Functional Evolution of PLP-dependent Enzymes based on Active-Site Structural Similarities

Families of distantly related proteins typically have very low sequence identity, which hinders evolutionary analysis and functional annotation. Slowly evolving features of proteins, such as an active site, are therefore valuable for annotating putative and distantly related proteins. To date, a complete evolutionary analysis of the functional relationship of an entire enzyme family based on active-site structural similarities has not yet been undertaken. Pyridoxal-5’-phosphate (PLP) dependent enzymes are primordial enzymes that diversified in the last universal ancestor. Using the Comparison of Protein Active Site Structures (CPASS) software and database, we show that the active site structures of PLP-dependent enzymes can be used to infer evolutionary relationships based on functional similarity. The enzymes successfully clustered together based on substrate specificity, function, and three-dimensional fold. This study demonstrates the value of using active site structures for functional evolutionary analysis and the effectiveness of CPASS

Metabolic Feedback Inhibition Influences Metabolite Secretion by the Human Gut Symbiont Bacteroides thetaiotaomicron

Microbial metabolism and trophic interactions between microbes give rise to complex multispecies communities in microbe-host systems. Bacteroides thetaiotaomicron (B. theta) is a human gut symbiont thought to play an important role in maintaining host health. Untargeted nuclear magnetic resonance metabolomics revealed B. theta secretes specific organic acids and amino acids in defined minimal medium. Physiological concentrations of acetate and formate found in the human intestinal tract were shown to cause dose-dependent changes in secretion of metabolites known to play roles in host nutrition and pathogenesis. While secretion fluxes varied, biomass yield was unchanged, suggesting feedback inhibition does not affect metabolic bioenergetics but instead redirects carbon and energy to CO2 and H2. Flux balance analysis modeling showed increased flux through CO2-producing reactions under glucose-limiting growth conditions. The metabolic dynamics observed for B. theta, a keystone symbiont organism, underscores the need for metabolic modeling to complement genomic predictions of microbial metabolism to infer mechanisms of microbemicrobe and microbe-host interactions

Bioinformatic and Biophysical Analyses of Proteins

The prevailing dogma in structural genomics is the existence of a strong correlation between protein sequence, structure, and biological function. Proteins with high sequence similarity typically have a similar, if not the same, structure and function. In many cases this logic can fail due to distantly related proteins having very low sequence similarity, a lack of a representative structure, structural novelty, or the absence of a characterized function. Further, the paradigm fails to account for dynamics, which have a significant effect on structural stability and enzymatic efficacy. Nuclear magnetic resonance (NMR) spectroscopy is uniquely capable of solving the structure, assisting with annotation, and deriving the dynamics of previously unstudied proteins. Historically, NMR has been used to calculate structures and dynamics of small or disordered proteins, which could then be used with computational methods to predict function. Predicted annotations are then confirmed by further experimentation such as ligand screens or titrations. The combination of NMR and bioinformatics, therefore, works synergistically to yield significant results, which has the ability to characterize highly complex proteins and fill gaps in the sequence to structure to function paradigm. This dissertation begins with work accomplished using the Comparison of Active Site Structures (CPASS) software to show the functional evolution of a class of cofactor dependent enzymes and also expands on the utility of CPASS with the implementation of a functional clustering of its database. Described next is an emphasis on protein and peptide structure and the relationship between the experimentally derived ensembles and biological function and dynamics. Recent improvements in the calculation of protein fast-timescale dynamics are then introduced before a final concluding chapter. Advisor: Robert Power

Bioinformatic and biophysical analyses of proteins

The prevailing dogma in structural genomics is the existence of a strong correlation between protein sequence, structure, and biological function. Proteins with high sequence similarity typically have a similar, if not the same, structure and function. In many cases this logic can fail due to distantly related proteins having very low sequence similarity, a lack of a representative structure, structural novelty, or the absence of a characterized function. Further, the paradigm fails to account for dynamics, which have a significant effect on structural stability and enzymatic efficacy. Nuclear magnetic resonance (NMR) spectroscopy is uniquely capable of solving the structure, assisting with annotation, and deriving the dynamics of previously unstudied proteins. Historically, NMR has been used to calculate structures and dynamics of small or disordered proteins, which could then be used with computational methods to predict function. Predicted annotations are then confirmed by further experimentation such as ligand screens or titrations. The combination of NMR and bioinformatics, therefore, works synergistically to yield significant results, which has the ability to characterize highly complex proteins and fill gaps in the sequence to structure to function paradigm. This dissertation begins with work accomplished using the Comparison of Active Site Structures (CPASS) software to show the functional evolution of a class of cofactor dependent enzymes and also expands on the utility of CPASS with the implementation of a functional clustering of its database. Described next is an emphasis on protein and peptide structure and the relationship between the experimentally derived ensembles and biological function and dynamics. Recent improvements in the calculation of protein fast-timescale dynamics are then introduced before a final concluding chapter

Bioinformatic and biophysical analyses of proteins

The prevailing dogma in structural genomics is the existence of a strong correlation between protein sequence, structure, and biological function. Proteins with high sequence similarity typically have a similar, if not the same, structure and function. In many cases this logic can fail due to distantly related proteins having very low sequence similarity, a lack of a representative structure, structural novelty, or the absence of a characterized function. Further, the paradigm fails to account for dynamics, which have a significant effect on structural stability and enzymatic efficacy. Nuclear magnetic resonance (NMR) spectroscopy is uniquely capable of solving the structure, assisting with annotation, and deriving the dynamics of previously unstudied proteins. Historically, NMR has been used to calculate structures and dynamics of small or disordered proteins, which could then be used with computational methods to predict function. Predicted annotations are then confirmed by further experimentation such as ligand screens or titrations. The combination of NMR and bioinformatics, therefore, works synergistically to yield significant results, which has the ability to characterize highly complex proteins and fill gaps in the sequence to structure to function paradigm. This dissertation begins with work accomplished using the Comparison of Active Site Structures (CPASS) software to show the functional evolution of a class of cofactor dependent enzymes and also expands on the utility of CPASS with the implementation of a functional clustering of its database. Described next is an emphasis on protein and peptide structure and the relationship between the experimentally derived ensembles and biological function and dynamics. Recent improvements in the calculation of protein fast-timescale dynamics are then introduced before a final concluding chapter

Functional Evolution of PLP-dependent Enzymes based on Active-Site Structural Similarities

Families of distantly related proteins typically have very low sequence identity, which hinders evolutionary analysis and functional annotation. Slowly evolving features of proteins, such as an active site, are therefore valuable for annotating putative and distantly related proteins. To date, a complete evolutionary analysis of the functional relationship of an entire enzyme family based on active-site structural similarities has not yet been undertaken. Pyridoxal-5’-phosphate (PLP) dependent enzymes are primordial enzymes that diversified in the last universal ancestor. Using the Comparison of Protein Active Site Structures (CPASS) software and database, we show that the active site structures of PLP-dependent enzymes can be used to infer evolutionary relationships based on functional similarity. The enzymes successfully clustered together based on substrate specificity, function, and three-dimensional fold. This study demonstrates the value of using active site structures for functional evolutionary analysis and the effectiveness of CPASS

The NMR Solution Structure and Function of RPA3313: A Hypothetical Protein from \u3ci\u3eR. palustris\u3c/i\u3e

Protein function elucidation often relies heavily on amino acid sequence analysis and other bioinformatics approaches. The reliance is further extended to structure homology modeling for ligand docking and protein-protein interaction mapping. However, sequence analysis of RPA3313 exposes a large, unannotated class of hypothetical proteins mostly from the Rhizobiales order. In the absence of sequence and structure information, further functional elucidation of this class of proteins has been significantly hindered. A high quality NMR structure of RPA3313 reveals that the protein forms a novel split βαβ fold with a conserved ligand binding pocket between the first β-strand and the N-terminus of the α-helix. Conserved residue analysis and protein-protein interaction prediction analyses reveal multiple protein binding sites and conserved functional residues. Results of a mass spectrometry proteomic analysis strongly point toward interaction with the ribosome and its subunits. The combined structural and proteomic analyses suggest that RPA3313 by itself or in a larger complex may assist in the transportation of substrates to or from the ribosome for further processing

Understanding interactions of Citropin 1.1 analogues with model membranes and their influence on biological activity

The rapid emergence of resistant bacterial strains has made the search for new antibacterial agents an endeavor of paramount importance. Cationic antimicrobial peptides (AMPs) have the ability to kill resistant pathogens while diminishing the development of resistance. Citropin 1.1 (Cit 1.1) is an AMP effective against a broad range of pathogens. 20 analogues of Cit 1.1 were prepared to understand how sequence variations lead to changes in structure and biological activity. Various analogues exhibited an increased antimicrobial activity relative to Cit 1.1. The two most promising, AMP-016 (W3F) and AMP-017 (W3F, D4R, K7R) presented a 2- to 8-fold increase in activity against MRSA (both = 4 µg/mL). AMP-017 was active against E. coli (4 µg/mL), K. pneumoniae (8 µg/mL), and A. baumannii (2 µg/mL). NMR studies indicated that Cit 1.1 and its analogues form a head-to-tail helical dimer in a membrane environment, which differs from a prior study by Sikorska et al. Active peptides displayed a greater tendency to form α-helices and to dimerize when in contact with a negatively-charged membrane. Antimicrobial activity was observed to correlate to the overall stability of the α-helix and to a positively charged N-terminus. Biologically active AMPs were shown by SEM and flow cytometry to disrupt membranes in both Gram-positive and Gram-negative bacteria through a proposed carpet mechanism. Notably, active peptides exhibited typical serum stabilities and a good selectivity for bacterial cells ove

Metabolic Feedback Inhibition Influences Metabolite Secretion by the Human Gut Symbiont Bacteroides thetaiotaomicron

Microbial metabolism and trophic interactions between microbes give rise to complex multispecies communities in microbe-host systems. Bacteroides thetaiotaomicron (B. theta) is a human gut symbiont thought to play an important role in maintaining host health. Untargeted nuclear magnetic resonance metabolomics revealed B. theta secretes specific organic acids and amino acids in defined minimal medium. Physiological concentrations of acetate and formate found in the human intestinal tract were shown to cause dose-dependent changes in secretion of metabolites known to play roles in host nutrition and pathogenesis. While secretion fluxes varied, biomass yield was unchanged, suggesting feedback inhibition does not affect metabolic bioenergetics but instead redirects carbon and energy to CO2 and H2. Flux balance analysis modeling showed increased flux through CO2-producing reactions under glucose-limiting growth conditions. The metabolic dynamics observed for B. theta, a keystone symbiont organism, underscores the need for metabolic modeling to complement genomic predictions of microbial metabolism to infer mechanisms of microbemicrobe and microbe-host interactions

Poly(cyclosilane) Postpolymerization Hydrosilylation

Postpolymerization functionalization is a key strategy in the diversification of polymeric materials for the conferral of tailored properties. Poly(cyclosilane)s are structurally complex polymers with an all-silicon backbone, featuring a periodic array of hydrido (Si–H) side chains that are potentially suitable for postpolymerization functionalization via hydrosilylation. At the same time, classical methods for hydrosilylation employing Pt- or Pd-based catalysts can result in Si–Si bond scission. Herein, we demonstrate borane-catalyzed hydrosilylation reactions between α-olefins and small molecules or three distinct poly(cyclosilane) architectures. We investigate chemoselectivity for end group versus internal Si–H groups and find that 29Si cross-polarization magic angle spinning can provide insight on site-selectivity in the functionalization of a complex poly(cyclosilane). We further show that postpolymerization hydrosilylation, converting oxidatively sensitive Si–H groups to Si–alkyl chains, modulates solubility and physical characteristics, optical properties, pyrolytic reactivity, and air sensitivity