Evolutionary rates vary vastly across intraspecific genes and the determinants of these rates is of central concern to the field of comparative genomics. Tradition has held that preservation of protein function conserved the sequence, however mounting evidence implicates the biophysical properties of proteins themselves as the elements that constrain sequence evolution. Of these properties, the exposure of a residue to solvent is the most prevalent determinant of its evolutionary rate due to pressures to maintain proper synthesis and folding of the structure. In this work, we have developed a model that considers the microenvironment of a residue in the estimation of its evolutionary rate. By working within the structural context of a protein's residues, we show that our model is better able to capture the overall evolutionary trends affecting conservation of both the coding sequences and the protein structures from a genomic level down to individual genes. / text

Identifying sites under evolutionary pressure and predicting the effects of substitutions at those sites are among the greatest standing problems in bioinformatics and computational biology. Moreover, the two problems have traditionally been separated by the enormous chasm that exists between molecular evolutionary biologists interested in the evolutionary process and theoretical chemists interested in free energy changes. As a result, identifying sites under selective pressure has most often left out any semblance of structural biology and biochemistry; likewise, theoretical chemistry tends to rely strictly on first principles calculations rather than thinking first about biologically simple and interpretable results. Here, I have tried to integrate these two intuitions with regard to protein function and evolution. First, I developed a model that implements structural measurements into a traditional structure-blind molecular evolutionary model. This structure-aware model performs significantly better at identifying sites under both purifying and diversifying selection than its structure-blind counter part. Second, I go further to understand the extent to which structural features of any kind can predict the evolutionary process. By comparing site-wise evolution between human and avian influenza, I find that structural features can account for 24% to 36% of the evolutionary pressure on influenza hemagglutinin. Third, I developed a computational method based on first principles molecular dynamics simulations to predict the biological effect of substitutions in the Machupo virus--Human receptor protein--protein interface. I found that relatively simple energetic proxies offer a reasonable substitute for rigorous free energy calculations; such simple proxies could allow non-experts to naively implement first principles methods without being forced to consider all possible degrees of freedom for post hoc calculations. / text

Biometals such as copper, cobalt and zinc are essential to life. These transition metals are used as cofactors in many enzymes. Nonetheless, these metals cause deleterious effects if their intracellular concentration exceeds the cells' requirement. Prokaryotic organisms usually employ efflux systems to maintain metals in appropriate intracellular concentrations.The Cus system of Escherichia coli plays a crucial part in the copper homeostasis of the organism. This system is a tetrapartite efflux system, which includes an additional component compared to similar efflux systems. This fourth component is a small periplasmic protein, CusF. CusF is essential for full copper resistance, yet its role within the Cus system has not been characterized. It could potentially serve in the role of a metallochaperone or as a regulator to the Cus system.To gain insight into the molecular mechanism of resistance of this system, I have structurally and biochemically characterized CusF. Using X-ray crystallography I determined the CusF structure. CusF displays a novel fold for a copper binding protein. Through multiple sequence alignment and NMR chemical shift experiments, I proposed a metal binding site in CusF, which I confirmed through determination of the structure of CusF-Ag(I). CusF displays a novel coordination of Ag(I) and Cu(I) through a Met2His motif and a cation-pi interaction between the metal ion and a tryptophan sidechain. Furthermore, I have shown that CusF binds Cu(I) and Ag(I) specifically and tightly.I investigated the role of the tryptophan at the binding site to establish its effect on metal binding and function of CusF. I have shown through competitive binding assays, NMR studies and through collaborative EXAFS studies that the tryptophan plays an essential role in CusF metal handling. The affinity of CusF for Cu(I) is influenced by this residue. Moreover, the tryptophan also caps the binding site such that oxidation of the bound metal as well access to adventitious ligands is prevented. In summary, these findings show that the structure and metal site of CusF are unique and are specifically designed to perform the function of CusF as a metallochaperone to the Cus system.

Tuberculosis and malaria are among the most deadly infectious diseases in the world. The prevalence in regions without well-established public health causes economical and financial burdens for both society and patients. There is an urgent need to find effective treatments due to the emergence of drug-resistant strains. The aim of the studies reported here was to gain knowledge from the protein structures that can lead to the elimination of these pathogens. In these studies, protein crystallography is the main method used to solve protein structure. Based on the protein structure, we used different methods to characterize the protein function of three lipid-binding proteins (LprG, LprA, and gp232), and to identify potent inhibitors against Plasmodium falciparum enoyl-ACP reductase (PfENR), a drug target protein involved in central lipid metabolism. To characterize the function of two lipid-binding proteins (LprG and LprA), liquid chromatography-mass spectrometry (LC-MS) was used to analyze the ligand extract. In the study of tail fiber protein from mycobacteriophage, we used protein sequence alignment to identify gp232 as a major tail fiber protein, which potentially binds to lipids on the cellular surface of mycobacteria. A pull-down assay and imaging methods (fluorescence microscopy and electron microscopy) were conducted to confirm the function of gp232. In the structural study of PfENR, the structure-activity relationships method was used to find potent inhibitors against PfENR, which would show stronger inhibition than the known inhibitor triclosan. The triclosan-like analogs with modification at the 5-position revealed a new binding site in PfENR that has great potential for improving the potency of inhibition. We found that two inhibitors containing the core structure of piperidine and tetrahydroquinoline reached this new binding site and were 10-fold more potent than triclosan. The structural study of PfENR provides structural insights into the inhibitor-binding site that can lead to the discovery of new drugs. The comprehensive knowledge that we gained from the structural studies of these lipid-binding proteins provide new information that could lead to a greater understanding of pathogen physiology or guide the discovery of effective treatments to eliminate the pathogens.

Accurate 3D protein structure prediction is one of the most challenging problems facing bioinformaticians today. This thesis develops and examines an evaluation module for ranking predicted super-secondary structures – specifically a-helix pairs – as part of a case-based reasoning system. The proposed module is part of the Triptych project, which aims at the accurate prediction of the three-dimensional structure of proteins from contact maps. Triptych is an advanced case-based reasoning system that utilizes a library of existing protein structures and motifs to help predict the structure of a known polypeptide chain of amino acids that represents a target a-helix pair. The proposed module evaluates possible solutions by integrating multiple strategies, learning methods and sources of knowledge in the form of expert advisors. It uses advisors which integrate knowledge from the fields of biology, biochemistry, classical physics, and statistical data analysis obtained from pre-determined structures. Lastly, the proposed evaluation module would allow for the integration of more sources of knowledge, in the form of expert advisors, as well as serve as a framework for evaluating other structural motifs in future. / Thesis (Master, Computing) -- Queen's University, 2007-09-09 19:42:59.094

1D protein sequences, 2D contact maps and 3D structures are three different representational levels of detail for proteins. Predicting protein 3D structures from their 1D sequences remains one of the complex challenges of bioinformatics. The "Divide and Conquer" principle is applied in our research to handle this challenge, by dividing it into two separate yet dependent subproblems, using a Case-Based Reasoning (CBR) approach. Firstly, 2D contact maps are predicted from their 1D protein sequences; secondly, 3D protein structures are then predicted from their predicted 2D contact maps. We focus on the problem of identifying common substructural patterns of protein contact maps, which could potentially be used as building blocks for a bottom-up approach for protein structure prediction. We further demonstrate how to improve identifying these patterns by combining both protein sequence and structural information. We assess the consistency and the efficiency of identifying common substructural patterns by conducting statistical analyses on several subsets of the experimental results with different sequence and structural information. / Thesis (Master, Computing) -- Queen's University, 2009-04-23 22:01:04.528

The three-dimensional structure of a protein molecule is the key to understanding its biological and physiological properties. A major problem in bioinformatics is to efficiently determine the three-dimensional structures of query proteins. Protein NMR structure de- termination is one of the main experimental methods and is comprised of: (i) protein sample production and isotope labelling, (ii) collecting NMR spectra, and (iii) analysis of the spectra to produce the protein structure. In protein NMR, the three-dimensional struc- ture is determined by exploiting a set of distance restraints between spatially proximate atoms. Currently, no practical automated protein NMR method exists that is without human intervention. We first propose a complete automated protein NMR pipeline, which can efficiently be used to determine the structures of moderate sized proteins. Second, we propose a novel and efficient semidefinite programming-based (SDP) protein structure determination method. The proposed automated protein NMR pipeline consists of three modules: (i) an automated peak picking method, called PICKY, (ii) a backbone chemical shift assign- ment method, called IPASS, and (iii) a protein structure determination method, called FALCON-NMR. When tested on four real protein data sets, this pipeline can produce structures with reasonable accuracies, starting from NMR spectra. This general method can be applied to other macromolecule structure determination methods. For example, a promising application is RNA NMR-assisted secondary structure determination. In the second part of this thesis, due to the shortcomings of FALCON-NMR, we propose a novel SDP-based protein structure determination method from NMR data, called SPROS. Most of the existing prominent protein NMR structure determination methods are based on molecular dynamics coupled with a simulated annealing schedule. In these methods, an objective function representing the error between observed and given distance restraints is minimized; these objective functions are highly non-convex and difficult to optimize. Euclidean distance geometry methods based on SDP provide a natural formulation for realizing a three-dimensional structure from a set of given distance constraints. However, the complexity of the SDP solvers increases cubically with the input matrix size, i.e., the number of atoms in the protein, and the number of constraints. In fact, the complexity of SDP solvers is a major obstacle in their applicability to the protein NMR problem. To overcome these limitations, the SPROS method models the protein molecule as a set of intersecting two- and three-dimensional cliques. We adapt and extend a technique called semidefinite facial reduction for the SDP matrix size reduction, which makes the SDP problem size approximately one quarter of the original problem. The reduced problem is solved nearly one hundred times faster and is more robust against numerical problems. Reasonably accurate results were obtained when SPROS was applied to a set of 20 real protein data sets.

NMR spectroscopy is not only for protein structure determination, but also for drug screening and studies of dynamics and interactions. In both cases, one of the main bottleneck steps is backbone assignment. When a homologous structure is available, it can accelerate assignment. Such structure-based methods are the focus of this thesis. This thesis aims for fast and robust methods for NMR assignment problems; in particular, structure-based backbone assignment and chemical shift mapping. For speed, we identified situations where the number of 15N-labeled experiments for structure-based assignment can be reduced; in particular, when a homologous assignment or chemical shift mapping information is available. For robustness, we modeled and directly addressed the errors. Binary integer linear programming, a well-studied method in operations research, was used to model the problems and provide practically efficient solutions with optimality guarantees. Our approach improved on the most robust method for structure-based backbone assignment on 15N-labeled data by improving the accuracy by 10% on average on 9 proteins, and then by handling typing errors, which had previously been ignored. We show that such errors can have a large impact on the accuracy; decreasing the accuracy from 95% or greater to between 40% and 75%. On automatically picked peaks, which is much noisier than manually picked peaks, we achieved an accuracy of 97% on ubiquitin. In chemical shift mapping, the peak tracking is often done manually because the problem is inherently visual. We developed a computer vision approach for tracking the peak movements with average accuracy of over 95% on three proteins with less than 1.5 residues predicted per peak. One of the proteins tested is larger than any tested by existing automated methods, and it has more titration peak lists. We then combined peak tracking with backbone assignment to take into account contact information, which resulted in an average accuracy of 94% on one-to-one assignments for these three proteins. Finally, we applied peak tracking and backbone assignment to protein-ligand docking to illustrate the potential for fast 3D complex determination.

Mitochondria evolved through endosymbiosis of an ancient prokaryote, and subsequently lost most genes to the host genome. In order for mitochondrial proteins to be correctly localized from the host cytosol to the mitochondrial compartments, a complex protein targeting and import machinery has evolved. Key receptor components in the protein translocase complex of the outer mitochondrial membrane, Tom20 and Tom22, recognize proteins to be imported and assist their insertion across the outer membrane. The solution structure of the Tom20 receptor domain from Arabidopsis thaliana was determined by nuclear magnetic resonance spectroscopy, and revealed that this protein has significant structural differences to its functional analogue found in animals and fungi.

Thesis (Ph. D.)--University of Oklahoma. / Includes bibliographical references.