Browsing by Subject "Structural biology"
Now showing 1 - 3 of 3
- Results Per Page
- Sort Options
Item Characterization of the conformational states of phospholamban and their roles in regulation of SR Calcium-ATPase(2012-12) Gustavsson, Bengt MartinMembrane proteins constitute 30% of the human genome but represent only a small fraction of the known three-dimensional protein structures. In this thesis I describe the characterization of the membrane protein complex between sarcoplasmic reticulum Ca2+-ATPase (SERCA) and phospholamban (PLN). SERCA drives cardiac muscle relaxation by active transport of Ca2+ ions into the SR. PLN is a small membrane protein that consists of a helical trans-membrane domain connected to a cytoplasmic domain through a short loop, and inhibits SERCA through intra-membrane interactions. The cytoplasmic domain of PLN is in equilibrium between a helical, membrane-associated T state and an unfolded, membrane-dissociated R state. Here, I summarize the work to probe the structures of the T and R states and elucidate the role of the conformational equilibrium in regulation of SERCA. Using solution and solid state NMR in combination with biochemical assays I show that the structures of T and R state but not their relative populations are conserved in different lipid environments and sample conditions. Furthermore, the T/ R equilibrium has a central role in SERCA regulation and is crucial to relieve the inhibition of the enzyme. These findings provide new insights into SERCA/PLN function and offer a unique view of the role of conformational equilibria and multiple conformational states in membrane protein structure and function.Item Computational methods for protein structure prediction and energy minimization(2013-07) Kauffman, Christopher DanielThe importance of proteins in biological systems cannot be overstated: genetic defects manifest themselves in misfolded proteins with tremendous human cost, drugs in turn target proteins to cure diseases, and our ability to accurately predict the behavior of designed proteins has allowed us to manufacture biological materials from engineered micro-organisms. All of these areas stand to benefit from fundamental improvements in computer modeling of protein structures. Due to the richness and complexity of protein structure data, it is a fruitful area to demonstrate the power of machine learning. In this dissertation we address three areas of structural bioinformatics with machine learning tools. Where current approaches are limited, we derive new solution methods via optimization theory.Identifying residues that interact with ligands is useful as a first step to understanding protein function and as an aid to designing small molecules that target the protein for interaction. Several studies have shown sequence features are very informative for this type of prediction while structure features have also been useful when structure is available. In the first major topic of this dissertation, we develop a sequence-based method, called LIBRUS, that combines homology-based transfer and direct prediction using machine learning. We compare it to previous sequence-based work and current structure-based methods. Our analysis shows that homology-based transfer is slightly more discriminating than a support vector machine learner using profiles and predicted secondary structure. We combine these two approaches in a method called LIBRUS. On a benchmark of 885 sequence independent proteins, it achieves an area under the ROC curve (ROC) of 0.83 with 45% precision at 50% recall, a significant improvement over previous sequence-based efforts. On an independent benchmark set, a current method, FINDSITE, based on structure features achieves a 0.81 ROC with 54% precision at 50% recall while LIBRUS achieves a ROC of 0.82 with 39% precision at 50% recall at a smaller computational cost. When LIBRUS and FINDSITE predictions are combined, performance is increased beyond either reaching an ROC of 0.86 and 59% precision at 50% recall. Coarse-grained models for protein structure are increasingly utilized in simulations and structural bioinformatics to avoid the cost associated with including all atoms. Currently there is little consensus as to what accuracy is lost transitioning from all-atom to coarse-grained models or how best to select the level of coarseness. The second major thrust of this dissertation is employing machine learning tools to address these two issues. We first illustrate how binary classifiers and ranking methods can be used to evaluate coarse-, medium-, and fine-grained protein models for their ability to discriminate between correctly and incorrectly folded structures. Through regularization and feature selection, we are able to determine the trade-offs associated with coarse models and their associated energy functions. We also propose an optimization method capable of creating a mixed representation of the protein from multiple granularities. The method utilizes a hinge loss similar to support vector machines and a max/L1 group regularization term to perform feature selection. Solutions are found for the whole regularization path using subgradient optimization. We illustrate its behavior on decoy discrimination and discuss implications for data-driven protein model selection.Finally, identifying the folded structure of a protein with a given sequence is often cast as a global optimization problem. One seeks the structural conformation that minimizes an energy function as it is believed the native states of naturally occurring proteins are at the global minimum of nature's energy function. In mathematical programming, convex optimization is the tool of choice for the speedy solution of global optimization problems. In the final section of this dissertation we introduce a framework, dubbed Marie, which formulates protein folding as a convex optimization problem. Protein structures are represented using convex constraints with a few well-defined nonconvexities that can be handled. Marie trades away the ability to observe the dynamics of the system but gains tremendous speed in searching for a single low-energy structure. Several convex energy functions that mirror standard energy functions are established so that Marie performs energy minimization by solving a series of semidefinite programs. Marie's speed allows us to study a wide range of parameters defining a Go-like potential where energy is based solely on native contacts. We also implement an energy function affecting hydrophobic collapse, thought to be a primary driving force in protein folding. We study several variants and find that they are insufficient to reproduce native structures due in part to native structures adopting non-spherical conformations.Item Fragment based inhibitor design of Mycobacterium tuberculosis BioA(2015-01) Dai, Ran7,8-Diaminopelargonic acid synthase (BioA) of Mycobacterium tuberculosis (Mtb) is a recently validated target for therapeutic intervention in the treatment of tuberculosis (TB). We herein report our fragment based inhibitor design of Mtb BioA. Using differential scanning fluorimetry (DSF) fragment screening, the Maybridge Ro3 library of 1000 molecules was screened. Twenty-one compounds giving rise to Tm shifts exceeding ±2°C were then investigated in crystallographic experiments. Six fragments have been co-crystallized with BioA to characterize binding. Each compound has a unique binding mode, and subtle variations in ligand binding site geometry are induced upon binding of different fragment molecules. Binding affinities of the fragments were characterized via isothermal titration calorimetry (ITC). A fragment extension strategy was used to rationally optimize these fragment hits. A commerce based SAR was used and identified 50 compounds containing the core of one of the fragments. These compounds were further screened virtually and experimently by DSF. Four optimized BioA ligands from fragment optimization were validated by X-ray crystallography, including a potent aryl hydrazine inhibitor of BioA that reversibly modifies the pyridoxal-5′-phosphate (PLP) cofactor. Binding affinities of these ligands have been characterized by ITC or kinetic assay. The six fragment complex structures were also used for optimization of HTS lead compounds. Six HTS lead compounds were co-crystallized with BioA at high resolution. Design of optimized compounds was by overlapping the fragments and HTS lead binding conformations in the BioA active site. Molecules predicted to have better potency were proposed. Two N-aryl piperazine inhibitors of BioA from HTS optimization were characterized using X-ray crystallography and ITC. One inhibitor that combines features of one HTS lead and one fragment was confirmed with improved binding affinity by ITC.