+publisher:"Indian Institute of Science" +contributor:("Chandra, Nagasuma")

Indian Institute of Science

1. Nagarajan, Deepesh. Computational and Experimental Approaches to Protein Design.

Degree: PhD, Faculty of Science, 2018, Indian Institute of Science

URL: http://etd.iisc.ac.in/handle/2005/4148

► Protein design is a rapidly maturing field, with the goal of designing proteins with customized structures and functions. A large number of protein structures are… (more)

▼ Protein design is a rapidly maturing field, with the goal of designing proteins with customized structures and functions. A large number of protein structures are experimentally determined, which have facilitated an understanding of sequence-structure relationships. However, very few studies have explored the full sequence space around each structural fold and further in understanding which of these sequences will lead to functional proteins. Moreover, there are not enough studies to date, reporting the design of entirely novel folds, especially with a designed functionality. There has been significant advance in the computational design methods, which enable us to explore de novo protein design. The challenges in protein design stem from the very large number of potential conformations that need to be explored, as well as scoring and ranking them. Designing proteins with predetermined functions is even more difficult since protein interactions with ligand molecules add a further layer of complexity. To date, reports of functional de novo protein designs remain rare. In this thesis, different algorithms have been successfully employed to solve four diverse protein and peptide design challenges. The algorithms were selected according to the design problem posed. For large proteins and binding site design, atomic resolution models and corresponding energetics were used. For antimicrobial peptides with non-specific targets, machine learning algorithms were used to understand sequence and structural features of existing proteins to incorporate into the new design. I will describe computational and experimental approaches for the following design challenges: The design and implementation of algorithms for understanding ligand binding-site (pocket) substructures. Three approaches for analysing and classifying pockets are described. The computational design and experimental characterization of symmetric TIM barrel proteins using the Rosetta software suite, followed by structural characterization through NMR studies. The design, biophysical characterization and NMR structure determination of a heme-binding peptide adopting a novel twin beta hairpin topology. The sequence-based design of antimicrobial peptides using a new long short-term memory network based algorithm, along with in vitro antimicrobial activity characterization. The structure-based design of a second set of antimicrobial peptides based on a maximum common sub-graph detection algorithm optimized using simulated annealing methods, followed by in vitro and in vivo antimicrobial activity characterization, and NMR structural determination of the best performing antimicrobial peptide . The applications of the algorithms presented to the broader field will also be discussed. Advisors/Committee Members: Chandra, Nagasuma (advisor).

Subjects/Keywords: Protein Folding; Protein Design; De novo Proteins; PocketMatch; Protein Therapeutics; TIM Barrel Proteins; Antimicrobial Peptides; Long Short-term Memory (LSTM) Networks; Biochemistry

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APA (6^th Edition):

Nagarajan, D. (2018). Computational and Experimental Approaches to Protein Design. (Doctoral Dissertation). Indian Institute of Science. Retrieved from http://etd.iisc.ac.in/handle/2005/4148

Chicago Manual of Style (16^th Edition):

Nagarajan, Deepesh. “Computational and Experimental Approaches to Protein Design.” 2018. Doctoral Dissertation, Indian Institute of Science. Accessed January 18, 2021. http://etd.iisc.ac.in/handle/2005/4148.

MLA Handbook (7^th Edition):

Nagarajan, Deepesh. “Computational and Experimental Approaches to Protein Design.” 2018. Web. 18 Jan 2021.

Vancouver:

Nagarajan D. Computational and Experimental Approaches to Protein Design. [Internet] [Doctoral dissertation]. Indian Institute of Science; 2018. [cited 2021 Jan 18]. Available from: http://etd.iisc.ac.in/handle/2005/4148.

Council of Science Editors:

Nagarajan D. Computational and Experimental Approaches to Protein Design. [Doctoral Dissertation]. Indian Institute of Science; 2018. Available from: http://etd.iisc.ac.in/handle/2005/4148

Indian Institute of Science

2. Anand, Praveen. Large-Scale Structural Analysis of Protein-ligand Interactions : Exploring New Paradigms in Anti-Tubercular Drug Discovery.

Degree: PhD, Faculty of Science, 2018, Indian Institute of Science

URL: http://etd.iisc.ac.in/handle/2005/3951

► BIOLOGICAL processes are governed through specific interactions of macromolecules. The three-dimensional structural information of the macromolecules is necessary to understand the basis of molecular recognition.… (more)

▼ BIOLOGICAL processes are governed through specific interactions of macromolecules. The three-dimensional structural information of the macromolecules is necessary to understand the basis of molecular recognition. A large number of protein structures have been determined at a high resolution using various experimental techniques such as X-ray crystallography, NMR, electron microscopy and made publicly available through the Protein Data Bank. In the recent years, comprehending function by studying a large number of related proteins is proving to be very fruitful for understanding their biological role and gaining mechanistic insights into molecular recognition. Availability of large-scale structural data has indeed made this task of predicting the protein function from three-dimensional structure, feasible. Structural bioinformatics, a branch of bioinformatics, has evolved into a separate discipline to rationalize and classify the information present in three-dimensional structures and derive meaningful biological insights. This has provided a better understanding of biological processes at a higher resolution in several cases. Most of the structural bioinformatics approaches so far, have focused on fold-level analysis of proteins and their relationship to sequences. It has long been recognized that sequence-fold or fold-function relationships are highly complex. Information on one aspect cannot be readily extrapolated to the other. To a significant extent, this can be overcome by understanding similarities in proteins by comparing their binding site structures. In this thesis, the primary focus is on analyzing the small-molecule ligand binding sites in protein structures, as most of the biological processes ranging from enzyme catalysis to complex signaling cascades are mediated through protein-ligand interactions. Moreover, given that the precise geometry and the chemical properties of the residues at the ligand binding sites dictate the molecular recognition capabilities, focusing on these sites at the structural level, is likely to yield more direct insights on protein function. The study of binding sites at the structural level poses several problems mainly because the residues at the site may be sequentially discontinuous but spatially proximal. Further, the order of the binding site residues in primary sequence, in most of cases has no significance for ligand binding. Compounding these difficulties are additional factors such as, non-uniform contribution to binding from different residues, and size-variations in binding sites even across closely related proteins. As a result, methods available to study ligand-binding sites in proteins, especially on a large-scale are limited, warranting exploration of new approaches. In the present work, new methods and tools have been developed to address some of these challenges in binding site analysis. First, a novel tool for site-based function annotation of protein structures, called PocketAnnotate was developed ( http://proline.biochem.iisc.ernet. in/pocketannotate/).… Advisors/Committee Members: Chandra, Nagasuma (advisor).

Subjects/Keywords: Protein-ligand Interactions; Anti-tubercular Drug Discovery; Protein Structure; Mycobacterium tuberculosis; Protein–ligand Complex Proteome; PDB Pocketome; Mycobacterium tuberculosis FtsZ; Helicobacter pylori DprA; Binding Site Analyses; PocketAnnotate; Protein-Ligand Interaction Clusters (PLIC); Mycobacterium tuberculosis Pocketome; Biochemistry

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

APA (6^th Edition):

Anand, P. (2018). Large-Scale Structural Analysis of Protein-ligand Interactions : Exploring New Paradigms in Anti-Tubercular Drug Discovery. (Doctoral Dissertation). Indian Institute of Science. Retrieved from http://etd.iisc.ac.in/handle/2005/3951

Chicago Manual of Style (16^th Edition):

Anand, Praveen. “Large-Scale Structural Analysis of Protein-ligand Interactions : Exploring New Paradigms in Anti-Tubercular Drug Discovery.” 2018. Doctoral Dissertation, Indian Institute of Science. Accessed January 18, 2021. http://etd.iisc.ac.in/handle/2005/3951.

MLA Handbook (7^th Edition):

Anand, Praveen. “Large-Scale Structural Analysis of Protein-ligand Interactions : Exploring New Paradigms in Anti-Tubercular Drug Discovery.” 2018. Web. 18 Jan 2021.

Vancouver:

Anand P. Large-Scale Structural Analysis of Protein-ligand Interactions : Exploring New Paradigms in Anti-Tubercular Drug Discovery. [Internet] [Doctoral dissertation]. Indian Institute of Science; 2018. [cited 2021 Jan 18]. Available from: http://etd.iisc.ac.in/handle/2005/3951.

Council of Science Editors:

Anand P. Large-Scale Structural Analysis of Protein-ligand Interactions : Exploring New Paradigms in Anti-Tubercular Drug Discovery. [Doctoral Dissertation]. Indian Institute of Science; 2018. Available from: http://etd.iisc.ac.in/handle/2005/3951

Indian Institute of Science

3. Padiadpu, Jyothi. An Integrated Systems Biology Approach to Study Drug Resistance in Mycobacteria.

Degree: PhD, Faculty of Engineering, 2018, Indian Institute of Science

URL: http://etd.iisc.ac.in/handle/2005/3750

► Emergence of drug resistance is a major problem in the treatment of many diseases including tuberculosis. To tackle the problem, it is essential to obtain… (more)

▼ Emergence of drug resistance is a major problem in the treatment of many diseases including tuberculosis. To tackle the problem, it is essential to obtain a global perspective of the molecular mechanisms by which bacteria acquire drug resistance. Systems biology approaches therefore become necessary. This work aims to understand pathways to drug resistance and strategies for inhibition of the resistant strains by using a combination of experimental genomics and computational molecular systems approaches. Laboratory evolution of Mycobacterium smegmatis MC2 155 by treatment with isoniazid (INH), a front-line anti-tubercular drug, resulted in a drug-resistant strain (4XR), capable of growth even at about 10-times the minimum inhibitory concentration of the drug. Whole genome sequence of the 4XR was determined, which indicated only 31 variations in the whole genome, including 3 point mutations, 17 indels and 11 frame-shifts. Two mutations were in proteins required for the pharmacological action of the drug, albeit in regions distant from the drug binding site. The variations however were insufficient to explain the observed resistance to isoniazid. For a better understanding of the global changes associated with drug resistance, whole genome-wide gene expression data was obtained for the resistant strain and compared with that of the WT strain. 716 genes were found to be differentially regulated in 4XR, spanning different biochemical, signaling and regulatory pathways. From this, some explanations for the emergence of drug resistance were obtained, such as the up-regulation of the enzymes in the mycolic acid biosynthesis pathway and also of the drug efflux pumps. In addition, enrichment analysis indicated that up-regulated genes belong to functional categories of response to stress, carbohydrate metabolism, oxidation-reduction process, ion transport, signaling as well as lipid metabolism. The differential gene regulations seemed to be partially responsible for conferring the phenotype to the organism. Alterations in the metabolic pathways in 4XR were characterized using the phenotypic microarray technology, which experimentally scanned the respiratory ability of the resistant bacteria under 280 different nutrient conditions and 96 different inhibitors. Phenotypic gain, where the resistant strain grows significantly better than the wild type and phenotypic loss, where the growth of the resistant strain is compromised as compared to the sensitive strains were derived from the comparison of the phenotypic responses. Differences in survival ability and growth rates in different nutrient sources in the resistant phenotype as compared to the wild type were observed, suggesting rewiring in the metabolic network of the drug-resistant strain. In particular, the pathways of central carbon metabolism and amino acid biosynthesis exhibit significant differences. The strain-specific metabolic pathway differences may guide in devising strategies to tackle the drug-resistant strains selectively and in a rational manner. … Advisors/Committee Members: Chandra, Nagasuma (advisor).

Subjects/Keywords: Drug Resistance Tuberculosis; Drug Resistance Mycobacteria; M. smegmatis; MC2 155; Mycobacterium smegmatis; M. tuberculosis; Anti-tubercular Drugs; Mycobacterium tuberculosis; Biochemistry

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

APA (6^th Edition):

Padiadpu, J. (2018). An Integrated Systems Biology Approach to Study Drug Resistance in Mycobacteria. (Doctoral Dissertation). Indian Institute of Science. Retrieved from http://etd.iisc.ac.in/handle/2005/3750

Chicago Manual of Style (16^th Edition):

Padiadpu, Jyothi. “An Integrated Systems Biology Approach to Study Drug Resistance in Mycobacteria.” 2018. Doctoral Dissertation, Indian Institute of Science. Accessed January 18, 2021. http://etd.iisc.ac.in/handle/2005/3750.

MLA Handbook (7^th Edition):

Padiadpu, Jyothi. “An Integrated Systems Biology Approach to Study Drug Resistance in Mycobacteria.” 2018. Web. 18 Jan 2021.

Vancouver:

Padiadpu J. An Integrated Systems Biology Approach to Study Drug Resistance in Mycobacteria. [Internet] [Doctoral dissertation]. Indian Institute of Science; 2018. [cited 2021 Jan 18]. Available from: http://etd.iisc.ac.in/handle/2005/3750.

Council of Science Editors:

Padiadpu J. An Integrated Systems Biology Approach to Study Drug Resistance in Mycobacteria. [Doctoral Dissertation]. Indian Institute of Science; 2018. Available from: http://etd.iisc.ac.in/handle/2005/3750

Indian Institute of Science

4. Mukherjee, Sumanta. Multi-scale Modelling of HLA Diversity and Its Effect on Cytotoxic Immune Responses in Influenza H1N1 Infection.

Degree: PhD, Faculty of Science, 2018, Indian Institute of Science

URL: http://etd.iisc.ac.in/handle/2005/3665

► Cytotoxic T-lymphocytes (CTLs) are important components of the adaptive immune system and function by scanning the intracellular environment so as to detect and de-stroy infected… (more)

▼ Cytotoxic T-lymphocytes (CTLs) are important components of the adaptive immune system and function by scanning the intracellular environment so as to detect and de-stroy infected cells. CTL responses play a major role in controlling virus-infected cells such as in HIV or influenza and cells infected with intracellular bacteria such as in tuberculosis. To do so they require the antigens to be presented to them, which is fulfilled by the major histocompatibility complex (MHC), commonly known as human leukocyte antigen or HLA molecules in humans. Recognition of antigenic peptides to Class-1 HLA molecules is a prerequisite for triggering CTL immune responses. Individuals differ significantly in their ability to respond to an infection. Among the factors that govern the outcome of an infection, HLA polymorphism in the host is one of the most important. Despite a large body of work on HLA molecules, much remains to be understood about the relationship between HLA diversity and disease susceptibility. High complexity arises due to HLA allele polymorphism, extensive antigen cross-presentability, and host-pathogen heterogeneity. A given allele can recognize a number of different peptides from various pathogens and a given peptide can also bind to a number of different individuals. Thus, given the plurality in peptide-allele pairs and the large number of alleles, understanding the differences in recognition profiles and the implications that follow for disease susceptibilities require mathematical modelling and computational analysis. The main objectives of the thesis were to understand heterogeneity in antigen presentation by HLA molecules at different scales and how that heterogeneity translates to variations in disease susceptibilities and finally the disease dynamics in different populations. Towards this goal, first the variations in HLA alleles need to be characterized systematically and their recognition properties understood. A structure-based classification of all known HLA class-1 alleles was therefore attempted. In the process, it was also of interest to see if understanding of sub-structures at the binding grooves of HLA molecules could help in high confidence prediction of epitopes for different alleles. Next, the goal was to understand how HLA heterogeneity affect disease susceptibilities and disease spread in populations. This was studied at two different levels. Firstly, modelling the HLA genotypes and CTL responses in different populations and assessing how they recognized epitopes from a given virus. The second approach involved modelling the disease dynamics given the predicted susceptibilities in different populations. Influenza H1N1 infection was used as a case study. The specific objectives addressed are: (a) To develop a classification scheme for all known HLA class-1 alleles that can explain epitope recognition profiles and further to dissect the physic-chemical features responsible for differences in peptide specificities, (b) A statistical model has been derived from a large dataset of HLA-peptide… Advisors/Committee Members: Chandra, Nagasuma (advisor).

Subjects/Keywords: Influenza H1N1 Infection; Cytotoxic T-lymphocytes (CTLs); Human Leucocyte Antigen (HLA); Cytotoxic Immune Responses Modeling; Human Immune System; Peptide Binding; Genetic Heterogeneity; Human Leucocyte Antigen (HLA) Genotype Modeling; Human Leucotyte Antigen (HLA) Gene Polymorphism; Structural Bioinformatics; HLAClassify; HLAffy; Flutope; Disease Spreader Network (DSN); Disease Dynamics; Mathematics

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

APA (6^th Edition):

Mukherjee, S. (2018). Multi-scale Modelling of HLA Diversity and Its Effect on Cytotoxic Immune Responses in Influenza H1N1 Infection. (Doctoral Dissertation). Indian Institute of Science. Retrieved from http://etd.iisc.ac.in/handle/2005/3665

Chicago Manual of Style (16^th Edition):

Mukherjee, Sumanta. “Multi-scale Modelling of HLA Diversity and Its Effect on Cytotoxic Immune Responses in Influenza H1N1 Infection.” 2018. Doctoral Dissertation, Indian Institute of Science. Accessed January 18, 2021. http://etd.iisc.ac.in/handle/2005/3665.

MLA Handbook (7^th Edition):

Mukherjee, Sumanta. “Multi-scale Modelling of HLA Diversity and Its Effect on Cytotoxic Immune Responses in Influenza H1N1 Infection.” 2018. Web. 18 Jan 2021.

Vancouver:

Mukherjee S. Multi-scale Modelling of HLA Diversity and Its Effect on Cytotoxic Immune Responses in Influenza H1N1 Infection. [Internet] [Doctoral dissertation]. Indian Institute of Science; 2018. [cited 2021 Jan 18]. Available from: http://etd.iisc.ac.in/handle/2005/3665.

Council of Science Editors:

Mukherjee S. Multi-scale Modelling of HLA Diversity and Its Effect on Cytotoxic Immune Responses in Influenza H1N1 Infection. [Doctoral Dissertation]. Indian Institute of Science; 2018. Available from: http://etd.iisc.ac.in/handle/2005/3665

Indian Institute of Science

5. Baloni, Priyanka. A Systems Biology Approach towards Understanding Host Response and Pathogen Adaptation in Latent Tuberculosis Infection.

Degree: PhD, Faculty of Science, 2018, Indian Institute of Science

URL: http://etd.iisc.ac.in/handle/2005/2967

► Mycobacterium tuberculosis, the etiological agent of tuberculosis, has adapted with the host environment and evolved to survive in harsh conditions in the host. The pathogen… (more)

▼ Mycobacterium tuberculosis, the etiological agent of tuberculosis, has adapted with the host environment and evolved to survive in harsh conditions in the host. The pathogen has successfully evolved strategies not only to evade the host immune system but also to thrive within the host cells. Upon infection, the pathogen is either cleared due to the host immune response, or it survives and causes active tuberculosis (TB) infection. In a number of cases however, the pathogen is neither killed nor does it actively proliferate, but it remains dormant in the host until the environment becomes favorable. This dormant state of pathogen is responsible for latent TB infection (LTBI). WHO reports indicated that as much as a third of the whole world’s population is exposed to the pathogen, of which a significant proportion could be latently infected (WHO report, 2015). These individuals do not show symptoms of active TB infection and hence are difficult to detect. The latent TB infected (LTBI) individuals serve as a reservoir for the pathogen, which can lead to epidemics when the conditions change. Hence, it is necessary to understand the host -pathogen interactions during LTBI, as this might provide clues to developing new strategies to detect and curb a latent infection. Host-pathogen interactions are multifaceted, in which both species attempt to recognize and respond to each other, all of these through specific molecules making distinct interactions with the other species. The outcome of the infection is thus decided by a complex set of host-pathogen interactions. The complexity arises since a large number of molecular components are involved, also multiplicity of interactions among these components and due to several feedback, feed forwards or other regulatory or influential loops within the system. The complexity of biological systems makes modeling and simulation an essential and critical part of systems– level studies. Systems biology studies provide an integrated framework to analyze and understand the function of biological systems. This work addresses some of these issues with an unbiased systems-level analysis so as to identify and understand the important global changes both in the host and in the pathogen during LTBI. The broad objectives of the work was to identify the key processes that vary in the host during latent infection, the set of metabolic reactions in the host which can be modulated to control the reactivation of infection, global adaptation in Mycobacterium tuberculosis (Mtb) and then to utilize this knowledge to identify strategies for tackling latent infection. A review of literature of the current understanding of latency from the pathogen and the host perspective is described in chapter 1. From this, it is clear that most available studies have focused on the role of individual molecules and individual biological processes such as granuloma formation, toll-like receptor signaling, T cell responses as well as cytokine signaling, in either initiating or maintaining a latent infection, but there… Advisors/Committee Members: Chandra, Nagasuma (advisor).

Subjects/Keywords: Mycobacterium Tuberculosis; Systems Biology; Latent Tuberculosis Infection (LTBI); Tuberculosis; Tuberculosis Pathogenesis; Tuberculosis Infection-Host Immune Response; Tuberculosis Vaccines; Antitubercular Agents; Mtb; Mycobacterium smegmatis; Genome-scale Metabolic Model (GSM); Latent TB Infection; Molecular Biophysics

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

APA (6^th Edition):

Baloni, P. (2018). A Systems Biology Approach towards Understanding Host Response and Pathogen Adaptation in Latent Tuberculosis Infection. (Doctoral Dissertation). Indian Institute of Science. Retrieved from http://etd.iisc.ac.in/handle/2005/2967

Chicago Manual of Style (16^th Edition):

Baloni, Priyanka. “A Systems Biology Approach towards Understanding Host Response and Pathogen Adaptation in Latent Tuberculosis Infection.” 2018. Doctoral Dissertation, Indian Institute of Science. Accessed January 18, 2021. http://etd.iisc.ac.in/handle/2005/2967.

MLA Handbook (7^th Edition):

Baloni, Priyanka. “A Systems Biology Approach towards Understanding Host Response and Pathogen Adaptation in Latent Tuberculosis Infection.” 2018. Web. 18 Jan 2021.

Vancouver:

Baloni P. A Systems Biology Approach towards Understanding Host Response and Pathogen Adaptation in Latent Tuberculosis Infection. [Internet] [Doctoral dissertation]. Indian Institute of Science; 2018. [cited 2021 Jan 18]. Available from: http://etd.iisc.ac.in/handle/2005/2967.

Council of Science Editors:

Baloni P. A Systems Biology Approach towards Understanding Host Response and Pathogen Adaptation in Latent Tuberculosis Infection. [Doctoral Dissertation]. Indian Institute of Science; 2018. Available from: http://etd.iisc.ac.in/handle/2005/2967

Indian Institute of Science

6. Bhagavat, Raghu B R. Structural Bioinformatics of Ligand Recognition in Proteins : A large-scale Analysis and Applications in Drug Discovery.

Degree: PhD, Faculty of Science, 2019, Indian Institute of Science

URL: http://etd.iisc.ac.in/handle/2005/4249

► Biological processes are governed by highly specific macromolecular interactions. Understanding the precise mechanism of ligand recognition in proteins is essential for deriving features responsible for… (more)

▼ Biological processes are governed by highly specific macromolecular interactions. Understanding the precise mechanism of ligand recognition in proteins is essential for deriving features responsible for such recognition capabilities. Although protein sequences give first-hand information about their function, their three-dimensional structures, which are the evolutionary units, convey the function better. Three-dimensional structures of many proteins determined through X-ray crystallography and/or NMR are available in the Protein Data Bank, a public repository. This resource has stimulated the development of computational techniques to read and analyse the wealth of structural data. Structural bioinformatics is an area that provides a means to transform information in the protein structures into functional insights and enable addressing a variety of questions about what defines and dictates ligand recognition. Large-scale analyses of several protein-ligand complexes have indicated that both one-to-many and many-to-one relationships of protein-folds and ligand-types are widely seen in the PDB. This means that a given ligand can be recognized by diverse proteins and a given protein can recognize different types of ligands at the same location, ligands referring to endogenous ligands, natural metabolites as well as small molecule inhibitors, and drugs. Given this, it is important to understand the determinants of recognition of a given ligand. This becomes important for applications in drug discovery that includes, lead design and lead optimization, assessment of draggability of a target, identification of off-target effects, polypharmacological targets and drug repurposing. The present work utilizes the information present at the functional sites, rationalizing many examples of ligand binding and deriving useful patterns that can be used for genome-wide function annotation and drug discovery applications. A large-scale analysis of the binding of two important classes of ligands, a sugar and nucleotides was carried out by analysing the sub-structures at their binding sites by matching, aligning and clustering. The two ligands studied are sialic acid, and nucleoside mon/di/tri phosphates (nucleotides or NTPs), for their binding to many proteins reported in the PDB. Sialic acid was found to be recognized by 170 different proteins representing 17 unique sequence families. Our approach deciphered a unified understanding of the basis of recognition of this ligand and showed six structural motifs, which contained different combinations of one or more key structural features, over a common scaffold. The site features refer to certain residues in the binding site that are seen to most frequently occur at their respective topological positions, a result that was evident upon binding site comparisons and 3-D alignment of sites in the different proteins. In the case of nucleotide ligands, 4,677 structures of protein-nucleotide complexes from PDB, belonging to 145 different structural folds and 394 sequence families were… Advisors/Committee Members: Chandra, Nagasuma (advisor), Srinivasan, N (advisor).

Subjects/Keywords: Protein-ligand Interactions; Ligand Recognition; Drug Discovery; Sialic Acid Binding Proteins; Pocketome; Nucleoside Diphosphate Kinase (NDK); Nucleotide Binding Proteins; Mycobacterial Genomes; Biotechnology

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

APA (6^th Edition):

Bhagavat, R. B. R. (2019). Structural Bioinformatics of Ligand Recognition in Proteins : A large-scale Analysis and Applications in Drug Discovery. (Doctoral Dissertation). Indian Institute of Science. Retrieved from http://etd.iisc.ac.in/handle/2005/4249

Chicago Manual of Style (16^th Edition):

Bhagavat, Raghu B R. “Structural Bioinformatics of Ligand Recognition in Proteins : A large-scale Analysis and Applications in Drug Discovery.” 2019. Doctoral Dissertation, Indian Institute of Science. Accessed January 18, 2021. http://etd.iisc.ac.in/handle/2005/4249.

MLA Handbook (7^th Edition):

Bhagavat, Raghu B R. “Structural Bioinformatics of Ligand Recognition in Proteins : A large-scale Analysis and Applications in Drug Discovery.” 2019. Web. 18 Jan 2021.

Vancouver:

Bhagavat RBR. Structural Bioinformatics of Ligand Recognition in Proteins : A large-scale Analysis and Applications in Drug Discovery. [Internet] [Doctoral dissertation]. Indian Institute of Science; 2019. [cited 2021 Jan 18]. Available from: http://etd.iisc.ac.in/handle/2005/4249.

Council of Science Editors:

Bhagavat RBR. Structural Bioinformatics of Ligand Recognition in Proteins : A large-scale Analysis and Applications in Drug Discovery. [Doctoral Dissertation]. Indian Institute of Science; 2019. Available from: http://etd.iisc.ac.in/handle/2005/4249

Indian Institute of Science

7. Dighe, Anasuya. Studies on Dynamic Plasticity of Ligand Binding Sites in Proteins.

Degree: PhD, Faculty of Science, 2019, Indian Institute of Science

URL: http://etd.iisc.ac.in/handle/2005/4236

► Molecular recognition between proteins and their associated ligands constitutes ligand-induced protein rewiring thereby enabling the formation of a stable protein-ligand complex. The studies presented in… (more)

▼ Molecular recognition between proteins and their associated ligands constitutes ligand-induced protein rewiring thereby enabling the formation of a stable protein-ligand complex. The studies presented in this thesis address the conformational plasticity inherent to proteins by virtue of which they adapt to diverse ligands and orchestrate complex biological processes like signal transduction, transcription and protein-protein interaction. Adopting network theory based formalisms for understanding protein-ligand associations involve deconstructing the three-dimensional structure of a protein in terms of nodes and edges. With this view, Protein Structure Networks (PSNs) of ligand-bound complexes are studied by considering their side-chain non-covalent interactions. Agonist and antagonist-bound G-Protein Coupled Receptors (GPCRs) are investigated to gain mechanistic insights into allostery and its role in signal transduction. The degree of similarity between PSNs of these complexes is quantified by means of Network Similarity Score (NSS). The physical nature of these networks is inspected by subjecting them to perturbations and major players in maintaining the stability of such networks are identified. Residue-wise groupings (at backbone and side-chain level) are obtained by applying graph spectral methods. All-atom Molecular Dynamics (MD) simulations are carried out to gain a better understanding of protein-ligand binding by analysing conformational ensembles of these complexes. In this scenario, two members from a highly versatile ligand-inducible transcription factor superfamily, i.e., Nuclear Receptors (NR) are studied, that are known to exhibit extremes of ligand binding behavior ranging from promiscuity to specificity. Diverse ligands are known to bind to proteins and the overall nature of their binding site is investigated. In particular, similarities among binding sites of diverse proteins are analysed by using PocketMatch. Percolation of these similarities to regions surrounding the binding site is reported and examples depicting this extended similarity are discussed. Overall, studies presented in this thesis provide a structural perspective into the adaptability of proteins for recognizing diverse ligands and undergoing local or global re-organizations in their framework to regulate complex biological processes. Advisors/Committee Members: Vishveshwara, Saraswathi (advisor), Chandra, Nagasuma (advisor).

Subjects/Keywords: Protein-ligand Interactions; Protein Ligand Interactions; Protein Structure Networks (PSNs); Graph Theory; Protein Side-chain Networks (PScN); Muscarinic Acetylcholine Receptors; Muscarinic Receptor Cmplexes; Protein-Protein Interactions; Pregnane X Receptor; G-Protein Coupled Receptors (GPCRs); Network Similarity Score (NSS); Biochemistry

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

APA (6^th Edition):

Dighe, A. (2019). Studies on Dynamic Plasticity of Ligand Binding Sites in Proteins. (Doctoral Dissertation). Indian Institute of Science. Retrieved from http://etd.iisc.ac.in/handle/2005/4236

Chicago Manual of Style (16^th Edition):

Dighe, Anasuya. “Studies on Dynamic Plasticity of Ligand Binding Sites in Proteins.” 2019. Doctoral Dissertation, Indian Institute of Science. Accessed January 18, 2021. http://etd.iisc.ac.in/handle/2005/4236.

MLA Handbook (7^th Edition):

Dighe, Anasuya. “Studies on Dynamic Plasticity of Ligand Binding Sites in Proteins.” 2019. Web. 18 Jan 2021.

Vancouver:

Dighe A. Studies on Dynamic Plasticity of Ligand Binding Sites in Proteins. [Internet] [Doctoral dissertation]. Indian Institute of Science; 2019. [cited 2021 Jan 18]. Available from: http://etd.iisc.ac.in/handle/2005/4236.

Council of Science Editors:

Dighe A. Studies on Dynamic Plasticity of Ligand Binding Sites in Proteins. [Doctoral Dissertation]. Indian Institute of Science; 2019. Available from: http://etd.iisc.ac.in/handle/2005/4236

Indian Institute of Science

8. Mehrotra, Prachi. Computational Studies on Structures and Functions of Single and Multi-domain Proteins.

Degree: PhD, Faculty of Science, 2018, Indian Institute of Science

URL: http://etd.iisc.ac.in/handle/2005/3611

► Proteins are essential for the growth, survival and maintenance of the cell. Understanding the functional roles of proteins helps to decipher the working of macromolecular… (more)

▼ Proteins are essential for the growth, survival and maintenance of the cell. Understanding the functional roles of proteins helps to decipher the working of macromolecular assemblies and cellular machinery of living organisms. A thorough investigation of the link between sequence, structure and function of proteins, helps in building a comprehensive understanding of the complex biological systems. Proteins have been observed to be composed of single and multiple domains. Analysis of proteins encoded in diverse genomes shows the ubiquitous nature of multi-domain proteins. Though the majority of eukaryotic proteins are multi-domain in nature, 3-D structures of only a small proportion of multi-domain proteins are known due to difficulties in crystallizing such proteins. While functions of individual domains are generally extensively studied, the complex interplay of functions of domains is not well understood for most multi-domain proteins. Paucity of structural and functional data, affects our understanding of the evolution of structure and function of multi-domain proteins. The broad objective of this thesis is to achieve an enhanced understanding of structure and function of protein domains by computational analysis of sequence and structural data. Special attention is paid in the first few chapters of this thesis on the multi-domain proteins. Classification of multi-domain proteins by implementation of an alignment-free sequence comparison method has been achieved in Chapters 2 and 3. Studies on organization, interactions and interdependence of domain-domain interactions in multi-domain proteins with respect to sequential separation between domains and N to C-terminal domain order have been described in Chapters 4 and 5. The functional and structural repertoire of organisms can be comprehensively studied and compared using functional and structural domain annotations. Chapter 6, 7 and 8 represent the proteome-wide structure and function comparisons of various pathogenic and non-pathogenic microorganisms. These comparisons help in identifying proteins implicated in virulence of the pathogen and thus predict putative targets for disease treatment and prevention. Chapter 1 forms an introduction to the main subject area of this thesis. Starting with describing protein structure and function, details of the four levels of hierarchical organization of protein structure have been provided, along with the databases that document protein sequences and structures. Classification of protein domains considered as the realm of function, structure and evolution has been described. The usefulness of classification of proteins at the domain level has been highlighted in terms of providing an enhanced understanding of protein structure and function and also their evolutionary relatedness. The details of structure, function and evolution of multi-domain proteins have also been outlined in chapter 1. ! Chapter 2 aims to achieve a biologically meaningful classification scheme for multi-domain protein sequences. The overall function… Advisors/Committee Members: Srinivasan, N (advisor), Chandra, Nagasuma (advisor).

Subjects/Keywords: Proteins - Building Blocks; Protein Sequences; Protein Domain; Hidden Markov Models (HMM); Multi-domain Proteins; Mycobacterium smegmatis MC2-155; Mycobacterium tuberculosis; Proteomes; Leptospira Interrogans; Leptospira Biflexa Proteomes; Leptospira Biflexa Genomes; Mycobacterium tuberculosis H37Rv; Mathematics

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

APA (6^th Edition):

Mehrotra, P. (2018). Computational Studies on Structures and Functions of Single and Multi-domain Proteins. (Doctoral Dissertation). Indian Institute of Science. Retrieved from http://etd.iisc.ac.in/handle/2005/3611

Chicago Manual of Style (16^th Edition):

Mehrotra, Prachi. “Computational Studies on Structures and Functions of Single and Multi-domain Proteins.” 2018. Doctoral Dissertation, Indian Institute of Science. Accessed January 18, 2021. http://etd.iisc.ac.in/handle/2005/3611.

MLA Handbook (7^th Edition):

Mehrotra, Prachi. “Computational Studies on Structures and Functions of Single and Multi-domain Proteins.” 2018. Web. 18 Jan 2021.

Vancouver:

Mehrotra P. Computational Studies on Structures and Functions of Single and Multi-domain Proteins. [Internet] [Doctoral dissertation]. Indian Institute of Science; 2018. [cited 2021 Jan 18]. Available from: http://etd.iisc.ac.in/handle/2005/3611.

Council of Science Editors:

Mehrotra P. Computational Studies on Structures and Functions of Single and Multi-domain Proteins. [Doctoral Dissertation]. Indian Institute of Science; 2018. Available from: http://etd.iisc.ac.in/handle/2005/3611

Indian Institute of Science

9. Gadiyaram, Vasundhara. Graph Spectral Methods for Analysis of Protein Structures.

Degree: PhD, Faculty of Science, 2019, Indian Institute of Science

URL: http://etd.iisc.ac.in/handle/2005/4280

► Network representation of protein structures is an information-rich mode of examining protein structure, dynamics and its interactions with biomolecules. Graph spectral methods are extremely useful… (more)

Subjects/Keywords: Protein Structure Networks (PSN); Spectral Theory; Protein Structure Models; Spectral Graph Theory; G-Protein Coupled Receptors; Graph Spectral Method; Correspondence Score (CRS); Eigenvalue-Weighted Cosine Score (EWCS); Eigenvalue-Weighted Cosine Score (EWCS); Network Similarity Score (NSS); Mathematics

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

APA (6^th Edition):

Gadiyaram, V. (2019). Graph Spectral Methods for Analysis of Protein Structures. (Doctoral Dissertation). Indian Institute of Science. Retrieved from http://etd.iisc.ac.in/handle/2005/4280

Chicago Manual of Style (16^th Edition):

Gadiyaram, Vasundhara. “Graph Spectral Methods for Analysis of Protein Structures.” 2019. Doctoral Dissertation, Indian Institute of Science. Accessed January 18, 2021. http://etd.iisc.ac.in/handle/2005/4280.

MLA Handbook (7^th Edition):

Gadiyaram, Vasundhara. “Graph Spectral Methods for Analysis of Protein Structures.” 2019. Web. 18 Jan 2021.

Vancouver:

Gadiyaram V. Graph Spectral Methods for Analysis of Protein Structures. [Internet] [Doctoral dissertation]. Indian Institute of Science; 2019. [cited 2021 Jan 18]. Available from: http://etd.iisc.ac.in/handle/2005/4280.

Council of Science Editors:

Gadiyaram V. Graph Spectral Methods for Analysis of Protein Structures. [Doctoral Dissertation]. Indian Institute of Science; 2019. Available from: http://etd.iisc.ac.in/handle/2005/4280

Indian Institute of Science

10. Raman, Karthik. Systems-Level Modelling And Simulation Of Mycobacterium Tuberculosis : Insights For Drug Discovery.

Degree: PhD, Faculty of Engineering, 2010, Indian Institute of Science

URL: http://etd.iisc.ac.in/handle/2005/685

► Systems biology adopts an integrated approach to study and understand the function of biological systems, particularly, the response of such systems to perturbations, such as… (more)

▼ Systems biology adopts an integrated approach to study and understand the function of biological systems, particularly, the response of such systems to perturbations, such as the inhibition of a reaction in a pathway, or the administration of a drug. The complexity and large scale of biological systems make modelling and simulation an essential and critical part of systems-level studies. Systems-level modelling of pathogenic organisms has the potential to significantly enhance drug discovery programmes. In this thesis, we show how systems – level models can positively impact anti-tubercular drug target identification. *Mycobacterium tuberculosis*, the principal aetiological agent of tuberculosis in humans, is estimated to cause two million deaths every year. The existing drugs, although of immense value in controlling the disease to some extent, have several shortcomings, the most important of them being the emergence of drug resistance rendering even the front-line drugs inactive. As drug discovery efforts are increasingly becoming rational, focussing at a molecular level, the identification of appropriate targets becomes a fundamental pre-requisite. We have constructed many system-level models, to identify drug targets for tuberculosis. We construct a constraint-based stoichiometric model of mycolic acid biosynthesis, and simulate it using flux balance analysis, to identify critical points in mycobacterial metabolism for targeting drugs. We then analyse protein – protein functional linkage networks to identify influential hubs, which can be targeted to disrupt bacterial metabolism. An important aspect of tuberculosis is the emergence of drug resistance. A network analysis of potential information pathways in the cell helps to identify important proteins as co-targets, targeting which could counter the emergence of resistance. We integrate analyses of metabolism, protein – protein interactions and protein structures to develop a generic drug target identification pipeline, for identifying most suitable drug targets. Finally, we model the interplay between the pathogen and the human immune system, using Boolean networks, to elucidate critical factors influencing the outcome of infection. The strategies described can be applied to understand various pathogens and can impact many drug discovery programmes. Advisors/Committee Members: Chandra, Nagasuma (advisor).

Subjects/Keywords: Microbacterium Tuberculosis - Modeling And Simulation; Pathway Modelling; Drug Discovery; Target Identification; Systems Biology; Tuberculosis; Biological Networks; Mycolic Acid Pathway; Protein-Protein Interactions; Drug Resistance; TargetTB; Mtb; Mycolic Acid Biosynthesis Pathway (MAP); Pathway–pathway Networks; Systems Biology

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

APA (6^th Edition):

Raman, K. (2010). Systems-Level Modelling And Simulation Of Mycobacterium Tuberculosis : Insights For Drug Discovery. (Doctoral Dissertation). Indian Institute of Science. Retrieved from http://etd.iisc.ac.in/handle/2005/685

Chicago Manual of Style (16^th Edition):

Raman, Karthik. “Systems-Level Modelling And Simulation Of Mycobacterium Tuberculosis : Insights For Drug Discovery.” 2010. Doctoral Dissertation, Indian Institute of Science. Accessed January 18, 2021. http://etd.iisc.ac.in/handle/2005/685.

MLA Handbook (7^th Edition):

Raman, Karthik. “Systems-Level Modelling And Simulation Of Mycobacterium Tuberculosis : Insights For Drug Discovery.” 2010. Web. 18 Jan 2021.

Vancouver:

Raman K. Systems-Level Modelling And Simulation Of Mycobacterium Tuberculosis : Insights For Drug Discovery. [Internet] [Doctoral dissertation]. Indian Institute of Science; 2010. [cited 2021 Jan 18]. Available from: http://etd.iisc.ac.in/handle/2005/685.

Council of Science Editors:

Raman K. Systems-Level Modelling And Simulation Of Mycobacterium Tuberculosis : Insights For Drug Discovery. [Doctoral Dissertation]. Indian Institute of Science; 2010. Available from: http://etd.iisc.ac.in/handle/2005/685

Open Access Theses and Dissertations