Biology subjects

Mahmoud, A. H.

Publications and source records attributed to Mahmoud, A. H..

5 recordsLinked to original sources

bioRxiv2026

A high-throughput compound screen identifies multiple druggable targets in Plasmodium falciparum transmission stages

Most antimalarials are ineffective against the sexual transmission stages, known as gametocytes, of the malaria parasite Plasmodium falciparum. Their low sensitivity to drugs is attributed to limited compound uptake and a poorly understood form of cellular quiescence. Our current understanding of druggable transmission-blocking processes is therefore limited. Based on genetically engineered parasites that facilitate the mass production of synchronous mature gametocytes, we developed a high throughput drug screening platform that allowed us to test more than 50,000 compounds for gametocytocidal effects in one day. By screening a diversity-oriented library, we identified over 40 molecules that kill mature gametocytes in the low nanomolar range. Using resistance selection coupled to whole genome sequencing and drug-target interaction modelling, we followed up on three chemically tractable compounds that are also highly active against asexual parasites and prevent gametocyte transmission to mosquitoes. We show that the compound ONX-0914, a specific inhibitor of the {beta}5i/LMP7 subunit of human immunoproteasomes, targets the parasite proteasomal {beta}5 subunit. In contrast, the compounds CR-1-31-B and brusatol interfere with translation by targeting eukaryotic initiation factor 4A (eIF4A) and the peptidyl transferase center (PTC) of the 80S ribosome, respectively. Interestingly, parasite resistance to brusatol, a broad-spectrum antitumor drug, is linked to the differential modification of specific rRNA bases near the ribosomal A-site, mediated by altered base specificity of a rRNA methyltransferase. In summary, we successfully combined high-throughput compound screening with drug target deconvolution to reveal the targets and mode-of-action for three potent gametocytocidal molecules and discover the mechanism of resistance to the anti-tumorigenic drug brusatol. In addition to critically advancing our understanding of mature gametocyte biology and druggable processes in P. falciparum transmission stages, our observations made with brusatol-resistant parasites may become relevant for anti-cancer drug research.

molecular biology↗

bioRxiv2024

Parallel Sampling of Protein-Ligand Dynamics

AO_SCPLOWBSTRACTC_SCPLOWMolecular dynamics (MD) simulations of protein-ligand complexes are essential for computer-aided drug design. In particular they enable the calculation of free energies and thus binding affinities. However, these simulations require significant computational resources and can take days to weeks to achieve relatively short timescales compared to biologically relevant timescales. To address this issue, we introduce a method for non-sequential generation of MD samples using a generative deep neural network trained on a large corpus of protein-ligand complex simulations. The method generates accurate protein-ligand complexes with full protein and ligand flexibility and is able to recapitulate the conformation space sampled by MD simulations with high coverage. This development is a step forward towards one-shot molecular sampling that can be utilized in the calculation of protein-ligand free energies.3

biophysics↗

bioRxiv2024

Do Deep Learning Models for Co-Folding Learn the Physics of Protein-Ligand Interactions?

Co-folding models are the newest innovation in deep-learning-based protein-ligand structure prediction. The recent publications of RoseTTAFold All-Atom and AlphaFold 3 have shown high-quality results on predicting the structures of proteins interacting with small-molecules, other proteins and nucleic-acids. Despite these advanced capabilities and broad potential, the current study presents critical findings that question these models adherence to fundamental physical principles and its susceptibility to overfitting specific protein families. Through adversarial examples based on established physical, chemical, and biological principles, we demonstrate notable discrepancies in protein-ligand structural predictions when subjected to biologically plausible perturbations. These discrepancies reveal a significant divergence from expected physical behaviors, indicating potential overfitting to particular data subsets within its training corpus. Our findings underscore the models limitations in generalizing effectively across diverse biological structures and highlight the necessity of integrating robust physical and chemical priors in the development of such predictive tools. The results advocate a measured reliance on deep-learning-based models for critical applications in drug discovery and protein engineering, where a deep understanding of the underlying physical and chemical properties is crucial.

bioinformatics↗

bioRxiv2023

Enhancing Ligand-Based Virtual Screening with 3D Shape Similarity via a Distance-Aware Transformer Model

Following the assumption that chemically similar molecules exhibit similar biologcial properties, ligand-based virtual screening can be a valuable starting point in drug discovery projects. While 2D-based similarity metrics generally focus on similar scaffolds or substructures, 3D-based methods can capture the shape of a molecule, allowing for the identification of compounds with different scaffolds. We recently published a proof-of-concept study which demonstrated how a Transformer model can be adapted to preserve 2D similarities in latent space in the form of Euclidean distances. In this work, we extend this research and prove that the approach can be adapted to 3D similarities. We use pharmacophore-based shape similarity as 3D similarity measure. We show that the model is able to enrich the predicted most similar hits with compounds with different scaffolds that are indeed similar in 3D space. Whereas classical pharmacophore- or shape-based 3D similarity methods rely on expensive alignment processes, in our approach, we identify similar compounds directly by the Euclidean distances in latent space. This enables for the first time the 3D screening of ultra-large databases with high efficiency.

bioinformatics↗

bioRxiv2018

Rubsicolins are naturally occurring G-protein-biased delta opioid receptor peptides

The impact that {beta}-arrestin proteins have on G-protein-coupled receptor trafficking, signaling and physiological behavior has gained much appreciation over the past decade. A number of studies have attributed the side effects associated with the use of naturally occurring and synthetic opioids, such as respiratory depression and constipation, to excessive recruitment of {beta}-arrestin. These findings have led to the development of biased opioid small molecule agonists that do not recruit {beta}-arrestin, activating only the canonical G-protein pathway. Similar G-protein biased small molecule opioids have been found to occur in nature, particularly within kratom, and opioids within salvia have served as a template for the synthesis of other G-protein-biased opioids. Here, we present the first report of naturally occurring peptides that selectively activate G-protein signaling pathways with minimal {beta}-arrestin recruitment. We find that rubiscolin peptides, which are produced as cleavage products of the plant protein rubisco, bind to and activate G-protein signaling at {delta} opioid receptors. However, unlike the naturally occurring {delta} opioid peptides leu-enkephalin and deltorphin II, the rubiscolin peptides only very weakly recruit {beta}-arrestin 2 and have undectable recruitment of {beta}-arrestin 1 at the {delta} opioid receptor.

pharmacology and toxicology↗