subject:(Structure prediction)

Rice University

1. Cheng, Yushao. Template-based Protein Structure Prediction and its Applications.

Degree: PhD, Engineering, 2013, Rice University

URL: http://hdl.handle.net/1911/76475

► Protein structure prediction, also called protein folding, is one of the most significant and challenging research areas in computational biophysics and structural bioinformatics. With the… (more)

▼ Protein structure prediction, also called protein folding, is one of the most significant and challenging research areas in computational biophysics and structural bioinformatics. With the rapid growth of PDB database, template-based modeling such as homology modeling and threading has become a popular method in protein structure prediction. However, it is still hard to detect good templates when the sequence identity is below 30%. In chapter 1, a profile-profile alignment method is proposed. It uses evolutionary and structural profiles to detect homologs, and a z-score-based method to rank templates. The performance of this method in the critical assessment of protein structure prediction experiments (CASP) was reported. In chapter 2, p53 mutations are studied as an application of protein structure prediction. The TP53 gene encodes a tumor suppressor protein called p53, and p53 mutations occur in about half of human cancers. Experimental studies showed that p53 cancer mutants can be reactivated by mutations on other sites. Machine learning technologies were used in this research. Multiple classifiers were built to predict whether a p53 mutant (single-point or multiple-point) would be transcriptionally active or not, based on features extracted from amino acid sequences and structures. The mutant structures were modeled using template-based protein structure prediction. Theses features were selected and analyzed using different feature selection methods, and classifiers were built under different learning settings, such as supervised learning and semi-supervised learning. The performances of these classifiers were analyzed and compared. Besides the study of single proteins, protein complexes in yeast are studied in chapter 3. Multiple classifiers were built to predict whether several given proteins can form a protein complex, based on features generated from amino acid sequences and protein-protein interaction network. Theses features were selected and analyzed using different feature selection methods. Also, these classifiers were built under different learning settings, such as supervised learning and active learning. The performances of these classifiers were analyzed and compared. Advisors/Committee Members: Ma, Jianpeng (advisor), Diehl, Michael R. (committee member), Tao, Yizhi Jane (committee member).

Subjects/Keywords: Protein structure prediction

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APA (6^th Edition):

Cheng, Y. (2013). Template-based Protein Structure Prediction and its Applications. (Doctoral Dissertation). Rice University. Retrieved from http://hdl.handle.net/1911/76475

Chicago Manual of Style (16^th Edition):

Cheng, Yushao. “Template-based Protein Structure Prediction and its Applications.” 2013. Doctoral Dissertation, Rice University. Accessed March 04, 2021. http://hdl.handle.net/1911/76475.

MLA Handbook (7^th Edition):

Cheng, Yushao. “Template-based Protein Structure Prediction and its Applications.” 2013. Web. 04 Mar 2021.

Vancouver:

Cheng Y. Template-based Protein Structure Prediction and its Applications. [Internet] [Doctoral dissertation]. Rice University; 2013. [cited 2021 Mar 04]. Available from: http://hdl.handle.net/1911/76475.

Council of Science Editors:

Cheng Y. Template-based Protein Structure Prediction and its Applications. [Doctoral Dissertation]. Rice University; 2013. Available from: http://hdl.handle.net/1911/76475

Vanderbilt University

2. Karakaş, Mert. BCL::Fold - de novo protein structure prediction by assembly of secondary structure elements.

Degree: PhD, Chemical and Physical Biology, 2011, Vanderbilt University

URL: http://hdl.handle.net/1803/14981

► Structural information facilitates understanding of protein function and activity. The limitations of experimental methods for protein structure elucidation in applicability to certain types and families… (more)

▼ Structural information facilitates understanding of protein function and activity. The limitations of experimental methods for protein structure elucidation in applicability to certain types and families of proteins, necessitates use of computational methods for protein structure prediction. Template-based methods utilize structural information from template proteins with available structures and high sequence similarity to the protein of interest. However, in the absence of such template proteins, de novo methods can be used to generate structural models. State of the art de novo methods are limited to smaller size proteins due to the size of the conformational search space that needs to be sampled. In this study, we introduce BCL::Fold, a novel de novo protein structure method and accompanying energy potentials. BCL::Fold discontinues the chain and works by assembling secondary structure elements (SSEs); namely α-helices and β-strands. This approach leverages the fact that SSEs more readily define the topology of a protein compared to flexible loop regions. This allows the decoupling of determination of a topology from building of flexible loop regions, which in turn divides the structure prediction problem into two more manageable portions. BCL::Fold employs a Monte-Carlo Metropolis minimization where SSE-based moves allow rapid sampling of conformational search space, while knowledge based-potentials are used to evaluate how native-like the generated structural models are. BCL::Fold was benchmarked on a set of proteins with diverse sequence lengths, secondary structure contents and topologies. A native-like structural model was obtained at comparable levels to Rosetta, one of the top-performing de novo methods. Energy potentials were also evaluated and shown to successfully discriminate native-like structural models. Accurate prediction of residue pairs, apart in the sequence but in close proximity in the structure, provides insight into the topology of a protein and therefore limits the conformational search space to be sampled in protein structure prediction. BCL::Contact is a novel method which utilizes artificial neural networks and provides rapid prediction of residue pair contacts. BCL::Contact improved the accuracy of protein structure prediction by Rosetta. Advisors/Committee Members: Brandt Eichman (committee member), Charles Sanders (committee member), Phoebe Stewart (committee member), Jens Meiler (committee member), Albert Beth (Committee Chair).

Subjects/Keywords: protein structure prediction; contact prediction; de novo

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Karakaş, M. (2011). BCL::Fold - de novo protein structure prediction by assembly of secondary structure elements. (Doctoral Dissertation). Vanderbilt University. Retrieved from http://hdl.handle.net/1803/14981

Chicago Manual of Style (16^th Edition):

Karakaş, Mert. “BCL::Fold - de novo protein structure prediction by assembly of secondary structure elements.” 2011. Doctoral Dissertation, Vanderbilt University. Accessed March 04, 2021. http://hdl.handle.net/1803/14981.

MLA Handbook (7^th Edition):

Karakaş, Mert. “BCL::Fold - de novo protein structure prediction by assembly of secondary structure elements.” 2011. Web. 04 Mar 2021.

Vancouver:

Karakaş M. BCL::Fold - de novo protein structure prediction by assembly of secondary structure elements. [Internet] [Doctoral dissertation]. Vanderbilt University; 2011. [cited 2021 Mar 04]. Available from: http://hdl.handle.net/1803/14981.

Council of Science Editors:

Karakaş M. BCL::Fold - de novo protein structure prediction by assembly of secondary structure elements. [Doctoral Dissertation]. Vanderbilt University; 2011. Available from: http://hdl.handle.net/1803/14981

University of Rochester

3. Seetin, Matthew G. RNA Structure Prediction:Advancing Both Secondary and Tertiary Structure Prediction.

Degree: PhD, 2011, University of Rochester

URL: http://hdl.handle.net/1802/17694

► RNAs can function without being translated into proteins. These RNAs adopt a structure or structures to perform these functions, and accurate prediction of structure is… (more)

▼ RNAs can function without being translated into proteins. These RNAs adopt a structure or structures to perform these functions, and accurate prediction of structure is a valuable tool for understanding these functions. RNA structure is hierarchical, beginning with the primary sequence, then the secondary structure, i.e. the set of canonical pairs, and ultimately the tertiary structure, i.e. the three-dimensional structure. One significant tool for prediction of secondary structure is the nearest neighbor model. This assumes the free energy change of forming a base pair depends on the identities of the pair and the adjacent pairs. Parameters were previously derived from optical melting on RNA duplexes where it was assumed all strands would be completely duplex or single-stranded. When individual base pairs are allowed to break as a function of temperature, the model does not agree with experiment. A new treatment of the data is presented. The probabilities of individual base pairs are calculated using a partition function, allowing internal loops and frayed ends. The parameters of the nearest neighbor model are recalculated using a nonlinear fit to the original data. These new parameters better fit the data and should provide improved structure prediction. Homologous RNAs adopt similar structures. One important structural motif is the pseudoknot, a structure difficult to predict and often found near functionally important regions. Combining information from thermodynamics and homology, the TurboKnot algorithm presented here finds ~80% of known base pairs, and ~75% of predicted pairs were found in the known structures. Pseudoknots are found with half or better of the false-positive rate of other methods. Finally, a novel protocol for RNA tertiary structure prediction employing restrained molecular mechanics and simulated annealing is presented. The restraints are from secondary structure, co-variation analysis, coaxial stacking predictions, and, when available, cross-linking data. Results are demonstrated on five different RNAs. The predicted structure is selected from a pool of decoy structures by maximizing radius of gyration and base-base contacts. This approach is sufficient to accurately predict the structure of RNAs compared to current crystal structures, as evaluated by root mean square deviation and the accuracy of base-base contacts.

Subjects/Keywords: RNA Structure; Secondary Structure Prediction; Tertiary Structure Prediction; Pseudoknots

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Seetin, M. G. (2011). RNA Structure Prediction:Advancing Both Secondary and Tertiary Structure Prediction. (Doctoral Dissertation). University of Rochester. Retrieved from http://hdl.handle.net/1802/17694

Chicago Manual of Style (16^th Edition):

Seetin, Matthew G. “RNA Structure Prediction:Advancing Both Secondary and Tertiary Structure Prediction.” 2011. Doctoral Dissertation, University of Rochester. Accessed March 04, 2021. http://hdl.handle.net/1802/17694.

MLA Handbook (7^th Edition):

Seetin, Matthew G. “RNA Structure Prediction:Advancing Both Secondary and Tertiary Structure Prediction.” 2011. Web. 04 Mar 2021.

Vancouver:

Seetin MG. RNA Structure Prediction:Advancing Both Secondary and Tertiary Structure Prediction. [Internet] [Doctoral dissertation]. University of Rochester; 2011. [cited 2021 Mar 04]. Available from: http://hdl.handle.net/1802/17694.

Council of Science Editors:

Seetin MG. RNA Structure Prediction:Advancing Both Secondary and Tertiary Structure Prediction. [Doctoral Dissertation]. University of Rochester; 2011. Available from: http://hdl.handle.net/1802/17694

NSYSU

4. Chen, Chun-jen. A New Fitness Function for Evaluating the Quality of Predicted Protein Structures.

Degree: Master, Computer Science and Engineering, 2010, NSYSU

URL: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0902110-103900

► For understanding the function of a protein, the protein structure plays an important role. The prediction of protein structure from its primary sequence has significant… (more)

Subjects/Keywords: prediction; tertiary structure; protein

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APA (6^th Edition):

Chen, C. (2010). A New Fitness Function for Evaluating the Quality of Predicted Protein Structures. (Thesis). NSYSU. Retrieved from http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0902110-103900

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Chen, Chun-jen. “A New Fitness Function for Evaluating the Quality of Predicted Protein Structures.” 2010. Thesis, NSYSU. Accessed March 04, 2021. http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0902110-103900.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Chen, Chun-jen. “A New Fitness Function for Evaluating the Quality of Predicted Protein Structures.” 2010. Web. 04 Mar 2021.

Vancouver:

Chen C. A New Fitness Function for Evaluating the Quality of Predicted Protein Structures. [Internet] [Thesis]. NSYSU; 2010. [cited 2021 Mar 04]. Available from: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0902110-103900.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Chen C. A New Fitness Function for Evaluating the Quality of Predicted Protein Structures. [Thesis]. NSYSU; 2010. Available from: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0902110-103900

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Rice University

5. Zang, Tianwu. Advanced Computational Methods for High-accuracy Refinement of Protein Low-quality Models.

Degree: PhD, Natural Sciences, 2016, Rice University

URL: http://hdl.handle.net/1911/95649

► Predicting the 3-dimentional structure of protein has been a major interest in the modern computational biology. While lots of successful methods can generate models with… (more)

▼ Predicting the 3-dimentional structure of protein has been a major interest in the modern computational biology. While lots of successful methods can generate models with 3~5Å root-mean-square deviation (RMSD) from the solution, the progress of refining these models is quite slow. It is therefore urgently needed to develop effective methods to bring low-quality models to higher-accuracy ranges (e.g., less than 2 Å RMSD). In this thesis, I present several novel computational methods to address the high-accuracy refinement problem. First, an enhanced sampling method, named parallel continuous simulated tempering (PCST), is developed to accelerate the molecular dynamics (MD) simulation. Second, two energy biasing methods, Structure-Based Model (SBM) and Ensemble-Based Model (EBM), are introduced to perform targeted sampling around important conformations. Third, a three-step method is developed to blindly select high-quality models along the MD simulation. These methods work together to make significant refinement of low-quality models without any knowledge of the solution. The effectiveness of these methods is examined in different applications. Using the PCST-SBM method, models with higher global distance test scores (GDT_TS) are generated and selected in the MD simulation of 18 targets from the refinement category of the 10th Critical Assessment of Structure Prediction (CASP10). In addition, in the refinement test of two CASP10 targets using the PCST-EBM method, it is indicated that EBM may bring the initial model to even higher-quality levels. Furthermore, a multi-round refinement protocol of PCST-SBM improves the model quality of a protein to the level that is sufficient high for the molecular replacement in X-ray crystallography. Our results justify the crucial position of enhanced sampling in the protein structure prediction and demonstrate that a considerable improvement of low-accuracy structures is still achievable with current force fields. Advisors/Committee Members: Ma, Jianpeng (advisor).

Subjects/Keywords: Protein Structure Prediction Enhanced Sampling Molecular DynamicsProtein Structure Prediction Enhanced Sampling Molecular DynamicsProtein Structure Prediction Enhanced Sampling Molecular DynamicsProtein Structure Prediction Enhanced Sampling Molecular DynamicsProtein Structure Prediction Enhanced Sampling Molecular DynamicsProtein Structure Prediction Enhanced Sampling Molecular DynamicsProtein Structure Prediction Enhanced Sampling Molecular Dynamics

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Zang, T. (2016). Advanced Computational Methods for High-accuracy Refinement of Protein Low-quality Models. (Doctoral Dissertation). Rice University. Retrieved from http://hdl.handle.net/1911/95649

Chicago Manual of Style (16^th Edition):

Zang, Tianwu. “Advanced Computational Methods for High-accuracy Refinement of Protein Low-quality Models.” 2016. Doctoral Dissertation, Rice University. Accessed March 04, 2021. http://hdl.handle.net/1911/95649.

MLA Handbook (7^th Edition):

Zang, Tianwu. “Advanced Computational Methods for High-accuracy Refinement of Protein Low-quality Models.” 2016. Web. 04 Mar 2021.

Vancouver:

Zang T. Advanced Computational Methods for High-accuracy Refinement of Protein Low-quality Models. [Internet] [Doctoral dissertation]. Rice University; 2016. [cited 2021 Mar 04]. Available from: http://hdl.handle.net/1911/95649.

Council of Science Editors:

Zang T. Advanced Computational Methods for High-accuracy Refinement of Protein Low-quality Models. [Doctoral Dissertation]. Rice University; 2016. Available from: http://hdl.handle.net/1911/95649

University of Georgia

6. Li, Qi. Graph tree decomposition enabled biopolymer folding.

Degree: 2014, University of Georgia

URL: http://hdl.handle.net/10724/26948

► Biopolymer tertiary structure prediction by computer programs plays a very important role in complementing the experimental determination method. There are two structure prediction approaches: template-based… (more)

Subjects/Keywords: template-based structure prediction; ab initio structure prediction; biopolymer sequence-structure prediction; graph tree decomposition; treewidth; dynamic programming

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Li, Q. (2014). Graph tree decomposition enabled biopolymer folding. (Thesis). University of Georgia. Retrieved from http://hdl.handle.net/10724/26948

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Li, Qi. “Graph tree decomposition enabled biopolymer folding.” 2014. Thesis, University of Georgia. Accessed March 04, 2021. http://hdl.handle.net/10724/26948.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Li, Qi. “Graph tree decomposition enabled biopolymer folding.” 2014. Web. 04 Mar 2021.

Vancouver:

Li Q. Graph tree decomposition enabled biopolymer folding. [Internet] [Thesis]. University of Georgia; 2014. [cited 2021 Mar 04]. Available from: http://hdl.handle.net/10724/26948.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Li Q. Graph tree decomposition enabled biopolymer folding. [Thesis]. University of Georgia; 2014. Available from: http://hdl.handle.net/10724/26948

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

7. Brunette, TJ. Adaptive Balancing of Exploitation with Exploration to Improve Protein Structure Prediction.

Degree: PhD, Computer Science, 2011, U of Massachusetts : PhD

URL: https://scholarworks.umass.edu/open_access_dissertations/375

► The most significant impediment for protein structure prediction is the inadequacy of conformation space search. Conformation space is too large and the energy landscape… (more)

▼ The most significant impediment for protein structure prediction is the inadequacy of conformation space search. Conformation space is too large and the energy landscape too rugged for existing search methods to consistently find near-optimal minima. Conformation space search methods thus have to focus exploration on a small fraction of the search space. The ability to choose appropriate regions, i.e. regions that are highly likely to contain the native state, critically impacts the effectiveness of search. To make the choice of where to explore requires information, with higher quality information resulting in better choices. Most current search methods are designed to work in as many domains as possible, which leads to less accurate information because of the need for generality. However, most domains provide unique, and accurate information. To best utilize domain specific information search needs to be customized for each domain. The first contribution of this thesis customizes search for protein structure prediction, resulting in significantly more accurate protein structure predictions. Unless information is perfect, mistakes will be made, and search will focus on regions that do not contain the native state. How search recovers from mistakes is critical to its effectiveness. To recover from mistakes, this thesis introduces the concept of adaptive balancing of exploitation with exploration. Adaptive balancing of exploitation with exploration allows search to use information only to the extent to which it guides exploration toward the native state. Existing methods of protein structure prediction rely on information from known proteins. Currently, this information is from either full-length proteins that share similar sequences, and hence have similar structures (homologs), or from short protein fragments. Homologs and fragments represent two extremes on the spectrum of information from known proteins. Significant additional information can be found between these extremes. However, current protein structure prediction methods are unable to use information between fragments and homologs because it is difficult to identify the correct information from the enormous amount of incorrect information. This thesis makes it possible to use information between homologs and fragments by adaptively balancing exploitation with exploration in response to an estimate of template protein quality. My results indicate that integrating the information between homologs and fragments significantly improves protein structure prediction accuracy, resulting in several proteins predicted with <1>°A RMSD resolution. Advisors/Committee Members: Oliver Brock, Lila Gierasch, David Kulp.

Subjects/Keywords: Optimization; Protein Structure Prediction; Search; Computer Sciences

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APA (6^th Edition):

Brunette, T. (2011). Adaptive Balancing of Exploitation with Exploration to Improve Protein Structure Prediction. (Doctoral Dissertation). U of Massachusetts : PhD. Retrieved from https://scholarworks.umass.edu/open_access_dissertations/375

Chicago Manual of Style (16^th Edition):

Brunette, TJ. “Adaptive Balancing of Exploitation with Exploration to Improve Protein Structure Prediction.” 2011. Doctoral Dissertation, U of Massachusetts : PhD. Accessed March 04, 2021. https://scholarworks.umass.edu/open_access_dissertations/375.

MLA Handbook (7^th Edition):

Brunette, TJ. “Adaptive Balancing of Exploitation with Exploration to Improve Protein Structure Prediction.” 2011. Web. 04 Mar 2021.

Vancouver:

Brunette T. Adaptive Balancing of Exploitation with Exploration to Improve Protein Structure Prediction. [Internet] [Doctoral dissertation]. U of Massachusetts : PhD; 2011. [cited 2021 Mar 04]. Available from: https://scholarworks.umass.edu/open_access_dissertations/375.

Council of Science Editors:

Brunette T. Adaptive Balancing of Exploitation with Exploration to Improve Protein Structure Prediction. [Doctoral Dissertation]. U of Massachusetts : PhD; 2011. Available from: https://scholarworks.umass.edu/open_access_dissertations/375

8. Weitzner, Brian D. Next-generation antibody modeling.

Degree: 2015, Johns Hopkins University

URL: http://jhir.library.jhu.edu/handle/1774.2/37835

► Antibodies are important immunological molecules that can bind a diverse array of foreign molecules. The genetic mechanism that gives rise to antibodies and many antibody… (more)

▼ Antibodies are important immunological molecules that can bind a diverse array of foreign molecules. The genetic mechanism that gives rise to antibodies and many antibody sequences is known, but only by studying three-dimensional structures of antibodies and antibody–antigen complexes can we reveal immunological mechanisms and provide a starting point for developing rationally designed antibodies. With the advent of high-throughput sequencing technologies, the gap between the number of sequences and structures is widening, demanding accurate antibody modeling methods. Our previously developed method, RosettaAntibody, served as a starting point for antibody structure prediction. In this dissertation, I detail my work assessing the predictive power of RosettaAntibody, and the development and testing of new methods to address its weaknesses. First, I describe an effort to assess the accuracy of RosettaAntibody on a set of unpublished crystal structures. This challenge enabled us to combine manual and automated methods for selecting models and compare RosettaAntibody to other antibody modeling methods. The most challenging aspect of structure prediction in this assessment proved to be modeling the third complementarity determining region loop on the heavy chain (CDR H3). Next I detail my work in studying CDR H3 loops to uncover why a vast majority of them contain a kink at the loop's C-terminus. Part of this work involved searching the Protein Data Bank (PDB) for structures with a similar geometry of the amino acid residues at the base of the loop, leading to a set of CDR H3-like loops from non-antibody proteins. With a clearer understanding of CDR H3 loop structures and the most detailed description of the kink to date, I developed a new loop modeling routine that utilizes this information to restrict the geometry of the loop to be kinked, resulting in an improvement in the weakest aspect of antibody structure prediction. In summary, the structure prediction methods I have developed and structural analyses I have performed provide a means to begin to address the widening sequence–structure gap. Additionally, these methods can be used to perform structural analysis in the development of rationally designed antibodies. Advisors/Committee Members: Gray, Jeffrey J (advisor).

Subjects/Keywords: Rosetta; antibodies; CDR H3; structure prediction

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APA (6^th Edition):

Weitzner, B. D. (2015). Next-generation antibody modeling. (Thesis). Johns Hopkins University. Retrieved from http://jhir.library.jhu.edu/handle/1774.2/37835

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Weitzner, Brian D. “Next-generation antibody modeling.” 2015. Thesis, Johns Hopkins University. Accessed March 04, 2021. http://jhir.library.jhu.edu/handle/1774.2/37835.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Weitzner, Brian D. “Next-generation antibody modeling.” 2015. Web. 04 Mar 2021.

Vancouver:

Weitzner BD. Next-generation antibody modeling. [Internet] [Thesis]. Johns Hopkins University; 2015. [cited 2021 Mar 04]. Available from: http://jhir.library.jhu.edu/handle/1774.2/37835.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Weitzner BD. Next-generation antibody modeling. [Thesis]. Johns Hopkins University; 2015. Available from: http://jhir.library.jhu.edu/handle/1774.2/37835

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Cornell University

9. Tipton, William. Ab-Initio Materials Discovery And Characterization Through Energy Landscape Exploration With An Evolutionary Algorithm.

Degree: PhD, Materials Science and Engineering, 2014, Cornell University

URL: http://hdl.handle.net/1813/37141

► We present an evolutionary algorithm which predicts stable atomic structures and phase diagrams by searching the energy landscape of empirical and ab-initio Hamiltonians. Composition and… (more)

▼ We present an evolutionary algorithm which predicts stable atomic structures and phase diagrams by searching the energy landscape of empirical and ab-initio Hamiltonians. Composition and geometrical degrees of freedom may be varied simultaneously. We show that this method utilizes information from favorable local structure at one composition to predict that at others, achieving far greater efficiency of phase diagram prediction than a method which relies on sampling compositions individually. We detail this and a number of other efficiency-improving techniques implemented in the Genetic Algorithm for Structure Prediction (GASP) code that is now publicly available. Applications are presented in three categories. First, we predict phase diagrams of elemental barium and europium under pressure and show that our methodology compliments experimental studies of those systems. Second, we show that phase diagram prediction is a primary component of ab initio Li-ion battery electrode characterization. We present studies of the Li-Si and Li-Ge binary phase diagrams that allow us to determine the voltage characteristics of silicon and germanium battery anodes. We also predict the stability of previouslyunreported binary structures in both of those materials systems. Third, we use the method to test empirical energy models. It is important that such models reproduce the energy landscape of the true system they are meant to represent. The GASP code can verify this if it is so and find errenous structures to augment the fitting database if it is not. Our results suggest that genetic algorithm searches can be used to improve the methodology of empirical potential design. This thesis takes advantage of the Cornell graduate school's "papers" option. That is, it is primarily composed from the author's first-author publications, in particular, Refs. [149, 148, 152, 151]. Additionally, one of the pleasures of computational materials science research is that it has a synergistic relationship with experiment and lends itself to many fruitful collaborations. These provide insights and richer publications than would be possible by either path alone. Thus this thesis also describes applications of our methodology to collaborative works described in Refs. [17, 145, 114]. In these cases, I focus on my own contributions in this thesis and refer the reader to the original publications for the full picture. Advisors/Committee Members: Hennig, Richard G. (chair), Shoemaker, Christine Ann (committee member), Van Dover, Robert B. (committee member).

Subjects/Keywords: structure prediction; genetic algorithm; phase diagram

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Tipton, W. (2014). Ab-Initio Materials Discovery And Characterization Through Energy Landscape Exploration With An Evolutionary Algorithm. (Doctoral Dissertation). Cornell University. Retrieved from http://hdl.handle.net/1813/37141

Chicago Manual of Style (16^th Edition):

Tipton, William. “Ab-Initio Materials Discovery And Characterization Through Energy Landscape Exploration With An Evolutionary Algorithm.” 2014. Doctoral Dissertation, Cornell University. Accessed March 04, 2021. http://hdl.handle.net/1813/37141.

MLA Handbook (7^th Edition):

Tipton, William. “Ab-Initio Materials Discovery And Characterization Through Energy Landscape Exploration With An Evolutionary Algorithm.” 2014. Web. 04 Mar 2021.

Vancouver:

Tipton W. Ab-Initio Materials Discovery And Characterization Through Energy Landscape Exploration With An Evolutionary Algorithm. [Internet] [Doctoral dissertation]. Cornell University; 2014. [cited 2021 Mar 04]. Available from: http://hdl.handle.net/1813/37141.

Council of Science Editors:

Tipton W. Ab-Initio Materials Discovery And Characterization Through Energy Landscape Exploration With An Evolutionary Algorithm. [Doctoral Dissertation]. Cornell University; 2014. Available from: http://hdl.handle.net/1813/37141

Vanderbilt University

10. Putnam, Daniel Kent. BCL::SAS- Small Angle X-Ray / Neutron Scattering Profiles to Assist Protein Structure Prediction.

Degree: PhD, Biomedical Informatics, 2016, Vanderbilt University

URL: http://hdl.handle.net/1803/11634

► The Biochemical Library (BCL) is a protein structure prediction algorithm developed in the Meiler Lab at Vanderbilt University based on the placement of secondary structure… (more)

Subjects/Keywords: BCL::Fold; SAXS; Protein Structure Prediction

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Putnam, D. K. (2016). BCL::SAS- Small Angle X-Ray / Neutron Scattering Profiles to Assist Protein Structure Prediction. (Doctoral Dissertation). Vanderbilt University. Retrieved from http://hdl.handle.net/1803/11634

Chicago Manual of Style (16^th Edition):

Putnam, Daniel Kent. “BCL::SAS- Small Angle X-Ray / Neutron Scattering Profiles to Assist Protein Structure Prediction.” 2016. Doctoral Dissertation, Vanderbilt University. Accessed March 04, 2021. http://hdl.handle.net/1803/11634.

MLA Handbook (7^th Edition):

Putnam, Daniel Kent. “BCL::SAS- Small Angle X-Ray / Neutron Scattering Profiles to Assist Protein Structure Prediction.” 2016. Web. 04 Mar 2021.

Vancouver:

Putnam DK. BCL::SAS- Small Angle X-Ray / Neutron Scattering Profiles to Assist Protein Structure Prediction. [Internet] [Doctoral dissertation]. Vanderbilt University; 2016. [cited 2021 Mar 04]. Available from: http://hdl.handle.net/1803/11634.

Council of Science Editors:

Putnam DK. BCL::SAS- Small Angle X-Ray / Neutron Scattering Profiles to Assist Protein Structure Prediction. [Doctoral Dissertation]. Vanderbilt University; 2016. Available from: http://hdl.handle.net/1803/11634

University of Cambridge

11. Wynn, Jamie Michael. First-principles structure prediction of extreme nanowires.

Degree: PhD, 2018, University of Cambridge

URL: https://doi.org/10.17863/CAM.30362 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.763577

► Low-dimensional systems are an important and intensely studied area of condensed matter physics. When a material is forced to adopt a low-dimensional structure, its behaviour… (more)

▼ Low-dimensional systems are an important and intensely studied area of condensed matter physics. When a material is forced to adopt a low-dimensional structure, its behaviour is often dramatically different to that of the bulk phase. It is vital to predict the structures of low-dimensional systems in order to reliably predict their properties. To this end, the ab initio random structure searching (AIRSS) method, which has previously been used to identify the structures of bulk materials, has been extended to deal with the case of nanowires encapsulated inside carbon nanotubes. Such systems are a rapidly developing area of research with important nanotechnological applications, including information storage, energy storage and chemical sensing. The extended AIRSS method for encapsulated nanowires (ENWs) was implemented and used to identify the structures formed by germanium telluride, silver chloride, and molybdenum diselenide ENWs. In each of these cases, a number of novel nanowire structures were identified, and a phase diagram predicting the ground state nanowire structure as a function of the radius of the encapsulating nanotube was calculated. In the case of germanium telluride, which is a technologically important phase-change material, the potential use of GeTe ENWs as switchable nanoscale memory devices was investigated. The vibrational properties of silver chloride ENWs were also considered, and a novel scheme was developed to predict the Raman spectra of systems which can be decomposed into multiple weakly interacting subsystems. This scheme was used to obtain a close approximation to the Raman spectra of AgCl ENWs at a fraction of the computational cost that would otherwise be necessary. The encapsulation of AgCl was shown to produce substantial shifts in the Raman spectra of nanotubes, providing an important link with experiment. A method was developed to predict the stress-strain response of an ENW based on a polygonal representation of its surface, and was used to investigate the elastic response of molybdenum diselenide ENWs. This was used to predict stress-radius phase diagrams for MoSe_2 ENWs, and hence to investigate stress-induced phase change within such systems. The X-ray diffraction of ENWs was also considered. A program was written to simulate X-ray diffraction in low-dimensional systems, and was used to predict the diffraction patterns of some of the encapsulated GeTe nanowire structures predicted by AIRSS. By modelling the interactions within a bundle of nanotubes, diffraction patterns for bundles of ENWs were obtained.

Subjects/Keywords: 620; DFT; Nanowires; Structure prediction; Carbon nanotubes

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APA (6^th Edition):

Wynn, J. M. (2018). First-principles structure prediction of extreme nanowires. (Doctoral Dissertation). University of Cambridge. Retrieved from https://doi.org/10.17863/CAM.30362 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.763577

Chicago Manual of Style (16^th Edition):

Wynn, Jamie Michael. “First-principles structure prediction of extreme nanowires.” 2018. Doctoral Dissertation, University of Cambridge. Accessed March 04, 2021. https://doi.org/10.17863/CAM.30362 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.763577.

MLA Handbook (7^th Edition):

Wynn, Jamie Michael. “First-principles structure prediction of extreme nanowires.” 2018. Web. 04 Mar 2021.

Vancouver:

Wynn JM. First-principles structure prediction of extreme nanowires. [Internet] [Doctoral dissertation]. University of Cambridge; 2018. [cited 2021 Mar 04]. Available from: https://doi.org/10.17863/CAM.30362 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.763577.

Council of Science Editors:

Wynn JM. First-principles structure prediction of extreme nanowires. [Doctoral Dissertation]. University of Cambridge; 2018. Available from: https://doi.org/10.17863/CAM.30362 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.763577

Vanderbilt University

12. Fischer, Axel Walter. In silico prediction of protein structures and ensembles.

Degree: PhD, Chemistry, 2018, Vanderbilt University

URL: http://hdl.handle.net/1803/10422

► Determination of a protein’s structural equilibrium constitution remains a challenge. Experimental techniques like X-ray crystallography or nuclear magnetic resonance spectroscopy either are only able to… (more)

▼ Determination of a protein’s structural equilibrium constitution remains a challenge. Experimental techniques like X-ray crystallography or nuclear magnetic resonance spectroscopy either are only able to determine single snapshots of the protein or are not applicable due to the protein's size or dynamics. Orthogonal techniques like electron paramagnetic resonance (EPR) spectroscopy are able to capture all significant populations of the protein but the obtainable data are typically too sparse to unambiguously determine a structural ensemble. Computational methods on the other hand, suffer from necessary simplifications of the structure sampling and free energy evaluation. In order to solve these existing problems, I developed a computational prediction pipeline for protein structures and ensembles that supports incorporation of limited experimental data from EPR spectroscopy and chemical cross-linking. The pipeline encompasses coarse-grained Monte Carlo Metropolis sampling using BCL::Fold, high-resolution refinement using Rosetta, and stability evaluations using molecular dynamics simulations. Novel methods were developed to incorporate the experimental data into the pipeline. Both types of experimental data significantly improved the average accuracy of the sampled models as well as the discrimination between accurate and inaccurate models. In addition, a novel loop sampling algorithm consisting of conformation hashing and cyclic coordinate descent was developed. The algorithm is substantially faster than other available algorithms and samples the conformation of the protein’s major population in 94 % of all cases. The developed methods were applied to determine the structure and dynamics of the Bcl-2-associated X protein (BAX), exotoxin U (ExoU), and the efflux-multidrug resistance protein (EmrE) in conjunction with structural data obtained through EPR spectroscopy. Advisors/Committee Members: Carlos F. Lopez (committee member), Terry P. Lybrand (committee member), Hassane S. Mchaourab (committee member), Michael P. Stone (committee member), Jens Meiler (Committee Chair).

Subjects/Keywords: de novo prediction; emre; protein modeling; epr; loop modeling; protein ensemble prediction; protein structure prediction

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APA (6^th Edition):

Fischer, A. W. (2018). In silico prediction of protein structures and ensembles. (Doctoral Dissertation). Vanderbilt University. Retrieved from http://hdl.handle.net/1803/10422

Chicago Manual of Style (16^th Edition):

Fischer, Axel Walter. “In silico prediction of protein structures and ensembles.” 2018. Doctoral Dissertation, Vanderbilt University. Accessed March 04, 2021. http://hdl.handle.net/1803/10422.

MLA Handbook (7^th Edition):

Fischer, Axel Walter. “In silico prediction of protein structures and ensembles.” 2018. Web. 04 Mar 2021.

Vancouver:

Fischer AW. In silico prediction of protein structures and ensembles. [Internet] [Doctoral dissertation]. Vanderbilt University; 2018. [cited 2021 Mar 04]. Available from: http://hdl.handle.net/1803/10422.

Council of Science Editors:

Fischer AW. In silico prediction of protein structures and ensembles. [Doctoral Dissertation]. Vanderbilt University; 2018. Available from: http://hdl.handle.net/1803/10422

13. Mukherjee, Srayanta. STRUCTURAL MODELING OF PROTEIN-PROTEIN INTERACTIONS USING MULTIPLE-CHAIN THREADING AND FRAGMENT ASSEMBLY.

Degree: PhD, Biochemistry & Molecular Biology, 2011, University of Kansas

URL: http://hdl.handle.net/1808/10694

► Since its birth, the study of protein structures has made progress with leaps and bounds. However, owing to the expenses and difficulties involved, the number… (more)

▼ Since its birth, the study of protein structures has made progress with leaps and bounds. However, owing to the expenses and difficulties involved, the number of protein structures has not been able to catch up with the number of protein sequences and in fact has steadily lost ground. This necessitated the development of high-throughput but accurate computational algorithms capable of predicting the three dimensional structure of proteins from its amino acid sequence. While progress has been made in the realm of protein tertiary structure prediction, the advancement in protein quaternary structure prediction has been limited by the fact that the degree of freedom for protein complexes is even larger and even fewer number of protein complex structures are present in the PDB library. In fact, protein complex structure prediction till date has largely remained a docking problem where automated algorithms aim to predict the protein complex structure starting from the unbound crystal structure of its component subunits and thus has remained largely limited in terms of scope. Secondly, since docking essentially treats the unbound subunits as "rigid-bodies" it has limited accuracy when conformational change accompanies protein-protein interaction. In one of the first of its kind effort, this study aims for the development of protein complex structure algorithms which require only the amino acid sequence of the interacting subunits as input. The study aimed to adapt the best features of protein tertiary structure prediction including template detection and ab initio loop modeling and extend it for protein-protein complexes thus requiring simultaneous modeling of the three dimensional structure of the component subunits as well as ensuring the correct orientation of the chains at the protein-protein interface. Essentially, the algorithms are dependent on knowledge-based statistical potentials for both fold recognition and structure modeling. First, as a way to compare known structure of protein-protein complexes, a complex structure alignment program MM-align was developed. MM-align joins the chains of the complex structures to be aligned to form artificial monomers in every possible order. It then aligns them using a heuristic dynamic programming based approach using TM-score as the objective function. However, the traditional NW dynamic programming was redesigned to prevent the cross alignment of chains during the structure alignment process. Driven by the knowledge obtained from MM-align that protein complex structures share evolutionary relationships and the current protein complex structure library already contains homologous/structurally analogous protein quaternary structure families, a dimeric threading approach, COTH was designed. The new threading-recombination approach boosts the protein complex structure library by combining tertiary structure templates with complex alignments. The query sequences are first aligned to complex templates using the modified dynamic programming algorithm, guided by a number of… Advisors/Committee Members: Vakser, Ilya (advisor), Zhang, Yang (advisor), Karanicolas, John (cmtemember), Deeds, Eric (cmtemember), Richter, Mark (cmtemember).

Subjects/Keywords: Bioinformatics; Ab intio prediction; Protein-protein interactions; Protein structure prediction; Threading

10 more images…

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APA (6^th Edition):

Mukherjee, S. (2011). STRUCTURAL MODELING OF PROTEIN-PROTEIN INTERACTIONS USING MULTIPLE-CHAIN THREADING AND FRAGMENT ASSEMBLY. (Doctoral Dissertation). University of Kansas. Retrieved from http://hdl.handle.net/1808/10694

Chicago Manual of Style (16^th Edition):

Mukherjee, Srayanta. “STRUCTURAL MODELING OF PROTEIN-PROTEIN INTERACTIONS USING MULTIPLE-CHAIN THREADING AND FRAGMENT ASSEMBLY.” 2011. Doctoral Dissertation, University of Kansas. Accessed March 04, 2021. http://hdl.handle.net/1808/10694.

MLA Handbook (7^th Edition):

Mukherjee, Srayanta. “STRUCTURAL MODELING OF PROTEIN-PROTEIN INTERACTIONS USING MULTIPLE-CHAIN THREADING AND FRAGMENT ASSEMBLY.” 2011. Web. 04 Mar 2021.

Vancouver:

Mukherjee S. STRUCTURAL MODELING OF PROTEIN-PROTEIN INTERACTIONS USING MULTIPLE-CHAIN THREADING AND FRAGMENT ASSEMBLY. [Internet] [Doctoral dissertation]. University of Kansas; 2011. [cited 2021 Mar 04]. Available from: http://hdl.handle.net/1808/10694.

Council of Science Editors:

Mukherjee S. STRUCTURAL MODELING OF PROTEIN-PROTEIN INTERACTIONS USING MULTIPLE-CHAIN THREADING AND FRAGMENT ASSEMBLY. [Doctoral Dissertation]. University of Kansas; 2011. Available from: http://hdl.handle.net/1808/10694

University of Rochester

14. Priore, Salvatore F. (1983 - ). Discovery and characterization of influenza virus RNA secondary structures.

Degree: PhD, 2013, University of Rochester

URL: http://hdl.handle.net/1802/27156

► PART I: Influenza virus is a significant public health threat, partially because of its capacity to readily exchange gene segments between different host species to… (more)

Subjects/Keywords: GORS; Influenza; RNA; Secondary structure; Structure prediction; Virology

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APA (6^th Edition):

Priore, S. F. (. -. ). (2013). Discovery and characterization of influenza virus RNA secondary structures. (Doctoral Dissertation). University of Rochester. Retrieved from http://hdl.handle.net/1802/27156

Chicago Manual of Style (16^th Edition):

Priore, Salvatore F (1983 - ). “Discovery and characterization of influenza virus RNA secondary structures.” 2013. Doctoral Dissertation, University of Rochester. Accessed March 04, 2021. http://hdl.handle.net/1802/27156.

MLA Handbook (7^th Edition):

Priore, Salvatore F (1983 - ). “Discovery and characterization of influenza virus RNA secondary structures.” 2013. Web. 04 Mar 2021.

Vancouver:

Priore SF(-). Discovery and characterization of influenza virus RNA secondary structures. [Internet] [Doctoral dissertation]. University of Rochester; 2013. [cited 2021 Mar 04]. Available from: http://hdl.handle.net/1802/27156.

Council of Science Editors:

Priore SF(-). Discovery and characterization of influenza virus RNA secondary structures. [Doctoral Dissertation]. University of Rochester; 2013. Available from: http://hdl.handle.net/1802/27156

University of Rochester

15. Sloma, Michael F. Computational Tools for RNA Structure Prediction.

Degree: PhD, 2018, University of Rochester

URL: http://hdl.handle.net/1802/33903

► RNA is a versatile biomolecule that functions in many cellular processes. In addition to acting as a template for protein synthesis, RNA plays a direct… (more)

Subjects/Keywords: RNA; Secondary structure; Structure prediction; Bioinformatics; Computational biology; Structural biology.

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APA (6^th Edition):

Sloma, M. F. (2018). Computational Tools for RNA Structure Prediction. (Doctoral Dissertation). University of Rochester. Retrieved from http://hdl.handle.net/1802/33903

Chicago Manual of Style (16^th Edition):

Sloma, Michael F. “Computational Tools for RNA Structure Prediction.” 2018. Doctoral Dissertation, University of Rochester. Accessed March 04, 2021. http://hdl.handle.net/1802/33903.

MLA Handbook (7^th Edition):

Sloma, Michael F. “Computational Tools for RNA Structure Prediction.” 2018. Web. 04 Mar 2021.

Vancouver:

Sloma MF. Computational Tools for RNA Structure Prediction. [Internet] [Doctoral dissertation]. University of Rochester; 2018. [cited 2021 Mar 04]. Available from: http://hdl.handle.net/1802/33903.

Council of Science Editors:

Sloma MF. Computational Tools for RNA Structure Prediction. [Doctoral Dissertation]. University of Rochester; 2018. Available from: http://hdl.handle.net/1802/33903

University of Missouri – Columbia

16. Alazmi, Meshari Saud. Protein structural models selection using 4-mer sequence and combined single and consensus scores.

Degree: 2012, University of Missouri – Columbia

URL: http://hdl.handle.net/10355/15238

► Quality assessment for protein structure models is an important issue in protein structure prediction. Consensus methods assess each model based on its structural similarity to… (more)

Subjects/Keywords: 4-mer sequence; protein structure prediction; protein structure model

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APA (6^th Edition):

Alazmi, M. S. (2012). Protein structural models selection using 4-mer sequence and combined single and consensus scores. (Thesis). University of Missouri – Columbia. Retrieved from http://hdl.handle.net/10355/15238

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Alazmi, Meshari Saud. “Protein structural models selection using 4-mer sequence and combined single and consensus scores.” 2012. Thesis, University of Missouri – Columbia. Accessed March 04, 2021. http://hdl.handle.net/10355/15238.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Alazmi, Meshari Saud. “Protein structural models selection using 4-mer sequence and combined single and consensus scores.” 2012. Web. 04 Mar 2021.

Vancouver:

Alazmi MS. Protein structural models selection using 4-mer sequence and combined single and consensus scores. [Internet] [Thesis]. University of Missouri – Columbia; 2012. [cited 2021 Mar 04]. Available from: http://hdl.handle.net/10355/15238.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Alazmi MS. Protein structural models selection using 4-mer sequence and combined single and consensus scores. [Thesis]. University of Missouri – Columbia; 2012. Available from: http://hdl.handle.net/10355/15238

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Duquesne University

17. Brunetta, Carl David. Structure Determination by X-Ray Diffraction Methods and Physicochemical Characterization of Quaternary Diamond-Like Semiconductors.

Degree: PhD, Chemistry and Biochemistry, 2013, Duquesne University

URL: https://dsc.duq.edu/etd/359

► Diamond-like semiconductors (DLSs) are a class of semiconductor materials having structures similar to that of either cubic or hexagonal diamond. These normal valence compounds are… (more)

▼ Diamond-like semiconductors (DLSs) are a class of semiconductor materials having structures similar to that of either cubic or hexagonal diamond. These normal valence compounds are of interest for their wide variety of technologically useful properties that can be tuned for specific applications. Until recently, DLS research has been focused on binary and ternary compositions due to their relative ease of synthesis. However, quaternary DLSs have gained considerable popularity due to their increased compositional flexibility and their potential as multifunctional materials. Despite their growing reputation, the vast number of possible combinations and conceivable solid solutions, DLSs remain fairly unexplored. This work focuses on quaternary DLSs of the formula Ag2-II-IV-S4 in order to advance the knowledge of structure-property relationships for this entire class of materials. Toward this goal, a more complete understanding of the crystal structures of these materials is necessary. This task is often problematic due to the presence of isoelectronic, or nearly isoelectonic elements, that can complicate X-ray structure refinements. In this work, Ag2CdGeS4 is used as a case study to demonstrate that this problem can be resolved with careful consideration of bonding environments as well as the use of high-resolution X-ray sources. For the novel DLS Ag2ZnSiS4, the relationship between the structure and optical properties is probed with the combination of single crystal X-ray diffraction, optical diffuse reflectance spectroscopy and electronic structure calculations using the software package Wien2k. Finally, the current set of predictive tools employed to forcast diamond-like structures are reviewed, including the adherence of these guidelines to the novel compound Ag2FeSiS4 as well all over 60 ternary and quaternary diamond-like materials currently reported in the literature. Furthermore, the most common radii sets used for the prediction of bond distance and cell parameters in these materials are compared to the observed bond distances in quaternary diamond-like nonoxide materials. Advisors/Committee Members: Jennifer Aitken, Ellen Gawalt, Jeffry Madura, Ralph Wheeler, Peter Wildfong.

Subjects/Keywords: Chalcogenide; Crystal structure; Diamond-Like; Electronic structure; Prediction; Semiconductor

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APA (6^th Edition):

Brunetta, C. D. (2013). Structure Determination by X-Ray Diffraction Methods and Physicochemical Characterization of Quaternary Diamond-Like Semiconductors. (Doctoral Dissertation). Duquesne University. Retrieved from https://dsc.duq.edu/etd/359

Chicago Manual of Style (16^th Edition):

Brunetta, Carl David. “Structure Determination by X-Ray Diffraction Methods and Physicochemical Characterization of Quaternary Diamond-Like Semiconductors.” 2013. Doctoral Dissertation, Duquesne University. Accessed March 04, 2021. https://dsc.duq.edu/etd/359.

MLA Handbook (7^th Edition):

Brunetta, Carl David. “Structure Determination by X-Ray Diffraction Methods and Physicochemical Characterization of Quaternary Diamond-Like Semiconductors.” 2013. Web. 04 Mar 2021.

Vancouver:

Brunetta CD. Structure Determination by X-Ray Diffraction Methods and Physicochemical Characterization of Quaternary Diamond-Like Semiconductors. [Internet] [Doctoral dissertation]. Duquesne University; 2013. [cited 2021 Mar 04]. Available from: https://dsc.duq.edu/etd/359.

Council of Science Editors:

Brunetta CD. Structure Determination by X-Ray Diffraction Methods and Physicochemical Characterization of Quaternary Diamond-Like Semiconductors. [Doctoral Dissertation]. Duquesne University; 2013. Available from: https://dsc.duq.edu/etd/359

University of Missouri – Columbia

18. Eickholt, Jesse. Predicting protein residue-residue contacts and disorder.

Degree: 2013, University of Missouri – Columbia

URL: https://doi.org/10.32469/10355/37829

► [ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI AT AUTHOR'S REQUEST.] Predicting a protein's three dimensional structure from its corresponding sequence has long been an extremely… (more)

Subjects/Keywords: protein structure; structure prediction; protein disorder; deep learning

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APA (6^th Edition):

Eickholt, J. (2013). Predicting protein residue-residue contacts and disorder. (Thesis). University of Missouri – Columbia. Retrieved from https://doi.org/10.32469/10355/37829

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Eickholt, Jesse. “Predicting protein residue-residue contacts and disorder.” 2013. Thesis, University of Missouri – Columbia. Accessed March 04, 2021. https://doi.org/10.32469/10355/37829.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Eickholt, Jesse. “Predicting protein residue-residue contacts and disorder.” 2013. Web. 04 Mar 2021.

Vancouver:

Eickholt J. Predicting protein residue-residue contacts and disorder. [Internet] [Thesis]. University of Missouri – Columbia; 2013. [cited 2021 Mar 04]. Available from: https://doi.org/10.32469/10355/37829.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Eickholt J. Predicting protein residue-residue contacts and disorder. [Thesis]. University of Missouri – Columbia; 2013. Available from: https://doi.org/10.32469/10355/37829

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Vanderbilt University

19. -8010-1279. Doctorate of Philosophy.

Degree: PhD, Chemical & Physical Biology, 2020, Vanderbilt University

URL: http://hdl.handle.net/1803/10124

► Bioinformatic and epitope mapping approaches have been successful, but are reactive, in determining the mutation preferences and commonly targeted B-cell epitopes of viral fusion proteins.… (more)

Subjects/Keywords: Protein Structure Prediction; multi-state design; contact distance changes; viral fusion; mutation tolerance; epitope prediction

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APA (6^th Edition):

-8010-1279. (2020). Doctorate of Philosophy. (Doctoral Dissertation). Vanderbilt University. Retrieved from http://hdl.handle.net/1803/10124

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

Chicago Manual of Style (16^th Edition):

-8010-1279. “Doctorate of Philosophy.” 2020. Doctoral Dissertation, Vanderbilt University. Accessed March 04, 2021. http://hdl.handle.net/1803/10124.

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

MLA Handbook (7^th Edition):

-8010-1279. “Doctorate of Philosophy.” 2020. Web. 04 Mar 2021.

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

Vancouver:

-8010-1279. Doctorate of Philosophy. [Internet] [Doctoral dissertation]. Vanderbilt University; 2020. [cited 2021 Mar 04]. Available from: http://hdl.handle.net/1803/10124.

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

Council of Science Editors:

-8010-1279. Doctorate of Philosophy. [Doctoral Dissertation]. Vanderbilt University; 2020. Available from: http://hdl.handle.net/1803/10124

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

KTH

20. Renhuldt, Nikos Tsardakas. Protein contact prediction based on the Tiramisu deep learning architecture.

Degree: Electrical Engineering and Computer Science (EECS), 2018, KTH

URL: http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-231494

►

Experimentally determining protein structure is a hard problem, with applications in both medicine and industry. Predicting protein structure is also difficult. Predicted contacts between… (more)

▼

Experimentally determining protein structure is a hard problem, with applications in both medicine and industry. Predicting protein structure is also difficult. Predicted contacts between residues within a protein is helpful during protein structure prediction. Recent state-of-the-art models have used deep learning to improve protein contact prediction. This thesis presents a new deep learning model for protein contact prediction, TiramiProt. It is based on the Tiramisu deep learning architecture, and trained and evaluated on the same data as the PconsC4 protein contact prediction model. 228 models using different combinations of hyperparameters were trained until convergence. The final TiramiProt model performs on par with two current state-of-the-art protein contact prediction models, PconsC4 and RaptorX-Contact, across a range of different metrics. A Python package and a Singularity container for running TiramiProt are available at https://gitlab.com/nikos.t.renhuldt/TiramiProt.

Att kunna bestämma proteiners struktur har tillämpningar inom både medicin och industri. Såväl experimentell bestämning av proteinstruktur som prediktion av densamma är svårt. Predicerad kontakt mellan olika delar av ett protein underlättar prediktion av proteinstruktur. Under senare tid har djupinlärning använts för att bygga bättre modeller för kontaktprediktion. Den här uppsatsen beskriver en ny djupinlärningsmodell för prediktion av proteinkontakter, TiramiProt. Modellen bygger på djupinlärningsarkitekturen Tiramisu. TiramiProt tränas och utvärderas på samma data som kontaktprediktionsmodellen PconsC4. Totalt tränades modeller med 228 olika hyperparameterkombinationer till konvergens. Mätt över ett flertal olika parametrar presterar den färdiga TiramiProt-modellen resultat i klass med state-of-the-art-modellerna PconsC4 och RaptorX-Contact. TiramiProt finns tillgängligt som ett Python-paket samt en Singularity-container via https://gitlab.com/nikos.t.renhuldt/TiramiProt.

Subjects/Keywords: protein contact prediction; contact prediction; protein structure; deep learning; machine learning; Computer Sciences; Datavetenskap (datalogi)

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APA (6^th Edition):

Renhuldt, N. T. (2018). Protein contact prediction based on the Tiramisu deep learning architecture. (Thesis). KTH. Retrieved from http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-231494

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Renhuldt, Nikos Tsardakas. “Protein contact prediction based on the Tiramisu deep learning architecture.” 2018. Thesis, KTH. Accessed March 04, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-231494.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Renhuldt, Nikos Tsardakas. “Protein contact prediction based on the Tiramisu deep learning architecture.” 2018. Web. 04 Mar 2021.

Vancouver:

Renhuldt NT. Protein contact prediction based on the Tiramisu deep learning architecture. [Internet] [Thesis]. KTH; 2018. [cited 2021 Mar 04]. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-231494.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Renhuldt NT. Protein contact prediction based on the Tiramisu deep learning architecture. [Thesis]. KTH; 2018. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-231494

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

NSYSU

21. Cheng, Rei-Sing. Protein Structure Prediction Based on Secondary Structure Alignment.

Degree: Master, Computer Science and Engineering, 2003, NSYSU

URL: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0821103-204917

► Sequence alignment is a basic but powerful technique in molecular biology. Macromolecular sequences (DNA, RNA and protein sequences) can be aligned based on some criteria.… (more)

Subjects/Keywords: prediction; structure; alignment; protein; secondary structure

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Cheng, R. (2003). Protein Structure Prediction Based on Secondary Structure Alignment. (Thesis). NSYSU. Retrieved from http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0821103-204917

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Cheng, Rei-Sing. “Protein Structure Prediction Based on Secondary Structure Alignment.” 2003. Thesis, NSYSU. Accessed March 04, 2021. http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0821103-204917.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Cheng, Rei-Sing. “Protein Structure Prediction Based on Secondary Structure Alignment.” 2003. Web. 04 Mar 2021.

Vancouver:

Cheng R. Protein Structure Prediction Based on Secondary Structure Alignment. [Internet] [Thesis]. NSYSU; 2003. [cited 2021 Mar 04]. Available from: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0821103-204917.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Cheng R. Protein Structure Prediction Based on Secondary Structure Alignment. [Thesis]. NSYSU; 2003. Available from: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0821103-204917

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Vanderbilt University

22. Leman, Julia Koehler. Membrane protein structure determination using NMR spectroscopy and computational techniques.

Degree: PhD, Chemical and Physical Biology, 2012, Vanderbilt University

URL: http://hdl.handle.net/1803/12894

► Membrane protein structures are very difficult to determine by solution NMR since severe line-broadening obstructs the measurement of restraints. To alleviate this problem we describe… (more)

Subjects/Keywords: paramagnetic NMR restraints; protein structure prediction; protein structure determination; membrane proteins; NMR spectroscopy

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APA (6^th Edition):

Leman, J. K. (2012). Membrane protein structure determination using NMR spectroscopy and computational techniques. (Doctoral Dissertation). Vanderbilt University. Retrieved from http://hdl.handle.net/1803/12894

Chicago Manual of Style (16^th Edition):

Leman, Julia Koehler. “Membrane protein structure determination using NMR spectroscopy and computational techniques.” 2012. Doctoral Dissertation, Vanderbilt University. Accessed March 04, 2021. http://hdl.handle.net/1803/12894.

MLA Handbook (7^th Edition):

Leman, Julia Koehler. “Membrane protein structure determination using NMR spectroscopy and computational techniques.” 2012. Web. 04 Mar 2021.

Vancouver:

Leman JK. Membrane protein structure determination using NMR spectroscopy and computational techniques. [Internet] [Doctoral dissertation]. Vanderbilt University; 2012. [cited 2021 Mar 04]. Available from: http://hdl.handle.net/1803/12894.

Council of Science Editors:

Leman JK. Membrane protein structure determination using NMR spectroscopy and computational techniques. [Doctoral Dissertation]. Vanderbilt University; 2012. Available from: http://hdl.handle.net/1803/12894

Iowa State University

23. Zimmermann, Michael Thomas. Mechanistic insights on important biomolecules derived using simple dynamics models from extending the reach of elastic network modeling.

Degree: 2011, Iowa State University

URL: https://lib.dr.iastate.edu/etd/12546

► The dynamics of biomolecules are important for carrying out their biologic functions, but these remain difficult to probe in detail experimentally, so that their accurate… (more)

▼ The dynamics of biomolecules are important for carrying out their biologic functions, but these remain difficult to probe in detail experimentally, so that their accurate computational evaluation is an important field of ongoing study. Critical questions remain open such as what are the importance of individual interactions within a structure, the composition of denatured states and equilibrium native ensembles, as well as the role and conservation of flexibility in functional dynamics. The tools of Molecular Dynamics, Monte Carlo simulation, and Normal Mode Analysis coupled with knowledge-based approaches represent the mainstay of computational approaches used in this field. The primary focus of this dissertation is to explore the functional dynamics of important biomolecules while extending the utility of Normal Mode Analysis using Elastic Network Models through the application of novel analysis methods. Many of these techniques have been made available to the scientific community through the software tool MAVEN which integrates and automates many of the steps in model building and analysis. By utilizing these tools, we have discerned structural dynamics characteristics and mechanistic behaviors of antibodies, ribosomes, telomerase, and efflux systems. Modes from multiple Anisotropic Network Models capture collective as well as local motions which accurately describe a large set of experimental tRNA structures. Mechanistic understanding of biomolecular motion can aid in the understanding of physiology, disease states, and our ability to engineer new structures with novel functions. The ability to distinguish native-like structures from a set of computational predictions is important not only in structure prediction, but also in molecular docking and for predicting conformational changes. We propose a new algorithm for evaluating the entropy of motion of biomolecules, showing that it leads to enhanced discrimination between native-like and non-native-like models in both structure predictions and protein-protein docking. Our findings indicate that the shape of a protein or complex contains sufficient information to distinguish it from poorer quality predictions. Graph theoretical approaches have also been employed to investigate the connectedness of the protein structure universe, showing that the modularity of protein domain architecture is of fundamental importance for future improvements in structure matching. All of the studies herein impact our understanding of protein domain evolution and modification.

Subjects/Keywords: Elastic Netowrk Modeling; Molecular Function; Molecular Motion; Protein Structure; Structural Biology; Structure Prediction; Bioinformatics

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APA (6^th Edition):

Zimmermann, M. T. (2011). Mechanistic insights on important biomolecules derived using simple dynamics models from extending the reach of elastic network modeling. (Thesis). Iowa State University. Retrieved from https://lib.dr.iastate.edu/etd/12546

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Zimmermann, Michael Thomas. “Mechanistic insights on important biomolecules derived using simple dynamics models from extending the reach of elastic network modeling.” 2011. Thesis, Iowa State University. Accessed March 04, 2021. https://lib.dr.iastate.edu/etd/12546.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Zimmermann, Michael Thomas. “Mechanistic insights on important biomolecules derived using simple dynamics models from extending the reach of elastic network modeling.” 2011. Web. 04 Mar 2021.

Vancouver:

Zimmermann MT. Mechanistic insights on important biomolecules derived using simple dynamics models from extending the reach of elastic network modeling. [Internet] [Thesis]. Iowa State University; 2011. [cited 2021 Mar 04]. Available from: https://lib.dr.iastate.edu/etd/12546.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Zimmermann MT. Mechanistic insights on important biomolecules derived using simple dynamics models from extending the reach of elastic network modeling. [Thesis]. Iowa State University; 2011. Available from: https://lib.dr.iastate.edu/etd/12546

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

New Jersey Institute of Technology

24. Wen, Dongrong. Design and implementation of a cyberinfrastructure for RNA motif search, prediction and analysis.

Degree: PhD, Computer Science, 2012, New Jersey Institute of Technology

URL: https://digitalcommons.njit.edu/dissertations/335

► RNA secondary and tertiary structure motifs play important roles in cells. However, very few web servers are available for RNA motif search and prediction.… (more)

▼ RNA secondary and tertiary structure motifs play important roles in cells. However, very few web servers are available for RNA motif search and prediction. In this dissertation, a cyberinfrastructure, named RNAcyber, capable of performing RNA motif search and prediction, is proposed, designed and implemented. The first component of RNAcyber is a web-based search engine, named RmotifDB. This web-based tool integrates an RNA secondary structure comparison algorithm with the secondary structure motifs stored in the Rfam database. With a user-friendly interface, RmotifDB provides the ability to search for ncRNA structure motifs in both structural and sequential ways. The second component of RNAcyber is an enhanced version of RmotifDB. This enhanced version combines data from multiple sources, incorporates a variety of well-established structure-based search methods, and is integrated with the Gene Ontology. To display RmotifDB’s search results, a software tool, called RSview, is developed. RSview is able to display the search results in a graphical manner. Finally, RNAcyber contains a web-based tool called Junction-Explorer, which employs a data mining method for predicting tertiary motifs in RNA junctions. Specifically, the tool is trained on solved RNA tertiary structures obtained from the Protein Data Bank, and is able to predict the configuration of coaxial helical stacks and families (topologies) in RNA junctions at the secondary structure level. Junction-Explorer employs several algorithms for motif prediction, including a random forest classification algorithm, a pseudoknot removal algorithm, and a feature ranking algorithm based on the gini impurity measure. A series of experiments including 10-fold cross- validation has been conducted to evaluate the performance of the Junction-Explorer tool. Experimental results demonstrate the effectiveness of the proposed algorithms and the superiority of the tool over existing methods. The RNAcyber infrastructure is fully operational, with all of its components accessible on the Internet. Advisors/Committee Members: Jason T. L. Wang, James A. McHugh, David Nassimi.

Subjects/Keywords: RNA motif; RNA motif search; Secondary structure; Motif prediction; Tertiary structure; Computer Sciences

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APA (6^th Edition):

Wen, D. (2012). Design and implementation of a cyberinfrastructure for RNA motif search, prediction and analysis. (Doctoral Dissertation). New Jersey Institute of Technology. Retrieved from https://digitalcommons.njit.edu/dissertations/335

Chicago Manual of Style (16^th Edition):

Wen, Dongrong. “Design and implementation of a cyberinfrastructure for RNA motif search, prediction and analysis.” 2012. Doctoral Dissertation, New Jersey Institute of Technology. Accessed March 04, 2021. https://digitalcommons.njit.edu/dissertations/335.

MLA Handbook (7^th Edition):

Wen, Dongrong. “Design and implementation of a cyberinfrastructure for RNA motif search, prediction and analysis.” 2012. Web. 04 Mar 2021.

Vancouver:

Wen D. Design and implementation of a cyberinfrastructure for RNA motif search, prediction and analysis. [Internet] [Doctoral dissertation]. New Jersey Institute of Technology; 2012. [cited 2021 Mar 04]. Available from: https://digitalcommons.njit.edu/dissertations/335.

Council of Science Editors:

Wen D. Design and implementation of a cyberinfrastructure for RNA motif search, prediction and analysis. [Doctoral Dissertation]. New Jersey Institute of Technology; 2012. Available from: https://digitalcommons.njit.edu/dissertations/335

University of Georgia

25. Takata, Mika. Analyses for protein tertiary structure prediction.

Degree: 2014, University of Georgia

URL: http://hdl.handle.net/10724/28133

► Protein fold classification is essential to recognition of protein tertiary structure. It is of particular interest to the structure analyses of proteins of low sequence… (more)

Subjects/Keywords: Protein; Secondary structure; Protein threading; Structure prediction; Sequence; Alpha-helix; Beta-strand; Coil; Interaction; Visualization

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APA (6^th Edition):

Takata, M. (2014). Analyses for protein tertiary structure prediction. (Thesis). University of Georgia. Retrieved from http://hdl.handle.net/10724/28133

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Takata, Mika. “Analyses for protein tertiary structure prediction.” 2014. Thesis, University of Georgia. Accessed March 04, 2021. http://hdl.handle.net/10724/28133.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Takata, Mika. “Analyses for protein tertiary structure prediction.” 2014. Web. 04 Mar 2021.

Vancouver:

Takata M. Analyses for protein tertiary structure prediction. [Internet] [Thesis]. University of Georgia; 2014. [cited 2021 Mar 04]. Available from: http://hdl.handle.net/10724/28133.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Takata M. Analyses for protein tertiary structure prediction. [Thesis]. University of Georgia; 2014. Available from: http://hdl.handle.net/10724/28133

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

University of Kansas

26. Roy, Ambrish. Protein structure prediction and structure-based protein function annotation.

Degree: PhD, Biochemistry & Molecular Biology, 2011, University of Kansas

URL: http://hdl.handle.net/1808/10695

► Nature tends to modify rather than invent function of protein molecules, and the log of the modifications is encrypted in the gene sequence. Analysis of… (more)

▼ Nature tends to modify rather than invent function of protein molecules, and the log of the modifications is encrypted in the gene sequence. Analysis of these modification events in evolutionarily related genes is important for assigning function to hypothetical genes and their products surging in databases, and to improve our understanding of the bioverse. However, random mutations occurring during evolution chisel the sequence to an extent that both decrypting these codes and identifying evolutionary relatives from sequence alone becomes difficult. Thankfully, even after many changes at the sequence level, the protein three-dimensional structures are often conserved and hence protein structural similarity usually provide more clues on evolution of functionally related proteins. In this dissertation, I study the design of three bioinformatics modules that form a new hierarchical approach for structure prediction and function annotation of proteins based on sequence-to-structure-to-function paradigm. First, we design an online platform for structure prediction of protein molecules using multiple threading alignments and iterative structural assembly simulations (I-TASSER). I review the components of this module and have added features that provide function annotation to the protein sequences and help to combine experimental and biological data for improving the structure modeling accuracy. The online service of the system has been supporting more than 20,000 biologists from over 100 countries. Next, we design a new comparative approach (COFACTOR) to identify the location of ligand binding sites on these modeled protein structures and spot the functional residue constellations using an innovative global-to-local structural alignment procedure and functional sites in known protein structures. Based on both large-scale benchmarking and blind tests (CASP), the method demonstrates significant advantages over the state-of-the- art methods of the field in recognizing ligand-binding residues for both metal and non- metal ligands. The major advantage of the method is the optimal combination of the local and global protein structural alignments, which helps to recognize functionally conserved structural motifs among proteins that have taken different evolutionary paths. We further extend the COFACTOR global-to-local approach to annotate the gene- ontology and enzyme classifications of protein molecules. Here, we added two new components to COFACTOR. First, we developed a new global structural match algorithm that allows performing better structural search. Second, a sensitive technique was proposed for constructing local 3D-signature motifs of template proteins that lack known functional sites, which allows us to perform query-template local structural similarity comparisons with all template proteins. A scoring scheme that combines the confidence score of structure prediction with global-local similarity score is used for assigning a confidence score to each of the predicted function. Large scale benchmarking shows that the… Advisors/Committee Members: Zhang, Yang (advisor), Karanicolas, John (cmtemember), Vakser, Ilya (cmtemember), Deeds, Eric J (cmtemember), Richter, Mark L (cmtemember).

Subjects/Keywords: Bioinformatics; Biophysics; Biochemistry; Local structure comparison; Protein function annotation; Protein-ligand binding; Protein structure prediction

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APA (6^th Edition):

Roy, A. (2011). Protein structure prediction and structure-based protein function annotation. (Doctoral Dissertation). University of Kansas. Retrieved from http://hdl.handle.net/1808/10695

Chicago Manual of Style (16^th Edition):

Roy, Ambrish. “Protein structure prediction and structure-based protein function annotation.” 2011. Doctoral Dissertation, University of Kansas. Accessed March 04, 2021. http://hdl.handle.net/1808/10695.

MLA Handbook (7^th Edition):

Roy, Ambrish. “Protein structure prediction and structure-based protein function annotation.” 2011. Web. 04 Mar 2021.

Vancouver:

Roy A. Protein structure prediction and structure-based protein function annotation. [Internet] [Doctoral dissertation]. University of Kansas; 2011. [cited 2021 Mar 04]. Available from: http://hdl.handle.net/1808/10695.

Council of Science Editors:

Roy A. Protein structure prediction and structure-based protein function annotation. [Doctoral Dissertation]. University of Kansas; 2011. Available from: http://hdl.handle.net/1808/10695

NSYSU

27. Liu, Chu-Kai. Prediction for the Domain of RNA with Support Vector Machine.

Degree: Master, Computer Science and Engineering, 2011, NSYSU

URL: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0901111-194100

► The three-domain system is a biological classification of RNA. In bioinformatics, predicting the domain of RNA is helpful in the research of DNA and protein.… (more)

Subjects/Keywords: secondary structure; SVM; prediction; RNA; three-domain system

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APA (6^th Edition):

Liu, C. (2011). Prediction for the Domain of RNA with Support Vector Machine. (Thesis). NSYSU. Retrieved from http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0901111-194100

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Liu, Chu-Kai. “Prediction for the Domain of RNA with Support Vector Machine.” 2011. Thesis, NSYSU. Accessed March 04, 2021. http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0901111-194100.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Liu, Chu-Kai. “Prediction for the Domain of RNA with Support Vector Machine.” 2011. Web. 04 Mar 2021.

Vancouver:

Liu C. Prediction for the Domain of RNA with Support Vector Machine. [Internet] [Thesis]. NSYSU; 2011. [cited 2021 Mar 04]. Available from: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0901111-194100.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Liu C. Prediction for the Domain of RNA with Support Vector Machine. [Thesis]. NSYSU; 2011. Available from: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0901111-194100

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

University of Rochester

28. Xu, Zhenjiang. Non-Coding RNA: From Structure Prediction to Discovery in Genomes.

Degree: PhD, 2013, University of Rochester

URL: http://hdl.handle.net/1802/26787

► RNA plays remarkably diverse roles in organisms, such as maintaining telomeres, regulating gene expression, and catalyzing reactions. With current techniques, it is often slow and… (more)

▼ RNA plays remarkably diverse roles in organisms, such as maintaining telomeres, regulating gene expression, and catalyzing reactions. With current techniques, it is often slow and expensive to solve the majority of those RNA structures experimentally. Thus computational RNA analysis remains an attractive tool. RNA secondary structure, the sum of canonical base pairs (A-U, G-U and G-C), can be predicted by free energy minimization using a nearest neighbor model. The prediction accuracy, however, is limited. Dynalign improves prediction by finding conserved structures of two homologous RNA sequences. A novel algorithm, Multilign, was developed to compute conserved structures for more than two sequences progressively using multiple Dynalign calculations. It keeps base pairs in low free energy structures predicted by all the Dynalign calculations and removes false competing base pairs. The benchmark on various RNA families showed that Multilign performs better than Dynalign. Traditionally, the averages of structure prediction accuracies are tabulated to compare the performance of different RNA secondary prediction algorithms without statistical testing. It was demonstrated here that the prediction accuracies of methods correlate with each other. The paired two-sample t-test was introduced to rigorously evaluate whether one method outperforms another. A pipeline of statistical analyses was proposed to guide the choice of data set size and performance assessment for benchmarks. Functional RNA motifs tend to have stable and conserved secondary structure and can be identified from genomes with algorithms derived from secondary structure prediction. The Streptomyces coelicolor genome was scanned using a Dynalign-based method to search ncRNA. The prediction result was compared with the results from RNAz (another program finding RNA genes) and 454 sequencing data, showing the three sets of data overlap little. Untranslated regions of human hypoxia-related genes were also scanned and many candidates of conserved, structural cis-regulatory motifs were found. Collaborating with Butler Lab, bioinformatics analysis was performed on RNA deep sequencing data generated from four S. cerevisiae genotypes, BY4724, rrp6-Δ , air1-Δ rrp6-Δ and air2-Δ rrp6-Δ to study the RNA exosome substrate specificity. The differentially expressed genes were identified in these genotypes, revealing that Air1p and Air2p convey substrate specificities during RNA degradation.

Subjects/Keywords: RNA; Secondary Structure; Prediction; Non-Coding RNA; Genomes

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Xu, Z. (2013). Non-Coding RNA: From Structure Prediction to Discovery in Genomes. (Doctoral Dissertation). University of Rochester. Retrieved from http://hdl.handle.net/1802/26787

Chicago Manual of Style (16^th Edition):

Xu, Zhenjiang. “Non-Coding RNA: From Structure Prediction to Discovery in Genomes.” 2013. Doctoral Dissertation, University of Rochester. Accessed March 04, 2021. http://hdl.handle.net/1802/26787.

MLA Handbook (7^th Edition):

Xu, Zhenjiang. “Non-Coding RNA: From Structure Prediction to Discovery in Genomes.” 2013. Web. 04 Mar 2021.

Vancouver:

Xu Z. Non-Coding RNA: From Structure Prediction to Discovery in Genomes. [Internet] [Doctoral dissertation]. University of Rochester; 2013. [cited 2021 Mar 04]. Available from: http://hdl.handle.net/1802/26787.

Council of Science Editors:

Xu Z. Non-Coding RNA: From Structure Prediction to Discovery in Genomes. [Doctoral Dissertation]. University of Rochester; 2013. Available from: http://hdl.handle.net/1802/26787

San Jose State University

29. Kicinski, Maciej. AB INITIO PROTEIN STRUCTURE PREDICTION ALGORITHMS.

Degree: MS, Computer Science, 2011, San Jose State University

URL: https://doi.org/10.31979/etd.dycd-k9fd ; https://scholarworks.sjsu.edu/etd_projects/165

► Genes that encode novel proteins are constantly being discovered and added to databases, but the speed with which their structures are being determined is… (more)

Subjects/Keywords: protein structure prediction hidden markov model; Bioinformatics; Other Computer Sciences

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APA (6^th Edition):

Kicinski, M. (2011). AB INITIO PROTEIN STRUCTURE PREDICTION ALGORITHMS. (Masters Thesis). San Jose State University. Retrieved from https://doi.org/10.31979/etd.dycd-k9fd ; https://scholarworks.sjsu.edu/etd_projects/165

Chicago Manual of Style (16^th Edition):

Kicinski, Maciej. “AB INITIO PROTEIN STRUCTURE PREDICTION ALGORITHMS.” 2011. Masters Thesis, San Jose State University. Accessed March 04, 2021. https://doi.org/10.31979/etd.dycd-k9fd ; https://scholarworks.sjsu.edu/etd_projects/165.

MLA Handbook (7^th Edition):

Kicinski, Maciej. “AB INITIO PROTEIN STRUCTURE PREDICTION ALGORITHMS.” 2011. Web. 04 Mar 2021.

Vancouver:

Kicinski M. AB INITIO PROTEIN STRUCTURE PREDICTION ALGORITHMS. [Internet] [Masters thesis]. San Jose State University; 2011. [cited 2021 Mar 04]. Available from: https://doi.org/10.31979/etd.dycd-k9fd ; https://scholarworks.sjsu.edu/etd_projects/165.

Council of Science Editors:

Kicinski M. AB INITIO PROTEIN STRUCTURE PREDICTION ALGORITHMS. [Masters Thesis]. San Jose State University; 2011. Available from: https://doi.org/10.31979/etd.dycd-k9fd ; https://scholarworks.sjsu.edu/etd_projects/165

San Jose State University

30. Mali, Meenakshee. RNA SECONDARY STRUCTURE PREDICTION TOOL.

Degree: MS, Computer Science, 2011, San Jose State University

URL: https://doi.org/10.31979/etd.v9y6-uzac ; https://scholarworks.sjsu.edu/etd_projects/164

► Ribonucleic Acid (RNA) is one of the major macromolecules essential to all forms of life. Apart from the important role played in protein synthesis,… (more)

Subjects/Keywords: RNA Secondary Structure Prediction; Bioinformatics; Other Computer Sciences

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APA (6^th Edition):

Mali, M. (2011). RNA SECONDARY STRUCTURE PREDICTION TOOL. (Masters Thesis). San Jose State University. Retrieved from https://doi.org/10.31979/etd.v9y6-uzac ; https://scholarworks.sjsu.edu/etd_projects/164

Chicago Manual of Style (16^th Edition):

Mali, Meenakshee. “RNA SECONDARY STRUCTURE PREDICTION TOOL.” 2011. Masters Thesis, San Jose State University. Accessed March 04, 2021. https://doi.org/10.31979/etd.v9y6-uzac ; https://scholarworks.sjsu.edu/etd_projects/164.

MLA Handbook (7^th Edition):

Mali, Meenakshee. “RNA SECONDARY STRUCTURE PREDICTION TOOL.” 2011. Web. 04 Mar 2021.

Vancouver:

Mali M. RNA SECONDARY STRUCTURE PREDICTION TOOL. [Internet] [Masters thesis]. San Jose State University; 2011. [cited 2021 Mar 04]. Available from: https://doi.org/10.31979/etd.v9y6-uzac ; https://scholarworks.sjsu.edu/etd_projects/164.

Council of Science Editors:

Mali M. RNA SECONDARY STRUCTURE PREDICTION TOOL. [Masters Thesis]. San Jose State University; 2011. Available from: https://doi.org/10.31979/etd.v9y6-uzac ; https://scholarworks.sjsu.edu/etd_projects/164

Open Access Theses and Dissertations