published_or_final_version / Medical Sciences / Master / Master of Medical Sciences

Thesis (Ph. D.)--Washington State University, May 2008. / Includes bibliographical references (p. 88-98).

Thesis (Ph.D.)--Biology, Georgia Institute of Technology, 2008. / Committee Chair: Jordan, I. King; Committee Member: Borodovsky, Mark; Committee Member: Bunimovich, Leonid; Committee Member: Choi, Jung; Committee Member: McDonald, John.

Thesis (Ph.D)--Electrical and Computer Engineering, Georgia Institute of Technology, 2009. / Committee Chair: Yucel Altunbasak; Committee Co-Chair: Mark Borodovsky; Committee Member: Brani Vidakovic; Committee Member: Ghassan Alregib; Committee Member: James McClellan; Committee Member: Russel Mersereau. Part of the SMARTech Electronic Thesis and Dissertation Collection.

Thesis (M.S.)--University of Missouri-Columbia, 2004. / Typescript. Vita. Includes bibliographical references (leaves 63-65). Also available on the Internet.

Thesis (Ph. D.)--Ohio State University, 2005. / Title from first page of PDF file. Document formatted into pages; contains xv, 151 p.; also includes graphics. Includes bibliographical references (p. 134-151). Available online via OhioLINK's ETD Center

Thesis (M.S.)--University of Missouri-Columbia, 2004. / Typescript. Vita. Includes bibliographical references (leaves 63-65). Also available on the Internet.

Protein tyrosine kinase and phosphatase domains have binding specificities that depend on the amino acid sequence surrounding the target (phospho)tyrosine residue on their substrates. Although the preferred recognition motifs of many kinase and phosphatase domains have been characterized, we lack a quantitative description of sequence specificity that could guide predictions about signaling pathways or be used to design sequences for biomedical applications. Here, we present a platform that combines genetically-encoded peptide libraries and deep sequencing to profile sequence recognition by tyrosine kinases. We screened several tyrosine kinases against a million-peptide random library and used the resulting profiles to design high-activity sequences and predict phosphorylation efficiencies of substrates. We then screened several kinases against a library containing thousands of human proteome-derived peptides and their naturally-occurring variants. These screens recapitulated independently measured phosphorylation rates and revealed hundreds of phosphosite-proximal mutations that impact phosphosite recognition by tyrosine kinases. Finally, we have made progress towards extending this platform to the analysis of tyrosine phosphatase domains, by optimizing methods to produce tyrosine-phosphorylated bacterial display libraries and implementing methods to detect peptide dephosphorylation on the cell surface. Collectively, these experiments demonstrate the utility of our platform for rapid profiling of sequence specificity by tyrosine kinases and will shed new light on phosphotyrosine signaling.

Wong Ka Shing = 兩個二級蛋白質結構組中的氨基酸序列的統計特性 / 黃嘉誠. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2004. / Includes bibliographical references (leaves 88-91). / Text in English; abstracts in English and Chinese. / Wong Ka Shing = Liang ge er ji dan bai zhi jie gou zu zhong de an ji suan xu lie de tong ji te xing / Huang Jiacheng. / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Predicting tertiary structures of proteins --- p.2 / Chapter 1.2 --- Introduction to secondary structures of protein --- p.5 / Chapter 1.3 --- Prediction of secondary structural classes --- p.9 / Chapter 1.3.1 --- Multi-dimensional space representation --- p.10 / Chapter 2 --- Method of analysis --- p.12 / Chapter 2.1 --- Data sets --- p.12 / Chapter 2.2 --- Basic statistics of the data sets --- p.13 / Chapter 2.3 --- Method of analysis --- p.17 / Chapter 2.4 --- An interesting sum rule --- p.20 / Chapter 3 --- Results of analysis of C-words --- p.22 / Chapter 3.1 --- A first insight --- p.22 / Chapter 3.2 --- Distributions of 〈C(j)〉 --- p.24 / Chapter 3.2.1 --- "Comparing distributions for different m, s and secondary structural classes" --- p.29 / Chapter 3.3 --- Features of each secondary structural class --- p.33 / Chapter 3.3.1 --- C-words with the largest or the smallest values of 〈C(j)〉 in the all-a class --- p.33 / Chapter 3.3.2 --- C-words with the largest or the smallest values of 〈C(j)〉 in the all-β class --- p.35 / Chapter 4 --- Results of analysis of H-words --- p.37 / Chapter 4.1 --- 〈C(j)〉 for each H-word --- p.38 / Chapter 4.1.1 --- "When m increases, 〈C(j)〉 deviates more from 1" --- p.39 / Chapter 4.1.2 --- "When spacing parameter s is small, 〈C(j)〉 deviates more from 1" --- p.40 / Chapter 4.2 --- The mean over all possible H-words --- p.41 / Chapter 4.3 --- Comparing 〈C(j)〉 of different H-words --- p.43 / Chapter 4.3.1 --- A few plots --- p.43 / Chapter 4.3.2 --- Results --- p.44 / Chapter 4.4 --- Features of each secondary structural class --- p.47 / Chapter 4.4.1 --- A first insight --- p.47 / Chapter 4.4.2 --- The H-words with the largest values of 〈C(j)〉 in the all-α class with particular number of letter m --- p.54 / Chapter 4.4.3 --- The H-words with the smallest values of 〈C(j) in the all-α class with particular number of letter m --- p.57 / Chapter 4.4.4 --- The H-words with the largest values of 〈C(j) in the all-β class with particular number of letter m --- p.60 / Chapter 4.4.5 --- The H-words with the smallest values of 〈C(j) in the all-β class with particular number of letter m --- p.63 / Chapter 4.4.6 --- A summary --- p.66 / Chapter 4.5 --- Possible indicators to predict secondary structural class --- p.67 / Chapter 4.5.1 --- H-words with the largest values of --- p.68 / Chapter 4.5.2 --- H-words with the largest magnitude of --- p.71 / Chapter 4.5.3 --- Discussion --- p.71 / Chapter 5 --- Prediction using patterns found in H-words --- p.72 / Chapter 6 --- Conclusion --- p.86 / Bibliography --- p.88

Thesis (doctoral)--Lund University, 1997. / Added t.p. with thesis statement inserted.