Energy Landscape Of Proteins Research Articles

Energy landscape of conformational changes for a single unmodified protein

Resolving the free energy landscapes that govern protein biophysics has been obscured by ensemble averaging. While the folding dynamics of single proteins have been observed using fluorescent labels and/or tethers, a simpler and more direct measurement of the conformational changes would not require modifications to the protein. We use nanoaperture optical tweezers to resolve the energy landscape of a single unmodified protein, Bovine Serum Albumin (BSA), and quantify changes in the three-state conformation dynamics with temperature. A Markov model with Kramers’ theory transition rates is used to model the dynamics, showing good agreement with the observed state transitions. This first look at the intrinsic energy landscape of proteins provides a transformative tool for protein biophysics and may be applied broadly, including mapping out the energy landscape of particularly challenging intrinsically disordered proteins.

Modeling of fluence-dependent hole-burned spectra and hole-growth kinetics using multiple two-level system model.

Numerical formalism is presented that perfectly describes resonant low-temperature hole-burned spectra (including zero-phonon holes, ZPHs) and spectral hole-growth dynamics of Al-phthalocyanine tetrasulphonate embedded in hyperquenched glassy water films over more than seven orders of fluence magnitude (0.4 µJ/cm2-5.9 J/cm2). Frequency changes during spectral hole-burning (HB) are traditionally explained with the help of a single extrinsic two-level-system (TLSext) associated with impurity molecules. The new multiple two-level system (n-TLSext) models and data analysis presented in this work show that each chromophore in an amorphous medium can couple with multiple independent TLSext, which maintain perfect photo-memory, allowing a full return of the photoproduct to the initial ("preburn") state. Modeling reveals that the experimentally observed narrow photoproduct peak at higher energies, in close vicinity of the zero-phonon hole (ZPH), reflects a dynamical feature of the HB process populating so-called "terminal" states (states that do not interact with laser excitation). Within the n-TLSext model, each chromophore possesses multiple possibilities to create a photoproduct when in interaction with the burning laser, i.e., chromophores can interact with burning laser-light multiple times until reaching the terminal states. Due to phonon-assisted absorption, terminal states are typically at higher energies than the ZPH, in agreement with the hole burned spectra reported for many molecules embedded in various amorphous solids. However, many HB systems reveal both blue- (high-energy) and red-shifted (low-energy) antiholes (i.e., photoproducts). We suggest that future modeling of resonant holes in various proteins using our n-TLSext model will provide more insight on the complexity of the protein energy landscape.

Contrasting behavior of urea in strengthening and weakening confinement effects on polymer collapse.

Biomolecules inhabit a crowded living cell that is packed with high concentrations of cosolutes and macromolecules that result in restricted, confined volumes for biomolecular dynamics. To understand the impact of crowding on the biomolecular structure, the combined effects of the cosolutes (such as urea) and confinement need to be accounted for. This study involves examining these effects on the collapse equilibria of three model 32-mer polymers, which are simplified models of hydrophobic, charge-neutral, and uncharged hydrophilic polymers, using molecular dynamics simulations. The introduction of confinement promotes the collapse of all three polymers. Interestingly, addition of urea weakens the collapse of the confined hydrophobic polymer, leading to non-additive effects, whereas for the hydrophilic polymers, urea enhances the confinement effects by enhancing polymer collapse (or decreasing the polymer unfolding), thereby exhibiting an additive effect. The unfavorable dehydration energy opposes collapse in the confined hydrophobic and charge-neutral polymers under the influence of urea. However, the collapse is driven mainly by the favorable change in polymer-solvent entropy. The confined hydrophilic polymer, which tends to unfold in bulk water, is seen to have reduced unfolding in the presence of urea due to the stabilizing of the collapsed state by urea via cohesive bridging interactions. Therefore, there is a complex balance of competing factors, such as polymer chemistry and polymer-water and polymer-cosolute interactions, beyond volume exclusion effects, which determine the collapse equilibria under confinement. The results have implications to understand the altering of the free energy landscape of proteins in the confined living cell environment.

Exploring the sequence and structural determinants of the energy landscape from thermodynamically stable and kinetically trapped subtilisins: ISP1 and SbtE.

A protein's energy landscape, all the accessible conformations, their populations, and their dynamics of interconversion, is encoded in its primary sequence. While we have a good understanding of how a protein's primary sequence encodes its native state, we have a much weaker understanding of how sequence encodes the kinetic barriers such as unfolding and refolding. Here we have looked at two subtiliase homologs from the Bacillus subtilis, Intracellular Subtilisin Protease 1 (ISP1) and Subtilisin E (SbtE) that are expected to have very different dynamics. As an intracellular protein, ISP1 has a small pro-domain thought to act simply as a zymogen, whereas the extracellular SbtE has a large pro-domain required for folding. We examined the global and local energetics of the mature proteases and how each pro-domain impacts their landscapes. We find that ISP1's pro-domain has limited impact on the energy landscape while the mature SbtE is thermodynamically unstable and kinetically trapped. The impact of the pro-domain has opposite effects on the flexibility of the core of the protein. ISP1's core becomes more flexible while SbtE's core becomes more rigid. ISP1 contains a conserved amino-acid insertion not present in extracellular subtilisin proteases, which points to a potential source for these differences. These homologs are an extreme example of how changes in the primary sequence can dramatically alter a proteins energy landscape, both stability and dynamics, and highlight the need for large scale, high throughput studies on the relationship between primary sequence and conformational dynamics.

AlphaFold predictions of fold-switched conformations are driven by structure memorization

Recent work suggests that AlphaFold (AF)–a deep learning-based model that can accurately infer protein structure from sequence–may discern important features of folded protein energy landscapes, defined by the diversity and frequency of different conformations in the folded state. Here, we test the limits of its predictive power on fold-switching proteins, which assume two structures with regions of distinct secondary and/or tertiary structure. We find that (1) AF is a weak predictor of fold switching and (2) some of its successes result from memorization of training-set structures rather than learned protein energetics. Combining >280,000 models from several implementations of AF2 and AF3, a 35% success rate was achieved for fold switchers likely in AF’s training sets. AF2’s confidence metrics selected against models consistent with experimentally determined fold-switching structures and failed to discriminate between low and high energy conformations. Further, AF captured only one out of seven experimentally confirmed fold switchers outside of its training sets despite extensive sampling of an additional ~280,000 models. Several observations indicate that AF2 has memorized structural information during training, and AF3 misassigns coevolutionary restraints. These limitations constrain the scope of successful predictions, highlighting the need for physically based methods that readily predict multiple protein conformations.

Protein engineering using circular permutation - structure, function, stability, and applications.

Protein engineering is important for creating novel variants from natural proteins, enabling a wide range of applications. Approaches such as rational design and directed evolution are routinely used to make new protein variants. Computational tools like de novo design can introduce new protein folds. Expanding the amino acid repertoire to include unnatural amino acids with non-canonical side chains in vitro by native chemical ligation and in vivo via codon expansion methods broadens sequence and structural possibilities. Circular permutation (CP) is an invaluable approach to redesigning a protein by rearranging the amino acid sequence, where the connectivity of the secondary structural elements is altered without changing the overall structure of the protein. Artificial CP proteins (CPs) are employed in various applications such as biocatalysis, sensing of small molecules by fluorescence, genome editing, ligand-binding protein switches, and optogenetic engineering. Many studies have shown that CP can lead to either reduced or enhanced stability or catalytic efficiency. The effects of CP on a protein's energy landscape cannot be predicted a priori. Thus, it is important to understand how CP can affect the thermodynamic and kinetic stability of a protein. In this review, we discuss the discovery and advancement of techniques to create protein CP, and existing reviews on CP. We delve into the plethora of biological applications for designed CP proteins. We subsequently discuss the experimental and computational reports on the effects of CP on the thermodynamic and kinetic stabilities of proteins of various topologies. An understanding of the various aspects of CP will allow the reader to design robust CP proteins for their specific purposes.

Molecular dynamics simulation of the effect of temperature on the conformation of ubiquitin protein.

Ubiquitin, a ubiquitous small protein found in all living organisms, is crucial for tagging proteins earmarked for degradation and holds pivotal importance in biomedicine. Protein functionality is intricately linked to its structure. To comprehend the impact of diverse temperatures on ubiquitin protein structure, our study delved into the energy landscape, hydrogen bonding, and overall structural stability of ubiquitin protein at varying temperatures. Through meticulous analysis of root mean square deviation and root mean square fluctuation, we validated the robustness of the simulation conditions employed. Within our simulated system, the bonding energy and electrostatic potential energy exhibited linear augmentation, while the van der Waals energy demonstrated a linear decline. Additionally, our findings highlighted that the α-Helix secondary structure of the ubiquitin protein gradually transitions toward helix destabilization under high-temperature conditions. The secondary structure of ubiquitin protein experiences distinct changes under varying temperatures. The outcomes of our molecular simulations offer a theoretical framework that enhances our comprehension of how temperature impacts the structural stability of ubiquitin protein. These insights contribute not only to a deeper understanding of iniquity's behavior but also hold broader implications in the realm of biomedicine and beyond. All the MD simulations were performed using the GROMACS software with GROMOS96 force field and SPC for water. The ubiquitin protein was put in the center of a cubic box with a length of 8nm, a setting that allowed > 0.8nm in the minimal distance between the protein surface and the box wall. To remove the possible coordinate collision of the configurations, in the beginning, the steepest descent method was used until the maximum force between atoms was under 100kJ/mol·nm with a 0.01nm step size. Minimization was followed by 30ps of position-restrained MD simulation. The protein was restrained to its initial position, and the solvent was freely equilibrated. The product phase was obtained with the whole system simulated for 10ns without any restraint using an integral time step of 1fs with different temperatures. The cutoff for short-range electronic interaction was set to 1.5nm. The long-range interactions were treated with a particle-mesh Ewald (PME) method with a grid width of 1.2nm.

Exploiting cyclodextrins as artificial chaperones to enhance enzyme protection through supramolecular engineering.

We report a method of enzyme stabilisation exploiting the artificial protein chaperone properties of β-cyclodextrin (β-CD) covalently embedded in an ultrathin organosilica layer. Putative interaction points of this artificial chaperone system with the surface of the selected enzyme were studied in silico using a protein energy landscape exploration simulation algorithm. We show that this enzyme shielding method allows for drastic enhancement of enzyme stability under thermal and chemical stress conditions, along with broadening the optimal temperature range of the biocatalyst. The presence of the β-CD macrocycle within the protective layer supports protein refolding after treatment with a surfactant.

Ancestral Reconstruction and the Evolution of Protein Energy Landscapes.

A protein's sequence determines its conformational energy landscape. This, in turn, determines the protein's function. Understanding the evolution of new protein functions therefore requires understanding how mutations alter the protein energy landscape. Ancestral sequence reconstruction (ASR) has proven a valuable tool for tackling this problem. In ASR, one phylogenetically infers the sequences of ancient proteins, allowing characterization of their properties. When coupled to biophysical, biochemical, and functional characterization, ASR can reveal how historical mutations altered the energy landscape of ancient proteins, allowing the evolution of enzyme activity, altered conformations, binding specificity, oligomerization, and many other protein features. In this article, we review how ASR studies have been used to dissect the evolution of energy landscapes. We also discuss ASR studies that reveal how energy landscapes have shaped protein evolution. Finally, we propose that thinking about evolution from the perspective of an energy landscape can improve how we approach and interpret ASR studies.

Differential effects of disulfide bond formation in TEM-1 versus CTX-M-9 β-lactamase.

To investigate how disulfide bonds can impact protein energy landscapes, we surveyed the effects of adding or removing a disulfide in two β-lactamase enzymes, TEM-1 and CTX-M-9. The homologs share a structure and 38% sequence identity, but only TEM-1 contains a native disulfide bond. They also differ in thermodynamic stability and in the number of states populated at equilibrium: CTX-M-9 is two-state whereas TEM-1 has an additional intermediate state. We hypothesized that the disulfide bond is the major underlying determinant for these observed differences in their energy landscapes. To test this, we removed the disulfide bridge from TEM-1 and introduced a disulfide bridge at the same location in CTX-M-9. This modest change to sequence modulates the stabilities-and therefore populations-of TEM-1's equilibrium states and, more surprisingly, creates a novel third state in CTX-M-9. Unlike TEM-1's partially folded intermediate, this third state is a higher-order oligomer with reduced cysteines that retains the native fold and is fully active. Sub-denaturing concentrations of urea shifts the equilibrium to the monomeric form, allowing the disulfide bond to form. Interestingly, comparing the stability of the oxidized monomer with a variant lacking cysteines reveals the disulfide is neither stabilizing nor destabilizing in CTX-M-9, in contrast with the observed stabilization in TEM-1. Thus, we can conclude that engineering disulfide bonds is not always an effective stabilization strategy even when analogous disulfides exist in more stable structural homologs. This study also illustrates how homo-oligomerization can result from a small number of mutations, suggesting complex formation might be easily accessed during a protein family's evolution.

Circular permutation at azurin's active site slows down its folding.

Circular permutation (CP) is a technique by which the primary sequence of a protein is rearranged to create new termini. The connectivity of the protein is altered but the overall protein structure generally remains unperturbed. Understanding the effect of CP can help design robust proteins for numerous applications such as in genetic engineering, optoelectronics, and improving catalytic activity. Studies on different protein topologies showed that CP usually affects protein stability as well as unfolding rates. Though a significant number of proteins contain metals or other cofactors, reports of metalloprotein CPs are rare. Thus, we chose a bacterial metalloprotein, azurin, and its CP within the metal-binding site (cpF114). We studied the stabilities, folding, and unfolding rates of apo- and Zn2+-bound CP azurin using fluorescence and circular dichroism. The introduced CP had destabilizing effects on the protein. Also, the folding of the Zn2+-CP protein was much slower than that of the Zn2+-WT or apo-protein. We compared this study to our previously reported azurin-cpN42, where we hadobserved an equilibrium and kinetic intermediate. cpF114 exhibits an apparent two-state equilibrium unfolding but has an off-pathway kinetic intermediate. Our study hinted at CP as a method to modify the energy landscape of proteins to alter their folding pathways. WT azurin, being a faster folder, may have evolved to optimize the folding rate of metal-bound protein compared to its CPs, albeit all of them have the same structure and function. Our study underscores that protein sequence and protein termini positions are crucial for metalloproteins. TOC Figure. (Top) Zn2+-azurin WT structure (PDB code: 1E67) and 2-D topology diagram of Zn2+-cpF114 azurin. (Bottom) Cartoon diagram representing folding (red arrows) and unfolding (blue arrows) of apo- and Zn2+- WT and cpF114 azurins. The width of the arrows represents the rate of the corresponding processes.

Predicting multiple conformations via sequence clustering and AlphaFold2.

AlphaFold2(ref.1) has revolutionized structural biology by accurately predicting single structures of proteins. However, a protein's biological function often depends on multiple conformational substates2, and disease-causing point mutations often cause population changes within these substates3,4. We demonstrate that clustering a multiple-sequence alignment by sequence similarity enables AlphaFold2 to sample alternative states of known metamorphic proteins with high confidence. Using this method, named AF-Cluster, we investigated the evolutionary distribution of predicted structures for the metamorphic protein KaiB5 and found that predictions of both conformations were distributed in clusters across the KaiB family. We used nuclear magnetic resonance spectroscopy to confirm an AF-Cluster prediction: a cyanobacteria KaiB variant is stabilized in the opposite state compared with the more widely studied variant. To test AF-Cluster's sensitivity to point mutations, we designed and experimentally verified a set of three mutations predicted to flip KaiB from Rhodobacter sphaeroides from the ground to the fold-switched state. Finally, screening for alternative states in protein families without known fold switching identified a putative alternative state for the oxidoreductase Mpt53 in Mycobacterium tuberculosis. Further development of such bioinformatic methods in tandem with experiments will probably have a considerable impact on predicting protein energy landscapes, essential for illuminating biological function.

Energy landscapes for proteins described by the UNRES coarse-grained potential

The self-assembly of proteins is encoded in the underlying potential energy surface (PES), from which we can predict structure, dynamics, and thermodynamic properties. However, the corresponding analysis becomes increasingly challenging with larger protein sizes, due to the computational time required, which grows significantly with the number of atoms. Coarse-grained models offer an attractive approach to reduce the computational cost. In this Feature Article, we describe our implementation of the UNited RESidue (UNRES) coarse-grained potential in the Cambridge energy landscapes software. We have applied this framework to explore the energy landscapes of four proteins that exhibit native states involving different secondary structures. Here we have tested the ability of the UNRES potential to represent the global energy landscape of proteins containing up to 100 amino acid residues. The resulting potential energy landscapes exhibit good agreement with experiment, with low-lying minima close to the PDB geometries and to results obtained using the all-atom AMBER force field. The new program interfaces will allow us to investigate larger biomolecules in future work, using the UNRES potential in combination with all the methodology available in the computational energy landscapes framework.

Engineering gain-of-function mutants of a WW domain by dynamics and structural analysis.

Proteins gain optimal fitness such as foldability and function through evolutionary selection. However, classical studies have found that evolutionarily designed protein sequences alone cannot guarantee foldability, or at least not without considering local contacts associated with the initial folding steps. We previously showed that foldability and function can be restored by removing frustration in the folding energy landscape of a model WW domain protein, CC16, which was designed based on Statistical Coupling Analysis (SCA). Substitutions ensuring the formation of five local contacts identified as "on-path" were selected using the closest homolog native folded sequence, N21. Surprisingly, the resulting sequence, CC16-N21, bound to Group I peptides, while N21 did not. Here, we identified single-point mutations that enable N21 to bind a Group I peptide ligand through structure and dynamic-based computational design. Comparison of the docked position of the CC16-N21/ligand complex with the N21 structure showed that residues at positions 9 and 19 are important for peptide binding, whereas the dynamic profiles identified position 10 as allosterically coupled to the binding site and exhibiting different dynamics between N21 and CC16-N21. We found that swapping these positions in N21 with matched residues from CC16-N21 recovers nature-like binding affinity to N21. This study validates the use of dynamic profiles as guiding principles for affecting the binding affinity of small proteins.

A geometrical framework for thinking about proteins.

We present a model, based on symmetry and geometry, for proteins. Using elementary ideas from mathematics and physics, we derive the geometries of discrete helices and sheets. We postulate a compatible solvent-mediated emergent pairwise attraction that assembles these building blocks, while respecting their individual symmetries. Instead of seeking to mimic the complexity of proteins, we look for a simple abstraction of reality that yet captures the essence of proteins. We employ analytic calculations and detailed Monte Carlo simulations to explore some consequences of our theory. The predictions of our approach are in accord with experimental data. Our framework provides a rationalization for understanding the common characteristics of proteins. Our results show that the free energy landscape of a globular protein is pre-sculpted at the backbone level, sequences and functionalities evolve in the fixed backdrop of the folds determined by geometry and symmetry, and that protein structures are unique in being simultaneously characterized by stability, diversity, and sensitivity.

Deep mining of the protein energy landscape

For over half a century, it has been known that protein molecules naturally undergo extensive structural fluctuations, and that these internal motions are intimately related to their functional properties. The energy landscape view has provided a powerful framework for describing the various physical states that proteins visit during their lifetimes. This Perspective focuses on the commonly neglected and often disparaged axis of the protein energy landscape: entropy. Initially seen largely as a barrier to functionally relevant states of protein molecules, it has recently become clear that proteins retain considerable conformational entropy in the "native" state, and that this entropy can and often does contribute significantly to the free energy of fundamental protein properties, processes, and functions. NMR spectroscopy, molecular dynamics simulations, and emerging crystallographic views have matured in parallel to illuminate dynamic disorder of the "ground state" of proteins and their importance in not only transiting between biologically interesting structures but also greatly influencing their stability, cooperativity, and contribution to critical properties such as allostery.

Toward protein fingerprinting with a solid-state nanopore.

Solid-state nanopores have shown great potential for single-molecule protein identification in recent years. As a first step toward single-molecule protein fingerprinting using a solid-state nanopore device, thorough characterization of the passage of proteins under the different conformational states is required. For this purpose, we use green fluorescent protein (GFP), antibiotin immunoglobulin G (IgG), and cytochrome c (cyt c) as model proteins, and show results from field-driven translocations through nanopores with diameters ranging from 2 to 10 nm in SiN membranes of thicknesses from 10 to 20 nm. As expected, the distinguishable current blockades observed during individual translocations allow the identification of different protein conformations adopted throughout its passage, ranging from partially unfolded to completely unfolded conformations. Here we show how different temperatures and voltages facilitate protein unfolding and alter their folding kinetics. This is achieved by varying voltages from 0.1 V to 1 V and by varying the temperature from room temperature to 60 oC while maintaining low-noise ionic current recording at 1 MHz bandwidth using a custom instrument with thermal control offering ±0.2 oC precision and ±1 oC repeatability. These results will serve to better understand the accessible states in the protein energy landscape and to benchmark future studies furthering protein fingerprinting.

Mapping protein energy landscapes with X-ray footprinting mass spectrometry.

Understanding how the linear sequence of a protein can encode the diversity of protein structures and functions has been a central goal of biology for the last half-century. Recent advances now allow one to predict the three-dimensional structure for a given protein sequence with incredible accuracy; the sequence of a protein, however, encodes much more than just this native structure - it encodes the entire energy landscape - an ensemble of conformations whose populations (energetics) and dynamics are finely tuned. Therefore, proteins should not be thought of as single static structures but as statistical ensembles. To truly predict protein function from sequence, we must also understand protein energy landscapes. Unfortunately, current methods for measuring protein stability suffer from several limitations, requiring milligram quantities of purified protein in isolation at micromolar concentrations. We have developed an approach based on x-ray radical footprinting mass spectrometry (XF-MS), in combination with chemical denaturation, that overcomes these challenges and can accurately measure protein stabilities using ∼103 fold less protein, including following the stabilities of individual proteins in complex mixtures. Importantly, we can follow the energetics of individual regions in a protein allowing us to study complex multidomain proteins refractory to standard tools for measuring protein stability. Together, this approach enables us to dramatically increase the number of proteins for which we can quantitatively measure energy landscapes.

Probing unfolding barriers in high throughput.

Energy barriers between protein states determine the rate at which a folded protein makes excursions to and from any high energy states. These may include functional states or potentially harmful states. Although barriers between states are important features of a protein's energy landscape, we do not understand how they are encoded in a protein's primary sequence. Therefore, we have developed an assay coupling quantitative proteolysis with a high throughput display system to probe the effect of sequence changes on a protein's unfolding barrier. Under certain conditions, proteolytic resistance arises solely from a large kinetic barrier to unfolding, allowing us to measure barrier heights directly. In addition to expanding our basic knowledge of protein folding, exploration of the relationship between sequence and kinetic barrier heights may contribute to a greater understanding of how pathogenic protein states arise and may aid to the development of long-lived, shelf-stable protein drugs.

Investigating differences between thermodynamically and kinetically stable subtilisins to identify sequence signatures of kinetic stability.

A protein's energy landscape, including the thermodynamic stability and dynamics of all accessible conformations, is encoded in its primary sequence. While the field has come a long way in understanding how thermodynamics are encoded, how the sequence encodes kinetic barriers is still poorly understood. For decades, researchers have attempted to uncover the sequence signatures of kinetically stable proteins by studying proteins with extreme kinetic barriers, such as extracellular proteases. It has been postulated that secreted proteases evolved large barriers to minimize local fluctuations that would leave them susceptible to damage in the harsh extracellular environment, including degradation by other proteases.