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Dengue virus is a single positive-strand RNA virus that is composed of three structural proteins including capsid, envelope, and precursor membrane while seven non-structural proteins (NS1, NS2A, NS2B, NS3A, NS3B, NS4, and NS5). Dengue is a viral infection caused by the dengue virus (DENV). DENV infections are asymptomatic or produce only mild illness. However, DENV can occasionally cause more severe cases and even death. There is no specific treatment for dengue virus infections. Therapeutic peptides have several important advantages over proteins or antibodies: they are small in size, easy to synthesize, and have the ability to penetrate the cell membranes. They also have high activity, specificity, affinity, and less toxicity. Based on the known peptide inhibitor, the current study designs peptide inhibitors for dengue virus envelope protein using an alanine and residue scanning technique. By replacing I21 with Q21, L14 with H14, and V28 with K28, the binding affinity of the peptide inhibitors was increased. The newly designed peptide inhibitors with single residue mutation improved the binding affinity of the peptide inhibitors. The inhibitory capability of the new promising peptide inhibitors was further confirmed by the utilization of MD simulation and free binding energy calculations. The molecular dynamics simulation demonstrated that the newly engineered peptide inhibitors exhibited greater stability compared to the wild-type peptide inhibitors. According to the binding free energies MM(GB)SA of these developed peptides, the first peptide inhibitor was the most effective against the dengue virus envelope protein. All peptide derivatives had higher binding affinities for the envelope protein and have the potential to treat dengue virus-associated infections. In this study, new peptide inhibitors were developed for the dengue virus envelope protein based on the already reported peptide inhibitor. [ABSTRACT FROM AUTHOR]

Single-point mutations in the Kirsten rat sarcoma (KRAS) viral proto-oncogene are the most common cause of human cancer. In humans, oncogenic KRAS mutations are responsible for about 30% of lung, pancreatic, and colon cancers. One of the predominant mutant KRAS G12D variants is responsible for pancreatic cancer and is an attractive drug target. At the time of writing, no Food and Drug Administration (FDA) approved drugs are available for the KRAS G12D mutant. So, there is a need to develop an effective drug for KRAS G12D. The process of finding new drugs is expensive and time-consuming. On the other hand, in silico drug designing methodologies are cost-effective and less time-consuming. Herein, we employed machine learning algorithms such as K-nearest neighbor (KNN), support vector machine (SVM), and random forest (RF) for the identification of new inhibitors against the KRAS G12D mutant. A total of 82 hits were predicted as active against the KRAS G12D mutant. The active hits were docked into the active site of the KRAS G12D mutant. Furthermore, to evaluate the stability of the compounds with a good docking score, the top two complexes and the standard complex (MRTX-1133) were subjected to 200 ns MD simulation. The top two hits revealed high stability as compared to the standard compound. The binding energy of the top two hits was good as compared to the standard compound. Our identified hits have the potential to inhibit the KRAS G12D mutation and can help combat cancer. To the best of our knowledge, this is the first study in which machine-learning-based virtual screening, molecular docking, and molecular dynamics simulation were carried out for the identification of new promising inhibitors for the KRAS G12D mutant. [ABSTRACT FROM AUTHOR]

Lung cancer is a disease with a high mortality rate and it is the number one cause of cancer death globally. Approximately 12–14% of non-small cell lung cancers are caused by mutations in KRASG12C. The KRASG12C is one of the most prevalent mutants in lung cancer patients. KRAS was first considered undruggable. The sotorasib and adagrasib are the recently approved drugs that selectively target KRASG12C, and offer new treatment approaches to enhance patient outcomes however drug resistance frequently arises. Drug development is a challenging, expensive, and time-consuming process. Recently, machine-learning-based virtual screening are used for the development of new drugs. In this study, we performed machine-learning-based virtual screening followed by molecular docking, all atoms molecular dynamics simulation, and binding energy calculations for the identifications of new inhibitors against the KRASG12C mutant. In this study, four machine learning models including, random forest, k-nearest neighbors, Gaussian naïve Bayes, and support vector machine were used. By using an external dataset and 5-fold cross-validation, the developed models were validated. Among all the models the performance of the random forest (RF) model was best on the train/test dataset and external dataset. The random forest model was further used for the virtual screening of the ZINC15 database, in-house database, Pakistani phytochemicals, and South African Natural Products database. A total of 100 ns MD simulation was performed for the four best docking score complexes as well as the standard compound in complex with KRASG12C. Furthermore, the top four hits revealed greater stability and greater binding affinities for KRASG12C compared to the standard drug. These new hits have the potential to inhibit KRASG12C and may help to prevent KRAS-associated lung cancer. All the datasets used in this study can be freely available at (https://github.com/Amar-Ajmal/Datasets-for-KRAS). [ABSTRACT FROM AUTHOR]

Helicobacter pylori is a Gram-negative spiral-shaped bacterium that infects half of the human population worldwide and causes chronic inflammation. In the present study, we used the art of computational biology for therapeutic drug targets identification and a multi-epitope vaccine against multi-strains of H. pylori. For drug target identification, we used different tools and softwares to identify human non-homologous but pathogen essential proteins, with virulent properties and involved in unique metabolic pathways of H. pylori. For this purpose, the core proteome of 84 strains of H. pylori was retrieved from EDGAR 2.3 database. There were 59,808 proteins sequences in these strains. Duplicates and paralogous protein sequence removal was followed by human non-homologous protein miningPathogen essential and virulent proteins were subjected to pathway analysis Subcellular localization of the virulent proteins was predicted and druggability was also checked, leading to 30 druggable targets based on their similarity with the approved drug targets in Drugbank. For immunoinformatics analysis, we selected two outer membrane proteins (HPAKL86_RS06305 and HPSNT_RS00950) and subjected to determined immunogenic B and T-Cell epitopes. The B and T-Cell overlapped epitopes were selected to design 9 different vaccine constructs by using linkers and adjuvants. Least allergenic and most antigenic construct (C-8) was selected as a promiscuous vaccine to elicit host immune response. Cloning and in silico expression of the constructed vaccine (C-8) was done to produce a clone having the desired (gene) vaccine construct. In conclusion, the prioritized therapeutic targets for 84 strains of H.pylori will be useful for future therapy design. Vaccine design may also prove useful in the quest for targeting multi-strains of H. pylori in patients. • Genome-wide core proteome of eighty-four strains of H.pylori was analysed for therapeutic drug targets and vaccine development. • 30 unique druggabble targets were predicted , which are non-homologous to host, virulent and essential for pathogen survival. • Metabolic pathways analysis was performed to identify essential and unique pathways in Helicobacter pylori. • Multi-epitope vaccine from B and T-cell epitopes by using linkers and adjuvants to enhance the immunogenicity of the vaccine. • Least allergenic and most antigenic construct was selected as a promiscuous vaccine to elicit host immune response. [ABSTRACT FROM AUTHOR]

Yersinia pestis, the causative agent of plague, is a Gram-negative bacterium. If the plague is not properly treated it can cause rapid death of the host. Bubonic, pneumonic, and septicemic are the three types of plague described. Bubonic plague can progress to septicemic plague, if not diagnosed and treated on time. The mortality rate of pneumonic and septicemic plague is quite high. The symptom-defining disease is the bubo, which is a painful lymph node swelling. Almost 50% of bubonic plague leads to sepsis and death if not treated immediately with antibiotics. The host immune response is slow as compared to other bacterial infections. Clinical isolates of Yersinia pestis revealed resistance to many antibiotics such as tetracycline, spectinomycin, kanamycin, streptomycin, minocycline, chloramphenicol, and sulfonamides. Drug discovery is a time-consuming process. It always takes ten to fifteen years to bring a single drug to the market. In this regard, in silico subtractive proteomics is an accurate, rapid, and cost-effective approach for the discovery of drug targets. An ideal drug target must be essential to the pathogen's survival and must be absent in the host. Machine learning approaches are more accurate as compared to traditional virtual screening. In this study, k-nearest neighbor (kNN) and support vector machine (SVM) were used to predict the active hits against the beta-ketoacyl-ACP synthase III drug target predicted by the subtractive genomics approach. Among the 1012 compounds of the South African Natural Products database, 11 hits were predicted as active. Further, the active hits were docked against the active site of beta-ketoacyl-ACP synthase III. Out of the total 11 active hits, the 3 lowest docking score hits that showed strong interaction with the drug target were shortlisted along with the standard drug and were simulated for 100 ns. The MD simulation revealed that all the shortlisted compounds display stable behavior and the compounds formed stable complexes with the drug target. These compounds may have the potential to inhibit the beta-ketoacyl-ACP synthase III drug target and can help to combat Yersinia pestis-related infections. The dataset and the source codes are freely available on GitHub. [ABSTRACT FROM AUTHOR]

BACKGROUND: Ficus benghalensis has been used by local health care practitioners to treat pain, inflammation, rheumatism, and other health issues. OBJECTIVE: In this study, the crude extract and diverse fractions, along with the isolated compound of F. benghalensis were examined for their roles as muscle relaxants, analgesics, and sedatives. METHODS: The extract and isolated compound 1 were screened for muscle-relaxant, analgesic, and sedative actions. The acetic acid-mediated writhing model was utilized for analgesic assessment, the muscle relaxant potential was quantified through traction and inclined plan tests, and the open field test was applied for sedative effects. RESULTS: The extract/fractions (25, 50, and 100 mg/kg) and isolated compounds (2.5, 5, 10, and 20 mg/kg) were tested at various doses. A profound (p < 0.001) reduce in the acetic acid-mediated writhing model was observed against carpachromene (64.44%), followed by ethyl acetate (60.67%) and methanol (58.42%) fractions. A marked (p < 0.001) muscle relaxant activity was noticed against the isolated compound (71.09%), followed by ethyl acetate (66.98%) and methanol (67.10%) fractions. Regarding the sedative effect, a significant action was noted against the isolated compound (71.09%), followed by ethyl acetate (66.98%) and methanol (67.10%) fractions. Furthermore, the binding modes of the isolated compounds were explored using molecular docking. The molecular docking study revealed that the isolated compound possessed good binding affinity for COX2 and GABA. Our isolated compound may possess inhibitory activity against COX2 and GABA receptors. CONCLUSION: The extract and isolated compounds of Ficus benghalensis can be used as analgesics, muscle relaxants, and sedatives. However, detailed molecular and functional analyses are essential to ascertain their function as muscle relaxants, analgesics, and sedatives. [ABSTRACT FROM AUTHOR]

A novel tick-borne orthonairovirus called the Yezo virus (YEZV), primarily transmitted by the Ixodes persulcatus tick, has been recently discovered and poses significant threats to human health. The YEZV is considered endemic in Japan and China. Clinical symptoms associated with this virus include thrombocytopenia, fatigue, headache, leukopenia, fever, depression, and neurological complications ranging from mild febrile illness to severe outcomes like meningitis and encephalitis. At present, there is no treatment or vaccine readily accessible for this pathogenic virus. Therefore, this research employed an immunoinformatics approach to pinpoint potential vaccine targets within the YEZV through an extensive examination of its structural proteins. Three structural proteins were chosen using specific criteria to pinpoint T-cell and B-cell epitopes, which were subsequently validated through interferon-gamma induction. Six overlapping epitopes for cytotoxic T-lymphocytes (CTL), helper T-lymphocytes (HTL), and linear B-lymphocytes (LBL) were selected to construct a multi-epitope vaccine, achieving a 92.29% coverage of the global population. These epitopes were then fused with the 50S ribosomal protein L7/L12 adjuvant to improve protection against international strains. The three-dimensional structure of the designed vaccine construct underwent an extensive evaluation through structural analysis. Following molecular docking studies, the YEZV vaccine construct emerged as a candidate for further investigation, showing the lowest binding energy (−78.7 kcal/mol) along with favorable physiochemical and immunological properties. Immune simulation and molecular dynamics studies demonstrated its stability and potential to induce a strong immune response within the host cells. This comprehensive analysis indicates that the designed vaccine construct could offer protection against the YEZV. It is crucial to conduct additional in vitro and in vivo experiments to verify its safety and effectiveness. [ABSTRACT FROM AUTHOR]

Approximately 3.9 billion individuals are vulnerable to dengue infection, a prevalent cause of tropical diseases worldwide. Currently, no drugs are available for preventing or treating Flavivirus diseases, including Dengue, West Nile, and the more recent Zika virus. The highly conserved Flavivirus NS2B-NS3 protease, crucial for viral replication, is a promising therapeutic target. This study employed in-silico methodologies to identify novel and potentially effective anti-dengue small molecules. A pharmacophore model was constructed using an experimentally validated NS2B-NS3 inhibitor, with the Gunner Henry score confirming the model's validity. The Natural Product Activity and Species Source (NPASS) database was screened using the validated pharmacophore model, yielding a total of 60 hits against the NS2B-NS3 protease. Furthermore, the docking finding reveals that our newly identified compounds from the NPASS database have enhanced binding affinities and established significant interactions with allosteric residues of the target protein. MD simulation and post-MD analysis further validated this finding. The free binding energy was computed in terms of MM-GBSA analysis, with the total binding energy for compound 1 (−57.3 ± 2.8 and − 52.9 ± 1.9 replica 1 and 2) indicating a stronger binding affinity for the target protein. Overall, this computational study identified these compounds as potential hit molecules, and these findings can open up a new avenue to explore and develop inhibitors against Dengue virus infection. [ABSTRACT FROM AUTHOR]

• Synthesis of novel indazole derivatives. • In vitro as inhibitors of diabetics II. • Identification of a new inhibitors of diabetics II. • Structure activity relationship established. • Molecular docking. • Kinetic study. In the light of the pharmacological significance of the indazole and 1, 3, 4-thiadiazole scaffold, hybrid analogs of indazole and 1, 3, 4-thiadiazole (1–14) were synthesized, identified using HREI-MS, 1H, and 13CNMR, and their capability for inhibition of α-amylase and α-glucosidase enzymes was evaluated. Generally, most of the synthesized derivatives of the series exhibited good inhibition activity in comparison to the reference drug acarbose with IC 50 value of 10.30 ± 0.20 for α-amylase, and 9.80 ± 0.20 for α-glucosidase. Amongst the synthesized derivatives, fluoro substituted analog 10 appeared to be the most potent inhibitor having IC 50 value of 9.90 ± 0.30 for α-amylase and 9.20 ± 0.30 for α-glucosidase. A structure activity relationship (SAR) for the series was established based on electronic effects and the positions of various groups on the phenyl ring. To comprehend the chemicals' binding interactions, molecular docking along with simulation study were also carried out. Compound 10 was ranked top in the series based on docking score and interactions with receptors. Compound 10 establish more H-bond contacts with the active site's residue of alpha-amylase and α-glucosidase as compared to all other compounds. Furthermore, 10 was found to be highly stable throughout 50 ns MD simulation. As compared to the control drug, 10 revealed high stability with both α-amylase and α-glucosidase enzymes during molecular dynamics simulation. [Display omitted] [ABSTRACT FROM AUTHOR]