Integrative Investigation of Flavonoids Targeting YBX1 Protein-Protein Interaction Network in Breast Cancer: From Computational Analysis to Experimental Validation.

Y-box-binding protein 1 (YBX1) is a multifunctional oncoprotein with its nuclear localization contributing to chemo-resistance in breast cancer. Through its interactions with various proteins and lncRNAs, YBX1 promotes cancer cell migration, invasion, and metastasis. Despite its significant role in cancer progression, studies on YBX1's protein-protein interactions (PPIs) remain limited.

Multifaceted computational profiling of thymol and geraniol against the human proteome for bio-repellent alternatives: Toxicity predictions, degradation analysis, and quantum mechanical approaches.

With growing interest in natural compounds as alternative mosquito repellents, assessing the toxicity and structure of potential repellent naturals like thymol (monoterpene phenol) and geraniol (monoterpene alcohol) is vital for understanding their stability and human impact. This study aimed to determine the structural, toxicity, and binding profiles of thymol and geraniol using computational predictions, xTB metadynamics, quantum mechanics, and principal component analysis. Toxicity studies using Protox-II, T.

Unified Aedes aegypti Protein Resource Database (UAAPRD): An Integrated High-Throughput In Silico Platform for Comprehensive Protein Structure Modeling and Functional Target Analysis to Enhance Vector Control Strategies.

A comprehensive examination of Aedes aegypti's proteome to detect key proteins that can be targeted with small molecules can disrupt blood feeding and disease transmission. However, research currently only focuses on finding repellent-like compounds, limiting studies on identifying unexplored proteins in its proteome. High-throughput analysis generates vast amounts of data, raising concerns about accessibility and usability.

Computational Insights into Papaveroline as an In Silico Drug Candidate for Alzheimer's Disease via Fyn Tyrosine Kinase Inhibition.

Alzheimer's disease (AD) poses a significant global health challenge, necessitating the exploration of novel therapeutic strategies. Fyn Tyrosine Kinase has emerged as a key player in AD pathogenesis, making it an attractive target for drug development. This study focuses on investigating the potential of Papaveroline as a drug candidate for AD by targeting Fyn Tyrosine Kinase.

Quantum synergy in peptide folding: A comparative study of CVaR-variational quantum eigensolver and molecular dynamics simulation.

One of the technological fields that is developing the fastest is quantum computing in biology. One of the main problems is protein folding, which calls for precise, effective algorithms with fast computing times. Mapping the least energy conformation state of proteins with disordered areas requires enormous computing resources.

Unraveling the regulatory landscape of Parkinson disease: A molecular symphony of miRNAs, transcription factors, and high-risk genes.

MicroRNAs (miRNAs) have emerged as critical regulators of post-transcriptional gene expression, impacting various biological processes (development, differentiation, and progression). In medicine, miRNAs are promising diagnostic biomarkers for neurodegenerative diseases, including Parkinson's disease (PD). The current study aims at exploring the role of miRNAs and transcription factors (TFs) in regulating genes-associated with PD.

Exploring the Role of Non-synonymous and Deleterious Variants Identified in Colorectal Cancer: A Multi-dimensional Computational Scrutiny of Exomes.

Introduction: Colorectal cancers are the world's third most commonly diagnosed type of cancer. Currently, there are several diagnostic and treatment options to combat it. However, a delay in detection of the disease is life-threatening.

Disaggregation of amyloid-beta fibrils via natural metabolites using long timescale replica exchange molecular dynamics simulation studies.

Context: Amyloid fibrils are self-assembled fibrous protein aggregates that are associated with several presently incurable diseases such as Alzheimer's. disease that is characterized by the accumulation of amyloid fibrils in the brain, which leads to the formation of plaques and the death of brain cells. Disaggregation of amyloid fibrils is considered a promising approach to cure Alzheimer's disease.

Comprehending interaction mechanism of natural actives of L. for rheumatoid arthritis using integrative chemoinformatic approaches.

This research delves into the realm of therapeutic potential within natural compounds derived from L., emphasizing a holistic perspective on medications used in human therapy. Rather than confining the study to their primary actions, the research endeavors to unveil molecular targets for these natural compounds, with a specific focus on their potential applicability in the treatment of rheumatoid arthritis (RA).

Computational molecular perspectives on novel carbazole derivative as an anti-cancer molecule against CDK1 of breast and colorectal cancers via gene expression studies, novel two-way docking strategies, molecular mechanics and dynamics.

With increase in cancer incidences, alternative strategies for disease management are of utmost importance. Carbazole, is a compound that is being studied extensively as an anti-cancer compound. In this work, we aimed to investigate a carbazole derivative against specific cancer types such as breast and colorectal, based on the off-target analyses of carbazole derivative.

Investigating the Molecular Interactions of Quinoline Derivatives for Antibacterial Activity Against Bacillus subtilis: Computational Biology and In Vitro Study Interpretations.

Bacterial infections are evolving and one of the chief problems is emergence and prevalence of antibacterial resistance. Moreover, certain strains of Bacillus subtilis have become resistant to several antibiotics. To counteract this menace, the present work aimed to comprehend the antibacterial activity of synthesized two quinoline derivatives against Bacillus subtilis.

Repurposing of known drugs for COVID-19 using molecular docking and simulation analysis.

We selected fifty one drugs already known for their potential disease treatment roles in various studies and subjected to docking and molecular docking simulation (MDS) analyses. Five of them showed promising features that are discussed and suggested as potential candidates for repurposing for COVID-19. These top five compounds were boswellic acid, pimecrolimus, GYY-4137, BMS-345541 and triamcinolone hexacetonide that interacted with the chosen receptors 1R42, 4G3D, 6VW1, 6VXX and 7MEQ, respectively with binding energies of -9.

Green synthesis, antibacterial and antifungal evaluation of new thiazolidine-2,4-dione derivatives: molecular dynamic simulation, POM study and identification of antitumor pharmacophore sites.

In this study, a series of thiazolidine-2,4-dione derivatives were synthesized and evaluated for antibacterial activity against Gram-positive and Gram-negative strains of , and . Newly prepared thiazolidine (TZD) derivatives were further screened separately for antifungal activity against cultures of fungal species, namely, , , and . The electron-donating substituents (-OH and -OCH) and electron-withdrawing substituents (-Cl and -NO) on the attached arylidene moieties of five-membered heterocyclic ring enhanced the broad spectrum of antimicrobial and antifungal activities.

Exploring the Synergistic Mechanism of AP2A2 Transcription Factor Inhibition via Molecular Modeling and Simulations as a Novel Computational Approach for Combating Breast Cancer: In Silico Interpretations.

Studies have shown that transcription factor AP2A2 (activator protein-2 alpha-2) is involved in the expression of DLEC1, a tumor suppressor gene, which, when mutated, will cause breast cancer and is thus an excellent target for breast cancer studies. Therefore, in the present research, a synergistic approach toward combating breast cancer is proposed by blocking AP2A2 factor, and allowing the cancer cells to be sensitive to anti-cancer drugs. The effect of AP2A2 on breast cancer was first understood via gene analysis from cBioPortal.

BRCA1/TP53 tumor proteins inhibited by novel analogues of curcumin - Insight from computational modelling, dynamic simulation and experimental validation.

The current study aimed to design novel curcumin analogue inhibitors with antiproliferative and antitumor activity towards BRCA1 and TP53 tumor proteins and to study their therapeutic potential by computer-aided molecular designing and experimental investigations. Four curcumin analogues were computationally designed and their drug-likeness and pharmacokinetic properties were predicted. The binding of these analogues against six protein targets belonging to BRCA1 and TP53 tumor proteins were modelled by molecular docking and their binding energies were compared with that of curcumin and the standard drug cyclophosphamide and its validated target.

Theoretical, antioxidant, antidiabetic and molecular docking and pharmacokinetics studies of heteroleptic oxovanadium(IV) complexes of thiosemicarbazone-based ligands and diclofenac.

A series of new heteroleptic oxovanadium(IV) complexes with the general formula [VOL(Dcf)] (), where L = thiosemicarbazone (TSC)-based ligands and Dcf = diclofenac have been synthesized and characterized. The spectral studies along with the density functional theory calculations evidenced the distorted square-pyramidal geometry around oxovanadium(IV) ion through imine nitrogen and thione sulfur atoms of TSC moiety, and two asymmetric carboxylate oxygen atoms of diclofenac drug. The complexes were evaluated for antioxidant activity using 2,2'-azino-bis(3-ethylbenzothiazoline-6-sulfonic acid) (ABTS), 2,2'-diphenyl-1-picrylhydrazyl (DPPH), hydrogen peroxide (HO) and superoxide radical scavenging assays with respect to the standard antioxidant drugs butylated hydroxyanisole (BHA) and rutin.

Protocol for the development of coarse-grained structures for macromolecular simulation using GROMACS.

Coarse-grained simulations have emerged as a valuable tool in the study of large and complex biomolecular systems. These simulations, which use simplified models to represent complex biomolecules, reduce the computational cost of simulations and enable the study of larger systems for longer periods of time than traditional atomistic simulations. GROMACS is a widely used software package for performing coarse-grained simulations of biomolecules, and several force fields have been developed specifically for this purpose.

De Novo Design of Anti-COVID Drugs Using Machine Learning-Based Equivariant Diffusion Model Targeting the Spike Protein.

The drug discovery and research for an anti-COVID-19 drug has been ongoing despite repurposed drugs in the market. Over time, these drugs were discontinued due to side effects. The search for effective drugs is still under process.

Design of Novel Imidazopyrazine Derivative against Breast Cancer Targeted NPY1R Antagonist.

Introduction: Breast cancer is the most frequent malignancy in women with more than one in ten new cancer diagnoses each year. Synthetic products are a key source for the identification of new anticancer medicines and drug leads.

Objectives: Imidazopyrazine is a highly favored skeleton for the design of new anticancer drugs.

Novel Biomarker Prediction for Lung Cancer Using Random Forest Classifiers.

Lung cancer is considered the most common and the deadliest cancer type. Lung cancer could be mainly of 2 types: small cell lung cancer and non-small cell lung cancer. Non-small cell lung cancer is affected by about 85% while small cell lung cancer is only about 14%.

Insights on Novel Effectors and Characterization of Metacaspase (RS107_6) as a Potential Cell Death-Inducing Protein in .

Effectors play an important role in host-pathogen interactions. Though an economically significant disease in rice, knowledge regarding the infection strategy of is obscure. In this study, we performed a genome-wide identification of the effectors in based on the characteristics of previously reported effector proteins.

Carboxymuconolactone decarboxylase is a prospective molecular target for multi-drug resistant Acinetobacter baumannii-computational modeling, molecular docking and dynamic simulation studies.

Multidrug-resistant Acinetobacter baumannii (MDRAb), a priority-I pathogen declared by the World Health Organization, became a potential healthcare concern worldwide with a high mortality rate. Thus, the identification of putative molecular targets and potential lead molecules is an important concern in healthcare. The present study aimed to screen a prospective molecular target and effectual binders for the drug discovery of MDRAb by computational virtual screening approach.

Decision Support System and Web-Application Using Supervised Machine Learning Algorithms for Easy Cancer Classifications.

Using a decision support system (DSS) that classifies various cancers provides support to the clinicians/researchers to make better decisions that can aid in early cancer diagnosis, thereby reducing chances of incorrect disease diagnosis. Thus, this work aimed at designing a classification model that can predict accurately for 5 different cancer types comprising of 20 cancer exomes, using the mutations identified from whole exome cancer analysis. Initially, a basic model was designed using supervised machine learning classification algorithms such as K-nearest neighbor (KNN), support vector machine (SVM), decision tree, naïve bayes and random forest (RF), among which decision tree and random forest performed better in terms of preliminary model accuracy.