Proteins orchestrate a number of cellular processes either alone or in concert with other biomolecules like nucleic acids, carbohydrates, and lipids. They exhibit an intrinsic ability to fold de novo to their functional states. The three–dimensional structure of a protein, dependent on its amino acid sequence, is important for its function. Understanding this sequence– structure–function relationship has become one of the primary goals in biophysics. Various experimental techniques like X–ray crystallography, Nuclear Magnetic Resonance (NMR), and site–directed mutagenesis have been used extensively towards this goal. Computational studies include mainly sequence based, and structure based approaches. The sequence based approaches such as sequence alignments, phylogenetic analysis, domain identification, statistical coupling analysis etc., aim at deriving meaningful information from the primary sequence of the protein. The structure based approaches, on the other hand, use structures of folded proteins. Recent advances in structure determination and efforts by various structural consortia have resulted in an enormous amount of structures available for analysis. Innumerable observations such as the allowed and disallowed regions in the conformations of a peptide unit, hydrophobic core in globular proteins, existence of regular secondary structures like helices, sheets, and turns and a limited fold space have been landmarks in understanding the characteristics of protein structures. The uniqueness of protein structure is attained through non–covalent interactions among the constituent amino acids. Analyses of protein structures show that different types of non–covalent interactions like hydrophobic interactions, hydrogen bonding, salt bridges, aromatic stacking, cation–π interactions, and solvent interactions hold protein structures together. Although such structure analyses have provided a wealth of information, they have largely been performed at a pair–wise level and an investigation involving such pair–wise interactions alone is not sufficient to capture all the determinants of protein structures, since they happen at a global level. This consideration has led to the development of graphs/networks for proteins. Graphs or Networks are a collection of nodes connected by edges. Protein Structure Networks (PSNs) can be constructed using various definitions of nodes and edges. Nodes may vary from atoms to secondary structures in Synopsis proteins, and the edges can range from simple atom–atom distances to distance between secondary structures. To study the interplay of amino acids in structure formation, the most commonly used PSNs consider amino acids as nodes. The criterion for edge definition, however, varies. PSNs can be constructed at a course grain level by considering the distances between Cα/Cβ atoms, any side–chain atoms, or the centroids of the amino acids. At a finer level, PSNs can be constructed using atomic details by considering the interaction types or by computing the extent of interaction between amino acids. Representation of proteins as networks and their analyses has given us a unique perspective on various aspects such as protein structure organization, stability, folding, function, oligomerization and so on. A variety of network properties like the degree distribution, clustering coefficient, characteristic path lengths, clusters, and hubs have been investigated. Most of these studies are carried out on protein structures alone. However, the interaction of proteins with other biopolymers like nucleic acids is vital for many crucial biological processes like transcription and translation. In this thesis, we have attempted to address this problem by constructing and analyzing combined graphs of the structures of protein and DNA. Also, in almost all of the PSN studies, the connections have been made solely on the basis of geometric criteria. In the later part of the thesis, we have taken PSN a step further by defining the non–covalent connections based on chemical considerations in the form of the energies of interactions. The thesis contains two sections. The first part mainly involves the construction and application of PSNs to study DNA binding proteins. The DNA binding proteins are involved in several high fidelity processes like DNA recombination, DNA replication, and transcription. Although the protein– DNA interfaces have been extensively analyzed using pair–wise interactions, we gain additional global perspective from network approach. Furthermore, most of the earlier investigations have been carried out from the protein point of view (protein centric) and the present network approach aims to combine both the protein centric and the DNA centric view points by construction and analyses of protein–DNA graphs. These studies are described in Chapters 3 and 4. The second part of the thesis discusses the development, characterization, and application of protein structure networks based on non– covalent interaction energies. The investigations are presented in chapters 5 and 6. Chapter 3 discusses the development of Protein–DNA Graphs (PDGs) where the protein–DNA interfaces are represented as networks. PDG is a bipartite network in which amino acids form a set of nodes and the nucleotides form the other set. The extent of interaction between the two diverse types of biopolymers is normalized to define the strength of interaction. Edges are then constructed based on the interaction strength between amino acids and nucleotides. Such a representation, reported here for the first time, provides a holistic view of the interacting surface. The developed PDGs are further analyzed in terms of clusters of interacting residues and identification of highly connected residues, known as hubs, along the protein–DNA interface and discussed in terms of their interacting motifs. Important clusters have been identified in a set of protein–DNA complexes, where the amino acids interact with different chemical components of DNA such as phosphate, deoxyribose and base with varying degrees of connectivity. An analysis of such fragment based PDGs provided insights into the nature of protein–DNA interaction, which could not have been obtained by conventional pair–wise analysis. The predominance of deoxyribose–amino acid clusters in beta–sheet proteins, distinction of the interface clusters in helix–turn–helix and the zipper type proteins are some of the new findings from the analysis of PDGs. Additionally, a potential classification scheme has been proposed for protein–DNA complexes on the basis of their interface clusters. This classification scheme gives a general idea of how the proteins interact with different components of DNA in various complexes. The present graph–based method has provided a deeper insight into the analysis of the protein–DNA recognition mechanisms from both protein and DNA view points, thus throwing more light on the nature and specificity of these interactions (Sathyapriya, Vijayabaskar et al. 2008). Chapter 4 delineates the application of PSN to an important problem in molecular biology. An analysis of interface clusters from multimeric proteins provides a clue to the important residues contributing to the stability of the oligomers. One such prediction was made on the DNA binding protein under starvation from Mycobacterium smegmatis (Ms–Dps) using PSNs. Two types of trimers, Trimer A (tA) and Trimer B (tB) can be derived from the dodecamer because of the inherent three fold symmetry of the spherical crystal structure. The irreversible dodecamerization of these native Ms--Dps trimers, in vitro, is known to be directly associated with the bimodal function (DNA binding and iron storage) of this protein. Interface clusters which were Synopsis identified from the PSNs of the derived trimers, allowed us to convincingly predict the residues E146 and F47 for mutation studies. The prediction was followed up by our experimental collaborators (Rakhi PC and Dipankar Chatterji), which led to the elucidation of the molecular mechanism behind the in vitro oligomerization of Ms--Dps. The F47E mutant was impaired in dodecamerization, and the double mutant (E146AF47E) was a native monomer in solution. These two observations suggested that the two trimers are important for dodecamerization and that the residues selected are important for the structural stability of the protein in vitro. From the structural and functional characterizations of the mutants, we have proposed an oligomerization pathway of Ms–Dps (Chowdhury, Vijayabaskar et al. 2008). The second part of the thesis involves the development, characterization (Chapter 5) and application (Chapter 6) of Protein Energy Networks (PENs). As mentioned above, the PSNs constructed on the geometric basis efficiently capture the topology and associated properties at the level of atom–atom contact. The chemistry, however, is not completely captured by these network representations, and a wealth of information can be extracted by incorporating the details of chemical interactions. This study is an advancement over the existing PSNs, in terms of edges being defined on the basis of interaction energies among the amino acids. This interaction energy is the resultant of various types of interactions within a protein. Use of such realistic interaction energies in a weighted network captures all the essential features responsible for maintaining the protein structure. The methodology involved in representing proteins as interaction energy weighted networks, with realistic edge weights obtained from standard force fields is described in Chapter 5. The interaction energies were derived from equilibrium ensembles (obtained using molecular dynamics simulations) to account for the structural plasticity, which is essential for function elucidation. The suitability of this method to study single static structures was validated by obtaining interaction energies on minimized crystal structures of proteins. The PENs were then characterized using network parameters like edge weight distributions, clusters, hubs, and shortest paths. The PENs exhibited three distinct behaviors in terms of the size of the largest connected cluster as a function of interaction energy; namely, the pre–transition, transition, and post transition regions, irrespective of the topology of the proteins. The pre– transition region (energies<–20 kJ/mol) comprises smaller clusters with mainly charged and polar residues as hubs. Crucial topological changes take place in the transition region (–10 to –20 kJ/mol), where the smaller clusters aggregate, through low energy van der Waals interactions, to form a single large cluster in the post–transition region (energies>–10 kJ/mol). These behaviors reinforce the concept that hydrophobic interactions hold together local clusters of highly interacting residues, keeping the protein topology intact (Vijayabaskar and Vishveshwara 2010). The applications of PENs in studying protein organization, allosteric communication, thermophilic stability and the structural relation of remote homologues of TIM barrel families have been outlined in Chapter 6. In the first case, the weighted networks were used to identify stabilization regions in protein structures and hierarchical organization in the folded proteins, which may provide some insights into the general mechanism of protein folding and stabilization (Vijayabaskar and Vishveshwara 2010). In the second case the features of communication paths in proteins were elucidated from PENs, and specific paths have been extensively discussed in the case of PDZ domain, which is known to bring together protein partners, mediating various cellular processes. Changes in PEN upon ligand binding, resulting in alterations of the shortest paths (energetically most favorable paths) for a small fraction of residues, indicated that allosteric communication is anisotropic in PDZ. The observations also establish that the shortest paths between functionally important sites traverse through key residues in PDZ2 domain. Furthermore, shortest paths in PENs provide us the exact pathways of communication between residues. Although the communication in PDZ has been extensively investigated, detailed information of pathways at the energy level has emerged for the first time from the present study from PEN analysis (Vijayabaskar and Vishveshwara 2010). In the third case, a set of thermophilic and mesophilic proteins were compared to determine the factors responsible for their thermal stability from a network perspective using PENs. The sub– graph parameters such as cluster population, hubs and cliques were the prominent contributing factors for thermal stability. Also, the thermophilic proteins have a better–packed hydrophobic core. The property of thermophilic protein to increase stability by increasing the connectivity but retain conformational flexibility is discussed from a cliques and communities (higher order inter–connection of residues) perspective (Vijayabaskar and Vishveshwara 2010). Finally, the remote homologues from the TIM barrel fold have been analyzed using PENs to identify the interactions responsible for the maintenance of the fold despite low sequence similarity. A study of conserved Synopsis interactions in family specific PENs reveals that the formation of the central beta barrel is vital for the TIM barrel formation. The beta barrel is being formed by either conserved long range electrostatic interactions or by tandem arrangement of low energy hydrophobic interactions. The contributions of helix–sheet and helix–helix interactions are not conserved in the families. This study suggests that the sequentially near residues forming the helix–sheet interactions are common in many folds and hence formed despite non– conservation, whereas formation of beta barrel requires long range interactions, thus more conserved within the families. The thesis also consists of an appendix in which a web–tool, developed to express proteins as networks and analyze these networks using different network parameters is discussed. The web based program–GraProStr allows us to represent proteins as structure graphs/networks by considering the amino acid residues as nodes and representing non–covalent interactions among them as edges. The different networks (classified based on edge definition) which can be obtained using GraProStr are Protein Side–chain Networks (PScNs), Cα/Cβ distance based networks (PcNs) and Protein– Ligand Networks (PLNs). The parameters which can be generated include clusters, hubs, cliques (rigid regions in proteins) and communities (group of cliques). It is also possible to differentiate the above mentioned parameters for monomers and interfaces in multimeric proteins. The well tested tool is now made available to the scientific community for the first time. GraProStr is available online and can be accessed from http://vishgraph.mbu.iisc.ernet.in/GraProStr/index.html. With a variety of structure networks, and a set of easily interpretable network parameters GraProStr can be useful is analyzing protein structures from a global paradigm (Vijayabaskar, Vidya et al. 2010). In summary, we have extensively studied DNA binding proteins using side– chain based protein structure networks and by integrating the DNA molecule into the network. Also, we have upgraded the existing methodology of generating structure networks, by representing both the geometry and the chemistry of residues as interaction energies among them. Using this energy based network we have studied diverse problems like protein structure formation, stabilization, and allosteric communication in detail. The above mentioned methodologies are a considerable advancement over existing structure network representations and have been shown in this thesis to shed more light on the structural features of proteins.

During the past decade the effects of macromolecular crowding on reaction pathways is gaining in prominence. The stress is to move out of the realms of ideal solution studies and make conceptual modifications that consider non-ideality as a variable in our calculations. In recent years it has been shown that molecular crowding exerts significant effects on all in vivo processes, from DNA conformational changes, protein folding to DNA-protein interactions, enzyme pathways and signalling pathways. Both thermodynamic as well as kinetic parameters vary by orders of magnitude in uncrowded buffer system as compared to those in the crowded cellular milieu. Ignoring these differences will restrict our knowledge of biology to a “model system” with few practical understandings. The recent expansion of the genome database has stimulated a study on numerous previously unknown proteins. This has whetted our thirst to model the cellular determinants in a more comprehensive manner. Intracellular extract would have been the ideal solution to re-create the cellular environment. However, studies conducted in this solution will be contaminated by interference with other biologically active molecule and relevant statistical data cannot be extracted out from it. Recent advances in methodologies to mimic the cellular crowding include use of inert macromolecules to reduce the volume occupancy of target molecules and the use of immobilization techniques to increase the surface density of molecules in a small volumetric region. The use of crowding agents often results in non-specific interaction and side-reactions like aggregation of the target molecules with the crowding agents themselves. Immobilization of one of the interacting partners reduces the probability of aggregation and precipitation of bio-macromolecules by restricting their degrees of freedom. Covalent linkage of molecules on solid support is used extensively in research for creating a homogeneous surface of bound molecules which can be interrogated for their reactivity. However, when it comes to biomolecules, direct immobilization on solid support or use of organic linkers often results in denaturation. The use of bio-affinity immobilization techniques can help us overcome this problem. Since mild conditions are needed to regenerate such a surface, it finds universal applicability as bio-memory chips. This thesis focuses on our attempts to design a physiologically viable immobilization technique for following rotein-protein/protein-DNA interactions. The work explores the mechanism for biological interactions related to transcription process in E. coli. Chapter 1 deals with the literary survey of the importance and effects of molecular crowding on biological reactions. It gives a brief history of the efforts been made so far by experimentalists, to mimic macromolecular crowding and the methods applied. The chapter tries to project an all-round perspective of the pros and cons of different immobilization techniques as a means to achieve a high surface density of molecules and the advancements so far. Chapter 2 deals with the detailed technicality and applicability of the Langmuir-Blodgett method. It discusses the rationale behind our developing this technique as an alternate means of bio-affinity immobilization, under physiologically compatible conditions. It then goes on to describe our efforts to follow the sequence-specific and sequential assembly process of a functional RNA polymerase enzyme with one immobilized partner and also explore the role of omega subunit of RNAP in the reconstitution pathway. This chapter uses the assembly process of a multi-subunit enzyme to evaluate the efficiency of the LB system as a universal two-dimensional scaffold to follow sequence-specific protein-ligand interaction. Chapter 3 discusses the application of LB technique to quantitatively evaluate the kinetics and thermodynamics of promoter-RNA polymerase interaction under conditions of reduced dimensionality. Here, we follow the interaction of T7A1 phage promoter with Escherichia coli RNA polymerase using our Langmuir-Blodgett technique. The changes in mechanistic pathway and trapping of kinetic intermediates are discussed in detail due to the imposed restriction in the degrees of freedom of the system. The sensitivity of this detection method is compared vis-a-vis conventional immobilization methods like SPR. This chapter firmly establishes the universal application of LB technique as a means to emulate molecular crowding and as a sensitive assay for studying the effects of such crowding on vital biological reaction pathway. Chapter 4 describes the mechanistic pathway for the physical binding of MsDps1 protein with long dsDNA in order to physically protect DNA during oxidative stress. The chapter describes in detail the mechanism of physical sequestering of non-specific DNA strands and compaction of the genome under conditions where a kinetic bottleneck has been applied. The data obtained is compared with results obtained in the previous chapter for the sequence-specific DNA-protein interaction in order to understand the difference in recognition process between regulatory and structural proteins binding to DNA. Chapter 5 deals with the evaluation of the σ-competition model in E. coli for three different sigma factors (all belonging to the σ-70 family). Here again, we have evaluated the kinetic and thermodynamic parameters governing the binding of core RNAP with its different sigma factors (σ70, σ32and σ38) and performed a comparative study for the binding of each sigma factor to its core using two different non-homogeneous immobilization techniques. The data has been analyzed globally to resolve the discrepancies associated with establishing the relative affinity of the different sigma factors for the same core RNA polymerase under physiological conditions. Chapter 6 summarizes the work presented in this thesis. In the Appendix section we have followed the unzipping of promoter DNA sequence using Optical Tweezers in an attempt to follow the temporal fluctuations occurring in biological reactions in real time and at a single molecule level.

This dissertation focuses on rationalised DNA design as a tool for the discovery and development of new therapeutic entities, as well as understanding the biological function of DNA beyond the storage of genetic information. The study is comprised of two main areas of study: (i) the use of DNA as a coding unit to illustrate the relationship between code-diversity and dynamics of self-assembly; and (ii) the use of DNA as an active unit that interacts and regulates a target protein. In the study of DNA as a coding unit in code-diversity and dynamics of self-assembly, we developed the DNA-Based Diversity Modelling and Analysis (DDMA) method. Using Polymerase Chain Reaction (PCR) and Real Time Polymerase Chain Reaction (RT-PCR), we studied the diversity and evolution of synthetic oligonucleotide populations. The manipulation of critical conditions, with monitoring and interpretation of their effects, lead to understanding how PCR amplification unfolding could reshape a population. This new take on an old technology has great value for the study of: (a) code-diversity, convenient in a DNA-based selection method, so semi-quantitation can evaluate a selection development and the population\'s behaviour can indicate the quality; (b) self-assembly dynamics, for the simulation of a real evolution, emulating a society where selective pressures direct the population's adaptation; and (c) development of high-entropy DNA structures, in order to understand how similar unspecific DNA structures are formed in certain pathologies, such as in auto-immune diseases. To explore DNA as an active unit in Tumour Necrosis Factor α (TNF-α) interaction and activity modulation, we investigate DNA's influence on its spatial conformation by physical environment regulation. Active TNF-α is a trimer and the protein-protein interactions between its monomers are a promising target for drug development. It has been hypothesised that TNF-α forms a very intricate network after its activation between its subunits and receptors, but the mechanism is still not completely clear. During our research, we estimate the non-specific DNA binding to TNF-α in the low micro-molar range. Cell toxicity assays confirm this interaction, where DNA consistently enhances TNF-α's cytotoxic effect. Further binding and structural studies lead to the same conclusion that DNA binds and interferes with TNF-α structure. From this protein-DNA interaction study, a new set of tools to regulate TNF-α's biological activity can be developed and its own biology can be unveiled.

Aus den frühen Bindungsstudien des globalen Regulators AbrB mit der ausgedehnten phyC-Promotorregion von Bacillus amyloliquefaciens FZB45 konnte ein mehrstufiger kooperativer Bindungsprozess abgeleitet werden. Dabei verlangt die AbrB-vermittelte Repression von phyC nach Integrität zweier großer Bindungsstellen, ABS1 und ABS2, die 162 bp voneinander entfernt liegen. In der vorliegenden Arbeit wurden die ersten Echtzeitkinetiken zur DNA-AbrB-Interaktion mittels der Oberflächenplasmonresonanz (SPR) gemessen und analysiert. AbrB zeigte hohe Affinitäten zu den 40 bp langen Oligonukleotiden, die den beiden Bindungsstellen entstammen. Dabei verursachten alle Oligonukleotide der ABS2 und nur eine kurze Region innerhalb der ABS1 bei der Bindung von AbrB Konformationsänderungen im Protein und in der DNA (CD - Zirkulardichroismusspektroskopie) und wiesen eine Kooperativität von 2 / In previous binding studies it could be demonstrated that a global regulator AbrB and the extensive phyC promoter region of Bacillus amyloliquefaciens FZB45 interact in a complex manner. AbrB binding is a multistep cooperative process. The integrity of both binding sites, ABS1 and ABS2, which are separated by 162 bp, is crucial for the AbrB-mediated repression of phyC. This work presents the first real-time binding kinetics of the AbrB-DNA interaction using surface plasmon resonance (SPR). AbrB exhibited high affinities to all analyzed 40-bp oligonucleotides that were derived from the ABSs of phyC. All parts of the ABS2, but only a small region within ABS1, were bound cooperatively to AbrB with a stoichiometry of 2 DNA to 1 AbrB tetramer and with 2

The flow of genetic information from genes to proteins is mediated through proteins which interact with the nucleic acids at several stages to successfully transmit the information from the nucleus to the cell cytoplasm. Unlike in the case of protein-protein interactions, the principles behind protein-nucleic acid interactions are still not very (Pabo and Nekludova, 2000) and efforts are still underway to arrive at the basic principles behind the specific recognition of nucleic acids by proteins (Prabakaran et al., 2006). This is mainly due to the innate complexity involved in recognition of nucleotides by proteins, where, even within a given family of DNA binding proteins, different modes of binding and recognition strategies are employed to suit their function (Luscomb et al., 2000). Such difficulties have also not made possible, a thorough classification of DNA/RNA binding proteins based on the mode of interaction as well as the specificity of recognition of the nucleotides. The availability of a large number of structures of protein-nucleic acids complexes (albeit lesser than the number of protein structures present in the PDB) in the past few decades has provided the knowledge-base for understanding the details behind their molecular mechanisms (Berman et al., 1992). Previously, studies have been carried out to characterize these interactions by analyzing specific non-covalent interactions such as hydrogen bonds, van der Walls, and hydrophobic interactions between a given amino acid and the nucleic acid (DNA, RNA) in a pair-wise manner, or through the analysis of interface areas of the protein-nucleic acid complexes (Nadassy et al., 1998; Jones et al., 1999). Though the studies have deciphered the common pairing preferences of a particular amino acid with a given nucleotide of DNA or RNA, there is little room for understanding these specificities in the context of spatial interactions at a global level from the protein-nucleic acid complexes. The representation of the amino acids and the nucleotides as components of graphs, and trying to explore the nature of the interactions at a level higher than exploring the individual pair-wise interactions, could provide greater details about the nature of these interactions and their specificity. This thesis reports the study of protein-nucleic interactions using graph and network based approaches. The evaluation of the parameters for characterizing protein-nucleic acid graphs have been carried out for the first time and these parameters have been successfully employed to capture biologically important non-covalent interactions as clusters of interacting amino acids and nucleotides from different protein-DNA and protein-RNA complexes. Graph and network based approaches are well established in the field of protein structure analysis for analyzing protein structure, stability and function (Kannan and Vishveshwara, 1999; Brinda and Vishveshwara, 2005). However, the use of graph and network principles for analyzing structures of protein-nucleic acid complexes is so far not accomplished and is being reported the first time in this thesis. The matter embodied in the thesis is presented as ten chapters. Chapter 1 lays the foundation for the study, surveying relevant literature from the field. Chapter 2 describes in detail the methods used in constructing graphs and networks from protein-nucleic acid complexes. Initially, only protein structure graphs and networks are constructed from proteins known to interact with specific DNA or RNA, and inferences with regard to nucleic acid binding and recognition were indirectly obtained . Subsequently, parameters were evaluated for representing both the interacting amino acids and the nucleotides as components of graphs and a direct evaluation of protein-DNA and Protein-RNA interactions as graphs has been carried out. Chapter 3 and 4 discuss the graph and network approaches applied to proteins from a dataset of DNA binding proteins complexed with DNA. In chapter 3, the protein structure graphs were constructed on the basis of the non-covalent interactions existing between the side chains of amino acids. Clusters of interacting side chains from the graphs were obtained using the graph spectral method. The clusters from the protein-DNA interface were analyzed in detail for the interaction geometry and biological importance (Sathyapriya and Vishveshwara, 2004). Chapter 4 also uses the same dataset of DNA binding proteins, but a network-based approach is presented. From the analysis of the protein structure networks from these DNA binding proteins, interesting observations relating the presence of highly connected nodes(or hubs) of the network to functionally important amino acids in the structure, emerged. Also, the comparison between the hubs identified from the protein-protein and the protein-DNA interfaces in terms of their amino acid composition and their connectivity are also presented (Sathyapriya and Vishveshwara, 2006) Chapter 5 and 6 deal with the graph and network applications to a specific system of protein-RNA complex (aminoacyl-tRNA synthetases) to gain insights into their interface biology based on amino acid connectivity. Chapter 5 deals with a dataset of aminoacyl-tRNA synthetase (aaRS) complexes obtained with various ligands like ATP, tRNA and L-amino acids. A graph based identification of side chain clusters from these ligand-bound aaRS structures has highlighted important features of ligand-binding at the catalytic sites of the two structurally different classes of aaRS (Class I and Class II). Side chain clusters from other regions of aaRS such as the anticodon binding region and the ligand-activation sites are discussed. A network approach is used in a specific system of aaRS(E.coli Glutaminyl-tRNA synthetase (GlnRS) complexed with its ligands, to specifically understand the effects of different ligand binding., in chapter 6. The structure networks of E.coli GlnRS in the ligand-free and different ligand-bound states are constructed. The ligand-free and the ligand-bound complexes are compared by analyzing their network properties and the presence of hubs to understand the effect of ligand-binding. These properties have elegantly captured the effects of ligand-binding to the GlnRS structure and have also provided an alternate method for comparing three dimensional structures of proteins in different ligand-bound states (Sathyapriya and Vishveshwara, 2007). In contrast to protein structure graphs (PSG), both the interacting amino acids and nucleotides (DNA/RNA) form the components of the protein-nucleic acid graphs (PNG) from protein-nucleic acid complexes. These graphs are constructed based on the non-covalent interactions existing between the side chains of the amino acids and nucleotides. After representing the interacting nucleotides and amino acids as graphs, clusters of the interacting components are identified. These clusters are the strongly interacting amino acids and nucleotides from the protein-nucleic acid complexes. These clusters can be generated at different strengths of interaction between the amino acid side chain and the nucleotide (measured in terms of its atomic connectivity) and can be used for detecting clusters of non-specific as well as specific interactions of amino acids and nucleotides. Though the methodology of graph construction and cluster identification are given in chapter 2, the details of the parameters evaluated for constructing PNG are given in chapter 7. Unlike in the previous chapters, the succeeding chapters deal exclusively with results that are obtained from the analyses of PNG. Two examples of obtaining clusters from a PNG are given, one each for a protein-DNA and a protein-RNA complex. In the first example, a nucleosome core particle is subjected to the graph based analysis and different clusters of amino acids with different regions of the DNA chain such as phosphate, deoxyribose sugar and the base are identified. Another example of aminoacyl-tRNA synthetase complexed with its cognate tRNA is used to illustrate the method with a protein-RNA complex. Further, the method of constructing and analyzing protein-nucleic acid graphs has been applied to the macromolecular machinery of the pre-translocation complex of the T. thermophilus 70S ribosome. Chapter 8 deals exclusively with the results identified from the analysis of this magnificent macromolecular ensemble. The availability of the method that can handle interactions between both amino acids and the nucleotides of the protein-nucleic acid complexes has given us the basis fro evaluating these interactions in a level higher than that of analyzing pair-wise interactions. A study on the evaluation of short hydrogen bonds(SHB) in proteins, which does not fall under the realm of the main objective of the thesis, is discussed in the Chapter 9. The short hydrogen bonds, defined by the geometrical distance and angle parameters, are identified from a non-redundant dataset of proteins. The insights into their occurrence, amino acid composition and secondary structural preferences are discussed. The SHB are present in distinct regions of protein three-dimensional structures, such that they mediate specific geometrical constraints that are necessary for stability of the structure (Sathyapriya and Vishveshwara, 2005). The significant conclusions of various studies carried out are summarized in the last chapter (Chapter 10). In conclusion, this thesis reports the analyses performed with protein-nucleic acid complexes using graph and network based methods. The parameters necessary for representing both amino acids and the nucleotides as components of a graph, are evaluated for the first time and can be used subsequently for other analyses. More importantly, the use of graph-based methods has resulted in considering the interaction between the amino acids and the nucleotides at a global level with respect to their topology of the protein-nucleic acid complexes. Such studies performed on a wide variety of protein-nucleic acid complexes could provide more insights into the details of protein-nucleic acid recognition mechanisms. The results of these studies can be used for rational design of experimental mutations that ascertain the structure-function relationships in proteins and protein-nucleic acid complexes.

Indiana University-Purdue University Indianapolis (IUPUI) / All platinum (Pt)-based chemotherapeutics exert their efficacy primarily via the formation of DNA adducts which interfere with DNA replication, transcription and cell division and ultimately induce cell death. Repair and tolerance of Pt-DNA lesions by nucleotide excision repair and homologous recombination (HR) can substantially reduce the effectiveness of the Pt therapy. Inhibition of these repair pathways, therefore, holds the potential to sensitize cancer cells to Pt treatment and increase clinical efficacy. Replication Protein A (RPA) plays essential roles in both NER and HR, along with its role in DNA replication and DNA damage checkpoint activation. Each of these functions requires RPA binding to single-stranded DNA (ssDNA). We synthesized structural analogs of our previously reported RPA inhibitor TDRL-505, determined the structure activity relationships and evaluated their efficacy in tissue culture models of epithelial ovarian cancer (EOC) and non-small cell lung cancer (NSCLC). These data led us to the identification of TDRL-551, which exhibited a greater than 2-fold increase in in vitro and cellular activity. TDRL-551 showed synergy with Pt in tissue culture models of EOC and in vivo efficacy, as a single agent and in combination with platinum, in a NSCLC xenograft model. These data demonstrate the utility of RPA inhibition in EOC and NSCLC and the potential in developing novel anticancer therapeutics that target RPA-DNA interactions.