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• W2109084433 abstract "This thesis describes the development and application of machine learning-basedmethods for the prediction of alpha-helical transmembrane proteinstructure from sequence alone. It is divided into six chapters.Chapter 1 provides an introduction to membrane structure and dynamics,membrane protein classes and families, and membrane protein structure prediction.Chapter 2 describes a topological study of the transmembrane proteinCLN3 using a consensus of bioinformatic approaches constrained by experimentaldata. Mutations in CLN3 can cause juvenile neuronal ceroidlipofuscinosis, or Batten disease, an inherited neurodegenerative lysosomalstorage disease affecting children, therefore such studies are importantfor directing further experimental work into this incurable illness.Chapter 3 explores the possibility of using biologically meaningful signaturesdescribed as regular expressions to influence the assignment of insideand outside loop locations during transmembrane topology prediction. Usingthis approach, it was possilbe to modify a recent topology prediction methodleading to an improvement of 6% prediction accuracy using a standard data set.Chapter 4 describes the development of a novel support vector machine-basedtopology predictor that integrates both signal peptide and re-entrant helix prediction,benchmarked with full cross-validation on a novel data set of sequences withknown crystal structures. The method achieves state-of-the-art performance in predictingtopology and discriminating between globular and transmembrane proteins.We also present the results of applying these tools to a number of complete genomes.Chapter 5 describes a novel approach to predict lipid exposure, residuecontacts, helix-helix interactions and finally the optimal helical packing arrangement of transmembrane proteins. It is based on two support vectormachine classifiers that predict per residue lipid exposure and residue contacts,which are used to determine helix-helix interaction with up to 65%accuracy. The method is also able to discriminate native from decoy helicalpacking arrangements with up to 70% accuracy. Finally, a force-directedalgorithm is employed to construct the optimal helical packing arrangementwhich demonstrates success for proteins containing up to 13 transmembrane helices.The final chapter summarises the major contributions of this thesis to biology,before future perspectives for TM protein structure prediction are discussed." @default.
• W2109084433 created "2016-06-24" @default.
• W2109084433 creator A5002637452 @default.
• W2109084433 date "2010-12-28" @default.
• W2109084433 modified "2023-09-26" @default.
• W2109084433 title "Transmembrane protein structure prediction using machine learning" @default.
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