Thesis (Ph. D.)--University of Virginia, 2008. / Title from title page. Includes bibliographical references. Also available online through Digital Dissertations.

Thesis (doctoral)--Karolinska Institutet, Stockholm, 1993. / Added t.p. with thesis statement inserted. Includes bibliographical references.

Thesis (doctoral)--Karolinska Institutet, Stockholm, 1993. / Added t.p. with thesis statement inserted. Includes bibliographical references.

Thesis (Ph. D.)--Virginia Commonwealth University, 2010. / Prepared for: Dept.of Computer Science. Title from resource description page. Includes bibliographical references.

Thesis (Ph. D.)--University of Wisconsin, 1967. / Typescript. Vita. eContent provider-neutral record in process. Description based on print version record. Includes bibliography.

Thesis (Ph. D.)--Chemistry and Biochemistry, Georgia Institute of Technology, 2006. / Dr. Allen M. Orville, Committee Chair ; Dr. Loren D. Williams, Committee Member ; Dr. Donald F. Doyle, Committee Member ; Dr. Dale E. Edmondson, Committee Member ; Dr. Giovanni Gadda, Committee Member.

Thesis (Ph. D.)--University of Missouri-Columbia, 2005. / The entire dissertation/thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file (which also appears in the research.pdf); a non-technical general description, or public abstract, appears in the public.pdf file. Title from title screen of research.pdf file viewed on (May 23, 2006) Vita. Includes bibliographical references.

Thesis (Ph.D.) - University of Birmingham. / PDF copy of thesis. Includes bibliographical references.

Thesis (Ph. D.)--University of Hawaii at Manoa, 2001. / Includes bibliographical references (leaves 180-195). Also available on microfiche.

X-ray crystallography is extensively deployed to determine the structure of proteins, both unbound and bound to different molecules. Crystallography has the power to visually reveal the binding of small molecules, assisting in their development in structure-based lead design. Currently, however, the methods used to detect binding, and the subjectivity of inexperienced modellers, are a weak-point in the field. Existing methods for ligand identification are fundamentally flawed when identifying partially-occupied states in crystallographic datasets; the ambiguity of conventional electron density maps, which present a superposition of multiple states, prevents robust ligand identification. In this thesis, I present novel methods to clearly identify bound ligands and other changed states in the case where multiple crystallographic datasets are available, such as in crystallographic fragment screening experiments. By applying statistical methods to signal identification, more crystallographic binders are detected than by state-of-the-art conventional approaches. Standard modelling practice is further challenged regarding the modelling of multiple chemical states in crystallography. The pervading modelling approach is to model only the bound state of the protein; I show that modelling an ensemble of bound and unbound states leads to better models. I conclude with a discussion of possible future applications of multi-datasets methods in X-ray crystallography, including the robust identification of conformational heterogeneity in protein structures.