Speed of protein conformational change?

Speed of protein conformational change?

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Although the speed may vary a lot based on factors like protein size/scale of conformational change/type of changes (small block change/arm movement,etc), are there examples of experimental results of time scale of such processes? (Only a single conformational change is considered i.e. the time delay between the begain and the end of a single conformational change event of a single protein (or subunit to be more precise?) )

This paper uses "ultrafast 2D-IR vibrational echo chemical-exchange spectroscopy" to track switching between different protein conformations and finds that they take place on the order of 50 pico-seconds ($ 1 imes 10^{-12} $ seconds). Another paper finds something similar but notes that full equilibriation can take on the order or nanoseconds:

Although the main conformational change of the backbone is completed after only 20 ps, the subsequent equilibration in the new region of conformational space continues for times >16 ns.

Speed of protein conformational change? - Biology

Adequate sampling of conformation space remains challenging in atomistic simulations, especially if the solvent is treated explicitly. Implicit-solvent simulations can speed up conformational sampling significantly. We compare the speed of conformational sampling between two commonly used methods of each class: the explicit-solvent particle mesh Ewald (PME) with TIP3P water model and a popular generalized Born (GB) implicit-solvent model, as implemented in the AMBER package. We systematically investigate small (dihedral angle flips in a protein), large (nucleosome tail collapse and DNA unwrapping), and mixed (folding of a miniprotein) conformational changes, with nominal simulation times ranging from nanoseconds to microseconds depending on system size. The speedups in conformational sampling for GB relative to PME simulations, are highly system- and problem-dependent. Where the simulation temperatures for PME and GB are the same, the corresponding speedups are approximately onefold (small conformational changes), between ∼1- and ∼100-fold (large changes), and approximately sevenfold (mixed case). The effects of temperature on speedup and free-energy landscapes, which may differ substantially between the solvent models, are discussed in detail for the case of miniprotein folding. In addition to speeding up conformational sampling, due to algorithmic differences, the implicit solvent model can be computationally faster for small systems or slower for large systems, depending on the number of solute and solvent atoms. For the conformational changes considered here, the combined speedups are approximately twofold, ∼1- to 60-fold, and ∼50-fold, respectively, in the low solvent viscosity regime afforded by the implicit solvent. For all the systems studied, 1) conformational sampling speedup increases as Langevin collision frequency (effective viscosity) decreases and 2) conformational sampling speedup is mainly due to reduction in solvent viscosity rather than possible differences in free-energy landscapes between the solvent models.


Proteins are flexible molecules that undergo conformational changes as part of their interactions with other proteins or drug molecules [1]. Changes in torsional angles may induce localized changes or large scale domain motions. Figure 1 shows an illustration of the closed structure of the GroEL 7-membered single ring complex taken from PDB code 1SS8 (Figure 1(a)) and the opened structure (GroEL-GroES-ADP7) taken from PDB code 1SX4 (Figure 1(b)). GroEL transitions between the closed and open conformations as part of its chaperone activity, but the structural details of the transition process are not fully understood. Tracing these changes is crucial for understanding the way these proteins perform their function. Existing physics-based computational methods that trace and simulate conformational changes in proteins include Molecular Dynamics (MD) [2], Monte Carlo (MC) [3] and their variants. These methods require large amounts of computational resources and are therefore hard to apply to conformational motions that take place over time scales larger than several hundreds of nanoseconds. In the past two decades several efficient conformational search algorithms have been developed. Some use a coarse representation of the protein molecule [4–6] and employ various efficient search methods such as Normal Mode Analysis (NMA) [7, 8] elastic network modeling [9–14], or morphing [15, 16]. In recent years sampling based motion planning methods have been successfully applied towards an efficient exploration of protein conformational space. Motion planning is an area in robotics concerned with finding a pathway for robot-like objects in constrained environments [17–19]. When applied to biological problems, the protein is represented as an articulated body with the degrees of freedom in all or selected torsional angles. The physical constraints are implicitly encoded in a penalty function which approximates the potential energy of the molecule. The conformational space of the protein is explored so that high energy regions are avoided and feasible conformational pathways are obtained more efficiently than with traditional simulation methods. Among the many applications of motion planning to biology are the characterization of near-native protein conformational ensembles [20], the study of conformational flexibility in proteins [21, 22], protein folding and binding simulation [23–25], modeling protein loops [21, 26], simulation of RNA folding kinetics [27] and recently the elucidation of conformational pathways in proteins, subject to pre-specified constraints [28]. The search methods described above strike a balance between accuracy and efficiency. Many of those methods are successful in sampling the conformational landscape of proteins but are often biased by the protein native conformation and some of them require additional, problem specific information. Additionally, when atomic details are skipped the conformational search process is greatly accelerated but fine details are missed.

GroEL (a) The GroEL complex (PDB structure 1ss8). (b) The GroEL-GroES-ADP7 complex (PDB structure 1sx4).

In this work we present a prototype of a novel, efficient motion-planning based methodology to perform conformational search on proteins requiring only backbone and limited side-chain information. The molecule is mapped into a reduced representation using a small number of parameters that represent its degrees of freedom. This allows for larger motions to be explored efficiently. We aim to make the conformational search as general as possible so it can be applied with as little system specific knowledge as possible. We use a coarse-grained physics based energy function which captures low energy conformations in a realistic but efficient way [29]. We identify the flexible parts of the proteins and manipulate them to simulate the conformational changes, treating the rest of the protein as rigid. In this way we reduce the dimensionality of the search space while still capturing the essential conformational flexibility of the protein. We tested our methodology on four proteins ranging in size from 101 to 525 residues that are known to undergo extensive conformational changes. The results show that we are able to efficiently produce low energy pathways for each one of them. The method can serve as a filtering tool which can provide biologists with useful hypotheses about the way proteins transition from one conformational state to another, and help to gain more insight about protein function.

Problem statement

Given two conformational states of a molecule, denoted by start and goal, our objective is to find conformational pathways connecting the start and goal conformations. A pathway is a sequence of affine transformations that, when applied successively to the degrees of freedom of the start conformation, the start conformation will be brought to within a tolerance range of the goal conformation under a defined distance metric. Furthermore, the energy of each intermediate conformation along the pathway must be lower than a given threshold as measured by a potential function that approximates the protein energy. The degrees of freedom of the structures lie in the flexible parts connecting rigid structural elements. Several assumptions are made in this paper. We assume that secondary structure elements do not change significantly during domain motion and that the flexible parts are the loops connecting secondary structure elements. While this assumption is true in many cases, there are cases where secondary structure elements melt or change. In these cases, it is possible to incorporate a more detailed modeling of the flexible parts into the general framework of the algorithm without limiting the proposed procedure. It should be emphasized that the algorithm does not always produce the same conformational pathway, but rather a possible pathway. This is due to randomness in the search algorithm (see Methods section below). By repeating the procedure a large number of times we produce a set of feasible pathways, thus limiting the huge search space to a manageable number of possibilities. These pathways can later be clustered, refined and filtered using information about the tested systems. The size of the clusters can give us information about the likelihood of given conformations along the pathway.

Conformational Dynamics of Proteins

Proteins are polypeptides which consist of typically 50 to several 100 amino acids and fold into protein-specific three-dimensional structures. This native structure is determined by the amino acid sequence of a protein, which is genetically encoded. A protein is not rigid, however. Rather, it can undergo a variety of (fast) vibrations and (slower) structural rearrangements, the latter being called &aposconformational transitions&apos.

This situation is best summarized by a one-dimensional sketch of the complex, high-dimensional energy landscape (see Figure), which determines the correspondingly complex dynamics of a protein, as originally suggested by Hans Frauenfelder. This energy landscape is characterized by a multitude of almost iso-energetic minima, which are separated from each other by energy barriers of various heights. Each of these minima corresponds to one particular protein structure (&aposconformational substate&apos) neighboring minima correspond to similar structures. Structural transitions are barrier crossings (arrows), and the transition rate is determined by the height of the barrier.

Principle of flooding. The free energy F along a collective coordinate, c, is approximated quasi-harmonically in the local educt minimum to yield F

. From this, a Gaussian shaped flooding potential Vfl is constructed which destabilizes the initial well and accelerates the transition across the barrier.

. From this, a Gaussian shaped flooding potential Vfl is constructed which destabilizes the initial well and accelerates the transition across the barrier.

Since in conventional molecular dynamics simulations only few nanoseconds can be covered, only the smallest barriers are overcome in simulations, and the observed structural changes are small (bottom right arrow and inset). The larger barriers are traversed more rarely (however the transition process itself may well be fast), and thus are not observed in molecular dynamics simulations. We have developed a method called &aposconformational flooding&apos [1,2], which accelerates conformational transitions in molecular dynamics simulations by several orders of magnitude and thereby actually can bring slow conformational transitions into the scope of simulations. This work was done at the Institut für Medizinische Optik, Univerity of Munich.

&aposConformational flooding&apos intimately links concepts of statistical mechanics to MD simulations and is mathematically involved, but the basic idea is simple (see Figure): In a typical nanosecond simulation, the structure is stuck in one of the minima of the high dimensional energy landscape discussed above (top of Figure). Thus, the question of in what direction of the huge number of possibilities in the high-dimensional conformational space the structure is going to cross a barrier (arrow) at, say, a millisecond time scale, cannot be answered in the first place.

An example for the motion of a small protein (BPTI, bovine pancreatic trypsin inhibitor, N=568 atoms) in 3*N=1704-dimensional configurational space, as studied by conformational flooding (2 MB). For the 3-dim. visualization, this motion has been projected onto the three most important collective degrees of freedom. Note the sudden 'jumps' between the regions of high trajectory density, which resemble conformations transitions.

However, from the ensemble generated by the simulation one can construct a localized artificial &aposflooding potential&apos of certain, but variable strength (mid). This potential is included within subsequent &aposflooding simulations&apos and rises the minimum of the initial conformation. Thereby, the barrier hight is reduced, and, following transition state theory, the transitions are accelerated. It is important to note that this behaviour is achieved solely by modifying the energy landscape within the minimum - where we know the dynamics already and thus are no longer interested in it as can be seen from the Figure, the barriers and all the other minima - which we are interested in - are not modified at all.

What determines the speed limit on enzyme catalysis?

Though the chemical mechanisms of many enzymes have been elucidated, the mechanisms by which specificity and rate acceleration are achieved remain less explored. A new study suggests that physically controlled processes, such as active site access and organization, are rate limiting for enzymatic catalysis.

Enzymes speed up the chemical reactions that are necessary for all living things. Understanding enzymes requires knowledge of three aspects: structure, dynamics and chemistry. Structure and dynamics are intimately related, but they are not equally well understood. Indeed, protein structural studies are far ahead of dynamical investigations. The elucidation of the first protein structures by Max Perutz and John Kendrew, using primitive X-ray sources and photographic plates, took years. Now, thanks to synchrotron radiation, vastly improved detection devices and improved computational methodologies, a very large number of protein structures have been determined. While structural studies have become an industry, the exploration of protein dynamics is still at the cottage stage. A new report from Henzler-Wildman et al. 1 provides a welcome step forward in understanding protein dynamics by exploring the mechanism of how a particular protein, adenylate kinase, achieves catalysis. They have shown that this enzyme must move to permit access to the catalytic site, that this motion is directed and that the protein motions, and not the actual chemistry, are rate limiting.


The crystallographic data for the S68A protein with and without bound aspartate are summarized in Table ​ Table1. 1 .

Table 1

Diffraction dataS68A apoS68A complex
Resolution (Å)1.91.9
Unique reflections12,07816,213
Completeness, %9799
Rmerge, %9.74.4
Rfree, %2729

The binding of aspartate, the initiating impulse for the conformation change, is seen by the crystallography to induce formation of a number of new hydrogen bonds and breaking of a number of bonds that existed in the apoprotein as described in Table ​ Table2. 2 . The bonds formed when aspartate is bound (designated as AspS to distinguish substrate from amino acid residues of the wild-type aspartate receptor) are Arg-64⋅⋅𢱚spS, Tyr-149⋅⋅𢱚spS, Gln-152⋅⋅𢱚spS, Thr-154⋅⋅𢱚spS, Arg-69′⋅⋅𢱚spS, and Arg-73′⋅⋅𢱚spS (Table ​ (Table2). 2 ). One drastic change is the swing of Ser-68′ in the second (unoccupied) site, resulting in a formation of a new hydrogen bond with Tyr-149′. The distance between Arg-64 and Gln-155 becomes less and that between Arg-64 and Gln-158 becomes longer.

Table 2

Bond changes when aspartate binds to the wild-type aspartate receptor

Bond Forming/strengtheningBond breaking/weakening

The structural change within a subunit of the S68A receptor protein depicted by the relative motion between helices 㬑 and 㬔 is shown in Fig. ​ Fig.1 1 b, and it seems to be similar to the motion for the wild type in Fig. ​ Fig.1 1 a. The conformational change triggered by the presence of aspartate involves a downward shift of helix 㬔 with respect to 㬑 by 𢒁.3 Å. For the wild-type periplasmic domain, helix 㬔 shifts downward with a magnitude of about 1.5 Å. Coupled to this downward shift, helix 㬔 also tilts by 5° in the wild-type protein. The tilt at helix 㬔, however, is more subtle in the case of the S68A mutant, where it is about 2° in magnitude (Table ​ (Table3). 3 ).

(a) The backbone atoms in the 㬔 helix, shown at the left, compared with the backbone atoms of the 㬑 helix, shown at the right, for the wild-type protein. The red line is the apoprotein and the yellow line is the protein with aspartate bound. (b) The same comparison as a, but for the S68A receptor.

Table 3

Differences from wild type when aspartate binds to the S68A receptor

Structural featureWild typeS68A
Downward shift at helix 㬔, Å1.51.3
Tilt at helix 㬔 relative to helix 㬑, degrees52
Inter-subunit angle, degrees40
New hydrogen bond formationSer-68′ to Tyr-149′ in the unoccupied siteBoth sites have aspartate

Conformational changes through helix 㬔 are accompanied by a piston-like motion in the helix interface. The main chain of helix 㬔 remains comparatively rigid and the side chains undergo small angular adjustments. This motion is also associated with a rearrangement of H-bond strengths between the side chains of helices 㬑 and 㬔. For example, the H-bond distance between Arg-64 and Gln-155 is 3.51 Å, and that between Arg-64 and Gln-158 is 2.85 Å in the apo crystal structure. Upon aspartate binding, these distances become 2.75 Å and 3.10 Å, respectively.

In Fig. ​ Fig.2 2 are shown the side chains of the residues near Thr-154 at the position of the ligand binding to the receptor, showing a similar 1-Å shift downwards of the side chains. Further down in the protein it can be seen that the side chains relative to each other and to the backbones are far enough apart so that there are no van der Waal's repulsions or steric hindrances to a 1 Å shift in the protein.

Side chains in the helices 㬑 and 㬔 that shift relative to each other on binding aspartate.

The mutant that lacks cooperativity and the wild type that has strong negative cooperativity have similar movements of 㬔 helix downward in both cases. The main difference is that the Ala-68 that replaces Ser-68 has no role in the interactions with Tyr-149. Because Ser-68 has been shown to play a major role in the cooperative interactions in the structure (9), it seems clear that this Ser-68 is a big actor in the cooperativity of the protein. It is intriguing that the negatively cooperative protein has, if anything, a greater movement downwards of helix 㬔 than the noncooperative protein, indicating that the cooperativity does not 𠇋leed off” energy from the excitation mechanism but, if anything, evolution has selected it for both the negative cooperativity and the propagation of the signal.


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Author summary

Protein motions are commonly quantified measuring structural differences between conformers. The extension of these differences are called conformational diversity. These motions are essential to understand protein biology. We have found that the distribution of conformational diversity in a large dataset of proteins could be explained in terms of three sets sharing structure-based features emerging from the conformer population for each protein. The first set, which we called rigid, involve proteins showing almost no backbone movements but with important changes in tunnels. In order of increasing conformational diversity, the other sets are called partially disordered and malleable, showing disordered regions and important cavities but with different behaviour to each other. Shared features in each set could represent conformational mechanisms related with biological functions.

Citation: Monzon AM, Zea DJ, Fornasari MS, Saldaño TE, Fernandez-Alberti S, Tosatto SCE, et al. (2017) Conformational diversity analysis reveals three functional mechanisms in proteins. PLoS Comput Biol 13(2): e1005398.

Editor: Christine A. Orengo, University College London, UNITED KINGDOM

Received: September 10, 2016 Accepted: February 2, 2017 Published: February 13, 2017

Copyright: © 2017 Monzon et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All relevant data are within the paper and its Supporting Information files.

Funding: Funding for this research was provided by COST Action (BM1405) Non-Globular Proteins-net (SCET) and Universidad Nacional de Quilmes (PUNQ 1004/11) (GP). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: The authors have declared that no competing interests exist.

Protein Conformational Dynamics

This book discusses how biological molecules exert their function and regulate biological processes, with a clear focus on how conformational dynamics of proteins are critical in this respect. In the last decade, the advancements in computational biology, nuclear magnetic resonance including paramagnetic relaxation enhancement, and fluorescence-based ensemble/single-molecule techniques have shown that biological molecules (proteins, DNAs and RNAs) fluctuate under equilibrium conditions. The conformational and energetic spaces that these fluctuations explore likely contain active conformations that are critical for their function. More interestingly, these fluctuations can respond actively to external cues, which introduces layers of tight regulation on the biological processes that they dictate. A growing number of studies have suggested that conformational dynamics of proteins govern their role in regulating biological functions, examples of this regulation can be found in signal transduction, molecular recognition, apoptosis, protein / ion / other molecules translocation and gene expression.

On the experimental side, the technical advances have offered deep insights into the conformational motions of a number of proteins. These studies greatly enrich our knowledge of the interplay between structure and function.

On the theoretical side, novel approaches and detailed computational simulations have provided powerful tools in the study of enzyme catalysis, protein / drug design, protein / ion / other molecule translocation and protein folding/aggregation, to name but a few. This work contains detailed information, not only on the conformational motions of biological systems, but also on the potential governing forces of conformational dynamics (transient interactions, chemical and physical origins, thermodynamic properties). New developments in computational simulations will greatly enhance our understanding of how these molecules function in various biological events.

Speed of protein conformational change? - Biology

Experimental Data Snapshot

  • Resolution: 1.90 Å
  • R-Value Free: 0.274 
  • R-Value Work: 0.244 
  • R-Value Observed: 0.096 

wwPDB Validation   3D Report Full Report

Propagating conformational changes over long (and short) distances in proteins.

(2001) Proc Natl Acad Sci U S A 98: 9517-9520

  • PubMed: 11504940  Search on PubMedSearch on PubMed Central
  • DOI: 10.1073/pnas.161239298
  • Primary Citation of Related Structures:  
  • PubMed Abstract: 

The problem of the propagation of conformational changes over long distances or through a closely packed protein is shown to fit a model of a ligand-induced conformational change between two protein states selected by evolution. Moreover, the kinetics of the pathway between these states is also selected so that the energy of ligand binding and the speed of the transition between conformational states are physiologically appropriate .

The problem of the propagation of conformational changes over long distances or through a closely packed protein is shown to fit a model of a ligand-induced conformational change between two protein states selected by evolution. Moreover, the kinetics of the pathway between these states is also selected so that the energy of ligand binding and the speed of the transition between conformational states are physiologically appropriate. The crystallographic data of a wild-type aspartate receptor that has negative cooperativity and a mutant that has no cooperativity but has native transmembrane signaling are shown to support this model.

Organizational Affiliation

Department of Molecular and Cell Biology, University of California, Berkeley 94720-3206, USA.

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