Dynamics of allosteric transitions in GroEL

The chaperonin GroEL-GroES, a machine which helps some proteins to fold, cycles through a number of allosteric states, the $T$ state, with high affinity for substrate proteins (SPs), the ATP-bound $R$ state, and the $R^{\prime\prime}$ ($GroEL-ADP-GroES$) complex. Structures are known for each of these states. Here, we use a self-organized polymer (SOP) model for the GroEL allosteric states and a general structure-based technique to simulate the dynamics of allosteric transitions in two subunits of GroEL and the heptamer. The $T \to R$ transition, in which the apical domains undergo counter-clockwise motion, is mediated by a multiple salt-bridge switch mechanism, in which a series of salt-bridges break and form. The initial event in the $R \to R^{\prime\prime}$ transition, during which GroEL rotates clockwise, involves a spectacular outside-in movement of helices K and L that results in K80-D359 salt-bridge formation. In both the transitions there is considerable heterogeneity in the transition pathways. The transition state ensembles (TSEs) connecting the $T$, $R$, and $R^{\prime\prime}$ states are broad with the the TSE for the $T \to R$ transition being more plastic than the $R\to R^{\prime\prime}$ TSE. The results suggest that GroEL functions as a force-transmitting device in which forces of about (5-30) pN may act on the SP during the reaction cycle.Comment: 32 pages, 10 figures (Longer version than the one published

Allosteric Transitions of Supramolecular Systems Explored by Network Models: Application to Chaperonin GroEL

Identification of pathways involved in the structural transitions of biomolecular systems is often complicated by the transient nature of the conformations visited across energy barriers and the multiplicity of paths accessible in the multidimensional energy landscape. This task becomes even more challenging in exploring molecular systems on the order of megadaltons. Coarse-grained models that lend themselves to analytical solutions appear to be the only possible means of approaching such cases. Motivated by the utility of elastic network models for describing the collective dynamics of biomolecular systems and by the growing theoretical and experimental evidence in support of the intrinsic accessibility of functional substates, we introduce a new method, adaptive anisotropic network model (aANM), for exploring functional transitions. Application to bacterial chaperonin GroEL and comparisons with experimental data, results from action minimization algorithm, and previous simulations support the utility of aANM as a computationally efficient, yet physically plausible, tool for unraveling potential transition pathways sampled by large complexes/assemblies. An important outcome is the assessment of the critical inter-residue interactions formed/broken near the transition state(s), most of which involve conserved residues

β Subunit M2–M3 Loop Conformational Changes Are Uncoupled from α1 β Glycine Receptor Channel Gating: Implications for Human Hereditary Hyperekplexia

Hereditary hyperekplexia, or startle disease, is a neuromotor disorder caused mainly by mutations that either prevent the surface expression of, or modify the function of, the human heteromeric α1 β glycine receptor (GlyR) chloride channel. There is as yet no explanation as to why hyperekplexia mutations that modify channel function are almost exclusively located in the α1 to the exclusion of β subunit. The majority of these mutations are identified in the M2–M3 loop of the α1 subunit. Here we demonstrate that α1 β GlyR channel function is less sensitive to hyperekplexia-mimicking mutations introduced into the M2–M3 loop of the β than into the α1 subunit. This suggests that the M2–M3 loop of the α subunit dominates the β subunit in gating the α1 β GlyR channel. A further attempt to determine the possible mechanism underlying this phenomenon by using the voltage-clamp fluorometry technique revealed that agonist-induced conformational changes in the β subunit M2–M3 loop were uncoupled from α1 β GlyR channel gating. This is in contrast to the α subunit, where the M2–M3 loop conformational changes were shown to be directly coupled to α1 β GlyR channel gating. Finally, based on analysis of α1 β chimeric receptors, we demonstrate that the structural components responsible for this are distributed throughout the β subunit, implying that the β subunit has evolved without the functional constraint of a normal gating pathway within it. Our study provides a possible explanation of why hereditary hyperekplexia-causing mutations that modify α1 β GlyR channel function are almost exclusively located in the α1 to the exclusion of the β subunit

Physicochemical properties of pore residues predict activation gating of CaV1.2: A correlation mutation analysis

Single point mutations in pore-forming S6 segments of calcium channels may transform a high-voltage-activated into a low-voltage-activated channel, and resulting disturbances in calcium entry may cause channelopathies (Hemara-Wahanui et al., Proc Natl Acad Sci U S A 102(21):7553–7558, 16). Here we ask the question how physicochemical properties of amino acid residues in gating-sensitive positions on S6 segments determine the threshold of channel activation of CaV1.2. Leucine in segment IS6 (L434) and a newly identified activation determinant in segment IIIS6 (G1193) were mutated to a variety of amino acids. The induced leftward shifts of the activation curves and decelerated current activation and deactivation suggest a destabilization of the closed and a stabilisation of the open channel state by most mutations. A selection of 17 physicochemical parameters (descriptors) was calculated for these residues and examined for correlation with the shifts of the midpoints of the activation curve (ΔVact). ΔVact correlated with local side-chain flexibility in position L434 (IS6), with the polar accessible surface area of the side chain in position G1193 (IIIS6) and with hydrophobicity in position I781 (IIS6). Combined descriptor analysis for positions I781 and G1193 revealed that additional amino acid properties may contribute to conformational changes during the gating process. The identified physicochemical properties in the analysed gating-sensitive positions (accessible surface area, side-chain flexibility, and hydrophobicity) predict the shifts of the activation curves of CaV1.2

Cooperative Transition between Open and Closed Conformations in Potassium Channels

Potassium (K+) ion channels switch between open and closed conformations. The nature of this important transition was revealed by comparing the X-ray crystal structures of the MthK channel from Methanobacterium thermoautotrophicum, obtained in its open conformation, and the KcsA channel from Streptomyces lividans, obtained in its closed conformation. We analyzed the dynamic characteristics and energetics of these homotetrameric structures in order to study the role of the intersubunit cooperativity in this transition. For this, elastic models and in silico alanine-scanning mutagenesis were used, respectively. Reassuringly, the calculations manifested motion from the open (closed) towards the closed (open) conformation. The calculations also revealed a network of dynamically and energetically coupled residues. Interestingly, the network suggests coupling between the selectivity filter and the gate, which are located at the two ends of the channel pore. Coupling between these two regions was not observed in calculations that were conducted with the monomer, which emphasizes the importance of the intersubunit interactions within the tetrameric structure for the cooperative gating behavior of the channel

Structure-Based Predictive Models for Allosteric Hot Spots

In allostery, a binding event at one site in a protein modulates the behavior of a distant site. Identifying residues that relay the signal between sites remains a challenge. We have developed predictive models using support-vector machines, a widely used machine-learning method. The training data set consisted of residues classified as either hotspots or non-hotspots based on experimental characterization of point mutations from a diverse set of allosteric proteins. Each residue had an associated set of calculated features. Two sets of features were used, one consisting of dynamical, structural, network, and informatic measures, and another of structural measures defined by Daily and Gray [1]. The resulting models performed well on an independent data set consisting of hotspots and non-hotspots from five allosteric proteins. For the independent data set, our top 10 models using Feature Set 1 recalled 68–81% of known hotspots, and among total hotspot predictions, 58–67% were actual hotspots. Hence, these models have precision P = 58–67% and recall R = 68–81%. The corresponding models for Feature Set 2 had P = 55–59% and R = 81–92%. We combined the features from each set that produced models with optimal predictive performance. The top 10 models using this hybrid feature set had R = 73–81% and P = 64–71%, the best overall performance of any of the sets of models. Our methods identified hotspots in structural regions of known allosteric significance. Moreover, our predicted hotspots form a network of contiguous residues in the interior of the structures, in agreement with previous work. In conclusion, we have developed models that discriminate between known allosteric hotspots and non-hotspots with high accuracy and sensitivity. Moreover, the pattern of predicted hotspots corresponds to known functional motifs implicated in allostery, and is consistent with previous work describing sparse networks of allosterically important residues