Folding superfunnel to describe cooperative folding of interacting proteins

This paper proposes a generalization of the well‐known folding funnel concept of proteins. In the funnel model the polypeptide chain is treated as an individual object not interacting with other proteins. Since biological systems are considerably crowded, protein–protein interaction is a fundamental feature during the life cycle of proteins. The folding superfunnel proposed here describes the folding process of interacting proteins in various situations. The first example discussed is the folding of the freshly synthesized protein with the aid of chaperones. Another important aspect of protein–protein interactions is the folding of the recently characterized intrinsically disordered proteins, where binding to target proteins plays a crucial role in the completion of the folding process. The third scenario where the folding superfunnel is used is the formation of aggregates from destabilized proteins, which is an important factor in case of several conformational diseases. The folding superfunnel constructed here with the minimal assumption about the interaction potential explains all three cases mentioned above. Proteins 2016; 84:1009–1016. © 2016 Wiley Periodicals, Inc.     

Folding funnels, binding funnels, and protein function.

Folding funnels have been the focus of considerable attention during the last few years. These have mostly been discussed in the general context of the theory of protein folding. Here we extend the utility of the concept of folding funnels, relating them to biological mechanisms and function. In particular, here we describe the shape of the funnels in light of protein synthesis and folding; flexibility, conformational diversity, and binding mechanisms; and the associated binding funnels, illustrating the multiple routes and the range of complexed conformers. Specifically, the walls of the folding funnels, their crevices, and bumps are related to the complexity of protein folding, and hence to sequential vs. nonsequential folding. Whereas the former is more frequently observed in eukaryotic proteins, where the rate of protein synthesis is slower, the latter is more frequent in prokaryotes, with faster translation rates. The bottoms of the funnels reflect the extent of the flexibility of the proteins. Rugged floors imply a range of conformational isomers, which may be close on the energy landscape. Rather than undergoing an induced fit binding mechanism, the conformational ensembles around the rugged bottoms argue that the conformers, which are most complementary to the ligand, will bind to it with the equilibrium shifting in their favor. Furthermore, depending on the extent of the ruggedness, or of the smoothness with only a few minima, we may infer nonspecific, broad range vs. specific binding. In particular, folding and binding are similar processes, with similar underlying principles. Hence, the shape of the folding funnel of the monomer enables making reasonable guesses regarding the shape of the corresponding binding funnel. Proteins having a broad range of binding, such as proteolytic enzymes or relatively nonspecific endonucleases, may be expected to have not only rugged floors in their folding funnels, but their binding funnels will also behave similarly, with a range of complexed conformations. Hence, knowledge of the shape of the folding funnels is biologically very useful. The converse also holds: If kinetic and thermodynamic data are available, hints regarding the role of the protein and its binding selectivity may be obtained. Thus, the utility of the concept of the funnel carries over to the origin of the protein and to its function.     

Protein folding mechanisms and the multidimensional folding funnel

An important idea that emerges from the energy landscape theory of protein folding is that subtle global features of the protein landscape can profoundly affect the apparent mechanism of folding. The relationship between various characteristic temperatures in the phase diagrams and landmarks in the folding funnel at fixed temperatures can be used to classify different folding behaviors. The one-dimensional picture of a folding funnel classifies folding kinetics into four basic scenarios, depending on the relative location of the thermodynamic barrier and the glass transition as a function of a single-order parameter. However, the folding mechanism may not always be quantitatively described by a single-order parameter. Several other order parameters, such as degree of secondary structure formation, collapse and topological order, are needed to establish the connection between minimalist models and proteins in the laboratory. In this article we describe a simple multidimensional funnel based on two-order parameters that measure the degree of collapse and topological order. The appearance of several different “mechanisms” is illustrated by analyzing lattice models with different potentials and sequences with different degrees of design. In most cases, the two-dimensional analysis leads to a classification of mechanisms totally in keeping with the one-dimensional scheme, but a topologically distinct scenario of fast folding with traps also emerges. The nature of traps depends on the relative location of the glass transition surface and the thermodynamic barrier in the multidimensional funnel. Proteins 32:136–158, 1998. © 1998 Wiley-Liss, Inc.     

Folding-unfolding energy change of a simple sphere model protein and an energy landscape of the folding process

We have calculated the free energy of a spherical model of a protein or part of a protein generated in the way of protein folding. Two spherical models are examined; one is a homogeneous model consisting of only one residue type—hydrophobic. The other is a heterogeneous model consisting of two residue types—strong hydrophobic and weak hydrophobic. Both models show a folding transition state, and the latter model reproduces the trend of the experimental folded-unfolded energy change. The heterogeneous model suggests that in the folding process of a protein of more than 70 residues, a specific region of the protein folds first to form a stable region, then the other residues follow the folding process. The energy landscape of folding of a small protein is approximately a funnel model, whereas a flatter energy landscape is suggested for larger proteins of more than 55–70 residues. Proteins 33:408–416, 1998. © 1998 Wiley-Liss, Inc.     

Cunning simplicity of protein folding landscapes

Funnel-like landscapes are widely used to visualize protein folding. It might seem that any funnel-like energy landscape helps to avoid the 'Levinthal paradox', i.e. to avoid sampling the impossibly large number of conformations for a folding protein. This cunning suggestion, reinforced by beautiful drawings of the energy funnels, stimulated some simple models of protein folding; one of them [D.J. Bicout and A. Szabo (2000) Protein Sci., 9, 452-465] is especially straightforward and instructive. A thorough analysis of this strict funnel model (which does not consider a nucleation of phase separation in the course of folding) shows that it cannot provide a simultaneous explanation for both major features observed for protein folding: (i) folding within non-astronomical time, and (ii) co-existence of the native and the unfolded states during the folding process. On the contrary, the nucleation mechanism of protein folding can account for both these major features simultaneously.     

Energetics of the native energy landscape of a two-domain calcium sensor protein: distinct folding features of the two domains

The protein folding energy landscape allows a thorough understanding of the protein folding problem which in turn helps in understanding various aspects of biological functions. Characterizing the cooperative unfolding units and the intermediates along the folding funnel of a protein is a challenging task. In this paper, we investigated the native energy landscape of EhCaBP, a calcium sensor, belonging to the same EF-hand superfamily as calmodulin. EhCaBP is a two-domain EF-hand protein consisting of two EF-hands in each domain and binding to four Ca2+ cations. Native-state hydrogen exchange (HX) was used to assess the folding features of the landscape and also to throw light on the structure-folding function paradigm of calcium sensor proteins. HX measurements under the EX2 regime provided the thermodynamic information about the protein folding events under native conditions. HX studies revealed that the unfolding of EhCaBP is not a two-state process. Instead, it proceeds through cooperative units. The C-terminal domain exhibits less denaturant dependence than the N-terminal domain, suggesting that the former is dominated by local fluctuations. It is interesting to note that the N- and C-terminal domains of EhCaBP have distinct folding features. In fact, these observed differences can regulate the domain-dependent target recognition of two-domain Ca2+ sensor proteins.     

Protein folding in the cell: reshaping the folding funnel

Models of protein folding have historically focused on a subset of 'well-behaved' proteins that can be successfully refolded from denaturants in vitro. Energy landscapes, including folding funnel 'cartoons', describe the largely uncomplicated folding of these isolated chains at infinite dilution. However, the frequent failure of many polypeptides to fold to their native state requires more comprehensive models of folding to accommodate the crucial role of interactions between partially folded intermediates. By incorporating additional deep minima, which reflect off-pathway interchain interactions, the folding funnel concept can be extended to describe the behavior of a more diverse set of proteins under more physiologically relevant conditions. In particular, the effects of ribosomes (translation), molecular chaperones and other aspects of the cellular environment on early chain conformations can be included to account for the folding behavior of polypeptide chains in cells.     

154 Protein folding and binding funnels: a common driving force and a common mechanism

Under the free energy landscape theory, both the protein-folding and protein–ligand binding processes are driven by the decrease in total Gibbs free energy of the protein-solvent or protein–ligand-solvent system, which involves the non-complementary changes between the entropy and enthalpy, ultimately leading to a global free energy minimization of these thermodynamic systems (Ji & Liu, 2011; Liu et al., 2012; Yang, Ji & Liu, 2012). In the case of protein folding, the lowering of the system free energy coupled with the gradual reduction in conformational degree of freedom of the folding intermediates determines that the shape of the free energy landscape for protein folding must be funnel-like (Dill & Chan, 1997), rather than non-funneled shapes (Ben-Naim, 2012). In the funnel-like free energy landscape, protein folding can be viewed as going down the hill via multiple parallel routes from a vast majority of individual non-native states on surface outside the funnel to the native states located around the bottom of the funnel. The first stage of folding, i.e. the rapid hydrophobic collapse process, is driven by the solvent entropy maximization. Concretely, the water molecules squeeze and sequestrate the hydrophobic amino acid side chains within the interior of the folding intermediates while exposing the polar and electrostatically charged side chains on the intermediate surface so as to minimize the solvent-accessible surface area of the solute and thus, the minimal contacts between the folding intermediates and the water molecules. This will maximize the entropy of the solvent, thus contributing substantially to lowering of the system free energy due to an absolute advantage of the solvent in both quantity and mass (Yang, Ji & Liu, 2012). The resulting molten globule states (Ohgushi & Wada, 1983), within which a few transient secondary structural components and tertiary contacts have been formed but many native contacts or close residue–residue interactions has yet to form, need to be further sculptured into the native states. This is a relatively slow “bottleneck” process because the competitive interactions between protein residues within the folding intermediates and between residues and water molecules may repeat many rounds to accumulate a large enough number of stable noncovalent bonds capable of counteracting the conformational entropy loss of the intermediates, thus putting this bottleneck stage under the enthalpy control (i.e. negative enthalpy change), contributing further to the lowering of the system free energy. Although the protein–ligand association occurs around the rugged bottom of the free energy landscape, the exclusion of water from the binding interfaces and the formation of noncovalent bonds between the two partners can still lower the system free energy. In conjunction with the loss of the rotational and translational degrees of freedom of the two partners as well as the loss of the conformational entropy of the protein, these processes could merge, downwards expand, and further narrow the free energy wells within which the protein–ligand binding process takes place, thereby making them look like a funnel, which we term the binding funnel. In this funnel, the free energy downhill process follows a similar paradigm to the protein-folding process. For example, if the initial collisions/contacts occur between the properly complementary interfaces of the protein and ligand, a large amount of water molecules (which usually form a water network around the solute surface) will be displaced to suit the need for maximizing the solvent entropy. This process is similar to that of the hydrophobic collapse during protein folding, resulting in a loosely associated protein–ligand complex that needs also to be further adapted into a tight complex, i.e. the second step which is mainly driven by the negative enthalpy change through intermolecular competitive interactions to gradually accumulate the noncovalent bonds and ultimately, to stabilize the complex at a tightly bound state. Taken together, we conclude that whether in the protein-folding or in the protein–ligand binding process, both the entropy-driven first step and the enthalpy-driven second step contribute to the lowering of the system free energy, resulting in the funnel-like folding or binding free energy landscape.     

Folding funnels and binding mechanisms.

The long-held views on lock-and-key versus induced fit in binding arose from the notion that a protein exists in a single, most stable conformation, dictated by its sequence. However, in solution proteins exist in a range of conformations, which may be described by statistical mechanical laws and their populations follow statistical distributions. Upon binding, the equilibrium will shift in favor of the bound conformation from the ensemble of conformations around the bottom of the folding funnel. Hence here we extend the implications and the usefulness of the folding funnel concept to explain fundamental binding mechanisms.     

Distinguishing between sequential and nonsequentially folded proteins: implications for folding and misfolding.

We describe here an algorithm for distinguishing sequential from nonsequentially folding proteins. Several experiments have recently suggested that most of the proteins that are synthesized in the eukaryotic cell may fold sequentially. This proposed folding mechanism in vivo is particularly advantageous to the organism. In the absence of chaperones, the probability that a sequentially folding protein will misfold is reduced significantly. The problem we address here is devising a procedure that would differentiate between the two types of folding patterns. Footprints of sequential folding may be found in structures where consecutive fragments of the chain interact with each other. In such cases, the folding complexity may be viewed as being lower. On the other hand, higher folding complexity suggests that at least a portion of the polypeptide backbone folds back upon itself to form three-dimensional (3D) interactions with noncontiguous portion(s) of the chain. Hence, we look at the mechanism of folding of the molecule via analysis of its complexity, that is, through the 3D interactions formed by contiguous segments on the polypeptide chain. To computationally splice the structure into consecutively interacting fragments, we either cut it into compact hydrophobic folding units or into a set of hypothetical, transient, highly populated, contiguous fragments ("building blocks" of the structure). In sequential folding, successive building blocks interact with each other from the amino to the carboxy terminus of the polypeptide chain. Consequently, the results of the parsing differentiate between sequentially vs. nonsequentially folded chains. The automated assessment of the folding complexity provides insight into both the likelihood of misfolding and the kinetic folding rate of the given protein. In terms of the funnel free energy landscape theory, a protein that truly follows the mechanism of sequential folding, in principle, encounters smoother free energy barriers. A simple sequentially folded protein should, therefore, be less error prone and fold faster than a protein with a complex folding pattern.     

Visualization of Protein Folding Funnels in Lattice Models

Protein folding occurs in a very high dimensional phase space with an exponentially large number of states, and according to the energy landscape theory it exhibits a topology resembling a funnel. In this statistical approach, the folding mechanism is unveiled by describing the local minima in an effective one-dimensional representation. Other approaches based on potential energy landscapes address the hierarchical structure of local energy minima through disconnectivity graphs. In this paper, we introduce a metric to describe the distance between any two conformations, which also allows us to go beyond the one-dimensional representation and visualize the folding funnel in 2D and 3D. In this way it is possible to assess the folding process in detail, e.g., by identifying the connectivity between conformations and establishing the paths to reach the native state, in addition to regions where trapping may occur. Unlike the disconnectivity maps method, which is based on the kinetic connections between states, our methodology is based on structural similarities inferred from the new metric. The method was developed in a 27-mer protein lattice model, folded into a 3×3×3 cube. Five sequences were studied and distinct funnels were generated in an analysis restricted to conformations from the transition-state to the native configuration. Consistent with the expected results from the energy landscape theory, folding routes can be visualized to probe different regions of the phase space, as well as determine the difficulty in folding of the distinct sequences. Changes in the landscape due to mutations were visualized, with the comparison between wild and mutated local minima in a single map, which serves to identify different trapping regions. The extension of this approach to more realistic models and its use in combination with other approaches are discussed.     

Disulfide bonds and protein folding

The applications of disulfide-bond chemistry to studies of protein folding, structure, and stability are reviewed and illustrated with bovine pancreatic ribonuclease A (RNase A). After surveying the general properties and advantages of disulfide-bond studies, we illustrate the mechanism of reductive unfolding with RNase A, and discuss its application to probing structural fluctuations in folded proteins. The oxidative folding of RNase A is then described, focusing on the role of structure formation in the regeneration of the native disulfide bonds. The development of structure and conformational order in the disulfide intermediates during oxidative folding is characterized. Partially folded disulfide species are not observed, indicating that disulfide-coupled folding is highly cooperative. Contrary to the predictions of "rugged funnel" models of protein folding, misfolded disulfide species are also not observed despite the potentially stabilizing effect of many nonnative disulfide bonds. The mechanism of regenerating the native disulfide bonds suggests an analogous scenario for conformational folding. Finally, engineered covalent cross-links may be used to assay for the association of protein segments in the folding transition state, as illustrated with RNase A.     

The folding funnel landscape for the peptide Met-enkephalin

We study the free energy landscape of the small peptide Met-enkephalin. Our data were obtained from a generalized-ensemble Monte Carlo simulation taking the interactions among all atoms into account. We show that the free energy landscape resembles that of a funnel, indicating that this peptide is a good folder. Our work demonstrates that the energy landscape picture and folding concept, developed in the context of simplified protein models, can also be used to describe the folding in more realistic models.     

Local interactions and the optimization of protein folding

The role of local interactions in protein folding has recently been the subject of some controversy. Here we investigate an extension of Zwanzig's simple and general model of folding in which local and nonlocal interactions are represented by functions of single and multiple conformational degrees of freedom, respectively. The kinetics and thermodynamics of folding are studied for a series of energy functions in which the energy of the native structure is fixed, but the relative contributions of local and nonlocal interactions to this energy are varied over a broad range. For funnel shaped energy landscapes, we find that 1) the rate of folding increases, but the stability of the folded state decreases, as the contribution of local interactions to the energy of the native structure increases, and 2) the amount of native structure in the unfolded state and the transition state vary considerably with the local interaction strength. Simple exponential kinetics and a well-defined free energy barrier separating folded and unfolded states are observed when nonlocal interactions make an appreciable contribution to the energy of the native structure; in such cases a transition state theory type approximation yields reasonably accurate estimates of the folding rate. Bumps in the folding funnel near the native state, which could result from desolvation effects, side chain freezing, or the breaking of nonnative contacts, significantly alter the dependence of the folding rate on the local interaction strength: the rate of folding decreases when the local interaction strength is increased beyond a certain point. A survey of the distribution of strong contacts in the protein structure database suggests that evolutionary optimization has involved both kinetics and thermodynamics: strong contacts are enriched at both very short and very long sequence separations. Proteins 29:282–291, 1997. © 1997 Wiley-Liss, Inc.     

Protein folding: matching theory and experiment.

The impact of folding funnels and folding simulations on the way experimentalists interpret results is examined. The image of the transition state has changed from a unique species that has a strained configuration, with a correspondingly high free energy, to a more ordinary folding intermediate, whose balance between limited conformational entropy and stabilizing contacts places it at the top of the free energy barrier. Evidence for a broad transition barrier comes from studies showing that mutations can change the position of the barrier. The main controversial issue now is whether populated folding intermediates are productive on-pathway intermediates or dead-end traps. Direct experimental evidence is needed. Theories suggesting that populated intermediates are trapped in a glasslike state are usually based on mechanisms which imply that trapping would only be extremely short-lived (e.g., nanoseconds) in water at 25 degrees C. There seems to be little experimental evidence for long-lived trapping in monomers, if folding aggregates are excluded. On the other hand, there is good evidence for kinetic trapping in dimers. alpha-Helix formation is currently the fastest known process in protein folding, and incipient helices are present at the start of folding. Fast helix formation has the effect of narrowing drastically the choice of folding routes. Thus helix formation can direct folding. It changes the folding metaphor from pouring liquid down a folding funnel to a train leaving a switchyard with only a few choices of exit tracks.     

Glassy dynamics in the folding landscape of cytochrome c detected by laser photolysis

Ferrocytochrome c liganded with CO refolds to a nativelike compact state, called the M state, where the non-native Fe2+-CO contact persists. The M state resembles the generic molten globule-like states and can be driven to the native state by pulsed laser photolysis of the CO ligand. The microsecond kinetics measured all across the guanidine hydrochloride unfolding transition of the protein produce a chevron plot with accentuated rollover in the folding limb, suggesting a glass transition of M en route to N. The rate of exit of the folding structure from the kinetic trap(s) limits the overall rate of folding of M to N. Sulfate-induced deceleration of the observed folding rate suggests that the folding structure indeed is transiently frozen in glassy traps. The results connect the post-transition features of the funnel paradigm.     

An evolutionary strategy for all-atom folding of the 60-amino-acid bacterial ribosomal protein l20

We have investigated an evolutionary algorithm for de novo all-atom folding of the bacterial ribosomal protein L20. We report results of two simulations that converge to near-native conformations of this 60-amino-acid, four-helix protein. We observe a steady increase of "native content" in both simulated ensembles and a large number of near-native conformations in their final populations. We argue that these structures represent a significant fraction of the low-energy metastable conformations, which characterize the folding funnel of this protein. These data validate our all-atom free-energy force field PFF01 for tertiary structure prediction of a previously inaccessible structural family of proteins. We also compare folding simulations of the evolutionary algorithm with the basin-hopping technique for the Trp-cage protein. We find that the evolutionary algorithm generates a dynamic memory in the simulated population, which leads to faster overall convergence.     

Energy Landscapes of Protein Aggregation and Conformation Switching in Intrinsically Disordered Proteins

The protein folding problem was apparently solved recently by the advent of a deep learning method for protein structure prediction called AlphaFold. However, this program is not able to make predictions about the protein folding pathways. Moreover, it only treats about half of the human proteome, as the remaining proteins are intrinsically disordered or contain disordered regions. By definition these proteins differ from natively folded proteins and do not adopt a properly folded structure in solution. However these intrinsically disordered proteins (IDPs) also systematically differ in amino acid composition and uniquely often become folded upon binding to an interaction partner. These factors preclude solving IDP structures by current machine-learning methods like AlphaFold, which also cannot solve the protein aggregation problem, since this meta-folding process can give rise to different aggregate sizes and structures. An alternative computational method is provided by molecular dynamics simulations that already successfully explored the energy landscapes of IDP conformational switching and protein aggregation in multiple cases. These energy landscapes are very different from those of ‘simple’ protein folding, where one energy funnel leads to a unique protein structure. Instead, the energy landscapes of IDP conformational switching and protein aggregation feature a number of minima for different competing low-energy structures. In this review, I discuss the characteristics of these multifunneled energy landscapes in detail, illustrated by molecular dynamics simulations that elucidated the underlying conformational transitions and aggregation processes.