690 research outputs found

    Capturing Atomic Interactions with a Graphical Framework in Computational Protein Design

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    A protein's amino acid sequence determines both its chemical and its physical structures, and together these two structures determine its function. Protein designers seek new amino acid sequences with chemical and physical structures capable of performing some function. The vast size of sequence space frustrates efforts to find useful sequences. Protein designers model proteins on computers and search through amino acid sequence space computationally. They represent the three-dimensional structures for the sequences they examine, specifying the location of each atom, and evaluate the stability of these structures. Good structures are tightly packed but are free of collisions. Designers seek a sequence with a stable structure that meets the geometric and chemical requirements to function as desired; they frame their search as an optimization problem. In this dissertation, I present a graphical model of the central optimization problem in protein design, the side-chain-placement problem. This model allows the formulation of a dynamic programming solution, thus connecting side-chain placement with the class of NP-complete problems for which certain instances admit polynomial time solutions. Moreover, the graphical model suggests a natural data structure for storing the energies used in design. With this data structure, I have created an extensible framework for the representation of energies during side-chain-placement optimization and have incorporated this framework into the Rosetta molecular modeling program. I present one extension that incorporates a new degree of structural variability into the optimization process. I present another extension that includes a non-pairwise decomposable energy function, the first of its kind in protein design, laying the ground-work to capture aspects of protein stability that could not previously be incorporated into the optimization of side-chain placement

    Doctor of Philosophy

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    dissertationThe coiled-coil is a common protein tertiary structural motif that is composed of two or more alpha helices intertwined together to formed a supercoil. In biological systems, the coiledcoil motif often forms the oligomerization domain of various proteins including DNA binding proteins, structural and transport proteins, and cellular transport and fusion proteins. It was first described by Crick in the 1950s while describing the structure of α-keratin and has since that time been the subject of numerous engineering and mutation studies. This versatile motif has been adapted to a number of nonbiological applications including environmentally responsive hydrogels, crosslinking agents, the construction of self-assembling fibers for tissue engineering, and biosensor surfaces. In this dissertation, we test the applicability of computational methods to understand the underlying energetics in coiled-coils as we apply molecular modeling approaches in the development of pharmaceutics. Two studies are described which test the limits of modern molecular dynamic force fields to understand the structural dynamics of the motif and to use energy calculation methodologies to predict favorable mutations for heterodimer formation and specificity. The first study considers the increasingly common use of fluorinated residues in protein pharmaceutics with regard to their incorporation in coiled-coils. Many studies find that fluorinated residues in the hydrophobic core increase protein stability against chemical and thermal denaturants. Often their incorporation fails to consider structural, energetic, and geometrical differences between these fluorinated residues and their nonfluorinated counterparts. To consider these differences, several variants of Hodges' very stable parallel heterodimer coiledcoil were constructed to examine the effect of salt bridge lengths and geometries with mixed fluorinated and nonfluorinated packed hydrophobic cores. In the second study, we collaborated with an experimental laboratory in the development of a mutant Bcr monomer with designed mutations to increase specificity and binding to the oncoprotein Bcr-Abl for use as an apoptosis inducing agent in chronic myelogenous leukemia (CML) cells. The final chapters of this dissertation discuss challenges and limitations that were encountered using force fields and energetic methods in our attempts to use computational chemistry to model this protein motif

    Development of hydroxyl free radical chemistry for the surface mapping of proteins

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    In order to rapidly probe the structure of proteins in solution, a protocol for chemical modification of solvent accessible amino acid side chains was developed, and the sites of modification were determined by mass spectrometry to describe the surface of the protein. The knowledge gained about side chain solvent accessibility allowed the critical evaluation of structural models for the proteins examined, allowing incorrect models to be rejected and more likely models to be proposed. Methods were developed using either Fenton chemistry or photolysis of hydrogen peroxide to generate hydroxyl radicals in situ. The oxidation chemistry of these radicals with the side chains of various amino acids were exploited to label solvent accessible sites on several model proteins of known tertiary structure. The relative apparent rate of oxidation of the side chains was shown to be a function of the known solvent accessibility and the chemical reactivity of the amino acid. The known properties of hydroxyl radical oxidation of amino acid side chains allows hydroxyl radical surface mapping data to be used as biophysical constraints for evaluating structural models of proteins and protein-protein interactions. Computational models of the yeast ribonucleotide reductase inhibitor protein Sml 1 p were evaluated using surface mapping data of the functional C 14S Sml 1 p protein. Various full atom computational models were discredited based on the surface mapping data, and a manually adjusted computational model was generated that possessed low free energy, and agreed with surface mapping data, partial NMR data, and tryptophan anisotropy and quenching data. In addition, the interaction between peptides forming AB fibrils, implicated in Alzheimer\u27s disease, were examined using hydroxyl radicals. This radical mapping suggests that the model proffered by Perutz et al, which states that the AB fibril is a solvent-filled nanotube, is incorrect. Overall, chemically-generated hydroxyl radicals have been developed as a general, multi-target labeling reagent for protein surfaces. Hydroxyl radical surface maps can be used to characterize protein tertiary and quaternary structure and to apply valuable biophysical constraints for structural modeling

    Explicit Consideration of Solubility and Interaction Specificity in Computational Protein Design

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    Most successes to date in computational protein design have relied on optimizing sequences to fit well for a single structure. Multistate design represents a new approach to designing proteins in which sequences are optimized for multiple contexts usually given by multiple structures (states). In multistate design simulations, sequences that either stabilize the target state or destabilize alternate competing states are selected. This dissertation describes the application of multistate design to two problems in protein design: designing sequences for solubility and increasing binding specificity in protein-protein interface design. Previous studies with the modeling program Rosetta have shown that many designed proteins have patches of hydrophobic surface area that are considerably larger than what is seen in native proteins. These patches can lead to nonspecific association and aggregation. We use a multistate design approach to address protein solubility by disfavoring the aggregated state through the addition of a new solubility term to the Rosetta energy function. The score term explicitly detects and penalizes the formation of hydrophobic patches during design. Designing with this new score term results in proteins with naturally occurring frequencies of hydrophobic amino acids on the surface without large hydrophobic patches. Designing protein-protein interfaces with high affinity and specificity is still a challenge for protein design algorithms. Multistate design is well-suited for addressing the problem of specificity because it can explicitly disfavor off-target interactions. Using a new implementation in Rosetta, multistate design is applied to the orthogonal interface design problem: redesign a protein with many partners to interact with only one of the partners. We use the RalA signaling network as the model system and make our design goal a redesigned RalA that only interacts with the effector RalBP1. Multistate design is able to recover several of the known mutants important for effector binding and predicts many new mutations that alter binding specificity. From in silico predictions, single-state design for Ral/RalBP1 by itself is not sufficient to destabilize RalA's interactions with its other effectors. Only multistate design is able to destabilize both of the negative states and give the desired interaction specificity.Doctor of Philosoph

    Computational protein design: assessment and applications

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    Indiana University-Purdue University Indianapolis (IUPUI)Computational protein design aims at designing amino acid sequences that can fold into a target structure and perform a desired function. Many computational design methods have been developed and their applications have been successful during past two decades. However, the success rate of protein design remains too low to be of a useful tool by biochemists whom are not an expert of computational biology. In this dissertation, we first developed novel computational assessment techniques to assess several state-of-the-art computational techniques. We found that significant progresses were made in several important measures by two new scoring functions from RosettaDesign and from OSCAR-design, respectively. We also developed the first machine-learning technique called SPIN that predicts a sequence profile compatible to a given structure with a novel nonlocal energy-based feature. The accuracy of predicted sequences is comparable to RosettaDesign in term of sequence identity to wild type sequences. In the last two application chapters, we have designed self-inhibitory peptides of Escherichia coli methionine aminopeptidase (EcMetAP) and de novo designed barstar. Several peptides were confirmed inhibition of EcMetAP at the micromole-range 50% inhibitory concentration. Meanwhile, the assessment of designed barstar sequences indicates the improvement of OSCAR-design over RosettaDesign

    Computational Design Of Protein–ligand And Protein–protein Interactions

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    Central to the function of proteins is the concept of molecular recognition. Protein–ligand and protein–protein interactions make up the bulk of the chemical processes that give rise to living things. Realizing the full potential of protein design technology will therefore require an increased understanding of the design principles of molecular recognition. We have tackled problems involving molecular recognition by using computational methods to design novel protein-ligand and protein-protein interactions. Firstly, we set out to design a protein capable of recognizing lanthanide metal ions. Protein-lanthanide systems are of interest for their potential to serve as purification agents for use under biological conditions. We have designed a highly dense 6-coordinate lanthanide binding at the core of a de novo protein, and used the dynamical aspects of the protein to achieve a degree of differentiation between elements in the lanthanide series. Secondly, we investigated systems of homo-oligomeric protein complexes that self-assemble into hollow cages. We have studied the structural determinants of naturally occurring self-assembling ferritin cages and identified a single mutation that greatly increased the stability of the ferritin cage, as well as dramatically altered the overall structure of the assembly. We have also used the formulation of probabilistic protein design to arrive at novel sequences for α-helical peptides that can adjust their surfaces in accordance to different local environments. This formulation was used to identify a sequence for a peptide designed to self-assemble into a spherical particle with broken symmetry. Taken together, these efforts will lead to an increased understanding of the role of kinetics and structural plasticity in protein-ligand and protein-protein interactions

    Protein Design Using Continuous Rotamers

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    Optimizing amino acid conformation and identity is a central problem in computational protein design. Protein design algorithms must allow realistic protein flexibility to occur during this optimization, or they may fail to find the best sequence with the lowest energy. Most design algorithms implement side-chain flexibility by allowing the side chains to move between a small set of discrete, low-energy states, which we call rigid rotamers. In this work we show that allowing continuous side-chain flexibility (which we call continuous rotamers) greatly improves protein flexibility modeling. We present a large-scale study that compares the sequences and best energy conformations in 69 protein-core redesigns using a rigid-rotamer model versus a continuous-rotamer model. We show that in nearly all of our redesigns the sequence found by the continuous-rotamer model is different and has a lower energy than the one found by the rigid-rotamer model. Moreover, the sequences found by the continuous-rotamer model are more similar to the native sequences. We then show that the seemingly easy solution of sampling more rigid rotamers within the continuous region is not a practical alternative to a continuous-rotamer model: at computationally feasible resolutions, using more rigid rotamers was never better than a continuous-rotamer model and almost always resulted in higher energies. Finally, we present a new protein design algorithm based on the dead-end elimination (DEE) algorithm, which we call iMinDEE, that makes the use of continuous rotamers feasible in larger systems. iMinDEE guarantees finding the optimal answer while pruning the search space with close to the same efficiency of DEE. Availability: Software is available under the Lesser GNU Public License v3. Contact the authors for source code

    Structural Analysis of Alkaline β-Mannanase from Alkaliphilic Bacillus sp. N16-5: Implications for Adaptation to Alkaline Conditions

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    Significant progress has been made in isolating novel alkaline β-mannanases, however, there is a paucity of information concerning the structural basis for alkaline tolerance displayed by these β-mannanases. We report the catalytic domain structure of an industrially important β-mannanase from the alkaliphilic Bacillus sp. N16-5 (BSP165 MAN) at a resolution of 1.6 Å. This enzyme, classified into subfamily 8 in glycosyl hydrolase family 5 (GH5), has a pH optimum of enzymatic activity at pH 9.5 and folds into a classic (β/α)8-barrel. In order to gain insight into molecular features for alkaline adaptation, we compared BSP165 MAN with previously reported GH5 β-mannanases. It was revealed that BSP165 MAN and other subfamily 8 β-mannanases have significantly increased hydrophobic and Arg residues content and decreased polar residues, comparing to β-mannanases of subfamily 7 or 10 in GH5 which display optimum activities at lower pH. Further, extensive structural comparisons show alkaline β-mannanases possess a set of distinctive features. Position and length of some helices, strands and loops of the TIM barrel structures are changed, which contributes, to a certain degree, to the distinctly different shaped (β/α)8-barrels, thus affecting the catalytic environment of these enzymes. The number of negatively charged residues is increased on the molecular surface, and fewer polar residues are exposed to the solvent. Two amino acid substitutions in the vicinity of the acid/base catalyst were proposed to be possibly responsible for the variation in pH optimum of these homologous enzymes in subfamily 8 of GH5, identified by sequence homology analysis and pKa calculations of the active site residues. Mutational analysis has proved that Gln91 and Glu226 are important for BSP165 MAN to function at high pH. These findings are proposed to be possible factors implicated in the alkaline adaptation of GH5 β-mannanases and will help to further understanding of alkaline adaptation mechanism
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