Src kinase conformational activation: thermodynamics, pathways, and mechanisms

Tyrosine kinases of the Src-family are large allosteric enzymes that play a key role in cellular signaling. Conversion of the kinase from an inactive to an active state is accompanied by substantial structural changes. Here, we construct a coarse-grained model of the catalytic domain incorporating experimental structures for the two stable states, and simulate the dynamics of conformational transitions in kinase activation. We explore the transition energy landscapes by constructing a structural network among clusters of conformations from the simulations. From the structural network, two major ensembles of pathways for the activation are identified. In the first transition pathway, we find a coordinated switching mechanism of interactions among the αC helix, the activation-loop, and the β strands in the N-lobe of the catalytic domain. In a second pathway, the conformational change is coupled to a partial unfolding of the N-lobe region of the catalytic domain. We also characterize the switching mechanism for the αC helix and the activation-loop in detail. Finally, we test the performance of a Markov model and its ability to account for the structural kinetics in the context of Src conformational changes. Taken together, these results provide a broad framework for understanding the main features of the conformational transition taking place upon Src activation.     

Instantaneous Normal Modes as an Unforced Reaction Coordinate for Protein Conformational Transitions

We present a novel sampling approach to explore large protein conformational transitions by determining unique substates from instantaneous normal modes calculated from an elastic network model, and applied to a progression of atomistic molecular dynamics snapshots. This unbiased sampling scheme allows us to direct the path sampling between the conformational end states over simulation timescales that are greatly reduced relative to the known experimental timescales. We use adenylate kinase as a test system to show that instantaneous normal modes can be used to identify substates that drive the structural fluctuations of adenylate kinase from its closed to open conformations, in which we observe 16 complete transitions in 4 μs of simulation time, reducing the timescale over conventional simulation timescales by two orders of magnitude. Analysis shows that the unbiased determination of substates is consistent with known pathways determined experimentally.     

Predicting order of conformational changes during protein conformational transitions using an interpolated elastic network model

The decryption of sequence of structural events during protein conformational transitions is essential to a detailed understanding of molecular functions ofvarious biological nanomachines. Coarse‐grained models have proven useful by allowing highly efficient simulations of protein conformational dynamics. By combining two coarse‐grained elastic network models constructed based on the beginning and end conformations of a transition, we have developed an interpolated elastic network model to generate a transition pathway between the two protein conformations. For validation, we have predicted the order of local and global conformational changes during key ATP‐driven transitions in three important biological nanomachines (myosin, F₁ ATPase and chaperonin GroEL). We have found that the local conformational change associated with the closing of active site precedes the global conformational change leading to mechanical motions. Our finding is in good agreement with the distribution of intermediate experimental structures, and it supports the importance of local motions at active site to drive or gate various conformational transitions underlying the workings of a diverse range of biological nanomachines. Proteins 2010. © 2010 Wiley‐Liss, Inc.     

A systematic study of the energetics involved in structural changes upon association and connectivity in protein interaction networks

The study of protein binding mechanisms is a major topic of research in structural biology. Here, we implement a combination of metrics to systematically assess the cost of backbone conformational changes that protein domains undergo upon association. Through the analyses of 2090 unique unbound → bound transitions, from over 12,000 structures, we show that two-thirds of these proteins do not suffer significant structural changes upon binding, and could thus fit the lock-and-key model well. Among the remaining proteins, one-third explores the bound conformation in the unbound state (conformational selection model) and, while most transitions are possible from an energetic perspective, a few do require external help to break the thermodynamic barrier (induced fit model). We also analyze the relationship between conformational transitions and protein connectivity, finding that, in general, domains interacting with many partners undergo smaller changes upon association, and are less likely to freely explore larger conformational changes.     

3D structure model of the principal neutralizing epitope of Minnesota HIV-1 isolate

A hierarchical procedure, using a "bottom-up" strategy and combining (i). a probabilistic approach for estimating all possible starting structures, (ii). restrained molecular mechanics algorithms for preliminary selection of all energetically preferred conformers, as well as (iii). quantum chemical computations for refining their geometry, was used to study the structural properties of the HIV-MN neutralizing epitope in terms of NMR spectroscopy data. As a result, only one of initial structures matching the experimental and theoretical data was found to be well-ground for implementing the function of immunoreactive conformation of the virus immunogenic crown. The geometric parameters of this structure in water solution were shown to correspond to a double beta-turn conformation similar to that revealed in crystal for synthetic molecules imitating the central region of the HIV-MN V3 loop. The following conclusion was drawn from the comparative analysis of simulated structure with the one computed previously: the HIV-MN immunogenic tip has some inherent conformational flexibility that manifests at the alterations of hexapeptide environment and leads to the structural transitions changing the local conformation of the stretch of interest but retaining its spatial main chain fold. As a matter of record, the high resolution 3D structure model for the HIV-MN principal neutralization site was constructed, and its geometric parameters were compared with the corresponding characteristics of conformers derived earlier for describing the conformational features of immunogenic tip of gp120 from Thailand HIV-1 isolate.     

Accurate flexible fitting of high-resolution protein structures to small-angle x-ray scattering data using a coarse-grained model with implicit hydration shell

Small-angle x-ray scattering (SAXS) is a powerful technique widely used to explore conformational states and transitions of biomolecular assemblies in solution. For accurate model reconstruction from SAXS data, one promising approach is to flexibly fit a known high-resolution protein structure to low-resolution SAXS data by computer simulations. This is a highly challenging task due to low information content in SAXS data. To meet this challenge, we have developed what we believe to be a novel method based on a coarse-grained (one-bead-per-residue) protein representation and a modified form of the elastic network model that allows large-scale conformational changes while maintaining pseudobonds and secondary structures. Our method optimizes a pseudoenergy that combines the modified elastic-network model energy with a SAXS-fitting score and a collision energy that penalizes steric collisions. Our method uses what we consider a new implicit hydration shell model that accounts for the contribution of hydration shell to SAXS data accurately without explicitly adding waters to the system. We have rigorously validated our method using five test cases with simulated SAXS data and three test cases with experimental SAXS data. Our method has successfully generated high-quality structural models with root mean-squared deviation of 1 ∼ 3 Å from the target structures.     

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.     

Driving Protein Conformational Cycles in Physiology and Disease: “Frustrated” Amino Acid Interaction Networks Define Dynamic Energy Landscapes

A general framework by which dynamic interactions within a protein will promote the necessary series of structural changes, or “conformational cycle,” required for function is proposed. It is suggested that the free-energy landscape of a protein is biased toward this conformational cycle. Fluctuations into higher energy, although thermally accessible, conformations drive the conformational cycle forward. The amino acid interaction network is defined as those intraprotein interactions that contribute most to the free-energy landscape. Some network connections are consistent in every structural state, while others periodically change their interaction strength according to the conformational cycle. It is reviewed here that structural transitions change these periodic network connections, which then predisposes the protein toward the next set of network changes, and hence the next structural change. These concepts are illustrated by recent work on tryptophan synthase. Disruption of these dynamic connections may lead to aberrant protein function and disease states.     

Mechanism of Cohesin Loading onto Chromosomes: A Conformational Dynamics Study

The structure-function relationship of cohesin, an essential chromosome maintenance protein, is investigated by analyzing its collective dynamics and conformational flexibility, enhancing our understanding of the sister chromatid cohesion process. A three-dimensional model of cohesin has been constructed by homology modeling using both crystallographic and electron microscopy image data. The harmonic dynamics of the cohesin structure are calculated with a coarse-grained elastic network model. The model shows that the bending motion of the cohesin ring is able to adopt a head-to-tail conformation, in agreement with experimental data. Low-frequency conformational changes are observed to deform the highly conserved glycine residues at the interface of the cohesin heterodimer. Normal mode analysis further reveals that, near large globular structures such as nucleosome and accessory proteins docked to cohesin, the mobility of the coiled-coil regions is notably affected. Moreover, fully solvated molecular dynamics calculations, performed specifically on the hinge region, indicate that hinge opening starts from one side of the dimerization interface, and is coordinated by highly conserved glycine residues.     

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.     

Site-specific variations in RNA folding thermodynamics visualized by 2-aminopurine fluorescence

The fluorescent base analogue 2-aminopurine (2-AP) is commonly used to study specific conformational and protein binding events involving nucleic acids. Here, combinations of steady-state and time-resolved fluorescence spectroscopy of 2-AP were employed to monitor conformational transitions within a model hairpin RNA from diverse structural perspectives. RNA substrates adopting stable, unambiguous secondary structures were labeled with 2-AP at an unpaired base, within the loop, or inside the base-paired stem. Steady-state fluorescence was monitored as the RNA hairpins made the transitions between folded and unfolded conformations using thermal denaturation, urea titration, and cation-mediated folding. Unstructured control RNA substrates permitted the effects of higher-order RNA structures on 2-AP fluorescence to be distinguished from stimulus-dependent changes in intrinsic 2-AP photophysics and/or interactions with adjacent residues. Thermodynamic parameters describing local conformational changes were thus resolved from multiple perspectives within the model RNA hairpin. These data provided energetic bases for construction of folding mechanisms, which varied among different folding-unfolding stimuli. Time-resolved fluorescence studies further revealed that 2-AP exhibits characteristic signatures of component fluorescence lifetimes and respective fractional contributions in different RNA structural contexts. Together, these studies demonstrate localized conformational events contributing to RNA folding and unfolding that could not be observed by approaches monitoring only global structural transitions.     

How well can we understand large-scale protein motions using normal modes of elastic network models?

In this article, we apply a coarse-grained elastic network model (ENM) to study conformational transitions to address the following questions: How well can a conformational change be predicted by the mode motions? Is there a way to improve the model to gain better results? To answer these questions, we use a dataset of 170 pairs having "open" and "closed" structures from Gerstein's protein motion database. Our results show that the conformational transitions fall into three categories: 1), the transitions of these proteins that can be explained well by ENM; 2), the transitions that are not explained well by ENM, but the results are significantly improved after considering the rigidity of some residue clusters and modeling them accordingly; and 3), the intrinsic nature of these transitions, specifically the low degree of collectivity, prevents their conformational changes from being represented well with the low frequency modes of any elastic network models. Our results thus indicate that the applicability of ENM for explaining conformational changes is not limited by the size of the studied protein or even the scale of the conformational change. Instead, it depends strongly on how collective the transition is.     

Comparative molecular dynamics—Similar folds and similar motions?

Proteins possessing the same fold may undergo similar motions, particularly if these motions involve large conformational transitions. The increasing amounts of structural data provide a useful starting point with which to test this hypothesis. We have performed a total of 0.29 micros of molecular dynamics across a series of proteins within the same fold family (periplasmic binding proteinlike) in order to address to what extent similarity of motion exists. Analysis of the local conformational space on these timescales (10-20 ns) revealed that the behavior of the proteins could be readily distinguished between an apo-state and a ligand-bound state. Moreover, analysis of the root-mean-square fluctuations reveals that the presence of the ligand exerts a stabilizing effect on the protein, with similar motions occurring, but with reduced magnitude. Furthermore, the conformational space in the presence of the ligand appears to be dictated by sequence but not by the type of ligand present. In contrast, apo-simulations showed considerable overlap of conformational space across the fold as a result of their ability to undergo larger fluctuations. Indeed, we observed several transitions from different simulations between states corresponding to the closed-cleft and open-cleft forms of the fold, with the predominant motions being conserved across the different proteins. Thus, large-scale conformational changes do indeed appear to be conserved across this fold architecture, but smaller conformational motions appear to reflect the differences in sequence and local fold.     

An improved structural characterisation of reduced French bean plastocyanin based on NMR data and local-elevation molecular dynamics simulation

Deriving structural information about a protein from NMR experimental data is still a non-trivial challenge to computational biochemistry. This is because of the low ratio of the number of independent observables to the number of molecular degrees of freedom, the approximations involved in the different relationships between particular observable quantities and molecular conformation, and the averaged character of the experimental data. For example, protein (3)J-coupling data are seldom used for structure refinement because of the multiple-valuedness and limited accuracy of the Karplus relationship linking a (3)J-coupling to a torsional angle. Moreover, sampling of the large conformational space is still problematic. Using the 99-residue protein plastocyanin as an example we investigated whether use of a thermodynamically calibrated force field, inclusion of solvent degrees of freedom, and application of adaptive local-elevation sampling that accounts for conformational averaging produces a more realistic representation of the ensemble of protein conformations than standard single-structure refinement in a non-explicit solvent using restraints that do not account for averaging and are partly based on non-observed data. Yielding better agreement with observed experimental data, the protein conformational ensemble is less restricted than when using standard single-structure refinement techniques, which are likely to yield a picture of the protein which is too rigid.     

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.     

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.     

3D Structure Model of the Principal Neutralizing Epitope of Minnesota HIV-1 Isolate

Abstract

A hierarchical procedure, using a “bottom-up” strategy and combining (i) a probabilistic approach for estimating all possible starting structures, (ii) restrained molecular mechanics algorithms for preliminary selection of all energetically preferred conformers, as well as (iii) quantum chemical computations for refining their geometry, was used to study the structural properties of the HIV-MN neutralizing epitope in terms of NMR spectroscopy data. As a result, only one of initial structures matching the experimental and theoretical data was found to be well-ground for implementing the function of immunoreactive conformation of the virus immunogenic crown. The geometric parameters of this structure in water solution were shown to correspond to a double β-turn conformation similar to that revealed in crystal for synthetic molecules imitating the central region of the HIV-MN V3 loop. The following conclusion was drawn from the comparative analysis of simulated structure with the one computed previously: the HIV-MN immunogenic tip has some inherent conformational flexibility that manifests at the alterations of hexapeptide environment and leads to the structural transitions changing the local conformation of the stretch of interest but retaining its spatial main chain fold. As a matter of record, the high resolution 3D structure model for the HIV-MN principal neutralization site was constructed, and its geometric parameters were compared with the corresponding characteristics of conformers derived earlier for describing the conformational features of immunogenic tip of gp120 from Thailand HIV-1 isolate.

The results are discussed in the light of literature data on HIV-1 neutralizing epitope structure.     

On the long-run sensitivity of probabilistic Boolean networks

Boolean networks and, more generally, probabilistic Boolean networks, as one class of gene regulatory networks, model biological processes with the network dynamics determined by the logic-rule regulatory functions in conjunction with probabilistic parameters involved in network transitions. While there has been significant research on applying different control policies to alter network dynamics as future gene therapeutic intervention, we have seen less work on understanding the sensitivity of network dynamics with respect to perturbations to networks, including regulatory rules and the involved parameters, which is particularly critical for the design of intervention strategies. This paper studies this less investigated issue of network sensitivity in the long run. As the underlying model of probabilistic Boolean networks is a finite Markov chain, we define the network sensitivity based on the steady-state distributions of probabilistic Boolean networks and call it long-run sensitivity. The steady-state distribution reflects the long-run behavior of the network and it can give insight into the dynamics or momentum existing in a system. The change of steady-state distribution caused by possible perturbations is the key measure for intervention. This newly defined long-run sensitivity can provide insight on both network inference and intervention. We show the results for probabilistic Boolean networks generated from random Boolean networks and the results from two real biological networks illustrate preliminary applications of sensitivity in intervention for practical problems.     

Free energy of helix propagation in short polyalanine chains determined from peptide growth simulations of La3+-binding model peptides. Comparison with experimental data

Molecular dynamics (MD) is, at present, a unique tool making it possible to study, at the atomic level, conformational transitions in peptides and proteins. Nevertheless, because MD calculations are always based on a more or less approximate physical model, using a set of approximate parameters, their reliability must be tested by comparison with experimental data. Unfortunately, it is very difficult to find a peptide system in which conformational transitions can be studied both experimentally and using MD simulations so that a direct comparison of the results obtained in both ways could be made. Such a system, containing a rigid alpha-helix nucleus stabilized by La(3+) coordination to a 12-residue sequence taken from an EF-hand protein has recently been used to determine experimentally the helix propagation parameters in very short polyalanine segments (Goch et al. (2003) Biochemistry 42: 6840-6847). The same parameters were calculated here for the same peptide system using the peptide growth simulation method with, alternatively, charmm 22 and cedar potential energy functions. The calculated free energies of the helix-coil transition are about two times too large for cedar and even three times too large for charmm 22, as compared with the experimental values. We suggest that these discrepancies have their origin in the incorrect representation of unfolded peptide backbone in solution by the molecular mechanics force fields.