFR): molecular docking and MD simulation studies.

The presence of drug-resistant variants of Plasmodium parasites within the population has presented a substantial obstacle to the eradication of Malaria. As a result, numerous research groups have directed their efforts towards creating new medication candidates that specifically target parasites. In this study, our main objective was to identify tri-peptide inhibitors for Plasmodium falciparum Dihydrofolate Reductase (PfDHFR) with the aim of finding a new peptide that exhibits superior binding properties compared to the current inhibitor, WR99210. In order to achieve this objective, a virtual library consisting of 8000 tripeptides was generated and subjected to computational screening against wild-type PfDHFR. The purpose of this screening was to discover the most effective binders at the active site. The four most optimal tripeptides identified (Trp-Trp-Glu, Trp-Phe-Tyr, Phe-Trp-Trp, Tyr-Trp-Trp) exhibited significant non-covalent interactions inside the active site of PfDHFR and had binding energies ranging from -9.5 to -9.0 kcal/mol and WR99210 had a binding energy of -6.2 kcal/mol. A 250 ns Molecular Dynamics (MD) simulation was performed to investigate the kinetic and thermodynamic characteristics of the protein-ligand complexes. The Root Mean Square Deviation (RMSD) values for the optimal tripeptides fell within the allowed range, indicating the stability of the ligands inside the protein complex. The Ki value for the most effective tripeptide was 0.3482 µM, whereas WR99210 had a Ki value of 1.02 µM. This article presents the initial discovery of peptide inhibitors targeting PfDHFR. In this text, we provide a comprehensive explanation of the interactions that occur between peptides and the enzyme.Communicated by Ramaswamy H. Sarma.

In-silico molecular modelling studies of some camphor imine based compounds as anti-influenza A (H1N1) pdm09 virus agents.

The advent of influenza A (H1N1) drug-resistant strains led to the search quest for more potent inhibitors of the influenza A virus, especially in this devastating COVID-19 pandemic era. Hence, the present research utilized some molecular modelling strategies to unveil new camphor imine-based compounds as anti-influenza A (H1N1) pdm09 agents. The 2D-QSAR results revealed GFA-MLR (R2train = 0.9158, Q2=0.8475) and GFA-ANN (R2train = 0.9264, Q2=0.9238) models for the anti-influenza A (H1N1) pdm09 activity prediction which have passed the QSAR model acceptability thresholds. The results from the 3D-QSAR studies also revealed CoMFA (R2train =0.977, Q2=0.509) and CoMSIA_S (R2train =0.976, Q2=0.527) models for activity predictions. Based on the notable information derived from the 2D-QSAR, 3D-QSAR, and docking analysis, ten (10) new camphor imine-based compounds (22a-22j) were designed using the most active compound 22 as the template. Furthermore, the high predicted activity and binding scores of compound 22j were further justified by the high reactive sites shown in the electrostatic potential maps and other quantum chemical calculations. The MD simulation of 22j in the active site of the influenza hemagglutinin (HA) receptor confirmed the dynamic stability of the complex. Moreover, the appraisals of drug-likeness and ADMET properties of the proposed compounds showed zero violation of Lipinski's criteria with good pharmacokinetic profiles. Hence, the outcomes in this work recommend further in-depth in vivo and in-vitro investigations to validate these theoretical findings.Communicated by Ramaswamy H. Sarma.

In silico and structure-based evaluation of deleterious mutations identified in human Chk1, Chk2, and Wee1 protein kinase.

Checkpoint kinases Chk1, Chk2, Wee1 are playing a key role in DNA damage response and genomic integrity. Cancer-associated mutations identified in human Chk1, Chk2, and Wee1 were retrieved to understand the function associated with the mutation and also alterations in the folding pattern. Therefore, an attempt has been made to identify deleterious effect of variants using in silico and structure-based approach. Variants of uncertain significance for Chk1, Chk2, and Wee1 were retrieved from different databases and four prediction servers were employed to predict pathogenicity of mutations. Further, Interpro, I-Mutant 3.0, Consurf, TM-align, and have (y)our protein explained were used for comprehensive study of the deleterious effects of variants. The sequences of Chk1, Chk2, and Wee1 were analyzed using Clustal Omega, and the three-dimensional structures of the proteins were aligned using TM-align. The molecular dynamics simulations were performed to explore the differences in folding pattern between Chk1, Chk2, Wee1 wild-type, and mutant protein and also to evaluate the structural integrity. Thirty-six variants in Chk1, 250 Variants in Chk2, and 29 in Wee1 were categorized as pathogenic using in silico prediction tools. Furthermore, 25 mutations in Chk1, 189 in Chk2, and 14 in Wee1 were highly conserved, possessing deleterious effect and also influencing the protein structure and function. These identified mutations may provide underlying genetic intricacies to serve as potential targets for therapeutic inventions and clinical management.

Molecular modelling studies of substituted indole derivatives as novel influenza a virus inhibitors.

The emergence of drug-resistant strains motivate researchers to find new innovative anti-IAV candidates with a different mode of action. In this work, molecular modelling strategies, such as 2D-QSAR, 3D-QSAR, molecular docking, molecular dynamics, FMOs, and ADMET were applied to some substituted indoles as IAV inhibitors. The best-developed 2D-QSAR models, MLR (Q2 = 0.7634, R2train = 0.8666) and ANN[4-3-1] (Q2 = 0.8699, R2train = 0.8705) revealed good statistical validation for the inhibitory response predictions. The 3D-QSAR models, CoMFA (Q2 = 0.504, R2train = 0.805) and CoMSIA/SEDHA (Q2 = 0.619, R2train = 0.813) are selected as the best 3D models following the global thresholds. In addition, the contour maps generated from the CoMFA and CoMSIA models illustrate the relationship between the molecular fields and the inhibitory effects of the studied molecules. The results of the studies led to the design of five new molecules (24a-e) with enhanced anti-IAV activities and binding potentials using the most active molecule (24) as the template scaffold. The conformational stability of the best-designed molecules with the NA protein showed hydrophobic and H-bonds with the key residues from the molecular dynamics simulations of 100 ns. Furthermore, the global reactivity indices from the DFT calculations portrayed the relevance of 24c in view of its smaller band gap as also justified by our QSAR and molecular simulation studies.Communicated by Ramaswamy H. Sarma.

Repurposing of drugs against methyltransferase as potential Zika virus therapies.

In recent years, the outbreak of infectious disease caused by Zika Virus (ZIKV) has posed a major threat to global public health, calling for the development of therapeutics to treat ZIKV disease. Several possible druggable targets involved in virus replication have been identified. In search of additional potential inhibitors, we screened 2895 FDA-approved compounds using Non-Structural Protein 5 (NS5) as a target utilizing virtual screening of in-silco methods. The top 28 compounds with the threshold of binding energy -7.2 kcal/mol value were selected and were cross-docked on the three-dimensional structure of NS5 using AutoDock Tools. Of the 2895 compounds screened, five compounds (Ceforanide, Squanavir, Amcinonide, Cefpiramide, and Olmesartan_Medoxomil) ranked highest based on filtering of having the least negative interactions with the NS5 and were selected for Molecular Dynamic Simulations (MDS) studies. Various parameters such as RMSD, RMSF, Rg, SASA, PCA and binding free energy were calculated to validate the binding of compounds to the target, ZIKV-NS5. The binding free energy was found to be -114.53, -182.01, -168.19, -91.16, -122.56, and -150.65 kJ mol-1 for NS5-SFG, NS5-Ceforanide, NS5-Squanavir, NS5-Amcinonide, NS5-Cefpiramide, and NS5-Ol_Me complexes respectively. The binding energy calculations suggested Cefpiramide and Olmesartan_Medoxomil (Ol_Me) as the most stable compounds for binding to NS5, indicating a strong rationale for their use as lead compounds for development of ZIKV inhibitors. As these drugs have been evaluated on pharmacokinetics and pharmacodynamics parameters only, in vitro and in vivo testing and their impact on Zika viral cell culture may suggest their clinical trials on ZIKV patients.

Modeling of chitosan modified PLGA atorvastatin-curcumin conjugate (AT-CU) nanoparticles, overcoming the barriers associated with PLGA: An approach for better management of atherosclerosis.

Conjugate drugs are evolving into potent techniques in the drug development process for enhancing the biopharmaceutical, physicochemical, and pharmacokinetic properties. Atorvastatin (AT) is the first line of treatment for coronary atherosclerosis; however its therapeutic efficacy is limited because of its poor solubility and fast pass metabolism. Curcumin (CU) is evidenced in several crucial signaling pathways linked to lipid regulation and inflammation. To enhance the therapeutic efficacy and physical properties of AT and CU, a new conjugate derivative (AT-CU) was synthesized and assessed by in silico, in vitro characterizations, and in vivo efficacy through mice model. Although the biocompatibility and biodegradability of Polylactic-co-Glycolic Acid (PLGA) in nanoparticles are well documented, burst release is a common issue with this polymer. Hence the current work used chitosan as a drug release modifier to the PLGA nanoparticles. The chitosan-modified PLGA AT-CU nanoparticles were prepaid by single emulsion and solvent evaporation technique. With raising the concentration of chitosan the particle size grew from 139.2 nm to 197.7 nm, the zeta potential rose from -20.57 mV to 28.32 mV, and the drug encapsulation efficiency improved from 71.81% to 90.57%. At 18 h, the burst release of AT-CU from PLGA nanoparticles was seen, hitting abruptly 70.8%. For chitosan-modified PLGA nanoparticles, the burst release pattern was significantly reduced which could be due to the adsorption of the drug on the surface of chitosan. The efficiency of the ideal formulation i.e F4 (chitosan/PLGA = 0.4) in treating atherosclerosis was further strongly evidenced by in vivo investigation.

Natural Metabolite Ursolic Acid as an Inhibitor of Dormancy Regulator DosR of Mycobacterium tuberculosis: Evidence from Molecular Docking, Molecular Dynamics Simulation and Free Energy Analysis.

BACKGROUND: DosR is a transcriptional regulator of Mycobacterium tuberculosis (MTB), governing the expression of a set of nearly 50 genes that is often referred to as 'dormancy regulon'. The inhibition of DosR expression by an appropriate inhibitor may be a crucial step against MTB. OBJECTIVE: We targeted the DosR with natural metabolites, ursolic acid (UA) and carvacrol (CV), using in silico approaches. METHODS: The molecular docking, molecular dynamics (MD) simulation for 200 ns, calculation of binding energies by MM-GBSA method, and ADMET calculation were performed to evaluate the inhibitory potential of natural metabolites ursolic acid (UA) and carvacrol (CV) against DosR of MTB. RESULTS: Our study demonstrated that UA displayed significant compatibility with DosR during the 200 ns timeframe of MD simulation. The thermodynamic binding energies by MM-GBSA also suggested UA conformational stability within the binding pocket. The SwissADME, pkCSM, and OSIRIS DataWarrior showed a drug-likeness profile of UA, where Lipinski profile was satisfied with one violation (MogP > 4.15) with no toxicities, no mutagenicity, no reproductive effect, and no irritant nature. CONCLUSION: The present study suggests that UA has the potency to inhibit the DosR expression and warrants further investigation on harnessing its clinical potential.

Functionalized Peptide-Based Nanoparticles for Targeted Cancer Nanotherapeutics: A State-of-the-Art Review.

Cancer mortality is increasing at an alarming rate across the globe. Albeit, many therapeutics are available commercially, they are not effective and have no cure up to today. Moreover, the knowledge gap in cancer therapy persists, representing a potential blind spot for the innovation of effective anticancer therapeutics. This review presents an update on current advancements in nanopeptide therapeutics. Herein, a detailed exploration of peptide-functionalized nanoparticles for the development of nanotherapeutics was carried out. Different approaches that include self-assembly nanostructures, solid phase peptide synthesis, ligand exchange, chemical reduction, and conjugation methods for assembling peptides for functionalizing nanodrugs are also highlighted. An outlook on biomedical applications is also reviewed. Additionally, a comprehensive discussion on targeted cancer cell therapy and mechanism of action are provided. The present review reflects the functional novelty of nanodrugs to improve stability, accessibility, bioavailability, and specificity toward cancerous cells. Finally, it summarizes the current challenges and future perspectives on the formulation of these nanodrugs.

Computational methods directed towards drug repurposing for COVID-19: advantages and limitations.

Novel coronavirus disease 2019 (COVID-19) has significantly altered the socio-economic status of countries. Although vaccines are now available against the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), a causative agent for COVID-19, it continues to transmit and newer variants of concern have been consistently emerging world-wide. Computational strategies involving drug repurposing offer a viable opportunity to choose a medication from a rundown of affirmed drugs against distinct diseases including COVID-19. While pandemics impede the healthcare systems, drug repurposing or repositioning represents a hopeful approach in which existing drugs can be remodeled and employed to treat newer diseases. In this review, we summarize the diverse computational approaches attempted for developing drugs through drug repurposing or repositioning against COVID-19 and discuss their advantages and limitations. To this end, we have outlined studies that utilized computational techniques such as molecular docking, molecular dynamic simulation, disease-disease association, drug-drug interaction, integrated biological network, artificial intelligence, machine learning and network medicine to accelerate creation of smart and safe drugs against COVID-19.

Richter's hernia after an intraperitoneal mesh repair.

For ventral hernia repair, laparoscopic surgery offers various advantages, including the application of a large mesh using the intraperitoneal approach. However, improper closure of the port site may lead to serious complications. Port-site hernia is a rare outcome of inadequate repair of the fascial or peritoneal layer, and Richter's hernia in a high risk patient, in the presence of an intraperitoneal mesh, is a particularly challenging scenario. Herein, we present a 58-year-old woman who, after a ventral hernia repair, was diagnosed with a small bowel hernia through a 10-mm port site. The patient complained of pain and mild swelling at the port site in the postoperative period, and her symptoms indicated intestinal obstruction. Clinical evaluation and abdominal X-ray confirmed the diagnosis, and early laparoscopic re-exploration and management were the key to a favorable outcome.