Serotype and genomic diversity of dengue virus during the 2023 outbreak in Pakistan reveals the circulation of genotype III of DENV-1 and cosmopolitan genotype of DENV-2.

Dengue, a mosquito-borne viral disease, poses a significant public health challenge in Pakistan, with a significant outbreak in 2023, prompting our investigation into the serotype and genomic diversity of the dengue virus (DENV). NS-1 positive blood samples from 153 patients were referred to the National Institute of Health, Pakistan, between July and October 2023. Among these, 98 (64.1%) tested positive using multiplex real-time PCR, with higher prevalence among males (65.8%) and individuals aged 31-40. Serotyping revealed DENV-1 as the predominant serotype (84.7%), followed by DENV-2 (15.3%). Whole-genome sequencing of 18 samples (DENV-1 = 17, DENV-2 = 01) showed that DENV-1 (genotype III) samples were closely related (>99%) to Pakistan outbreak samples (2022), and approx. > 98% with USA (2022), Singapore and China (2016), Bangladesh (2017), and Pakistan (2019). The DENV-2 sequence (cosmopolitan genotype; clade IVA) shared genetic similarity with Pakistan outbreak sequences (2022), approx. > 99% with China and Singapore (2018-2019) and showed divergence from Pakistan sequences (2008-2013). No coinfection with dengue serotypes or other viruses were observed. Comparisons with previous DENV-1 sequences highlighted genetic variations affecting viral replication efficiency (NS2B:K55R) and infectivity (E:M272T). These findings contribute to dengue epidemiology understanding and underscore the importance of ongoing genomic surveillance for future outbreak responses in Pakistan.

Study on structural insight of the analysis of negative effects of opioids analgesics in naltrexone with TLR4 Mutations.

Chronic pain has been defined as the persistence that remained for more than three months. The extent of previous time duration with the normal time of natural healing phase becomes poor and results in reduced life quality and morbidity. Opioids are well recognized therapy for pain management and the clinical prescriptions based on opioids have been defined with increasing implicating behavior among patients suffering with chronic pain. The association between the pain and immunity has long been established since the involvement of interleukin-1ß (IL-1ß) in sickness that is considered with the induced hyperalgesia. In the context of pharmacodynamics Toll like receptors (TLRs) are involved in the negative effects of opioids as analgesics. The soluble factors released by immune cells as well as from the disruptive cells bind to TLRs. This binding leads the pre and post-synaptic ends on endothelial and microglial cells that exhibit the activation of complex inhibitory and excitatory process at the synapses site. In TLRs, TLR4 is mostly reported that is strongly associated in specifically in areas of T cells and macrophages. The current study is designed to investigate the structural insights of the opioids and TLR4 interactions by using computational approach in the aspect of recognizing the chemical combinatorial factors that are involved in the pain management. This study targets that how opioids interact with TLR4 and the process of chemical interaction that leads to negative effects of opioids at neuroimmune interface as well as to investigate the extent of particular naltrexone that mediates with the negative effects of opioids.

De novo generation of dual-target ligands for the treatment of SARS-CoV-2 using deep learning, virtual screening, and molecular dynamic simulations.

De novo generation of molecules with the necessary features offers a promising opportunity for artificial intelligence, such as deep generative approaches. However, creating novel compounds having biological activities toward two distinct targets continues to be a very challenging task. In this study, we develop a unique computational framework for the de novo synthesis of bioactive compounds directed at two predetermined therapeutic targets. This framework is referred to as the dual-target ligand generative network. Our approach uses a stochastic policy to explore chemical spaces called a sequence-based simple molecular input line entry system (SMILES) generator. The steps in the high-level workflow would be to gather and prepare the training data for both targets' molecules, build a neural network model and train it to make molecules, create new molecules using generative AI, and then virtually screen the newly validated molecules against the SARS-CoV-2 PLpro and 3CLpro drug targets. Results shows that novel molecules generated have higher binding affinity with both targets than the conventional drug i.e. Remdesivir being used for the treatment of SARS-CoV-2.Communicated by Ramaswamy H. Sarma.

Abrogation of SARS-CoV-2 interaction with host (NRP1) neuropilin-1 receptor through high-affinity marine natural compounds to curtail the infectivity: A structural-dynamics data.

The evolution of new severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants around the globe has made the coronavirus disease 2019 (COVID-19) pandemic more worrisome, pressuring the health care system and resulting in an increased mortality rate. Recent studies recognized neuropilin-1 (NRP1) as a key facilitator in the invasion of the new SARS-CoV-2 into the host cell. Therefore, it is considered an imperative drug target for the treatment of COVID-19. Hence, a thorough analysis was needed to understand the impact and to guide new therapeutics development. In this study, we used structural and biomolecular simulation techniques to identify novel marine natural products which could block this receptor and stop the virus entry. We discovered that the binding affinity of CMNPD10175, CMNPD10017, CMNPD10114, CMNPD10115, CMNPD10020. CMNPD10018, CMNPD10153, CMNPD10149 CMNPD10464 and CMNPD10019 were substantial during the virtual screening (VS). We further explored these compounds by analyzing their absorption, distribution, metabolism, and excretion and toxicity (ADMET) properties and structural-dynamics features. Free energy calculations further established that all the compounds exhibit stronger binding energy for NRP1. Consequently, we hypothesized that these compounds might be the best lead candidates for therapeutic interventions hindering virus binding to the host cell. This study provides a strong impetus to develop novel drugs against the SARS-CoV-2 by targeting NRP1.

OpenBloodFlow: A User-Friendly OpenCV-Based Software Package for Blood Flow Velocity and Blood Cell Count Measurement for Fish Embryos.

The transparent appearance of fish embryos provides an excellent assessment feature for observing cardiovascular function in vivo. Previously, methods to conduct vascular function assessment were based on measuring blood-flow velocity using third-party software. In this study, we reported a simple software, free of costs and skills, called OpenBloodFlow, which can measure blood flow velocity and count blood cells in fish embryos for the first time. First, videos captured by high-speed CCD were processed for better image stabilization and contrast. Next, the optical flow of moving objects was extracted from the non-moving background in a frame-by-frame manner. Finally, blood flow velocity was calculated by the Gunner Farneback algorithm in Python. Data validation with zebrafish and medaka embryos in OpenBloodFlow was consistent with our previously published ImageJ-based method. We demonstrated consistent blood flow alterations by either OpenBloodFlow or ImageJ in the dorsal aorta of zebrafish embryos when exposed to either phenylhydrazine or ractopamine. In addition, we validated that OpenBloodFlow was able to conduct precise blood cell counting. In this study, we provide an easy and fully automatic programming for blood flow velocity calculation and blood cell counting that is useful for toxicology and pharmacology studies in fish.

Structure-guided design of multi-epitopes vaccine against variants of concern (VOCs) of SARS-CoV-2 and validation through In silico cloning and immune simulations.

Severe Acute Respiratory Syndrome Corovirus2 (SARS-CoV-2) has been determined to be the cause of the current pandemic. Typical symptoms of patient having COVID-19 are fever, runny nose, cough (dry or not) and dyspnea. Several vaccines are available in markets that are tackling current pandemic. Many different strains of SAR-CoV-2 have been evolved with the passage of time. The emergence of VOCs particularly the B.1.351 ("South African") variant of SARS-CoV-2 has been reported to be more resistant than other SARS-CoV-2 strains to the current vaccines. Thus, the current research is focused to design multi-epitope subunit Vaccine (MEV) using structural vaccinology techniques. As a result, the designed MEV exhibit antigenic properties and possess therapeutic features that can trigger an immunological response against COVID-19. Furthermore, validation of the MEV using immune simulation and in silico cloning revealed that the proposed vaccine candidate effectively triggered the immune response. Conclusively, the developed MEV needs further wet lab exploration and could be a viable vaccine to manage and prevent COVID-19.

Computational Method for Classification of Avian Influenza A Virus Using DNA Sequence Information and Physicochemical Properties.

Accurate and fast characterization of the subtype sequences of Avian influenza A virus (AIAV) hemagglutinin (HA) and neuraminidase (NA) depends on expanding diagnostic services and is embedded in molecular epidemiological studies. A new approach for classifying the AIAV sequences of the HA and NA genes into subtypes using DNA sequence data and physicochemical properties is proposed. This method simply requires unaligned, full-length, or partial sequences of HA or NA DNA as input. It allows for quick and highly accurate assignments of HA sequences to subtypes H1-H16 and NA sequences to subtypes N1-N9. For feature extraction, k-gram, discrete wavelet transformation, and multivariate mutual information were used, and different classifiers were trained for prediction. Four different classifiers, Naïve Bayes, Support Vector Machine (SVM), K nearest neighbor (KNN), and Decision Tree, were compared using our feature selection method. This comparison is based on the 30% dataset separated from the original dataset for testing purposes. Among the four classifiers, Decision Tree was the best, and Precision, Recall, F1 score, and Accuracy were 0.9514, 0.9535, 0.9524, and 0.9571, respectively. Decision Tree had considerable improvements over the other three classifiers using our method. Results show that the proposed feature selection method, when trained with a Decision Tree classifier, gives the best results for accurate prediction of the AIAV subtype.

Dynamics Insights Into the Gain of Flexibility by Helix-12 in ESR1 as a Mechanism of Resistance to Drugs in Breast Cancer Cell Lines.

Incidents of breast cancer (BC) are on the rise on a daily basis and have proven to be the most prevelant cause of death for women in both developed and developing countries. Among total BC cases diagnosed after menopause, 70% of cases are Estrogen Receptor (ER) positive (ER-positive or ER+). Mutations in the LBD (ligand-binding domain) of the ER have recently been reported to be the major cause of resistance to potent antagonists. In this study, the experimentally reported mutations K303R, E380Q, V392I, S463P, V524E, P535H, P536H, Y537C, Y537N, Y537S, and D538G were analyzed, and the most significant mutations were shortlisted based on multiple analyses. Initial analyses, such as mCSM stability, occluded depth analysis, mCSM-binding affinity, and FoldX energy changes shortlisted only six mutations as being highly resistant. Finally, simulations of force field-based molecular dynamics (MD on wild type (WT) ERα) on six mERα variants (E380Q, S463P, Y537S, Y537C, Y537N, and D538G) were carried out to justify mechanism of the resistance. It was observed that these mutations increased the flexibility of the H12. A bonding analysis suggested that previously reported important residue His524 lost bonding upon mutation. Other parameters, such as PCA (principal component analysis), DCCM (dynamics cross-correlation), and FEL (free energy landscape), verified that the shortlisted mutations affect the H12 helix, which opens up the co-activator binding conformation. These results provide deep insight into the mechanism of relative resistance posed to fulvestrant due to mutations in breast cancer. This study will facilitate further understanding of the important aspects of designing specific and more effective drugs.