Biology subjects

Massahi, S.

Publications and source records attributed to Massahi, S..

5 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↗

bioRxiv2022

Candida albicans exhibits heterogeneous and adaptive cytoprotective responses to anti-fungal compounds

Candida albicans is an opportunistic human pathogen which represents a significant threat to human health and is associated with substantial socio-economic burden. Current antifungal treatments fail at least in part because C. albicans can initiate a strong drug tolerance response, allowing cells to grow at concentrations above their minimal inhibitory concentration. Our goal is to better characterize this cytoprotective tolerance program at the molecular single cell level. We present here a nano-liter droplet-based fungal single cell transcriptomics platform capable of profiling thousands of individual C. albicans SC5314 cells in an efficient manner. Profiles of untreated cells partition into three transcriptional clusters with each highlighting a cell cycle checkpoint coupled with specific metabolic and stress responses, as perhaps expected. After just two days post-treatment with fluconazole, surviving cells bifurcate into two distinct subpopulations: the so-called response involving upregulation of protein translation, rRNA processing and mitochondrial cellular respiration, and the {beta} response involving processes and stress responses that assist damaged cells. By extending our time series to six days and profiling with other antifungals and bioactive compounds, we provide evidence that surviving cells transition from the to {beta} responses mediated by the Ribosome Assembly Stress Response (RASTR).

microbiology↗

bioRxiv2021

A deep learning approach to capture the essence of Candida albicans morphologies

We present deep learning-based approaches for exploring the complex array of morphologies exhibited by the opportunistic human pathogen C. albicans. Our system entitled Candescence automatically detects C. albicans cells from Differential Image Contrast microscopy, and labels each detected cell with one of nine vegetative, mating-competent or filamentous morphologies. The software is based upon a fully convolutional one-stage object detector and exploits a novel cumulative curriculum-based learning strategy that stratifies our images by difficulty from simple vegetative forms to more complex filamentous architectures. Candescence achieves very good performance on this difficult learning set which has substantial intermixing between the predicted classes. To capture the essence of each C. albicans morphology, we develop models using generative adversarial networks and identify subcomponents of the latent space which control technical variables, developmental trajectories or morphological switches. We envision Candescence as a community meeting point for quantitative explorations of C. albicans morphology.

microbiology↗

bioRxiv2020

Candida albicans exhibits distinct cytoprotective responses to anti-fungal drugs that facilitate the evolution of drug resistance

We developed a modified protocol for nanolitre droplet-based single cell sequencing appropriate for fungal settings, and used it to transcriptionally profiled several thousands cells from a prototrophic Candida albicans population and several drug exposed colonies (incl. fluconazole, caspofungin and nystatin). Thousands of cells from each colony were profiled both at early and late time points post-treatment in order to infer survival trajectories from initial drug tolerance to drug resistance. We find that prototrophic C. albicans populations differentially and stochastically express cytoprotective epigenetic programs. For all drugs, there is evidence that tolerant individuals partition into distinct subpopulations, each with a unique survival strategy involving different regulatory programs. These responses are weakly related to changes in morphology (shift from white to opaque forms, or shift from yeast to filamentous forms). In turn, those subpopulations that successfully reach resistance each have a distinct multivariate epigenetic response that coordinates the expression of efflux pumps, chaperones, transport mechanisms, and cell wall maintenance. Live cell fluorescent imaging was used to validate predictions of which molecular responses most often led to survival after drug exposure. Together our findings provide evidence that C. albicans has a robust toolkit of short-term epigenetic cytoprotective responses designed to "buy time" and increase the chance of acquiring long-term resistance.

genomics↗