The crystal and molecular structure of thiamine hydroiodide /|nWilliam Edward Lee. -- 260 St. Catharines [Ont.] : Dept. of Chemistry, Brock University,

The x-ray crystal structure of thiamine hydroiodide,C1ZH18N40S12' has been determined. The unit cell parameters are a = 13.84 ± 0.03, o b = 7.44 ± 0.01, c = 20.24 ± 0.02 A, 8 = 120.52 ± 0.07°, space group P2/c, z = 4. A total of 1445 reflections having ,2 > 2o(F2), 26 < 40° were collected on a Picker four-circle diffractometer with MoKa radiation by the 26 scan technique. The structure was solved by the heavy atom method. The iodine and sulphur atoms were refined anisotropically; only the positional parameters were refined for the hydrogen atoms. Successive least squares cycles yielded an unweighted R factor of 0.054. The site of protonation of the pyrimidine ring is the nitrogen opposite the amino group. The overall structure conforms very closely to the structures of other related thiamine compounds. The bonding surrounding the iodine atoms is distorted tetrahedral. The iodine atoms make several contacts with surrounding atoms most of them at or near the van der Waal's distances A thiaminium tetrachlorocobaltate salt was produced whose molecular and crystal structure was j~dged to be isomorphous to thiaminium tetrachlorocadmate.

Modeling Metal Protein Complexes from Experimental Extended X-ray Absorption Fine Structure using Computational Intelligence

Experimental Extended X-ray Absorption Fine Structure (EXAFS) spectra carry information about the chemical structure of metal protein complexes. However, pre- dicting the structure of such complexes from EXAFS spectra is not a simple task. Currently methods such as Monte Carlo optimization or simulated annealing are used in structure refinement of EXAFS. These methods have proven somewhat successful in structure refinement but have not been successful in finding the global minima. Multiple population based algorithms, including a genetic algorithm, a restarting ge- netic algorithm, differential evolution, and particle swarm optimization, are studied for their effectiveness in structure refinement of EXAFS. The oxygen-evolving com- plex in S1 is used as a benchmark for comparing the algorithms. These algorithms were successful in finding new atomic structures that produced improved calculated EXAFS spectra over atomic structures previously found.

X-ray diffraction of a gram-negative bacterial membrane mimetic

This thesis applies x-ray diffraction to measure he membrane structure of lipopolysaccharides and to develop a better model of a LPS bacterial melilbrane that can be used for biophysical research on antibiotics that attack cell membranes. \iVe ha'e Inodified the Physics department x-ray machine for use 3.'3 a thin film diffractometer, and have lesigned a new temperature and relative humidity controlled sample cell.\Ve tested the sample eel: by measuring the one-dimensional electron density profiles of bilayers of pope with 0%, 1%, 1G :VcJ, and 100% by weight lipo-polysaccharide from Pse'udo'lTwna aeTuginosa. Background VVe now know that traditional p,ntibiotics ,I,re losing their effectiveness against ever-evolving bacteria. This is because traditional antibiotic: work against specific targets within the bacterial cell, and with genetic mutations over time, themtibiotic no longer works. One possible solution are antimicrobial peptides. These are short proteins that are part of the immune systems of many animals, and some of them attack bacteria directly at the membrane of the cell, causing the bacterium to rupture and die. Since the membranes of most bacteria share common structural features, and these featuret, are unlikely to evolve very much, these peptides should effectively kill many types of bacteria wi Lhout much evolved resistance. But why do these peptides kill bacterial cel: '3 , but not the cells of the host animal? For gramnegative bacteria, the most likely reason is that t Ileir outer membrane is made of lipopolysaccharides (LPS), which is very different from an animal :;ell membrane. Up to now, what we knovv about how these peptides work was likely done with r !10spholipid models of animal cell membranes, and not with the more complex lipopolysa,echaricies, If we want to make better pepticies, ones that we can use to fight all types of infection, we need a more accurate molecular picture of how they \vork. This will hopefully be one step forward to the ( esign of better treatments for bacterial infections.