Nuclear magnetic resonance (NMR) spectroscopy technique is becoming exceedingly significant due to its capability of studying protein structures in solution. However, NMR protein structure determination has remained a laborious and costly process until now, even with the help of currently available computer programs. After the NMR spectra are collected, the main road blocks to the fully automated NMR protein structure determination are peak picking from noisy spectra, resonance assignment from imperfect peak lists, and structure calculation from incomplete assignment and ambiguous nuclear Overhauser enhancements (NOE) constraints. The goal of this dissertation is to propose error-tolerant and highly-efficient methods that work well on real and noisy data sets of NMR protein structure determination and the closely related protein structure prediction problems. One major contribution of this dissertation is to propose a fully automated NMR protein structure determination system, AMR, with emphasis on the parts that I contributed. AMR only requires an input set with six NMR spectra. We develop a novel peak picking method, PICKY, to solve the crucial but tricky peak picking problem. PICKY consists of a noise level estimation step, a component forming step, a singular value decomposition-based initial peak picking step, and a peak refinement step. The first systematic study on peak picking problem is conducted to test the performance of PICKY. An integer linear programming (ILP)-based resonance assignment method, IPASS, is then developed to handle the imperfect peak lists generated by PICKY. IPASS contains an error-tolerant spin system forming method and an ILP-based assignment method. The assignment generated by IPASS is fed into the structure calculation step, FALCON-NMR. FALCON-NMR has a threading module, an ab initio module, an all-atom refinement module, and an NOE constraints-based decoy selection module. The entire system, AMR, is successfully tested on four out of five real proteins with practical NMR spectra, and generates 1.25A, 1.49A, 0.67A, and 0.88A to the native reference structures, respectively. Another contribution of this dissertation is to propose novel ideas and methods to solve three protein structure prediction problems which are closely related to NMR protein structure determination. We develop a novel consensus contact prediction method, which is able to eliminate server correlations, to solve the protein inter-residue contact prediction problem. We also propose an ultra-fast side chain packing method, which only uses local backbone information, to solve the protein side chain packing problem. Finally, two complementary local quality assessment methods are proposed to solve the local quality prediction problem for comparative modeling-based protein structure prediction methods.

Nuclear magnetic resonance (NMR) spectroscopy technique is becoming exceedingly significant due to its capability of studying protein structures in solution. However, NMR protein structure determination has remained a laborious and costly process until now, even with the help of currently available computer programs. After the NMR spectra are collected, the main road blocks to the fully automated NMR protein structure determination are peak picking from noisy spectra, resonance assignment from imperfect peak lists, and structure calculation from incomplete assignment and ambiguous nuclear Overhauser enhancements (NOE) constraints. The goal of this dissertation is to propose error-tolerant and highly-efficient methods that work well on real and noisy data sets of NMR protein structure determination and the closely related protein structure prediction problems. One major contribution of this dissertation is to propose a fully automated NMR protein structure determination system, AMR, with emphasis on the parts that I contributed. AMR only requires an input set with six NMR spectra. We develop a novel peak picking method, PICKY, to solve the crucial but tricky peak picking problem. PICKY consists of a noise level estimation step, a component forming step, a singular value decomposition-based initial peak picking step, and a peak refinement step. The first systematic study on peak picking problem is conducted to test the performance of PICKY. An integer linear programming (ILP)-based resonance assignment method, IPASS, is then developed to handle the imperfect peak lists generated by PICKY. IPASS contains an error-tolerant spin system forming method and an ILP-based assignment method. The assignment generated by IPASS is fed into the structure calculation step, FALCON-NMR. FALCON-NMR has a threading module, an ab initio module, an all-atom refinement module, and an NOE constraints-based decoy selection module. The entire system, AMR, is successfully tested on four out of five real proteins with practical NMR spectra, and generates 1.25A, 1.49A, 0.67A, and 0.88A to the native reference structures, respectively. Another contribution of this dissertation is to propose novel ideas and methods to solve three protein structure prediction problems which are closely related to NMR protein structure determination. We develop a novel consensus contact prediction method, which is able to eliminate server correlations, to solve the protein inter-residue contact prediction problem. We also propose an ultra-fast side chain packing method, which only uses local backbone information, to solve the protein side chain packing problem. Finally, two complementary local quality assessment methods are proposed to solve the local quality prediction problem for comparative modeling-based protein structure prediction methods.

Dynamic development of nuclear magnetic resonance spectroscopy (NMR) allowed fast acquisition of experimental data which determine structure and dynamics of macromolecules. Nevertheless, due to lack of appropriate computational methods, NMR spectra are still analyzed manually by researchers what takes weeks or years depending on protein complexity. Therefore automation of this process is extremely desired and can significantly reduce time of protein structure solving. In presented work, a new approach to automated three-dimensional protein NMR spectra analysis is presented. It is based on Histogram of Oriented Gradients and Bayesian Network which have not been ever applied in that context in the history of research in the area. Proposed method was evaluated using benchmark data which was established by manual labeling of 99 spectroscopic images taken from 6 different NMR experiments. Afterwards subsequent validation was made using spectra of upstream of N-ras protein. With the use of proposed method, a three-dimensional structure of mentioned protein was calculated. Comparison with reference structure from protein databank reveals no significant differences what has proven that proposed method can be used in practice in NMR laboratories.

Nuclear magnetic resonance (NMR) is one of the main approaches for protein struc- ture determination. The biggest advantage of this approach is that it can determine the three-dimensional structure of the protein in the solution phase. Thus, the natural dynamics of the protein can be studied. However, NMR protein structure determina- tion is an expertise intensive and time-consuming process. If the structure determi- nation process can be accelerated or even automated by computational methods, that will significantly advance the structural biology field. Our goal in this dissertation is to propose highly efficient and error tolerant methods that can work well on real and noisy data sets of NMR. Our first contribution in this dissertation is the development of a novel peak pick- ing method (WaVPeak). First, WaVPeak denoises the NMR spectra using wavelet smoothing. A brute force method is then used to identify all the candidate peaks. Af- ter that, the volume of each candidate peak is estimated. Finally, the peaks are sorted according to their volumes. WaVPeak is tested on the same benchmark data set that was used to test the state-of-the-art method, PICKY. WaVPeak shows significantly better performance than PICKY in terms of recall and precision. Our second contribution is to propose an automatic method to select peaks pro- duced by peak picking methods. This automatic method is used to overcome the limitations of fixed number-based methods. Our method is based on the Benjamini- Hochberg (B-H) algorithm. The method is used with both WaVPeak and PICKY to automatically select the number of peaks to return from out of hundreds of candidate peaks. The volume (in WaVPeak) and the intensity (in PICKY) are converted into p-values. Peaks that have p-values below some certain threshold are selected. Ex- perimental results show that the new method is better than the fixed number-based method in terms of recall. To improve precision, we tried to eliminate false peaks using consensus of the B-H selected peaks from both PICKY and WaVPeak. On average, the consensus method is able to identify more than 88% of the expected true peaks, whereas less than 17% of the selected peaks are false ones. Our third contribution is to propose for the first time, the 3D extension of the Median-Modified-Wiener-Filter (MMWF), and its novel variation named MMWF*. These spatial filters have only one parameter to tune: the window-size. Unlike wavelet denoising, the higher dimensional extension of the newly proposed filters is relatively easy. Thus, they can be applied to denoise multi-dimensional NMR-spectra. We tested the proposed filters and the Wiener-filter, an adaptive variant of the mean-filter, on a benchmark set that contains 16 two-dimensional and three-dimensional NMR- spectra extracted from eight proteins. Our results demonstrate that the adaptive spatial filters significantly outperform their non-adaptive versions. The performance of the new MMWF* on 2D/3D-spectra is even better than wavelet-denoising. Finally, we propose a novel framework that simultaneously conducts slice picking and spin system forming, an essential step in resonance assignment. Our framework then employs a genetic algorithm, directed by both connectivity information and amino acid typing information from the spin systems to assign the spin systems to residues. The inputs to our framework can be as few as two commonly used spectra, i.e., CBCA(CO)NH and HNCACB. Different from existing peak picking and resonance assignment methods that treat peaks as the units, our method is based on slices, which are one-dimensional vectors in three-dimensional spectra that correspond to certain (N, H) values. Experimental results on both benchmark simulated data sets and four real protein data sets demonstrate that our method significantly outperforms the state-of-the-art methods especially on the more challenging real protein data sets, while using a less number of spectra than those methods. Furthermore, we show that using the chemical shift assignments predicted by our method for the four real proteins can lead to accurate calculation of their final three-dimensional structures by using CS-ROSETTA server.

The gradual deterioration and failure of old buildings, bridges and other civil engineering structures invoked the need for Structural Health Monitoring (SHM) systems to develop a means to monitor the health of structures. Dozens of sensing, processing and monitoring mechanisms have been implemented and widely deployed with wired sensors. Wireless sensor networks (WSNs), on the other hand, are networks of large numbers of low cost wireless sensor nodes that communicate through a wireless media. The complexity nature and high cost demand of the highly used wired traditional SHM systems have posed the need for replacement with WSNs. However, the major fact that wireless sensor nodes have memory and power supply limitations has been an issue and many efficient options have been proposed to solve this problem and preserve the long life of the network. This is the reason why data processing algorithms in WSNs focus mainly on the accomplishment of efficient utilization of these scarce resources. In this thesis, we design a low-power and memory efficient data processing algorithm using in-place radix-2 integer Fast Fourier Transform (FFT). This algorithm requires inputs with integer values; hence, increases the memory efficiency by more than 40% and highly saves processor power consumption over the traditional floating-point implementation. A standard-deviation-based peak picking algorithm is next applied to measure the natural frequency of the structure. The algorithms together with Contiki, a lightweight open source operating system for networked embedded systems, are loaded on Z1 Zolertia sensor node. Analogue Device’s ADXL345 digital accelerometer on board is used to collect vibration data. The bridge model used to test the target algorithm is a simply supported beam in the lab.

<p>Mass spectrometry (MS) is an important tool in analytical chemistry today, particularly in the field of proteomics where identification of proteins is the central activity. The focus in this thesis has been to improve the mass accuracy of MS-analyses in order to improve the possibility for unambiguous identification of proteins.</p><p>In paper I a new peak picking algorithm has been developed for Matrix Assisted Laser Desorption/Ionization - Time of Flight - Mass Spectrometry (MALDI-TOF-MS). The new algorithm is based on the assumption that two sets of ions are formed during the ionisation, and that these two sets have different Gaussian-distributed velocity profiles. The algorithm then deconvolutes the spectral peak into two Gaussian distributions, were the narrower of the two distributions is utilized for peak picking. The two-Gaussian peak picking algorithm proved to be especially useful when dealing with weak, distorted peaks.</p><p>In paper II a novel chip-based target for MALDI analysis is described. The target features pairs of 50x50 μm anchors in close proximity. Each anchor within a pair could be individually addressed with different sample solutions. Each pair could then be irradiated with the MALDI laser, which allowed ionization to take place on separated anchors simultaneously. This made it possible for us to calibrate analytes with calibration standards that where physically separated from the analyte, but ionized simultaneously. The use of new chip-based MALDI target resulted in a 2-fold reduction of relative mass errors. We could also report a significant reduction of ion suppression. The small size of the anchors provided a good platform for efficient utilization of sample. This resulted in a detection limit of ca. 1.5 attomole of angiotensin I at a S/N of 22:1.</p>

The aim of this diploma thesis is a creation of modal parameter estimation application. Modal properties (natural frequencies, damping factors and mode shapes) are used in many dynamics analysis and their accurate determination is very important therefore the modal parameter estimation is one of the most significant part of the experimental modal analysis. Many methods have been developed for modal parameter estimation, each of them with different assumptions and with different accuracy. In the beginning of this thesis, a theory connected with modal analysis and a theory which is necessary for understanding to presented modal parameter methods are given. Then four different modal parameter estimation methods are presented - Peak Picking, Circle Fit, Least Square method and Eigensystem Realization Algorithm. The application for the modal parameter estimation is the output of this diploma thesis. In addition, the application allows performing all experimental modal analysis such as estimation of frequency response functions, animation of the found mode shapes, different kinds of comparison etc. In the conclusion, three structures are shown on which the application and modal parameter estimation methods were tested.

Mass spectrometry (MS) is an important tool in analytical chemistry today, particularly in the field of proteomics where identification of proteins is the central activity. The focus in this thesis has been to improve the mass accuracy of MS-analyses in order to improve the possibility for unambiguous identification of proteins. In paper I a new peak picking algorithm has been developed for Matrix Assisted Laser Desorption/Ionization - Time of Flight - Mass Spectrometry (MALDI-TOF-MS). The new algorithm is based on the assumption that two sets of ions are formed during the ionisation, and that these two sets have different Gaussian-distributed velocity profiles. The algorithm then deconvolutes the spectral peak into two Gaussian distributions, were the narrower of the two distributions is utilized for peak picking. The two-Gaussian peak picking algorithm proved to be especially useful when dealing with weak, distorted peaks. In paper II a novel chip-based target for MALDI analysis is described. The target features pairs of 50x50 μm anchors in close proximity. Each anchor within a pair could be individually addressed with different sample solutions. Each pair could then be irradiated with the MALDI laser, which allowed ionization to take place on separated anchors simultaneously. This made it possible for us to calibrate analytes with calibration standards that where physically separated from the analyte, but ionized simultaneously. The use of new chip-based MALDI target resulted in a 2-fold reduction of relative mass errors. We could also report a significant reduction of ion suppression. The small size of the anchors provided a good platform for efficient utilization of sample. This resulted in a detection limit of ca. 1.5 attomole of angiotensin I at a S/N of 22:1. / QC 20101206

No description available.