Salmonella Typhimurium effector SseI regulates host peroxisomal dynamics to acquire lysosomal cholesterol.

Salmonella enterica serotype Typhimurium (Salmonella) resides and multiplies intracellularly in cholesterol-rich compartments called Salmonella-containing vacuoles (SCVs) with actin-rich tubular extensions known as Salmonella-induced filaments (SIFs). SCV maturation depends on host-derived cholesterol, but the transport mechanism of low-density lipoprotein (LDL)-derived cholesterol to SCVs remains unclear. Here we find that peroxisomes are recruited to SCVs and function as pro-bacterial organelle.

Supervised Screening of EGFR Inhibitors Validated through Computational Structural Biology Approaches.

One of the prominent challenges in breast cancer (BC) treatment is human epidermal growth factor receptor (EGFR) overexpression, which facilitates tumor proliferation and presents a viable target for anticancer therapies. This study integrates multiomics data to pinpoint promising therapeutic compounds and employs a machine learning (ML)-based similarity search to identify effective alternatives. We used BC cell line data from the Cancer Cell Line Encyclopedia (CCLE) and Genomics of Drug Sensitivity in Cancer (GDSC) databases and single-cell RNA sequencing (scRNA-seq) information that established afatinib as an efficacious candidate demonstrating superior IC values.

AI screening and molecular dynamic simulation-driven identification of novel inhibitors of TGFßR1 for pancreatic cancer therapy.

Pancreatic cancer, with a 5-year survival rate below 10 %, is one of the deadliest malignancies. The TGF-ß pathway plays a crucial role in this disease, making it a key target for therapeutic intervention. Clinical trials targeting TGF-β have faced challenges of toxicity and limited efficacy, highlighting the need for more potent small molecule inhibitors.

Unveiling the Therapeutic Potential of Paclitaxel Combinations Against Breast Carcinoma and Identification of In Vivo Biomarkers.

Breast cancer (BC) is one of the leading causes of high mortality rates in women worldwide. Although advancements have been made in the design of therapeutic strategies and drug discovery, drug resistance remains one of the key challenges. One of the ways to overcome drug resistance is finding potential drug combinations since the efficacy of combined drugs is higher than their individual efficacies if the combination is a synergistic pair.

DDSBC: A Stacking Ensemble Classifier-Based Approach for Breast Cancer Drug-Pair Cell Synergy Prediction.

Breast cancer (BC) ranks as a leading cause of mortality among women worldwide, with incidence rates continuing to rise. The quest for effective treatments has led to the adoption of drug combination therapy, aiming to enhance drug efficacy. However, identifying synergistic drug combinations remains a daunting challenge due to the myriad of potential drug pairs.

A high-throughput structural dynamics approach for identification of potential agonists of FFAR4 for type 2 diabetes mellitus therapy.

Diabetes mellitus is a metabolic disorder that persists as a global threat to the world. A G-protein coupled receptor (GPCR), free fatty acid receptor 4 (FFAR4), has emerged as a potential target for type 2 diabetes mellitus (T2DM) and obesity-related disorders. The current study has investigated the FFAR4, deploying 3-dimensional structure modeling, molecular docking, machine learning, and high-throughput virtual screening methods to unravel the receptor's crucial and non-crucial binding site residues.

Identification of Prognostic Markers and Potential Therapeutic Targets using Gene Expression Profiling and Simulation Studies in Pancreatic Cancer.

Background: Pancreatic ductal adenocarcinoma (PDAC) has a 5-year relative survival rate of less than 10% making it one of the most fatal cancers. A lack of early measures of prognosis, challenges in molecular targeted therapy, ineffective adjuvant chemotherapy, and strong resistance to chemotherapy cumulatively make pancreatic cancer challenging to manage.

Objective: The present study aims to enhance understanding of the disease mechanism and its progression by identifying prognostic biomarkers, potential drug targets, and candidate drugs that can be used for therapy in pancreatic cancer.

Machine learning-driven exploration of drug therapies for triple-negative breast cancer treatment.

Breast cancer is the second leading cause of cancer death in women among all cancer types. It is highly heterogeneous in nature, which means that the tumors have different morphologies and there is heterogeneity even among people who have the same type of tumor. Several staging and classifying systems have been developed due to the variability of different types of breast cancer.

Supervised screening of Tecovirimat-like compounds as potential inhibitors for the monkeypox virus E8L protein.

Monkeypox virus (MPXV) is a budding public health threat worldwide, and there lacks a personalized drug availability to treat MPXV infections. Tecovirimat, an antiviral drug against pox viruses, is recently confirmed to be effective against the MPXV using nanomolar concentrations. Therefore, the current study considers Tecovirimat as a reference compound for a machine learning-based guided screening to scan bioactive compounds from the DrugBank with similar chemical features or moieties as the Tecovirimat to inhibit the MPXV E8L surface binding protein.

Utilization of Deep Convolutional Neural Networks for Accurate Chest X-Ray Diagnosis and Disease Detection.

Chest radiography is a widely used diagnostic imaging procedure in medical practice, which involves prompt reporting of future imaging tests and diagnosis of diseases in the images. In this study, a critical phase in the radiology workflow is automated using the three convolutional neural network (CNN) models, viz. DenseNet121, ResNet50, and EfficientNetB1 for fast and accurate detection of 14 class labels of thoracic pathology diseases based on chest radiography.

Combination of furosemide, gold, and dopamine as a potential therapy for breast cancer.

Breast cancer is one of the leading causes of death in women worldwide. Initially, it develops in the epithelium of the ducts or lobules of the breast glandular tissues with limited growth and the potential to metastasize. It is a highly heterogeneous malignancy; however, the common molecular mechanisms could help identify new targeted drugs for treating its subtypes.

Ranking Breast Cancer Drugs and Biomarkers Identification Using Machine Learning and Pharmacogenomics.

Breast cancer is one of the major causes of death in women worldwide. It is a diverse illness with substantial intersubject heterogeneity, even among individuals with the same type of tumor, and customized therapy has become increasingly important in this sector. Because of the clinical and physical variability of different kinds of breast cancers, multiple staging and classification systems have been developed.

Mutational Impacts on the N and C Terminal Domains of the MUC5B Protein: A Transcriptomics and Structural Biology Study.

Cholangiocarcinoma (CCA) involves various epithelial tumors historically linked with poor prognosis because of its aggressive sickness course, delayed diagnosis, and limited efficacy of typical chemotherapy in its advanced stages. In-depth molecular profiling has exposed a varied scenery of genomic alterations as CCA's oncogenic drivers. Previous studies have mainly focused on commonly occurring TP53 and KRAS alterations, but there is limited research conducted to explore other vital genes involved in CCA.

Discovering potent inhibitors against the Mpro of the SARS-CoV-2. A medicinal chemistry approach.

The global pandemic caused by a single-stranded RNA (ssRNA) virus known as the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is still at its peak, with new cases being reported daily. Although the vaccines have been administered on a massive scale, the frequent mutations in the viral gene and resilience of the future strains could be more problematic. Therefore, new compounds are always needed to be available for therapeutic approaches.

Computational Methods for Structure-Based Drug Design Through System Biology.

The advances in computational chemistry and biology, computer science, structural biology, and molecular biology go in parallel with the rapid progress in target-based systems. This technique has become a powerful tool in medicinal chemistry for the identification of hit molecules. The recent developments in target-based systems have played a major role in the creation of libraries of compounds, and it has also been widely applied for the design of molecular docking methods.

Prediction of Protein Solubility Based on Sequence Feature Fusion and DDcCNN.

Background: Prediction of protein solubility is an indispensable prerequisite for pharmaceutical research and production. The general and specific objective of this work is to design a new model for predicting protein solubility by using protein sequence feature fusion and deep dual-channel convolutional neural networks (DDcCNN) to improve the performance of existing prediction models.

Methods: The redundancy of raw protein is reduced by CD-HIT.

Exosomal ncRNAs profiling of mycobacterial infection identified miRNA-185-5p as a novel biomarker for tuberculosis.

Background: There are ever increasing researches implying that noncoded RNAs (ncRNAs) specifically circular RNAs (circRNAs) and microRNAs (miRNAs) in exosomes play vital roles in respiratory disease. However, the detailed mechanisms persist to be unclear in mycobacterial infection.

Methods: In order to detect circRNAs and miRNAs expression pattern and potential biological function in tuberculosis, we performed immense parallel sequencing for exosomal ncRNAs from THP-1-derived macrophages infected by Mycobacterium tuberculosis H37Ra, Mycobacterium bovis BCG and control Streptococcus pneumonia, respectively and uninfected normal cells.

Insight into the drug resistance whole genome of Mycobacterium tuberculosis isolates from Khyber Pakhtunkhwa, Pakistan.

Whole genome sequencing (WGS) is one of the most reliable methods for detection of drug resistance, genetic diversity in other virulence factor and also evolutionary dynamics of Mycobacterium tuberculosis complex (MTBC). First-line anti-tuberculosis drugs are the major weapons against Mycobacterium tuberculosis (MTB). However, the emergence of drug resistance remained a major obstacle towards global tuberculosis (TB) control program 2030, especially in high burden countries including Pakistan.

CoronaPep: An Anti-Coronavirus Peptide Generation Tool.

The novel coronavirus (COVID-19) infections have adopted the shape of a global pandemic now, demanding an urgent vaccine design. The current work reports contriving an anti-coronavirus peptide scanner tool to discern anti-coronavirus targets in the embodiment of peptides. The proffered CoronaPep tool features the fast fingerprinting of the anti-coronavirus target serving supreme prominence in the current bioinformatics research.

Pan-Cancer Analysis and Drug Formulation for GPR139 and GPR142.

GPR (G protein receptor) 139 and 142 are novel foundling GPCRs (G protein-coupled receptors) in the class "A" of the GPCRs family and are suitable targets for various biological conditions. To engage these targets, validated pharmacophores and 3D QSAR (Quantitative structure-activity relationship) models are widely used because of their direct fingerprinting capability of the target and an overall accuracy. The current work initially analyzes GPR139 and GPR142 for its genomic alteration via tumor samples.

Characterization of Piezoelectric Properties of Ag-NPs Doped PVDF Nanocomposite Fibres Membrane Prepared by Near Field Electrospinning.

Background: In this study, Near-field electrospinning (NFES) technique is used with a cylindrical collector to fabricate a large area permanent piezoelectric micro and nanofibers by a prepared solution. NFES requires a small electric field to fabricate fibers Objective: The objective of this paper to investigate silver nanoparticle (Ag-NP)/ Polyvinylidene fluoride (PVDF) composite as the best piezoelectric material with improved properties to produced tremendously flexible and sensitive piezoelectric material with pertinent conductance Methods: In this paper, we used controllable electrospinning technique based on Near-field electrospinning (NFES). The process parameter for Ag-NP/PVDF composite electrospun fiber based on pure PVDF fiber.

Globally ncRNAs Expression Profiling of TNBC and Screening of Functional lncRNA.

One of the most well-known cancer subtypes worldwide is triple-negative breast cancer (TNBC) which has reduced prediction due to its antagonistic biotic actions and target's deficiency for the treatment. The current work aims to discover the countenance outlines and possible roles of lncRNAs in the TNBC via computational approaches. Long non-coding RNAs (lncRNAs) exert profound biological functions and are widely applied as prognostic features in cancer.

Bringing Structural Implications and Deep Learning-Based Drug Identification for Mutants.

Colorectal cancer is considered one of the leading causes of death that is linked with the Kirsten Rat Sarcoma () harboring codons 13 and 61 mutations. The objective for this study is to search for clinically important codon 61 mutations and analyze how they affect the protein structural dynamics. Additionally, a deep-learning approach is used to carry out a similarity search for potential compounds that might have a comparatively better affinity.

Structures of SARS-CoV-2 RNA-Binding Proteins and Therapeutic Targets.

Background: The severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) epidemic has resulted in thousands of infections and deaths worldwide. Several therapies are currently undergoing clinical trials for the treatment of SARS-CoV-2 infection. However, the development of new drugs and the repositioning of existing drugs can only be achieved after the identification of potential therapeutic targets within structures, as this strategy provides the most precise solution for developing treatments for sudden epidemic infectious diseases.

ACPS: An accurate bioinformatics tool for precision-based anti-cancer peptide generation via omics data.

The anti-cancer targets play a crucial role in the signaling processes of cells, and therefore, it becomes nearly impossible to engage these targets without affecting the native cellular function. Thus, an approach has been taken to develop an anti-cancer Scanner (ACPS) tool aimed toward the recognition of anti-cancer marks in the form of peptides. The proposed ACPS tool allows fast fingerprinting of the anti-cancer targets having extreme significance in the current bioinformatics research.