Members of the C-type lectin domain (CTLD) superfamily are metazoan proteins functionally important in glycoprotein metabolism, mechanisms of multicellular integration and immunity. This thesis presents the results of several computational and experimental studies of the CTLD structure, function and evolution.¶ Core structural properties of the CTLD fold were explored in a comparative analysis of the 37 distinct CTLD structures available publicly, which demonstrate significant structural conservation despite low or undetectable sequence similarity. Pairwise structural alignments of all CTLD structures were created with three different methods (DALI, CE and LOCK) and analysed manually and using a computational algorithm developed for this purpose. The analysis revealed a set of conserved positions and interactions, which were classified based on their role in CTLD structure maintenance.¶ The CTLD family is large and diverse. To organize and annotate the several thousand of known CTLD-containing protein sequences and integrate the information on their evolution, structure and function a local database and a web-based interface to it were developed. The software is written in Perl, is based on bioperl, bioperl-db and Apache::ASP modules, and can be used for collaborative annotation of any collection of phylogenetically related sequences.¶ Several studies of CTLD genomics were performed. In one such study, carried out in collaboration with the RIKEN structural genomics centre, CTLD sequences from the Caenorhabditis elegans genome were identified and clustered into groups based on similarity. The most representative members of the groups were then selected, which if characterized structurally would tell most about the C. elegans CTLDs and provide templates for homology modelling of all C. elegans CTLD structures.¶ In the other whole-genome study, the CTLD family in the puffer fish Fugu rubripes was analysed using the draft genome sequence. This work extended and complemented three genome-level surveys on human, C. elegans and D. melanogaster reported previously. The study showed that the CTLD repertoire of Fugu rubripes is very similar to that of mammals, although several interesting differences exist, and that Fugu CTLD-encoding genes are selectively duplicated in a manner suggesting an ancient large-scale duplication event. Another important finding was the identification of several new CTLDcps, which had mammalian orthologues not recognized previously.¶ CBCP, a novel CTLD-containing protein highly conserved between fish and mammals with previously unknown domain architecture, was predicted in the Fugu study based solely on ab initio gene models from the Fugu locus and cross-species genomic DNA alignments. To test if the prediction was correct, a full-length cDNA of the mouse CBCP was cloned, its tissue distribution characterized and untranslated regions determined by RACE. The full-length mCBCP transcript is 10 kb long, encodes a protein of 2172 amino acids and confirms the original prediction. The presence of a large N-terminal NG2 domain makes CBCP a member of a small but very interesting family of Metazoan proteins.

Flavonoid glycosides are required for a number of crucial roles in planta and have the potential for development in a variety of agricultural, medicinal, and biotechnological applications. A number of recent advancements have been made in characterizing glycosyltransferases, the enzymes that are responsible for the synthesis of these important molecules. In this review, glycosyltransferases are considered with regard to biochemical properties, expression patterns, levels of enzyme activity during development, and structure/function relationships. This is presented with historical context to highlight critical findings, particularly with regard to the innovative work that has come from research on citrus species. The plant glycosyltransferase crystal structures that have been solved over the past decade, either alone or in complex with sugar donor and/or acceptor molecules, are discussed. The application of results from these structures to inform current structure/function work as well as implications and goals for future crystallography and tertiary modeling studies are considered. A thorough understanding of the properties of glycosyltransferases will be a critical step in any future biotechnological application of these enzymes in areas such as crop improvement and custom design of enzymes to produce desired compounds for nutritional and/or medicinal usage.

Indiana University-Purdue University Indianapolis (IUPUI) / Protein malfunction can cause human diseases, which makes the protein a target in the process of drug discovery. In-depth knowledge of how protein functions can widely contribute to the understanding of the mechanism of these diseases. Protein functions are determined by protein structures and their dynamic properties. Protein dynamics refers to the constant physical movement of atoms in a protein, which may result in the transition between different conformational states of the protein. These conformational transitions are critically important for the proteins to function. Understanding protein dynamics can help to understand and interfere with the conformational states and transitions, and thus with the function of the protein. If we can understand the mechanism of conformational transition of protein, we can design molecules to regulate this process and regulate the protein functions for new drug discovery. Protein Dynamics can be simulated by Molecular Dynamics (MD) Simulations. The MD simulation data generated are spatial-temporal and therefore very high dimensional. To analyze the data, distinguishing various atomic interactions within a protein by interpreting their 3D coordinate values plays a significant role. Since the data is humongous, the essential step is to find ways to interpret the data by generating more efficient algorithms to reduce the dimensionality and developing user-friendly visualization tools to find patterns and trends, which are not usually attainable by traditional methods of data process. The typical allosteric long-range nature of the interactions that lead to large conformational transition, pin-pointing the underlying forces and pathways responsible for the global conformational transition at atomic level is very challenging. To address the problems, Various analytical techniques are performed on the simulation data to better understand the mechanism of protein dynamics at atomic level by developing a new program called Probing Long-distance interactions by Tapping into Paired-Distances (PLITIP), which contains a set of new tools based on analysis of paired distances to remove the interference of the translation and rotation of the protein itself and therefore can capture the absolute changes within the protein. Firstly, we developed a tool called Decomposition of Paired Distances (DPD). This tool generates a distance matrix of all paired residues from our simulation data. This paired distance matrix therefore is not subjected to the interference of the translation or rotation of the protein and can capture the absolute changes within the protein. This matrix is then decomposed by DPD using Principal Component Analysis (PCA) to reduce dimensionality and to capture the largest structural variation. To showcase how DPD works, two protein systems, HIV-1 protease and 14-3-3 σ, that both have tremendous structural changes and conformational transitions as displayed by their MD simulation trajectories. The largest structural variation and conformational transition were captured by the first principal component in both cases. In addition, structural clustering and ranking of representative frames by their PC1 values revealed the long-distance nature of the conformational transition and locked the key candidate regions that might be responsible for the large conformational transitions. Secondly, to facilitate further analysis of identification of the long-distance path, a tool called Pearson Coefficient Spiral (PCP) that generates and visualizes Pearson Coefficient to measure the linear correlation between any two sets of residue pairs is developed. PCP allows users to fix one residue pair and examine the correlation of its change with other residue pairs. Thirdly, a set of visualization tools that generate paired atomic distances for the shortlisted candidate residue and captured significant interactions among them were developed. The first tool is the Residue Interaction Network Graph for Paired Atomic Distances (NG-PAD), which not only generates paired atomic distances for the shortlisted candidate residues, but also display significant interactions by a Network Graph for convenient visualization. Second, the Chord Diagram for Interaction Mapping (CD-IP) was developed to map the interactions to protein secondary structural elements and to further narrow down important interactions. Third, a Distance Plotting for Direct Comparison (DP-DC), which plots any two paired distances at user’s choice, either at residue or atomic level, to facilitate identification of similar or opposite pattern change of distances along the simulation time. All the above tools of PLITIP enabled us to identify critical residues contributing to the large conformational transitions in both HIV-1 protease and 14-3-3σ proteins. Beside the above major project, a side project of developing tools to study protein pseudo-symmetry is also reported. It has been proposed that symmetry provides protein stability, opportunities for allosteric regulation, and even functionality. This tool helps us to answer the questions of why there is a deviation from perfect symmetry in protein and how to quantify it.

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<p>Protein malfunction can cause human diseases, which makes the protein a target in the process of drug discovery. In-depth knowledge of how protein functions can widely contribute to the understanding of the mechanism of these diseases. Protein functions are determined by protein structures and their dynamic properties. Protein dynamics refers to the constant physical movement of atoms in a protein, which may result in the transition between different conformational states of the protein. These conformational transitions are critically important for the proteins to function. Understanding protein dynamics can help to understand and interfere with the conformational states and transitions, and thus with the function of the protein. If we can understand the mechanism of conformational transition of protein, we can design molecules to regulate this process and regulate the protein functions for new drug discovery. Protein Dynamics can be simulated by Molecular Dynamics (MD) Simulations.</p> <p>The MD simulation data generated are spatial-temporal and therefore very high dimensional. To analyze the data, distinguishing various atomic interactions within a protein by interpreting their 3D coordinate values plays a significant role. Since the data is humongous, the essential step is to find ways to interpret the data by generating more efficient algorithms to reduce the dimensionality and developing user-friendly visualization tools to find patterns and trends, which are not usually attainable by traditional methods of data process. The typical allosteric long-range nature of the interactions that lead to large conformational transition, pin-pointing the underlying forces and pathways responsible for the global conformational transition at atomic level is very challenging. To address the problems, Various analytical techniques are performed on the simulation data to better understand the mechanism of protein dynamics at atomic level by developing a new program called Probing Long-distance interactions by Tapping into Paired-Distances (PLITIP), which contains a set of new tools based on analysis of paired distances to remove the interference of the translation and rotation of the protein itself and therefore can capture the absolute changes within the protein.</p> <p>Firstly, we developed a tool called Decomposition of Paired Distances (DPD). This tool generates a distance matrix of all paired residues from our simulation data. This paired distance matrix therefore is not subjected to the interference of the translation or rotation of the protein and can capture the absolute changes within the protein. This matrix is then decomposed by DPD</p> <p>using Principal Component Analysis (PCA) to reduce dimensionality and to capture the largest structural variation. To showcase how DPD works, two protein systems, HIV-1 protease and 14-3-3 σ, that both have tremendous structural changes and conformational transitions as displayed by their MD simulation trajectories. The largest structural variation and conformational transition were captured by the first principal component in both cases. In addition, structural clustering and ranking of representative frames by their PC1 values revealed the long-distance nature of the conformational transition and locked the key candidate regions that might be responsible for the large conformational transitions.</p> <p>Secondly, to facilitate further analysis of identification of the long-distance path, a tool called Pearson Coefficient Spiral (PCP) that generates and visualizes Pearson Coefficient to measure the linear correlation between any two sets of residue pairs is developed. PCP allows users to fix one residue pair and examine the correlation of its change with other residue pairs.</p> <p>Thirdly, a set of visualization tools that generate paired atomic distances for the shortlisted candidate residue and captured significant interactions among them were developed. The first tool is the Residue Interaction Network Graph for Paired Atomic Distances (NG-PAD), which not only generates paired atomic distances for the shortlisted candidate residues, but also display significant interactions by a Network Graph for convenient visualization. Second, the Chord Diagram for Interaction Mapping (CD-IP) was developed to map the interactions to protein secondary structural elements and to further narrow down important interactions. Third, a Distance Plotting for Direct Comparison (DP-DC), which plots any two paired distances at user’s choice, either at residue or atomic level, to facilitate identification of similar or opposite pattern change of distances along the simulation time. All the above tools of PLITIP enabled us to identify critical residues contributing to the large conformational transitions in both HIV-1 protease and 14-3-3σ proteins.</p> <p>Beside the above major project, a side project of developing tools to study protein pseudo-symmetry is also reported. It has been proposed that symmetry provides protein stability, opportunities for allosteric regulation, and even functionality. This tool helps us to answer the questions of why there is a deviation from perfect symmetry in protein and how to quantify it.</p>