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- W2738508617 abstract "The success in sequential resonance assignment is fundamental to protein three dimensional structure determination via NMR spectroscopy. In general, the sequential resonance assignment consists of four components, namely, peak grouping, connectivity determination, string assignment and scoring scheme. The objective of peak grouping is to classify the detected resonance peaks from multiple NMR spectra into spin systems. Connectivity determination aims to find the true connectivity among the grouped spin systems in order to chain them into some strings. The goal of string assignment is to map the strings of spin systems to non-overlapping consecutive amino acid residues in the target protein. The task of scoring scheme is to measure the correlations between the amino acid types and the grouped spin systems. This thesis thoroughly addresses the computational issues that remain to be resolved in each component in the sequential resonance assignment process. Several novel computational models are developed to tackle those issues. We organize this thesis according to the issues we tackle in the development. First, we discuss the difficulties in scoring scheme learning, evaluate the existing learning methods with the string assignment algorithms, and identify the best one. Second, we provide our solutions to resolve the connectivity determination problem, which supplies valuable constraints for computing the reliable resonance assignment. A vital heuristic is designed and applied to our solutions. Third, we reveal that the peak grouping, which is often assumed to be less important and neglected by most researchers, is the bottleneck in automated sequential resonance assignment. We present our graph-based solutions based on the improved automated assignment framework to resolve the peak grouping and sequential resonance assignment simultaneously. The value of our approaches to solving the different issues is explored by conducting comparison experiments with many recently published similar methods. The experimental results show that this study has made a significant contribution to the field of NMR protein structure determination. The performance comparisons demonstrate the fact that our models would be more promising for practical use. We conclude this thesis with a discussion of the limitations in our models as well as related future work." @default.
- W2738508617 created "2017-07-31" @default.
- W2738508617 creator A5045345341 @default.
- W2738508617 date "2006-01-01" @default.
- W2738508617 modified "2023-09-23" @default.
- W2738508617 title "Automated sequential resonance assignment in nmr protein structure determination" @default.
- W2738508617 hasPublicationYear "2006" @default.
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