4 research outputs found

    An improved formalism for assigning proteins using nuclear vector replacement framework

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    Tezin basılısı İstanbul Şehir Üniversitesi Kütüphanesi'ndedir.Proteins are macromolecules in living systems used in crucial functions in all biological processes. In order to understand the function of a protein it is necessary to determine the structure of it. There are various techniques to obtain structural information and Nuclear Magnetic Resonance (NMR) Spectroscopy is one of the most important ones. In this technique, an essential step is the backbone resonance assignment and Structure Based Assignment (SBA) is a method solving this problem with the help of a template structure. NVR is an NMR protein SBA program, that takes as input 15N and HN chemical shifts and unambiguous NOEs, as well as RDCs, HD-exchange and TOCSY data. To run NVR, there is a sequence of steps in obtaining the datales from NMR data and the template structure. In this study, the process of preparing these datales is simpliedandautomatized, whichisanimportantpracticalstepinrunningNVRonnovel proteins. A method to distinguish NH2 peaks from HSQC peaks is generated. Finally, rather than computing a single assignment, an ensemble of assignments is computed. Using this ensemble of assignment results, degree of reliability for individual peak-amino acid assignments is obtained and assignment accuracy is improved. The results show that these improvements bring NVR closer to a tool to be useful and practical tool, able to handle the input data automatically and analyze the reliability of assignments.Declaration of Authorship ii Abstract iii Öz iv Acknowledgments v List of Figures vii List of Tables viii Abbreviations ix 1 Introduction 1 2 Literature Review 6 2.1 Related Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 2.2 NVR Framework . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 2.2.1 Mathematical Formulation . . . . . . . . . . . . . . . . . . . . . . . 8 2.2.2 Template Selection . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 3 Methodology 10 3.1 Automatization of the Data Preparation . . . . . . . . . . . . . . . . . . . 10 3.2 Distinguishing NH2 peaks . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 3.2.1 Experimental Analysis of Distinguishing NH2 peaks . . . . . . . . 13 3.3 Providing a Measure of Reliability of Assignments . . . . . . . . . . . . . 14 4 Results 17 4.1 Automatization of the Data Preparation Results . . . . . . . . . . . . . . 17 4.1.1 Test Results on Two Novel Proteins . . . . . . . . . . . . . . . . . 17 4.2 Distinguishing NH2 peaks Results . . . . . . . . . . . . . . . . . . . . . . 17 4.3 Reliability Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 5 Conclusion 21 A NH2 Removal Scores of Randomly Selected Proteins 23 Bibliography 2

    Automating the usage of unambiguous noes in nuclear vector replacement for NMR protein structure-based assignments

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    Proteins perform various functions and tasks in living organisms. The structure of a protein is essential in identifying the protein function. Therefore, determining the protein structure is of upmost importance. Nuclear Magnetic Resonance (NMR) is one of the experimental methods used to determine the protein structure. The key bottleneck in NMR protein structure determination is assigning NMR peaks to corresponding nuclei, which is known as the assignment problem. This assignment process is manually performed in many laboratories. In this thesis, we have developed methodologies and software to automate this process. The Structure Based Assignment (SBA) is an approach to solve this computationally challenging problem by using prior information about the protein that is obtained from a template structure. NVR-BIP is an approach that uses the Nuclear Vector Replacement (NVR) framework to model SBA as a binary integer programming problem. NVR-TS is a tabu search algorithm equipped with a guided perturbation mechanism to handle the proteins with larger residue numbers. NVR-ACO is an ant colony optimization approach that is inspired by the behavior of living ants to minimize peak-nuclei matching cost. One of the input data utilized in these approaches is the Nuclear Overhauser Effect (NOE) data. NOE is an interaction observed between two protons if the protons are located close in space. These protons could be amide protons (HN), protons attached to the alpha-carbon atom in the backbone of the protein (HA), or side chain protons. NVR only uses backbone protons. In the previous approaches using the NVR framework, the proton type was not distinguished in the NOEs and only the HN coordinates were used to incorporate the NOEs into the computation. In this thesis, we fix this problem and use both the HA and HN coordinates and the corresponding distances in our computations. In addition, in the previous studies within this context the distance threshold value for the NOEs was manually tuned for different proteins. However, this limits the application of the methodology for novel proteins. In this thesis we set the threshold value in a standard manner for all proteins by extracting the NOE upper bound distances from the data. Furthermore, for Maltose Binding Protein (MBP), we extract the NOE upper bound distances from the NMR peak intensity values directly and test this protein on real NMR data. We tested our approach on NVR-ACO's data set and compared our new approaches with NVR-BIP, NVR-TS, and NVR-ACO. The experimental results show that the proposed approach improves the assignment accuracies significantly. In particular, we achieved 100% assignment accuracy on EIN and 80% assignment accuracy on MBP proteins as compared to 83% and 73% accuracies, respectively, obtained in the previous approaches

    Automating unambiguous NOE data usage in NVR for NMR protein structure-based assignments

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    Nuclear Magnetic Resonance (NMR) Spectroscopy is an important technique that allows determining protein structure in solution. An important problem in protein structure determination using NMR spectroscopy is the mapping of peaks to corresponding amino acids, also known as the assignment problem. Structure Based Assignment (SBA) is an approach to solve this problem using a template structure that is homologous to the target. Our previously developed approach NVR-BIP computed the optimal solution for small proteins, but was unable to solve the assignments of large proteins. NVR-ACO extended the applicability of the NVR approach for such proteins. One of the input data utilized in these approaches is the Nuclear Overhauser Eect (NOE) data. NOE is an interaction observed between two protons if the protons are located close in space. These protons could be amide protons, protons attached to the alpha-carbon atom in the backbone of the protein, or side chain protons. NVR only uses backbone protons. In this paper, we reformulate the NVR-BIP model to distinguish the type of proton in NOE data and use the corresponding proton coordinates in the extended formulation. In addition, the threshold value over interproton distances is set in a standard manner for all proteins by extracting the NOE upper bound distance information from the data. We also convert NOE intensities into distance thresholds. Our new approach thus handles the NOE data correctly and without manually determined parameters. We accordingly adapt NVR-ACO solution methodology to these changes. Computational results show that our approaches obtains optimal solutions for small proteins. For the large proteins our ant colony optimization based approach obtains promising results
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