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.

Pyrazinamide (PZA) is one of the essential anti-mycobacterium drugs, active against non-replicating Mycobacterium tuberculosis (MTB) isolates. PZA is converted into its active state, called pyrazinoic acid (POA), by action of pncA encoding pyrazinamidase (PZase). In the majority of PZA-resistance isolates, pncA harbored mutations in the coding region. In our recent report, we detected a number of novel variants in PZA-resistance (PZA^R) MTB isolates, whose resistance mechanisms were yet to be determined. Here we performed several analyses to unveil the PZA^R mechanism of R123P, T76P, G150A, and H71R mutants (MTs) through molecular dynamics (MD) simulations. In brief, culture positive MTB isolates were subjected to PZA susceptibility tests using the WHO recommended concentration of PZA (100 μg ml⁻¹). The PZA^R samples were screened for mutations in pncA along sensitive isolates through polymerase chain reactions and sequencing. A large number of variants (GeneBank accession no. MH461111), including R123P, T76P, G150A, and H71R, have been spotted in more than 70% of isolates. However, the mechanism of PZA^R for mutants (MTs) R123P, T76P, G150A, and H71R was unknown. For the MTs and native PZase structures (WT), thermodynamic properties were compared using molecular dynamics simulations for 100 ns. The MTs structural activity was compared to the WT. Folding effect and pocket volume variations have been detected when comparing between WT and MTs. Geometric matching further confirmed the effect of R123P, T76P, G150A, and H71R mutations on PZase dynamics, making them vulnerable for activating the pro-drug into POA. This study offers a better understanding for management of PZA^R TB. The results may be used as alternative diagnostic tools to infer PZA resistance at a structural dynamics level.

A.D. is a common disease among other neurodegenerative disorders primarily developing due to amyloid-β (Aβ) neurotoxicity derived from the amyloid-β protein precursor (AβPP). The amyloid precursor-like proteins 1 and 2 (APP1 and APLP2) biochemically behave similarly in many aspects to AβPP. We, therefore, proposed to test WGX-50 and Alpha-M for their interaction mechanism with APLP1 and APLP2 because both these drug candidate compounds previously showed inhibition of Aβ aggregation. We employed a comparative atomic investigation on Alpha-M and WGX-50 in complex with novel targets, i.e., APLP1 and APLP2, using biophysical and molecular simulation methods. The docking score was −6.83 kcal mol⁻¹ for Alpha-M–APLP1, −8.41 kcal mol⁻¹ for WGX-50–APLP1, −7.02 kcal mol⁻¹ for Alpha-M–APLP2 and −8.25 kcal mol⁻¹ for the WGX-50–APLP2 complex. Our results also elaborate that in the case of their interaction with both APLP1 and APLP2, the WGX-50 complex exhibits better stability than the APLP1/2-Alpha-M complexes during simulation. Furthermore, WGX50 in both APLP1 and APLP2 stabilized the internal flexibility upon binding in contrast to the Alpha-M complexes. The data showed that the BFE for Alpha-M–APLP1 was calculated to be −27.38 ± 0.93 kcal mol⁻¹, for WGX-50–APLP1 −39.65 ± 0.95 kcal mol⁻¹, for Alpha-M–APLP2 −24.80 ± 0.63 kcal mol⁻¹ while for WGX-50–APLP2 the BFE was −57.16 ± 1.03 kcal mol⁻¹ respectively. These results highlight that APLP2-WGX50 has greater binding energies in all four systems. PCA and FEL analysis further revealed variations in the dynamic behavior of these complexes. Overall, our findings demonstrate that WGX50 potentially acts as a more potent inhibitor for APLP1 and APLP2 than Alpha-M and thus shows the diverse pharmacological potential of WGX50. Due to its stable binding interaction, WGX50 might be a suitable candidate drug compound for targeting these precursors under pathological conditions.

Aims: The research aims to determine quantitative values of HBeAg and HBeAb concentrations in chronic hepatitis B patients while assessing their relationship with serum hepatitis B DNA levels. Place and Duration of Study: This study was conducted at Saidu Teaching Hospital, Swat. This study lasts for 6 months, from December 2023 to April 2024. Methodology: This study comprised 100 patients with confirmed HBV DNA positive and a chronic hepatitis B. Exclusion criteria included those using antiviral medication, those with autoimmune or metabolic liver disorders, and those with co-infections (HCV, HIV, or HDV). HBeAg and HBeAb levels were assessed using CLIA, and HBV DNA was processed using qRT-PCR. Accuracy was ensured using internal quality control measures. To evaluate the correlations between HBV DNA, HBeAg, and HBeAb, data analysis was done using SPSS Version 17.0 and Pearson correlation. P-values less than 0.05 were regarded as statistically significant. Results: The mean viral load of the 100 patients was 60,800 IU/mL, and all of them tested positive for HBV DNA. HBeAb was 8.75 IU/mL on average, while HBeAg was 12.82 IU/mL on average. HBV DNA and HBeAg were found to be 59% associated, HBV DNA and HBeAb to be 46%, and HBeAg and HBeAb to be 77%, according to the correlation study. Epidemiological insights into the distribution and correlation of HBV markers in chronic hepatitis B patients are provided by these findings. Conclusion: This study concluded that not all chronic HBV patients have HBeAg and HBeAb levels, which are lower than those of HBV DNA. These findings offer valuable information on the serological trends of chronic hepatitis B, which might aid in comprehending how the illness develops.

Alzheimer's disease (A.D.) is ranked as the third leading cause of death in developed countries and is characterized by the typical presence of senile plaques and neurofibrillary tangles within the brain, usually caused by beta-amyloid like Aβ40 and Aβ42. Aβ42 aggregates more readily, exhibits higher neurotoxicity than Aβ40 and is more significant in the development of A.D. Nowaday, design of new drugs is a costly and time-consuming process, thus it needs in silico approaches to reduce the costs. Here, we used state-of-the-art machine learning (M.L.) methods including random forests (R.F.), support vector machine (SVM), Gaussian Naïve Bayes (GNB), Multilayer perceptron (MLP), logistic regression (L.R.), Gradient boosting (G.B.), extra trees (E.T.) and K-nearest neighbor (KNN) combined with molecular simulations to find novel inhibitors against the Aβ42. The models were trained based on the previously available drugs from BindingDB. A total of 28 out of 1012 phytochemicals from the South African Natural Compounds database (SANCDB) were predicted as active against the amyloid beta 42 by the R.F. model. Virtual screening of the ZINC database revealed that out of the 12000 compounds, a total of 26 compounds were found active by the R.F. model. The 28 hits from the SANCDB and 26 hits from the ZINC database were docked into the model of Aβ42. Additionally, a 200 ns molecular dynamics simulation was performed on the top four docking complexes and the reference complex with WGX-50 bound to assess the binding of the compounds. As compared to the reference compound WGX-50, the four compounds SANC00672, SANC00552, ZINC04234505, and ZINC73633247 showed great binding stability during 200 ns molecular dynamics (M.D.) simulations, which were further validated by determining the best binding free energy (BFE) of the shortlisted hits. To the best of our knowledge, this is the first work to systematically identify novel, potentially effective inhibitors for the Aβ42 using in silico methods, including machine learning-based virtual screening, molecular docking, and molecular dynamics simulations together.