ETD Collection

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    Support vector machine prediction of HIV-1 drug resistance using The Viral Nucleotide patterns
    (2007-02-23T12:45:30Z) Araya, Seare Tesfamichael
    Drug resistance of the HI virus due to its fast replication and error-prone mutation is a key factor in the failure to combat the HIV epidemic. For this reason, performing pre-therapy drug resistance testing and administering appropriate drugs or combination of drugs accordingly is very useful. There are two approaches to HIV drug resistance testing: phenotypic (clinical) and genotypic (based on the particular virus’s DNA). Genotyping tests HIV drug resistance by detecting specific mutations known to confer drug resistance. It is cheaper and can be computerised. However, it requires being able to know or learn what mutations confer drug resistance. Previous research using pattern recognition techniques has been promising, but the performance needs to be improved. It is also important for techniques that can quickly learn new rules when faced with new mutations or drugs. A relatively recent addition to these techniques is the Support Vector Machines (SVMs). SVMs have proved very successful in many benchmark applications such as face recognition, text recognition, and have also performed well in many computational biology problems where the number of features targeted is large compared to the number of available samples. This paper explores the use of SVMs in predicting the drug resistance of an HIV strain extracted from a patient based on the genetic sequence of those parts of the viral DNA encoding for the two enzymes, Reverse Transcriptase or Protease, which are critical for the replication of the HIV virus. In particular, it is the aim of this reseach to design the model without incorporating the biological knowledge at hand to enable the resulting classifier accommodate new drugs and mutations. To evaluate the performance of SVMs we used cross validation technique to measure the unbiased estimate on 2045 data points. The accuracy of classification and the area under the receiver operating characteristics curve (AUC) was used as a performance measure. Furthermore, to compare the performance of our SVMs model we also developed other prediction models based on popular classification algorithms, namely neural networks, decision trees and logistic regressions. The results show that SVMs are a highly successful classifier and out-perform other techniques with performance ranging between (94.13%–96.33%) accuracy and (81.26% - 97.49%) AUC. Decision trees were rated second and logistic regression performed the worst.
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    Role of a topologically conserved Isoleucine in the structure and function of Glutathione Transferases
    (2006-11-15T08:19:55Z) Fisher, Loren Tichauer
    Proteins in the glutathione transferase family share a common fold. The close packing of secondary structures in the thioredoxin fold in domain 1 forms a compact hydrophobic core. This fold has a bababba topology and most proteins/domains with this fold have a topologically conserved isoleucine residue at the N-terminus of a-helix 3. Class Alpha glutathione transferases are one of 12 classes within the glutathione transferase family. To investigate the role of the conserved isoleucine residue in the structure, function and stability of glutathione transferases, homodimeric human glutathione transferase A1-1 (hGST A1-1) was used as a representative of the GST family. Ile71 was replaced with valine and the properties of I71V hGST A1-1 were compared with those of wildtype hGST A1-1. The spectral properties monitored using far-UV CD and tryptophan fluorescence indicated little change in secondary or tertiary structure confirming the absence of any gross structural changes in hGST A1-1 due to the incorporation of the mutation. Both wildtype and mutant dimeric proteins were determined to have a monomeric molecular mass of 26 kDa. The specific activity of I71V hGST A1-1 (130 mmol/min/mg) was three times that of wildtype hGST A1-1 (48 mmol/min/mg). I71V hGST A1-1 showed increased kinetic parameters compared to wildtype with a 10-fold increase in kcat/Km for CDNB. The increase in Km of I71V hGST A1-1 suggests the mutation had a negative effect on substrate binding. The DDG for transition state stabilisation was –5.82 kJ/mol which suggest the I71V mutation helps stabilise the transition state of the SNAR reaction involving the conjugation of reduced glutathione (GSH) to 1-chloro-2,4-dinitrobenzene (CDNB). A 2-fold increase in the IC50 value for I71V hGST A1-1 (11.3 mM) compared to wildtype (5.4 mM) suggests that the most noticeable change due to the mutation occurs at the H-site of the active site. Conformational stability studies were performed to determine the contribution of Ile71 to protein stability. The non-superimposability of I71V hGST A1-1 unfolding curves and the decreased m-value suggest the formation of an intermediate state. The conformational stability of I71V hGST A1-1 (16.5 kcal/mol) was reduced when compared to that of the wildtype (23 kcal/mol). ITC was used to dissect the binding energetics of Shexylglutathione to wildtype and I71V hGSTA1-1. The ligand binds 5-fold more tightly to wildtype hGST A1-1 (0.07 mM) than I71V hGST A1-1 (0.37 mM). The I71V mutant displays a larger negative DCp than wildtype hGST A1-1 (DDCp = -0.41 kJ/mol/K). This indicates that a larger solvent-exposed hydrophobic surface area is buried for I71V hGST A1-1 than for wildtype hGST A1-1 upon the binding of S-hexylglutathione. Overall the results suggest that Ile71 conservation is for the stability of the protein as well as playing a pivotal indirect role in catalysis and substrate binding.