Structural insights into the mechanism of resistance to bicalutamide by the clinical mutations in androgen receptor in chemo-treatment resistant prostate cancer.

Despite advanced diagnosis and detection technologies, prostate cancer (PCa) is the most prevalent neoplasms in males. Dysregulation of the androgen receptor (AR) is centrally involved in the tumorigenesis of PCa cells. Acquisition of drug resistance due to modifications in AR leads to therapeutic failure and relapse in PCa. An overhaul of comprehensive catalogues of cancer-causing mutations and their juxta positioning on 3D protein can help in guiding the exploration of small drug molecules. Among several well-studied PCa-specific mutations, T877A, T877S and H874Y are the most common substitutions in the ligand-binding domain (LBD) of the AR. In this study, we combined structure as well as dynamics-based in silico approaches to infer the mechanistic effect of amino acid substitutions on the structural stability of LBD. Molecular dynamics simulations allowed us to unveil a possible drug resistance mechanism that acts through structural alteration and changes in the molecular motions of LBD. Our findings suggest that the resistance to bicalutamide is partially due to increased flexibility in the H12 helix, which disturbs the compactness, thereby reducing the affinity for bicalutamide. In conclusion, the current study helps in understanding the structural changes caused by mutations and could assist in the drug development process.Communicated by Ramaswamy H. Sarma.

ASAVACT: Arabic sentiment analysis for vaccine-related COVID-19 tweets using deep learning.

COVID-19 has become a global pandemic that has affected not only the health sector but also economic, social, and psychological well-being. Individuals are using social media platforms to communicate their feelings and sentiments about the pandemic. One of the most debated topics in that regard is the vaccine. People are divided mainly into two groups, pro-vaccine and anti-vaccine. This article aims to explore Arabic Sentiment Analysis for Vaccine-Related COVID-19 Tweets (ASAVACT) to quantify sentiment polarity shared publicly, and it is considered the first and the largest human-annotated dataset in Arabic. The analysis is done using state-of-the-art deep learning models that proved superiority in the field of language processing and analysis. The models are the stacked gated recurrent unit (SGRU), the stacked bidirectional gated recurrent unit (SBi-GRU), and the ensemble architecture of SGRU, SBi-GRU, and AraBERT. Additionally, this article presents the largest Arabic Twitter corpus on COVID-19 vaccination, with 32,476 annotated Tweets. The results show that the ensemble model outperformed other singular models with at least 7% accuracy enhancement.

Target-Specific Machine Learning Scoring Function Improved Structure-Based Virtual Screening Performance for SARS-CoV-2 Drugs Development.

Leveraging machine learning has been shown to improve the accuracy of structure-based virtual screening. Furthermore, a tremendous amount of empirical data is publicly available, which further enhances the performance of the machine learning approach. In this proof-of-concept study, the 3CLpro enzyme of SARS-CoV-2 was used. Structure-based virtual screening relies heavily on scoring functions. It is widely accepted that target-specific scoring functions may perform more effectively than universal scoring functions in real-world drug research and development processes. It would be beneficial to drug discovery to develop a method that can effectively build target-specific scoring functions. In the current study, the bindingDB database was used to retrieve experimental data. Smina was utilized to generate protein-ligand complexes for the extraction of InteractionFingerPrint (IFP) and SimpleInteractionFingerPrint SIFP fingerprints via the open drug discovery tool (oddt). The present study found that randomforestClassifier and randomforestRegressor performed well when used with the above fingerprints along the Molecular ACCess System (MACCS), Extended Connectivity Fingerprint (ECFP4), and ECFP6. It was found that the area under the precision-recall curve was 0.80, which is considered a satisfactory level of accuracy. In addition, our enrichment factor analysis indicated that our trained scoring function ranked molecules correctly compared to smina's generic scoring function. Further molecular dynamics simulations indicated that the top-ranked molecules identified by our developed scoring function were highly stable in the active site, supporting the validity of our developed process. This research may provide a template for developing target-specific scoring functions against specific enzyme targets.