Energy Landscape Of Proteins Research Articles

Learning Organizations of Protein Energy Landscapes: An Application on Decoy Selection in Template-Free Protein Structure Prediction.

The protein energy landscape, which lifts the protein structure space by associating energies with structures, has been useful in improving our understanding of the relationship between structure, dynamics, and function. Currently, however, it is challenging to automatically extract and utilize the underlying organization of an energy landscape to the link structural states it houses to biological activity. In this chapter, we first report on two computational approaches that extract such an organization, one that ignores energies and operates directly in the structure space and another that operates on the energy landscape associated with the structure space. We then describe two complementary approaches, one based on unsupervised learning and another based on supervised learning. Both approaches utilize the extracted organization to address the problem of decoy selection in template-free protein structure prediction. The presented results make the case that learning organizations of protein energy landscapes advances our ability to link structures to biological activity.

The entropic force generated by intrinsically disordered segments tunes protein function.

Protein structures are dynamic and can explore a large conformational landscape1,2. Only some of these structural substates are important for protein function (i.e. ligand binding, catalysis and regulation)3–5. How evolution shapes the structural ensemble to optimize a specific function is poorly understood>3,4. One of the constraints on the evolution of proteins is the stability of the folded ‘native’ state. Despite this, 44% of the human proteome contains intrinsically disordered (ID) peptide segments >30 residues in length6, the majority of which have no known function7–9. Here we show that the entropic force produced by an ID carboxy-terminus (ID-tail) shifts the conformational ensemble of human UDP-α-D-glucose-6-dehydrogenase (hUGDH) toward a substate with a high affinity for an allosteric inhibitor. The function of the ID-tail does not depend on its sequence or chemical composition. Instead, the affinity enhancement can be accurately predicted based on the length of the ID segment and is consistent with the entropic force generated by an unstructured peptide attached to the protein surface10–13. Our data show that the unfolded state of the ID-tail rectifies the dynamics and structure of hUGDH to favor inhibitor binding. Because this entropic rectifier does not have any sequence or structural constraints, it is an easily acquired adaptation. This model implies that evolution selects for disordered segments to tune the energy landscape of proteins, which may explain the persistence of ID in the proteome.

From mutations to mechanisms and dysfunction via computation and mining of protein energy landscapes

BackgroundThe protein energy landscape underscores the inherent nature of proteins as dynamic molecules interconverting between structures with varying energies. Reconstructing a protein’s energy landscape holds the key to characterizing a protein’s equilibrium conformational dynamics and its relationship to function. Many pathogenic mutations in protein sequences alter the equilibrium dynamics that regulates molecular interactions and thus protein function. In principle, reconstructing energy landscapes of a protein’s healthy and diseased variants is a central step to understanding how mutations impact dynamics, biological mechanisms, and function.ResultsRecent computational advances are yielding detailed, sample-based representations of protein energy landscapes. In this paper, we propose and describe two novel methods that leverage computed, sample-based representations of landscapes to reconstruct them and extract from them informative local structures that reveal the underlying organization of an energy landscape. Such structures constitute landscape features that, as we demonstrate here, can be utilized to detect alterations of landscapes upon mutation.ConclusionsThe proposed methods detect altered protein energy landscape features in response to sequence mutations. By doing so, the methods allow formulating hypotheses on the impact of mutations on specific biological activities of a protein. This work demonstrates that the availability of energy landscapes of healthy and diseased variants of a protein opens up new avenues to harness the quantitative information embedded in landscapes to summarize mechanisms via which mutations alter protein dynamics to percolate to dysfunction.

An Energy Landscape Treatment of Decoy Selection in Template-Free Protein Structure Prediction

The energy landscape, which organizes microstates by energies, has shed light on many cellular processes governed by dynamic biological macromolecules leveraging their structural dynamics to regulate interactions with molecular partners. In particular, the protein energy landscape has been central to understanding the relationship between protein structure, dynamics, and function. The landscape view, however, remains underutilized in an important problem in protein modeling, decoy selection in template-free protein structure prediction. Given the amino-acid sequence of a protein, template-free methods compute thousands of structures, known as decoys, as part of an optimization process that seeks minima of an energy function. Selecting biologically-active/native structures from the computed decoys remains challenging. Research has shown that energy is an unreliable indicator of nativeness. In this paper, we advocate that, while comparison of energies is not informative for structures that already populate minima of an energy function, the landscape view exposes the overall organization of generated decoys. As we demonstrate, such organization highlights macrostates that contain native decoys. We present two different computational approaches to extracting such organization and demonstrate through the presented findings that a landscape-driven treatment is promising in furthering research on decoy selection.

Modulating the Folding Landscape of Superoxide Dismutase 1 with Targeted Molecular Binders

AbstractAmyotrophic lateral sclerosis, or Lou Gehrig's disease, is characterized by motor neuron death, with average survival times of two to five years. One cause of this disease is the misfolding of superoxide dismutase 1 (SOD1), a phenomenon influenced by point mutations spanning the protein. Herein, we used an epitope‐specific high‐throughput screen to identify a peptide ligand that stabilizes the SOD1 native conformation and accelerates its folding by a factor of 2.5. This strategy may be useful for fundamental studies of protein energy landscapes as well as designing new classes of therapeutics.

Modulating the Folding Landscape of Superoxide Dismutase 1 with Targeted Molecular Binders.

Amyotrophic lateral sclerosis, or Lou Gehrig's disease, is characterized by motor neuron death, with average survival times of two to five years. One cause of this disease is the misfolding of superoxide dismutase 1 (SOD1), a phenomenon influenced by point mutations spanning the protein. Herein, we used an epitope-specific high-throughput screen to identify a peptide ligand that stabilizes the SOD1 native conformation and accelerates its folding by a factor of 2.5. This strategy may be useful for fundamental studies of protein energy landscapes as well as designing new classes of therapeutics.

How and why internal cavities destabilize proteins

Proteins are marginally stable as a consequence of the large and opposing contributions from enthalpy and entropy to the folded state. The microscopic contributions to the total system ΔS and ΔH are still highly enigmatic. Here, we use hydrostatic pressure ‐– coupled to NMR spectroscopy detection – to explore the protein energy landscape in residue‐specific detail. As the conjugate of pressure is volume, this approach offers a mild and clearly defined perturbation of the system, as opposed to mutagenesis, temperature or denaturants. We could thus directly examine the Van der Waals contribution to stability. Pressure‐variation also allowed us to detect several partially unfolded states that consist of cooperative modules, a result that argues for a rather discrete route that connects the folded and unfolded forms of a protein.This abstract is from the Experimental Biology 2018 Meeting. There is no full text article associated with this abstract published in The FASEB Journal.

Principal component analysis in protein tertiary structure prediction.

We discuss applicability of principal component analysis (PCA) for protein tertiary structure prediction from amino acid sequence. The algorithm presented in this paper belongs to the category of protein refinement models and involves establishing a low-dimensional space where the sampling (and optimization) is carried out via particle swarm optimizer (PSO). The reduced space is found via PCA performed for a set of low-energy protein models previously found using different optimization techniques. A high frequency term is added into this expansion by projecting the best decoy into the PCA basis set and calculating the residual model. This term is aimed at providing high frequency details in the energy optimization. The goal of this research is to analyze how the dimensionality reduction affects the prediction capability of the PSO procedure. For that purpose, different proteins from the Critical Assessment of Techniques for Protein Structure Prediction experiments were modeled. In all the cases, both the energy of the best decoy and the distance to the native structure have decreased. Our analysis also shows how the predicted backbone structure of native conformation and of alternative low energy states varies with respect to the PCA dimensionality. Generally speaking, the reconstruction can be successfully achieved with 10 principal components and the high frequency term. We also provide a computational analysis of protein energy landscape for the inverse problem of reconstructing structure from the reduced number of principal components, showing that the dimensionality reduction alleviates the ill-posed character of this high-dimensional energy optimization problem. The procedure explained in this paper is very fast and allows testing different PCA expansions. Our results show that PSO improves the energy of the best decoy used in the PCA when the adequate number of PCA terms is considered.

Rational Engineering of Multiple Active Sites in an Ester Hydrolase.

Effects of altering the properties of an active site in an enzymatic homogeneous catalyst have been extensively reported. However, the possibility of increasing the number of such sites, as commonly done in heterogeneous catalytic materials, remains unexplored, particularly because those have to accommodate appropriate residues in specific configurations. This possibility was investigated by using a serine ester hydrolase as the target enzyme. By using the Protein Energy Landscape Exploration software, which maps ligand diffusion and binding, we found a potential binding pocket capable of holding an extra catalytic triad and oxyanion hole contacts. By introducing two mutations, this binding pocket became a catalytic site. Its substrate specificity, substrate preference, and catalytic activity were different from those of the native site of the wild type ester hydrolase and other hydrolases, due to the differences in the active site architecture. Converting the binding pocket into an extra catalytic active site was proven to be a successful approach to create a serine ester hydrolase with two functional reactive groups. Our results illustrate the accuracy and predictive nature of modern modeling techniques, opening novel catalytic opportunities coming from the presence of different catalytic environments in single enzymes.

Investigating energy-based pool structure selection in the structure ensemble modeling with experimental distance constraints: The example from a multidomain protein Pub1.

The structural variations of multidomain proteins with flexible parts mediate many biological processes, and a structure ensemble can be determined by selecting a weighted combination of representative structures from a simulated structure pool, producing the best fit to experimental constraints such as interatomic distance. In this study, a hybrid structure-based and physics-based atomistic force field with an efficient sampling strategy is adopted to simulate a model di-domain protein against experimental paramagnetic relaxation enhancement (PRE) data that correspond to distance constraints. The molecular dynamics simulations produce a wide range of conformations depicted on a protein energy landscape. Subsequently, a conformational ensemble recovered with low-energy structures and the minimum-size restraint is identified in good agreement with experimental PRE rates, and the result is also supported by chemical shift perturbations and small-angle X-ray scattering data. It is illustrated that the regularizations of energy and ensemble-size prevent an arbitrary interpretation of protein conformations. Moreover, energy is found to serve as a critical control to refine the structure pool and prevent data overfitting, because the absence of energy regularization exposes ensemble construction to the noise from high-energy structures and causes a more ambiguous representation of protein conformations. Finally, we perform structure-ensemble optimizations with a topology-based structure pool, to enhance the understanding on the ensemble results from different sources of pool candidates.

The effects of pressure on the energy landscape of proteins

Protein dynamics is characterized by fluctuations among different conformational substates, i.e. the different minima of their energy landscape. At temperatures above ~200 K, these fluctuations lead to a steep increase in the thermal dependence of all dynamical properties, phenomenon known as Protein Dynamical Transition. In spite of the intense studies, little is known about the effects of pressure on these processes, investigated mostly near room temperature. We studied by neutron scattering the dynamics of myoglobin in a wide temperature and pressure range. Our results show that high pressure reduces protein motions, but does not affect the onset temperature for the Protein Dynamical Transition, indicating that the energy differences and barriers among conformational substates do not change with pressure. Instead, high pressure values strongly reduce the average structural differences between the accessible conformational substates, thus increasing the roughness of the free energy landscape of the system.

Probing the Effect of the Ribosome on the Protein Folding Pathway using Single-Molecule Chemo-Mechanical Folding

In vivo, proteins function in a complex environment where they are subject to many stresses that can modulate the protein's energy landscape. Using a combination of force and chemical denaturant (chemo-mechanical unfolding), we have demonstrated that a simple, two-state folding protein can fold through multiple parallel pathways and that seemingly small changes in force, denaturant concentration and protein sequence can strongly modulate the flux between the pathways. This result suggests that in vivo, the heterogeneous crowded cellular environment could strongly influence the mechanisms of protein folding and unfolding. Another perturbant that could affect the folding pathway in vivo is the ribosome since many proteins fold while they are being translated. To explore how the ribosome affects the folding mechanism, we use chemo-mechanical unfolding to characterize folding pathways both on and off the ribosome.

Quantitative Prediction of Bacterial Fitness from a Protein's Energy Landscape

Quantitative Prediction of Bacterial Fitness from a Protein's Energy Landscape

The Effect of the Ribosome on Nascent Chain Dynamics

Protein folding on the ribosome is fundamentally different from full-length protein refolding. Constrictions presented by the structure of the ribosome exit tunnel, the large size of the ribosome, and the negative charge of the ribosome surface, among other factors, have been suggested to modulate protein folding on the ribosome, and the ribosome has been shown to thermodynamically destabilize nascent chains (Samelson et al., PNAS 2016). In contrast to the energy landscape for a full-length protein, translation poses a situation in which the nascent chain can access conformations before the entire amino acid sequence is present. Elongation of the nascent chain gradually increases the conformational space available to the protein until it can access its native state. We are interested in how the energy landscape of a protein changes during translation and how the conformations a nascent chain samples differ from those a full-length protein can occupy. Here, we measure protein unfolding kinetics and assess the dynamics of a ribosome-stalled nascent chain in comparison with off-the-ribosome proteins using pulse proteolysis and smFRET.

Engineered synthetic antibodies as probes to quantify the energetic contributions of ligand binding to conformational changes in proteins

Conformational changes in proteins due to ligand binding are ubiquitous in biological processes and are integral to many biological systems. However, it is often challenging to link ligand-induced conformational changes to a resulting biological function because it is difficult to distinguish between the energetic components associated with ligand binding and those due to structural rearrangements. Here, we used a unique approach exploiting conformation-specific and regio-specific synthetic antibodies (sABs) to probe the energetic contributions of ligand binding to conformation changes. Using maltose-binding protein (MBP) as a model system, customized phage-display selections were performed to generate sABs that stabilize MBP in different conformational states, modulating ligand-binding affinity in competitive, allosteric, or peristeric manners. We determined that the binding of a closed conformation-specific sAB (sAB-11M) to MBP in the absence of maltose is entropically driven, providing new insight into designing antibody-stabilized protein interactions. Crystal structures of sABs bound to MBP, together with biophysical data, delineate the basis of free energy differences between different conformational states and confirm the use of the sABs as energy probes for dissecting enthalpic and entropic contributions to conformational transitions. Our work provides a foundation for investigating the energetic contributions of distinct conformational dynamics to specific biological outputs. We anticipate that our approach also may be valuable for analyzing the energy landscapes of regulatory proteins controlling biological responses to environmental changes.

The Exclusive Effects of Chaperonin on the Free Energy Landscape of Proteins with Complex Knots

A survey across the PDB shown that at least 7% of known protein structures are entangled. Proteins can be knotted, possess lassos and links. In particular knotted structures are known to act as enzymes and DNA binding proteins, and are also found in the mitochondria and in the membrane. However self-tying of such proteins is still an unresolved question. Based on representative members of Ubiquitin C-terminal Hydrolases (UCHs) that contain the 52 knot in the native state, we explain how UCHs are able to unfold and refold in vitro reversibly within the structure-based model. In particular, we identify two, topologically different folding/unfolding pathways and corroborate our results with experiment, recreating the Chevron plot. We show that confinement effect of chaperonin or weak crowding greatly facilitates folding, simultaneously slowing down the unfolding process of UCHs, compared with bulk conditions. The results of the work show that the crowded environment of the cell should have a positive effect on the kinetics of complex knotted proteins, especially now when proteins with deeper knots are found in this family. These results are also the first step to the identification of possible oligomerization-prone forms of UCHs, which may cause neurodegenerative diseases. Based on analysis of proteins with very low sequence similarity, different depths of a knot but with a similar fold, and the same type of topology (VirC2, DndE and MJ0366), we found that encapsulation facilitates knotting at the early stage of folding and can enhance an alternative folding route. Comparison to unknotted proteins with the same fold shows directly how encapsulation influences the free energy landscape.

Examination of the Effect of a Histidine Tag on the Energy Landscape of ACBP

Proteins have complex energy landscapes that can be altered by perturbations like force, mutation, or changes in environment. Exploring these landscapes gives us insight to the folding and stability of a given protein in different environmental conditions. A 6 Histidine residue tag, is often attached to the end of a protein, strictly for use in protein purification. It is interesting and necessary to determine if the His-tag has an effect on protein folding and stability. Experiments with Acyl CoA binding protein (ACBP) reveal that effects of chemical denaturation, mutation, and force can depend on the presence of a His-tag. To characterize how the His-tag affects the protein energy landscape, we study the effects of mutations on ACBP stability and folding kinetics with and without a His-tag.

Using Ancestral Proteins to Probe the Thermodynamic and Kinetic Properties of the Alpha-Lytic Protease Family

The linear sequence of a protein defines its entire energy landscape, which includes all of the accessible conformations of the polypeptide chain, their relative stabilities, and dynamics. Although small changes in the sequence do not alter the native fold of a protein, they invariably alter the energetics and dynamics of the landscape, and thus, the function and fitness of the protein. Through their effect on fitness and function, these changes drive evolution. We can use these evolutionary sequence changes to probe the code between sequence and the energy landscape. Here we use the technique of Ancestral Sequence Reconstruction (ASR) to probe the biophysical properties of the kinetic barrier to protein folding through the lens of evolutionary history. We use ASR to study the protein alpha-lytic protease (αLP) and its unusual kinetic stability to investigate the nature and origin of kinetic stability and conformational barriers in general. This protease has an unusual energy landscape: the native state of αLP is not its most thermodynamically stable state. Instead, αLP requires its own pro-region chaperone to fold, and the native state of αLP is kinetically trapped, preventing unfolding at biologically relevant time scales. Thus, understanding how this unique folding landscape of αLP arose over an evolutionary process provides insights into how the amino acid sequence can modulate kinetic barriers and thermodynamic stabilities of the protein energy landscape.

From Extraction of Local Structures of Protein Energy Landscapes to Improved Decoy Selection in Template-Free Protein Structure Prediction.

Due to the essential role that the three-dimensional conformation of a protein plays in regulating interactions with molecular partners, wet and dry laboratories seek biologically-active conformations of a protein to decode its function. Computational approaches are gaining prominence due to the labor and cost demands of wet laboratory investigations. Template-free methods can now compute thousands of conformations known as decoys, but selecting native conformations from the generated decoys remains challenging. Repeatedly, research has shown that the protein energy functions whose minima are sought in the generation of decoys are unreliable indicators of nativeness. The prevalent approach ignores energy altogether and clusters decoys by conformational similarity. Complementary recent efforts design protein-specific scoring functions or train machine learning models on labeled decoys. In this paper, we show that an informative consideration of energy can be carried out under the energy landscape view. Specifically, we leverage local structures known as basins in the energy landscape probed by a template-free method. We propose and compare various strategies of basin-based decoy selection that we demonstrate are superior to clustering-based strategies. The presented results point to further directions of research for improving decoy selection, including the ability to properly consider the multiplicity of native conformations of proteins.

Computational Methods for Efficient Sampling of Protein Landscapes and Disclosing Allosteric Regions.

Methods developed toward computational exploration of protein landscapes have become standardized tools to assess biophysical experimental findings. They are also used on their own right to discover the workings of the protein as a molecular machine, potential sites of interest for protein functioning, allosteric regions in proteins, and communication pathways between different sites on a protein. With the development of reliable force fields that describe interactions in biomolecules, molecular dynamics (MD) simulations have become the prime tool for this purpose. While it is now straightforward to carry out MD simulations up to microseconds with current computers readily available to researchers, many processes of biological interest occur on several of orders of magnitudes slower timescales. Thus, the latter problems are attackable through MD by a handful of researchers that have access to the most powerful computers. Alternatively, physics-based methods to interrogate the protein energy landscape are in continuous development to circumvent this problem. In addition to opening the routes for advancement to a large number of researchers that have access to modest computational resources, they have the advantage of providing an understanding of the mechanisms that govern protein dynamics. Here we discuss network-based approaches geared toward understanding protein dynamics. These include (i) construction of residue networks which view proteins as networks of nodes connected through local interactions and (ii) construction of proteins as elastic networks whose modes of motion may be manipulated to achieve allowed conformational changes. Limitations of the methods as well as opportunities for future exploitation are described.