@article{Interpretation:11337,
      recid = {11337},
      author = {Lian, Xinran},
      title = {Data-Driven Interpretation and Design of Orthologs and  Paralogs of a Signaling Protein},
      publisher = {University of Chicago},
      school = {Ph.D.},
      address = {2024-03},
      pages = {104},
      abstract = {Protein design has emerged as an important field in  contemporary biology, driven in part by the accumulation of  vast amounts of protein data in public databases like the  Protein Data Bank (PDB). The challenge now is to use this  data to decipher the principles underlying protein design,  as guided by nature, and to develop novel proteins with  desired properties. To this end, we investigated the design  principles of orthologs and paralogs of a small binding  protein - Sho1-SH3 - in the yeast osmosensing pathway.  Using this natural system as a template, we employed deep  learning models to design novel functional osmosensing  orthologs. Our results demonstrate that these models not  only accurately captured the distribution of functionality  of natural proteins, but also expanded the functional space  by designing novel proteins that extended beyond the  functional constraints of natural proteins. This work  provides valuable insights into the principles governing  protein design and opens up new avenues for the development  of novel proteins with desirable functions.},
      url = {http://knowledge.uchicago.edu/record/11337},
      doi = {https://doi.org/10.6082/uchicago.11337},
}