Electrostatic effects on funneled landscapes and structural diversity in denatured protein ensembles

The denatured state of proteins is heterogeneous and susceptible to general hydrophobic and electrostatic forces, but to what extent does the funneled nature of protein energy landscapes play a role in the unfolded ensemble? We simulate the denatured ensemble of cytochrome c using a series of models. The models pinpoint the efficacy of incorporating energetic funnels toward the native state in contrast with models having no native structure-seeking tendency. These models also contain varying strengths of electrostatic effects and hydrophobic collapse. The simulations based on these models are compared with experimental distributions for the distances between a fluorescent donor and the heme acceptor that were extracted from time-resolved fluorescence energy transfer experiments on cytochrome c. Comparing simulations to detailed experimental data on several labeling sites allows us to quantify the dominant forces in denatured protein ensembles

Prediction of native-state hydrogen exchange from perfectly funneled energy landscapes

Simulations based on perfectly funneled energy landscapes often capture many of the kinetic features of protein folding. We examined whether simulations based on funneled energy functions can also describe fluctuations in native-state protein ensembles. We quantitatively compared the site-specific local stability determined from structure-based folding simulations, with hydrogen exchange protection factors measured experimentally for ubiquitin, chymotrypsin inhibitor 2, and staphylococcal nuclease. Different structural definitions for the open and closed states based on the number of native contacts for each residue, as well as the hydrogen-bonding state, or a combination of both criteria were evaluated. The predicted exchange patterns agree with the experiments under native conditions, indicating that protein topology indeed has a dominant effect on the exchange kinetics. Insights into the simplest mechanistic interpretation of the amide exchange process were thus obtained.Fil: Craig, Patricio Oliver. Fundación Instituto Leloir; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigaciones Bioquimicas de Buenos Aires; Argentina. University of California San Diego. Department of Chemistry and Biochemistry; Estados UnidosFil: Lätzer, Joachim. Rutgers University. BioMaPS Institute; Estados UnidosFil: Weinkam, Patrick. University of California at San Francisco. Department of Bioengineering and Therapeutic Sciences; Estados UnidosFil: Hoffman, Ryan M. B.. University Of California At San Diego; Estados UnidosFil: Ferreiro, Diego. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Química Biológica de la Facultad de Ciencias Exactas y Naturales; ArgentinaFil: Komives, Elizabeth A.. University Of California At San Diego; Estados UnidosFil: Wolynes, Peter G.. University Of California At San Diego; Estados Unido

The folding energy landscape of Cytochrome c : theoretical and experimental investigations

The folding energy landscape of cytochrome c is complicated by a large, covalently bound heme cofactor. The heme significantly stabilizes the native structure by providing a hydrophobic core and two ligation sites for a histidine and a methionine. Other residues or solvent molecules can compete for the heme ligation sites thereby affecting the protein's stability and folding mechanism. The relative stability of the heme ligands has a large effect on the folding and the dynamics of cytochrome c and is sensitive to the solvent conditions as well as the heme redox state. With the added complexity of the heme, some may think that cytochrome c is unique and has a folding mechanism that is unrelated to single domain proteins without cofactors. We will see however that the general principles used to describe the folding energy landscape are sufficient to describe the folding of cytochrome c. Models based on energy landscape ideas can predict behavior consistent with many experimental results. The most simple structure-based model, in which the energetics are based on information in the native fold, successfully predicts the sequential ordering of protein substructures. Changing the solvent conditions by varying pH, salt concentration, or by adding denaturant will destabilize the protein and perturb the energy landscape. By including nonnative effects into structure-based models, we can determine what features of the energy landscape are important in partially unfolded and denatured ensemble

The folding energy landscape of Cytochrome c : theoretical and experimental investigations

The folding energy landscape of cytochrome c is complicated by a large, covalently bound heme cofactor. The heme significantly stabilizes the native structure by providing a hydrophobic core and two ligation sites for a histidine and a methionine. Other residues or solvent molecules can compete for the heme ligation sites thereby affecting the protein's stability and folding mechanism. The relative stability of the heme ligands has a large effect on the folding and the dynamics of cytochrome c and is sensitive to the solvent conditions as well as the heme redox state. With the added complexity of the heme, some may think that cytochrome c is unique and has a folding mechanism that is unrelated to single domain proteins without cofactors. We will see however that the general principles used to describe the folding energy landscape are sufficient to describe the folding of cytochrome c. Models based on energy landscape ideas can predict behavior consistent with many experimental results. The most simple structure-based model, in which the energetics are based on information in the native fold, successfully predicts the sequential ordering of protein substructures. Changing the solvent conditions by varying pH, salt concentration, or by adding denaturant will destabilize the protein and perturb the energy landscape. By including nonnative effects into structure-based models, we can determine what features of the energy landscape are important in partially unfolded and denatured ensemble

Mapping Polymerization and Allostery of Hemoglobin S Using Point Mutations

Hemoglobin is a complex system that undergoes conformational changes in response to oxygen, allosteric effectors, mutations, and environmental changes. Here, we study allostery and polymerization of hemoglobin and its variants by application of two previously described methods: (i) AllosMod for simulating allostery dynamics given two allosterically related input structures and (ii) a machine-learning method for dynamics- and structure-based prediction of the mutation impact on allostery (Weinkam et al. J. Mol. Biol. 2013, 425, 647-661), now applicable to systems with multiple coupled binding sites, such as hemoglobin. First, we predict the relative stabilities of substates and microstates of hemoglobin, which are determined primarily by entropy within our model. Next, we predict the impact of 866 annotated mutations on hemoglobin's oxygen binding equilibrium. We then discuss a subset of 30 mutations that occur in the presence of the sickle cell mutation and whose effects on polymerization have been measured. Seven of these HbS mutations occur in three predicted druggable binding pockets that might be exploited to directly inhibit polymerization; one of these binding pockets is not apparent in the crystal structure, but only in structures generated by AllosMod. For the 30 mutations, we predict that mutation-induced conformational changes within a single tetramer tend not to significantly impact polymerization; instead, these mutations more likely impact polymerization by directly perturbing a polymerization interface. Finally, our analysis of allostery allows us to hypothesize why hemoglobin evolved to have multiple subunits and a persistent low frequency sickle cell mutation

Impact of Mutations on the Allosteric Conformational Equilibrium

Allostery in a protein involves effector binding at an allosteric site that changes the structure and/or dynamics at a distant, functional site. In addition to the chemical equilibrium of ligand binding, allostery involves a conformational equilibrium between one protein substate that binds the effector and a second substate that less strongly binds the effector. We run molecular dynamics simulations using simple, smooth energy landscapes to sample specific ligand-induced conformational transitions, as defined by the effector-bound and effector-unbound protein structures. These simulations can be performed using our web server (http://salilab.org/allosmod/). We then develop a set of features to analyze the simulations and capture the relevant thermodynamic properties of the allosteric conformational equilibrium. These features are based on molecular mechanics energy functions, stereochemical effects, and structural/dynamic coupling between sites. Using a machine-learning algorithm on a data set of 10 proteins and 179 mutations, we predict both the magnitude and the sign of the allosteric conformational equilibrium shift by the mutation; the impact of a large identifiable fraction of the mutations can be predicted with an average unsigned error of 1k(B)T. With similar accuracy, we predict the mutation effects for an 11th protein that was omitted from the initial training and testing of the machine-learning algorithm. We also assess which calculated thermodynamic properties contribute most to the accuracy of the prediction