Yeast Resource Center | Technology driven by biology.
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Collaborate with the YRC. Click to learn more.

Learn more about the technology being developed by the YRC.

Find software developed by the YRC.

Find research articles published by the YRC.

The YRC is a NIGMS Biomedical Technology Research Center based at the University of Washington in Seattle. Click to learn more about us.

Learn more about the technologies being developed by the YRC and how they are being applied to biomedical problems.

Learn how to collaborate with the YRC–leveraging our technology and expertise in your research.

197

Active Projects

230

Collaborators Since 2011

$38M

Annual NIH Dollars Impacted

727

YRC Publications

New Research

We are developing technology to produce biosensors based on a ligand-binding domain (LBD) that may, in principle, be applied to any target molecule. The power of this method is illustrated in this paper by development of biosensors for digoxin and progesterone.

Read more in eLIFE or in PubMed Central.

In this work, we combine the results of protein cross-linking mass spectrometry with iterative structural modelling to determine the molecular architecture of the 10-member Dam1p protein complex in S. cerevisiae. Using this technique, we can model conformational changes resulting from binding with microtubules.

Read more in Nature Communications or in PubMed Central.

Latest Publications

Johnson, RS et al. (2020) Assessing Protein Sequence Database Suitability Using De Novo Sequencing. Mol. Cell Proteomics 19 (1):198-208. PubMed PMID:31732549

Jaiswal, D et al. (2020) Function of the MYND Domain and C-Terminal Region in Regulating the Subcellular Localization and Catalytic Activity of the SMYD Family Lysine Methyltransferase Set5. Mol. Cell. Biol. 40 (2):. PubMed PMID:31685550

Wang, J et al. (2019) Biochemical analysis of protein arginylation. Meth. Enzymol. 626 :89-113. PubMed PMID:31606094

Lancaster, SM et al. (2019) Fitness benefits of loss of heterozygosity in Saccharomyces hybrids. Genome Res. 29 (10):1685-1692. PubMed PMID:31548357

Read, DF et al. (2019) Predicting gene expression in the human malaria parasite Plasmodium falciparum using histone modification, nucleosome positioning, and 3D localization features. PLoS Comput. Biol. 15 (9):e1007329. PubMed PMID:31509524