Amir Ata Saei

This study uses multi-omics to uncover Gardnerella genomic features linked to biofilm formation and interspecies interactions in the vaginal microbiome, identifying potential therapeutic targets for bacterial vaginosis.

The paper introduces ThermoTargetMiner, a database that identifies protein targets of lung cancer therapeutics using the PISA assay, revealing novel targets for 77% of tested compounds.

This paper introduces CoPPRA, a structural proteomics method to analyze drug combinations in AML, revealing peptide-level accessibility changes linked to conjunctive targeting.

This study uses CoPISA and proteomics to analyze venetoclax-azacitidine response in AML cells, identifying adaptive vulnerabilities like NRP2.

This study shows lipid nanoparticles form protein coronas through lipoprotein fusion, not surface adsorption, using cryo-EM and simulations.

HOLSER improves proteome coverage and depth for structural analysis of protein-ligand interactions and conformational changes.

This paper shows that extracellular vesicles contaminate nanoparticle protein corona profiles in human plasma, leading to inaccurate protein origin assignments.

This paper identifies a stage-specific proteomic signature of Alzheimer's disease using plasma proteomics and finds a proteomic switch associated with disease progression.

DORSSAA provides a platform for analyzing stability and solubility alteration data to identify drug-protein interactions and therapeutic targets.

This paper introduces CoPISA, a proteomics workflow that identifies combination-specific protein targets in AML, revealing conjunctional targeting mechanisms beyond classical drug synergy.

CoPISA provides a proteomics dataset profiling protein solubility and stability responses to single and combined drug treatments in AML cell lines, enabling analysis of combinatorial drug effects.

This paper identifies NaR as a liver clock-controlled metabolite that links circadian rhythms to adipogenesis via the prefoldin complex and UPR signaling.

The paper introduces AFDIP, a chemical proteomics method that identifies drug target binding sites with high structural resolution using digestion rate analysis and MD simulations.

The paper introduces KISSE, a statistical framework for species identification using bone collagen tandem mass spectrometry data, which provides species identification and probability estimates.

This paper uses causality analysis to identify phosphatidylcholine as a key factor enhancing plasma proteome profiling through protein corona composition changes on nanoparticles.

This study investigates trypsin cleavage preferences in native proteins using AFDIP, finding lysine cleavage is more abundant and faster than arginine in native conditions.

This study identifies xenorceptides, a new class of antibiotics that target BamA in Gram-negative bacteria, showing potent activity and limited resistance.

The paper introduces OPTI-PISA, a method that automates and reduces sample requirements for drug target identification, demonstrating its effectiveness in cell lysates with sub-microgram samples.

This paper demonstrates that adding small molecules to plasma enhances nanoparticle protein corona diversity, significantly increasing the number of detectable proteins in plasma proteome profiling.

The paper introduces GAPPIS, a method to recover molecular weight information from shotgun proteomics, identifying novel caspase 3 substrates in apoptotic cells.

This study standardizes protein corona characterization across proteomics centers, improving data consistency and reproducibility through unified protocols and workflows.

This study shows that ultralight Caenorhabditis elegans, grown on isotopically depleted bacteria, have shorter lifespans despite faster growth and maturation.

This study simulates traumatic brain injury using a dummy head to generate g-force and shockwave, revealing proteome solubility changes and identifying shockwave as the main cause of irreversible cell damage.