No description available.

Transcription of the immunoglobulin heavy chain is influenced, in part, by an intronic enhancer region located 3' to the V,D, and J gene segment regions. The regulatory elements within this enhancer show high levels of sequence homology between mouse, rat, rabbit, and human, indicating their evolutionary significance. SRY, SpiB, LM02, and Rzra have been shown elsewhere to bind recognition sequences found within the evolutionarily conserved regulatory elements of this enhancer. We seek to demonstrate previously unreported individual and cooperative DNA binding activities for I these factors at regulatory' elements within the immunoglobulin 3' enhancer region. To facilitate this investigation, clones representing E2A, p65, SRY, SpiB LM02, ABF-1, and Rzra were retrieved from a plasma cell eDNA library. E2A, p65, and ABF-1 have been previously reported to bind elements within this enhancer. EMSA studies of these factors individually and in concert support previous characterization of DNA binding activities at this enhancer, and demonstrate the hereto unreported individual binding activities of Rzra, LM02, SpiB and SRY. Mixing studies with LM02 and SpiB show that when simultaneously present, these factors block the binding activity of one another. Furthermore, SpiB and LM02 are capable of blocking the in vitro DNA binding activity ofE2A and p65, presumably through the formation of a non-DNA binding complex. Mixing studies with Rzra demonstrate it to be a high affinity DNA binding factor capable of blocking p65 and E2A binding activity without relinquishing its own activity. We suspect this blocking activity to be a result of steric hinderance based on the close linear proximity of these factors recognition sequences.

No description available.

No description available.

No description available.

DNA-binding proteins play an important role in various essential biological processes such as DNA replication, recombination, repair, gene transcription, and expression. The identification of DNA-binding proteins and the residues involved in the contacts is important for understanding the DNA-binding mechanism in proteins. Moreover, it has been reported in the literature that the mutations of some DNA-binding residues on proteins are associated with some diseases. The identification of these proteins and their binding mechanism generally require experimental techniques, which makes large scale study extremely difficult. Thus, the prediction of DNA-binding proteins and their binding sites from sequences alone is one of the most challenging problems in the field of genome annotation. Since the start of the human genome project, many attempts have been made to solve the problem with different approaches, but the accuracy of these methods is still not suitable to do large scale annotation of proteins. Rather than relying solely on the existing machine learning techniques, I sought to combine those using novel “stacking technique” and used the problem-specific architectures to solve the problem with better accuracy than the existing methods. This thesis presents a possible solution to the DNA-binding proteins prediction problem which performs better than the state-of-the-art approaches.

Thesis (Ph. D.)--University of Missouri--Columbia, 1999. / Typescript. Vita. Includes bibliographical references (leaves 128-129). Also available on the Internet.

Diss. (sammanfattning) Stockholm : Karol inst., 2004. / Härtill 4 uppsatser.

Diss. (sammanfattning) Stockholm : Karol inst., 2004. / Härtill 5 uppsatser.

Thesis (Ph. D.)--University of Missouri--Columbia, 1998. / Typescript. Vita. Includes bibliographical references (leaves : 172-189). Also available on the Internet.