Thermodynamics and Kinetics of Non-native Interactions in Protein Folding: A Single Point Mutant Significantly Stabilizes the N-terminal Domain of L9 by Modulating Non-native Interactions in the Denatured State

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Unfolded and partially unfolded proteins participate in a wide range of biological processes from pathological aggregation to the regulation of normal cellular activity. Unfolded states can be populated under strongly denaturing conditions, but the ensemble which is relevant for folding, stability, and aggregation is that populated under physiological conditions. Characterization of nonnative states is critical for the understanding of these processes, yet comparatively little is known about their energetics and their structural propensities under native conditions. The standard view is that energetically significant coupled interactions involving multiple residues are generally not present in the denatured state ensemble (DSE) or in intrinsically disordered proteins. Using the N-terminal domain of the ribosomal protein L9, a small α-β protein, as an experimental model system, we demonstrate that networks of energetically significant, coupled interactions can form in the DSE of globular proteins, and can involve residues that are distant in sequence and spatially well separated in the native structure. X-ray crystallography, NMR, dynamics studies, native state pK a measurements, and thermodynamic analysis of more than 25 mutants demonstrate that residues are energetically coupled in the DSE. Altering these interactions by mutation affects the stability of the domain. Mutations that alter the energetics of the DSE can impact the analysis of cooperativity and folding, and may play a role in determining the propensity to aggregate.protein folding | protein stability | ϕ-value analysis | double-mutant cycle T he properties of nonnative states of proteins have attracted considerable attention because they can play critical roles in cell signaling, translocation across membranes, temperaturesensitive phenotypes, and in a wide range of protein deposition diseases (1-14). The properties of unfolded states are also relevant to any discussion of intrinsically disordered proteins (IDPs) (1). The denatured state ensemble (DSE) can be populated under strongly denaturing conditions and studied at equilibrium; however, the physiologically relevant DSE is the state that is in equilibrium with the folded state under native conditions. This state is difficult to study directly and most studies of the DSE under native conditions have made use of indirect approaches. Nevertheless, experimental and theoretical work has shown that the DSE populated under physiological conditions is not a random coil, but can contain native and nonnative structure, stabilized by local and nonlocal interactions. However, very little is known about the energetics of these interactions, particularly ones involving residues distant in sequence, or about energetically coupled interactions involving multiple residues in the . These are a critical piece of the folding puzzle; they impact protein aggregation and folding, and have important implications for the properties of IDPs. Recent studies offer evidence for hydrophobic clusters, electrostatic interactio...

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

mentioning

confidence: 99%

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Energetically significant networks of coupled interactions within an unfolded protein

Cho

Meng

Sato

et al. 2014

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“…This suggests that the denatured states of thermophilic proteins may be more compact than the denatured states of mesophilic proteins. The reasons for these differences are not yet known, but may include differences in hydrophobic residues, disulfide bonds, electrostatic interactions (7,(26)(27)(28)(29)(30)(31)(32)(33)(34)(35)(36), different amino acid flexibilities (37-41), or loop deletions (42).…”

Section: Proteomes Are Marginally Stable To Denaturationmentioning

confidence: 99%

Physical limits of cells and proteomes

Dill

Ghosh

Schmit

2011

281

280

What are the physical limits to cell behavior? Often, the physical limitations can be dominated by the proteome, the cell's complement of proteins. We combine known protein sizes, stabilities, and rates of folding and diffusion, with the known protein-length distributions PðNÞ of proteomes (Escherichia coli, yeast, and worm), to formulate distributions and scaling relationships in order to address questions of cell physics. Why do mesophilic cells die around 50°C? How can the maximal growth-rate temperature (around 37°C) occur so close to the cell-death temperature? The model shows that the cell's death temperature coincides with a denaturation catastrophe of its proteome. The reason cells can function so well just a few degrees below their death temperature is because proteome denaturation is so cooperative. Why are cells so dense-packed with protein molecules (about 20% by volume)? Cells are packed at a density that maximizes biochemical reaction rates. At lower densities, proteins collide too rarely. At higher densities, proteins diffuse too slowly through the crowded cell. What limits cell sizes and growth rates? Cell growth is limited by rates of protein synthesis, by the folding rates of its slowest proteins, and-for large cells-by the rates of its protein diffusion. Useful insights into cell physics may be obtainable from scaling laws that encapsulate information from protein knowledge bases. . So, some behaviors of cells are likely to be dominated by the physical properties of its proteomethe collection of its thousands of different types of proteins. We develop here some biophysical scaling relationships of proteomes, and we use those relationships to make estimates of the physical limits of cell behavior. Our scaling relationships come from combining current databases of the properties of proteins that have been measured in vitro, with PðNÞ, the length distributions of proteins that are known for several proteomes. Some of the hypotheses we explore are not new; what is previously undescribed is the use of modern databases to make quantitative estimates of physical limits. A key point, previously also made by Thirumalai (4), is that many physical properties of proteins just depend on N, the number of amino acids in the protein. We estimate the folding stabilities for mesophiles and thermophiles, the folding rates, and the diffusion coefficients of whole proteomes, and we compare these various rates at the end. We first consider the folding stabilities of proteomes. Proteomes Are Marginally Stable to DenaturationFor at least 116 monomeric, two-state and reversible folding proteins there are calorimetric measurements of the folding stability, ΔG ¼ G unfolded − G folded , enthalpy ΔH, entropy ΔS, and heat capacity, ΔC p . Data are now available for both mesophilic and thermophilic proteins. Taken over the full set of proteins, these thermal quantities depend, remarkably, mainly just on the number, N, of amino acids in the chain. The relationship is simply linear. For both enthalpy and entropy the correlati...

“…This description has been successfully used to obtain sets of conformations from the TS ensemble of several proteins (5)(6)(7)(8)(9) and to bias molecular dynamics (MD) trajectories toward the TS (10). On the other hand, specific nonnative interactions may be formed at both the TS and denatured-state ensemble and lead to a wrong picture of TS if not taken into account (11). Furthermore, different experimental conditions or mutations may determine detectable changes in the TS structure, showing the presence of parallel pathways (12,13) and, thus, a heterogeneous TS.…”

Section: And References Therein)mentioning

confidence: 99%

Φ-Value analysis by molecular dynamics simulations of reversible folding

Settanni

Rao

Caflisch

2005

In ⌽-value analysis, the effects of mutations on the folding kinetics are compared with the corresponding effects on thermodynamic stability to investigate the structure of the protein-folding transition state (TS). Here, molecular dynamics (MD) simulations (totaling 0.65 ms) have been performed for a large set of single-point mutants of a 20-residue three-stranded antiparallel ␤-sheet peptide. Between 57 and 120 folding events were sampled at near equilibrium for each mutant, allowing for accurate estimates of folding͞unfolding rates and stability changes. The ⌽ values calculated from folding and unfolding rates extracted from the MD trajectories are reliable if the stability loss upon mutation is larger than Ϸ0.6 kcal͞mol, which is observed for 8 of the 32 single-point mutants. The same heterogeneity of the TS of the wild type was found in the mutated peptides, showing two possible pathways for folding. Single-point mutations can induce significant TS shifts not always detected by ⌽-value analysis. Specific nonnative interactions at the TS were observed in most of the peptides studied here. The interpretation of ⌽ values based on the ratio of atomic contacts at the TS over the native state, which has been used in the past in MD and Monte Carlo simulations, is in agreement with the TS structures of wild-type peptide. However, ⌽ values tend to overestimate the nativeness of the TS ensemble, when interpreted neglecting the nonnative interactions. ⌽ values are usually interpreted in terms of native contacts (4). This description has been successfully used to obtain sets of conformations from the TS ensemble of several proteins (5-9) and to bias molecular dynamics (MD) trajectories toward the TS (10). On the other hand, specific nonnative interactions may be formed at both the TS and denatured-state ensemble and lead to a wrong picture of TS if not taken into account (11). Furthermore, different experimental conditions or mutations may determine detectable changes in the TS structure, showing the presence of parallel pathways (12, 13) and, thus, a heterogeneous TS. In addition, the ensemble average associated with the use of certain folding observables, like the degree of tryptophan burial, may disguise the presence of multiple folding pathways and folding intermediates (14). Namely, a recent study (15) suggests that not all conformations obtained in MD simulations by using ⌽ values as restraints on a subset of the native contacts belong to the TS.The TS structures can be identified by MD simulations through the calculation of their folding probability P fold (16), i.e., the probability that a trajectory started from a given structure reaches the folded state before unfolding. The concept of P fold calculation was first introduced in a method for determining transmission coefficients, starting from a known TS (17), and used to identify TSs of simple conformational changes (e.g., tyrosine ring flips) (18). The approach has recently been used to study the otherwise very elusive folding TS by atomistic Monte Carlo off...