Biology subjects

Zareei, S.

Publications and source records attributed to Zareei, S..

3 recordsLinked to original sources

bioRxiv2024

Targeting GSK-beta with Peptide Inhibitors: A Rational Computational Strategy for Alzheimer's Disease Intervention

Glycogen synthase kinase-3 beta (GSK-3{beta}) is a pivotal serine/threonine kinase implicated in Alzheimers disease (AD) pathogenesis, particularly through the hyperphosphorylation of tau protein and increased production of amyloid-beta (A{beta}) peptides. This study investigates kappa casein-derived peptides as potential inhibitors of GSK-3{beta}. A peptide library of 42 sequences was generated from kappa casein and docking studies identified IP8 (LRFFVAPFPE) as the top candidate for binging to the previously approved inhibition site of GSK-3{beta}. Molecular dynamics (MD) simulations revealed that IP8s first three residues contributed unfavorably to binding, promoting the design of mutated peptides (MPs) MP27, MP31, and MP39. Of these, MP31 (HPDFVAPFPE) demonstrated the most stable interaction with GSK-3{beta}, exhibiting the most favorable binding score (-96.6) and interacting with 19 residues of the ATP-binding pocket of the enzyme. Structural analyses confirmed MP31s superior stability, with minimal RMSD deviations, and stable hydrogen bond formation. The results showed that peptide binding stabilizes GSK-3{beta} by reducing both domain-level dynamics and local side-chain flexibility, leading to a more structurally constrained enzyme. This dual stabilization of the backbone and side chains underscores the critical role of peptide interactions in modulating the conformational landscape of GSK-3{beta} and potentially obstructing substrate access or product release, which are crucial for enzyme activity. These results suggest that kappa casein-derived peptides, particularly MP31, could be promising therapeutic candidates for inhibiting GSK-3{beta} in AD.

biochemistry↗

bioRxiv2024

Computational Disruption of Paired Helical Filaments (PHFs) Assembly Using Milk Lactalbumin-derived Peptides Against Alzheimer's Disease

Peptides show great potential in diagnosing and treating Alzheimers disease (AD), particularly by targeting amyloid-beta plaques and neurofibrillary tangles (NFTs) formed by hyperphosphorylated tau proteins. This study focuses on designing peptide inhibitors from bovine milk alpha-lactalbumin to reduce tau aggregation in AD. Using computational techniques, such as docking, molecular dynamics simulations, and mutagenesis, we evaluated the binding and stability of these peptides against the paired helical filament (PHF) core. Our results identified promising inhibitors, with p136 emerging as the most effective. It significantly altered the PHF cores structure, preventing further aggregation by blocking additional subunits. Additionally, p76 displayed strong binding against straight filaments (SFs). These findings highlight the potential of peptides derived from bovine milk alpha-lactalbumin as diagnostic and therapeutic tools for AD, with p136 standing out as a promising candidate for disrupting tau aggregation.

biochemistry↗

bioRxiv2024

Machine Learning Approaches for Predicting Virus-Human Protein-Protein Interactions: An Evaluation of Retroviral Interaction Networks

Virus-human protein-protein interactions (VHPPI) are key to understanding how viruses manipulate host cellular functions. This study constructed a retroviral-human PPI network by integrating multiple public databases, resulting in 1,387 interactions between 29 retroviral and 1,026 human genes. Using minimal sequence similarity, we generated a pseudo-negative dataset for model reliability. Five machine learning models--Logistic Regression (LR), Support Vector Machine (SVM), Naive Bayes (NB), Decision Tree (DT), and Random Forest (RF)--were evaluated using accuracy, sensitivity, specificity, PPV, and NPV. LR and KNN models demonstrated the strongest predictive performance, with sensitivities up to 77% and specificities of 52%. Feature importance analysis identified GC content and semantic similarity as influential predictors. Models trained on selected features showed enhanced accuracy with reduced complexity. Our approach highlights the potential of computational models for VHPPI predictions, offering valuable insights into viral-host interaction networks and guiding therapeutic target identification. SignificanceThis study addresses a crucial gap in antiviral research by focusing on the prediction of virus-host protein-protein interactions (VHPPI) for retroviruses, which are linked to serious diseases, including certain cancers and autoimmune disorders. By leveraging machine learning models, we identified essential host-pathogen interactions that underlie retroviral survival and pathogenesis. These models were optimized to predict interactions accurately, offering valuable insights into the complex mechanisms that retroviruses use to manipulate host cellular processes. Our approach highlights key host and viral proteins, such as ENV_HV1H2 and CD4, that play pivotal roles in retroviral infection and persistence. Targeting these specific interactions can potentially disrupt the viral lifecycle while minimizing toxicity to human cells. This study thus opens avenues for the development of selective therapeutic strategies, contributing to more effective and targeted antiviral interventions with fewer side effects, marking a significant step forward in computational virology and drug discovery.

bioinformatics↗