Molecular evolution of protein conformational changes revealed by a network of evolutionarily coupled residues

An improved understanding of protein conformational changes has broad implications for elucidating the mechanisms of various biological processes and for the design of protein engineering experiments. Understanding rearrangements of residue interactions is a key component in the challenge of describing structural transitions. Evolutionary properties of protein sequences and structures are extensively studied; however, evolution of protein motions, especially with respect to interaction rearrangements, has yet to be explored. Here, we investigated the relationship between sequence evolution and protein conformational changes and discovered that structural transitions are encoded in amino acid sequences as coevolving residue pairs. Furthermore, we found that highly coevolving residues are clustered in the flexible regions of proteins and facilitate structural transitions by forming and disrupting their interactions cooperatively. Our results provide insight into the evolution of protein conformational changes and help to identify residues important for structural transitions.     

Protein conformational transitions explored by mixed elastic network models

We develop a mixed elastic network model (MENM) to study large-scale conformational transitions of proteins between two (or more) known structures. Elastic network potentials for the beginning and end states of a transition are combined, in effect, by adding their respective partition functions. The resulting effective MENM energy function smoothly interpolates between the original surfaces, and retains the beginning and end structures as local minima. Saddle points, transition paths, potentials of mean force, and partition functions can be found efficiently by largely analytic methods. To characterize the protein motions during a conformational transition, we follow "transition paths" on the MENM surface that connect the beginning and end structures and are invariant to parameterizations of the model and the mathematical form of the mixing scheme. As illustrations of the general formalism, we study large-scale conformation changes of the motor proteins KIF1A kinesin and myosin II. We generate possible transition paths for these two proteins that reveal details of their conformational motions. The MENM formalism is computationally efficient and generally applicable even for large protein systems that undergo highly collective structural changes.     

Using robotics to fold proteins and dock ligands

The problems of protein folding and ligand docking have been explored largely using molecular dynamics or Monte Carlo methods. These methods are very compute intensive because they often explore a much wider range of energies, conformations and time than necessary. In addition, Monte Carlo methods often get trapped in local minima. We initially showed that robotic motion planning permitted one to determine the energy of binding and dissociation of ligands from protein binding sites (Singh et al., 1999). The robotic motion planning method maps complicated three-dimensional conformational states into a much simpler, but higher dimensional space in which conformational rearrangements can be represented as linear paths. The dimensionality of the conformation space is of the same order as the number of degrees of conformational freedom in three-dimensional space. We were able to determine the relative energy of association and dissociation of a ligand to a protein by calculating the energetics of interaction for a few thousand conformational states in the vicinity of the protein and choosing the best path from the roadmap. More recently, we have applied roadmap planning to the problem of protein folding (Apaydin et al., 2002a). We represented multiple conformations of a protein as nodes in a compact graph with the edges representing the probability of moving between neighboring states. Instead of using Monte Carlo simulation to simulate thousands of possible paths through various conformational states, we were able to use Markov methods to calculate the steady state occupancy of each conformation, needing to calculate the energy of each conformation only once. We referred to this Markov method of representing multiple conformations and transitions as stochastic roadmap simulation or SRS. We demonstrated that the distribution of conformational states calculated with exhaustive Monte Carlo simulations asymptotically approached the Markov steady state if the same Boltzman energy distribution was used in both methods. SRS permits one to calculate contributions from all possible paths simultaneously with far fewer energy calculations than Monte Carlo or molecular dynamics methods. The SRS method also permits one to represent multiple unfolded starting states and multiple, near-native, folded states and all possible paths between them simultaneously. The SRS method is also independent of the function used to calculate the energy of the various conformational states. In a paper to be presented at this conference (Apaydin et al., 2002b) we have also applied SRS to ligand docking in which we calculate the dynamics of ligand-protein association and dissociation in the region of various binding sites on a number of proteins. SRS permits us to determine the relative times of association to and dissociation from various catalytic and non-catalytic binding sites on protein surfaces. Instead of just following the best path in a roadmap, we can calculate the contribution of all the possible binding or dissociation paths and their relative probabilities and energies simultaneously.     

Path Similarity Analysis: A Method for Quantifying Macromolecular Pathways

Diverse classes of proteins function through large-scale conformational changes and various sophisticated computational algorithms have been proposed to enhance sampling of these macromolecular transition paths. Because such paths are curves in a high-dimensional space, it has been difficult to quantitatively compare multiple paths, a necessary prerequisite to, for instance, assess the quality of different algorithms. We introduce a method named Path Similarity Analysis (PSA) that enables us to quantify the similarity between two arbitrary paths and extract the atomic-scale determinants responsible for their differences. PSA utilizes the full information available in 3N-dimensional configuration space trajectories by employing the Hausdorff or Fréchet metrics (adopted from computational geometry) to quantify the degree of similarity between piecewise-linear curves. It thus completely avoids relying on projections into low dimensional spaces, as used in traditional approaches. To elucidate the principles of PSA, we quantified the effect of path roughness induced by thermal fluctuations using a toy model system. Using, as an example, the closed-to-open transitions of the enzyme adenylate kinase (AdK) in its substrate-free form, we compared a range of protein transition path-generating algorithms. Molecular dynamics-based dynamic importance sampling (DIMS) MD and targeted MD (TMD) and the purely geometric FRODA (Framework Rigidity Optimized Dynamics Algorithm) were tested along with seven other methods publicly available on servers, including several based on the popular elastic network model (ENM). PSA with clustering revealed that paths produced by a given method are more similar to each other than to those from another method and, for instance, that the ENM-based methods produced relatively similar paths. PSA applied to ensembles of DIMS MD and FRODA trajectories of the conformational transition of diphtheria toxin, a particularly challenging example, showed that the geometry-based FRODA occasionally sampled the pathway space of force field-based DIMS MD. For the AdK transition, the new concept of a Hausdorff-pair map enabled us to extract the molecular structural determinants responsible for differences in pathways, namely a set of conserved salt bridges whose charge-charge interactions are fully modelled in DIMS MD but not in FRODA. PSA has the potential to enhance our understanding of transition path sampling methods, validate them, and to provide a new approach to analyzing conformational transitions.     

Protein phosphatases and their regulation in the control of mitosis

Cell cycle transitions depend on protein phosphorylation and dephosphorylation. The discovery of cyclin-dependent kinases (CDKs) and their mode of activation by their cyclin partners explained many important aspects of cell cycle control. As the cell cycle is basically a series of recurrences of a defined set of events, protein phosphatases must obviously be as important as kinases. However, our knowledge about phosphatases lags well behind that of kinases. We still do not know which phosphatase(s) is/are truly responsible for dephosphorylating CDK substrates, and we know very little about whether and how protein phosphatases are regulated. Here, we summarize our present understanding of the phosphatases that are important in the control of the cell cycle and pose the questions that need to be answered as regards the regulation of protein phosphatases.     

Elucidating the ensemble of functionally-relevant transitions in protein systems with a robotics-inspired method

Background

Many proteins tune their biological function by transitioning between different functional states, effectively acting as dynamic molecular machines. Detailed structural characterization of transition trajectories is central to understanding the relationship between protein dynamics and function. Computational approaches that build on the Molecular Dynamics framework are in principle able to model transition trajectories at great detail but also at considerable computational cost. Methods that delay consideration of dynamics and focus instead on elucidating energetically-credible conformational paths connecting two functionally-relevant structures provide a complementary approach. Effective sampling-based path planning methods originating in robotics have been recently proposed to produce conformational paths. These methods largely model short peptides or address large proteins by simplifying conformational space.

Methods

We propose a robotics-inspired method that connects two given structures of a protein by sampling conformational paths. The method focuses on small- to medium-size proteins, efficiently modeling structural deformations through the use of the molecular fragment replacement technique. In particular, the method grows a tree in conformational space rooted at the start structure, steering the tree to a goal region defined around the goal structure. We investigate various bias schemes over a progress coordinate for balance between coverage of conformational space and progress towards the goal. A geometric projection layer promotes path diversity. A reactive temperature scheme allows sampling of rare paths that cross energy barriers.

Results and conclusions

Experiments are conducted on small- to medium-size proteins of length up to 214 amino acids and with multiple known functionally-relevant states, some of which are more than 13Å apart of each-other. Analysis reveals that the method effectively obtains conformational paths connecting structural states that are significantly different. A detailed analysis on the depth and breadth of the tree suggests that a soft global bias over the progress coordinate enhances sampling and results in higher path diversity. The explicit geometric projection layer that biases the exploration away from over-sampled regions further increases coverage, often improving proximity to the goal by forcing the exploration to find new paths. The reactive temperature scheme is shown effective in increasing path diversity, particularly in difficult structural transitions with known high-energy barriers.

     

Disorder transitions and conformational diversity cooperatively modulate biological function in proteins

Structural differences between conformers sustain protein biological function. Here, we studied in a large dataset of 745 intrinsically disordered proteins, how ordered‐disordered transitions modulate structural differences between conformers as derived from crystallographic data. We found that almost 50% of the proteins studied show no transitions and have low conformational diversity while the rest show transitions and a higher conformational diversity. In this last subset, 60% of the proteins become more ordered after ligand binding, while 40% more disordered. As protein conformational diversity is inherently connected with protein function our analysis suggests differences in structure‐function relationships related to order‐disorder transitions.     

Characterization of the reversible conformational equilibrium of the cytoplasmic domain of erythrocyte membrane band 3

The cytoplasmic domain of the erythrocyte membrane protein, band 3, contains binding sites for hemoglobin, several glycolytic enzymes, and ankyrin, the linkage to the cytoskeleton. In an earlier study, we found evidence which suggested that band 3 might undergo a native conformational change. We demonstrate here that the cytoplasmic domain of band 3 does exist in a reversible, pH-dependent conformational equilibrium among 3 native states. At physiological salt concentrations this equilibrium is characterized by apparent pKa values of 7.2 and 9.2; however, these apparent pKa values change if the domain's sulfhydryl groups are modified. A major component of the structural change appears to involve the pivoting of two subdomains of the cytoplasmic domain at a central hinge, as evidenced by both hydrodynamic and fluorescence energy transfer measurements. The probable site of this hinge is between residues 176 and 191, a region highly accessible to proteases and also rich in proline. These structural rearrangements also apparently extend to the cluster of tryptophan residues near the N terminus, since the domain's intrinsic fluorescence more than doubles between pH 6.5 and 9.5. No measurable change in band 3 secondary or quaternary structure could be detected during the conformational transitions. A structural model of the cytoplasmic domain of band 3 is presented to show the possible spatial relationships between the regions of conformational change and the sites of peripheral protein binding.     

Apo-Hsp90 coexists in two open conformational states in solution

Background information. Hsp90 (90 kDa heat‐shock protein) plays a key role in the folding and activation of many client proteins involved in signal transduction and cell cycle control. The cycle of Hsp90 has been intimately associated with large conformational rearrangements, which are nucleotide‐binding‐dependent. However, up to now, our understanding of Hsp90 conformational changes derives from structural information, which refers to the crystal states of either recombinant Hsp90 constructs or the prokaryotic homologue HtpG (Hsp90 prokaryotic homologue). Results and discussion. Here, we present the first nucleotide‐free structures of the entire eukaryotic Hsp90 (apo‐Hsp90) obtained by small‐angle X‐ray scattering and single‐particle cryo‐EM (cryo‐electron microscopy). We show that, in solution, apo‐Hsp90 is in a conformational equilibrium between two open states that have never been described previously. By comparing our cryo‐EM maps with HtpG and known Hsp90 structures, we establish that the structural changes involved in switching between the two Hsp90 apo‐forms require large movements of the NTD (N‐terminal domain) and MD (middle domain) around two flexible hinge regions. Conclusions. The present study shows, for the first time, the structure of the entire eukaryotic apo‐Hsp90, along with its intrinsic flexibility. Although large structural rearrangements, leading to partial closure of the Hsp90 dimer, were previously attributed to the binding of nucleotides, our results reveal that they are in fact mainly due to the intrinsic flexibility of Hsp90 dimer. Taking into account the preponderant role of the dynamic nature of the structure of Hsp90, we reconsider the Hsp90 ATPase cycle.     

Dynamic alpha-helices: conformations that do not conform

Structural transitions are important for the stability and function of proteins, but these phenomena are poorly understood. An extensive analysis of Protein Data Bank entries reveals 103 regions in proteins with a tendency to transform from helical to nonhelical conformation and vice versa. We find that these dynamic helices, unlike other helices, are depleted in hydrophobic residues. Furthermore, the dynamic helices have higher surface accessibility and conformational mobility (P-value = 3.35e-07) than the rigid helices. Contact analyses show that these transitions result from protein-ligand, protein-nucleic acid, and crystal-contacts. The immediate structural environment differs quantitatively (P-value = 0.003) as well as qualitatively in the two alternate conformations. Often, dynamic helix experiences more contacts in its helical conformation than in the nonhelical counterpart (P-value = 0.001). There is differential preference for the type of short contacts observed in two conformational states. We also demonstrate that the regions in protein that can undergo such large conformational transitions can be predicted with a reasonable accuracy using logistic regression model of supervised learning. Our findings have implications in understanding the molecular basis of structural transitions that are coupled with binding and are important for the function and stability of the protein. Based on our observations, we propose that several functionally relevant regions on the protein surface can switch over their conformation from coil to helix and vice-versa, to regulate the recognition and binding of their partner and hence these may work as "molecular switches" in the proteins to regulate certain biological process. Our results supports the idea that protein structure-function paradigm should transform from static to a highly dynamic one.     

Conformational Dynamics of Light-Harvesting Complex II in a Native Membrane Environment

Photosynthetic light-harvesting complexes (LHCs) of higher plants, moss, and green algae can undergo dynamic conformational transitions, which have been correlated to their ability to adapt to fluctuations in the light environment. Herein, we demonstrate the application of solid-state NMR spectroscopy on native, heterogeneous thylakoid membranes of Chlamydomonas reinhardtii (Cr) and on Cr light-harvesting complex II (LHCII) in thylakoid lipid bilayers to detect LHCII conformational dynamics in its native membrane environment. We show that membrane-reconstituted LHCII contains selective sites that undergo fast, large-amplitude motions, including the phytol tails of two chlorophylls. Protein plasticity is also observed in the N-terminal stromal loop and in protein fragments facing the lumen, involving sites that stabilize the xanthophyll-cycle carotenoid violaxanthin and the two luteins. The results report on the intrinsic flexibility of LHCII pigment-protein complexes in a membrane environment, revealing putative sites for conformational switching. In thylakoid membranes, fast dynamics of protein and pigment sites is significantly reduced, which suggests that in their native organelle membranes, LHCII complexes are locked in specific conformational states.     

Sequence-dependent dynamical instability of the human prion protein: a comparative simulation study

The present study aimed to explore the most probable regions of the human prion protein backbone for which the initial steps of conformational transitions as a result of intrinsic and extrinsic perturbing factors on the protein structure can be assigned. A total of 0.3-μs molecular dynamics simulations on several analog structures of the protein have been performed. To mimic the impact of the extrinsic and intrinsic destructive parameters on the dynamical characteristics of the protein, mild acidic conditions and R208H mutation have been simulated. The findings indicated that distribution of conformational flexibilities along the protein chain was almost independent of the induced perturbing factors, and was mostly centralized on certain distinct parts of the structure comprising residues 132–145 and 187–203. Analyses also revealed that the segment comprising residues 187–203 may be considered as a peptide sequence, possessing high potential to start the initial steps of conformational rearrangements due to the induced physicochemical alterations. Sequence alignment and molecular dynamics data also revealed that segment 178–203 prefers to accommodate in extended structures rather than α-helices. Region 178–203 may be considered as a peptide switch capable of initiating the conformational transitions due to the introduced modifications and perturbing parameters.     

Investigation of pathways for the low-pH conformational transition in influenza hemagglutinin

Targeted molecular dynamics simulations were used to study the conformational transition of influenza hemagglutinin (HA) from the native conformation to putative fusogenic or postfusion conformations populated at low pH. Three pathways for this conformational change were considered. Complete dissociation of the globular domains of HA was observed in one pathway, whereas smaller rearrangements were observed in the other two. The fusion peptides became exposed and moved toward the target membrane, although occasional movement toward the viral membrane was also observed. The effective energy profiles along the paths show multiple barriers. The final low-pH structures, which are consistent with available experimental data, are comparable in effective energy to native HA. As a control, the uncleaved precursor HA0 was also forced along the same pathway. In this case both the final energy and the energy barrier were much higher than in the cleaved protein. This study suggests that 1) as proposed, the native conformation is the global minimum energy conformation for the uncleaved precursor but a metastable state for cleaved HA; 2) the spring-loaded conformational change is energetically plausible in full-length HA; and 3) complete globular domain dissociation is not necessary for extension of the coiled coil and fusion peptide exposure, but the model with complete dissociation has lower energy.     

Elucidation of interactions regulating conformational stability and dynamics of SARS-CoV-2 S-protein

The ongoing COVID-19 pandemic caused by the new coronavirus, SARS-CoV-2, calls for urgent developments of vaccines and antiviral drugs. The spike protein of SARS-CoV-2 (S-protein), which consists of trimeric polypeptide chains with glycosylated residues on the surface, triggers the virus entry into a host cell. Extensive structural and functional studies on this protein have rapidly advanced our understanding of the S-protein structure at atomic resolutions, although most of these structural studies overlook the effect of glycans attached to the S-protein on the conformational stability and functional motions between the inactive down and active up forms. Here, we performed all-atom molecular dynamics simulations of both down and up forms of a fully glycosylated S-protein in solution as well as targeted molecular dynamics simulations between them to elucidate key interdomain interactions for stabilizing each form and inducing the large-scale conformational transitions. The residue-level interaction analysis of the simulation trajectories detects distinct amino acid residues and N-glycans as determinants on conformational stability of each form. During the conformational transitions between them, interdomain interactions mediated by glycosylated residues are switched to play key roles on the stabilization of another form. Electrostatic interactions, as well as hydrogen bonds between the three receptor binding domains, work as driving forces to initiate the conformational transitions toward the active form. This study sheds light on the mechanisms underlying conformational stability and functional motions of the S-protein, which are relevant for vaccine and antiviral drug developments.     

Intramolecular Movements in EF-G, Trapped at Different Stages in Its GTP Hydrolytic Cycle, Probed by FRET

Elongation factor G (EF-G) is one of several GTP hydrolytic proteins (GTPases) that cycles repeatedly on and off the ribosome during protein synthesis in bacterial cells. In the functional cycle of EF-G, hydrolysis of guanosine 5′-triphosphate (GTP) is coupled to tRNA-mRNA translocation in ribosomes. GTP hydrolysis induces conformational rearrangements in two switch elements in the G domain of EF-G and other GTPases. These switch elements are thought to initiate the cascade of events that lead to translocation and EF-G cycling between ribosomes. To further define the coupling mechanism, we developed a new fluorescent approach that can detect intramolecular movements in EF-G. We attached a fluorescent probe to the switch I element (sw1) of Escherichia coli EF-G. We monitored the position of the sw1 probe, relative to another fluorescent probe anchored to the GTP substrate or product, by measuring the distance-dependent, Förster resonance energy transfer between the two probes. By analyzing EF-G trapped at five different functional states in its cycle, we could infer the cyclical movements of sw1 within EF-G. Our results provide evidence for conformational changes in sw1, which help to drive the unidirectional EF-G cycle during protein synthesis. More generally, our approach might also serve to define the conformational dynamics of other GTPases with their cellular receptors.     

Molecular dynamics simulation of SRP GTPases: Towards an understanding of the complex formation from equilibrium fluctuations

Signal recognition particle (SRP) and its receptor (SR) play essential role in the SRP‐dependent protein targeting pathway. They interact with one another to precisely regulate the targeting reaction. The mechanism of this interaction consists of at least two discrete conformational states: complex formation and GTPase activation. Although structural studies have provided valuable insights into the understanding of the SRP‐SR interaction, it still remains unclear that how SRP and SR GTPases use their intrinsic conformational flexibilities to exert multiple allosteric regulations on this interaction process. Here, we use computational simulations to present the dynamic behavior of the SRP GTPases at an atomic level to gain further understanding of SRP‐SR interaction. We show that: (i) equilibrium conformational fluctuations contain a cooperative inter‐ and intradomain structural rearrangements that are functionally relevant to complex formation, (ii) a series of residues in different domains are identified to correlate with each other during conformational rearrangements, and (iii) α3 and α4 helices at domain interface actively rearrange their relative conformation to function as a bridge between the N domain and the core region of the G domain. These results, in addition to structural studies, would harness our understanding of the molecular mechanism for SRP and SR interaction. Proteins 2010. © 2010 Wiley‐Liss, Inc.     

The sequence TGAAKAVALVL from glyceraldehyde-3-phosphate dehydrogenase displays structural ambivalence and interconverts between alpha-helical and beta-hairpin conformations mediated by collapsed conformational states.

The peptide TGAAKAVALVL from glyceraldehyde-3-phosphate dehydrogenase adopts a helical conformation in the crystal structure and is a site for two hydrated helical segments, which are thought to be helical folding intermediates. Overlapping sequences of four to five residues from the peptide, sample both helical and strand conformations in known protein structures, which are dissimilar to glyceraldehyde-3-phosphate dehydrogenase suggesting that the peptide may have a structural ambivalence. Molecular dynamics simulations of the peptide sequence performed for a total simulation time of 1.2 micros, starting from the various initial conformations using GROMOS96 force field under NVT conditions, show that the peptide samples a large number of conformational forms with transitions from alpha-helix to beta-hairpin and vice versa. The peptide, therefore, displays a structural ambivalence. The mechanism from alpha-helix to beta-hairpin transition and vice versa reveals that the compact bends and turns conformational forms mediate such conformational transitions. These compact structures including helices and hairpins have similar hydrophobic radius of gyration (Rgh) values suggesting that similar hydrophobic interactions govern these conformational forms. The distribution of conformational energies is Gaussian with helix sampling lowest energy followed by the hairpins and coil. The lowest potential energy of the full helix may enable the peptide to take up helical conformation in the crystal structure of the glyceraldehyde-3-phosphate dehydrogenase, even though the peptide has a preference for hairpin too. The relevance of folding and unfolding events observed in our simulations to hydrophobic collapse model of protein folding are discussed.