Chin-Hsien (Emily) Tai

Chin-Hsien (Emily) Tai
Staff Scientist

Team Member of:

Emily developed programs to compare protein structures, to generate more accurate alignments and to parse protein structures into domains in a reliable and rational fashion. She teamed up with UCSF Chimera group to develop new app for assessing protein structure models more efficiently. She also has interests in studying protein structures with internal symmetry. Currently, she performs Next Generation Sequencing Analysis for different studies in the LMB.

Areas of Expertise
computational biology and bioinformatics, NGS data analysis, protein structure modeling

Contact Info

Chin-Hsien (Emily) Tai
Center for Cancer Research
National Cancer Institute
Bldg. 37, Rm 5120
Bethesda, MD 20892-4262
Ph: 240-760-7887
taic@mail.nih.gov

Our lab focused on developing programs to compare protein structures, to generate more accurate alignments and to parse protein structures into domains in a reliable and rational fashion. We also have interests in studying protein structures with internal symmetry. The long term goal is to discover the sequence-structure relation, the relation between the active sites and the structural types and the evolution of the structure types. We were assessors in the 6th Community Wide Experiment on the Critical Assessment of Techniques for Protein Structure Prediction (CASP6) for 'Prediction of New Folds' and 'Domain Prediction' categories in 2004. Once again, we were invited to assess 'Template free modeling', 'CASP ROLL', and 'Contact-Assisted modeling' in 2012 CASP10. We developed new algorithm for predicting B cell epitope hot spot residues and evaluated T cell epitope prediction programs.  Currently, we are studying the mechanism of resistance of immunotoxin by using Next Generation Sequencing.

Scientific Focus Areas:
Computational Biology, Immunology, Structural Biology

Selected Publications

  1. Mazor R, Addissie S, Jang Y, Tai CH, Rose J, Hakim F, Pastan I.
    AAPS J. 19(1): 117-12, 2017. [ Journal Article ]
  2. Leshem Y, O'Brien J, Liu X, Bera TK, Terabe M, Berzofsky JA, Bossenmaier B, Niederfellner G, Tai CH, Reiter Y, Pastan I.
    Cancer Immunol Res. 5(8): 685-694, 2017. [ Journal Article ]
  3. Mazor R, Tai CH, Lee B, Pastan I.
    J Immunol Methods.. 425: 10-20, 2015. [ Journal Article ]
  4. Tai CH, Paul R, Dukka K, Shilling JD, Lee B.
    Nucleic Acids Res.. 2014. [ Journal Article ]
  5. Tai CH, Bai H, Taylor TJ, Lee B.
    Proteins. 82 Suppl 2: 57-83, 2014. [ Journal Article ]

Emily obtained her BS in Zoology and MS in Immunology from National Taiwan University while studying T-cell signal transduction in the Institute of Molecular Biology, Academia Sinica.  She received another MS in Computer and Information Science from New Jersey Institute of Technology. Before joining the Molecular Modeling and Bioinformatics Section in LMB/CCR/NCI as a staff scientist in 2002, she was a Programmer Analyst at Information Technology Division serving global Compliance Reporting and Equities Trading systems in Goldman, Sachs & Co. in New York City and Goldman, Sachs International Ltd. in London as well.  Emily played key roles in CASP6 and CASP10 assessment for new fold and template free protein structure predictions.  Currently, she is the Co-Chair of the NCI CCR SSSC Organization.

SymD Web Server: Symmetry Detection (SymD) is a tool for detecting internally symmetric protein structures.

Seed Extension Algorithm (SE) is a novel algorithm for deriving sequence alignment from a pair of superimposed structures. It does not use a gap penalty as the dynamic programming algorithm does and usually generates a more accurate alignment using less CPU time.

NDO (Normalized Domain Overlap) scoring scheme was developed when we assessed the domain boundary prediction in CASP6.