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rewiring of proteolytic systems and the deployment of innate immune strategies that enable survival during phage attack. In addition, another major focus of our research is protein engineering and the
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properties of natural and artificial proteins is a growing research discipline with applications in many different areas, primarily medicine and diagnostics. Research at the Division of Protein Engineering
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: computational ancient metabolomics and natural products, carbohydrate-active enzymes and carbohydrate metabolism, and protein language models for ancient metagenomics. Our research AEGIS is a pioneering
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Position Summary The Dickson, Feig, Vermaas, Wei, Woldring laboratories together form Team Green, a collaborative research effort funded by DARPA to predict protein structural ensembles, ligand
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 7 hours ago
-line model systems. The project focuses on vertical integration of multimodal single-cell datasets, including single-cell DNA copy number, RNA expression, chromatin accessibility, protein abundance
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Overview Under the limited direction of Dr. Cathy H. Wu, Professor of Department of Biochemistry and Molecular & Cellular Biology and Director of Protein Information Resource, the Bioinformatics Team Lead
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The University of Texas Southwestern Medical Center (UT Southwestern Medical Center) | Dallas, Texas | United States | 20 days ago
collaborative postdoctoral researcher to help develop the next generation of cancer immunotherapies. Our research integrates cancer immunology, T-cell and protein engineering, and translational science
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recombinant protein engineering, super resolution microscopy and intracellular transport assays. In addition, the student will also gain experience in generic approaches such as molecular biology, cell
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Mathjobs.org | 29 days ago
families of algorithms for cryo-electron microscopy (cryo-EM) and broader applications in data science * Protein structure prediction: Combining traditional and machine learning approaches to protein
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families of algorithms for cryo-electron microscopy (cryo-EM) and broader applications in data science * Protein structure prediction: Combining traditional and machine learning approaches to protein